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Mapping punk music and its relative subgenres

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Mapping punk music and its relative subgenres

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Mapping Punk Music and its Relative Subgenres

by

Kurtis Eisenhuth

A Thesis Presented to the
FACULTY OF THE USC DORNSIFE COLLEGE OF LETTERS, ARTS AND SCIENCES
University of Southern California
In Partial Fulfillment of the
Requirements for the Degree
MASTER OF SCIENCE
(GEOGRAPHIC INFORMATION SCIENCE AND TECHNOLOGY)

December 2022

Copyright © 2022 Kurtis Eisenhuth

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To my father, Ed – for introducing me to good music.

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Acknowledgements
I am grateful to my thesis advisor, Dr. John Wilson, who single-handedly guided my thesis to
what it is today. Throughout the Summer of 2022, Dr. Wilson juggled teaching multiple classes,
guiding other thesis students, the ESRI User Conference, editing multiple articles for publication,
tragic loss, and COVID-19 – all with a smile on his face. I would like to thank my employers,
AgriCapture and Vanderbilt University, who allowed me to complete some of this work using
my office computers. I would also like to thank USC’s Spatial Science Institute for providing me
with the knowledge and insight required to complete this thesis.

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Table of Contents
Dedication ....................................................................................................................................... ii
Acknowledgements ........................................................................................................................ iii
List of Tables ................................................................................................................................. vi
List of Figures ............................................................................................................................... vii
Abbreviations ............................................................................................................................... viii
Abstract .......................................................................................................................................... ix
Chapter 1 Introduction .................................................................................................................... 1
1.1. Punk Music ........................................................................................................................2
1.1.1 Subgenres ......................................................................................................................3
1.2 Motivation ............................................................................................................................4
1.3 Thesis Organization .............................................................................................................6
Chapter 2 Related Research ............................................................................................................ 7
2.1. Musical Geography ........................................................................................................... 7
2.2 GIS and Music ................................................................................................................. 10
2.3 Punk Music ...................................................................................................................... 12
Chapter 3 Methodology ................................................................................................................ 16
3.1 Data Description .............................................................................................................. 17
3.2 Research/Design Methods ............................................................................................... 19
3.3 Geoprocessing/Analysis .................................................................................................. 22
3.3.1 Exploratory Analysis ................................................................................................ 23
3.3.2 Spatial Autocorrelation (Global Moran’s I) .............................................................. 26
3.3.3 Find Point Clusters (Geoprocessing Tool) ................................................................ 27
3.3.4 Census Data Analysis ............................................................................................... 28

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Chapter 4 Results .......................................................................................................................... 30
4.1 Exploratory Analysis ....................................................................................................... 30
4.2 Cluster Analysis............................................................................................................... 34
4.2.1 Spatial Autocorrelation (Global Moran’s I) .............................................................. 34
4.2.2 Determining Clusters ................................................................................................. 35
4.3 Census Data Analysis ...................................................................................................... 36
Chapter 5 Conclusion .................................................................................................................... 41
5.1 Summary & Discussion ................................................................................................... 41
5.2 Limitations ....................................................................................................................... 44
5.2.1 Data Limitations ........................................................................................................ 45
5.2.2 Analysis Limitations .................................................................................................. 47
5.3 Future Work..................................................................................................................... 48
References ..................................................................................................................................... 51

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List of Tables
Table 1 Early punk subgenres explored (Clark 2021) .................................................................... 5
Table 2 The spatial and tabular data used in this study ................................................................ 17
Table 3 Areas of highest frequency from 1971 – 1988 (*denotes earliest instance) ................... 31
Table 4 Date ranges and locations for the earliest observances of punk subgenres recorded ...... 31
Table 5 Areas of highest frequency observed for early punk music venue openings ................... 32
Table 6 No. of early punk venue openings by decade .................................................................. 34
Table 7 Variables used in census data analysis ............................................................................ 37
Table 8 Percentage of population by race in census tracts with punk music bands or venues ..... 37
Table 9 Percentage of population by income in census tracts with punk music bands or venues 38
Table 10 Percentage of population by age group in census tracts with punk music bands or
venues .................................................................................................................................... 39
Table 11 Percentage of population by education level in census tracts with punk music bands or
venues .................................................................................................................................... 40

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List of Figures
Figure 1 The New York Dolls - the earliest band observed in the research dataset (1971) ........... 3
Figure 2 Black Flag performing in Los Angeles, CA in 1976 ........................................................ 4
Figure 3 Data collection/preparation (left) and analysis/thesis preparation (right) ...................... 20
Figure 4 Workflow to conduct exploratory and cluster analysis of point vector data .................. 23
Figure 5 Number of bands (by subgenre) by year ........................................................................ 24
Figure 6 Number of bands (by subgenre) by location .................................................................. 25
Figure 7 Workflow for census data analysis ................................................................................. 29
Figure 8 Geographic locations associated with early punk bands forming in the 1970s .............. 30
Figure 9 Frequency of early punk music venues observed over time ........................................... 32
Figure 10 Geographic locations associated with early punk venues opening in the 1970s .......... 34
Figure 11 Oi punk scene in London, UK (1979) .......................................................................... 43
Figure 12 Ian MacKaye singing for hardcore band Minor Threat (left) in Washington, D.C.
(1980) and post hardcore band Fugazi (right) in 1987 (Washington, D.C.) .......................... 45
Figure 13 Fat Mike – founder of Fat Wreck Chords and lead singer/bassist of NOFX (1990) .... 46
Figure 14 Example of punk music lineage analysis...................................................................... 49

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Abbreviations
CA California
CBGB Country Bluegrass Blues
DIY Do-It-Yourself
GIS Geographic Information System
IPUMS International Public Use Microdata Series
LA Los Angeles
NHGIS National Historical Geographic Information System
NJ New Jersey
NY New York
NYC New York City
R&B Rhythm and Blues
UK United Kingdom
US United States

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Abstract
Geographic information systems (GIS) contain visualization and analytical tools that assist users

to better understand the spatial and temporal relationships between mapped entities. The

formation of music genres, for example, is a complex phenomenon that can be explored through

spatial analysis using GIS software. The genre of punk music emerged in New York City, NY in

the early 1970s with the appearance of bands such as the New York Dolls and The Ramones.

Punk music is unique because commercial or mainstream success is most likely not the sole

motivator for musicians who propagate the genre. The Do-It-Yourself (DIY) mentality of those

who originally played punk music, coupled with the unique subcultures that stemmed from local

groupings of popular bands raises questions as to the nature of the environment and people

closest in proximity to the phenomenon. This study aimed to explore the spatial and temporal

relationships between genre-specific punk bands and their local environments within the context

of sociodemographics and time. A literature review was conducted to identify the temporal and

spatial evolution of punk music. ArcMap and ArcGIS Pro were used to analyze, display, and

prepare the spatial data, which included locations of band formation sites, venue openings, and

census data from 1970. Point cluster and proximity analysis, along with historic census data

quantification were employed to tell the story of punk music within the context of time and

space. The people, environment, and spatial diffusion of locations (as well as attributes)

associated with early punk music is characterized through the use of GIS. Findings revealed

through this research exemplified the versatility of GIS and created a repeatable process for

examining other music genres.

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Chapter 1 Introduction
The GIS-based analysis conducted in this thesis aimed to explore the relevant locations and

sociodemographic variables associated with the creation of the punk music genre (and relative

subgenres) in New York City, NY and Los Angeles, CA beginning in the early 1970s. There are

four research questions that this thesis answers: (1) When and where in the world did punk music

originate? (2) When and where in the world did punk music venues pop up? (3) What kinds of

neighborhoods did punk music arise in New York, NY and the Los Angeles area in the 1970s?

and (4) Were the source neighborhoods of punk bands and punk music venues one and the same

for both NY City and LA?

The spatial data used to pinpoint the earliest punk bands across the globe can be

characterized as trimodal in nature due to the number of bands that have originated in New York

City, Los Angeles, and London in the United Kingdom (UK). Because census data collected by

the UK in the 1970s was unattainable, the region could not be analyzed at the same granularity as

its American counterparts. As a result, the scope of this study only included the contiguous

United States (US), and the cities from which the earliest punk bands hailed.

The research describes formation sites of source bands and locations of influential

venues, and characterizes local regions with census information from intersecting and adjacent

census blocks. Spatial data analysis was conducted to quantify findings and provide inferences

about the spatial origins and spread as a whole. The use of GIS in this study provided

visualization tools and analytical diagnostics for those wishing to explore the subject matter

researched. The intended audience of this research is anyone interested in learning more about

the origins of punk, or those who are interested in the role environment plays in the creation of

music genres. The possible end-users of the data and subsequent diagnostics can range from the

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casual listener to the academic in need of reference or comparative material.
1.1. Punk Music
Punk music can be characterized as music with extremely fast tempos (4/4 prestissimo),
overly distorted guitars, and harsh, aggressive (even snarling) vocals. Though technical talent
can be found across the genre, it is not necessarily the cornerstone of many of its most popular
acts. In fact, the rejection of solid musicianship appears to be a common sentiment embraced by
the earliest punk bands observed in this study. The themes of punk lyrics can vary from
irreverent musings to subversive and/or exceptionally anti-establishment compositions. Unlike
many mainstream acts in the 1970s, punk musicians were aesthetically unkempt and did not fit
the mold of their more popular counterparts (Clark 2021).
The earliest band observed during the data collection portion of this study was the New
York Dolls (Figure 1), who were active from 1971 to 1976 in New York City. At the tail
end of the disco era, and with the emergence of glam musicians like David Bowie gaining
mainstream popularity, the New York Dolls entered the east coast music scene as a non-
conformist alternative to what was musically accepted. While David Bowie was both eccentric
and provocative, the New York Dolls provided a grittier option to those seeking unconventional
music in the early 1970s. Another early punk band in New York City was The Ramones, who
many believe is one of the most influential punk bands of all time (Goshert 2000). The Ramones
came about in 1974, and were integral in the spread of punk music to the UK with bands like
Cock Sparrer and the Sex Pistols emerging shortly after their tour of London, UK in the 1970s.

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Within the same timeframe, bands like Black Flag (Figure 2) and The Germs were
establishing themselves in the Los Angeles music scene. While both the east and west coast
groupings of early bands played punk music explicitly, the actual music varied greatly between
the two locations (Clark 2021). This difference is what defines the subgenres that have branched
off of the parent genre. This research analyzed the spatiotemporal relationship between
subgenres in order to better understand how (and where) punk music has changed and evolved
over time and space.
Figure 1 The New York Dolls, the earliest band observed in the research dataset
(1971). source: https://www.rollingstone.com/music/music-album-reviews/too-much-too-soon-246070/

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1.1.1 Subgenres
The following punk subgenres summarized in Table 1 were examined and used to

characterize early punk music throughout the study.
1.2. Motivation
Although punk music and the musicians who play it rarely reach the level of commercial
success and influence as their more radio-friendly counterparts, the genre itself is remarkable due
to the subcultures and subgenres that originated from locations where the music was most
popular (Goshert 2000). The parent genre has endured from the early 1970s, with the evolving
music eventually branching into several subgenres that remain relevant today. This thesis project
explored the geographic locations and local environments that sustain these subgenres and
provided a specific sociodemographic profile for each formation site.
In terms of spatial research, conducting analysis that gives audiences insight into the
creation of music genres allows for a deeper understanding of local cultures and the populations
Figure 2 Black Flag performing in Los Angeles, CA in 1976.
source: https://www.theatlantic.com/entertainment/archive/2016/06/black-flag/488906/

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Table 1 Early punk subgenres explored.
Subgenre
Earliest
Instance
Observed
Location Characteristics Early Bands
Anarcho 1975 London, UK
Politically inspired lyrics;
themes that promote
anarchism, anti-government
movements, and animal rights
Sex Pistols,
Poison Girls, Crass,
Zounds, Icons of
Filth

Crust 1978 Devon, UK
Politically inspired themes
that touch on social ills;
guttural vocals, extremely
fast tempo
Amebix, Killing
Joke, Rudimentary,
Penti, Anti-Cemix,
Antisect
Glam 1971
New York
City, NY
Mix of punk music and glam
rock; some of the earliest
garage bands were glam punk
bands
New York Dolls,
Arrows,
Slaughter and The
Dogs,
Adam and the Ants,
Hanoi Rocks
Hardcore 1976
Hermosa
Beach, CA
Themes that promote anti-
commercialism and anything
outside of the mainstream;
known for influencing
independent recording labels
Black Flag,
Bad Brains, The
Germs, D.O.A.,
T.S.O.L
Horror 1976
Sacramento,
CA
Known for its horror-movie
inspired aesthetic and violent
lyrics
The Cramps, Misfits,
45 Grave, Christian
Death, Screaming
Dead
Oi 1977
South
Shields, UK
Themes the promote working
class values, anti-poverty,
anti-police
Angelic Upstarts,
Peter and the Test
Tube Babies,
Splodgenessabounds,
One Way System,
The 4-Skins
Pop 1974
New York
City, NY
Known for upbeat melodies,
catchy choruses, and fast
tempo; most mainstream
subgenre
Ramones, Buzzcocks
The Queers, The
Offspring, Green
Day
Ska 1977
Coventry,
UK
Fuses American rhythm and
blues (R&B) with Jamaican
folk; uses horn section
(trumpets, saxophones and
trombones); upbeat tempo
and rhythms
The Specials,
Fishbone, The
Toasters, Sublime,
Voodoo Glow Skulls
Skate 1977
Manhattan
Beach, CA
Also known as pop hardcore;
uses vocal melodies and
Descendents, Dead
Kennedys, Agent

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technical guitar rhythms;
extremely fast tempos
Orange, Bad
Religion, The
Vandals
Street 1977
Stoke-On-
Trent, UK
Rebellious antithesis to early
British punk; started classic
punk archetype (leather
jacket, mohawk, etc.)
Cock Sparrer, Sham
69, UK Subs,
Discharge, Charged
GBH
Source: Clark 2021

living in and around their creation. Often, music plays a significant role in defining meaningful
characteristics of individual societies and cultures (regardless of their size) (Treloyn 2016).
Though the study only illustrated the formation of punk music specifically, the spatial and
environmental variables used to exemplify its creation can serve as reference parameters for
further studies focused on music and urban society.
NY City and LA are large metropolitan areas with diverse ethnic populations of varying
ages and education levels. Even in the early days of punk music, both cities had several areas
within their geographic boundaries with widely varying median annual incomes. Studying the
origins of punk music can also serve to allow a greater understanding of the affluence in these
places relative to the surrounding areas (Cross 2001). The results may benefit those interested in
human landscape and the production of culture.
This study details both the spatial and temporal representations of punk music from its
inception through various subgenres. The reader can see where the earliest bands came from and
how and where the music has evolved over time. They can also learn about the demographics of
the formation sites, as well as explore live music venues that have propagated the genre since
1970 to the present-day.

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1.3. Thesis Organization

The remainder of this thesis is organized as follows. Chapter 2 reviews the related work
and the foundational musical and spatial knowledge that this thesis builds upon. Chapter 3
describes the methods used to explore punk music through the lens of GIS. Chapter 4 details the
results and Chapter 5 concludes the thesis and discusses the steps that could be taken to expand
this particular study of music and geography.

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Chapter 2 Related Research
The relevant spatial science and sociology literature used to bolster the research for this study
focused mainly on various music mapping studies, the use of GIS in musical research, and punk
music within the context of its local environment. Since there are limited studies that my thesis
work can build upon, the following literature was used to combine credible knowledge of music
culture and GIS technology to illustrate both in terms of space and time.
2.1. Musical Geography
Considering the presence (and largely the significance) of distinctive music in almost
every culture around the world, the music people listen to can be used to characterize boundaries
of similarity in human terrain. Having a strong research foundation based on music and location
assisted with characterizing New York and Los Angeles not only within the context of punk
music, but within the context of the people that resided in these locations during the rise of punk.
The following articles were used to gain insight into the role geography plays in deciding the
type of music that exists at a particular location.
Sara Cohen’s article “Bubbles, Tracks, Borders and Lines: Mapping Music and Urban
Landscape” investigates the connection between music and the urban environments in which it
originates through pointed demographic research. Geography, age, ethnicity, and economics play
a significant role in shaping the genres of music that exist in a specific area (Cohen 2012a). The
main ideas expressed in the article illustrate how the material environment in which musicians
play creates meaning for those particular locations, the obvious correlation between genre, race,
and economics, that musicians from different genres view and map the musical landscape around
them differently, and that age, class and locality shape music subcultures.

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While Cohen’s (2012a) article addresses the human terrain within the context of music
and location, it does not acknowledge the influence of music industry sites located within a
particular environment. In the article, “Music Scenes to Music Clusters: The Economic
Geography of Music in the US, 1970–2000,” Florida, Mellander, and Stolarick explore the
nature of economics as it pertains to the clustering of localized music scenes (of various genres)
and their associated industries (Florida et al. 2010). The role of scope and scale economics were
used to identify spatial distribution patterns connected to music industries within larger
populations. Much of this study focuses on how population and income dictate an area’s
suitability for musical economics, comparing areas based on lagged variables to see their success
in fostering such an environment, and the ratio of musicians and music industry professionals to
the larger populations in which they exist (Florida et al. 2010).
The cataloging of spatial data to assist in visualizing global music distribution is another
essential step in analyzing the creation of punk music. The article "Globe of Music-Music
Library Visualization Using Geosom,” provides an excellent example of locational, web-based
development that centers on the subject of the music itself (Leitich and Topf 2007). The article
outlines the creation of a global music library that serves as an interactive map showing the
geographic positions of popular musicians on Earth. The author’s methodology and development
of the musical visualization tool in GIS is the main topic addressed throughout the article.
Leigh Michael Harrison’s article entitled “Factory Music: How the Industrial Geography
and Working-Class Environment of Post-War Birmingham Fostered the Birth of Heavy Metal”
explains how geography, politics, and economics encouraged the birth of heavy metal music in
Birmingham, England during the 1960s. Harrison describes how living in a post-war, working-
class environment affected Birmingham youth, and how it fostered the perfect atmosphere for the

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creation of heavy metal (Harrison 2010). The main ideas expressed throughout this article
discuss the geographic and social conditions of post-war Birmingham, the mentality associated
with living in highly populated industrial neighborhoods, the changing youth culture of the
1960s, and the phenomenon of place-specific genres of music. The limitations of this article
include the fact that GIS was not used for the study, and that the research was time (1960s), place
(Birmingham, England), and genre (heavy metal) specific.
Jorge Leal’s article entitled “Mapping Ephemeral Music Forums in Latina/o Los
Angeles” examines the musical subcultures that stem from local music scenes in the city of Los
Angeles. This article explores the observed sense of belonging that music venues gave to Latin-
American youth in the early 1990s in Los Angeles, CA (Leal 2020). Interestingly, the author
uses hand drawn maps of Los Angeles to draw conclusions on music and personal identity.
Youthful participants in this study used musical boundaries (originating at venue locations)
within the city to create maps that visualized areas to which they believed they belonged.
Significant in supporting arguments that note the importance of music and local
populations, Sally Treloyn’s article, “Music in Culture, Music as Culture, Music Interculturally:
Reflections on the Development and Challenges of Ethnomusicological Research in Australia,”
examines the importance of music as an element of individual culture (Treloyn 2016).
Throughout this study, Treloyn delves into the significance of music as it pertains to cultural
identity and characterization. The spectrum of music associated with cultures around the world
are singular to those from which each music originates – a distinct identifier that can be
attributed to distinct populations.
In summation, acknowledging similar research into music, culture, and geography sets a
strong foundation for the analysis and inferences conducted throughout this research. Since

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music is a universal outlet, created and appreciated by people worldwide, it is important for this
research project to recognize the role local culture (however similar to that of surrounding
populations) plays on deciding the type of music that forms in specific places across the globe.
The related research associated with music and geography set the stage for the sociodemographic
analysis conducted in Chapter 3, and served as a continuous (and referenceable) reminder of how
important culture is to the creation of new music genres. The next section explains how GIS has
been involved to help explore the connections between music and place in specific locations.
2.2. GIS and Music
The use of GIS in anthropological studies (such as the research conducted for this thesis)
is an interesting approach to capturing and analyzing spatial data about music and local cultures.
These works usually start by attributing vector data with music-related information, but the
treatment of the spatial data afterwards (as well as the type of analysis conducted) varies
tremendously from one study to the next.
An article by Cohen (2012b) entitled, “Live music and urban landscape: Mapping the
beat in Liverpool,” illustrates this point with its use of GIS to map the human terrain in an urban
city. Her work was particularly helpful with lending insight into how to structure the spatial data
used to explore punk music’s origins. Cohen (2012b) draws conclusions between live-music
venues and local culture in Liverpool, England by documenting how music that is consistently
played in a particular location creates analogous cultural archetypes. The majority of this article
focuses on the challenges experienced specifying the locations of the live-music venues to
describe their spatial distribution within the study area, and the subsequent social classifications
assigned to musical subcultures in urban Liverpool (Cohen 2012b).

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Juho Hänninen’s article entitled "Urban DIY Enclaves? The ‘Alternative’ Cultural
Spaces of Helsinki’s Music Scenes 2000–2019” focuses on the social connotations (i.e., race,
income, education level) associated with alternative music ideologies and space (Hänninen
2020). This thesis sought to identify the mentality connected with alternative DIY music scenes
and the sociodemographic environments that accompany Helsinki, Finland’s musical
subcultures. The DIY attitude towards recording, branding, promotion, and even venue selection
is essential to punk music’s lasting notoriety – something that appears to be replicated in
Helsinki’s alternative music scene. Much of this thesis deals with defining spaces within
Helsinki’s urban environments that can be deemed cultural enclaves that foster DIY music
(Hänninen 2020).
Stan Renard’s purpose in “Mapping Music Cities: A Case Study of the Musical
Landscape of San Antonio” was to assess the impact that San Antonio, Texas’ live-music sector
has on the city’s economy. Live music, as an industry, has grown globally to the point that
money and resources around the world are being used to study the value of maintaining it within
city limits (Renard 2018). The article focuses on mapping the musical landscape of San Antonio,
location intelligence associated with GIS mapping and analysis, and the musical (cultural) tastes
of the local environment.
Hunter Shobe’s and David Banis’ 2010 article entitled “Music Regions and Mental Maps:
Teaching Cultural Geography” examines the perception of music and culture in geographic terms
via mental visualization (Shobe and Banis 2010). Study participants labeled maps of the
contiguous US with music genres based on where they believed each particular genre was most
prevalent. The authors argue that music is an excellent vehicle for delineating cultural
understanding and identity.

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Taylor et al. (2014) explore the shifting dynamics of musicians and live music venue
locations in Sydney and Melbourne, Australia. Over time, the music genres and performance
locations for both city’s music industries have changed dramatically. These authors created a
personalized historical geodatabase to analyze gig dates, locations, and band genres in GIS. The
results show an increase in live music events in Melbourne, notwithstanding the city’s decrease
in size and the fact that the performance locations are far more dispersed than in Sydney. The
scope of this article was modest given the use of just three variables – city-size, population, and
the collected gig listings, and its focus on Sydney and Melbourne.
The data collection and data structuring elements in each of these aforementioned articles
were particularly helpful in creating the data used in this study. While there was some focus on
creating complex geodatabases to house and maintain music-related spatial data over time, the
composition of the feature classes themselves appeared to be similar across most of the
contributions (creating a helpful template for the punk-related data). Hänninen’s (2020)
treatment of sociodemographic information bolstered many of his arguments, which was the aim
of including census data from 1970 for NYC and LA. All of the studies in this section provided
great examples of how GIS can be used to better understand or communicate musical
environments, with Hanninen’s (2020) approach serving to guide the sociodemographic analysis
outlined in later chapters of this thesis.
2.3. Punk Music
Punk music was a definite outlier in comparison to the mainstream music that was
popular in NYC and LA in 1970. Aggressive or explicit music at any time is rarely the most
popular, making the groupings (and subcultures) formed by those who have embraced the genre
all the more interesting. To explore these groupings, researching the genre of punk music itself

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was a necessary step in defining the study areas and timelines chosen for this study. The cluster
analysis described in later chapters was motivated by the desire to characterize these groupings
by their spatial distribution, with the hope of also uncovering the distributions by subgenre. The
related work detailed below was the informational source for much of the spatial data used and
drove the underlying narrative throughout the research.
Differing from the previous articles that employ GIS for musical research, Paula Guerra’s
(2018) article entitled "Raw power: Punk, DIY and underground cultures as spaces of resistance
in contemporary Portugal" lends insight into punk music subcultures that extend beyond NYC
and LA. Guerra delves into the social subsets and resistance ideologies that have arisen over 37
years within Portugal’s various punk music scenes. The article focuses on punk music’s DIY
methodology and the social, political, and economic influence that non-English speaking people
have on the music genre (Guerra 2018).
The article entitled, "Punk: The do-it-yourself subculture" by Ian Moran investigates the
individual genre-specific attributes that are unique to punk music. Stripping away the politics,
ideology, and fashion, Moran focuses on how punk musicians were able to foster the genre
through self-reliance and entrepreneurship (Moran 2010). The article is relevant because it
highlights how the music has spread from local epicenters to satellite-like communities where
the parent genre is maintained (and subgenres are formed in their own right). Nearly all of the
academic articles on punk music point to the resourcefulness of its contributors, and the
emergence of punk music as a social phenomenon.
In Alastair Gordon’s 2005 dissertation entitled "The authentic punk: An ethnography of
DIY music ethics," punk music as a culture is examined as its own separate phenomenon within
local music scenes in Leeds and Bradford, England. The author acknowledges genre-specific

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ethea, such as artist’s self-reliant proactivity towards self-recording, promotion, and venue
selection that are not prevalent in many other musical subcultures (Gordon 2005). Gordan’s
treatment of punk music throughout his research is similar to that of differing cultures around the
world – lending validity to his ethnographic approach. The dissertation evaluates local punk
music scenes in the framework of counter-communities with their own individualized ethics and
identifying characteristics (both politically and ideologically) and describes sub-genre formation
and the spreading distribution of punk music (and influence) over time.
The arguments made by Rebecca Johinke in her article entitled “Take a Walk on the Wild
Side: Punk Music Walking Tours in New York City” support claims that NYC is the epicenter of
punk music (Johinke 2018). This article focuses attention on a lower East Side punk music
walking tour in New York City that seeks to explore the “psychogeographic” importance of
connecting location and music history. The results were useful for the thesis research project at
hand because they provide a credible source of location information dealing with the birthplace
of punk music in New York City.
Dave Laing’s 1978 article entitled "Interpreting punk rock" provided an insightful look
into the subversive nature of punk music’s overarching ideologies that have been foundational
since the music genre’s inception. The political and social implications of punk music are
examined through the lens of Marxism and are often contradicted by the actions of the musicians
and general participants themselves (Laing 1978). The author exemplifies the burgeoning
movement as pockets of “social bases,” which serves to cluster similar attitudes, musical
preference, and aesthetics.
In terms of related research, the articles published on punk music were helpful in
speaking factually about the genre and in thinking about how to create the necessary data. The

15

people and places discussed in this section told the story, but the locations of punk music bands
and venues and the accompanying census data for NYC and LA in 1970 gives another dimension
to what can be understood about early punk music. These data and the various methods used to
analyze and visualize the data are described in more detail in Chapter 3.

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Chapter 3 Methodology
This study aimed to analyze the sociodemographics of New York City, NY and Los Angeles, CA

during the early stages of punk music’s creation in the 1970s. The specific locations of band

formations and historic venues were identified and used to characterize the variables that most

likely contributed to an environment where subversive music could flourish. The two areas

examined in this research were chosen due to the number of bands that formed in New York City

and Los Angeles, CA. Although the earliest punk bands arose in NYC, several early bands

formed in the LA area at about the same time as their east coast counterparts. In looking at the

actual number of bands from each location, the data set appears to be bimodal, which is a

phenomenon that should be explored to properly represent the genre.

Like many other music genres, punk music can be subdivided into several

subgenres that have branched off the parent genus throughout the years (Lena and Peterson

2011). Initial observations showed a greater degree of subgenre diversity in the Los Angeles

area, which may answer questions regarding individuals who are directly involved in the

subgenre’s inception (influential band members), as well as whether similarity in subgenre

branching can be attributed to location. To evaluate the spatiotemporal distribution of punk

bands, subgenres, and venues, spatial autocorrelation and point clustering analysis was employed

to test whether these phenomena were randomly dispersed.

Given the two study locations, the census data were used to characterize the types of

individuals living within the intersecting tracts. The ethnicity, age, education level, and

household income of each census tract was displayed and the point data representing specific

punk locations were used for cluster analysis, to query census tracts, and visualization.

Essentially, the point data were used to choose census tracts and to describe the parts of the two

17

cities where punk music flourished within this study.

3.1. Data Description

The data required for this study was researched and collected from various credible

online resources. Historic and current census data were found at https://www.census.gov/

and https://www.ipums.org/. The spatial and tabular data described in Table 2 were fundamental

to the work at hand.

Census data were collected and analyzed in ArcGIS Pro to determine the spatial

positioning and demographics of each punk-related census tract. Individual punk bands

representative of specific subgenres were researched in order to create a more comprehensive

dataset. Census data that allowed for site profile characterization included attributes such as age,

race, gender, education, and annual household income. The location data researched and

compiled included sites where subgenre-specific bands have formed, and popular live-music

venues where the subgenres were most prevalent.

The self-authored datasets (PunkBandPoints and PunkVenues) were gathered and

verified using Google search and various online resources to cross-reference individual bands

(i.e. Cooper 2018, 2019). Both self-authored datasets have been published on ArcGIS Online at

https://services1.arcgis.com/ZIL9uO234SBBPGL7/arcgis/rest/services/PunkBandPoints/Feature

Server and https://services1.arcgis.com/ ZIL9uO234SBBPGL7/arcgis/rest/services/PunkVenues/

/FeatureServer, respectively. For this research, bands were defined as groups of musicians who

recorded at least one album and played live music regularly. The criteria for choosing and

including a band was the year in which it was formed or that it was one of the first 100 bands

selected. Once a band was selected and confirmed in at least two online sources, a single pair of

latitude and longitude coordinates was selected to represent the city from which they hail from.

18

Table 2 The spatial and tabular data used in this study.
Names Sources Formats Contents
Date of
Compilation
PunkBandPoints
Self-authored
dataset created
through Internet
research
(published on
ArcGIS Online).
Vector and
tabular
(shapefiles and
an Excel csv
file)
Subgenre, band
name, year
formed, city where
the band was
formed, and
coordinates for the
city
Nov 2016
PunkVenues
Self-authored
dataset
created through
Internet research
(published on
ArcGIS Online).
Vector and
tabular
(shapefiles
and an Excel
csv file)
Name of historic
genre-related
venues,
music genre, city
where venue is
located,
coordinates for the
venue’s address
Apr 2020
NewYorkTracts IPUMS
1

Vector
(shapefile)
State of New York
census tracts
1970
AgeDataNY IPUMS
1

Tabular (Excel
csv file)
New York City
age data
1970
GenderDataNY IPUMS
1

Tabular (Excel
csv file)
New York City
gender data
1970
MedianIncomeNY IPUMS
1

Tabular (Excel
csv file)
New York City
household income
data
1970
RaceDataNY IPUMS
1

Tabular (Excel
csv file)
New York City
race data
1970
CaliforniaTracts IPUMS
1

Vector
(shapefile)
State of California
census tracts
1970
RaceDataLA IPUMS
1

Tabular (Excel
csv file)
Los Angeles
County race data
1970
AgeDataLA IPUMS
1

Tabular (Excel
csv file)
Los Angeles
County age data
1970
GenderDataLA IPUMS
1

Tabular (Excel
csv file)
Los Angeles
County gender
data
1970
MedianIncomeLA IPUMS
1

Tabular (Excel
csv file)
Los Angeles
County household
income data
1970
1
https://www.ipums.org/

These coordinates were gathered using the Latitude and Longitude Finder (https://www.latlong.

net/) and then converted to decimal degrees for spatial analysis and display in ArcGIS Pro.

19

The census data for New York City and Los Angeles County in 1970 were downloaded

from the National Historical Geographic Information System (NHGIS) on the University of

Minnesota sponsored IPUMS website (https://www.ipums.org/). The age and race tables were

organized by gender, which allowed for the aggregation and creation of an individual gender

table. Education and household income were organized by years accomplished and household

type and income amount, respectively. The shapefile containing census tract boundaries from

1970 was also downloaded from NHGIS.

3.2. Research Design/Methods

The GIS work was performed on a Dell Precision 7740 laptop with a Netgear N150

wireless router to support internet connectivity. The data was stored in Microsoft Excel and

converted to comma-separated value (.csv) files before being imported into ArcGIS Pro 3.0.0.

The steps shown in Figure 3 describe the methodology and cadence that was deployed for this

study.

The next four paragraphs describe the data preparation tasks in a little more detail and

Section 3.3 describes the spatial analysis in considerably more detail. Once the historic

education, income, sex by age, sex by race census data was downloaded from IPUMS NHGIS

website (https://data2.nhgis.org/main), the following preparation was required to facilitate

meaningful analysis and representation.

20

The 1970 education, income, sex by age, and sex by race census data were reformulated

and used in the subsequent steps in the following ways. Three attributes were used to

characterize the low, median, and high education levels in New York City and Los Angeles

County in 1970: (1) no school years completed; (2) high school: 4 years; (3) and college: 5 years

or more. After the tables were downloaded as csv files, they were imported into an ArcGIS

geodatabase. The 1970 census tract shapefile was then joined with the education table to create

three separate feature classes showing the range of education in New York City and Los Angeles

County.

The 1970 household income data was broken down into: (1) married head-of-household;

(2) primary head-of-household; (3) and other head-of-household. For each of these

classifications, there was three classes – less than $2,000; $2,000 to $24,999; and $25,000 or
Figure 3 Data collection/preparation (left) and analysis/thesis preparation steps (right).
Prepare self-authored spatial
datasets for analysis and modeling

Download census tract shapefiles
for both New York City and Los
Angeles County

Download historic tabular census
data for both New York City and
Los Angeles County

Select relevant variables (age, race,
education, household/median
annual income) from census data

Convert tabular census data to .csv
files and join to vector census tract
data

Non-spatial characterization of New
York City and Los Angeles from historic
census data

Visualize data of New York City and
Los Angeles using historic census data
and results of sociodemographic analysis

Conduct exploratory and cluster
analysis for self-authored datasets

Create figures, tables, and graphs
from the aforementioned analysis to
include in the thesis

21

more for each study area. All rows were aggregated by household type for each income bin.

Using the statistics option within the ArcGIS Pro attribute table allowed for quantiles to be

created for the data. These values guided the criteria for low, median, and high-income

households. As with the education table, the income table was then imported into the project

geodatabase so it could be joined to the 1970 census tract shapefile. Individual feature classes

were then created to display the three relevant levels of income.

Gender data for 1970 consisted of a very large table with many columns representing

population counts by age for both males and females from 0 to 100 years old. To make the table

useful for analysis, ages were combined in five groups: (1) years 0-11 children, (2) years 12-17

teenagers, (3) years 18-39 adults, (4) years 40-59 middle-aged adults, and (5) 60 years and older

adults. The table columns were then aggregated by age group and gender. The table was then

imported into the project geodatabase and joined with the 1970 census tract polygons. Feature

classes were created for each age group, with a separate feature class showing ages 18-39 years

to represent those within the population that are most likely to attend a punk show. This age

group was picked based on the assumption that more young adults would attend a live punk

show, rather than children or people aged 40 years or more.

The 1970 sex by race table was limited in the sense that few ethnicities were represented

overall. For each ethnicity, both male and female population counts were reported in separate

columns. Asian ethnicities were aggregated to form a single column, while Pacific Islanders and

mixed ethnicities (other) were combined for ease of analysis. The male and female data for each

race were then combined and (as with the other 1970 census tables), the sex by race data was

included in the project geodatabase and joined with the census tract layer. Each ethnic group

(Black, White, Asian, Pacific Islander/Other) was visualized by creating a separate feature class

22

in the project database.

With all of the data now in one place, the geoprocessing and analysis tasks described in

the next section completes the workflow summarized in Figure 3.

3.3. Geoprocessing/Analysis

The analysis conducted for the thesis research began with exploratory examination of the

point datasets containing the locations associated with punk music (Figure 4). This allowed for

an initial empirical understanding of when subgenres originated, when (and where) they were

most popular, as well as when (and where) bands and venues were most prominent. Cluster

analysis was then used to confirm locations that identify groupings of instances as epicenters of

the genre. This involved using the PunkBandPoints and PunkVenues feature classes, the Spatial

Autocorrelation (Global Moran’s I) tool, and the Find Point Clusters geoprocessing tool in

ArcGIS Pro. Having the ability to determine spatiotemporal clusters within the data gave

quantifiable evidence of subgenre epicenters and the origin of punk in terms of the initial band

clusters.

The final analysis method employed required the characterization of each study area in

terms of age, gender, race, education level, and annual household income – essentially the

description of each census tract related to punk music through the lens of sociodemographic

variables. The results of this analysis allowed for the reader to understand what kind of

neighborhoods punk music popped up in NY City and the LA area in the 1970s.

23

3.3.1 Exploratory Analysis

Exploratory analysis of the point datasets gave preliminary insight as to the distribution
of early punk entities over space and time. Using the PunkBandPoints, the change in counts for
bands by subgenre were compared to the years in which they were formed (Figure 5). The
subsequent line chart shows peaks in subgenre popularity – essentially dating the pinnacle of
each and illustrating when the subgenre began. Visualizing where bands formed (by counts and
subgenres) served as a predecessor to cluster analysis (Figure 6). While the list of locations is
lengthy, obvious groupings of bands in individual locations confirms the locations or places
chosen to characterize the beginning of punk music. Initial analysis also showed trends in the
distribution of subgenres, which appears to require further attention due to the rise of similar
subgenres in several locations.

Figure 4 Workflow to conduct exploratory and cluster analysis of point vector data.

Conduct exploratory analysis
Test for spatial autocorrelation
(Global Moran’s I)
Find point clusters
(Geoprocessing Tool)
Conduct exploratory and
cluster analysis for self-
authored datasets

Conduct point data analysis
(PunkBandPoints &
PunkVenues)

24

Figure 5 Number of bands (by subgenre) per year.

25

Figure 6 Number of bands (by subgenre) by location.

26

3.3.2 Spatial Autocorrelation (Global Moran’s I)

The Moran’s I statistic for spatial autocorrelation is given as (ESRI 2022):

𝐼 =
𝑛 𝑆 0
𝑛 ∑
𝑛 𝑖 =1
∑ 𝑤 𝑖 ,𝑗 𝑧 𝑖 𝑧 𝑗
𝑛 𝑗 =1
𝑆 0
∑ 𝑧 𝑖 2 𝑛 𝑖 =1
(1)

where zi is the deviation of an attribute for feature i from its mean (xi – X), wij is the spatial

weight between feature i and j, n is equal to the total number of features, and S0 is the aggregate

of all the spatial weights:

𝑆 0
= ∑
𝑛 𝑖 =1
∑ 𝑤 𝑖 ,𝑗 𝑛 𝑗 =1
(2)

The zI-score for the statistic is computed as:

𝑧 𝐼 =
I−E[𝐼 ]
√𝑉 [𝐼 ]
(3)

where 𝐸 [𝐼 ] = −1/(𝑛 − 1) (4)

𝑉 [𝐼 ] = 𝐸 [𝐼 2
] − 𝐸 [𝐼 ]
2
(5)

To determine the spatial nature of punk music’s formation, the Spatial Autocorrelation

(Global Moran’s I) tool in ArcGIS Pro was used to characterize the spatial distribution of punk

bands by subgenre. The aforementioned ArcGIS Pro tool sets a default null hypothesis that

assumes the creation dates of early punk bands and venues were randomly distributed. Equations

(1-5) show the workflow, with Equation (3) showing how the z score for the statistic was

calculated. The spatiotemporal distribution of the PunkBandPoints and PunkVenues datasets was

tested for the years the bands were formed, the years in which a venue was established, as well as

for the geographic locations associated with a band’s subgenre. In order to use an individual

band’s subgenre as an input parameter (and meet the tool’s numerical input requirement), a

numerical identifier (SubgenreID) was created to represent it throughout the analysis.

27

Inputting additional parameters for the Spatial Autocorrelation (Global Moran’s I) tool in

ArcGIS Pro was required for the tool to work properly. To determine the relationship between

the location of a punk band forming and the time at which it formed, the PunkBandPoints dataset

was used as the input feature class, with the year the band was formed chosen as the input field.

Rerunning the tool to examine the relationship between the location of a punk band forming and

differing subgenres, the PunkBandPoints dataset was used once more as the input feature class,

with the abovementioned SubgenreID used as the input field. Determining the relationship

between the opening of an early punk venue and the time at which it opened, the PunkVenues

dataset was used as the input feature class, with the year the venue opened as the input field.

3.3.3 Determining Clusters

The PunkBandPoints feature class contained 100 of the earliest punk bands

separated by subgenre and was used with the Find Point Clusters tool in ArcGIS

Pro to find clusters of bands based on geographic location and observed frequency. Using the

tool’s DBSCAN (or defined distance) algorithm, Find Point Clusters identified the number of

features within a defined distance chosen by the user. Once the number of features were counted,

the tool determined whether or not they were clustered based on a minimum feature count value

also selected by the user. If the number of features counted was equal to or greater than the

minimum feature count within the defined distance, then the tool identified such as group of

features as a cluster (and all else as noise).

Time clusters showed whether subgenre creation was driven by proximity to similar

bands in a specific period, or if the categorization was arbitrarily assigned (regardless of

geographic location or year the band came to be). Using the Find Point Clusters tool, a cluster

would be defined as a group of at least 3 punk bands existing within 1 mile of each other. The

28

same analysis was given to the feature class containing music venues, with a cluster of early

punk venues defined as 5 or more locations within 15 miles of each other. Establishing these

distance and count parameters set a reasonable area and frequency threshold that could identify

grouping within the data. The results of this analysis allowed for the reader to determine whether

or not the source neighborhoods of punk bands and punk music venues were one and the same

for both New York City and Los Angeles, CA.

3.3.4 Census Data Analysis

The 1970 census data was used to characterize the residents in study locations over time

(Figure 7). The census tracts which intersected the band formation sites and venues were selected

and used to depict life in those locations. To denote whether or not an instance of early punk

coincided with a particular census tract, a binary (numerical) field was created to represent

whether or not a band or venue existed within it. A 0 was assigned to tracts without an early

punk instance, and a 1 was assigned for those that had at least one band or venue located within

its boundary. This allowed for ease of analysis and querying out census tracts that did not

intersect with an instance of either a band forming or venue opening. Census tracts adjacent to

those that intersected instances of early punk were not analyzed due to the subjectivity involved

with determining adequate boundaries which may have introduced statistical bias.

The census data was then compiled for New York City and Los Angeles County to

determine the age, gender, race, education level, and annual household income of those living

within areas where punk music began in the 1970s. Census tracts associated with early punk

bands and venues were separated from the larger dataset containing all census tracts for ease of

quantitative analysis and statistical findings. These separate census tracts were then compared to

each other, both within their individual locations and to those that existed across the country

29

from each other. This allowed for conjecture regarding the similarity of tracts found mutually

within one location, and those that were separated by thousands of miles.

Figure 7 Workflow for census data analysis.
Census Data
(Tabular/Polygon)

Aggregate Tabular Data

Join Tables & Polygons

Combine Relevant Layers

Visualization

Analysis

30

Chapter 4 Results
This chapter details the results of the work described in the previous chapter in three parts: (1)
exploratory analysis; (2) cluster analysis; and (3) census data analysis. The exploratory and
cluster analysis answers the research questions which ask when and where punk bands and
venues originated, while the census data analysis answers the research questions dealing with
sociodemographic profiles for locations associated with early punk music.
4.1. Exploratory Analysis

The exploratory analysis of the early punk bands (PunkBandPoints) data showed three

locations where punk music was most prevalent during the 1970s and 1980s (Figure 8).
Figure 8 Geographic locations associated with early punk bands forming in the
1970s.

31

Table 3 shows the locations, counts, and date ranges for three locations where punk music was

formed.

Table 3 Areas of highest frequency from 1971 – 1988 (*denotes earliest instance).
Location No. of Bands Date Range Observed
London, UK 15 1973 - 1983
New York, NY 7 1971* - 1988
Los Angeles, CA 7 1977 - 1983

Locationally, the data is definitely tri-modal in nature, with more than twice as many

instances occurring in London, UK than in New York City and Los Angeles. Because the earliest

instance of punk music was observed in the conterminous US (and the census data in the UK

was not collected at the same level of granularity as in the US during this period), the next part of

the thesis focused on New York, NY, and Los Angeles, CA. The timeline of early punk bands in

New York shows a larger temporal range compared to Los Angeles, where the punk bands

popped up over 6 years at the end of the 1970s and the early 1980s.

While each subgenre was represented equally in the data collection process (ten bands

per subgenre), Table 4 shows the date ranges and locations of the earliest instance

recorded for each subgenre observed in the study.

Table 4 Date ranges and locations for the earliest observances of punk subgenres recorded.
Subgenre Date Range Observed Location of Earliest
Observance
Anarcho 1975-1986 London, UK
Crust 1978-1987 Devon, UK
Glam 1971-1989 New York, NY
Hardcore 1976-1981 Los Angeles, CA
Horror 1976-1987 Sacramento, CA
Oi 1977-1982 South Shields, UK
Pop 1974-1992 New York, NY
Ska 1977-1996 Coventry, UK
Skate 1977-1989 Manhattan Beach, CA
Street 1974-1994 London, UK

32

Five of the first ten subgenres observed can be attributed to locations within the UK.

Three are located in California and the final pair to New York. Certain subgenres appear to have

larger date ranges, with groupings of fewer instances observed over a longer period (i.e., glam,

horror, pop, ska, skate, and street punk), while anarcho, crust, hardcore, and oi punk appear to

have surges of formation observed within shorter periods. Subgenres associated with high

frequency over shorter periods are located almost entirely within the UK, with hardcore punk

being the only American exception. Subgenres that tend to have longer temporal influence can

be mainly attributed to locations within the US.

Much like the PunkBandPoints data, initial exploratory analysis of the early punk venue

dataset (PunkVenues) shows the locations and highest frequency of where early punk music was

played in live music settings (Figure 9). This plot and the counts and date ranges summarized in

Table 5 shows the opening of historic punk music venues collected in this thesis research project.

Table 5 Areas of highest frequency observed for early punk music venue openings.
Location No. of Venues Date Range Observed
New York, NY 8 1965-1981
Los Angeles, CA 7 1973-2001
San Francisco, CA 4 1970-1980
Boston, MA 3 1970-1980
Figure 9 Frequency of early punk music venues observed over
time.

33

Highly populated coastal regions within the US hosted the highest frequency of early

punk music venues observed (Figure 10). While instances of venues opening in London, UK

were recorded, acknowledgement of the study area (New York and Los Angeles) biased the data

collection for this particular dataset.

It appears that venues that already existed transitioned into locations where early punk

music was played for certain New York, San Francisco, and Boston venues, while locations that

were opened in Los Angeles after the first instance of punk music was observed. Los Angeles

also has the largest date range associated with historic music venues, revealing a greater level of

influence over a longer period of time. New York is second to Los Angeles in this regard, with

San Francisco and Boston showing similar values (i.e., ~10 years) that appear to create

spatiotemporal boundaries that accurately depict the popularity of punk music observed in the

PunkBandPoints data. The venue dataset also allows for popularity to be evaluated in terms of

time itself, which shows a steady decline (or fewer venues being opened) beyond 1990 (Table 6).

4.2. Cluster Analysis

The results of using the Spatial Autocorrelation (Global Moran’s I) and Find Point

Clusters tools to describe the distribution of punk music across the US are described in the next

two subsections.

4.2.1 Spatial Autocorrelation (Global Moran’s I)

The Spatial Autocorrelation (Global Moran’s I) tool in ArcGIS Pro was utilized to better

understand the spatiotemporal distribution of both the early punk band and early punk venue

vector datasets. Given the nature of the data (and the tool’s requirement of a numeric input field),

the distribution of punk bands by subgenre, punk bands by year formed, and punk venues

by year opened were analyzed in this manner.

34

Table 6 No. of early punk venue openings by decade.
Decade No. of Venue Openings Observed
1970-1979 22
1980-1989 15
1990-1999 6
2000-2010 4

The Spatial Autocorrelation (Global Moran’s I) tool in ArcGIS Pro started with a default

null hypothesis that assumes that early punk bands by subgenre are randomly distributed. The

tool generates a report that lists the Moran’s Index, the variance, the z-score, and p value, among

others, and using these metrics, the null hypothesis was accepted, which means the spatial

distribution of punk bands by subgenre was random. The p value of 0.71 implies that location is

not a determining factor within the scope of subgenre creation.
Figure 10 Geographic locations associated with early punk venues opening in the 1970s.

35

The same tool was used to check whether the geographic distribution of the start dates of

early punk bands was randomly distributed. The report generated using the tool and the

PunkBandPoints dataset included a p value of 0.040 and suggest that the spatial distribution of

punk bands by year formed was clustered. This result suggests that location and time,

considered together, did influence where and when punk bands were formed.

The Spatial Autocorrelation (Global Moran’s I) tool in ArcGIS Pro was also used to

examine the distribution of punk venues using a null hypothesis that assumes that the locations

of the opening dates of early punk venues were randomly distributed. The resulting z-score

(2.39) and p value (0.018) show that the spatial distribution of punk venues by year opened was

Clustered as well. This suggests that location and time, considered together, did influence where

and when punk music venues opened.

4.2.2 Determining Clusters

The Find Point Clusters tool in ArcGIS Pro was used to identify regions where early

punk musicians gathered. Using the tool, a cluster would be identified if at least 3 punk bands

were formed within 1 mile of each other. The following locations contained clusters of early

punk bands based on this criterion: (1) London, UK (15 bands); (2) Los Angeles, CA (7 bands);

(3) New York, NY (7 bands); (4) Lodi, NJ (4 bands); and Berkeley, CA (3 bands).

The Find Point Clusters tool in ArcGis Pro was used next to identify locations where

early punk bands played live music regularly. Using the tool, a cluster would be identified if at

least 5 venues were found within 10 miles of each other. The following locations contained

clusters of early punk venues based on this criterion: (1) New York, NY (8 venues); (2) Southern

California – Los Angeles, Hollywood, and West Hollywood, CA (7 venues); (3) Northern

California – Berkeley, Oakland, and San Francisco, CA (5 venues).

36

4.3. Census Data Analysis

The historic census data from 1970 was used in ArcGIS Pro to describe the places where
punk music originated. Thirty-one census tracts intersected the PunkBandPoints and PunkVenue
vector data, and were used to characterize the population living within them. The variables in
Table 7 were analyzed to determine the relationships between age, race, education, and income
level, and the presence of a punk band or punk venue. While the variables observed in census
tracts specifically located within either location (New York City or Los Angeles County) were
more similar than dissimilar, variance was observed in median annual income and education
level.
In 31 census tracts representing areas where punk music began in New York City and
Los Angeles County, the majority of the people living within them were Caucasian (Table 8).
African Americans and Asians were similar in count to each other, but had decidedly fewer
people living within census tracts that were associated with early punk music. Pacific Islanders
and mixed races made up a negligible portion of these populations. These findings were evident
in both locations, with little variance observed in accordance to race and population for both
New York City and Los Angeles County.
In terms of annual income, areas associated with middle to slightly upper middle class

annual income values align with instances of either a punk band forming or a venue opening

(Table 9). That being said, there is a proportionately larger subset of the population that are

lower-middle to lower class depicted in the analysis. Punk music is definitely not associated with

areas of extremely high median annual income, but with areas that are slightly above median

annual income.

37

Table 7 Variables used in census data analysis.
Variable Sociodemographic
Caucasian Race
African American Race
Asian Race
Pacific Islander Race
Other Race
< $2000 Annual Income
$2,000-$2,999 Annual Income
$3,000-$4,999 Annual Income
$5,000-$6,999 Annual Income
$7,000-$9,999 Annual Income
$10,000-$14,999 Annual Income
$15,000-$24,999 Annual Income
> $25,000 Annual Income
Children Age
Teenagers Age
Adults Age
Middle Aged Adults Age
Older Adults Age
No School years Education
Elementary 1-4 years Education
Elementary 5-6 years Education
Elementary 7 years Education
Elementary 8 years Education
High School 1-3 years Education
High School 4 years Education
College 1-3 years Education
College 4 years Education
College 5 > years Education

Table 8 Percentage of population by race in census tracts with punk music bands or venues.
Race Percent of Total Population
Caucasian 83.6
African American 8.3
Asian 7.3
Pacific Islander 0.06
Other 0.82

38

While the majority of the population living within census tracts associated with

early punk music made between $7,000-9,000, this was not equally evident for both locations.

Populations in Los Angeles County were more likely to make between $2,000-6,999, while

those that lived in New York City were more likely to make between $5,000-14,999. In terms

of relative affluence, populations living in census tracts associated with early punk music in New

York City generally made more money than populations living in census tracts associated with

early punk in Los Angeles County.

Table 9 Percentage of population by income in census tracts with punk music bands or venues.
Annual Income Percent of Total Population
< $2000 14.3
$2,000-$2,999 8.7
$3,000-$4,999 13.8
$5,000-$6,999 13.3
$7,000-$9,999 17.1
$10,000-$14,999 15.8
$15,000-$24,999 11.1
> $25,000 6.3

Ages associated with punk music creation sites tend to show a relatively young
population living within the census tract boundaries. Over 60% of the total population is between
the ages of 0-39, with the majority of individuals being ages 18-39. The number of children
living in these areas represent more than double their teenage counterparts. While there are a fair
number of middle-aged adults living within these regions, they represent less than a quarter of
the overall population. In terms of young vs. old, children and teenagers outnumber those who
are 60 years and older by nearly 3%. To summarize, it appears that census tracts that intersect
with punk music generally have a large number of young adults who tend to have a relatively

39

large number of offspring – an observation that is evident for both New York City and Los
Angeles County.
Table 10 Percentage of population by age group within punk music creation sites.
Age Groups Percent of Total Population
Children (0-11 years) 14.4
Teenagers (12-17 years) 6.1
Adults (18-39) 39.7
Middle Aged Adults (40-59 years) 22.4
Older Adults (60+ years) 17.7

Approximately 56% of the populations associated with early punk music have an
education level between one year of high school and three years of college. The majority of
people living within these census tracts maintain at least a high school level education, with the
second highest frequency of education level associated with an Associate level degree, or
possibly an unfinished Bachelor’s degree. Roughly 10% of the population is highly educated,
which is triple the size of the population that have no education whatsoever. At least 62% of the
population is high school educated or higher, with 38% never graduating from high school.
While the percentage of education levels in census tracts associated with early punk
appear to increase for both locations for 1-3 high school years and begins to decrease at 1-3
college years, populations living within Los Angeles County were observed to be slightly more
educated than populations living within New York City. Populations living within Los Angeles
County were observed to have a higher frequency of individuals that have completed 1-3 college
years, while those that lived within New York City were observed to have a higher frequency of
individuals that have completed 1-3 high school years (but never graduating).

40

Table 11 Percentage of population by education level in census tracts with punk music bands or
venues.
Education Level Percent of Total Population
No School years 3.3
Elementary 1-4 years 3.8
Elementary 5-6 years 6.0
Elementary 7 years 2.7
Elementary 8 years 8.1
High School 1-3 years 14.1
High School 4 years 25.8
College 1-3 years 16.0
College 4 years 9.9
College 5 > years 10.3

41

Chapter 5 Conclusion
Based on the findings of this study, it remains unclear whether punk music can be described as a
phenomenon directly related to local geography and sociodemographics. However, this raises
questions about the geographic specificity of music genres more generally. Music is understood
as being rooted in place; however, its spatial diffusion patterns are less well-realized. Using a
similar approach to analyze other music genres could provide a comparison as to the relationship
between punk rock and its geographic context. Similar conditions for differing music would
bolster the counterargument. These findings show that the beginning of punk music was
associated with clusters of youthful individuals living in lower to middle-class neighborhoods in
coastal metropolitan areas. This may or may not be the same for other music genres. As punk
music is a definitive outlier in terms of mainstream popularity, it is reasonable to assume that it
more than likely sets itself apart from other music based on the variables examined within this
thesis.
5.1. Summary and Discussion
The clustering of early punk music shows a relatively narrow time frame during which
punk was new and popular, after which it declined in popularity and frequency. While there are
several instances of punk music being played in the early 1970s across the country, it was and is
played more prevalently near creation sites on the east and west coasts of the United States. Of
course, New York City and Los Angeles County are entertainment and music industry
epicenters, so it makes sense that these were the areas in which these musical genres emerged.
That said, this analysis did not show that punk music mapped perfectly onto regions that simply
incubate music more generally. For example, the cities of Boston and San Francisco are not
traditionally known to be entertainment hubs, and yet were strongly connected with the

42

emergence and spread of punk music. Another anomaly that was uncovered by the cluster
analysis was how subgenres were seemingly randomly distributed across the country and over
time.
With respect to subgenres, the null hypothesis before analysis was the assumption bands
that sounded alike would originate from the same locations. This is largely true of oi punk, with
multiple bands coming from London, UK between the late 1970s and early 1980s. This was not
true for the other subgenres included in the study. Oi punk was observed to be (initially) a
distinctly British phenomenon – essentially a reaction of the working-class youth to economic
and living conditions experienced in London. In comparison to other subgenres, oi is interesting
because of the brief time frame in which it was most popular, and for the fact that it remained
centralized in one location. Figure 11 shows members of the oi punk scene outside of a popular
venue in London – note the similar ways in which the participants dress. This particular aesthetic
originated with the oi punk scene, but its influence can be observed in other locations discussed
throughout the study.
The subgenre of horror punk can be attributed to influential band members (specifically
Glenn Danzig), who generated a cluster in Lodi, NJ, with very few instances observed elsewhere.
It is important to remember this while considering how music was shared and grew in popularity
during the research timeline, not within the context of how easily and quickly music can be
shared presently. An analysis of current punk subgenres would have to include the advent of the
internet and music streaming services available today, which would dramatically change the
results coming from the type of clustering methods used in this research. Thus, this study can
only explain subgenres as varying punk music categories that are either directly related to

43

influential band members or local music scenes in which the particular subgenre thrives in
popularity.
This study suggests that early live music venues associated with punk music were largely
reactionary in their openings, meaning that the venues opened specifically to meet the demand of
punk music. There were only a few instances of established venues becoming known for punk
music after the first instance of a band forming was observed. The analysis created groupings
Figure 11 Oi punk scene in London, UK (1979).
source: https://www.documentjournal.com/2019/07/before-boris-there-
was-thatcher-youth-revolt-in-70s-london/

44

within New York City and Los Angeles County, but it also showed clusters in other highly
populated coastal cities around the country. These observations seem to similarly be reactionary
in nature, or exemplify how large, populated areas provide live-music venues within their city (or
downtown area). While acknowledging that large cities offer more opportunities for live-music
settings, there is evidence of punk specific venues being located in and around punk music
creation epicenters in both New York and Los Angeles. The only difference is that those located
in New York and Los Angeles have conflicting sociodemographic variables attributed to creation
sites, namely with Los Angeles having lower income. That being said, there were definitive ties
to geographic location, time, and punk music observed throughout the study – specifically in
New York, Los Angeles, and London in the early 1970s.
When examining the census data, it became apparent that young, Caucasian, moderately
educated, lower-middle class populations have a strong association to the epicenters of punk
music. Considering the observed sociodemographics, punk musicians would hardly have a leg to
stand on if pressed to justify the level of angst and aggression expressed through their music and
aesthetics. While living in and around 1970 New York City and Los Angeles County may come
with its own set of difficulties, the fact remains that the overwhelming majority of the people
who listened to and played punk music were not poverty-stricken, or part of an oppressed group
of underrepresented individuals. This speaks to the inauthenticity of many punk ideologies, but it
also sheds light on the fact that punk music draws in youthful participants, who (for their own
individual reasons) seek to be part of something loud, aggressive, and rebellious.
5.2. Limitations
The following subsections outline the limitations observed throughout the research and
analysis portions of this thesis.

45

5.2.1 Data Limitations
A major limitation of the PunkBandPoints dataset was the lack of attributes dedicated to

band members. Had fields such as SINGER, GUITAR_LEAD, GUITAR_RYTHM, BASS, and

DRUMS been included, the nature of subgenres could have been further explored to

acknowledge individuals who were members of different bands. Musicians who played in

multiple bands across multiple subgenres (Figure 12) could possibly create a common lineage

between dissimilar types of punk music. Identifying this common lineage could further connect

subgenres with specific locations, with core members serving as the impetus. Given the current

dataset, cluster analysis can only characterize the distribution of subgenres as a random

phenomenon, with only a few that were explained in the context of both time and space.

Another limitation of the early punk band data was the manner in which the locational

data was generalized during collection. There were no specific street addresses available for

every instance of a punk band forming (unlike the punk venue data), and thus there were several

duplicate longitude and latitude values associated with bands forming within the same city.

Figure 12 Ian MacKaye singing for hardcore band Minor Threat (left) in Washington, D.C.
(1980) and post hardcore band Fugazi (right) in 1987 (Washington, D.C.)
source: https://www.rebelnoise.com/interviews/ian-mackaye-2009

46

Providing more granular locational data would have given a more accurate depiction of the

data’s distribution, as well as refined the census tract characterization. While the dataset

containing early punk venues did have a sample size of more than thirty separate sites associated

with where punk music was first played, it would have been advantageous to include other

locations (such as influential recording studios) to further refine geographic boundaries of punk

influence. This would either expand or densify the data among common locations, creating a

more well-rounded and accurate dataset. Like adding band members to the PunkBandPoints data,

attributing this layer with FOUNDER, OWNER, and OPERATOR fields would tie individuals

within heavily distributed punk areas (Figure 13) to bands playing various subgenres during

specific timeframes.

Figure 13 Fat Mike – founder of Fat Wreck Chords and lead singer/bassist of
NOFX (1990).
source: https://diffuser.fm/nofx-fat-mike-interview-2015/

47

The only real limitation attributed to the census data from 1970 was the way several races
and ethnicities were grouped into broad categories. Even after aggregating the data for ease of
manipulation and analysis, the categories represented very few ethnic groups (as opposed to the
number of groups that are represented in modern census data). While the annual income data
represented the distribution of wealth in both New York City and Los Angeles County, the cost
of living during the 1970s was vastly less than it is today. When looking to income data as a
strong explanatory variable, much was lost contextually due to how little annual income values
from the past align with those from the present. A possible fix for this would be to include a table
that equates economic classes proportionately to what is earned today.
5.2.2 Analysis Limitations
The only limitations observed from the spatial autocorrelation analysis of the point vector
data were the limitations of the datasets themselves. Although the tool (by default) assumes a
random distribution, the null hypothesis proposed throughout the study assumed that every
aspect of the data would be clustered due to the nature of the phenomenon being observed. Punk
subgenres were randomly distributed within the context of time and space, but if the dataset
included band members, then this could have been analyzed to find clusters of duplicate values.
As it currently stands, the analysis only states that geographic location does not play a large role
in the type of punk music being played. Had members been a variable, then this could possibly
have been an explanation for similar subgenres occurring at random locations (member
relocation).
The geographic cluster analysis of the early punk band and venue data was likely limited
due to how clusters were defined. Three punk bands forming within one mile of each other
appeared to be the most conservative representation of a cluster, but reducing the number (or

48

increasing the distance value) may have created clusters not acknowledged in the first analysis.
This was the nature of the cluster analysis conducted for this thesis, insofar as one must choose
the parameters for each cluster, making them inherently subjective. While New York City, Los
Angeles County, and London appeared as obvious clusters (even from rudimentary tabular
examination), other locations could have been realized as possible subgenre epicenters or areas
where punk was becoming popular depending on the dates observed.
5.3. Future Work
Moving forward, this research can be expanded by examining similar genres of music,
defining the genealogy of punk music from its inception, and using predictive modeling to
include punk music (and related census demographics) across the United States. Punk music is
aggressive, with lyrical subject matter that is counter to mainstream social ideals, both
aesthetically and ideologically. Finding other genres with similar qualities can lead to a better
understanding of how genres are related, and to what degree they differ based on the region in
which they form and develop. For example, metal and reggae music are tied to punk music
through both band member lineage (i.e. Glenn Danzig in The Misfits vs Glenn Danzig in
Samhain or Danzig [horror punk vs heavy metal]) and musical composition. A novel approach
would be to include locational values as explanatory variables in regression analysis.
Including the genealogy of punk music in future work would create new data for analysis
in GIS. Bands that come from or are directly influenced by specific bands of similar subgenres
create a traceable directory of punk instances that can be visualized and examined geographically
(Figure 14). While not addressed in this study, punk music does have circuitous ties to reggae
music, including how comparable it sounds to ska punk, as well as its rebellious subject matter.
Both punk and reggae bands play music as a form of social sedition. Thus, the analysis of the

49

people and environment must demonstrate similar circumstances, if not similar or proportionate
sociodemographics. Metal music was also created from the same angst and aggression that drove
the ideology of oi punk – essentially disgruntled lower-middle class workers who question the
system through which they must negotiate to survive. The ties to both reggae and metal music
could further the understanding of how music genres themselves form within the context of
similar genres.

Another way this work can be expanded upon would be to introduce predictive modeling
and apply it to all census tracts within the US. If the spatial and sociodemographic analysis of
punk music can serve as a piece of the puzzle that defines the nature of music formation overall,
then employing predictive analysis to define punk-type locations outside of the study area would
create training data that can be compared to other music genres, regardless of where they are
Figure 14 Example of punk music lineage analysis.
source: https://www.wired.com/2016/10/lets-obsess-intricate-map-alt-music-history/

50

formed. Comparing the sociodemographic variables associated with specific music genres would
then lead to a better understanding of how localized culture and economic conditions drive the
type of music that can originate at a given location.
In conclusion, the beginning of punk music is geographically tied to New York City,
New York, Los Angeles County, California, and London, UK. In terms of being a spatial
phenomenon, both bands and locations where live music is played can be defined as clusters that
exist in all three locations. Along with spatial clustering, temporal clustering can be observed
across all subgenres that tie the beginning of punk music to the early-to-mid 1970s. The only real
anomaly uncovered in this research is the fact that bands that played similar types of punk music
did not create spatial or temporal clusters. The results from this study acknowledge that punk
music is both a spatiotemporal and micro-cultural phenomenon, while also characterizing the
genre for further musical research as a whole.

51

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Abstract (if available)

Abstract Geographic information systems (GIS) contain visualization and analytical tools that assist users to better understand the spatial and temporal relationships between mapped entities. The formation of music genres, for example, is a complex phenomenon that can be explored through spatial analysis using GIS software. The genre of punk music emerged in New York City, NY in the early 1970s with the appearance of bands such as the New York Dolls and The Ramones. Punk music is unique because commercial or mainstream success is most likely not the sole motivator for musicians who propagate the genre. The Do-It-Yourself (DIY) mentality of those who originally played punk music, coupled with the unique subcultures that stemmed from local groupings of popular bands raises questions as to the nature of the environment and people closest in proximity to the phenomenon. This study aimed to explore the spatial and temporal relationships between genre-specific punk bands and their local environments within the context of sociodemographics and time. A literature review was conducted to identify the temporal and spatial evolution of punk music. ArcMap and ArcGIS Pro were used to analyze, display, and prepare the spatial data, which included locations of band formation sites, venue openings, and census data from 1970. Point cluster and proximity analysis, along with historic census data quantification were employed to tell the story of punk music within the context of time and space. The people, environment, and spatial diffusion of locations (as well as attributes) associated with early punk music is characterized through the use of GIS. Findings revealed through this research exemplified the versatility of GIS and created a repeatable process for examining other music genres.

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Asset Metadata

Creator Eisenhuth, Kurtis Ausbin (author)

Core Title Mapping punk music and its relative subgenres

School College of Letters, Arts and Sciences

Degree Master of Science

Degree Program Geographic Information Science and Technology

Degree Conferral Date 2022-12

Publication Date 08/31/2022

Defense Date 08/25/2022

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

Tag census,cluster,GIS,mapping,OAI-PMH Harvest,punk music,spatial autocorrelation,subgenres

Format application/pdf (imt)

Language English

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Advisor Wilson, John (committee chair), Duan, Leilei (committee member), Sedano, Elisabeth (committee member)

Creator Email eisenhut@usc.edu,keisenhuth@agricapture.com

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