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The social ecology of delinquency in Los Angeles county: a structural analysis

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The social ecology of delinquency in Los Angeles county: a structural analysis

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Content THE SOCIAL ECOLOGY OF DELINQUENCY IN L OS ANGELES COUNTY: A STRUCTURAL ANALYSIS by Leo Anthony Schuerman A Dissertation Presented to the FACULTY OF THE GRADUATE SCHOOL UNIVERSITY OF SOUTHERN CALIFORNIA In Partial Fulfillment of the Requirements for the Degree DOCTOR OF PHILOSOPHY (Sociology) February, 1977 Copyright Leo Anthony Schuerman 1977 l UNIVERSITY OF SOUTHERN CALIFORNIA THE GRADUATE SCHOOL UNIVERSITY PARK LOS ANGELES, CALIFORNIA 90007 This dissertation} written by ........ o ... LEO .. ANTHONY . SCHUERMAN .................... . under the direction of h.iB ... Dissertation Com mittee} and approved by all its members} has been presented to and accepted by The Graduate School} in partial fulfillment of requirements of the degree of DOCTOR OF PHILOSOPHY Dean Date ....... ..J.anuar..y ... 14., ... 1.9.7.7 .... . ------ --------------------- -- ,., p 2)0 77 I l ACKNOWLEDGMENTS Many persons have been instrumental, either explicitly or implicitly, to the completion of this work. I am most grateful to my parents for their constant support and encouragement over the years. To my wife, Muriel, I am surely grateful for maintaining ~nd stimulating my interest in the subject; as a colleague she was afforded the usual opportunity to memorize the dissPrtation through the numer ous readings of the formulative analyses. For the typing, formating, and editing, I would like to express my appreci ation to Mary Sears for her skills, diligence, and forbear ance. I In addition to usual committee duties, thanks are due to Professor Maurice D. Van Arsdol, Jr. for the many fruit ful graduate student hours in the Population Research Laboratory which provided the time and intellectual environ ment for much of the methodological orientation of this study. Likewise, my gratitude is extended to Professor Fred Krinsky for his inspired consultation and crucial sup port at always the correct moments d~ring the process to the Ph.D. Finally, I am most indebted to Professor Solomon Kobrin. Both as a colleague and advisor, Dr. Kobrin has been continuously invaluable during the study by generously giving his beneficial gifts of many, many hours of stimulating discussions and insightful guidance which orchestrated the conceptual design and research of the dissertation. It has been a delight and pleasure to study and learn with him. TABLE OF CONTENTS Page 1 ACKNOWLEDGMENTS LIST OF TABLES LIST OF FIGURES I • • • • . . . . • • • • • • . . • • . . . . . . . . . . . . . . . . . . • • • • • • • • • • • • • • • • • • Chapter I . I I. STUDY ORIENTATION . . . . . . . . . . . . . Introduction Theoretical Context of the Investigation Value Conflict Theory and the Typification of Delinquency Areas Subsequent Delinquency Theory: Statement of Problem RESEARCH DESIGN . . . . . . . . . . . . . . Introduction Delinquency Areas Spatial clustering Mathematical clustering A measurement of subareal autocorrelation Structuring Delinquency Areas Empirical referent for an elemental typology of structurally determined instrumental and expressive delinquency Composite indicators of social structure Spatial standardization of rate formu lation for indicator construction Social Structural Stability Considerations in measuring conventional elements of neighborhood social structure Deviational change used as a measure of stability Composite indicators of deviational change Ancillary summary measures of stability . . I. . . . 11 Vll lX 1 19 1V Chapter Page II. Continued III. Summary and E pectations: A Restatement of the Value Conflict Hypotheses Hypotheses respecting the identification of delinquency areas Structural impact on delinquency areas TEST AND ANALYSIS THROUGH AN EMPIRICAL APPLICATION . . . . . . . . . / Introduction Universe of Territory Data Sources and Areal Unjt of Analysis Delinquency Measurement Development and Composite Indicator Construction Delinquency summary record construction Measurement construction of juvenile instrumental and expressive delinquency Instrumental and expressive composite indicators of juvenile delinquency Statistical Simulation of Delinquency Areas in Los Angeles County Examining spatially contiguous subareas for different types of delinquency areas: Hypothesis Hl Spatial and magnitude differences in delinquency areas: Hypothesis HZ The relationship and overlapping patterns between the designated instrumental and expressive delinquency areas: Hypo theses H3 and H4 Measurement Development and Composite Indi cator Construction of Specific Elements of Neighborhood Social Structure Composite indicators for the measurement of neighborhood adult criminal activity Composite indicators of deviational change Delinquency Area Determinants Effects of conventional structural stability: Hypotheses HS and H6 Effects of adult criminal patterns: Hypotheses H7, HS, and H9 86 Chapter IV. DISC GJ SION . . . . . . . . . . . . Summary Orientation • • • • • Test site and measurement considerations Main findings Caveats Conclusions BIBLIOGRAPHY . . . . . . . . . . . . . . . . . . Page 169 191 VI Table 1. 2 . LIST OF TABLES Summary of Legally Specified Juvenile Delin quency Acts Separated Into Instrumental or Expressive Categories ......... . Summary of Legally Specified Adult Criminal Offenses Separated Into Instrumental or Expressive Categories ......... . • • • • 3. Summary Statistics for Selected Variables and Composite Indicator Construction of Instru mental Juvenile Delinquency Over 1,142 Sub- Page 48 49 areas in Los Angeles County: 1970 . . . . . 99 4. Summary Statistics for Selected Variables and Composite Indicator Construction of Expres sive Juvenile Delinquency Over 1,142 Subareas in Los Angeles County: 1970 ........ 101 5. Summary Statistics and Contiguity Ratios for Instrumental and Expressive Composite Indi- cators of Juvenile Delinquency for Los Angeles County and Thirteen Designated Delinquency Statistical Spatial Clusters: 1970 ..... 110 6. Difference of Means Test Between Each Desig nated Instrumental Delinquency Area 1n Los Angeles County: 1970 .......... 112 7 . Difference of Means Test Between Each Desig nated Expressive Delinquency Area in Los Angeles County: 1970 ......... . 8. Correlation Coefficients Comparing Instru mental and Expressive Composite Indicators of Juvenile Delinquency for Designated Instrumental and Expressive Delinquency • • 113 Areas in Los Angeles County: 1970 ..... 121 9. Summary Statistics for Selected Variables and Composite Indicator Construction of Instru mental Adult Criminal Activity Over 1,142 Subareas in Los Angeles County: 1970 .... 126 . . Vll Table Page 10. Summary Statistics for Selected Variables and Composite Indicator Construction of Expres sive Adult Criminal Activity Over 1,142 Subareas in Los Angeles County: 1970 ..... 128 11. 12. Summary Statistics for Selected Change Measures and Composite Construction of Land Use Over areas in Los Angeles County: Summary Statistics for Selected Deviational Indicator 1,142 Sub- 1970 on 1960 Deviational Indicator Status Over County: . . 13 2 . Change Measures and Composite Construction of Socioeconomic 1,142 Subareas in Los Angeles 1970 on 1960 . . . . . ... • • • • • • . . . 13 5 13. Summary Statistics for Selected Deviational Change Measures and Composite Indicator Construction of Ascribed Demographic Character Over 1,142 Subareas in Los Angeles County: 1970 on 1960 ............. 137 1 14. Simple and Multiple Correlations and Multiple Standardized Regressions for an Indicator of Instrumental Juvenile Delinquency with Specified Indicators of Elements of Social Structure in Each Subarea, Measured Over Seven Instrumental Delinquency Areas in Los Angeles County .............. 146 15. Simple and Multiple Correlations and Multiple Standardized Regressions for an Indicator of Expressive Juvenile Delinquency with Specified Indicators of Elements of Social Structure in Each Subarea, Measured Over Six Expressive Delinquen~y Areas in Los Angeles County ...... · .......... 151 LIST OF FIGURES Figure Page 1. Distribution of Instrumental Delinquency Areas, 1970: County of Los Angeles • . • • • 115 2 . Distribution of Expressive Delinquency Areas, 1970: County of Los Angeles • • • • • 116 - lX CHAPTER I STUDY ORIENTATION Introduction This study addresses two issues. First, given the fact that in any metropolitan area there exist spatial enclaves of officially adjudicated juvenile delinquency, the problem arises of identifying in an empirically valid way the spatial patterning, intensity, and location of such enclaves. The second issue concerns the differen tiation of identified and located "delinquency areas" in terms of their sociodemographic features and their associ ated local institutional structure, social stability, social control, and characteristic patterns of delinquency. The focus of the study is exclusively "structural." It attempts to account for the distribution of forms or types of delinquent behavior by reference to "social facts" in the Durkheimian sense. 1 Both delinquent bebavior itself as well as its subtypes are viewed as structural effects; that is, the behavior is generally constrained by the attributes of social collectivities rather than those of their individual members. Collectivity attributes that are here of concern include the stability of the social 1 and institutional order of residential areas; the norms and values that characterize the subcultures of local populations; and the prevailing forms of cross-generational social interaction. These variables embrace the two types of social fact that may be analytically distinguished: the values and norms embodied in a culture or subculture; and the modal forms of social relationships in terms of which the institutional orders of groups may be differentiated. 2 The major general hypothesis investigated holds that delinquency areas, as defined above, vary on the dimension of abnormal change in three key neighborhood structural features: sociodemographic characteristics, social class, and residential land use. Low values of change in these structural features imply stability of the local institu tional order, accompanied by integration of age groups; high levels of cross-generational social control; a neigh borhood culture characterized by a view of criminal and conventional values as continuous with one another (i.e., as essentially morally indistinguishable); and a pattern of delinquency oriented primarily to its instrumental values. By contrast, delinquency areas charucterized by high values of structural change imply instability of the local institutional order and low levels of integration across age grades; low levels of cross-generational social control; a local neighborhood culture marked by a view of 2 conventional and criminal values as morally mutually ex clusive; and a pattern of delinquency oriented primarily to its expressive values. Theoretical Context of the Investigation Empirical study relating social conditions to crime and delinquency has antecedents going back to the nine teenth century. For example, studies by Guerry, Rawson, Fletcher, and Mayhew during the 1800's presented research into the relationship between crime rates and geographical location. However, no systematic body of theory guided their research; hence, these disparate works were not generally cumulative. 3 Interest in this type of research was not renewed until the 1930's, wen systematic descrip tions of the distributional aspects of delinquency emerged in the sociological literature. What is perhaps striking about delinquency research since the 1930's has been the effort to discover causal linkages between specific pat terns of delinquent behavior and general types of social conditions. Delinquency research during the first half of this century was substantially advanced in the work of the "Chicago School." The or.iginal orientation of these studies was derived from the development of urban soci ology and human ecology, with a focus mainly on social 3 structure resulting from the processes of city growth. As early as 1925, in a study of urban spatial patterns, Burgess described the city as developing outward from the city's center according to a pattern of concentric zones. He observed that delinquency and other social problems were concentrated in a "zone of transition." This zone consisted of deteriorated residential areas facing en croachment of land use by business and light manufacturing. Burgess characterized these areas as the slums of the . 4 city. Following this original statement of urban growth, other studies concentrated more directly on juvenile delin quency as a distinctive feature of areas of urban deteri oration.5 Most representative of this development, the studies of Shaw and McKay suggested one of the more dominant themes of the first half of the twentieth century. Specific to the notion of delinquency areas, Shaw and McKay noted that juvenile delinquency was persistently concen trated in the poverty areas of the city; and that its persistence was the result of a delinquent tradition or culture passed on from one generation to another and/or from one socioethnic group to the next. 6 In a more concise summary statement of crime producing conditions, Sutherland 7 suggests that the durability of delinquent " behavior is an outcome of "differential association with - 4 the bearers of the existing criminal value system. Basic to this perspective is that delinquency is learned from the society in which the juvenile resides; and that the proba- 1 bility of becoming delinquent depends upon the existence in a neighborhood of more or less str.ble delinquent pattern and the degree of exposure the juve11ile has to these pat terns. During this period in the development of delinquency theory, a number of other hypotheses were advanced in the effort to explain delinquency in a cultural setting. For example, Sellin 8 proposed a "cultural conflict" theory holding that delinquency occurred not necessarily from a learning experience but rather was spawned· in neighborhoods in which the norms of immigrant cultures clashed with the legal prescriptions of the host society. The overall framework of differential association and culture conflict was amplified and acquired a new dimension in a theory advancej by Solomon Kobrin. 9 By synthesizing the concept of differential association with Sellin's culture conflict argument, Kobrin assimilated what was essentially a description of types of delinquent behavior to a structural context. He then suggested possible causal relationships between delinquency areas, neighborhood social structure, and the specific characteristics of local subcultural values and norms. Furthermore, unlike many 5 previous models which relegated delinquency to the slums of the city, Kobrin's model promised to be applicable to any 1 neighborhood within a metropolitan area. I Value Conflict Theory and the Typification of Delinquency Areas In 1951 Kobrin proposed a conflict of values model which places patterns of delinquent behavior along a con tinuum between two polar types of delinquency areas. The location along the continuum is affected by the degree of structural integration or change. At the conceptual level, he proposed that highly organized and regulative delin quency areas develop in neighborhoods that have patterns of "professional" adult criminality along with stable conven tional social values and institutions, with delinquency tending to be planful, sophisticated, and oriented to economic gain. At the opposite end of the continuum delin quency areas still may contain adult law violators furnish ing deviant models, but the delinquency is principally expressive of conflict behavior and opposition to tradition al social constraints. Kobrin argued that in the first polar type of delin quency area, social institutions must be stable and controls at a maximum in order to have "integration between the conventional and criminal value systems ... ," and that this type of area generates the kinds of delinquent 6 behavior accepted and tolerated in the community. While there may be patterns of boisterousness and destructiveness !in juvenile activity, it is nevertheless controlled and channeled. In general the delinquent behavior affords an opportunity for training and for the development of skill in income producing forms of delinquency. Such activity I tends to be regarded by local residents as not incongruous with neighborhood norms, and is consequently status en hancing. In the second polar type of delinquency area, insti tutional controls are at a minimum due to rapid changes 1n population composition, with the juvenile population exposed to confusion and uncertainty in local normative controls. Hence, little integration between the conven tional behavior norms and adult criminality can be expected. Prevailing types of adult crime are essentially unorganized and provide no consistent model for the channeling of delin quent behavior through the usual social control mechanisms available in neighborhoods of the opposite polar type. Thus, neither conventional persons nor adult violators exer1 control over juvenile behavior. The delinquency patterns in these areas reflect a lack of socially imposed restraint, and is expressed in the predominance of individual and gang assaults, in wanton property destruction, and in open oppo sition to adult controls. 7 Subsequent Delinquency Theory: Statement of the Problem Since its original statement, some of the maJor theo rists in the delinquency field have variously incorporated in their work, either implicitly or explicitly, elements of the "value conflict" position. 10 In their work, however, the explanatory concepts were generally not retained at the social structural level. They tended to transform what was initially proposed as a structural theory to a social psychological theory, with attention to the explanation of individual motivation. In general the focus of attention shifted from the structural bases of the differentiation of delinquency areas to why and how delinquent norms develop and are elaborated, and how they crystallize as delinquent subcultures. The interest in explaining the emergence of deviant norms and delinquent subcultures assigned to social structure a distinctly subsidiary role in the development of delinquency theory. In part this ancillary positioning of social structure is reflected in the fact that sub sequent theories did not concern themselves with the distri bution of delinquency over the entire urban area, but rather focused on the types of delinquent behavior found only in slum or low income neighborhoods. Likewise, little emphasis was placed on the relation between types of delin quency areas and the dynamics of urban change and growth, a 8 ,-------------------------------------, I fundamental dimension in th formulation of the value con flict position. Thus, the focus of attention had generally shifted from an interest in the relationship between social structure and characteristic types of delinquency areas to lthe microsociology and social psychology of the origin of I d 1 . . 1 . d h . . 11 e inquent patterns in ow income an g etto communities. In theories developed since 1951 social structure was viewed as an accepted lower class static condition in which individual juveniles must function. Delinquent patterns that emerged tended to be treated as products of "delin quent subcultures." Theories of delinquent subcultures have been, and largely remain, central in th~ study of juvenile delinquency. Of these, the work of Albert Cohen, and Cloward and Ohlin have perhaps done the most in develop- . . f. h . 1 f 1 · 12 ing signi icant t eoretica ormu ations. Their writings will be briefly reviewed as representative of the way theory concerning delinquency subareas has evolved since 1 1952. Particular attention will be given to how the speci fication of delinquency areas was developed in their theories. Other studies which seem relevant to the formu lation of delinquent spatial patterns will also be discussed. I I Both Cohen and Cloward and Ohlin regard delinquent subcultures in general, and types of delinquent groups in particular, as developing in response to juvenile 9 adjustment problems. The nature of the adjustment problem, lhowever, was differently defined by each. Moreover, the concepts extracted from Kobrin's work by Cloward and Ohlin were placed in the context of structural-functional theory. Cohen first published his formulation concerning the origins of delinquent subcultures in 1955. 13 He restricted the main content of his thesis to lower socioeconomic neighborhoods and argued that the delinquent subculture was a group elaborated reaction of lower class youth to the norms and values of the dominant middle class segment of society, to which they found it difficult to conform by virtue of their lower class training. Through a psycho logical process of adjustment to status frustration, shared by companions of similar background, there develops a com mon pattern of intentional rejection of conventional values. The essential character of the subculture that develops is characterized by non-utilitarian, malicious, and negativ istic behavior. In joint authorship with Short, Cohen in 1958 14 included three specific variants of the basic or "parent" delinquent subculture. They were defined as conflict-oriented, semi -professional theft, and a drug usin subculture. While they recognized that two types of delin quent behavior patterns were explained within the value conflict framework, Cohen and Short eschewed this explana tion and suggested that the delinquent patterns could be L _____________ 10 better understood from a social-psychological perspective. However, the variant forms of the subculture were not crucial to the main thrust of Cohen's theory. They were viewed rather as variant outcomes of the same social psychological factors involved in the emergence of the 15 parent subculture. In his work subsequent to 1958, Cohen moved further in the same direction, utilizing a symbolic interactionist model to elaborate his account of the origin of the delinquent subculture, in lieu of the use of a socia structural model. This contribution centered around George 16 Herbert Mead's role-self theory. 17 Cloward and Ohlin, writing in 1960, also explored the individual motivational dimensions of juvenile delin quency. In their theory, types of delinquent patterns were more central to an explanatory model than found in Cohen's work. In developing their account of the formation of the delinquent subculture, Cloward and Ohlin synthesized the anomie theory and cultural transmission-differential association theory. Using the word "gang" and "delinquent subculture" interchangeably and extracting from Merton's derivation of Durkheim's anomie theory, they argued that th delinquent subculture originates from what Merton noted as perceived discrepancies lower class juveniles experience between the culturally prescribed goals and the actual means available to lower class youth to achieve these 11 18 goals. This disjunction produces social pressure s which result in anomie, or state of normlessness. Thus lacking institutional means and responding to the pressures to suc ceed in achieving middle class goals, various illegitimate means are accepted as a substitute. Cloward and Ohlin note however, that the availability of the illegitimate means is also differentially distributed throughout the social struc ture. With citation to aspects of Kobrin's conflict of value thesis, Cloward and Ohlin propose that there are various degrees of inter-weaving of conventional and criminal value structures in different lower class com munities. The degree of integration determines the kind of illegitimate avenues available to lower class youth. They thus differentiate three types of delinquent subculture: 1. cr·iminal: This subculture develops mainly in neighborhoods where there 1s (a) a high degree of association between those 1n the juvenile age levels and adults engaged in illegitimate activity, and (l; where behavior commonly exhibits both criminal and conventional values. 2. Conflict: This pattern flourishes where (a) adult criminal models exist; (b) integration between age grades is weak or non -existent, such that juvenile typically have little contact with adult criminal models; (c) no systematic learning opportunities 12 are available for illegitimate success; and ~ there is similarly an absence of institutional means for the achievement of the prescribed suc cess goals of the wider society. 3. Retreatist: This variant of the delinquent sub culture is a product of double failure. First, there is failure to achieve success goals through use of legitimate or illegitimate means. Second, there is an inability for personal or idiosyncrati reasons to adopt the patterns of violence charac teristic of boys 1n conflict gangs. The retreatis is usually involved in heavy use of druge. Like Cohen, Cloward and Ohlin differentiated three types of delinquency patterns, and again two of the types are similar to those found in delinquency areas as de scribed by Kobrin. Unlike Cohen, however, who saw the three types as "gangs" all manifesting the characteristics of the parent subculture, Cloward and Ohlin argued that there were three quite different subcultures which develope out of the individual's responses to differentiated social conditions within delimited areas. It was not long after the advent of these theories that empirical studies were conducted to test the various subculture typologies as defined by Cohen and more partic ularly Cloward and Ohl in. For example, whe_ n Short and 13 r 19 Strodtbeck studied sixteen gangs in Chicago, they tried to identify the subcultures that would more or less fit the I neighborhood gangs of Cohen and/or Cloward and Ohlin. Per- ltaining to Cohen's key theoretical position of a "reaction formation,'' Short and Strodtbeck reported no clear evidence that such attitudes prevailed in the gangs sampled. They also tried to identify patterns of behavior that would fit Cloward and Ohlin's criminal, retreatist, and conflict typology. In general, the study found only one drug group, could not locate a "true" criminal group, and only margin ally accepted the notion of a strictly conflict-oriented gang. Iri ... a summary of the study' s findings, Short and Strodtbeck sharply questioned the general postulate that neighborhood delinquent subcultures are organized around specific types or patterns of delinquent activity. I In another study that followed Cloward and Ohlin's theoretical lead, Irving Sperge1 20 presented research findings that required the addition of still more types of delinquent subcultures in lower class populations. Instead of a single criminal pattern, Spergel differentiated this type into a racket and theft adaptation. Furthermore, he felt it necessary to identify a drug pattern as a subtype of the three main subcultures. Thus Cloward and Ohlin's three subcultures were expanded to six distinct patterns: racket, racket-drug related, theft, theft-drug related, 14 M I 1 conflict, and conflict-drug related. The different types lof delinquent subcultures identified by Spergel did not originate from a theoretical formulation, as was the case 1 in Cloward and Ohlin's work. Rather, it appears that the typology suggested by Spergel is the result of exam1n1ng three urban neighborhoods with reputations for high levels of racket activity, violent gang fighting, and car theft and burglary, respectively. The extension of Cloward and Ohlin's basic typology from three to six types of sub culture seems to be primarily a highly unparsimonious means to account for empirically encountered variations in delin quency patterns rather than an extension of the theoretical constructs detailed by Cloward and Ohlin. In their theory, the three specific types of subcultures are regarded as developing from specific behavior motivated by anomic pres sures and differential association. Spergel's typology seems unrelated to this basic set of constructs. I Since the advent of the two major theories of Cohen anc Cloward and Ohlin, little has been added to the delinquency literature that suggests that delinquent subcultures or similar constructs oriented to motivational theory might be useful in explicating the differential location of delin quent patterns. It is interesting to note also that while since 1951 the value conflict perspective has been adopted, principally by Cloward and Ohlin, to serve as one of the 15 I pivotal foundations for a motivational theory of juvenile delinquency, no research has attempted to test it empiri caily or extend its original theoretical suggestions. Furthermore, its use as an axiom in specific motivational theories of delinquent subcultures may have led to a pre mature dead end in empirical research concerning the re lationship between patterns of urban social structure and differentiated delinquency areas and patterns of delin quency. The overall task of the present research, therefore, is to empirically ascertain the utility of value conflict theory. The analysis will require formulating operational specifications of, and tests for, the location and characteristics of urban neighborhoods constituting a set of areas differentiated with respect to the incidence and type of delinquent behavior in their juvenile populations. 1 If such areas can be successfully identified empirically, the further question will be addressed whether their distri bution in urban space, their structural characteristics, anc their associated delinquency patterns can be accounted for within the framework of a value conflict social structural paradigm for an entire metropolitan region. 16 ---- CHAPTER I FOOTNOTES 1. Emile Durkheim, The Rules of Sociological Method (New York: The Free Press, 1964). 2. Peter M. Blau, "Structural Effects," American Socio logical Review, XXV (April, 1960), 178. 3. Yale Levin and Alfred Lindesmith, "English Ecology and Criminology of the Past Century," Journal of Criminal Law and Criminolb~y, XXVII (March, 1937), 801-816; Terence Morris, Te Criminal Area (London: Routledge & Kegan Paul Ltd, 1957), pp. 42-53. 4. Ernest W. Burgess, "The Growth of the City: An Introduction to a Research Project," The City, ed. Robert E. Park, Ernest W. Burgess, and R. D. McKenzie (Chicago: University of Chicago Press, 1925), pp. 4 7 - 6 2. 5. Walter C. Reckless, "The Distribution of Commercialize Vice in the City: A Sociological Analysis," Publi cations of the American Sociological Societ, XX 1926, 164-76; Harvey Zorbaug, The Gol Coast and the Slum (Chicago: The University of Chicago Press 1929); Frederic M. Thrasher, The Gang (Chicago,202-213. University of Chicago Press, 1936); Stuart Lottier, "Distribution of Criminal Offenses in Metropolitan Regions," Journal of Criminal Law and Criminolo , XXIX (1938 , 37-50; Marshall B. Clinar , "Te Process of Urbanization and Criminal Behavior," American Journal of Sociology, XLVIII (September, 1942),202-213. 6. Clifford R. Shaw and Henry D. McKay, Social Factors in Juvenile Delinquency, U.S. National Commission on Law Enforcement and Observance,No. 13, Vol. 2 (Washington, D.C.: Government Printing Office, 1931); Clifford R. Shaw and Henry D. McKay, Juvenile Delin~uency and Urban Areas (2nd ed. rev.; Chicago: University of c'n~cago Press, 1972). 7. Edwin H. Sutherland and Donald R. Cressey, Principles of Criminology (6th ed.; New York: J. B. Lippincott Co., 1960) 74-81. 17 8. Thorsten Sellin, Culture Conflict and Crime (New York: Social Science Research Council, 1938). 9. Solomon Kobrin, "The Conflict of Values 1n Delin quency," American Sociological Review, XVI (October, 1951), 653-661. 10. Richard A. Cloward and Lloyd E. Ohlin, Delinquency and Opportunity: A Theory of Delinquent Gangs (New York: The Free Press of Glencoe, 1960); Marshall B. Clinard, Slums and Community Development: Experiments 1n Self-Help (New York: The Free Press of Glencoe, 1966); David M. Downes, The Delinquent Solution: A Study of Subcultural Theory (New York: The Free Press, 1966), pp. 2 2 - 5 6. 11. Jackson Toby, "Delinquency and Opportunity," British Journal of Sociology, XII (Sept., 1961), 282-289. 12. Donald R. Cressey and David A. Ward, Delinquency, Crime, and Social Process (New York: Harper & Row, Publishers, 1969), pp. 634-639. 13. Albert K. Cohen, Delinquent Boys: The Culture of the Gang, (Glencoe, Ill.: The Free Press, 1955) . 14. Albert Cohen and James F. Short, Jr., "Research 111 Delinquent Subcultures," Journal of Social Issues, XIV (1958), 20-37. 15. Albert K. Cohen, Deviance and Control, (Englewood Cliffs, New Jersey: Prentice-Hall, Inc., 1966). 16. George H. Mead, Mind, Self, and Society (Chicago: University of Chicago Press, 1934). 17. Cloward and Ohlin, et passim. 18. Robert K. Merton, Social Theory and Social Structure (2nd ed.; Glencoe Ill.: The Free Press, 1957) pp. 131-194. 19. James F. Short, Jr., and Fred L. Strodtbeck, Group Process and Gang Delinquency (Chicago: University of Chicago Press, 1965), pp. 10-13. 20. Irving Spergel, Racketville, Slumtown, Haulburg (Chicago: The University of Chicago Press, 1964). 18 CHAPTER II RESEARCH DESIGN Introduction A conceptual overview was presented in Chapter I which described the role of conflict of values 1n the formation of delinquency areas. Adopting this conceptual paradigm as the study framework, the task of the present chapter is to describe the measurements and methodological procedures devised to transform the conceptual hypotheses outlined earlier into research hypotheses. To this end, three key terms from the value conflict thesis need to be operation ally specified so they can be used in a research design geared to actual data limited by the exigencies and real ities of a concrete metropolitan test site. The terms to be discussed in detail are: delinquency areas, social structure of delinquency areas, and social structural stability. Following these three sections, the value con flict hypotheses will be reformulated as they apply to analytical statements using the three operational terms. Delinquency Areas The Chicago ecological studies, already described, 19 indicate that a delinquency area is a spatial clustering of subareas, or neighborhoods, with similar delinquency rate values. However, since the 1950's there has been little research utilizing a spatial clustering approach. Rather, most of the studies concerned with the ecology of delin quency in urban areas have proceeded from a more abstract mathematical view of delinquency areas . Thus, even though the present study adopts the view of a spatial cluster analysis, it is heuristically important for purposes of comparison and clarification also to examine as a point of reference most of the more recent ecological studies of delinquency areas. Therefore, the two perspectives of clustering will be briefly examined and identified as "spatially" and "mathematically" formulated delinquency areas. Following the discussion of the spatial and mathe matical perspective, a synthesis measurement is suggested, and it is developed in a description of a "Spatial Program to Automatically Cluster Environs" (SPACE). Spatial clustering As mentioned in Chapter I, Burgess, 1 in 1928, de scribed juvenile delinquency, boy's gangs, poverty, and related social problems as located mainly in coterminous neighborhoods in a zone of transition that surrounded the downtown area. Subsequent studies by Thrasher, 2 Reckless, 3 20 Sutherland, 4 Shaw and McKay, 5 and others 6 were all concerne !with what was defined as "natural" delinquency areas in the zone of transition. For instance in describing the spatial location of gangs in Chicago in 1926, Thrasher noted that most of his 1,313 gangs were located in a c1rcu- 1 1ar spatial zone, a sort of "interstitial" barrier between the central business area and the more traditional resi dential areas. Within this general zone he further noted areas which he described as "smaller kingdoms:" They are natural areas, differentiated in the process of human interaction, and having their own characteristic 7 place in the mosaic of the city's life. This description of natural gang areas by Thrasher was essentially an extension of a term that was used to de scribe neighborhoods that developed within the different zonal rings as the city expanded outward. Because of the implicit and explicit reference to delinquency areas as a form of natural area,. the concept of "natural areas" is now briefly reviewed. It was generally recognized by the Chicago urban ecologists that concentric zones in a city were not neatly homogeneous gradients. 8 Rather, the concept of gradient zoning of urban growth was meant to be a generalization of the typical pattern of urban· development that occurred as the city expanded outward from the central business 21 I 1 district. Each zone, however, was viewed as having a unique mixture of social neighborhoods. These neighborhoods were based on spatial contiguity, personal association, and a general sense of community. 9 As part of a process of im- personal and unplanned competition, dominance, and mobility, city growth produced natural areas as products of selective land use and natural boundary restrictions (such as trans portation networks, business organizations, parks, and natural topography) . 1 ° Furthermore, the competition among various populations for living space tended to have a selec tive and restrictive effect upon the kinds of social groups that cluster together in space. Thus, the total urban growth process resulted in specific combinations of land use and social structure, producing spatially defined com munities having unique subcultures characterizing each t . 1 1 1· 11 par 1cu ar oca 1ty. The formal application of the natural area concept to delinquency areas was perhaps most fully elaborated by Shaw and McKay. 12 In their view the development of a delinquency area was as natural in process as the develop ment of a "Gold Coast" or "Skid Row." They defined a set of subareas as a delinquency area where delinquent activity constituted the dominant pattern of juvenile behavior rather than the exception. The characteristics of high delinquency rate areas most often noted were physical 22 ~·--------------·----------------- - I deterioration, overcrowded housing, industrial concentra- tion, high residential mobility, and high concentrations of h . . . 13 et n1c 1mm1grant groups. In a more recent statement expanding on the delin quency area research of the 30's and 40's and the general urban environment, McKay in 1972 amplified the generality of reference of the term, "delinquency areas." He included not only the areas of highest delinquency rates, but also those with less intense patterns of delinquency. The term "delinquency areas," used originally to indicate the areas or communities in a city where the rates of delinquents are highest, has been used often to suggest that delinquent behavior is common to only a few areas and that the other areas do not have to face conduct problems among young people. This is probably never true, and surely it is not the situation either in Chicago or its suburbs. Examination of the maps reveals that rates spread rather evenly between areas with the lowest and those with the highest rates.14 Any atte1npt to use this more generalized definition of the delinquency area concept raises two problems. First, techniques of measurement must be devised and, second, techniques must be developed that locate with reasonable precision areas across the range of delinquency rate magni tudes in the actual areal universe of territory. For over the past forty years, urban ecologists have recognized that the different schemes subsumed under the traditional orien tation were difficult to verify through empirical studies. 15 23 ---------·---------------------------- With particular reference to juvenile delinquency, a number of critical studies focused on the delinquency area concept, some using data from the original Chicago studies, and reported results that did not support the traditional eco- 1 . l . . 16 og1ca or1entat1on. Many of these studies concluded tha the concept of the · delinquency area, as traditionally de fined, may not be generalizable. A principal problem in the use of the concept was the implicit assumption of homogeneity in the characteristics of delinquency areas. Though conceptualized as homogeneous areas with specific cultural, physical, and delinquency patterns, the empirically located delinquency area was in the final analysis found to be an ad hoc spatial area whose uniformity of structural characteristics and delinquency patterns was highly questionable. The question always seemed to remain: How much deviation can be permitted from mean values of measured characteristics of the neighborhoods comprising a given delinquency area before there is no longer homogeneity? Or, how homogeneous is homogeneous? The criticism of the natural area concept in general and of the delinquency area concept in particular suggested that more sophisticated concepts and more complex measure ment devices might have to be devised. At a minimum, any definition of a delinquency area must account for the various degrees of dissimilar qualities within the 24 I I - conscribed boundaries of the natural or delinquency areas, as well as account for the gradient amount of natural area traits outside the ultimately defined area. Many studies of what is defined below as "mathematical clustering" con sisted, first, of attempts to test the question of homo geneity; and, second, of utilizing statistical devices to modify the concept of natural areas and/or delinquency areas to fit into a more acceptable analytical concept of urban space. Mathematical cluste·ring Mathematical clustering refers to the use of measures representing areal subareas clustered according to simi larity of unit measurement for a sample or universe of cases. Each variable might be said to be located only 1n the sense of "belonging to" a mathematical cluster. Thus the primary question being addressed is location 1n ab stract mathematical space; and it may be of only incidental concern to know if the cluster of cases has coterminous boundaries in real space. As it relates specifically to delinquency areas, the mathematical clustering approach is typified first by Bernard Lander's critical work in 1954, where he reexamined the theses of Shaw and McKay; and later when other studies either explicitly replicated and/or im plicitly tested Lander's findings. 17 Most of these studies 25 !focused on the use of the census tract as the unit of I /analysis and applied standard correlational, cluster, factor analysis techniques. The main concern of these I and studies has centered around the differential rates of I 'juvenile delinquency in relation to other social and 1 economic measures in mathematical space for various cities. Another form of mathematically oriented analysis of a universe of territory that has been used to define and analyze delinquency areas is social area analysis. 18 1 technique was first developed by Shevky and Williams 19 This 1n I l 1949. Though used in juvenile delinquency research, the general development of the technique did not stem from an interest in delinquency research. Instead, social area analysis developed to organize urban census tract measures 1n an "attribute of space" of social areas according to three dimensions: social rank, urbanization, and segre- . 20 gat1on. While much of the delinquency research referenced above as mathematical clustering can be generally traced back 1n time to the Chicago school, it was not a mathematical notion of clustering that was of primary concern to the initial Chicago studies. Furthermore, the reconceptuali zation of delinquency area as capable of representation by mathematical space also sacrificed the original idea of such areas as being a contiguous set of neighborhoods with 26 unique and characteristic delinquency patterns. The theo retical importance of the spatial aspect of the delinquency area is predicated on the thesis that its social structural features and its delinquency pattern develop as a result of its differentiation in real space and time. In view of the above discussion, the position taken in the present study is that it is important in any delineatin definition of delinquency areas, that on the one hand the element of neighborhood contiguity be included. On the other hand, it is also essential in the use of measurement in the identification of delinquency areas to use some form of a variance reduction statistical framework to measure th similarity of the neighborhoods comprising any given delin quency area. Such duality of approach would allow a degree of uncertitude in the actual rates of delinquency for con tiguous subareas and still permit the identification of a delinquency area. A measurement of subareal autocorrelations To obtain an empirically based simulate of delinquency areas, a statistical measure of spatial clustering was developed as an algorithm that is a particular application f R C G ' C . . R . ( ) 21 H b f o .. eary s ontiguity atio c . owever, e ore presenting the derivation of the Contiguity Ratio that 27 !isolates a cluster of subareas in an areal universe of territory according to a specified measure, a discussion of Geary's general statistic is reviewed. This is necessary in order to provide the background logic for the special ized development of the statistics for this study. I in its simplest form, c is a ratio measure that indi cates the degree of spatial autocorrelation; it measures the pattern of "likeness" of a subarea by a quality due to . . 1 . . 2 2 Th its coterminous ocation in space. enumerator is composed of the "within cluster" variation for i th contig uous or join satellite subareas (denoted by i') around each observational parent subarea, i. The denominator of the ratio is the total variation for the entire (i = 1 ... n) universe of territory. Thus the contiguity ratio c for an entire universe of territory values is defined as I:.I:~.;.·, (Y. - Y.,) 2 i iri i i , I:. (Y. - M) i i C = (n -· 1) . 2K 1 where i = parent subarea, i' = contiguous subarea or Join, n = total number of subareas in the tot~l universe of territory, k. = the number of contiguous subareas (i.e., i 11 . ) f h . th b ( . t sate ites or eac i su area i.e., paren subarea), 2E -- Kl = I: k. ' 1 Y. value for .th parent subareas, = 1 1 y. ' = value for . ' contiguous subare a to y. ' 1 1 1 I: = sum over all subareas, I:* = sum over contiguous subareas i', and where K 1 is the number of joins for a particular parent subarea i, M = mean value for total universe of territory. If the values for the subareas are randomly arranged throughout the universe, c will equal unity; values less than unity indicate positive spatial autocorrelation; and values greater than unity indicate negative spatial auto- Jcorrelation.23 Unlike many other correlation coefficients, this form of autocorrelation does not have an upper or lower boundary. Thus, c is a summary measure that provides a statistic that can be built upon to inferentially test whether or not a defined problem is distributed in a random fashion throughout the subareas within a universe of terri tory. That is, the measure provides a way of examining spatial patterns to determine if subareas spatially adjacen to each other are more similar (in value) to each other than subareas that are not spatially adjacent. 24 Geary has shown, for example, that if the Y measures are a random sample n from a normal universe with the moment frame of reference, additional statistics can be 29 calculated as follows: where where 1. Var (c) = (n - 1)/n 2 (n + 1) Ki {n 2 Ki + 2n(K 1 + K 2 )} -1, Kl Kz 2 . 3 . = = z:.k. 1 1 n ' 2 Z: • k. 1 1 • n SE (c) Z* = ✓Var (c). = (1 - c) SE ( c) ' Z* = the test statistic that describes the normal deviate or distributions of the observed value of c from unity. As indicated by the symbol used here, Z* can be interpreted as equivalent to the usual Z score. Therefore, if the Z* value exceeds a predetermined signi ficance level, it can be said that a spatial autocorrelatio does exist; and that similar values are more likely than not to be adjacent to each other in spatial location. Geary also considered the sampling distribution which has only a randomization function underlying the rationale 25 of c. On the one hand, it was noted by Geary that one advantageous aspect of a randomized model is that it can be 30 I utilized without concern for the normality of the random sample. On the other hand, this particular theoretical limitation has been demonstrated to have little pragmatic relevance and it appears to be immaterial which point of view 15 adopted regarding the type of sampling distribution, even if the n sample size 15 relatively small. 26 In its general form, the Contiguity Ratio 15 a summary measure of an entire areal territory's degree of spatial clustering. This is also true of other summary auto- ,correlation statistics developed. Therefore, as it was developed, c does not isolate clusters of similar subareas spatially located throughout a given universe of territory; c only indicates if such tendency to cluster does exist. I It should be noted in passing that as a general statistical approach the actual application of the various spatial auto correlation techniques in sociological literature has been h . f 28 rat er in requent. It is the task of this study to develop a simulation model that can statistically isolate subarea spatial clusters within a total universe of territory that will reflect delinquency areas. To accomplish this, a derivation of c was designed where the procedures already described may be applied to a series of smaller sets of subareas within the universe. These algorithmic procedures, instead of treating the whole universe of territory as a single L _______________ 3___. l I data set, are such that a resultant cluster of contiguous subareas may be isolated through a gradual "building up" lvia a decision tree process of statistically related and coterminous subareas. Because of the complexity, the repetitive nature, and the elective alternatives available at various junctures within the methodological procedures, a generalized computer program was developed to actually execute the total process: a "Spatial Program to Auto- ,matically Cluster Environs" (SPACE). The computer program is written in ANS-FORTRAN. The steps which are posited to be specifically germane to the spatial identification of delinquency areas are outlined below. Following these steps, the rationale for the decisions and possible alterna tive procedures are discussed. The steps are as follows: Step 1. The highest observed score Y. (i, ... ,n) for l the i subareas is isolated. In general, this value could be any defined interval variable. For example, it might be the greatest proportion of juveniles that are somehow defined as delinquent for a given urban subarea. Step 2. Defining Y. and its contiguous subarea Y., as l l a "temporary subuniverse," a test for spatial similarity 1n the form of a special application of a Z test of proportior p lis computed between Yi and the largest value Yi, join. The rationale for this particular test is discussed below, following the individual procedural steps. [ ___ _ If the Z value p 32 does not indicate a spatial similarity (i.e., support of the null hypothesis) according to a predefined level (e.g., significance< .OS), the next largest observed contiguous i' value is tested. If all joins Y., are tested and the i null hypothesis rejected in each case, Y. is given a unique i numeric identification and is definedasa detached subarea (i.e., the subarea stands alone in relation to all adjacent subareas), and the next largest observed Y. in the universe i of territory is isolated. Steps 1 and 2 are repeated until a contiguous subuniverse (i.e., there must be at least two joined subareas) is found, and/or the total universe of Y. i values is exhausted. If a continuing subuniverse has been defined, it is given a unique numeric identification label and vectors from the contiguous subareas Y., are obtained. i Step 3. A vector of contiguous subareas is developed by starting with the highest unencumbered (i.e., not pre viously used or rejected) join value Y., statistically i defined to have a similar spatial value as the original parent Y. of the subuniverse. i Step 4. This Y., is now treated as the new subsequent i observation Y. in the subuniverse. i Its spatial joins Y., l are in turn tested for subuniverse spatial homogeneity- excluding all subareas that have been previously rejected from the subuniverse, or already in another separate sub universe, or determined to be a detached subarea (i.e., a -------------------- 3 ~ subuniverse with only one subarea). The vector direction is in a continuing hierarchically ordered and contiguous set of branches and subbranches until all join values are exhausted according to the predefined level of statistical spatial similarity. If the number of subareas permanently belonging to the subuniverse is less than, say, four to six · th b h b d . . . 1 1 . 1 su areas, t e test to e use 1s again a spec1a app 1- cation of Z test of proportions. For a larger number of p subareas the formulae above for c, Var(c),SE(c), and Z* may be applied to test for subuniverse spatial contiguity. Step 5. As long as the subareas of a particular decision branch are found to be homogeneously clustered 1n space to the total subuniverse of values, the particular 1 vector is increased by repeating Step 4. However, if the predetermined significance level for Z* is not reached, and all subbranches are exhausted, the vector is terminated along this particular direction, and a new decision branch 1s initiated, based on its value size and spatial adjacency to the terminated branch. Step 6. The procedure 1s repeated from Step 3 to Step 5 for each succeeding unencumbered and hierarchically ordered Y., subarea of the original Y. of Step 2 until all 1 1 !branches and subbranches along each vector have been ex- hausted. Step 7. After a subuniverse has been totally defined. 34 the next largest observed Y. in the total universe of terri 1 ltory that is outside all defined statistically contiguous areas or detached subareas is isolated, and Steps 2 through Step 7 are repeated until all subareas have been defined as detached or part of a unique subuni verse. Within the general steps just described, several pro cedural decisions were indicated without discussion as to why the particular methodological form was adopted. For example, by following the procedures just described, if there are spatial clusters 1n a universe of territory, they must be uniquely defined. That is each subarea will be assigned to a "mutually exclusive" spatial set, depending on the statistical similarity of the spatially neighboring subarea values. Also, since the initial starting point in the cluster space is always the uncommitted subarea with th largest measurement value (i.e., sample selection without replacement), there is inherently a diminished degree of freedom of the clustering process as each subarea is remove from the domain of possible acceptance. An alternative approach could take the form of "mutually inclusive" spatial clustering of subareas in a universe of territory. In this case each subarea in the universe 1s placed back into the universe after each sub universe 1s isolated. It follows from this approach that how ·the initial selection is determined is not procedurally 35 important, because each subarea can have its turn at being the starting point in the cluster buildup. This form of developing a spatial subuniverse might be particularly use ful under conditions where one subarea can legitimately belong to more than one subuniverse; the formation of school attendance areas might be such an example. In the current study, however, the mutually exclusive approach was adopted for its heuristic clarity to: (1) morE fully reflect the concept that a delinquency area is a set of subareas that are unique in their similarity of delin quency intensity; (2) permit the examination of differences in delinquency areas as to the levels of delinquency inten sity, especially delinquency areas that in themselves are spatially adjacent to each other; (3) isolate unique spatia] subareal cluster delinquency patterns that can be concomi tantly related to independent indicators of social structurE specified in the value conflict theory. In addition to the rationale justifying the mutually exclusive approach, the actual indicators for delinquency used in this study were also initially subjected to the mu tually inclusive technique of spatial cluster. This pretes1 was designed to ascertain whether a "best" spatial cluster could be determined among all the clusters which contained the highest measure of delinquency. The results seemed to indicate that no one cluster was statistically more homo geneous than the initial cluster selected through the 36 I mutually exclusive procedure. In Steps 2 and 4 of the above procedures, it was noted that below a minimum number of subareas, a special appli cation of a Z test of proportions might be used for testinf p spatial similarity of observed Y values. This condition 1s imposed on the procedures due to the fact that SE (c) is totally determined by the number of subareas and the number of connections between the subareas for a given spatial cluster. Therefore, as the number of subareas and the number of joins go down, the less the Z* statistic can be expected to reflect a normal distribution, a condition assumed for the test to be useful. 29 The most extreme case of the limiting effect is found in procedural Step 2. In this step the initial comparison is always restricted to two subareas which, of course, also has only a total of two connections. Thus, 1n order to provide a way to adjust for the distributional bias that occurs as the number of sub areas become smaller (say somewhere between two and ten 30 subareas), a special application of the one sample critical ratio for proportions has been provided as an option in the computerized algorithm for determining if two contiguous subareas are random. 31 Replacing Z by Z , the p C critical ratio for contiguous proportions is defined in this study as z = IP - Pl/ IPQ/N, C 37 '----------------------------------·- where nl = value for first subarea, n2 = value for second subarea, N = nl + nz, p ;: n 1 /N, p = hypothesized proportion, Q - 1 - p. In the usual application, the actual input values into Z p are in the form of nominal frequency counts N, and pis 1 derived from the partitioning of a subset n 1 from the total frequency . This subset n 1 is compared to the total count N to create a relative measurement of frequency. This is not the case in Zc' however. Here, n 1 and n 2 are at least interval measurements which are treated as pure numbers and when added together= N. Since the objective is to statis tically test the argument that two numbers are the same, the original measurement form of the numbers is not impor tant. Thus, as long asp= n 1 /N (i.e., n 1 + n 2 ), it is clear these values can be subjected to the same inferential treatment formulated by the critical ratio one sample proportion test. Thus, the primary purpose of Z is to C provide a test for the two contiguous subarea case that can complement Z* when a small number of subareas are involved. I By definition it should be expected when Z is used to test p for similari t y in a dichotomy of N values, the null or no 38 difference proportion (P) is equal to .50; because if there is no difference in a dichotomy, an equal division of the 32 total N must occur. In general, then, with the P value established, a Z test can be applied to test the distance p p, the sample proportion statistic, is from P, the hypo- ' thesized parameter proportion. If the distance is not significantly different, it is said that the null hypo thesis H 0 is sustained. Thus replacing P with .SO, Zc ' becomes z = IP - .sol / ✓.so x .so/N = IP - .sol / ✓.zs/N. C , Also, since direction is not important in determining if one contiguous subarea value is similar to another, the absolute value of p-P can be used in the current study. A note should be made here regarding the slight functional difference between using Zc and Z*. When Z is used, the C focus is only on similarity and is, therefore, more uni dimensional; the only concern of the statistic is with the similarity of the value in subarea n 1 to the value in sub area n~. The fact that the two subareas have coterminous boundaries does not enter into the actual calculation of Z, C and the variance of the statistic is determined only by the proportional value between the two subareas. Thus the actual statistic is insensitive to spatial position, and the spatial arrangement of the values is controlled only by the methodological procedure of defining the proportional 39 I . comparison (of n 1 and n 2 as N) through the sharing of a common spatial boundary. Of course, at the initial start of the subuniverse there will be only two subareas, and spatial location is unimportant, except that the subareas are coterminous. Clearly, then, it is at this beginning 1 stage (i.e., Step 2 above) and Z is most effective in de- e fining homogeneity in the subuniverse. It should be noticed and cautioned, here, that a continual accumulation of subareas into a subuniverse through the sole use of this statistical procedure becomes a more dubious procedure. As the number of subareas increases in the cluster, through the use of Z , there is also a greater chance for error, C since the only spatial link is the assumption of procedural additivity of contiguous areas of similar value. There fore, Z should not be the only determining test of the C final cluster; it should be considered as simply a m1n1mum statistical expediency that may identify the beginnings of a spatial cluster. In the practical situation of attempting to identify delinquency areas in this study, however, Z insensitivity C to spatial location as the number of subareas increases in the subuniverse is more or less checked by the introduction of Z* into the total algorithmic process. The latter test statistically addresses the question: !s the observed arrangement of subarea values in physical space 40 I significantly different from what a random arrangement of the same values would be? However, while spatial position is explicitly prominent as part of the statistic, it is also indicative that reliability of the statistic improves as the number of the subareas increases. To state the con verse, the unique identification of a subuniverse of con tiguous subareas with similar values decreases as the number of subareas decreases,because the variance in Z* 1s I totally dependent on the number of subareas and the number of connections. For this reason it is suggested that the procedural algorithm include both Zc and Z*. The first test is most useful for the identification of the first few subareas into a subuniverse as being statistic2lly similar in value; while Z* becomes more effective as the number of subareas increases and assures that initial subareas under Z and the later included subareas identified by Z* compose C a unique set of contiguous and statistically homogeneous subareas. The advantages gained by operationalizing the spatial structure of delinquency areas in the above simulation process have been already alluded to in previous discus sions; however, for purposes of a more concise summary, four points are reiterated: 1. The clustering procedures described can be execu ted for as many different types of delinquency areas as 41 ------------------------------------- desired. 2. Each simulation can be adjusted quickly for rele vant conceptual conditions as well as levels of acceptable measurement error (i.e., statistical Type I or Type II error conditions), (a) either before the simulate process begins, (b) during the process, or (c) treating the result ant delinquency spatial areal clusters to further statist ical analysis (e.g., do the delinquency subarea values 1n a cluster have meaningful concomitant relationships with social structure). 3. Unlike mathematical clustering, or most of the current delinquency research analyzing structural patterns, the present approach tracks, isolates, and uniquely identi fies the delinquency area in space and thus allows individ ual areal analysis of each cluster. 4. Finally, the areas isolated by the above simula tion process are posited on statistical probabilistic statements which 1n turn allows for certain degrees of incertitude and gradient qualities within the isolated sub un1verse. Thus, this approach 1s 1n contrast to the implicit assumptions of the traditional approach to natural! or delinquency area analysis that treated metropolitan subcommunities as having a finite homogeneous set of sub areas with specific and definite sets of cultural and physical traits. 42 Structuring Delinquency Areas In the previous section the focus of attention was on I the analytical spatial definition of delinquency areas in I a total urban universe of territory. The actual composi tion of the delinquency area was only generally indicated. This section addresses the analytical content of the defi nition of delinquency area used in this study. It adopts 33 the point of view expressed by Blau, namely, that "social structure is conceived explicitly as being composed of dif ferent elements and their interrelations or abstractly as a theoretical construct or model." The basic task, here, then is to develop operational specifications that define in testable form the elements hypothesized to be inter related in such a way as to identify the differentiated types of delinquency areas pos~ulated in value conflict theory. Empirical referent for an elemental typology of struc turally determined instrumental and expressive delinquency As discussed in Chapter I, a delinquency area is con ceptualized as an urban area with a consistent pattern of delinquent activity. This pattern is characterized by the degree to which delinquent activity is capable of being integrated with the conventional social and cultural 43 I structure of the area. Ranging between two extreme polar types, the integration can be total or minimal. In general the degree of integration depends on the relative stability of conventional institutions in such neighborhood subareas to which there is a substantial degree of collective attach ment. These include the family, often of the extended type the church with its associated social activities, and social and political organizations based on ethnic or racial identity. 34 While neighborhood cultures possessing h 1 . f . . 11 35 h t ese e ements 1n pure orm are emp1r1ca y rare, t e pattern is paradigmatic for all instances of neighborhoods in which instrumental delinquency may be found. The pro position here proposed is that local institutional orders which combine stability with the presence of successful adult criminal models will tend to generate delinquency of the instrumental type. Thus a high degree of structural stability in areas also characterized by persistent delin quency will tend to be reflected in patterns of what is here labeled "instrumental" delinquency. Since a high re gard for success in illicit enterprise is included as a stable element of the local value structure, only similarly successful delinquent activity is regarded as status en hancing. Kobrin, for example~noted that: ... delinquency tends to occur within a partial framework of social controls, insofar as delinquent activity in these 44 areas represents a tolerated means for the acq~~sition of an approved role and status. In contrast, those areas that are undergoing relatively 1 rapid social and economic change generally weaken estab lished social institutions, resulting in a weakening of the socializing effect. As a consequence, juvenile delinquency is less subject to adult controls and tends to acquire a pattern of what might be labeled "expressive" delinquency. Although adult crime may also be present in these areas, it is unorganized, and there is little or weak integration be tween the conventional adult social structure and no toler ance of the prevailing type of delinquency. The delinquenc pattern in such areas reflects this instability; delinquent acts are viewed by residents as alien to accepted societal norms. 37 Bandura characterized the distinction between the two types in the following passage: Delinquents who strike victims on the head to extract their wallets expediently are generally considered semiprofessional thieves who are using income-producing instrumental aggression responses. By contrast, delinquents who simply beat up strangers but show no interest in their victim's material possessions are sup posedly displaying gmotional aggression of a · disturbed sort.3 In using this example, a caution should be raised as to the !level of relevance (or abstraction) represented by the instrumental and expressive categories, as applied to this study. 45 It should be noted that if the focus of attention is shifted from the structural to the emotional level, the actual motivation of an individual expressive act may be also construed as an instrumental act. For example, the same aggressive act described above, where beating up a stranger without interest in the material reward, might also be the result of the juvenile seeking approval, respect, and/or social status in a neighborhood peer group. 39 No matter what the individual's incentive 1s, however, the form of delinquency as .pictured in the latter example is posited in this study to be most pronounced in neighborhoods with relatively high rates of change in the various elements of neighborhood social structure. Thus, as the two terms are here used, they represent typification of delinquent acts in which the element of personal moti vation is crucially constrained by the context of structura forces 1n which the delinquent act occurs. In identifying specific types of delinquent and criminal activities that express either one or the other polar types of behavior, Chambliss 40 similarly typified acts such as theft, forgery, shoplifting, various white collar crimes, and robbery, as instrumental acts. Express ive offenses included among others drug and alcohol offenses, usually murder, sex offenses, and assault. Thus, using the logical constructs described above and the _____________________________ 46 I specific examples just presented, all officially defined delinquent acts and adult violations may be usefully re duced to their instrumental or expressive content. The purpose here is to transform these conceptual polar types of delinquency areas into an empirical reference that can be used as the basic data inputs into the previously de scribed spatial clustering algorithm. Therefore, all legally defined deviant or criminal activities have been separated into either an instrumental or expressive cate gory and are presented in Tables 1 and 2. As is the case of all typologies, the conceptual constructs may not have a precise fit to all possible conditions of the actual data. The pragmatic applicability of the empirical typology, however, will depend upon the usefulness in the actual analysis of the formal hypotheses. 41 The separation of all officially defined deviant acts into either instrumental or expressive categories does not provide a spatially useful structural indicator. Such cate gorization only furnishes a summary count by the two re spective categories that can be attributed to a specific subarea or neighborhood within the total urban area. Since each subarea 1n the total metropolitan area is not neces sarily equal in spatial size or total number of persons, a form of areal standardization is necessary to transform raw tallies into conceptually meaningful indicators that are 47 1 ~ Instrumental Burglary Checks/W.F. Forgery Fraud Liquor Violations Possession/Marijuana Sale Possession/Narcotics Sale Prostitution or Visiting Receiving Stolen Property Robbery Tampering with Auto Theft Auto and Joy Riding Theft, Grand Theft, Petty TABLE 1 SUMMARY OF LEGALLY SPECIFIED JUVENILE DELINQUENCY ACTS SEPARATED INTO INSTRUMENTAL OR EXPRESSIVE CATEGORIES Arson Assault Assault/Weapon Battery Curfew Dangerous Weapons Disturbing Peace Disorderly Conduct Drunk Drunk Driving Fail to Obey Court Order Glue Sniffing Hit/Run Vehicle Indecent Exposure Acts on Child Malicious Mischief Trespassing Murder Possession of Marijuana Possession/Marijuana Use Possession of Narcotics Possession/Narcotics Use Miscellaneous Narcotics Rape, Forcible ExEressive Rape, Statutory Resisting an Officer Riot Miscellaneous Sex Offenses Traffic Violations Beyond Control of Parents, Incorrigible Beyond Control of School Officials Danger of Leading Lewd, Immoral Life Runaway Transient Truancy Miscellaneous Delinquent Tendencies No Parent or Guardian No Parent or Guardian Willing to Exercise Control No Parent or Guardian Actually Exercising Control:_ Unfit Home - Neglect Unfit Home - Cruelty Unfit Home - Requires Medical Consent or Treatment All Other Dependent Situations Miscellaneous Felonies Miscellaneous Misdemeanors +:>, ~ Instrumental Auto Theft Bookmaking Burglary Checks/NSF Forgery and Checks Liquor Violations Miscellaneous Fraud Petty Theft Petty Theft with Prior Possessi9n of Narcotics For Sale Receiving Stolen Property Robbery Theft Except Auto Theft, Auto Unemploymentihsurance Act TABLE 2 SUMMARY OF LEGALLY SPECIFIED ADULT CRIMINAL OFFENSES SEPARATED INTO INSTRUMENTAL OR EXPRESSIVE CATEGORIES Acts Against Public Decency Assault Assault/Battery Assault/Deadly Weapon Carrying Concealed Weapon Child Neglect Child or Wife Beating Expressive Contributing to Delinquency of Minor Deadly Weapons Disturbing Peace Disorderly Conduct Drunk Driving Escape Failure to Render Aid Hit and Run Lewd and Lascivious Conduct Lewd Vagrancy Malicious Mischief/Trespassing Manslaughter Manslaughter, Vehicle Miscellaneous Felonies Miscellaneous Misdemeanors Miscellaneous Narcotics Murder Non-Support Possession of Marijuana Possession of Narcotics Rape Rape, Statutory Sex Offenses Temporarily Taking Auto Traffic Violations, Other- Felony 1 comparable throughout the total urban universe of terri tory. Thus, the formulation of a neighborhood's degree of instrumental or expressive patterning requires further ioperational specification. I Before discussing individual specifications for each I !particular indicator that is conceived to reflect the typology of relevant delinquency areas and elements of the conventional social structure, however, it will be necessary to examine how the study design specifies the general devel- 1 opment of any "structural indicator." Therefore, at this point in the chapter the discussion turns to, first, how all spatial indicators are formulated and then to measure ment construction before moving to the actual specification of instrumental and expressive delinquency area measures and indicators. 1 Co~po~ite inditato~~ b" f ~6c·ial structure The concept of a spatial indicator of all aspects of neighborhood social structure will be shown below to be ,most useful only if a variety of standardized summary I statistics are used in a composite form of indicator con struction. The formulation of a composite score from several individual measures is based on the expectation that any one variable is only imperfectly correlated to a so ----------------------------------- particular structural characteristic; and even through the I use of composite scores, the derived social structural indicators at best can only indirectly measure the total concept. This approach, in essence, assumes that in the formation of an operational specification of an indicator of some element of social structure, it is highly unlikely that any single constructed variable can stand as a primary 42 measure of the structural element. As will be indicated, this is particularly so when the structure also has a spatial context. Therefore, the term "indicator" as it , , is used 1n this study, is itself a form of abstraction, be cause each specific measure included in the construction of the indicator score is hypothesized as being conceptually related to an aspect of social structure which is not measureable by a single variable. 43 However, it is posited that by including and appropriately weighting several criterion measures to represent a specific delinquency pat tern or other related elements of neighborhood social structure, the more abstract composite indicator should present a fuller and more reliable picture of the neighbor hoods within the various delinquency areas. Knowledge of the weighting coefficients, for example, provides a way of determining which dimensional facets are more relevant and how they should be treated in the total indicator construc tion. This, in turn, should improve the total comparative L 51 reliability between the subareas in the total universe of t erritory, in that the composite accounts for the differ entiation of the importance of the individual measures 1 across all subareas. 44 Within the context of the study's definition of com posite indicator, what is needed is a mathematical model which can produce a single comparative score for each sub area for each element of the social structure relevant to the constructs of the value conflict theory. As such, the model equation should treat the various relevant individual measures as independent variables and intercorrelate them so that the dependent variable is represented by the under lying common dimension or pattern of the variables. The pattern, of course, would then represent the abstract ele ment of the social structure. Perhaps the most efficient method of developing such scores is through the use of factor analytic techniques. In this study the use of factor analysis, however, is applied in a very restricted way, and it is in contrast to the usual use of the technique. For example, any one of the many factor analytic techniques is often applied for the sake of reducing the number of variables by extracting unknown or "hidden" factors. The factors that emerge are then subjected to a form of ex post facto logical meaning of the factorial results. 45 52 In contrast, and perhaps the most defensible approach 1n using factor analysis, is the use of the technique in 1the present study. Here the factors to be extracted are a priori determined. The actual variables are initially developed as being conceptually relevant as indirect measures of a particular element of a spatial social struc ture. As such, the unique measures can be applied to a one factor model of factor analysis 1n order to measure the contribution the variables have to a single common factor 46 pattern. The general procedural steps in the process of a one factor model as it pertains to the current study are generalized as follows: Step 1. The unique predefined measures of a particular structural element are subjected to a principal component solution for a single factor extraction. The method actu ally used is somewhat arbitrary; in the special case of a single factor model, there can be only one set of final factor loadings which can reproduce the original input cor relation matrix. Since these same factor loadings are the only loadings that can be obtained, it should also be clear that the factor loadings are not subject to adjustment by f h . h . 47 any o t e rotation tee n1ques. Step 2. The factoring process 1s initiated by a "best" estimate of the proportion of the variance (i.e., 53 commonality) of a particular variable due to the factor. In general, the closest estimate is obtained through the initial insertion of the squared multiple correlation of each variable with all remaining variables in the main diag anal values of the correlation matrix, and refactoring iterations (i.e., new commonalities estimated with the process starting at the beginning) are performed until the recomputed commonality estimates stabilize (i.e., there is no change in the commonality estimates beyond .001). 48 Step 3. After Step 2 is completed, the coefficients from the factor matrix obtained are also the final factor loadings; as discussed above, no adjustment rotation techni que is warranted. These factor loadings are expressions of the correlation each variable has with the factor; and since there is by definition only one factor, the square of the loading also expresses the obtained commonality of each variable. Thus, each final commonality obtained also re flects the amount of variance in the variable accounted for by the factor. Step 4. Given the factor loadings which are the cor relations between each variable and the factor, and given the intercorrelation between the variables, it is possible to apply multiple regression methods to the variables to obtain regression coefficients as optimal weights to maxi mize each factor loading to form a composite with the 54 49 factor. Of course, once the regression equation is deter- 1 mined whereby the factor is the dependent variable, a multi ple correlation coefficient and, more important to this study, factor scores can be calculated for each subarea. In order to fit the factor loading and intercorrelations to a least squares solution, the general format of the equatior should include a standardization of the input variables to Z score form. These standard scores are then converted to standard score factor scores. The general equation is where f 2 = the composite factor score in the form of standard score form, a= calculated regression coefficients, Z = a standard scale of the original variable (i.e., x - M/s), x = actual variable for a particular subarea, M = means of the variable for all subareas, s = standard deviation, n = number of variables in the single factor. Standard score values are used for the independent variables to assure that the intercept value will be zero and hence does not enter into the calculations. In summary, the above procedures of obtaining factor 55 loadings for a priori determined variables through a one factor solution provides the basic statistical procedures which can be used to develop a composite indicator for each subarea in the universe of territory. By carrying the "conceptualized" factor loadings through a least-square 1 multiple regression format, the weights produced will give a maximum correlation between the abstract concept of an element of a social structure and the individual measures selected as being relevant to a spatial analysis. The actual validity of any composite indicator score obviously is dependent on available data, how the data are transformed into relevant variables, and how the variables are trans generated into indicators that reflect a reasonable opera tional specification that represents an element of the conceptualized social structure. Thus, with the form of indicator construction speci fied, the discussion of this section now turns to the general formulation of the input variables to the indicator procedures. Here the concern shifts to the development of each variable generated from unit data counts of the sub areas . Because characteristics of areal data can be greatly influenced by the construction of the measurement, data items summarized to reflect an aspect of a particular element of social structure are als o transformed into various common spatial dimensions of standardization. These 56 ' 'various forms of different summary units are then available as inputs into the composite indicator construction pro cedures. Spatial standardization of rate formulation for indicator cons tr· uction Concrete variables constructed to be input to the indicator logic described above may generally have at least four forms of spatial measurement construction. Each of these rates is constructed to represent a different type of subarea standardization of some aspect of a neighborhood's areal social or economic condition. Therefore, the numer ator is always the same and is a tally summary of a parti cular set of data and the denominator is an expression which provides the standard relation of comparison. The principal difference in each rate for a single dimension of the structural indicator is that the denominator is predi cated on a different basis of spatial comparison. Having a different denominator in turn provides a totally differ ent interpretation of a specific measurement. The ele mental forms include expressions defined below as (1) concentration, (2) distribution, (3) density, and (4) unit --• share measures. In addition to these four common measure- ment forms, however, different measures are developed which are unique to the construction of either instrumental or expressive delinquency indicators or are expected ·to be 57 germane to other elements in the construction of indi- 1 cators of the social structure of delinquency areas. The explication of these special measurements will be discussed below in Chapter III. The four forms of measurement dis cussed in this subsection are not unique to any structural indicator; and, therefore, they are presented in this study as a generic form of measurement construction applicable to all relevant elements of neighborhood social structure. Concentration measures.--This measurement form is constructed to compare a specific summary count of some f h . 1 1 . 50 part o a group p enomenon to its tota popu ation. For example, a concentration rate for instrumental juvenile delinquency would include in the numerator a summary count of all persons in a subarea recorded as having committed instrumental delinquent offenses. The denominator of the rate would include all juvenile offenses in Table I for the same subarea. The concentration measure provides a de scription of the prevalence of a specified type of delin quent act as a proportion of all delinquent acts. Distribution measures.--This form of rate compares a specific phenomenon to a population at risk. 51 For example, the numerator is the number of expressive or instrumental delinquent acts committed by juveniles in a particular sub area for a particular period, and the denominator consists 58 of all juveniles 10-17 years of age in the same space and time period. In contrast to a concentration measure, this measure standardizes not only on the phenomenon of interest but it expands the comparison to the specific population at risk. Thus, in the example of instrumental juvenile delinquency, the measure reflects the distribution of such acts among all those eligible to commit delinquent acts. Density measures.--The basic form of this measure is a ratio of the phenomenon of interest (e.g . , number of acts o instrumental delinquency) to some areal unit, such as acres ·1 52 or square mi es. Here the denominator base is not re- lated to delinquency or potential delinquents but is shifte to the area unit. This is a different facet of the distri bution of the phenomenon, for it measures the degree to which the unit count prevails in a unit space. This measurement is often considered an essential supplement to any statistical analysis using measurement based on areal . 53 units. Unit share measures . --The last rate to be presented here compares a particular subarea's unit count (e.g., number of instrumental delinquent acts) to the tally of the h f h 1 . f . S 4 Th same p enomenon or t e tota universe o territory. e denominator may be considered an external reference of the same scale dimension. Thus, unlike the three forms of 59 I I measurement described above, the phenomenon of interest 1s in both the numerator and the denominator, and the •standardization unit of the subareas is the count for the 'total universe of territory. Soci~ Struc tu·ral S'tabi 1 i ty To keep the exposition of the operational terminology relatively simple , all discussion thus far in this chapter has centered around spatial considerations involved in delineating delinquency areas and the statistical procedures and methodology designed to isolate static elements of the social structure in the area, as reflected by the composite indicators. In general, such procedures are only descrip- tive of the elements of social structure described earlier. These procedures are adequate only for cross~sectional analysis: specified independent variables constituting indicators of social structure are related only concur rently to indicators of types of delinquent acts. The procedures as described provide no information relevant to the central general hypothesis of the study, namely, that the rate of change in selected features of local social structure constitutes the major determinant of its delin quency pattern. A test of this proposition requires the introduction of the temporal dimension as a major condition in determining specific group patterns of delinquency 60 'in delinquency areas. Considerations i~ measuring conventional elements of neighborhood social structure Previousiyit was argued that delinquency areas can be rangeJ along a continuum between polar types generating clearly instrumental or expressive delinquency, depending on the degree of integration between conventional and criminal valnes in neighborhood culture. And the degree of integration in the delinquency area in turn was postulated as largely determined by the stability of those elements of local social structure supportive of conventional values. These structural elements are hypothesized to include socio economic status (i . e., class), land use, and demographic (i.e., ascribed) character. 55 Given this orientation con cerning the conditions related to the differentiation of delinquency areas, a strict cross-sectional comparison would be insufficient and misleading in testing the nature of the relationship between type of delinquency area and the degree of stability of the essential elements of the social structure. Therefore, the next step in the defini tion of operational terms is to specify how the indicator methodology can be further enhanced to measure the relative degree of stability of the appropriate elements of social structure in the spatially defined delinquency area. 61 One approach to the development of indicators of change in local social structure is to extend the procedure~ 1 of composite indicator construction to include a second I I facet of multidimensionality. Here the indicator is still conceived to be a composite of conceptually and statisti- cally related variables on the one hand; on the other hand, each input variable is used as a measure of change over I • time. There are, of course, a number of ways that trends over time can be summarized. For example, Duncan, Cuzzort, and Duncan 56 noted that in a sample of cases for two points ,in time, at least four different types of indexes of changes might be calculated. They defined the types as 1 . Absolute change = tz - tl 2 . Relative change = tz - tl/tl = t 2 /t 1 - 1 3. Positional change = {(tz t 2 )/st} 2 {(tl - tl)/st} 1 4 . Deviational change = tz - tz* where tz * = a + b tl, tztl tzt1 t = areal unit value for time one (tl) or time two (tz), t = mean value for all areal units in the sample for time one (t 1 ) or time two (i 2 ), s = standard deviation for time one (st ) or 1 time two ( st ) ' 2 a = the intercept point on the tz axis of a tz,t1 standard least squares regression line, b the coefficient of tz tl (i.e., tz,t1 = regression on the increase in variable t 2 of every unit increase in variable t 1 ). Of the four types of indexes of change, the authors note that: a meaningful measure of relative change can be computed only for a variable measured on a rate scale, i.e., a scale with truly equal intervals and an absolute zero point. It is obviously inapplicable to the kind of index number in which the location of the zero point is determined arbitrarily .... the only one of the four change measures that fully takes into account the level at the beo/inning of the period is the deviational change. 5 Deviational change used as a measure of stability What is especially important to the present study is that a deviational change measure reflects a "true" degree of relative change over time. The degree of change is calculated from an absolute zero point by utilizing a transformed set of variables. This measure is particularly suited for the present problem, because it reflects the velocity of change between points in time of the observa tion, while also accounting for the "natural" influences on 63 stability and change that may be occurring for the entire urban area. In that the measure accounts for the various forms of variable maturation, decay, and inflation, it will not indicate instability in the selected delinquency sub areas unless the change occurring is above or below that of the total urban area. For example, if median income for all urban subareas in a metropolitan area were measured for change between, say, 1960 and 1970 using a deviational measure, no additional adjustments would be required to account for inflation. It would be accounted for auto matically. Furthermore, since the measure is actually a special form of the residual differences between estimated and expected values of a standard linear least square re gression line, another useful statistical interpretation is justified when used in an interannual application to the measurement of subuniverses of stability. For example, once delinquency areas are determined for the metropolitan area, the selected measures which are to reflect the various aspects of social structure can be examined for neighborhood stability. A zero value, of course would indicate relatively no change in a subarea as compared to the total trend in the metropolitan area. At the same time it would also follow that relatively accelerated changes in neighborhood structure would be indicated by the size of the positive values. Likewise, relatively low social change L_· _____________ 64 would be reflected by the size of the negative values for subareas. 58 Com· pos'i te indicators o"f ·devi a tional change Given that the procedures described are a way of 1 measuring relative stability in a universe of territory, it 1is not a difficult task to transform the appropriate 'spatial rates into measures of deviational change. By 1 using these change rates 1n lieu of simple cross-sectional I spatial rates, the logic of the composite indicator con - 1 struction described earlier can be extended to represent I the degree of neighborhood stability for elements of the social structure such as socioeconomic status, land use, anc demographic characteristics. In this study, then, the additional application of its methodological procedures can be used to create "composite indicators of deviational change." Ancillary summary measures of stability Though not directly applicable to interpretation of the particular subuniverses of the delinquency areas, the 1 use of deviation change measures as reflections of metro politan subarea stability can be extended to allow descrip tive summary statistics to be calculated. Such statistics 65 ··--------------------------------------' are to be used here, then,as general descriptions of the conditions of stability that exist in the larger community of which the delinquency areas are part. The most useful summary statistics would be those used to determine the degree of relative stability over time, and the general segments of the distribution that are changing more or less rapidly over the period of observation. While the measures are generalizable to any number of temporal observations, 59 they are perhaps most often used for two points in time, 60 as they will be used in this study. Thus, two summary sta tistics seem particularly important 1n describing the over all distribution of deviational change measures for the total universe of territory (i.e., the defined metropolitan area). The two summary statistics are labeled in this study as (a) interannual correlations and (b) interannual regression coefficients. Interannual correlations.--In order to summarize the degree of stability (or change) for a given measure over two points in time, a standard product-moment correlation (for t 1 , t 2 ) can be obtained on an interannual basis. In this special application, the correlation is calculated whereby the independent variable (t 1 ) represents time one and the dependent variable (t 2 ) represents time two for each neighborhood characteristic. The higher the rt t 2' 1 66 value, the more it is indicative of an overall stable pat tern of neighborhoods in the urban area. Interannu~l regression coefficients.--In the equation above describing the measure of deviational change, the regression (bt t) was described as defining the slope of 2' 1 t 2 on t 1 in unit values. In this special case where the regression coefficient is calculated from the same variable over two points in time, bt t can be interpreted as an 2' 1 index of average change from the beginning to the end of the two periods of time. Given the same unit of measuremen for two different periods of time, projected on the abscissa (as t 1 ) and ordinate (as t 2 ) of an axis, ab tz,tl value of unity can clearly mean that any change that took place from t 1 to t 2 is on the average equal (or no change) along the entire bivariate distribution. It also follows that a bt t less than unity indicates that subareas' low 2' 1 values increased or decreased (depending on the sign) faste on the average from t 1 to t 2 than did the higher valued sub areas in the total universe of territory. It should be als clear that the reciprocal interpretation can be made with regard to bt t values that are greater than unity (i.e., 2' 1 the initially larger values in the variables' distribution increased faster). Consequently while r can describe the degree of tz,t1 67 [ov~rall relative rank stability for the defined measurementi, of neighborhood structure in this study, b provides a tz,t1 summary description of where and how the average degree of ,change in subareas has occurred. Thus the latter co- 1efficient 1s a gauge of the relative equal increase through - out the mathematical distribution, and if it is not coexten sive, where it is not. Summary and Expectations: A Restatement of the Val~e Conflict Hypotheses The main objective of this chapter has been to estab lish operational specifications for key conceptual terms of value conflict theory in relation to delinquency areas. In this section the specifications are summarized into a set of procedures which can be expressed as specific research able hypotheses. An analysis and test of the hypotheses as they apply to an actual metropolitan area will be the ' subject of Chapter III. Hypotheses respe·cting the identification of delinquency areas As has been indicated, delinquency patterns have here · been radically reduced to the two category typology of strumental and expressive, as reflected by officially . 1n- 68 . defined delinquent acts in Table 1. Also, the discussion above suggested that through a set of specified procedures, involving the construction of a minimum set of spatial measures, a separate set of composite standardized indi cators can be developed for each category. As such, each indicator is a measure of the degree to which each subarea can be characterized by instrumental and expressive delin quency. Finally, each indicator set can be processed through a statistical spatial clustering algorithm. These procedures simulate a search pattern, starting always with the highest ordered value and uncommitted subareas in a universe of territory, to determine and isolate mutually exclusive subareas that are contiguous and statistically similar at a predefined level of significance. At this point in the total set of procedures, then, a number of instrumental and expressive delinquency areas should be identified for a conscribed metropolitan area. Thus, the first hypothesis to be tested should be: Hl - Spatially uniform and functionally related delinquency patterns should statistically cluster into areas characterized by rela tively dense spatially contiguous neigh borhoods in a metropolitan area. A second hypothesis that implicitly follows from Hl 1s concerned with overall intensity or magnitude between 69 delinquency areas of the same type of pattern (i.e., instrumental or exµ,ressive). Since it is expected that: (1) a delineated delinquency area is statistically homo geneous, (2) each is an exclusive area in the universe of territory, (3) each area starts with an initial clustering of subareas with the highest uncommitted value, it should follow that each type of delinquency area should tend to have a different intensity factor. That is, the delinquenc area concept as here defined refers not only to those with the highest rates of delinquents; as stated by McKay, they are also to be found "between areas with the lowest and those with the highest rates." 61 Therefore, a second hypo thesis to be examined is: H2 - Functionally differentiated delinquency areas should be significantly different in overall delinquency magnitude. It should be noted in these two hypotheses that while each delinquency area delimited will be spatially exclusive among the respective types, nothing is assumed about a spatial division between the delinquency area types. In fact, it is expected that there will be some overlapping between expressive and instrumental types of delinquency areas. In an earlier conceptual statement, it was argued that the spatial distinction is not categorical; rather, the delinquency areas in reality should be on a continuum 70 between the two distinctive polar types. The research procedures are designed to reflect this condition by allow ling the indicator of instrumental delinquency to vary in dependently of an indicator of expressive delinquency for any subarea in the universe of territory. If there is spatial overlap and/or some congruence between the two categories, however, it is expected to occur in those delin quency areas where the respective magnitudes are relatively high for that defined type of delinquency area. In part, of course, this should be expected because of the sheer volume of delinquent activity reflected by high indicator scores. In addition, in the instrumental delinquency areas, some forms of expressive activity are to be expected as par1 of the learning process associated with delinquent activity. For example, Kobrin noted regarding this type of delinquenc) area that: ... delinquent activity in these areas constitutes a training ground for the acquisition of skill in the use of violence, concealment of offense, evasion of detection and arrest .... Thus, while delinquent activity here possesses the usual characteristics of violence and destructiveness, there tend to develop effective limits of permissible activity in this direction.62 Accordingly, then, areas reflective of higher magni tudes of instrumental delinquency may tend to have a con comitant relationship with elements of expressive activity, especially when the instrumental areas also display a stronf 71 stable neighborhood social structure. At the same time, 1 such a relationship should not be as pronounced in the expressive delinquency areas. As discussed below, expres sive delinquent activity should vary positively with the degree of structural instability in such neighborhoods. This in turn should tend to weaken the bond between the conventional elements of local social structure and the youth group, a bond needed for instrumental delinquency to be most fully developed. In summary, then, a third and fourth hypothesis are concerned with the relationship between instrumental and expressive activity as manifested within each of the de fined delinquency areas. The third hypothesis is more general, and is stated as: H3 - The higher the overall delinquency activity of an area, the greater the concomitant direct relationship between instrumental and expressive delinquency indicator scores. The fourth hypothesis 1s related to the latter statement, but in this postulate the degree of interrelation between the two forms of delinquency is expected to occur over a broader range of magnitude for instrumental delinquency areas. This expectation is based on the argument that 1n p areas characterized by a predominance of instrumental delinquency a certain amount of expressive activity is 72 I expected and tolerated, whereas in areas where expressive delinquency predominates instrumental activity should be absent because of a lack of control and training provided by a stable neighborhood structure. Thus the fourth hypothesis is: H4 - The concomitant relationship between the two types of delinquency indicators should be more pronounced over a broader range of over all levels of magnitude in instrumental than 1n expressive delinquency areas. Structural impact on de.lin4u~ricy areas Once spatially uniform and functionally related delin quency patterns are established, the next consideration is the nature of the relationship between the type of delin quency area and other elements of neighborhood social structure. One aspect of this relationship was described in the discussion of the last hypothesis above. Here it was suggested that delinquency areas defined as having a pre dominantly instrumental pattern of delinquency should also exhibit relative stability of social structure. This rela tionship should predominate despite the presence there of expressive delinquency as well. Of course, the reciprocal condition should be expected to exist in delinquency areas defined by a generally uniform patterning of expressive 73 juvenile delinquency. In these areas there should be indi- cations of disruption in the stability of the social struc- jture, even though there may be present a limited amount of instrumental delinquency. Such seeming contradictions may exist because, it should be remembered, delinquency areas are defined in this study as statistically homogeneous sets of contiguous subareas. Therefore, some degree of hetero geneity should be expected within the delinquency pattern of any given area. Despite some heterogeneity within the subareas of any given delinquency area, there should also be evidence of a concomitant relationship between indicators of the social structure and the magnitude of the delinquency indicators that define the area. Here the direction of the relation ship is important. There should be a different form in the relationships between the elements of social structure and instrumental and expressive delinquency areas. This expec tation is based on the fact that elements of the social structure in this study are represented by composite indi- 63 cators of deviational change. Since these indicators are designed to reflect the degree of structural stability 1n neighborhoods, with the highest values indicating the greatest amount of relative change and the lowest ralues indicating the least amount of relative change 1n the ele ments of local social structure, the following two formal 74 I 1 hypotheses express the general form of the relationships I !between neighborhood social structure and delinquency are type: HS - Indicators reflecting a high degree of relative change in elements of neighborhood social struc ture should be negatively related to indicators of instrumental delinquency that have been definec statistically as spatial clusters of homogeneous areas. H6 - Indicators reflecting a high degree of relative change in elements of neighborhood social struc ture should be positively related to indicators of expressive delinquency that have been defined statistically as spatial clusters of homogeneous areas. In addition to the simple forms of relationship just described, attention must also be given to how and to what degree, if any, the criminal value system in the delinquency area is related to the type of spatial group patterns of delinquency. In earlier discussions it was stated that instrumental delinquency activity, in particular, assumes integration between the conventional and criminal value 'systems. Since this integration is most likely to occur in those areas with an enduring social structure, it should be L I expected that in instrumental delinquency areas with the !strongest cross-generational relationships between adult 1 and juvenile group patterns there should be also evidence jof a relatively stable social structure within the area. I Iu addition, this simultaneous effect of structural sta- 1 bility and adult criminal activity on instrumental delin- 1 quency might well be expected to be most pronounced in the delinquency areas with the higher average magnitudes of I instrumental delinquency. This expectation follows from 1 the fact that without the nece·s·sary c·o'ndi tion of a strong ,association between structural stability and adult criminal activity there can be little opportunity for the development of the learning opportunities needed for acquisition of the skills and support required for the instrumental type of delinquency. In contrast to the above prerequisites for instru mental delinquency areas, earlier discussions have stressed that rapid change in the social structure is a sufficient condition for the existence of expressive types of delin quency areas. Therefore expressive delinquency areas should be less spatially confined in the metropolitan area because such areas may exist merely in the absence of social control. That is, expressive delinquency areas do not depend on the integration of both conventional and criminal values and thus do not require an enduring 76 I neighborhood institutional order. Rather, the expressive delinquency area is a reflection of the lack of such order and stability. are: Hence, the next two formal hypotheses to be examined H7 - Instrumental group patterns of juvenile delinquency will be more positively related to instrumental adult criminal patterns in instrumental delinquency areas, with negative associations with neighborhood social struc tural change. HS - Conversely, expressive delinquency areas will exhibit strong positive association with rapid changes in local social structure and show a much reduced positive association with adult expressive criminal activity. Finally, it might also be expected that: H9 - Regardless of delinquency area type, those spatial clusters with the higher overall magnitude in activity of delinquency will also exhibit a stronger positive relationship with a similar magnitude of adult criminal activity. Here, it is assumed that the level of juvenile delin quency is in part a reflection of differential association 77 I with adults. In areas predom·nantly instrumental in delin quency pattern adult criminal models are assumed to be directly accessible to juveniles, 64 and adult illicit enterprise is postulated as relatively integrated with the more conventional elements of the local institutional 65 order. Areas predominantly expressive in delinquency pattern also provide adult criminal models, but direct contact with juveniles is largely absent. Further, in areas with a predominance of expressive delinquency, adults who engage in criminal activity are likely to have little direct contact or personal relationships with members of the conventional institutions of the neighborhood. How ever, in both types of areas the level of delinquent activity is expected to be positively associated with the level of adult criminal activity, disregarding specific type of delinquency pattern. 78 CHAPTER II FOOTNOTES 1. Burgess, ~.cit. 2. Thrasher,~- cit. 3. Reckless,~- cit. 4. Edwin H. Sutherland, "Juvenile Delinquency and Community Organization," Edwin H. Sutherland: On Analyzing Crime, ed. Karl Schuessler (Chicago: The University of Chicago Press, 1973), pp. 141 - 159. 5. Shaw and McKay, Juvenile Delinquency. 6. Andrew W. Lind, "Some Ecological Patterns of Community Disorganization in Honolulu," American Journal of Sociology, XXXVI (September, 1930), 206-220; R. Clyde White, "The Relation of Felonies to Environmental Factors 1n Indianapolis," Social Forces, X (1932), 498-513; Pauline V. Young, The Pilgrims of Russian Town (Chicago: The University of Chicago Press, 1932); Stuart Lottier, "The Distribution of Criminal Offenses in Metropolitan Regions," Journal of Criminal Law and Criminology, XXIX (1938), 37-50. 7. Thrasher,~- cit., p. 6. 8. Burgess,~- cit. Variant themes and supplemental concepts to the concentric zones have been developed as zones based on sectors, multiple nuclei, and urban settlement. See: Homer Hoyt, The Structure and Growth of Residential Neighborhoods in American Cities, Washington: Government Printing Office, 1939); Chauncy D. Harris and Edward L. Ullman, "The Nature of Cities," The Annals, CXLII (November, 1945), 7-17; Beverly Duncan, Georges Sabagh, and Maurice D. Van Arsdol Jr., "Patterns of City Growth," American Journal of Sociology, LXVII (January, 1962), 419-421; Leo A. Schuerman, "Assimilation of Minority Subpopu lations 1n Los Angeles County." (unpublished M.A. thesis, Department of Sociology, University of Southern California, 1969); Maurice D. Van Arsdol Jr., and Leo A. Schuerman, "Redistribution and Assimilation of Ethnic Populations: The Los Angeles Case," Demography, 79 ------------------------------------' 9. 1. VIII (November, 1971), 459-480 t Robert E. Park, "Personal Competition and the Evolution of Individual Types," Introduction to the Science of Sociology, ed. Robert E. Park and Ernest W. Burgess (Chicago: The University of Chicago Press, 1969), pp. 352-354. Harvey W. Zorbaugh, "The Natural Areas of the City," Publications of the American Sociological Society, XX (1926), 188-97. Zorbaugh, Ibid.; R. D. McKenzie, "The Ecological Approach to the Study of the Human Community," The City, ed. Robert E. Park, Ernest W. Burgess and R. D. McKenzie (Chicago: University of Chicago Press, 1925), pp. 63-64; R. D. McKenzie, "The Scope of Human Ecology," Publications of the American Sociological Society, XX (1926), 141-154· Paul K. Hatt, "The Concept of Natural Area," American Sociological Review, XI (August, 1946), 423-428; Robert Park, Human Communities (New York: The Free Press of Glencoe, 1952); Terrence Morris,~- cit., pp. 9-10. 12. Clifford R. Shaw et al., Delinquency Areas, (Chicago: University of ChicagoPress, 1929); Clifford R. Shaw and Henry D. McKay, Social Factors in Juvenile Delin quency, (Washington: U.S. Government Printing Office, 1931). 13. Ibid., pp. 60-108; Shaw and McKay, Juvenile Delinquency. 14. Shaw and .McKay, Juvenile Delinquency, p. 381. 15. Milla A. Alihan, Social Ecology (New York: Columbia University Press, 1936); Maurice R. Davie, "The Pattern of Urban Growth," Studies 1n the Science of Society, ed. George P. Murdock (New Haven: Yale University Press, 1938) pp. 133-161; Paul Hatt,~- cit.; Walter Firey, Land Use 1n Central Boston (Cambridge: Harvard University Press, 1947); Homer Hoyt, QE_. cit.; Harris and Ullman,~- cit. 16. Sophie Robison, Can Delinquency be Measured? (New York: Columbia University Press, 1936); Christan T. Jonassen, "A Re-evaluation and Critique of the Logic and Some Methods of Shaw and McKay," American Sociological Review, XIV (October, 1949), 608-615; Bernard Lander, 80 [I Toward an Understanding of Juvenile Delinquency (New York: Columbia University Press, 1954); Terence Morris, ~- cit., pp. 92-105. 17. Bernard Lander,~· cit.; Roland J. Chilton, "Delin quency Area Research in Baltimore, Detroit and Indianapolis," American Socio·log·ical Review, XXIX (February, 1964), 71-83; Charles V. Willie and Anita Gershenovitz, "Juvenile Delinquency in Racially Mixed Area," American Sociological Review, XXIX (October 1964), 740-744; Lawrence Rosen and Stanley H. Turner, "An Evaluation of the Lan.der Approach to Ecology of De 1 in q u ency , " Soc i a 1 Prob 1 ems , XV ( Fa 11 , 1 9 6 7) , 18 9 - 2 0 0. 18. Kenneth Polk, "Juvenile Delinquency and Social Areas," Social Problems, V (1957), 214-217; Kenneth Polk, "Urban Social Areas and Delinquency," Social Problems, XIV (1967), 320-325. 19. Eshref Shevky and Marilyn Williams, The Social Areas of Los Angeles: Analysis and Typology (Berkeley: University of California Press, 1949). 20. Various statistical procedures (including ranking, cluster analysis, scale analysis, and factor analysis) have been used to mathematically isolate the social areas in a mathematical space. For examples see: Eshref Shevky and Wendell Bell, The Social Areas: Theory, Illustrative Application, and Computational Procedures (Stanford: Stanford University Press, 1955); Robert C. Tryon, Identification of Social Area by Cluster Analysis (Berkeley: University of Cali fornia Press, 1955); Maurice D. Van Arsdol, Jr., Santo F. Cammilleri, and Calvin F. Schmid, "The Generality of Urban Social Area Indexes," American Soc'io'logic·a1 Review, XXI I I (June, 19 58) , 2 7 7-2 8 ~ 21. R. C. Geary, "The Contiguity Ratio and Statistical Mapping," The Incorporated Statistician, V, 115-145. 22. For one of the most complete treatises of spatial autocorrelation see: A. D. Ord, Spatial Autocorrelation (London: 1973). 23. Ibid., p. 21. 24. Geary, ~- cit., p. 121. on the subject Cliff and J. K. Pion Limited, l 81 I 25. Ibid., pp. 116-119. Geary also discusses some theoretical difficulties and limitations to the use of a strict randomization model, particularly if the values used to obtain care regression residues. The details of the discussion are not empirically relevant to the current discussion; however, Geary's original article has a detailed review of the problem. 26. Ibid., pp. 133-135; Cliff and Ord,~- cit., pp. 20-21 Sample sizes as small as seven were tested by the author and found to have no pr~ctical difference in a series of tests using real data. 127. Cliff and Ord,~- cit. I 128. ' 129. 30. , 31. 32. I I I 7. 3 ..) . I I Otis Dudley Duncan, Ray P. Cuzzort, and Beverly Duncan, Statistical Geography (Glencoe, Illinois: The Free Press, 1961), pp. 131-136; Roland K. Hawkes, "Spatial Patterns of Urban Population Characteristics,' American Journal of Sociology, LXXVIII (March, 1973), 1216-1235; Bradley R. Hertel, "Measuring Homogeneity and Stability with the Contiguity Correlation Coeffi cient," paper presented at American Sociological Association Annual Meeting, San Francisco, August 26, 1975. Cliff and Ord,~- cit., p. 38. Cliff and Ord suggest that a rule of thumb for a small n is any number of subareas below 10. Ibid., p. 39. A general discussion concerning the logic and circum stances when Zp should or should not be used can be found in most standard text books. For examples see: Perry E. Jacobson, Jr., Introduction to Stastical Measures for the Social and Behavioral Sciences (Hins dale, Illinois: The Dryden Press, 1976), pp. 144-147, 231-235; Frederick E. Croxton, Dudley J. Cowden, and Sidney Klein, Applied General Statistics (3d ed.; Englewood Cliffs, N.J.; Prentice-Hall, Inc., 1967), pp. 567-589; John G. Peatman, Introduction to Applied Statistics (New York: Harper & Row, Publishers, 1963) ) pp. 2 2 9-244. Ibid., pp.- 232-234; Croxton, Cowden, and Klein, Q£• ~it., pp. 567-575. Peter M. Blau, "Presidential .A.ddress: Parameters of Social Structure," American Sociological Review, XXXIX (October, 1974), 615-635. 82 I 35. 36. 37. 38. 39. 40. 41. 42. 43. 44. Kobrin~- cit., pp. 657-658. Ibid., p. 659. Ibid., p. 658. Ibid., pp. 658-659. Albert Bandura, "A Social Learning Interpretation of Psychological Dysfunctions," Foundation of Abnormal Psycholo~y, ed. Perry London and David Rosenhan (New Yor : Holt, Rinehart and Winston, Inc., 1968), p. 296. Daniel Glaser, "The Classification of Offenses and Offenders," Handbook of Criminology, ed. Daniel Glaser (Chicago: Rand McNally College Publishing Company, 1974), pp. 75-76. William J. Chambliss, "Types of Deviance and the Effectiveness of Legal Sanctions," Wisconsin Law Review, ( 19 6 7) , 7 0 3-719. John C. McKinney, "Constructive Typology: Explication of a Procedure," An Introduction to Social Research, ed. John T. Doby (New York: Appleton-Century-Crofts, 1967), pp. 213-229 J. P. Guilford, Fundamental Statistics in Psychology and Education (4th ed.; New York: McGraw-Hill Book Company, 1965), pp. 470-473; Herbert Bixhorn and Albert Mindlin, "Composite Social Indicators for Small Areas - Methodology and Results in Washington, D.C.," Social Indicators for Small Areas 1 Census Tract Papers ! Series GE-40, No. 9 (Washington, D.C.: U.S. Bureau of the Census, 1972), 3-17. David R. Heise, "Some Issues in Sociological Measure ment," Sociological Methodology 1973-1974, ed. Herbert L. Costner (San Francisco: Jossey-Bass Publishers, 1974), pp. 2-13; Michael Patrick Allen, "Construction I of Composite Measures By the Canonical Factor- 1 Regression Method," Sociological Methodology 1973-1974~ ed. Herbert L. Costner (San Francisco: Jossey-Bass Publishers, 1974), pp. 51-54. J. P. Guilford, Psychometric Methods (New York: McGraw-Hill Book Co., 1954), pp. 402-404. 83 I 45. J. Scott Armstrong, "Derivation of Theory by Means of Factor Analysis or Tom Swift and His Electric Factor Analysis Machine," The American Statistician, XXI (December, 1967), 17-21. Karl Schuessler, Analyzing Social Data: A Statistical Orientation (Boston: Houghton Mifflin Company, 1971), pp. 4 7-60. 4 7. Ibid., p. 119. 48. Harry H. Harman, Modern Factor Analysis (Chicago: The University of Chicago Press, 1967), pp. 84-88; Schuessler,~- cit., pp. 89-91. 49. J. P. Guilford, Psychometric Methods, pp. 524-526; Harman,~- cit., 350-354. SO. Duncan, Cuzzort, and Duncan,~- cit., pp. 8-9. 51. Henry S. Shryock and Jacob S. Siegal and Associates, The Methods and Materials of Demography (Washington, D.C.: U.S. Bureau of the Census, 1971), p. 7. 52. Duncan, Cuzzort, and Duncan,~- cit., pp. 34-38. 53. Ibid., pp. 46-48. 54. Ibid, pp. 49-52. 55. Kobrin,~- cit., p. 658; David M. Downes, QE_. cit., pp. 51- 5 3. 56. Duncan, Cuzzort, and Duncan,~- cit., pp. 162-165. 57. Ibid., p. 163. 58. U.S. Bureau of the Census, Social and Health Indi cators Slstem: Los Anreles, (Wasliington, D.C.: U.S. Bureau o Census, 1972 , pp. 281-302. 59. Leo A. Schuerman, E. Wayne Hansen, and Charles Hubay, "Combining Ratio-Correlation and Composite Methods for Intercensal Social and Economic Small-Area Esti mates," Intercensal Estimates for Small Areas and Public Data Files for Research, Small-Area Statistics Papers, Series GE-41, No. 1 (Washington, D.C., U.S. Bureau of Census, 1974), pp. 2-15. 84 I I I I 60. For examples of the use and interpretation of inter annual correlations and regressions applied to studies concerned with stability between two points in time :;ee: Otis Dudley Duncan and Ray P. Cuzz6rt, "Regional Differentiation and Social-Economic Change," Papers and Proceedings of the Regional Science Association, IV (1958), 163-176; George C. Myers, "Variations in Urban Population Structure," Demography, I (1964), 156-163; Karl E. Taeuber, and Alma F. Taeuber, Negroes in Cities (Chicago: Aldine Publishing Co., 1965); Schuerman,~- cit., pp. 71-91; Van Arsdol and Schuerman,~- cit., pp. 475-476. 61. Supra, footnote 14. 62. Kobrin, ~- cit., p. 658. 63. Supra, p. 65. 64. Cloward and Ohlin, ~- cit., pp. 150-160. 6 5. Kobrin, ~. cit. , pp. 6 5 7 - 6 5 8. l ______________ 8- 5 CHAPTER III TEST AND ANALYSIS THROUGH AN EMPIRICAL APPLICATION Introduction In the last two chapters the conceptual orientation, operational specifications, and research hypotheses were formulated. In general, all discussions were abstracted and absent of any reference to an actual situation. This chapter builds on the previous two chapters by applying to a concrete metropolitan area the methods and procedures presented that will allow tests of the hypotheses presented 1n Chapter II. Here, then, the hypotheses, methodology, and statisti cal procedures are conscribed by the realities and limi tations of data available for a large metropolitan area. Specific procedures to accommodate and transform the real data into a working set of measurements and indicators are presented below. Thus, 1n this chapter, a summary of the practical procedures, results, and analysis of the actual data are described as they apply to the stated hypotheses. In concluding the introduction of this chapter, refer- I ence should be made regarding the adoption of a statistical 86 level of significance. In this study the test of the null hypothesis is used in two ways. First, it is an implicit decision node at various junctures of the methodological procedures described in Chapter II. For example, this use of the test of significance is particularly important with reference to the SPACE simulation algorithm. Second, statistical tests may be used more traditionally in the assessment of descriptive summary statistics such as cor relation coefficients and tests of difference between two sample means. Since this investigation has been designed as a form of testing methodological procedures as well as a test of broad theoretical concepts, the higher risk of rejecting the null hypothesis when in fact it may actually be true (i.e., Type I error), was felt warranted and useful 1 for the purpose of the study. Thus, the somewhat common .OS level of significance was adopted for all inferential statistical tests applied throughout the rest of this chapter. The Universe· of Territory The concrete urban site used to test the study's theoretical and operational specifications was the mainland of the County of Los Angeles. In part, the County was selected because it is the largest metropolitan area where the data sources needed are available under one permanent jurisdiction and for more than one point in time. The I I latter condition was especially important for the construe- tion of indicators of social structural stability and more reliable indicators of juvenile delinquency and adult criminal activity. Other factors also prompted the selection of Los Angeles County. Of particular importance to the testing of the study's hypotheses is the diverse and the type of neighborhood growth throughout the County. For example, it has experienced high intra-metropolitan residential mobil ity.2 Also, because the County is the largest metropolitan area to emerge concurrently with the development of the automobile, and because arterial network of the freeways provides easy spatial access, and because of the size of the area (approximately 4,000 square miles), many struc tural changes are often conspicuously magnified. 3 Further more, most of the maJor growth has occurred during a brief thirty year period of rapid economic expansion accompanied by a continual increase in family standard of living. 4 The physical environment includes arid desert land, military installations, mountains, forest regions, and flat 1 land. The political organization of the space consists of over seventy - five "inner cities," as well as the central city of Los Angeles. The age of the various cities ranges from communities established approximately two hundred years 88 ago to cities with incorporated status of less than five years. Land use ranges from total industrialization to 1 . . . 5 tota retirement commun1t1es. During the time period covered 1n this study, the two major cities of Long Beach and Los Angeles comprised a [population of almost three million; the smaller fifty-two cities (ranging between 25,000 and 134,000) 2.8 million; with the balance of the County housing almost one million 6 persons. Total population for the County was 7,032,075. jin summary, the County was seen as a particularly relevant imetropolitan area for this study, because the heterogeneous and diversified nature of the social, economic, and land use patterns provided the needed structural distribution to test the various hypotheses specified. Da·ta Sources and Areal Unit of Analysis All data for the study were taken from administrative sources that were machine readable in quality. Data per- 1taining to juvenile delinquency and adult criminal activity were obtained from the transaction files of the Los Angeles Department of Probation for the calendar years of 1969,1970, and 1971. All data items used to reflect elements of areal social structure were extracted from the 1960 and 1970 U.S. I Census tract summary tapes for the respective Censuses of Population and Housing. The only other data used 89 1n the analysis were total acreage for each census tract; this information was originally developed by the Los Angeles Regional Planning Department. In the analysis all data were aggregated to appropri ate tally counts for 1,142 "comparability" spatial subareas for the entire mainland of Los Angeles County. In general, these subareas were equivalent to 1970 U.S. Bureau of the Census defined census tracts. Because much of the data used was coded only to the level of 1960 census tract specifications, certain spatial boundary adjustments had to be made. Thus 425 out of 1,584 1970 mainland County census tracts did not have the same areal definition as 1960 census tract boundaries. Therefore, all areal discrepan cies between 1960 and 1970 were resolved by aggregating either the 1960 and/or 1970 census tracts until comparable borders were obtained. Tracts were eliminated which had more than fifty per cent of the population housed in group quarters or had fewer than five hundred persons in 1970. These last two steps were included in the procedures to improve the reli ability of the areal analysis. In the case of group quarters, such populations (e.g., hospitals, jails, mili tary housing) were not considered to be part of the social structure of an urban neighborhood, and this 1s reflected in the fact that most institutional quarters are designated 90 I 1 as separate census tracts by the U.S. Bureau of the Census. j The minimum number of five hundred persons was selected to maintain the level of sampling reliability of census infor mation and confidentiality as set by the Bureau of the Census; these two considerations can be a particular prob lem when subcategories of the census population are selected, as was th~ case in the current study. There were a total of seventeen tracts deleted under these last two procedural definitions. Thus, in this study there were a total number of 1,142 subareas or areal cases in the total universe of territory for Los Angeles County. In addition to the development of a correspondence table for areal integrity between 1960 and 1970 census tracts, another file was needed for the SPACE algorithm which identified ·all contiguous satellite subareas for each of the 1,142 parent subareas in the universe of territory. That is each subarea's coterminous boundary subarea had to be identified to the original subarea. A subarea was de fined as being coterminous or contiguous if any part of the subareas shared a common boundary or point on an inter section. For the 1,142 subareas in Los Angeles County there were 7,262 contiguous connections or Joins. an average of six joins for each subarea. This 1s L _________________ 91__. Delinquency Measurement Development and Composite Indicator Construction As indicated above, all information concerning delin quency activity was obtained from official records of formal complaint petitions files for a three year period with the County Probation Department. The initial set of records, however, consists of all transactions including all petitions. A petition can be initiated by many sources, such as police, courts, schools, social service agencies, parents, and private citizens. The total number of records for the three year period consisted of 116,486 transactions. Of course, there are more transactions than individual cases, because one case can have many official trans actions. For example, each time an active file address of residence changes, there is a transaction created; there also may be a transaction record made at the opening and closing of a case file, and an additional transaction for every new offense reported for the same case. Therefore, the original set of transaction records on individuals for the three year period had to be transformed into a usable I analytical file concerned with tallies of delinquent activ ity for individual subareas. 92 Delinquency summary record construction The procedure used to develop summary tallies for spatial subareas from a juvenile probation file are out lined in the following four general steps: Step 1. All records dated between 1969 and 1971 were examined for an indication that the transaction was a new filing for one of the official delinquency acts defined in Table 1. During this processing phase, all continuances, unwanted codes (e.g., dependency cases), and other adminis trative transactions were removed from the study file. Step 2. All records remaining in the study file were examined for an acceptable spatial identification. Accord ing to County Probation Department administrative proce dures, all records were to be geocoded with a 1960 census tract number corresponding to the individual's address of residence. Therefore, these codes were checked for a legi timate 1960 census tract number, and -all records with non identifiable numbers were purged from the study file. Reasons for rejection were missing numbers, out of County residency, and incorrect numbers. Nevertheless, the final total was 76,809 separate delinquency acts recorded for the three year period. Of this number, 55,221 were male (i.e., 71.89 percent) and 21,588 were female (i.e., 28.11 percent). Step 3. Up to this point, three years of data were 93 I processed. This particular set of data items was developed in order to provide a mean score summary for each subarea for each offense code by sex for one calendar year of data. Since the average was based on 1969, 1970, and 1971 calen dar years, the summary tallies were assumed to be approxi- 1 mately commensurate with the 1970 Census of Population and I I Housing (i.e., April 1, 1970). As already mentioned, the latter information was also a data source for the study. Furthermore, through the averaging of each score for each subarea, much of the unwanted and unsystematic variance interjected into the scores because of possible data deletion or bias during Step 2 should be controlled. 7 Step 4. In this last phase of file preparation, the juvenile tallies for each subarea were further summarized into categories of instrumental or expressive delinquency as defined in Table 1. Furthermore, because of the tradi tional differences in the number and type of delinquent acts committed by male and female juveniles, 8 the same set of data items was also separated into the same binary cate gories by sex. This additional information was of parti cular interest because research findings on the subject suggest that female delinquency may be expected to be most pronounced in expressive types of delinquency areas. 9 Thus, for Los Angeles County, the offense by type and by - sex for the 1970 "averaging" calendar year tallies are: 94 ; i Instrumental Expressive Total No. 9.: 0 ·No. 9.: 0 No. 9.: 0 Male 8,011 89.35 10,396 62.49 18,407 71.89 Female 955 10.65 6,241 37.51 7,196 28.11 Total 8,966 100.00 16,637 100.00 25,603 100.00 It should be noted in this summary tabulation that the distribution between male and female by the two categories is indeed quite different. For example, by using the total percentages as the average distribution between males and females, it is readily apparent that the respective sex distributions by type of activity are reversed. That 1s, while the female juvenile group made up 28.11 percent of the total distribution, they exceeded this overall average in the expressive category of activity but not in the instrumental category. Still another more pronounced dif ference can be seen if the distribution is examined between categories for each sex. This distribution is shown below: Male Female Total No. 9.: 0 No. ~ 0 No. ~ 0 Instrumental 8,011 43.52 955 13.27 8,966 35.02 Expressive 10,396 56.48 6,241 86.73 16,637 64.98 Total 18,407 100.00 7,196 100.00 25,603 100.00 In this comparison the female group can be seen to be much more concentrated in the expressive category than the male group. The substantive significance of the difference is further reflected by the fact that the chi square for the above distribution is x 2 ~ 2079.1. Thus, because of this 95 .__ ______________________________ - differentiation between males and females by instrumental and expressive activity, special measurements by sex were developed as part of the composite indicator construction. The specific delinquency measurements and indicators developed are discussed below. Measurement construction of juvenile instrumental and expressive ·delinquency The set of instrumental and expressive summary tallies for each subarea was transformed into generalized areal comparable units defined in Chapter II as measurements of distribution, concentration, density, and unit share. 10 Each measure produced was for total instrumental and total expressive activity. Thus, eight measures were developed for each of the 1,142 subareas. Another set of measures was developed to reflect an additional input form for indicator construction of instru mental and expressive delinquency. These measurements stem from the overall findings described above in the discussion of male and female delinquency pattern differences. The summary data items indicated that female juvenile delin quency is most readily reflected in those offense cate gories defined as expressive. The male pattern of delin quency, as might be expected, was represented in both cate- gories. However, when compared to the females for each L ____ _ 96 type of delinquency, males are relatively more prominent in the instrumental category. Therefore, to distinguish between type of activity and sex, two additional sets of measures of concentration, density, and unit share, were developed. In one set, the numerator was the number of offenses by females. These three measures were included as ! additional measures of expressive activity. In the second I set, the numerator was the number of offenses by males. I I These three measures were developed as additional inputs I into the final composite indicator of instrumental delin- 1 I I quency. In earlier discussions, it was suggested that instru mental delinquency areas may tend more to a mixture of both types of delinquent patterns when contrasted with expres sive delinquency areas. Therefore, a single ratio measure was created between the two delinquency categories for each subarea. The numerator of the ratio consisted of the number of instrumental acts of delinquenc½ and the denomi- I nator was the summary count of the expressive activity. , This measure was included as part of the construction of I the composite indicator for instrumental activity. Instru~ental and expressive composite indicators of juvenile delinquency The two delinquency categories were identified 1n the 97 last subsection in terms of specific data elements being transformed into fifteen different measurement formulations I of a subarea. In the total procedural process described 1n I Chapter II, these measures are also the basis of composite I indicators which then represent two specific elements of the neighborhood total social structure. 11 The elements to be represented by two different com posite indicators here, then, are instrumental and expres- 1 s1ve delinquency. In this analysis these two indicators serve two functions. First, when each set of indicator 1 scores 1s processed through the SPACE algorithm, the values serve to identify delinquency areas as being of the instrumental or the expressive type. Second, the indi cators are used as dependent variables 1n testing the relationship between neighborhood juvenile delinquency and other elements of the social structure hypothesized as determinants of specific group patterns of delinquency. The result of the total process of data item defini- 1 tion, measurement formulation, and composite indicator I 1 construction 1s reflected in generalized parameters. , Summary statistics for each of the fifteen measures of I , delinquency were generated at various stages 1n the con- I struction of the two composite indicators. These descrip- 1 tive statistics are shown in Tables 3 and 4. A cursory I examination of these two tables reveals at least two 98 <..D l <..O TABLE 3 SUMMARY STATISTICS FOR SELECTED VARIABLES AND COMPOSITE INDICATOR CONSTRUCTION OF INSTRUMENTAL JUVENILE DELINQUENCY OVER 1,142 SUBAREAS Defined Measures REPORTED INSTRUMENTAL OFFENSES BY:J (1) All Persons 10-17 Years of Age x 100 (2) All Reported Offenses X 100 (3) Number of Acres (4) All Reported Instrumental Offenses in Los Angeles County x 100 (5) Reported Expressive Offenses x 100k ¾f = Mean bs = Standard Deviation = Skewness = Kurtosis = Estimated Communality cg d 1 g2 eh e fh = f Final Communality IN LOS ANGELES COUNTY: 1970 Ma (1) b s (2) 0.97 1.84 30.71 20.35 0.02 0.04 0.09 0.12 54.90 51.67 C gl (3) d g2 (4) 17.44 443.87 0.58 0.99 4.85 40.55 3.52 20.69 2.35 10.94 gF.L. = Factor Loading he e (5) .26 .40 .70 .58 .36 hF.S.C. = Factor Score Coefficient hf f (6) .26 .40 .70 .58 .36 F.L g • (7) .51 .63 .84 .76 .60 F.S.C.h,i (8) I .06 .16 .11 .86 .04 1 = Summary statistics of composite scores over 1,142 sub areas: M = 0.00, s = 0.95, minimum value= -1.15, maximum value= 7.68, g 1 = 2.09, g 2 = 7.55. J k = See Table 1· for instrumental offense category designation. = See Table 1 for expressive offense category designation. ,~ 0 1 0 TABLE 3 (continued) SUMMARY STATISTICS FOR SELECTED VARIABLES AND COMPOSITE INDICATOR CONSTRUCTION OF INSTRUMENTAL JUVENILE DELINQUENCY OVER 1,142 SUBAREAS Defined Measures REPORTED DELINQUENCY OFFENSES FOR MALES BY: (6) All keported Offenses X 100 (7) All Reported Delinquency Offenses for Males in Los Angeles County X 100 (8) Number of Acres '½-1 = Mean b = Standard Deviation s C g 1 = Skewness d K . g 2 = urtosis IN LOS ANGELES COUNTY: 1970 Ma (1) 64.71 0.09 0.05 eh f e hf gF.L. b s (2) C gl (3) d g2 (4) 23.22 -1.37 1.96 0.12 0.06 4.34 30.35 4.06 29.69 = Estimated Communality = Final Communality = Factor Loading he e (5) .17 .33 .66 hF.S.C. = Factor Score Coefficient hf f (6) .17 .33 .66 F.L.g (7) .41 .57 .81 F.s.c_h,i (8) I .11 -.47 .27 1 = Summary statistics of composite scores over 1,142 sub- areas: M = 0.00, s = 0.95, minimum value= -1.15, maximum value= 7.68, g 1 = 2.09, g 2 = 7.55. L _______________________________________________ _____. 1--J 0 1--J TABLE 4 SUMMARY STATISTICS FOR SELECTED VARIABLES AND COMPOSITE INDICATOR CONSTRUCTION OF EXPRESSIVE JUVENILE DELINQUENCY OVER 1,142 SUBAREAS IN LOS ANGELES COUNTY: 1970 Ma a a a ha ha a s gl g2 F. L. e f Defined Measures (1) (2) (3) (4) (5) (6) (7) REPORTED EXPRESSIVE OFFENSES BY:c (1) All Persons 10-17 Years of Age x 100 1.65 2.02 15.48 336.65 .15 .15 .38 (2) All Reported Offenses X 100 65.25 23.53 -0.98 1.31 .00 .00 .01 (3) Number of Acres 0.03 0.04 3.12 16.55 .63 .63 .80 (4) All Reported Expressive Offenses in Los Angeles County x 100 0.09 0.11 4.92 37.75 .42 .42 .65 aSee Table 3 for column definitions. bSummary statistics of composite scores over 1,142 subareas: M = 0.00, s = 0.93, minimum value= -1.04, maximum value= 6.15, g 1 = 2.07, g 2 = 7.12. cSee Table 1 for expressive offense category designations. --- - - - - - --- ------ - _ i F.s.c_a,b (8) .06 .00 .84 -.47 ~ l~ TABLE 4 (continued) SUMMARY STATISTICS FOR SELECTED VARIABLES AND COMPOSITE INDICATOR CONSTRUCTION OF EXPRESSIVE JUVENILE DELINQUENCY OVER 1,142 SUBAREAS IN LOS ANGELES COUNTY: 1970 Ma a a a ha ha a s gl &z F.L. e f Defined Measures (1) (2) (3) (4) (5) (6) (7) REPORTED DELINQUENCY OFFENSES FOR FEMALES BY: (5) All Reported Offenses X 100 31.26 20.11 1.16 2.57 .00 .00 -.01 (6) All Reported Delinquency Offenses for Females in Los Angeles County x 100 0.09 0.10 4.57 37.71 .52 .52 .72 (7) Number of Acres 0.02 0 . 02 3.26 18.54 .56 .56 .75 aSee Table 3 for column definitions . bSummary statistics of composite scores over 1,142 subareas: M = o.oo· , s = 0.93, nun1mum value= -1.04 , maximum value= 6.15, g 1 = 2.07, g 2 = 7.12. F.S.C.a,b (8) -.01 .95 -.29 patterns. First, except perhaps for variable number 2 1n each table, all measures have substantial skewness and kurtosis. This would indicate that any subsequent results using these measures (i.e., factor loading) must be con- servative estimates, because an important assumption in the I development of factor loadings is that the distributions ' of the input variables be normally distributed. However, I I the skewness and kurtosis indexes indicate that this is 1 not the case. Nevertheless the second pattern found 1n the 1 statistics is the two single factor, factor analyses (and consequently the factor scores) was not gravely affected I by the distorted distribution. For example, if the com monly accepted notion that a coefficient greater than .30 ! . " b . l" f 1 d. 12 . . 1 h 1 1s a su stant1a actor oa 1ng, it 1s c ear tat on y two of the fifteen measures fall below this level. In I general then, it appears that much of the total variance I (i.e., communality) of the variables is accounted for by the correlation with other variables which make the factor. 1 In addition, the use of the factor score coefficients, 1 shown in column 8 (i.e., multiple regression coefficients developed from each of the variable's factor loading in I column 7), as optimum weights should yield a more reliable analysis of the conceptual elements of neighborhood instru- I mental or expressive delinquency than would be possible by I f h . l 13 using any one o t e singe measures. 103 I The outcome of the total process just described was two sets of standardized scores. Of course, these two sets of scores for the 1,142 subareas are also composite indicators, representing fifteen different measurements. In this study these two indicators were defined as quantitative repre sentations for the specific group patterns of instrumental and expressive delinquency in urban neighborhoods. Statistical Simulation of Delinquency Areas in Lo·s Angeles County The relations and descriptive statistics presented to this point have all been centered around the development of the two sets of composite indicators of delinquency. In this section of the analysis, these indicators are used to focus on questions concerned with the development and the form of delinquency areas 1n the metropolitan area of Los Angeles. The results presented in the next three sub sections address a set of interdependent statements ab- 14 stracted from the first four hypotheses 1n Chapter II. Examining spatially contiguous subareas for different types of delinquency areas: Hypo thesis Hl To determine the type of delinquency areas and the intensity of delinquent activity 1n Los Angeles County, 104 I each set of subareal indicator scores for instrumental and I expressive delinquency was applied to the spatial cluster- ! ing algorithm, called SPACE. 15 As presented in Chapter II, the computerized algorithm was defined as a set of pro cedures which include special applications of one sample test of proportion (Z ) and a test (Z*) of Geary's C Contiguity Ratio (c). The procedures were designed to isolate all statistical uniform patterns in a universe of territory which are mutually exclusive spatially contiguous clusters. Thus, clusters from the SPACE algorithm should reflect the delinquency areas by type of specific group patterns of delinquency. However, for the clusters to be useful in the present study, identified clusters also had to include a sufficient number of contiguous subareas so that as defined delinquency areas they could be used also as a test of the various research statements specified at the end of Chapter II. Prior to the actual application of the delinquency indicator scores to the SPACE algorithm, certain necessary adjustments had to be made to the structure of the data. Remembering that all composite indicators in the study are also standard scores, any two contiguous subarea values (n 1 and n 2 ) before being tested by Zc were automatically checked to determine if the combined values of n 1 + n 2 (N) vere at 1 ea s t 1 0 . If not, each of the two values was L ____________ 1 __ ___, os multiplied by a constant, starting with 2, and incremented by 1 until the minimum value of N = 10 was reached. After the Z test, all score values were converted back to the C I original composite score for any additional processing (e.g., Z*). This additional preprocessing step was per formed to overcome a minimum size sample requirement when using a test of proportions. 16 For example, it can be shown that the minimum Nin this special case of Zc must be at least 5 when the significance level is set at .OS for a two tail test--the significance level of the study. How ever, a more general rule of thumb sets the minimum value for N somewhere around 10. 17 In the particular case of standard scores with a mean value of zero, as was the case in this study, adjustments had to take into account the fact that values could be between O and 1 and/or negative in sign. Since many of the actual scores were small, it should be expected that even with the additional transformation of the standardized scores to a minimum N, many results from z will still be C inexact. 18 As it was used in the present study, the in- exactness of Z was not considered a maJor restriction, C however. The Z test was viewed only as a minimum statis- c tical expediency which may give a close estimate of the beginning of a spatial cluster. And once there were more than five subareas identified, all statistical testing for 106 I spatial contiguity in the clustering process was measured through the modified procedures of Geary's c, as tested by Z*. The choice of six subareas as a point of changing 1 from a Z to Z* test was adopted in order to use a midpoint I C between the most effective use of z~ in a spatial cluster- 1ng process (i.e., where there are only two subareas) and the m1n1mum level (i.e., somewhere around ten subareas) where Z* sampling distribution approaches normality. Hence, any inappropriate preliminary clustering by zc would be more or less tempered by the more powerful Z* as h b f b . d 19 t e num er o su areas increase . Because of restric- 1 tions imposed by additional analyses (to be discussed below), only the "larger" subarea clusters are useful for statistical descriptive summaries and tests of the study hypotheses. Results of the SPACE clustering algorithm of the two types of delinquency indicators are summarized as: Instrumental Expressive Cluster Cluster No. of Subareas No. 9.: 0 No. 9.: 0 1 678 87.94 585 87.06 2 46 5.96 40 5.95 3 15 1.94 10 1.49 4 8 1.04 8 1.19 5 4 0.52 9 1.34 6 5 0.65 6 0.89 7 2 Q.26 6 o.89 8 1 0.13 2 o.30 9 4 0.52 0 o.oo 10 1 0.13 0 o.oo 11 and over 7 0.91 6 o.89 Total 771 100.00 672 100.00 107 - ' " In this summary, it 1s clear that most of the sub - I areas 1n Los Angeles County are detached or are located 1n 1 space as indicative of little or no pattern of relation to all adjacent subareas. However, it was also clear that at least 13 clusters had 11 or more subareas that were spatially clustered into single spatially statistical homogeneous areas. These 13 clusters were designated as the delinquency areas for further analyses. The choice of a m1n1mum level of 11 was somewhat arbitrary, but for the present study this cutoff point was useful for the following three reasons: 1. A "natural"break in the two distributions could be established where an approximately even number of clusters was defined for instrumental and expressive areas, respectively; 2. with regard to further analyses of hypotheses HZ through H9 of the study, 11 subareas were about the minimum number of areal units that could be sustained and still have statistical reliability in many of the descriptive correlations and tests; and finally, as discussed above, 3. the larger number of subareas gave greater statis tical reliability that the homogeneity of the sub areas was a result of both a functionally related delinquency pattern and overall contiguous spatial 108 position and not an artifact of an inexact statis cal test. This rationale provided the selective criteria for the areal clusters to be used for the rest of the analysis. Therefore, in this study, these 13 clusters were defined as reperesenting the instrumental and expressive type of delinquency areas in Los Angeles County. An overview of the descriptive parameters for the County and the 13 selected areas in Table 5 reveals four uniform patterns. First, areas in both the instrumental and expressive areas generally showed a larger contiguity ratio coefficient than the respective value for the County total. In this comparison, it should be recalled that a c = 1 indicates no pattern of contiguity. Second, the indexes of skewness and kurtosis, reflecting the form of each cluster's distribution, indicate much less distortion than the respective indexes for the total County, parti cularly the degree of skewness. Also, skewness appears to be highest 1n those "delinquency areas" with the larger magnitudes of delinquency activity. Third, remembering that O (see County total) is the arithmetic midpoint 1n the overall indicator distribution, the spatial clusters with I larger number of subareas seem to be spread over the entire 1 range of delinquency intensities for the two types of delinquent activities. For each group of clusters, 109 I TABLE 5 SUMMARY STATISTICS AND CONTIGUITY RATIOS FOR INSTRUMENTAL AND EXPRESSIVE COMPOSITE INDICATORS OF JUVENILE DELINQUENCY FOR LOS ANGELES COUNTY AND THIRTEEN DESIGNATED DELINQUENCY STATISTICAL SPATIAL CLUSTERS: 1970 Number b of Ma a a a s gl g2 C Cluster Category and Subareas Selection Number (1) ~ - ~-~.--. (2) {3) - --~- - ( 4) (5) (6) Instrumental TOTAL 1,142 0.00 0.95 2.09 7.55 0.45 CLUSTER le 63 2.30 1.32 1.34 3.63 1.24 3 20 1.52 0.77 0.45 -0.65 1.54 5 28 1.42 0.61 0.27 -0.48 1.28 660 12 -0.71 0.18 -1.21 0.65 -0.23 661 27 -0.77 0.15 -0.04 -1.40 -0.56 708 15 -0.90 0.17 -0.48 -1.17 -0.62 710 12 -0.87 0.15 -0.25 -1.08 -1.22 Expressive TOTAL 1,142 0.00 0.93 2.07 7.12 0.70 CLUSTER ld 49 1.44 1.05 1.40 6.51 1.29 3 16 1.99 1.09 1.87 3.43 1.37 14 11 1.29 0.58 -0.82 -0.03 2.78 591 34 -0.69 0.11 -0.42 -1.14 2.27 599 149 -0.78 0.14 -0.02 -1.27 0.56 618 17 -0.87 0.10 0.52 -1.04 -1.18 aSee Table 3 for column definitions. b c = Contiguity Ratio cSequence number for cluster out of a total of 771 isolated areas. ~ dSequence number for cluster out of a total of 672 isolated areas. ~ 0 I approximately half of each type of delinquency 1s above the midpoint, as measured by the mean of the area. Finally, there was only one reversal pattern within each set of ,delinquency areas as indicated by the cluster selection ' I sequence number. The magnitude of each area was generally in an ordered decreasing level of intensity. Spatial and magnitude differences in delinquency areas: Hypothesis HZ From the above descriptive summary, it would seem that ,spatial clusters large enough to be statistically defined I as functionally related delinquency areal patterns also are distributed over much of the range of indicator scores. To determine the actual magnitudes of variance between each designated instrumental or expressive delinquency area, two difference of means test matrixes were calculated; 20 the results are presented 1n Table 6 and 7. The t values in both tables generally substantiate the more cursory findings described above. However, there 1s an additional pattern detected in the two tables. For example, the two cluster selection reversals of overall magnitude mentioned above were shown not to be significantly different in distri bution. Also, Tables 6 and 7 reveal that for instrumental 'delinquency areas the non-significant areal patterns tended to be concentrated in the lower overall magnitude areas. L 111 ~ ~ N TABLE 6 DIFFERENCE OF MEANS TEST BETWEEN EACH DESIGNATED INSTRUMENTAL DELINQUENCY AREA IN LOS ANGELES COUNTY: 1970 Instrumental M -M M -M Mx-M660 Mx-M661 Mx-M708 Mx-M710 Cluster X 3 X 5 Selection Number (x) I 2.46 3.35 7.80 11.92 9.25 3 0.49a 9.56 14.75 11.61 5 11.58 17.80 14.09 660 1.osa 2.70 661 2.50 708 aNot significant at .OS level, one tail test; all other values significant at .OS level, one tail test. ---- 8.21 10.29 12.51 4.95 1.87 -0.46a ~ ~ t.N Expressive Cluster Selection Number (x) 1 3 14 591 599 TABLE 7 DIFFERENCE OF MEANS TEST BETWEEN EACH DESIGNATED EXPRESSIVE DELINQUENCY AREA IN LOS ANGELES COUNTY: 1970 M -M X 3 Mx-Ml4 Mx-M591 -l.78b 0.43a 11.58c 1.88c 13.90c 6.40c aNot significant at .05 level, one tail test. Mx-MS99 Mx-M618 ' 24.93c 8.87c 28.71c 10.44c 32.37c 14.47c 3.49c 5.56c 2.56c bSignificant at .05 level, one tail test; however, the difference is in the reverse direction. cSignificant at .05 level, one tail test. -- - ------------------------ In contrast, significant t values in Table 7 indicate that when there are similar distributional patterns, they are all associated with the higher overall magnitudes for the expressive delinquency areas. It should be recalled that differentiated delinquency areas are also located within the spatial context of a universe of territory. The actual spatial location of each ,area in relation to each other area has been reproduced ' ,through the production of two computerized proximal maps. lrhe results reflecting the various instrumental and expres sive delinquency areas in Los Angeles County are shown in Figures 1 and 2, respectively. Each shading level r~pre sents a different delinquency area cluster of subareas as designated by the rank order of the selection sequence number. Except for two areas, this in turn indicates that the darker the shading, the greater the overall magnitude or intensity of delinquency activity. In Figure 1, the grouping patterns of instrumental delinquency show two distinct spatial concentrations: the higher overall magnitude areas are 1n the south-central portion of the County, and the less prominent areas are all in the near-western portions of the County. In both of these _ general regions, the delinquency areas are shown to 1 be contiguous to each other. However, within the two con centrations, two delinquency areas (clusters 3 and 5) with 114 ' ........... ~ .., ....... . .. . . ,. . " - ........ . ,, ..... .c .. • ··••'f• ··•• 'C ........ .... . 000 ,o 00~ ... uoo "-••a-.,. • ')!)<.,.,. >cO<.oo o .. o._-:,., • • <.·oo ..,,..,:.~ •• Ugo ¢u<e ..•.. 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" ..... . . i ~~~[f . . ..... ..... ..... ...... ····· . .... .. . . . . . . . . . .. .. .. . .. .. 1 •••• •••• . ... ......... :: :: : ::i ~n~ni . ..... . . ..... . .. ........ . .. :::::::i" ... .. · ==1m 1 .. ;; '.'.:·· _J) . . . ·= . . . . . . . i! : : : : : =1. ..... { ... . ·······························j'' •• > .. , - :.. . ... 11G. : ----" ----------• ____ ,.. ____ " ---------• ----· ----• ____ ,, ----• ---- 41----• ........... --- .. • -------- .. • ----·----• ---------• ---- -----• ---- flo----• ---------• ----· ----• ----~ ----·----·· ---• ................. .. • ---------• ----•-• overall high amplitudes were shown in Table 6 to have no significant difference as to their subareal distributional patterns. Yet, an examination of Figure 1 shows these two !areas to be totally separated in space, and they are only I spatially connected by being coterminous with the highest I :level delinquency area. Though not as absolute in separa- ltion, a similar spatial pattern is reflected in the non- I .significant comparison values found in the less intense I instrumental delinquency areas. Thus, the two larger spatial concentration patterns may be reflecting what is 1 one of the major requirements of instrumental delinquent activity; it is in the instrumental areal pattern develop ment which most requires the transmission of special delin quency skills through the spatial contagion process of differential association. Such a process would be less likely to operate across delinquency areas that are spa ·tially separate and scattered throughout the metropolitan region. An examination of Figure 2 shows important differences 1n the clustering patterns of expressive delinquency areas. The most striking divergence from the pattern found 1n Figure 1 for instrumental delinquency areas is that expres sive areas are not concentrated in just two areas of the County. For example, while the two most prominently intense areas (i.e., cluster sequence numbers 1 and 3) are 117 again located in the south-central portion of the County, an area shown 1n Table 7 to have no significant difference 1n subareal distribution to one of these areas (i.e., cluster sequence numbers 1 and 14) is in the eastern part of the County. This latter area 1s spatially separated by a distance exceeding ten miles. It is also interesting to note that unlike the two very distinct and spatially con centrated cluster patterns illustrated in Figure 1 for instrumental delinquency areas, one of the more intense expressive areas is actually spatially adjacent to an area of substantially less expressive intensity (i.e., cluster sequence numbers 1 and 599). The expressive areal distri bution is further divergent from the instrumental pattern in that only two of the lower overall intensity level areas have coterminous boundaries. figure 2 indicates these areas to be generally spread over a somewhat large portion of the western and central parts of the County. The expres sive delinquency area with the lowest overall level of magnitude was found to be the most unique in spatial loca tion, when compared to any other expressive (or instru mental) delinquency area. This least prominent area is situated in the general northeastern part of the region and is totally spatially separate from any other delinquency area. Thus, in an overall comparison of the two types of delinquency areas, it appears that while there are 118 I coalescenses of instrumental and expressive areas 1n parts 1 0£ the County, the expressive type of delinquency areas are much more dispersed throughout the total Los Angeles Metropolitan Area. The relationship· and ov·erlapping patterns betwe·en the· a· e·signated instru~e~tal and e~pres~ive· de.lin quencf areas: Hypothes~s H3 ~nd H4 It was noted in the discussion above that a visual cor relation of Figures 1 and 2 indicated a convergence of some of the different types of delinquency areas. In this sub section, then, the interweaving of the two types of delin quency 1s more specifically examined to determine: 1. if and where relationships exist within the different delinquency areas; and 2. how the relationship is affected by the type of delinquency area. It was proposed at the end of Chapter II as Hypothesis H3 that in the higher intensity delinquency area it should be expected the two forms of delinquent activity would be more likely to be simultaneously present than 1n areas with relatively lower magnitude values. Likewise, it was furthe J argued as Hypothesis H4 that these relationships should be more pronounced over a broader range of intensities for the instrumental in contrast to the expressive delinquency areas. In the present subsection these statements of 1 ~ ~-------------------------------- I relationship can perhaps best be assessed through a measure ment of the degree or the strength of the concomitant relationship. One such statistic is, of course, the product-moment correlation. This procedure was applied to !th8 appropriate samples and the correlation coefficients in Table 8 summarize the degree of relationship between magnitudes representing the two forms of delinquent activ ity found in the specified delinquency areas. I It is generally understood when using product-moment (or any multiple correlation form of linear regression equations) correlations that one also assumes a symmetrical and unimodal distribution of the data. Yet earlier, it was revealed that some of the study distributions are somewhat skewed and thus may violate a basic theoretical condition of the product-moment statistic. However, since the study is primarily concerned with discovery of general associ ations and relative comparative trends between various ratic scales - -and not concerned with an exact correlation value- all measures of concomitant correlation 1n the study should at the minimum meet the study objectives. This is assumed because any results obtained are necessarily conservative estimates of the "actual" relationships. 21 Table 8 indicates that both expectations above are at least partially supported. Both the instrumental and expressive areas which were shown in Table 5 to have the 120 I TABLE 8 CORRELATION COEFFICIENTS COMPARING INSTRUMENTAL AND EXPRESSIVE COMPOSITE INDICATORS OF JUVENILE DELINQUENCY FOR DESIGNATED INSTRUMENTAL AND EXPRESSIVE DELINQUENCY AREAS IN LOS ANGELES COUNTY: 1970 -------------------------------- Cluster Categories and Selection Number Number of Subareas Correlation Coefficient 1 Instrumental Areas I I I I Cluster 1 3 5 660 661 708 710 Expressive Areas Cluster 1 3 14 591 599 618 63 20 28 12 27 15 12 49 16 11 34 149 17 .74a .43 .72a .64a .00 -.08 -.47 .73a .76a .32 -.14 .02 .41 aCorrelations differ significantly from zero at the .OS level, one tail test; all other coefficients not significant at .OS level. 121 /higher means for their respective areal values are shown !Table 8 to have the most substantial correlation coeffi- . 1n cients between instrumental and expressive indicator scores. Moreover, the general direction 1n correlation values tends to reflect the similar trend found in mean values. Also, the number of significant correlations substantiates the expectation that the instrumental areas will have a more pronounced set of correlations over a broader range of sub areas. For example, the four highest magnitude areas are significant. Furthermore, one of the instrumental areas (i.e., selection number 660) which had a high correlation value of .637 was shown to have a mean average level of -0.71, a value below the County average. No similar pattern is found for expressive areas. Finally, examination of Figure 1 reveals that the latter instrumental delinquency area is not in the same cluster concentration as the other areas with significant correlation values. In the above correlational comparison of the two delin quency indicators, it should be remembered that each desig nated delinquency area had already been determined to be a statistically homogeneous area. Therefore, indicator score differences within (or the general distribution of) the defined area are in one sense only an artifact of random variance when compared to the subareal values surrounding the spatial cluster. However, when comparing different 122 - I 'values totally within each area, the level of magnitudes for the subareas defining indicator (i.e., composite indi cator scores for instrumental or expressive juvenile delin quency) may have a concomitant relationship with other neighborhood conditions, where the latter conditions are not themselves a spatial cluster. This may be the case because the actual cluster is dependent upon not only the within contiguous variant distribution, but also the be tween variance with the subareas surrounding the cluster. Measurement Development and Composite Indicator Con·struction of Specific Elements of Neighbbrhobd ·social Structure Thus far this chapter has concentrated on the develop ment of composite indicators representing the two dependent variables of the study and how these variables were used in the construction and analysis of instrumental and expressive 1 delinquency areas. Similar to the objectives of delin quency subareal scores, composite indicators were designed to reflect patterns of adult instrumental and expressive criminal activity, neighborhood socioeconomic status, land use, and ascribed demographic character. These five struc tural elements were described earlier as important independ ent variables in the determination of the specific offense patterns of delinquency areas. 123 The special procedures and summary statistics of the !five composite indicators are described in the following ltwo subsections. In the last section of the chapter the !results and analysis are presented relating these independ lent variables to delinquency scores within the context of I I the designated urban delinquency areas. Compos i ·te indica to'rs· £: or· ·the measurement of neighborh~od adult cri~inal a~t- ivity In general, procedures and measurement design de scribed earlier regarding development of juvenile instru mental and expressive delinquency indicators were also used in the construction of adult measures. Except for the fact that actual input data consisted of only those adults convicted of criminal offenses, the files used were again transaction records from the Los Angeles County Probation Department for 1969, 1970, and 1971 calendar years. For these three years there were 92,775 new criminal offense convictions in Los Angeles County. After these usable transactions were checked for correct geocode specifi cations, offense designations, and transformed into a singl ~ calendar year average conviction file, there were a total of 21,396 records available for creation of the study's analytical file of adult criminal activity. The disparity in the criterion for the inclusion of 124 juveniles and adults 1n the transaction files imposed on the study the need to accept the noncomparability of the two groups. In general, the definition of official adult criminal activity is more restrictive in that a person must be convicted of a criminal offense. The welfare of the adult is not considered a point of judicial interest as 1s the case of juveniles. Nevertheless, the appropriateness of the information is not in the absolute numbers. Rather, the transaction records were useful in the development of comparative rates (similar to measures discussed under the above section for juveniles) which were then used to form subareal instrumental and expressive adult composite indi cator scores of criminal activity for Los Angeles County. The summary statistical descriptions for the specified measurements and transgenerated composite scores of adult instrumental and expressive subareal activity are shown in Tables 9 and 10, respectively. By comparing the parameters in these two tables for adults to the juvenile results in Tables 3 and 4, it becomes readily apparent that the dis tributions of the adult categories generally show a more normal distribution for the County as a whole. However, it l should also be noticed that: first, much of the variation (column 6) within each measure is accountable to the factor and second, the relative weighting pattern (i.e., the factor score coefficients in column 8) is similar for the 125 ~ N °' TABLE 9 SUMMARY STATISTICS FOR SELECTED VARIABLES AND COMPOSITE INDICATOR CONSTRUCTION OF INSTRUMENTAL ADULT CRIMINAL ACTIVITY OVER 1,142 SUBAREAS IN LOS ANGELES COUNTY: 1970 Ma a a a ha ha a s gl g2 F.L. e f Defined Measures (1) (2). (3) (4) (5) (6) (7) INSTRUMENTAL CONVICTIONS BY:c (1) All Persons 18 Years and Over x 100 0.22 0.27 2.28 5.95 .76 .76 .87 (2) All Convictions x 100 38.18 20.70 -0.10 0.60 .21 .21 .46 (3) Number of Acres 0.02 0.03 2.47 7.67 .78 .78 .88 (4) All Instrumental Convictions in Los Angeles County x 100 0.09 0.12 5.14 47.86 .so .so .71 (5) Expressive Convictions x 100d 75.35 58.38 1.95 9.51 .15 .15 .39 aSee Table 3 for column definitions. bSummary statistics of composite scores over 1,142 subareas: M = 0.00, s = 0.96, minimum value= -1.13, maximum value= 5.63, g 1 = 1.65, g 2 = 3.25. cSee Table 2 for instrumental offense category designations. dSee Table 2 for expressive offense category designations. F.S.C.a,b (8) .27 .08 .27 .so .02 I-' N '-.l TABLE 9 (continued) SUMMARY STATISTICS FOR SELECTED VARIABLES AND COMPOSITE INDICATOR CONSTRUCTION OF INSTRUMENTAL ADULT CRIMINAL ACTIVITY OVER 1,142 SUBAREAS IN LOS ANGELES COUNTY: 1970 Ma a a a ha ha a s gl g2 F. L. e f Defined Measures (1) (2) (3) (4) (5) (6) (7) CONVICTIONS FOR MALES BY: (6) All Convictions x 100 73.20 25.72 -1.79 2.69 .11 .11 .33 (7) All Convictions for Males in Los Angeles County x 100 0.09 0.12 5.81 57.81 .39 .39 .62 (8) Number of Acres 0.04 0.06 2.23 5.68 .75 .75 .86 aSee Table 3 for column definitions. bSummary statistics of composite scores over 1,142 subareas: M = 0.00, s = 0.96, minimum value =-1.13, maximum value= 5.63, g 1 = 1.65, g 2 = 3.25. F.S.C.a,b (8) .07 -.21 .18 I I ~ N 00 TABLE 10 SUMMARY STATISTICS FOR SELECTED VARIABLES AND COMPOSITE INDICATOR CONSTRUCTION OF EXPRESSIVE ADULT CRIMINAL ACTIVITY OVER 1,142 SUBAREAS IN LOS ANGELES COUNTY: 1970 Ma a a a ha ha s gl g2 e f Defined Measures (1) (2) (3) (4) (5) (6) EXPRESSIVE CONVICTIONS BY:c (1) All Persons 18 Years and Over x 100 0.28 0.30 2.10 5.98 .71 .71 (2) All Convictions x 100 56.03 23.06 -0.53 0.97 .00 .00 (3) Number of Acres 0.03 0.04 2.21 5.83 .79 .79 (4) All Expressive Con- victions in Los Angeles County x 100 0.09 0.12 6. 24 64.43 .43 .43 aSee Table 3 for column definitions. bSummary statistics of composite scores over 1,142 subareas: M = 0.00, s = 0.96, minimum value= -0.95, maximum value= 5.00, g 1 = 1.84, g 2 = 3.99. cSee Table 2 for expressive offense category designations. F.L. a (7) .84 • OS .89 .65 F.S.C.a,b (8) .20 -.01 .76 -.19 --------- ------ - I ~ N I.O TABLE 10 (continued) SUMMARY STATISTICS FOR SELECTED VARIABLES AND COMPOSITE INDICATOR CONSTRUCTION OF EXPRESSIVE ADULT CRIMINAL ACTIVITY OVER 1,142 SUBAREAS IN LOS ANGELES COUNTY: 1970 Ma a a a ha ha s gl g2 e f Defined Measures (1) (2) (3) (4) (5) (6) CONVICTIONS FOR FEMALES BY: (5) All Convictions x 100 21.02 18.96 1.90 5.28 .00 .00 (6) All Convictions for Females in Los Angeles County x 100 0.09 0.11 5.39 53.10 .46 .46 (7) Number of Acres 0.01 0.01 2.52 9.29 .61 .61 aSee Table 3 for column definitions. bSummary statistics of composite scores over 1,142 subareas: M = 0.00, s = 0.96, minimum value =-0.95, maximum value= 5.00, g 1 = 1.84, g 2 = 3.99 F.L. a (7) -.01 .68 .78 F.S.C.a,b (8) .03 .53 -.22 complementary adult and juvenile tables. Thus, it appears that overall contribution of the different variables used in formulating composite instrumental and expressive indi cators is similar for adults and juveniles. Com~osite indicato~s of dev1ational change In Chapter II statistical procedures were outlined whereby composite indicators of stability were to be devel oped through the use of appropriate measures of deviational change. 22 Also in earlier discussions, it was suggested that within the context of the current study's objectives the degree of subareal stability (or change) in neighbor hood socioeconomic status, land use, and ascribed demo graphic character should play an important part in deter mining the specific group patterns of juvenile delinquency in delinquency areas. This subsection, therefore, describe the development of change measures in these three elements of local social structure. The basic source of data for all deviational change measures was the automated computer files of census tract summary tabulations for the 1960 and 1970 U.S. Census of Population and Housing. This single source of input data, of course restricted the actual kinds of variables that could be developed . This notwithstanding, a variety of data items was selected to reflect different facets of the l 130 three elements of local social structure. By combining the various measurements into three different composite indi cators, it was anticipated that the element being repre sented by the composite would be more valid and reliable than any single measure of the abstract concept of social structure. 23 While by no means exhaustive, the items selected were based on standard demographic and sociological uses of such data to represent the three relevant elements of social structure. 24 Furthermore, except for the measures of m~dia family income, median housing unit value, and a ratio of number of persons to the number of occupied housing units for each subarea, all subareal measurements of the selected data items were constructed according to the procedural conventions established above for concentration, density, and unit share measures. Distribution measures were not developed, because measurement of elements of the social structure involves no populations at risk. With the use of these guidelines, a total of 33 measurements were formu lated to represent the three elements of local social struc ture. The description of each measurement is presented in Tables 11, 12, and 13 . Unlike all previous composite indicator construction 1n the study, here the input measures must first be trans formed into deviational measures of change. Therefore, the 131 1---1 , ~ --- - - -- -- --- - - --- .. ---- - ----· TABLE 11 SUMMARY STATISTICS FOR SELECTED DEVIATIONAL CHANGE MEASURES AND COMPOSITE INDICATOR CONSTRUCTION OF LAND USE OVER 1,142 SUBAREAS IN LOS ANGELES COUNTY: 1970 ON 1960 Defined Measures (1) Population by Acres (2) Population by Population for County x 100 Occupied Housing Units With a r7,6 (1) .93 .92 More Than One Person Per Room By: (3) Total Occupied Units x 100 .85 (4) Acres .84 (5) Occupied Housing Units With More Than One Person Per Room for County x 100 .91 b b7,6 (2) 0.96 1. 12 0.82 0.95 0.94 he e (3) .64 .13 .00 .14 .12 he f (4) . 64 .13 .00 .14 .12 F. L.c (5) .80 .36 .03 .37 .35 F.S.C.c,d (6) .06 .07 .07 .06 .03 a r7,6 = interannual correlation of 1970 on 1960 measures; all coefficients differ significantly from zero at .OS level. b b7, ' t) = interannual regression coefficients of 1970 on 1960 measures. cSee Table 3 for column definitions. dSummary statistics of composite scores over 1,142 subareas: M = 0.00, s = 0.97, minimum value= -9.89, maximum value= 6.02, g 1 = -1.18, g 2 = 20.91. ~ v-l t,.l TABLE 11 (continued) SUMMARY STATISTICS FOR SELECTED DEVIATIONAL CHANGE MEASURES AND COMPOSITE INDICATOR CONSTRUCTION OF LAND USE OVER 1,142 SUBAREAS IN LOS ANGELES COUNTY: 1970 ON 1960 Defined Measures Occupied Housing Units Rented By: (6) Total Occupied Units X 100 (7) Acres (8) Total Rented Occupied Units for Countr x 100 Vacant Units for Rent or Purchase By: (9) Total Units x 100 (10) Acres (11) Housing Units for Rent or Purchase for County x 100 a r7,6 (1) .80 .89 .90 .41 .71 .72 b b7,6 (2) 0.80 0.97 0.86 0.28 0.45 0.68 he e (3) .32 .71 .55 . 01 .24 .32 he f (4) .32 .71 .55 . 01 .24 .32 F.L.c (5) .56 .84 .74 .09 .49 .57 F.S.C.c,d (6) .09 .10 .07 .10 -.13 .12 a = r7,6 interannual correlation of 1970 on 1960 measures; all coefficients differ significantly from zero at .OS level. bb 7 , 6 = intei annual regression coefficients of 1970 on 1960 measures. cSee Table 3 for column definitions. dSummary statistics of composite scores over 1,142 subareas: M = 0.00, s = 0.97, minimum value= -9.89, maximum value= 6.02, g 1 = -1.18, g 2 = 20.91. TABLE 11 (continued) SUMMARY STATISTICS FOR SELECTED DEVIATIONAL CHANGE MEASURES AND COMPOSITE INDICATOR CONSTRUCTION OF LAND USE OVER 1,142 SUBAREAS IN LOS ANGELES COUNTY: 1970 ON 1960 Defined Measures Moved Into Unit In Last Two Years: a r7,6 (1) (12) Total Occupied Units x 100 .69 (13) Acres .92 (14) Moved Into Unit In Last Two Years for County x 100 .91 (15) Number of -Persons By Occupied Uni ts . 66 (16) HousinR Units by Acres .93 b b7,6 (2) 0.73 1.01 1.04 3.54 0.96 he e (3) .09 .70 .34 .00 .81 he f (4) .09 .70 .34 .00 .81 F.L.c (?) .31 .84 .59 .03 .90 F.S.C.c,d (6) .06 .09 .07 .02 .55 a = r7,6 interannual correlation of 1970 on 1960 measures; all coefficients differ significantly from zero at .05 level. bb = 7,6 interannual regression coefficients of 1970 on 1960 measures. cSee Table 3 for column definitions. dSummary statistics of composite scores over 1,142 subareas: M = 0.00, s = 0.97, minimum value= -9.89, maximum value= 6.02, g 1 = -1.18, g 2 = 20.91. TABLE 12 SUMMARY STATISTICS FOR SELECTED DEVIATIONAL CHANGE MEASURES AND COMPOSITE INDICATOR CONSTRUCTION OF SOCIOECONOMIC STATUS OVER 1,142 SUBAREAS IN LOS ANGELES COUNTY: 1970 ON 1960 a,b r7 6 , Defined Measures (I) Population Over 24, College Graduate or More Education By: (I) All Persons Over 24 Years Old x 100 .92 (2) Acres .90 (3) Total Population Over 24, College Graduate or More Education For County x 100 .90 Population Over 13 and Employed As a Professional, Technical, Manager, Administrator, and Kindred Workers By: (4) Employed Population Over 13 X 100 .92 (5) Acres .89 aSee Table 11 for column definitions. a b7,6 (2) 1.17 1.22 1.14 0.92 0.99 he e (3) .48 .58 .48 .42 .51 bAll coefficients differ significantly from zero at the .OS level. cSee Table 3 for column definitions. he b (4) .48 .58 .47 .42 .51 F.L.c (5) .69 .76 .69 .64 .71 ~ ~ u, dSummary statistics of composite scores over 1,142 subareas: M = 0.00, s = 0.93, ~---- m _ i_ n_ i_ mum value =~~78, maximum value= 7.19, g 1 = 1.81, g 2 = 10.55. F.S.C.c,d (6) .14 .27 .28 .20 .18 ~ vl °' TABLE 12 (continued) SUMMARY STATISTICS FOR SELECTED DEVIATIONAL CHANGE MEASURES AND COMPOSITE INDICATOR CONSTRUCTION OF SOCIOECONOMIC STATUS OVER 1,142 SUBAREAS IN LOS ANGELES COUNTY: 1970 ON 1960 a,b a he he C F.S.C.c,d r7 6 b7 6 F. L. f , , e Defined Measures (1) (2) (3) (4) (5) (6) (6) Total Population Over 13 and Employed As a Pro- fessional, Technical, Manager, Administrator, and Kindred Workers For County x 100 .87 1.12 .40 .40 .64 .08 (7) Median Family Income .95 1. 70 .15 .15 . 38 .07 (8) Median Home Value .85 1.31 .11 .11 .34 .06 aSee Table 11 for column definitions. bAll coefficients differ significantly from zero at the .OS level. cSee Table 3 for column definitions. dSummary statistics of composite scores over 1,142 subareas: M = 0.00, s = 0.93, minimum value =-3.78, maximum value= 7.19, g 1 = 1.81, g 2 = 10.55. 1-J tN -.....J ------------------------------------~ TABLE 13 SUMMARY STATISTICS FOR SELECTED DEVIATIONAL CHANGE MEASURES AND COMPOSITE INDICATOR CONSTRUCTION OF ASCRIBED DEMOGRAPHIC CHARACTER OVER 1,142 SUBAREAS IN LOS ANGELES COUNTY: 1970 ON 1960 Defined Measures Black Population By: (1) Total Population x 100 (2) Acres (3) Total Black Population For County x 100 Mexican Foreign Stock By: (4) Total Population x 100 (5) Acres (6) Total Mexican Foreign Stock For County x 100 a,b r7,6 (12 .88 .86 .76 .92 .92 .91 aSee Table 11 for column definitions. a b7,6 (2) 1.12 1.01 0.64 1.29 1.33 0.82 he e (3) .83 .85 .78 .06 .03 .03 bAll coefficients differ significantly from zero at the .OS level. cSee Table 3 for column definitions. he f (4) .83 .85 .78 .06 .03 .03 F.L.c (5) .91 .92 .88 -.25 -.18 -.16 dSummary statistics of composite scores over 1,142 subareas: M = 0.00, s = 0.96 minimum value =-2.88, maximum value= 8.41, g 1 = 3.41, g 2 = 15.65. F.S.C.c,d (6) .28 .46 .21 -.OS -.00 -.03 ~ vl 00 ~ - - -- - -~ - - ---------------------------------------------- TABLE 13 (continued) SUMMARY STATISTICS FOR SELECTED DEVIATIONAL CHANGE MEASURES AND COMPOSITE INDICATOR CONSTRUCTION OF ASCRIBED DEMOGRAPHIC CHARACTER OVER 1,142 SUBAREAS IN LOS ANGELES COUNTY: 1970 ON 1960 Defined Measures Person Age 10-17 Years By: (7) Total Population x 100 (8) Acres (9) Total Person Age 10-17 Years for County x 100 a,b r7,6 (1) .77 .86 .86 aSee Table 11 for column definitions. a b7,6 (2) 1.07 1.11 1.13 he e (3) .09 .15 .04 bAll coefficients differ significantly from zero at the .05 level. cSee Table 3 for column definitions. he f (4) .09 .15 . 04 F.L.c (5) .30 .39 .20 dSummary statistics of composite scores over 1,142 subareas: M ~ 0.00, s = 0.96, minimum value= -2.88, maximum value= 8.41, g 1 = 3.41, g 2 = 15.65. , F.S.C.c,d (6) .13 -.02 .06 33 selected measures for each of the 1,142 subareas were constructed for 1960 and for 1970. Using the 1960 measures to predict the 1970 measures, linear interannual regression statistics were calculated which in turn were used to produce deviational or residual measures (i.e., residual= actual value - expected value) for each subarea. These residuals, representing relative velocity of change between 1960 and 1970, were then input measurements in the develop ment of the three composite indicators of deviational change. This difference in the measurement development of inputs for composite indicator construction of elements of social structure is reflected also in the summary of de scriptive statistics in columns 1 and 2 in Tables 11, 12, and 13. Here the univariate descriptive parameters of pre vious tables have been replaced by the more appropriate bivariate statistics of interannual correlations and regres sion coefficients. It should be recalled from the discus sion in Chapter II that an interannual correlation sum marizes the degree of relative stability (or change) occurring within a given measure, and provides a measure of relative similar positions in rank magnitude over time. Th higher the correlation value, the more stable the particula structural characteristic. Of course, the square of the correlation (i.e., coefficient of determination) can be use to show the amount of variance the study period's L ___________ 13 !distribution (i.e., 1970) was accounted for by the initial period's distribution (i.e., 1960). In addition, it should be recalled that interannual regressions are indicators of average change within the distributions of the subareas; it provides a descriptive summary of where and how much the average degree of change has occurred. Thus, it can be viewed as a gauge of the relative equal increase throughout a subareal distribution, and if the distribution is not coextensive, where it is not. These two descriptive statis tics of interannual change are useful in examining the individual measurement patterns for each set of measures. If there are discernible patterns, they should in part explain the final deviational composite indicator distri butions of land use, socioeconomic status, and ascribed demographic characteristics. An overview of the interannual correlations in column 1 of the three tables indicates that of the three groups of measures, the pattern of greatest stability is found in Table 12: measures of subareal socioeconomic characteris tics. Here only three correlations were below .90 and none of the coefficients were below .70. In contrast, the measure of land use shown in Table 11 had nine correlations below .90 and three below .70. Likewise, Table 13 reveals demographic characteristics to have six of nine of the measures with correlations below .90 and two of these are 140 I below .70. The general highlights of the interannual regressions in column 2 of the three tables show that in addition to :having the most stable measures, Table 12 also indicates that the greatest increases in socioeconomic status have occurred more rapidly in subareas which already had high measures at the beginning of the decade. All but two of the eight measures exceeded unity (i.e., the value of no change) and three of these measures were in excess of 1.20. This suggests then that socioeconomic measures are relatively the most stable, and any gains in status that have taken place occurred generally in the areas with already high status. The measures of ascribed status in Table 13 also show a pattern of average increase in subareas which were ini tially high. Only two of nine measures were less than unity, and both of these measures were unit share measures representing Black and Mexican foreign stock categories. In general contrast to measures of demographic and socio economic status, measures of land use in Table 11 are the most different. Not only are the measures in this table the least stable as indicated by the interannual correlations, but most of the measures also show the greatest average amounts of change occurring in areas which showed lowest initial values. Of the 16 measures included in Table 11, 141 only four were above unity. Since the degree of instability also indicates the amount of deviation of an actual value from a calculated 1 value, based on a least squares linear regression line, it might also be expected that the range of standardized composite scores would be greatest where interannual cor relations were lowest. This was found to be the case for the composite land use indicator. In Table 11, footnoted, the absolute range between the minimum and maximum value 1s 15.91, whereas the absolute range of the composite standard scores in Tables 12 and 13 are only 10.97 and 11.29. Furthermore, consideration of the interannual regression coefficient patterns sugges ted that land use was the only set of measures which were increasing the fastest in those subareas with initially low values. Hence, it might be expected that the composite indicator distribution would be more negatively skewed than either socioeconomic or demographic composite indicator distributions. Tables 11, 12, and 13, footnoted, reveal this to be true; the respec tive skewness indexes are -1.18, 1.81, and 3.41. Looking more closely at the summary univariate distributional statistics also reveals that the lowest value of the stand ard values for land use is -9 89 in contrast to the -3.79 and -2.88 minimum values for socioeconomic and demographic characteristics. From the differences in total absolute 142 ranges and m1n1mum values, it is obvious that maximum values, then, must also be higher for socioeconomic and demographic composite scores. The various differences notwithstanding, the total 1 contribution of the different measurements to their respec- I 1 tive composite indicator was substantial. This is reflectec !in the factor loading (column 8, Tables 11, 12, and 13). For example, in Table 11 only three of the sixteen land use deviational measures has a factor loading below .30. Socio economic status deviational measures in Table 12 reveal no ,factor loading below .3o.· Finally ascribed demographic character deviational measures in Table 13 displayed four out of nine factor loadings to be below .30, with only two ,of these loadings below .20, however. Delin· quency Area· Determinants The five composite indicators described 1n the last two sections were formulated as the study's main independent variables. They were developed to measure particular ele- 1 ments of the social structure deemed relevant to the study's last five hypotheses (i.e., HS through H9) which stated ideal relationship conditions between subareal structural ,features and group patterns of delinquent activity in the I delinquency areas. Thus, this section builds on results discussed above in the definition of delinquency spatial 143 I enclaves. There is an assumption here that: (a) the 1spatially contiguous subareas identified earlier were representative of two types of delinquency areas; (b) each 'type in turn was generally found to have a number of desig- 1 nated spatial enclaves consisting of different overall levels of delinquency intensity, as measured by mean levels of composite scores representing instrumental or expressive group patterns of delinquency. Effects of structural ·stabil"ity: Hypotheses HS and. H6 At the outset of Chapter I, the argument was set forth that delinquency activity patterns within delinquency areas were affected by the degree of stability in local social structure. This general postulate was made more explicit at the end of Chapter II in the form of two research hypo theses expressing not only the form but also the direction of the relationships. On the one hand, Hypothesis HS stated that in designated instrumental delinquency areas inverse concomitant relationships might be expected hetween subareal indicator scores of instrumental delinquency and indicators of neighborhood structural change. On the other hand, Hypothesis H6 was a statement of the converse by noting that in designated expressive delinquency areas there should be a tendency of positive covariation between subareal indicator scores of expressive delinquency and indicators of 144 ------------------------------------J ' 1 neighborhood structural change. It should be remembered that in the formulation of Hypotheses HS and H6 other inter relationships postulated to be important to delinquency areas were not considered in order first to examine the simple relationship between structural stability and specific group patterns of delinquency. After reviewing the results directly related to the two specific hypotheses, however, other relationships are also examined which may further explain the trends of association between structura] stability and patterns of delinquency. Hypothesis HS.--To assess the tenability of HS, linear regression concomitant relationships between instrumental delinquency indicators within the defined instrumental delinquency areas and the three deviational change indi cators were obtained. The results are shown in Table 14 (variables 1-3, columns 1-6). An overall comparison of the zero-order correlation coefficients in Table 14 calculated for the designated instrumental delinquency areas would indicate support of Hypothesis HS in that a general trend of "proper direction" negative relationships does exist, particularly in the instrumental delinquency areas shown 1n Table S to have the higher overall activity of instrumental delinquency (i.e., cluster selection 1, 3, and 5). And as indicated in Figure 1, these three delinquency areas are 145 1--J ~ °' TABLE 14 SIMPLE AND MULTIPLE CORRELATIONS AND MULTIPLE STANDARDIZED REGRESSIONS FOR AN INDICATOR OF INSTRUMENTAL JUVENILE DELINQUENCY WITH SPECIFIED INDICATORS OF ELEMENTS OF SOCIAL STRUCTURE IN EACH SUBAREA, MEASURED OVER SEVEN INSTRUMENTAL DELINQUENCY AREAS IN LOS ANGELES COUNTY Instrumental Clustered Number 1 3 5 660 661 708 710 (1) (2) (3) (4) (5) (6) (7) simple correlation coefficient Deviational Composite Indicator (1) Subareas Land Use (lu) -.39c -.44c .01 -.07 .03 -.03 .21 (2) Socioeconomic Status (ses) .15 -.19 .29 .01 .08 -.04 -.03 (3) Ascribed Demographic Character (demo) .OS -.21 -.24 .13 .21 .00 .06 Adult Instrumental Criminal Activity (4) Composite Indicator (adult) .69c .64c .69c .13 .05 .08 .28 multiple correlation a and standardized . ff. . b ~egress1on coe 1c1ent (S) Rlu + ses + demo .46d 8 1u -.56c B .09 ses B demo .3le aR = multiple correlation. bB = standardized regression coefficient. cSignificant at .05 level, one tail test. dSignificant at .05 level, two tail test. .45 -.38 -.09 -.12 .36 .18 .22 .10 .22 -.12 .08 -.14 .15 .OS -.02 .15 -.35 .18 .23 -.03 eSignificant at .OS level, one tail test; however the direction is not as hypothesized. .30 .23 -.05 -.27 1---J ~ -......J TABLE 14 (continued) SIMPLE AND MULTIPLE CORRELATIONS AND MULTIPLE STANDARDIZED REGRESSIONS FOR AN INDICATOR OF INSTRUMENTAL JUVENILE DELINQUENCY WITH SPECIFIED INDICATORS OF ELEMENTS OF SOCIAL STRUCTURE IN EACH SUBAREA, MEASURED OVER SEVEN INSTRUMENTAL DELINQUENCY AREAS IN LOS ANGELES COUNTY Instrumental Cluster Number 1 3 5 660 661 708 710 (1) (2) (3) (4) (5) (6) (7) multiple correlationa and standardized regression coefficientb ( 6 ) Rlu + ses +demo+ adult .75d .7ld .73d .21 .26 .14 .44 8 1u -.33c -.25 .22 -.14 .09 -.03 .29 B .10 ses .01 -.02 .12 -.01 -.03 .OS B demo .24e -.13 -.37 .07 .27 -.08 -.33 B adult .62c .SBC .67c .19 .14 .OS .40 aR = multiple correlation. bB = standardized regression coefficient. cSignificant at .05 level, one tail test . dSignificant at .OS level, two tail test. eSignificant at .05 level, one tail test; however the direction is not as hypothesized. I themselves spatially adjacent to each other in the south- central region of Los Angeles County. It should also be noted, however, that the level of these negative relation ships ranges from low to moderate associations. Only two spatial enclaves demonstrate a statistically significant relationship, and in both cases the structural element was subareal land use. The lack of statistical significance, of course, is due in part to the rather small sample size of the respective clusters, and may also be a function of the conservative nature of all correlations in this study. The latter condition, it will be remembered, is a result of the somewhat skewed distribution of all of the independent and dependent variables. In contrast to the delinquency areas clustered in the south-central part of the County, the western concentration of delinquency areas (shown in Figure 1) which have the lower averages of instrumental delinquency magnitudes (as noted in Table 5) also revealslittle or no zero-order associations or patterns of relationships with the three indicators of social structure (Table 14, lines 1-3, columns 4-7). Thus, the zero-order correlations discussed here seem to suggest that for patterns of relationships to exist be tween relative stable elements of social structure and instrumental delinquency areas there must be a relatively 148 I . high overall average of delinquency commitment to the instrumental type. Furthermore, what is particularly im portant in support of Hypothesis HS is the fact that the zero-order correlations for the different delinquency areas in Table 14 (lines 1, 2, and 3) do not suggest any meaning ful pattern of positive relationships between levels of structural change and magnitudes of instrumental delin quency: the pattern that is revealed is always towards a need for stability in neighborhood social structure in the instrumental delinquency area with the increasing magnitudes of instrumental delinquent activity. Finally, it should be remembered that stable neighborhood conditions are only necessary conditions, particularly in instrumental delin quency areas. For there to be a cross-generational trans mission of instrumental type of skills, there must also be present in the delinquency area a substantial relationship with an adult criminal value system. The importance of this form of simultaneous relationship will be more fully exam ined below in the analysis of Hypothesis H7. Hyp?thesis H6.--In contrast to the instrumental delin quency areas, it was postulated in Hypothesis H6 that neigh borhood structural change is a sufficient condition under which expressive delinquency areas may emerge. Here, then, expressive areas should indicate a trend of positive rela tionships between deviational scores reflecting changing 149 L neighborhood structural patterns and indicators of expres sive delinquency. The zero-order correlations for expres sive delinquency areas, comparing this form of subarea delinquency activity with deviational change indicators of the three elements of social structure, are summarized in Table 15 (variables 1-3, columns 1-6). An initial overview of the results would indicate clea support for Hypothesis H6 only in expressive delinquency areas 591, 599, and 618. It can be seen from the correla tions in columns 4, 5, and 6 of Table 15 that a distinctive pattern of positive relationships exists as the overall expressive activity falls below the County average (i.e., remembering from Table 5 that these three areas all had mean delinquency scores of less than 0). Five of the nine correlations were positive and four of these coefficients were significant, in the statistical sense, in spite of the rather small sample size of the three different delinquency areas. It is also important to remember from Table 8 that these three expressive delinquency areas reveal no signifi cant relationship with instrumental delinquency activity. The zero-order correlations in columns 1, 2, and 3, however, do not exhibit a similar incisive trend. While expressive area 1 exhibited no relationship, the correla tions in columns 2 and 3 seem to suggest a strong pattern of relationship that would be expected for only instrumenta 150 --------------------------------- ____j 1-J lF TABLE 15 SIMPLE AND MULTIPLE CORREL4TIONS AND MULTIPLE STANDARDIZED REGRESSIONS FOR AN INDICATOR OF EXPRESSIVE JUVENILE DELINQUENCY WITI-I SPECIFIED INDICATORS OF ELEMENTS OF SOCIAL STRUCTURE IN EACH SUBAREA, MEASURED OVER SIX EXPRESSIVE DELINQUENCY AREAS IN LOS ANGELES COUNTY Expressive Cluster Number 1 3 14 591 599 618 (1) (2) (3) (4) (5) (6) simple correlation coefficient Deviational Composite Indicator (1) Subareal Land Use (lu) -.08 -.62e .03 .52c .2lc -.36 (2) Socioeconomic Status (ses) .11 -.02 -.74e .00 .13 .44c (3) Ascribed Demographic Character (demo) .01 -.soe -.41 -.OS -.07 .57c Adult Expressive Criminal Activity (4) Composite Indicator (adult) .78c .28 .63c .32c -.08 .11 multiple correlationa and standardized regression coefficientb (S) Rlu + ses + demo 8 1u B ses B demo aR = multiple correlation. bB = standardized regression coefficient. cSignificant at .OS level, one tail test. dSignificant at .OS level, two tail test. .17 -.16 .13 .14 .66d .84d .54d .22d -.S6e .29 .S7c .20c -.21 -.8Se -.14 .03 -.17 -.22 .OS -.08 eSignificant at .OS level, one tail test; however the directior is not as hypothesized. .8ld -.40e .4lc .SBC TABLE 15 (continued) SIMPLE AND MULTIPLE CORRELATIONS AND MULTIPLE STANDARDIZED REGRESSIONS FOR AN INDICATOR OF EXPRESSIVE JUVENILE DELINQUENCY WITH SPECIFIED INDICATORS OF ELEMENTS OF SOCIAL STRUCTURE IN EACH SUBAREA, MEASURED OVER SIX EXPRESSIVE DELINQUENCY AREAS IN LOS ANGELES COUNTY Expressive Cluster Number 1 (1) 3 (2) 14 (3) 591 (4) 599 (5) 618 (6) multiple correlationa~an~~tapdardized regression coefficientb ( 6 ) Rlu + ses +demo+ adult 8 1u B ses B demo B adult aR = multiple correlation. bB = standardized regression coefficient. cSignificant at .OS level, one tail test. dSignificant at .05 level, two tail test. .79d .06 .02 -.12 .soc .69d -.67e -.22 .03 .25 .8Sd .56d .23d .25 .Slc .20c -.76e -.15 .03 -.08 .06 -.07 .22 .17 -.03 eSignificant at .05 level, one tail test; however the direction is not as hypothesized. .87d -.27 .45c .74c .39c I enclaves; and in fact, these coefficients in Table 15 are substantically higher than any similar zero-order associ- lations in Table 14 for instrumental delinquency areas. Re calling from results in Table 8, however, that expressive cluster 3 also had the highest concomitant association be tween instrumental and expressive delinquency scores of all the expressive areas (r = .76), it was suspected that what seems to be a conceptual and areal contradiction in Table 15, column 2, may be accounted for 1n part by the levels of instrumental delinquency in the area. For example, in this particular expressive delinquency area, the zero-order cor relations between instrumental delinquency activity were -.64 and -.65 for land use and demographic character scores, respectively. Statistically there is no significant dif ference between these relationships and the correlations in Table 15 (column 2, variables 1 and 3). It would seem use- ful then to measure the importance of each element of structural stability to each one of the delinquency patterns separately, while at the same time accounting for the co variation with the other type of delinquency activity. Therefore, to nullify the effect of one of the delinquency patterns while correlating the other to a structural stabil ity variable, first-order partial correlations were per formed. Thus, by partialing out the instrumental delin quent activity 1n expressive delinquency area 3, the 153 frelationship between expressive delinquency and subarea l1and use dropped from -.62 to -.27, and ascribed demo jgraphic character was reduced from -.50 to -.02. In both cases the relationships droped from a statistically signi ficant to a statistically nonsignificant status. Similarly, the relationships in this expressive area for instrumental delinquency shifted from -.64 to -.34 and -.65 to -.48 for the respective variables of structural stability. Unlike the former drop in correlation magnitude, however, only the partial correlation for land use was nonsignificant. It is also interesting to note that such a large reduction in association, as observed for expressive area 3 when con trolling for instrumental delinquency activity, was only found for this particular designated delinquency area (this included a similar procedure of partialling out of expressive delinquency in instrumental delinquency areas). Therefore, even though this is a designated expressive area, it may be concluded that neighborhood stability is as important an influence on instrumental as on expressive delinquent activity. A careful visual re-review of Figures 1 and 2 would also suggest that expressive delinquency area 3 is more or less composed of parts of instrumental delinquency areas 1 and 3. In fact 11 of the 16 subareas (i.e., 68.8 percent) of expressive area 3 are also found to be part of ----------------------------------- r instrumental delinquency area 1; and four of the subareas, or 25 percent, are also part of instrumental delinquency area 3. Therefore, over 93 percent of expressive delin quency area 3 is 1n fact a coalescence of two fragments of the instrumental delinquency areas with the highest magni tude of instrumental delinquency activity (i.e., mean value for instrumental delinquency, area 1 = 2.30; area 3 = 1.52). An examination of all other delinquency areas did not indi cate any other area with so complete inclusion of one type of subarea within the boundaries of another. Moreover, it should be noticed that in Figures 1 and 2 there is also some degree of spatial redundance in expressive area 1. Expressive delinquency area 1 includes 38.8, 4.1, and 40.8 percent of instrumental delinquency areas 1, 3, and 5 re spectively, and 16.3 percent of subareas outside any area. Thus, since both instrumental delinquency areas 1 and 3 encompass many more subareas (e.g., instrumental delin quency area 1 = 63 subareas and area 3 = 20 subareas), and considering that these are the two most intense areas of instrumental delinquency activity, and in view of the results of the partial correlation above, it seems reason~ able to conclude tentatively that expressive delinquency area 3 is not an exception to Hypothesis 6. Rather the above conditions seem to reflect in more detail the results first revealed in Table 8 in support of Hypotheses H3 and 155 . I I H4. There it was noted that as the magnitude of delin quency rates increase there tends to be a greater inter twining between instrumental and expressive activity, parti cularly in delinquency areas designated as instrumental enclaves. 25 Results for expressive area 14 (Table 15, column 3) appear to indicate the only contrary results in the trend supporting Hypothesis H6. For example, on the one hand, this area displayed expressive delinquency scores that were statistically significantly higher than the mean average fo1 the metropolitan area. But within the delinquency area, there was not a significant relationship between instru mental and expressive delinquent activity (see Table 8) as was exhibited in other high intensity expressive delin quency areas (i.e., areas 1 and 3). Nor did this designatec expressive area display in Figures 1 and 2 any spatial over lap with any of the instrumental delinquency areas. On the other hand this delinquency area did not reveal positive relationships between group patterns of expressive delin quency and the composite scores of change in elements of local social structure as expected. On the contrary, zero order correlations in this area were negative. Thus, of the 13 instrumental and expressive delinquency areas, only expressive area 14 shows a clear-cut reversal in delinquency area+ pattern as predicted by Hypotheses HS and H6. 156 ' Effects of adult criminal patterns: !:!l£otheses H7, H8, ·and· H9 The importance of adult criminal activity has not been discussed up to this point of the analysis. Yet in the total development of delinquency areas, adult criminal patterns have been stressed as an integral part of the culture of delinquency areas. In general it is expected that the influence of an adult criminal model would be most pronounced in those delinquency areas which reflect high delinquent activity. Furthermore, the presence of an adult criminal value orientation is expected to be more influen tial in the instrumental than in the expressive areas. In the former, access to adult criminal models is essential in the cross-generational training required for the acquisitior of the criminal skills that characterize instrumental delin quent activities. In expressive delinquency areas the specific group pattern of adult criminal activity is not as vital and may serve merely as a remote and abstract model for activities that are illegal. In order to analyze these issues and ascertain results that are sensitive to the interrelationships between delinquency areas, neighborhood structural stability, and the role of adult criminal acti vity, the specific expectations were separated earlier into Hypotheses H7, HS, and H9. 157 I Hypotpesis H7. - -This formal statement is essentially Ian expansion of Hypothesis HS. The focus of H7 is not only on neighborhood structural stability as was the case of HS. !Here the concern is with the total effect of a neighbor hood's structural stability as a condition for adult crimi nal activity to have influence on group patterns of delin quency 1n instrumental delinquency areas. Earlier, 1n reference to Hypothesis HS, evidence was presented that in delinquency areas with relatively high levels of function ally related instrumental delinquency patterns, there was also a relative concomitant pattern of neighborhood stability. Since stability in local social structure 1s only a necessary condition in producing instrumental delin quency areas, it was hypothesized in H7 that in addition to structural stability there must also be a positive relation ship with indicator scores of adult instrumental criminal activity in the areas. In view of the fact that in instru- mental criminal activities there is a requirement of inte gration of adult conventional and criminal values and a neec for a stable community in order to provide for cross generational training in such activities, it should be evident that such processes will be most manifest in the high instrumental delinquency areas, with a downward trend 1n the less intense instrumental delinquency areas. The results reported in Table 14 (variable sets 4, 5, 158 ----------------------------------- 1 and 6) furnish findings and a general directional trend in the results which support the argument of Hypothesis H7. For example, the highest zero-order correlations between instrumental adult and juvenile activity are found in the first three instrumental delinquency areas (i.e., variable 4, clusters 1, 3, and 5). Perhaps more important as an indication of the importance of adult criminal activity are the results of the multiple correlations and their attend ant standardized regressions in Table 14. An examination of variable set 5 and 6 shows that in combination an in crease in variance accountability is not greatly improved over zero-order correlations of structural stability until indicator scores of adult criminal activity and structural stability are both part of the total multiple regression equation, particularly in instrumental delinquency areas 1, 3, and 5. Furthermore, the standardized regression coeffi cients show a clear trend that the contribution of instru mental adult criminal activity is relatively substantial within each of the instrumental delinquency areas. Except in delinquency areas 661 and 708, the highest regression coefficient is attributed to scores of adult criminal acti vity. However, it is also important to note that the second highest standardized regression coefficient in each area (except cluster 661) has a negative value which, of course, signifies the higher the instrumental delinquency activity 159 the higher the neighborhood stability on the particular indicator of neighborhood structural stability. Hypothesis H8.--In contrast to instrumental delin quency areas where structural stability is only a necessary condition, rapid structural change is posited to be a suf ficient condition for spatial enclaves of group patterns of expressive delinquent activity. As such, then, adult criminal activity in these areas should not play an inte gral role in the overall development or pattern of the delinquency area. This expectation is demonstrated in Table 15. For instance the zero-order correlations between adult and juvenile expressive activity (i.e., variable 4) are generally not higher than zero-order correlations re flecting high concomitant associations with rapid change in social structure (i.e . , variables 1 2, and 3). The only exception is expressive area 1 , and perhaps expressive area 14. The latter area, of course, has high correlation values; but as was discussed earlier, the sign does not indicate rapid social change. Furthermore, as will be dis cussed below, the importance of adult criminal activity diminishes when this variable is combined with other inde pendent variables to form a multiple correlation. The importance of structural change is more pointedly apparent when the multiple correlations representing only 160 I lthe three elements of neighborhood structural change I (variable set 5) are compared to multiple correlation which also includes the adult expressive criminal activity vari able (variable set 6). Here again, except for expressive area 1, it is obvious that the greatest increase in account ing for variance over the initial zero-order correlations occurs in the multiple regression correlation with only the 1 structural variables. The addition of the adult criminal I variable to the equation adds little to the reduction of ivariance (variable set 6 - variable set 5). The importance of structural change is further exemplified in an exami nation of the standardized regression coefficients which arE part of variable set 6 in Table 15. Only in expressive area 1 is there indication that adult criminal activity is a major contribution to the generally high multiple cor relation coefficients. That is, four of the six multiple 1 correlations exceed .75, and all correlations are signifi- , cant. Another trend which is unique to expressive areas, as contrasted to instrumental delinquency areas, presented in 'Table 14, and furnishing further support for Hypothesis HS is the increase in importance of the dimension of abnormal neighborhood structural change as the mean level of expres- 1 sive delinquency in the area goes down. For example, in expressive areas 591, 599, and 618, all having mean levels 161 of delinquency significantly below the County average, the standardized regression coefficients clearly indicate that relatively rapid structural change is a sufficient condi tion, particularly in expressive delinquency areas with !generally lower magnitudes of delinquent activity. Since the positive standardized regression coefficients tend to I I be most pronounced in the expressive areas with less in- 1 tensity of delinquent activity, it might suggest that the 1 effect of rapid structural change is most sensitive to the jpresence of delinquent activity when the area is in a stage ,of formulation. I ' Hyppt~esis· H9.--Throughout this subsection it has been pointed out that the strongest relationships between adult criminal activity and juvenile delinquency group patterns exist in the delinquency areas with the highest mean levels of delinquency intensity. This evidence supported the general contentions of Hypothesis H9 which were posited on the argument that: on the one hand, instrumental delin quency areas of high delinquency intensities could only be maintained through ·differential association with similar patterns of adult activity. On the other hand, in the high expressive areas the influence of adult criminal activity may be confined only to the presence ·of adult models legi- timizing illegal activity in general. Nevertheless, such l 162 ... adult criminal activity must be relatively intense 1n the high expressive delinquency areas in order to have this effect. On the basis of these considerations, the areas of 1 particular interest here are instrumental delinquency areas 1 1, 3, and 5 and expressive delinquency areas 1, 3, and 14. IAll of these areas were found to have mean levels of delin- 1 1 quent activity above the average delinquency scores for the ' 1entire County. Similarly, the mean values for adult crimi- lnal activity also show higher overall magnitudes in these I areas than found in the County as a whole. Remembering that both the instrumental and expressive composite indi cators are standardized scores with a mean of zero, the overall magnitude levels of adult criminal activity are shown to be comparable 'in size to the mean values of delin- 1 quency, and all mean values for the six areas are 1n excess of zero. The means for instrumental and expressive delin- 1 quency areas on adult criminal scores and juvenile delin- quency scores are shown below: Delinquency Areas Instrumental Expressive Cluster Juvenile Adult Cluster Juvenile Adult 1 2.30 2.00 1 1.44 1.62 3 1.52 1.21 3 1.99 2.32 5 1.42 1.55 14 1.29 0.72 660 -0.71 -0.51 591 -0.69 -0.64 661 -0.77 -0.60 599 -0.78 -0.39 708 -0.90 -0.78 618 -0.87 -0.86 710 -0.87 -0.94 163 In view of the fact that order of magnitude is an 1m- 1 portant expectation of Hypothesis H9, the correlation between juvenile delinquency activity and adult criminal activity across delinquency areas in the above summary was I obtained in the form of Spearman Rhos (rs); the results were .93 and .94 for instrumental and expressive areas, respectively. Since both Rhos are statistically signifi cant, the findings provide additional evidence of a meaning ful association between the overall level of delinquency activity and adult involvement in illegal activity. It is also interesting to note that when the juvenile delinquency mean scores for the instrumental areas were compared to mean scores of adult expressive activity in the various areas, the r was also .93. However, in the expressive s areas the converse (i.e., mean scores of instrumental activ- ity in expressive areas) was only r = .82, and this value s was not significant. And as might be expected, the dispar- ity which produced the lower r was from the expressive s areas with the lower level magnitudes. Thus here again the expected overlap between instrumental and expressive activ ity is expressed more convincingly in instrumental than ex- . press1ve areas. Recalling results earlier which revealed adult criminal activity as being more essential within instrumental than expressive areas, it is important to remember here that the 164 Rho values above pertain only to the order of overall magn1 I 1tudes between delinquency areas and not the relative co- variant relationships within the delinquency areas. It is clear from results presented earlier from Tables 14 and 15, when discussing Hypotheses HS through H8, that adult crimi nal activity is most important to the form of neighborhood group delinquency in the instrumental delinquency areas with evidence of structural stability (see Table 14, vari able sets 4, 5, and 6, cluster 1, 3, and 5). On the other hand, within the expressive delinquency areas the relative contribution of adult criminal activity did not manifest any substantial independent relationship except in expres sive area 1 (see Table 15, variable sets 4, 5, and 6). 165 CHAPTER III FOOTNOTES 1. Hubert M. Blalock, Social Statistics (New York: McGraw Hill Book Co., 1960), pp. 122-1125; J. P. Guilford, Fundamental Statistics, pp. 103-105, 205-214; Sanford I. Labovitz, "Criteria for Selecting a Significance Level: A Note on the Sacredness of .OS," American Sociologist, III (1968), 220-222. 2. Maurice D. Van Arsdol, Jr., Georges Sabagh, and Edgar W. Butler, "Retrospective and Subsequent Metropolitan Residential Mobility," Demography, V (1968) 249-267. 3. Arthur Grey, Jr., "Los Angeles: Urban Prototype," Land Economics, XXXV (August, 1959), 232-242. 4. Ralph Thomlinson, Urban Structure (New York: Random House, Inc., 1969), pp. 299-304. 5. 6. 7. 8. 9. Editors of Sunset Books, Los Angeles (Menlo Park, Cali fornis: Lane Book Co., 1974). U.S. Bureau of the Census, Census of Population and Housing: 1970 Census Tracts, Final Re ort PHC (1)-117 Los Angeles-Long Beac , Cali . SMSA Washington, D. C.: U.S. Government Printing Office, 1972). George W. Barclay, Techniques of Po ulation Anal sis (New York: John Wiley & Sons, Inc., 1958 , pp. 35-36. Sutherland and Cressy, QQ_. cit., pp. 111 - 115; Walter C. Reckless, The Crime ProoTem--mew York: Appleton Century-Crofts, Inc., 1961), pp. 68-95. Thrasher,~- cit. pp. 155-173; Shaw and McKay, Juvenile Delinquency and Urban Areas, pp. 262-263, 346, 356; Jackson Toby, "The Differential Impact of Family Disorganization," American Sociological Review, XXII ( 0 ct ob er , 1 9 5 7 ) , 5 0 5 - 51 2 ; Jame s F . Short , Ga· n g De 1 in - uency and Delin uent Subcultures (New York: Harper Row Pub., 1968 , pp. 4-6; Ewin M. Schur, Our Criminal Society (Englewood Cliffs, N.J., Prentice-Hall, Inc., 1969), pp. 41-45. 166 I 10. The detailed discussion of the rationale and construc tion of concentration, distribution, density, and unit share measurements was presented earlier. Supra, pp. 5 7 -6 0. 11. Supra, pp. 50-57. 1 2 . J . P . Gui 1 ford , Psycho nie · t ri c Me tho d s , p . 5 0 0 ; Ron a 1 d D . Schwartz, "Operational Techniques of a Factor Analysis Model," The American Statistician, XXV (October, 1971), 38-42. 13. J. P. Guilford, Psychometric Methods, pp. 524-525. 14. Supra, pp. 68-73. 15. Supra, pp. 31-42. 16. Croxton, Cowden, and Klein,~- cit., pp. 567-575. 17. Peatman, op. c·it., p. 234; Jacobson, op. cit., p. 231. 18. Croxton, Cowden, and Klein,~- cit., pp. 567-573. 19. Supra, pp. 27-31. 20. The formula used assumed that the samples were from populations with the same standard deviation, and therefore, the expression fort 1s where M 1 and M 2 = means of two delinquency areas, = standard deviations of two delinquency areas, N 1 and N 2 = number of subareas 1n delinquency areas. 21. Schuessler,~- cit., pp. 5-6. 22. Supra, pp. 63-67. 167 23. Supra, pp. 50 - 57. 24. For discussions of the use of the study's specific data items in the formulatio~ of measures of social structure see: Milton M. Gordon, Assimilation in America (New York: Oxford -University Press, 1964), pp. 30-31; Alber·t J. Reiss, Jr., and Albert L. Rhodes, "The Distribution of Juvenile - Delinquency in the Social Class Structure," Ame·ric·an Socio1o· gi"ca1 Review, XXVI (October, 1961), 720-732; Charles ·R. Wellford, "Crime and the Police: A Multivariate Analysis," Gri· min· o1o·gy, XII (August, 1974), 195-213. 25. Supra, pp. 119 - 123. 168 Orientation CHAPTER IV DISCUSSION Summary This study was concerned with the identification, location, and characteristics of spatial clusters of I officially adjudicated juvenile delinquency cases in a I l metropolitan area. On the basis of prior research, a j I spatial cluster was defined as a delinquency area when I · there was a more or less consistent pattern of functionally 1 related delinquent activity within the subareas or neigh- borhoods that made up the spatial cluster. The analytic 1 meaning of the existence of such spatial clusters of delin ! quent activity has been surrounded in the past by contro versy. Hence, two issues were addressed in the current I study: (1) the specification of the delinquency area con- I cept in terms of the patterning, intensity, and location of I : the areas; and (2) the differentiation of neighborhood 1 social structure in relation to the emergence and develop ment of delinquency areas and to the specific group pat- 1 terns of delinquency in various urban locations. The general hypothesis which guided the investigation suggested that the de~elopment and form of the delinquency 169 area was greatly affected by the relative degree of stabil ity (or change) in three major elements of neighborhood social structure: sociodemographic character, social classi and residential land use. It was argued that a high degree of neighborhood structural stability was a necessary condi- 1 tion for the emergence of patterns of delinquency oriented I primarily to "instrumental" values. Stability 1n these components of structure is seen as necessary 1n stabilizing sustained cross-generational ties between the youth group and a core of locally prestigeful adults who engage succes~ fully 1n income producing illicit activity, and who simul- ~ taneously participate in the normal conventional institu- 1 tions of the neighborhood. The cross-generational ties tend to socialize members of the youth group to the instru mental values of delinquent activity and to provide both the models and the training to acquire the requisite skills. In contrast, delinquency areas undergoing rela tively rapid neighborhood structural change generally experience a weakening of established institutions and a I low level of socializing effect across the generations. As a consequence, delinquency there escapes adult controls of any kind and is much less likely to take the form of locally "acceptable" illegal activity. Consequently, delinquency activity becomes oriented primarily around "expressive" values. Although there may also be some adult 170 illicit activity in these areas, it too will tend to be expressive in nature, with adult violators excluded from participation in local conventional institutions and thus generally unavailable as models to the youth group. Thus, in these delinquency areas social instability is the suf ficient condition for inducing a pattern of expressive delinquency that is alien to local norms as well as to those of the wider society. From the foregoing conceptual framework (developed in Chapter I) and from the study's two main objectives (opera tionally specified in Chapter II), nine specific research hypotheses were presented at the end of Chapter II. In summary, it was expected that an empirical test of data for a metropolitan area would reveal the following conditions: Differentiation of instrumentally and expressively oriented delinquency areas Functionally related delinquency areas with various ! levels of delinquency intensity The higher intensity level delinquency areas to have an interactive presence of instrumental and expressive illicit activity The interrelationship between the two types of illicit activity to be more pronounced over a greater range of high intensity instrumental delin quency areas 171 Indications that relatively stable social structure is related to an instrumental delinquency pattern Indications that relatively rapid change in social structure is related to an expressive delin quency pattern Stability of social structure coupled with adult instrumental illicit activity is related to the emergence of instrumental delinquency areas Expressive delinquency areas do not require an adult criminal counterpart to produce expressive delinquency activity The strongest relationships between juvenile and adult illicit activity exist in the areas of highest rates of delinquent activities. The remainder of this section summarizes pertinent empirical factors and the results of the analysis of a metropolitan area within the conceptual orientation out lined above and in Chapter I, and guided by the research design amplified in Chapter II and parts of Chapter III. In summarizing the study findings, the rest of this section parallels the overall research statements outlined above and the sequential order of Chapter III. These abridged • findings were placed in a perspective relative to the detailed results and analysis of Chapter III. The possible limitations and conclusions of the study follow this 172 summary of the findings. Test site and measurement ~onsid~rations The above specifications were tested using data per taining to the County of Los Angeles. The units of I analysis were 1,142 subareas that were comparable to the 1960-1970 U.S. Bureau of the Census defined census tracts. Subareal delinquency and adult criminal activity for Los Angeles County were extracted from County official I probation transactions which were analytically developed to represent all newly adjudicated delinquency and adult cases for an equivalence of the 1970 calendar year. An analyti cal file was then tallied to produce separate juvenile and adult instrumental and expressive aggregates for each of the 1,142 subareas in Los Angeles County. These categories were in turn transformed into 15 subareal spatial measures of instrumental and expressive juvenile delinquency and 15 similar measures for adult criminal activity. Through the application of a one factor, factor analysis technique the 30 measures were transgenerated into four composite indi cators. As such, each indicator was a set of scores that measured on a standardized scale the degree to which each of the 1,142 subareas in Los Angeles County was character ized by juvenile and adult illicit instrumental and expressive activity. 173 The basic source of data for measures of neighborhood social structure was the automated computer files of census tract summary tabulations of the 1960 and 1970 U.S. Census of Population and Housing. Through the extraction of specific data items, 33 different measurements were devel oped to represent the study's three key elements of social structure for 1960 and 1970. Unlike the above indicators of instrumental or expressive activity, these measures of social structure had to be transformed into measures re flecting neighborhood stability. Therefore, using the 1960 i measures to predict the 1970 measures, linear interannual ~ regressions were calculated in order to extract a devia tional change measure in the form of the residual value of the 1970 actual from the expected measure. The resultant 33 measures were representative of the relative velocity of change between 1960 and 1970 for each of the 1,142 subareas in Los Angeles County. These deviational measures were then treated in a similar procedure of composite indicator construction as used for neighborhood i nstrumental and expressive juvenile and adult illicit activity. Here, 1 however, the final product was three composite indicators of deviational change in neighborhood sociodemographic character, social class, and residential land use. j Main findings With the establishment of an empirical referent score 174 I for instrumental and expressive activity, the first task of the research design was to determine whether delinquency scores do indeed cluster into spatial enclaves, differen tiated with respect to the pattern of delinquent behavior in the subareas. To ascertain if there were actually functionally related spatial clusters of delinquency activ ity, a special statistical simulation process was developed and computer programmed. The basic logic of the program consisted of an areal clustering algorithm composed of a combination of a particular spatial application of a "test of proportion" and a derivation of R. C. Geary's Contiguity Ratio. In general the process assesses the degree of spatial or areal autocorrelation by measuring the similar ity patterns of subareas according to a given interval or ratio scale and coterminous location in space. The algo rithmic procedures are such that through an a priori level of statistical confidence a clustering of contiguous spatial subareas may be isolated through a gradual "build ing up" via a decision tree process of statistically related and mutually exclusive coterminous subareas. Thus, • by processing the two sets of composite subareal scores of delinquency through the spatial clustering simulation, a number of instrumental and/or expressive delinquency areas were expected and, in fact, weie identified within the universe of territory of Los Angeles County. Areas 175 consisting of sufficient numbers of subareas to be usefu1 7 for further analysis (i.e., a spatial cluster of 11 or more subareas) were specified as the study's delinquency areas. 6even instrumental and six expressive delinquency areas were, identified. As expected, each delinquency area was charac terized by a relatively even density of spatially contiguous subareas, and each area generally showed a statistically significant different overall delinquency magnitude with respect to its type of delinquency activity. In the above finding it was generally anticipated from the conceptual orientation that functionally related delin quency areas (i.e., instrumental or expressive in character) should be different in overall magnitude of activity. How ever, it was no_! assumed that instrumental or expressive delinquency areas would be devoid of the bi-polar type of delinquency activity. As discussed in detail in Chapters I and II, delinquency areas in reality should be somewhere on a continuum between the two distinctive polar types. Furthermore, because of the conditions needed for a delin quency area to actually exist in general, and the instru mental delinquency areas in particular, if there was to be a spatial congruence of the two delinquency types of activity, it should occur in the delinquency areas which reflect the higher overall intensity levels of activity. Finally, it was also expected that the interweaving of the two types of 176 delinquency activity should occur over a broader range of 7 levels of magnitudes in the instrumental than in expressive delinquency areas. Results reported in Chapter III in the form of both zero-order correlations between the two types of delinquency for each of the delinquency areas and compu terized spatial maps of the delinquency areas generally supported both of the above expectations. For example, four of the seven instrumental delinquency areas revealed signi ficant correlations, and three of these were greater than .60. Furthermore, these relationships appeared in the four areas with the highest mean levels of instrumental activity. On the other hand, only two of the expressive areas showed significant correlations with instrumental delinquency. However, both of the correlations were 1n excess of .70, and these two areas exhibited the two most intensive levels of expressive delinquent activity. Likewise, the overlap be tween these two expressive areas and the three highest areas of instrumental activity was noticeably evident in two computerized proximal maps representing areal locations of instrumental and expressive areas. Once the patterning, intensity, and location of the delinquency areas were established, the remainder of the analysis in Chapter III focused on the last five research statements outlined above. In gener~l, it was expected that if the designated delinquency areas were statistically ------·-----~-------------177 spatial representations of instrumental and expressive delinquency activity, the intensity level would not neces sarily be the same for all subareas within a defined delin quency area, but that the subareas would be within the bounds of statistical confidence of spatial homogeneity. At the same time, however, it was further expected that distributions of specific group delinquency patterns would show a specific kind of relationship with the relative degree of stability (or change) on key elementsof neighbor hood social structure and the concomitant relationship with adult criminal activity. These relationships would on the one hand reflect the fact that a delinquency area in reali ty cannot and should not be expected to be a totally uni form area, but rather should have a tendency toward (in the statistical sense) being homogeneous. On the other hand, the distribution of the delinquency activity should also reflect a covariant response to certain expected structural realities within the neighborhoods of the delinquency area. In summary, it was argued that a sufficient condition for expressive areal existence is relatively rapid neighborhood structural change; while in the case of instrumental delin quency areas, structural stability is only a necessary condition. In addition, the latter delinquency areas also were expected to have a rather well established relation ship with instrumental adult illicit activity in the areas 178 displaying the higher average intensities of instrumental delinquency activity. Findings reported in Chapter III in the form of zero-order correlations and multiple correla tions and regressions generally supported the expectations. One expressive area located in the eastern part of the County, however, exhibited a direction in the significant I correlation coefficients pertaining to structural stability I contrary to what was expected. All of the other 12 delin- 1 quency areas were interpreted to have correlation and regression patterns that were more or less consistent with expectations. In addition to the minimum expectations, the correla tional and standardized regressional patterns disclosed that neighborhood structural change progressively becomes a more important factor in expressive delinquency areas as the overall delinquency intensity of the area went down. For example, the higher positive correlations between structural change and expressive delinquency activity occurred in the expressive delinquency area with the lowest overall mean delinquency score. A final finding empha- sizing this trend was the fact that this area had the highest multiple correlation coefficient of all expressive 1 delinquency areas with an R = .87; and the statistically , significant standardized regressions demonstrated that the , maJor contributions to this substantial value came from 179 indicators of neighborhood change in demographic character (B = .74), and socioeconomic status (B = .45), and then in adult expressive illicit activity (B = .39). Conversely, the overall trend of structural stability is meaningful only in instrumental delinquency areas which have the higher mean levels of intensity. These areas also have a substantial trend of high instrumental adult criminal ' activity. Caveats Before the implications of the study are discussed, some cautions should be restated. Most of these admoni tions have been explicitly _ discussed or implied in greater detail in Chapters II and III, and they are summarized here as (a) generality of test site, (b) usefulness of the data base, and (c) the adequacy of the research design. Perhaps the greatest limitation of the study is the fact that it examines only one metropolitan area. Even though this is the largest and most diverse urban area in the United States, the restriction of the test and analysis to the single case of Los Angeles County for the limited time period covered by the study makes it clear that any confirmation of the hypotheses and the results found, while perhaps applicable to other metropolitan areas, are only provisionally conclusive. 180 It should also be remembered that the study's focus has been upon officially adjudicated delinquency, and the results do not purport to be representative of "all" delin quency activity in Los Angeles County. Thus, it may be the case that an expanded definition of juvenile delinquency and adult illicit activity that took into account the "dark figure" of crime would produce some modification as to the spatial patterning, intensity, and location of delinquency areas. On the other hand, given the relative size and range of the current enumeration of juvenile and adult illegal activity, it was decided early in the study that an enumeration of official offenses would not only be consist ent with the past literature pertaining to the concept of delinquency areas, but would also provide the most uniform and consistent aggregate report of neighborhood delinquent and criminal activity over an entire metropolitan area. In addition, a review of other types of data and/or arguments pertaining to what actually constitutes neighborhood delin quency provides no assurance of any measurable improvement in the accuracy, reliability, or validity of the results at the subareal level and for the study's universe of terri tory. A potential problem related to the use of official enumerations in this study was the separation of official categories into the binary typology of instrumental and 181 expressive activity. At the outset it was recognized that any application of a paradigmatic bi-polar typology to actual official categories would entail radical reduction and would incur possible inaccuracies and discrepancies 1n typing the activity. To deal with this problem, any cate gory of offense that seemed ambiguous was tested first as one type and then as the other type of activity. In an initial analysis those officially defined categories of offense having this character indicated little or no impact in changing the final set of instrumental or expressive delinquency areas selected for this study. Nevertheless, I it should be recognized that using a different set of data, I I metropolitan area, or point in time may have resulted 1n I some meaningful modification of the results. Another qualification -pertaining to the data input I into the analysis 1s, of course, the use of only U.S . I 1 Census information and the areal aggregate of the census I I tracts in characterizing the structural features of urban neighborhoods. While over 33 variables were utilized from , this one source and represented many of the measures of the I study's key elements of the social structure, these vari- ables were possible limiting factors from the standpoint of I I being able to cover the complete range of neighborhood I structure, appropriate level of aggregation, and temporal 1 change. Elements, for example, of the voluntary and 182 economic institutions were not included. Likewise, the aggregate level of the census tract may in some locations of the metropolitan area be too large. A more useful areal representation might well be urban "block groups." Also, the measure of neighborhood cha~ge was by necessity limited to a single decade, and the trend as measured by this temporal frame of reference may not be the most appropriate for capturing the neighborhood changes involved in the development of instrumental or expressive types of delin quency areas. Finally, the study findings rest on two key assump tions. In the discussion of the results it was assumed that the research design and methodology as developed for this study in Chapter II were the correct procedures. In addition, the assumption was made that the analytical pro cedures employed in Chapter III were appropriate to the , task of bridging the gap between the theoretical orienta tion of this study and the limitations of real data utili zed to represent delinquency are~s and neighborhood struc tural and institutional conditions. ·conc·lusion The summary of potential limitations of the present study suggests that any final conclusions must await addi tional research which addresses the problems described 183 above. Nevertheless, it also seems clear that until such future research can be undertaken, at least two tentative but tenable general themes are discernible from the study's 1 findings pertinent to delinquency patterns within Los Angeles County. The essential content of the two themes and the implications of the research findings are summa rized below. The first conclusion that may be drawn from the re sults supports the viability of the traditional notion of ' delinquency areas as urban spatial enclaves. In its re- generation, however, the original concept has been method ologically modified to reflect more accurately urban growth processes. As such, the delinquency area is recomposed as a totally structural concept. Here the areas are viewed as only relatively homogeneous (i.e., within the limits of a given level of statistical confidence)with respect to a specific delinquency pattern for a set of contiguous urban 1 subareas. For the total urban universe there may be many such spatial configurations consisting of different trends in group patterning and overall intensity of delinquent activity. Related to the above conclusion, a second maJor theme based on the empirical findings supports the utility of a value conflict orientation as a conceptual tool with which to describe the structural content that differentiates the 1 184 , development of delinquency areas. Unlike many other delin quency theories which are either restricted to the lower social class (e.g., slums of the city) and/or concentrate on individual motivation as the explanatory level of analysis, value conflict theory 1s applicable to the total metropolitan area and can be applied at the explanatory level of neighborhood structural processes. Within the framework of these two themes, delinquency areas are viewed as spatially contiguous neighborhoods I characterized by a particular overall pattern of delin l quent activity. In its static form, the pattern of the 1 activity may be conceptualized paradigmatically as located at some point on a bi-polar continuum between expressive and instrumental types of delinquent activity. The find ings of the current study, however, provide evidence that the location on the continuum is not static but 1s, rather, subject to a dynamic developmental process. Before describing the process, a word of caution 1s 1n order concerning the use of what are essentially cross sectional data of the dependent variable of delinquency for I interpreting social processes and change. While longi- 1 I tudinal data were used to assess neighborhood structural I change as independent variables, there was no counterpart 01 1 I I longitudinal analysis of adult or juvenile illicit activ- ity. In the formulation of the study design such 185 information was not relevant to the initial conceptual ization of delinquency areas within a value conflict con text. However, the study findings clearly suggest that any change in neighborhood social structure is bound to be re- 1 fleeted in a changed location of the neighborhood delin quency pattern on the continuum between the expressive and instrumental types. Essentially the findings as a whole suggest that social structural change in neighborhoods is a sufficient condi tion and is the sensitive process which generates the initial formation of a relatively low intensity delinquency area. In these areas, neighborhoods undergoing structural change have a reduced probability of maintaining local institutional order through the usual mechanisms of cross generational social control. Likewise, these areas might be said generally to exhibit an incipient form of value conflict, since there still exists a neighborhood culture markeded by a view of conventional and criminal values as morally mutually exclusive. Because such a condition is tantamount to a weakening of the general socializing con ditions in neighborhoods, this form of value conflict will be reflected in the predominance of a pattern of expressive delinquent activity. These areas emerge early in the de velopmental sequence referred to above, and are character ized by the less serious types of delinquent activity 186 (e.g., runaway, truancy, delinquent tendencies, etc.). On the other hand, expressive delinquency areas with relatively high overall amplitudes of delinquent activity do not appear to be tied as closely to a concomitant re lationship of neighborhood change. The evidence seems to indicate that as the delinquency activity of an expressive area increases there tends also to occur an increase in the incidence of instrumental delinquency and adult instru mental illicit activity, associated with greater stability in neighborhood social structure. Such patterns suggest that expressive areas of higher rates of delinquency are 1n the process of becoming instrumental delinquency areas. Evidence of this trend is furnished by other study findings indicating that expressive areas with the highest measures of expressive delinquency were identifiable as pockets or subareas, surrounded by larger areas with very high meas ures of instrumental delinquency. Thus, the overall findings of the study suggest that instrumental areas are an outgrowth of expressive areas. This general trend would indicate in turn that instrumental delinquency areas do not develop readily: such areas may be clearly established only after passing through a period , of expressive activity and under a rather complex set of neighborhood conditions. For example, urban growth processes, and consequently the possible subsequent development of instrumental areas, 187 do not necessarily affect equally all sectors of a total metropolitan region. Some neighborhoods may evolve rapidly through various stages of development, while in other instances urban communities may undergo slow change, or stagnate. During the periods of rapid change in neighbor hood social structure as a result of the urban growth process, areas characterized by expressive delinquent activity may develop and persist for only a short period. However, as is suggested above, if expressive areas persist 1 over a long period of time with continued increase 1n delinquency rates, there may develop the necessary neigh borhood conditions for the evolution of an instrumental delinquency area. The conditions, however, include a more delicate and integrated balance between the perennial con flicting values of conventional and illicit norms within the neighborhoods involved. There needs to be established an enduring set of neighborhood structural features plus a clear and concomitant presence of a cross-generational criminal learning model in the community. Thus, there are intrinsically many more conditions necessary for the estab lishment of instrumental delinquency areas, and the balance of the various factors may be expected to be most evident 1n the most pronounced instrumental delinquency areas. This expectation rests on the assumption that an instru mental delinquency area can take shape only if there are 188 simultaneously present both structural stability and adult criminal activity. The latter is required for the develop ment of training and learning opportunities needed for acquisition of the skills and support necessary for the total development of instrumental delinquency areas. In summary, the study findings suggest that delin quency areas may undergo a process of development, start ing with a relatively simple,low intensity form of expres sive delinquency, progress to an intensification of such activity, and in some instances emerge as an area in which instrumental delinquency is the predominant but not exclus ive pattern. It is further suggested that the sufficient condition for the onset of the developmental process is relatively rapid change 1n neighborhood social structure for a number of contiguous subareas in the metropolitan area. For the expressive delinquency area to evolve as an instrumental delinquency area two conditions are necessary: stabilization of its structural features and the presence of adult models engaged in instrumental criminal activity. The present study has in effect redefined the delin quency area construct. In its earlier form the concept was designed to account solely for the location and character of urban neighborhoods in which the highest rates of delin quents were historically endemic. Relationships between ecological and social control processes were examined to 189 explain only those features of urban growth and change involved in the creation of these neighborhoods. That ecologically oriented structural analysis need not be lim ited in this manner has now become apparent. Delinquent behavior is hardly confined to the high rate areas of the city. In its various forms it is epidemic in its diffusion rather than endemic. If a claim is to be made for the utility of a social structural theory in the explanation of delinquent behavior its concern must be with all of its forms. The analysis here presented provides some evidence of continuity and linkage between structural features of urban social organization, the state of social control pro cesses under a range of structural conditions, and, in radically reduced form, the associated patterns of delin quent behavior. 190 BIBLIOGRAPHY Books Alihan, Milla A. Social Ecology. New York: Columbia 1 University Press, 1936. Barclay, George W. Techniques of Population Analysis. New York: John Wiley & Sons, Inc., 1958. Blalock, Hubert M. Social Statistics. New York: McGraw Hill Book Co., 1960. Cliff, A. 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Creator Schuerman, Leo Anthony (author)

Core Title The social ecology of delinquency in Los Angeles county: a structural analysis

School College of Letters, Arts and Sciences

Degree Doctor of Philosophy

Degree Program Sociology

Degree Conferral Date 1977-02

Publication Date 01/14/1977

Defense Date 01/14/1977

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

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Language English

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Permanent Link (DOI) https://doi.org/10.25549/usctheses-oUC25328

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Legacy Identifier etd-schuermanL-1977

Document Type Dissertation

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Type texts

Source 20230120-usctheses-microfilm-box5 (batch), University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the author, as the original true and official version of the work, but does not grant the reader permission to use the work if the desired use is covered by copyright. It is the author, as rights holder, who must provide use permission if such use is covered by copyright. The original signature page accompanying the original submission of the work to the USC Libraries is retained by the USC Libraries and a copy of it may be obtained by authorized requesters contacting the repository e-mail address given.

Repository Name University of Southern California Digital Library

Repository Location USC Digital Library, University of Southern California, University Park Campus MC 2810, 3434 South Grand Avenue, 2nd Floor, Los Angeles, California 90089-2810, USA

Repository Email cisadmin@lib.usc.edu