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Site selection of medium density housing in Tacoma, Washington: where to put “missing middle” housing

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Site selection of medium density housing in Tacoma, Washington: where to put “missing middle” housing

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Content SITE SELECTION OF MEDIUM DENSITY HOUSING IN TACOMA, WASHINGTON:
WHERE TO PUT “MISSING MIDDLE” HOUSING?
By
Kevin Congthanh Le
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 2023
Copyright 2023 Kevin Congthanh Le

ii
Dedication
To Kacie, Frankie, and Blossom!

iii
Acknowledgements
I am grateful to my committee chair, Dr. Elisabeth Sedano, and committee members, Dr. AnMin Wu and Dr. Siqin Wang, for their guidance, and feedback during the writing of this
manuscript. I would also like to extend my gratitude to the experts that took time from their
schedules to provide feedback on the factors used in this project. This project would not have
been possible without the contributions of all of these individuals.

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Table of Contents
Dedication....................................................................................................................................... ii
Acknowledgements........................................................................................................................iii
List of Tables ................................................................................................................................. vi
List of Figures............................................................................................................................... vii
Abbreviations................................................................................................................................. ix
Abstract........................................................................................................................................... x
Chapter 1 Introduction .................................................................................................................... 1
1.1 Study Area .......................................................................................................................... 3
1.2 Motivation........................................................................................................................... 8
1.3 Document Overview......................................................................................................... 11
Chapter 2 Related Work................................................................................................................ 13
2.1 Urban Sprawl .................................................................................................................... 13
2.2 Medium Density Housing Typology ................................................................................ 15
2.3 Barriers to MDH Development......................................................................................... 19
2.4 MCDA and Site Selection................................................................................................. 20
2.5 Criteria Relevant to MDH Site Selection.......................................................................... 23
2.5.1 Access Criteria ......................................................................................................... 23
2.5.2 Socioeconomic Criteria............................................................................................ 28
2.5.3 Administrative Criteria ............................................................................................ 31
Chapter 3 Methods and Data......................................................................................................... 33
3.1 Variable Selection............................................................................................................. 34
3.1.1 Infrastructure and Amenities.................................................................................... 36
3.1.2 Property Characteristics........................................................................................... 37
3.1.3 Population Characteristics ....................................................................................... 37
3.1.4 Local Economy ........................................................................................................ 38
3.2 Data ................................................................................................................................... 39
3.3 Data Preparation................................................................................................................ 43
3.4 Analytical Hierarchy Process............................................................................................ 51
3.5 Weighted Overlay ............................................................................................................. 56
Chapter 4 Results.......................................................................................................................... 58
4.1 Spatial Qualities of Criteria of Interest ............................................................................. 58
4.2 Service Area Results......................................................................................................... 76
4.3 Site Suitability Results...................................................................................................... 85
4.4 Service Area Spatial Trends.............................................................................................. 92
4.5 Site Suitability Spatial Trends .......................................................................................... 96

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4.6 Limitations...................................................................................................................... 102
Chapter 5 Conclusions................................................................................................................ 104
5.1 Policy Implications ......................................................................................................... 104
5.2 Future Work.................................................................................................................... 106
5.3 Conclusions..................................................................................................................... 107
References................................................................................................................................... 109
Appendix A................................................................................................................................. 113
Appendix B................................................................................................................................. 116
Appendix C................................................................................................................................. 117

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List of Tables
Table 1. General characteristics of medium density housing types in the GMA.......................... 9
Table 2. Selected criteria for analysis ......................................................................................... 36
Table 3. Project Data ................................................................................................................... 39
Table 4. Reclassification scheme for counts of crime incidences............................................... 48
Table 5. Reclassification schemes for the criteria layers before Weighted Sum input............... 51
Table 6. The AHP scale of importance for pairwise comparisons.............................................. 52
Table 7. Weighting scheme for Weighted Sum .......................................................................... 55
Table 8. Suitable parcels scores and counts................................................................................ 97
Table 9. Net unit gain based on two scenarios of MDH development...................................... 100

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List of Figures
Figure 1. Study Area of Tacoma, Washington ............................................................................... 4
Figure 2. Middle housing types as defined by Daniel Parolek ....................................................... 8
Figure 3. The general workflow for this project........................................................................... 34
Figure 4. The modified environment settings for this project ...................................................... 44
Figure 5. Workflow diagram of dataset cleaning.......................................................................... 45
Figure 6. Preparation of cleaned data layers for MCDA .............................................................. 47
Figure 7. Preparing the criteria layers for input into the Weighted Sum tool............................... 49
Figure 8. Production of a weighting scheme using AHP.............................................................. 54
Figure 9. Final combination of data layers to produce the MDH suitability results..................... 57
Figure 10. Tacoma’s residentially zoned areas............................................................................. 59
Figure 11. Land values for all Tacoma parcels............................................................................. 61
Figure 12. Unemployment rate by census block group ................................................................ 62
Figure 13. Property crime counts by census block group............................................................. 63
Figure 14. Personal Crime counts for Tacoma. ............................................................................ 64
Figure 15. Proportion of census block groups earning below the poverty level........................... 65
Figure 16. Airborne particulate levels for Tacoma....................................................................... 67
Figure 17. Local bus stops in Tacoma .......................................................................................... 68
Figure 18. Locations of public K-12 schools................................................................................ 69
Figure 19. Daycare facilities throughout Tacoma......................................................................... 70
Figure 20. Network of Tacoma's higher capacity roadways......................................................... 71
Figure 21. Tacoma's parks and community centers...................................................................... 72
Figure 22. Designated neighborhood business districts in Tacoma.............................................. 73

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Figure 23. Public medical facilities in Tacoma ............................................................................ 74
Figure 24. Full-service grocery stores in Tacoma ........................................................................ 75
Figure 25. Walksheds to daycare facilities................................................................................... 77
Figure 26. Drivesheds to daycare facilities................................................................................... 77
Figure 27. Walksheds to grocery stores........................................................................................ 78
Figure 28. Drivesheds to grocery stores ....................................................................................... 79
Figure 29. Walksheds to public clinics......................................................................................... 80
Figure 30. Drivesheds to public clinics......................................................................................... 80
Figure 31. Walksheds to high-capacity roads............................................................................... 81
Figure 32. Drivesheds to high-capacity roads............................................................................... 82
Figure 33. Walksheds to public schools ....................................................................................... 83
Figure 34. Drivesheds to public schools....................................................................................... 83
Figure 35. Walksheds to bus stops................................................................................................ 84
Figure 36. Drivesheds to bus stops............................................................................................... 85
Figure 37. Weighted Sum output with the walksheds .................................................................. 87
Figure 38. Weighted Sum output with the drivesheds.................................................................. 87
Figure 39. MDH Walking Suitability map ................................................................................... 89
Figure 40. MDH Driving Suitability............................................................................................. 90
Figure 41. Tacoma parcels with assigned MDH suitability scores for walking ........................... 91
Figure 42. Tacoma parcels with assigned MDH suitability scores for driving ............................ 92
Figure 43. Zoning for central/southern Tacoma ........................................................................... 99

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Abbreviations
ADU Accessory dwelling unit
CR Consistency ratio
GIS Geographic information system
GIST Geographic information science and technology
GMA Growth Management Act
MCDA Multi-criteria decision analysis
MDH Medium density housing
MFH Multi-family housing
MH Middle housing
NBD Neighborhood business district
SFH Single family housing/home
US United States

x
Abstract
The Puget Sound region in the Pacific Northwest has failed to provide adequate housing for its
residents for decades, leaving behind a trail of displacement and inequity throughout many of its
cities. The city of Tacoma, the second largest city in the region, is expected to grow in
population by nearly 50% between 2020 and 2040, highlighting the urgency to build sufficient
housing to home both current and future residents. The state legislature responded in 2023 with
the passage of legislation mandating increased minimum density regulations in all low-density
residential zones and allow a greater variety of housing type where they were once restricted.
This project identified tens of thousands of parcels in Tacoma that would be suitable for denser
housing within the context of this new legislation. Service areas were created to measure access
to a variety of amenities and resources such as schools, public clinics, and grocery stores,
deemed important for the success of denser housing. Weighted overlay methods combined these
service areas with other layers to build suitability maps scoring the entire city of Tacoma for
medium density housing. The results indicate that significant portions of Tacoma would be
moderately or highly suitable for increased housing density. The methods of this project
highlight how geographic information science and technology can support housing policy to
ensure accessible, affordable housing for the state of Washington.

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Chapter 1 Introduction
The Seattle metropolitan region, the urban portion of the Puget Sound region, is feeling the
effects of longtime urban sprawl and inadequate housing policies and is on track to fall a million
homes short in 2040 (Growth Management Act 2023). Like many other urban centers across the
US, the region experienced a boom after World War II that saw the growth of suburban, singlefamily neighborhoods. While cheap and relatively plentiful at the time, single-family homes
(SFH) and the neighborhoods they are in have grown increasingly unaffordable in recent decades
across the nation. Recently passed Washington state legislation – an amendment to the Growth
Management Act (GMA) of 1990 – eases residential zoning standards across Washington to
allow medium-density housing (MDH) in previously low-density neighborhoods by cutting
down municipal red tape. The potential impact is great; Washington can build up their housing
stock incrementally on a wide scale without massively intensifying sprawl between urban centers
or driving residents into displacement and poverty due to rising costs of living. By increasing
housing stock and variety in multiple locations through the updated GMA, state representatives
hope that home prices and rents would become more affordable (GMA 2023).
Tacoma’s housing situation is familiar to many Americans: a majority of residential
buildings are detached homes or medium to large apartment buildings with few deviations in
between. Between 2013 and 2018, approximately 63% of residential units (both owned and
rented) in Tacoma were single family homes and 27% were apartments with five or more units.
Thus, 90% of units are one of only two varieties of housing. Among renters, a quarter spend
more than 30% of their incomes on rent and another quarter spend over 50% (Root Policy 2020).
These groups would be labeled “cost-burdened” and “severely cost-burdened,” respectively.

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These statistics indicate that current conditions form an extremely high barrier for
Tacoma renters to make the transition to homeownership while being provided no safety nets
such as eviction protections, rent control or other measures. Surprisingly, homeownership does
not guarantee stability either, as 25% of Tacoma homeowners spend over 30% of household
income on housing as well, indicating that even if a renter can obtain homeownership, they are
still at risk of continued financial instability (Root Policy 2020). This difficulty in securing
financial footing is despite local homeowner households tending to have more than two times the
median income of renter households. Thus, increased income is not enough to resolve the
housing issue, prices need to fall. The updated GMA will attempt to address the issue of housing
variety and supply to shrink the economic gap between renters and homeowners and provide
more stability for both groups.
The new provisions will allow for more duplexes up to sixplexes, courtyard style
apartment buildings and condo structures to be built up throughout the city. To be clear,
instances of these housing types are present in multiple areas of the city, but they are a small
minority of overall housing units. Further while these housing types exist, restrictive zoning
policies prohibit them in many residential areas of the city. The legislation includes verbiage
intended to cut down on restrictions and the bureaucracy of construction, reducing the time and
energy invested in getting a housing project funded and approved (GMA 2023). In simplest
terms, the legislation will ease the planning process for housing developments while allowing a
greater variety of housing stock to enter the market. The hope is that this flexibility will spur
affordable housing development.
Further, standards around form and scale intend to make the new, denser housing
compatible with existing homes so that the fabric of neighborhoods may be preserved. A

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common concern from residents when dense redevelopment is proposed is the fear of large
apartment buildings towering over detached homes. While the new developments may not
perfectly match the existing neighborhoods, the intention of the new law is to make the new
structures more palatable to nearby residents (GMA 2023). The MDH types listed in the previous
paragraph aim to be approximately the same size or slightly larger than a typical two- or threestory SFH, a common sight throughout Tacoma. Coupled with guidelines for a consistency of
aesthetics, neighborhoods with new MDH units could feel more unified rather than a messy
patchwork of buildings built at different times and by different builders. Regardless, low density
cities such as Tacoma could see massive gains in housing stock in the coming decades as much
of the city appears to be well suited to accept the new housing type. However, barriers and
opponents to MDH have prevented it from tackling the affordable housing issue.
This research examines residential parcels in Tacoma, Washington through the lens of
multi-criteria decision analysis (MCDA) to identify suitable locations for MDH. This analytical
technique identifies and balances several factors such as pollution, access to locations, cost of
land, crime and other local characteristics that contribute to urban planning decision-making.
With the identification of highly suitable areas, two scenarios of MDH adoption are proposed to
estimate added housing units. The analytical hierarchy process (AHP) method applied in this
analysis of Tacoma’s housing exemplifies how geographic information science and technology
(GIST) can support the representation and combination of characteristics to solve spatial MCDA
problems.
1.1 Study Area
The City of Tacoma is located south of Seattle and north of the state capital of Olympia
and is the county seat for Pierce County. Pierce County itself is the second most populous in the

4
state behind King County, which contains Seattle. As seen in Figure 1, the city sits on a
peninsula and is completely bounded by other municipalities. Therefore, there is no way for the
city to expand its borders, density must be increased on current land to handle the anticipated
population growth.
Figure 1. Study Area of Tacoma, Washington

5
Tacoma, with a population of 240,000, is expected to receive about 125,000 new
residents by 2040 according to a study ordered by the city as part of its comprehensive plan
update (Tacoma 2015). After accounting for the initial global disruption of the COVID-19
pandemic, this population increase translates to approximately 45,000-50,000 units that need to
be added from 2021 to 2040 (Root Policy 2020) at a minimum. Thousands more units are needed
to house the existing houseless and shelter populations, with the county government counting
nearly 2200 homeless individuals across the county in a January 2023 census. The county admits
this is likely an undercount, as there are over 6000 individuals connected to the county’s
homelessness crisis system (Pierce County Human Services 2023). Finally, the region’s
population will not simply stop growing in 2040, so even more units are needed to house those
future residents in the second half of the 21st century. To fully house current and future residents
of Tacoma, close to 60,000 units will be needed, with even more housing needed for population
growth beyond 2040. This 60,000 is derived from dividing the sum of the expected population
growth and houseless population with the average household size in Tacoma of two or three
people. Thus, it is critical to assess and critique the new legislation so that policymakers have
time to revise their approaches to maximize the number of units constructed under the GMA.
The City of Tacoma has a history of housing shortfalls coupled with a laissez-faire
attitude in constructing housing. The Tacoma Housing Authority was established in 1940,
following its federal counterpart in 1937 (Montange 2015). The housing authority built its first,
and still largest, public housing complex in the Salishan neighborhood in East Tacoma from
1942-43. The Salishan neighborhood housed a number of families flocking to Tacoma to
participate in its war industries at the time. Researchers examining the history of public housing
in Tacoma have noted that this housing development was part of the first major effort of the US

6
government to construct subsidized housing and it was not specifically for low-income people.
Following the war, 900 units were redesignated as low-income and qualifying families began to
move in. These units did not meet the housing need at the time, with the 1950 Census finding
that over 6000 families were living in substandard housing in Tacoma (Montange 2015). Policies
in the decades following that were intended to reduce poverty only deepened social segregation
by restricting the mobility for historically poor or marginalized groups. For example, providing
housing only for single parents and their children is a seemingly noble task that will ease their
struggles. But without proper management and additional holistic support from local agencies,
that single parent and their children may not be able to accrue wealth or knowledge and could
fall deeper into poverty rather than rising out of it, remaining trapped in the social benefits
system for generations (Van Atta 2013). Similar trends can be seen with other marginalized
groups, where they are housed at the physical and social peripheries of society, out of sight of the
majority. These groups can be ignored and their struggles unresolved
Further poor decisions from the government were made by putting public housing in the
hands of private landowners. The creation of Section 8 vouchers in 1974 shows a major shift to
the privatization of public housing. Public housing had become increasingly stigmatized and
politically unpopular, so the vouchers were seen as a way to publicly stem the flow of federal tax
dollars to those in public housing projects (Montange 2015). These federal vouchers instead went
directly to private landlords housing qualified low-income tenants across the nation. In reality,
the vouchers just obscured the federal money going into housing by putting it in the hands of
landowners rather than renters or potential homeowners. This privatization was further
compounded by Low Income Housing Tax Credits in 1986, which encouraged private
investment in affordable rental housing as federal funds for building and maintaining public

7
housing were decreased year after year (Montange 2015). These actions show a reliance from the
federal government on private businesses and individuals to solve the housing issue through
business dealings rather than taking on a more active role itself. Unfortunately, this poor
administration is far reaching and trickles down to Tacoma’s authorities and agencies as well.
In the late 1990s and early 2000s, the Tacoma Housing Authority applied for and
eventually received funding to redevelop the Salishan Public Housing complex into a mixedincome residential area to spur private development in adjacent residential neighborhoods. The
mentality appears to be by uplifting the Salishan neighborhood, nearby areas would become
more attractive to property developers. The redevelopment was spurred by the 1980s and 1990s,
where unemployment rose as high as 9%, driving an increase in crime and violence throughout
the city. The Salishan neighborhood in particular was characterized by Tacoma residents as an
area of concentrated violence, gang activity and drug sales (Montange 2015). Salishan’s
redevelopment was completed in 2013, following several delays due to the 2008 Great Recession
and funding-related changes that removed planned amenities such as an adult education center.
Some amenities such as a health clinic and childcare were successfully established, creating
access to these services where there was previously none.
While the redevelopment sought to improve living conditions, the housing authority
displaced residents in order to renovate the physical units. Existing Vietnamese and Cambodian
residents were forced out and sent to the back of the line for public housing. While greater
numbers of black, white, mixed, and single parent households obtained housing, it came at the
cost of taking that away from other people in need. Montange (2015) indicates in their account of
public housing history in Tacoma, that one form of diversity was essentially replaced with
another. The Salishan redevelopment successfully brought public investment in the

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neighborhood, such as the founding of a nearby public middle school. Unfortunately, there was
little evidence of any significant private investment aside from a handful of individual homes.
Evidently, an updated public housing development did not make the surrounding neighborhoods
much more attractive to developers than before the update. Like with the Section 8 vouchers, this
shows that a government body taking less than an active or leading role in public housing
development will lead to failure or unsatisfying results.
1.2 Motivation
The update to the GMA refers to the concept of “middle housing,” (MH) coined by urban
designer Daniel Parolek (2020), which is almost synonymous with MDH. Parolek is an advocate
for more walkable American cities and middle housing plays a role in those goals. MH is a broad
category of housing larger than SFH but smaller than mid-rise apartment buildings (Figure 2).
Figure 2. Middle housing types as defined by Daniel Parolek
For the purposes of this project, MDH is not as wide in scope and refers to the housing
types specifically named in the text of the GMA (Table 1). As Table 1 shows, MDH is on the
smaller end of the spectrum with structures about the same size of a large SFH or two. It does not
include options such as larger multiplexes of live/work units. There are endless configurations
and designs for middle housing from urban areas around the world, but for this project, only the
typologies mentioned by the GMA and Parolek will be the focus. For this project, MDH will be

9
used when speaking of the improved GMA specifically while MH will be when speaking of
denser housing broadly.
Table 1. General characteristics of medium density housing types in the GMA
Type Units per Lot Unit Size (sq. ft) Floors Width (ft) Height (ft)
Duplex 2 600-2400 1-2 28-55 14-24
Triplex 3 700-1600 3-3.5 24-40 30-45
Fourplex 4 500-1200 2-2.5 34-56 20-28
Sixplex 6 500-1200 2-2.5 50-80 25-40
Bungalow Court 5-10 500-800 1-1.5 18-24 12-18
Townhouse 2-16 1000-3000 2-3.5 18-25 (per unit) 25-40
The language of the GMA mandates cities at least double the number of units on SFH
lots, indicating duplexes up to sixplexes may compose the first wave of new developments under
the new legislation, especially as many SFH can be renovated into one of these classifications
(GMA 2023). These smaller developments would be faster to construct and more cost effective
in meeting the minimum legislative requirements in comparison to larger, newly built MH
developments. Larger developments may still be constructed early on, but their scale makes them
inherently riskier investments than renovating an existing building.
The concept of MH is that not everyone wants to or has the capacity to care for a
detached single-family home in the suburbs. While detached homes became prolific across many
US cities in the 20th century, they only fit well with certain types of lifestyles: middle class,
traditional work schedule, car owner, stable socioeconomic status, and other idealistic
characteristics. This level of socioeconomic status is certainly attractive to many, but is not
equally attainable by all, dwindling single family housing stock aside.

10
The range of MDH allows for different configurations and sizes at a spectrum of price
points (Parolek 2020). The needs and resources of a recent college graduate would be different
than those of a dual-income couple with a newborn, which would be different from those of a
senior individual on disability benefits. The flexibility and variety of MDH can be used to satisfy
the needs of these diverse communities. Further, these needs and resources may shift with time,
so it is prudent that developers or municipalities work to build a variety of options to fit different
stages of life. In an ideally equitable world, MDH would be spread out throughout a city so that
when a household is looking for a new home, they are given the ability to choose to live in more
places, or even remain in their communities, and not compromise their needs or give up more of
their resources to live in less than adequate housing (Mallach 2009). In other words, middle
housing emphasizes inclusivity to build diverse, strong communities rather than the current
status quo of only allowing in those with enough resources, furthering socioeconomic divides.
One component of the GMA notes that all Washington municipalities must comply with
this state legislation, unless the municipality has their own policies that are stronger in favor of
MH than the bill (GMA 2023). In other words, these edits to the GMA serve as a new baseline
for absolute minimum residential density standards while encouraging Washington
municipalities to build upon it in their own ways. Tacoma is currently in the process of refining
its zoning code as part of a city project called “Home in Tacoma.” It is generally in line with the
state bill but is slightly wider reaching. Home in Tacoma puts special attention on transit
corridors and central business districts as localities for even higher residential density with
additional housing typologies than what is stated in the GMA. Home in Tacoma also aims to
increase commercial density near the central business districts, building up and enhancing
multiple neighborhood hubs for residents to access goods and services, goals not explicitly

11
covered by the GMA. These policies intend to shift Tacoma away from its current driver-centric
climate to one that encourages public transit usage.
While Home in Tacoma is not yet officially law, it is in the public comment phase. The
GMA and Home in Tacoma represent relatively more conservative and more expansive solutions
to the housing crisis. Both are policies that seek to build up housing stock to address affordability
issues, but Home in Tacoma takes it further to have a more specialized, local response for its
population by considering the unique spatial composition of the city in revising its zoning
policies. Home in Tacoma will not be intensely examined in this project, but following the site
selection analysis, there will be a discussion of how it can support the GMA in filling the
housing gap in Tacoma.
This project will apply the policies contained within the updated GMA to simulate a
scenario for MH in Washington State where the bill is the only legislation requiring increased
density. If the only mandate was to double the number of units on all parcels zoned for SFH in
Washington, would that build enough housing for current and future residents? Would it be
enough in Tacoma? To what degree are these goals not met or exceeded? The answers to these
inquiries could motivate state planners and legislators to reassess the GMA and add even more
improvements or expansions for the benefit of future residents.
1.3 Document Overview
The remaining text in this work describe the research, methodology and results of this
site suitability analysis. Chapter Two explores research describing the merits of MDH and
background on analytical techniques used in this project. Chapter Three will describe the
analytical workflow and specific methods such as the analytical hierarchy process. Chapter Four
describes the results of the workflow and discusses implications and shortcomings in the results.

12
Finally, Chapter Five will highlight how the results of this project could translate to the physical
world in addition to describing potential development paths for future iterations of this project.

13
Chapter 2 Related Work
To fully understand why MDH is part of the solution to Tacoma’s shortage of homes, previous
works are explored below. This chapter begins with a brief overview of urban sprawl and its
consequences in the US. This leads to Section 2.2, where a description of MDH will show its
overall benefits, particularly in increasing housing affordability as many Americans struggle to
pay for it. Following the benefits, Section 2.3 explores barriers to implementing MDH, from both
governments and their people. Section 2.4 describes MCDA and its utility in balancing multiple
factors within the context of housing selection. Section 2.5 explores the variety of criteria that
are relevant to selecting a suitable location for MDH.
2.1 Urban Sprawl
Around the same time of public housing’s rise was the explosive growth of suburban
sprawl across the US. Sprawl is the tendency of cities to lower their population densities as their
footprints expand. This idea became part of the public consciousness in the mid-20th century,
when rising incomes and affordable personal transportation allowed significant portions of the
population to migrate away from urban centers to low density neighborhoods of detached homes
(Banai and Depriest 2014). At the time, being far from urban centers, these brand-new
neighborhoods had relatively cheap land values, further encouraging residents to spread out into
suburbs to seek affordable homes. While these households obtained certain residential qualities
that they were seeking, it came at the cost of long daily journeys to and from work and the
deterioration of local community life as people did not spend their time in the communities that
they lived in. This shift in urban lifestyle has led to a slew of consequences across the urban
landscape from increased pollution due to motor vehicle congestion, loss of open space amenities

14
and residential segregation (Nechbya and Walsh 2004). MDH can be considered a counter to
urban sprawl as it promotes the increased density and walkability of neighborhoods. Through the
walkability of neighborhoods, reliance of personal vehicles can be reduced and replaced with
public transit usage.
A sprawled city increases the strain on infrastructure and associated construction and
maintenance costs. Every additional vehicle adds wear and tear to the highway system, which
itself needs to be expanded and maintained to make travel by personal vehicles more viable.
These vehicles need a network of gas stations and repair shops to keep moving. Road
maintenance and ongoing construction could cause additional congestion, compounding each
issue even more. There are additional increased costs in terms of energy use per capita and for
communal amenities (Banai and Depriest 2014). On the other hand, a bus assigned to a specific
route will have a flat operating cost in terms of fuel regardless of the number of riders
transported. Conversely, ten different people with personal vehicles may have radically different
mileage day to day, each drawing upon an individual fuel source while being limited in the
number of passengers. Ultimately, personal vehicles end up consuming more energy with less
efficiency compared to public transportation options.
This is assuming public transit is even available in a sprawled area, as it would cost far
more to maintain a line going to and from the suburbs over having it remain in the urban center.
Transit use is discouraged while car ownership is left as the only option, adding significant
expenses to every household compared to transit passes. This description is representative of a
disconnected transportation network, where its components compete against one another rather
than in harmony.

15
The Washington State Legislature recognized how unmanaged urban growth was hurting
its cities, and passed the Growth Management Act (GMA) in 1990 to mitigate population growth
in urban areas to preserve public, private and natural resources. The GMA directed
municipalities to develop comprehensive, long-term strategies rather than continue allowing
unchecked development to haphazardly fill in space between cities (GMA 2023). Over 30 years
later, housing throughout the region continues to rise in cost, showing that the original GMA was
not a silver bullet to the situation. Thus, an amendment was needed to enhance the previous bill
and direct cities to build additional housing.
2.2 Medium Density Housing Typology
As mentioned in Chapter 1, MDH represents the types of housing that exist on the
spectrum between detached homes and mid-rise apartment buildings (Figure 2). This range of
housing can generally accommodate two up to 19 units per building, with some designs adding
even more units (Table 1) (Parolek 2020). Comparable in size to or moderately larger than most
single-family homes, MDH will allow multiple households to live on the same parcel of land that
only one household previously occupied. For example, on a small residential street of ten houses,
there may be a capacity for as many households. If every home was renovated into a triplex, the
total capacity would increase to 30 households without having to expand any property
boundaries. Even if only half of the homes were renovated, the capacity could increase to 20.
This expansion would lead to the land being more efficiently used rather than continuing to
spread out in order to provide every household with a detached home, yard, garage and needless
extra space. Without MDH, housing the equivalent number of households would take up
multiple blocks. As Tacoma is on a peninsula and is fully bounded by other municipalities,
border expansion is not an option.

16
Further, the intended variety of housing can create a spectrum of unit sizes,
configurations, and features for a household to select from, contributing to a variety of price
points (Parolek 2020). On the example residential street above, some units may be studios
intended for individuals while other units may have multiple bedrooms to serve a growing family
unit. Similar structural designs that minimize initial costs can be used across luxury and standard
housing units, with finishing materials defining the final costs (Adabre and Chan 2019). In other
words, more types of housing can be placed in more locations without continuing to expand
borders and increase sprawl.
Because MDH does not have many hard and fast rules due to a lack of agreed upon
standards or special governing body, municipalities can take the general characteristics of MDH
as described by urban planning researchers and tweak them to better serve their localities. While
this housing type has traditionally served middle income families, adjustments to materials used
and finishes can lower the overall cost for lower earners or increase aesthetics for higher earners
(Parolek 2020). This means that a single base design could be attractive for a range of people to
rent or purchase while reducing costs where possible thanks to the economies of scale.
In his book that positions MDH as a solution to America’s housing crisis, Parolek (2020)
describes MDH as being geared towards walkability, sustainability, and affordability. If similar
design templates are used, bulk materials can be purchased in advance and sourced from a small
number of manufacturers rather than piecemeal, possibly speeding up the construction of new
MDH. This would also help developers as well, as they could draw from a handful of designs
rather than starting from scratch with each project. As developers become more experienced with
certain designs, construction timelines could be further compressed. Businesses and services can
coalesce near these denser neighborhoods to provide amenities and capture consistent customer

17
bases. Residents will be closer to transit options so they may not require cars, removing the
additional costs of car ownership. These last two points together help develop the notion of
social sustainability, where the design of an urban setting is conducive to human interaction to
maintain the social and cultural fabric of a neighborhood (Ancell and Thompson-Fawcett 2008).
People are more likely to speak to their neighbors when they run into them on the sidewalk or on
public transit, rather than being isolated within the walls of their vehicle or home. In the long
term, these interactions can help build a strong, stable community.
Affordability is one of the key motivating factors when looking at strategies to address
housing shortages. Dwindling space in cities have led to skyrocketing home prices and rents
globally. While constructing MDH is by no means the panacea to rising costs, it can be part of an
assortment of policies and standards to increase the affordability of housing. In a study siting
low-income public housing in Buffalo, New York, Saleh and Setyowati (2020) criticize the
placement of existing public housing as most of it occurs in high deprivation neighborhoods.
They describe these neighborhoods as not being able to provide long-term social connections or
job opportunities for low-income households while providing insufficient housing. They argue
that placing subsidized MDH in more affluent areas allows lower-income residents greater
access to sources of income and social capital, public amenities, and greater quality of life. In
other words, placing affordable MDH in higher income areas can contribute to strong social and
economic bases for households to sustain themselves with.
In both Angola and South Africa, studies have investigated integrating accessory
dwelling units(ADUs) with existing detached homes. ADUs can be single rooms up to entire
apartments built as additions to an existing structure or as standalone structures within the
property boundaries and are named as an MH type in the GMA (Mitchell 2020). Depending upon

18
the size of the property, multiple ADUs could be constructed to house additional friends and
family members or generate income for the property owner. A full-sized ADU for a detached
home could double the density of that plot of land (Poulsen and Silverman 2005).
In these studies, the researchers find that the enabling of ADUs allowed for homebuyers
to buy a modular starter home or core unit of a home with the intent to expand the building in the
future. This allowed for lower initial costs and the flexibility to expand the home to meet the
specific needs of the household. Further, rooms or units could be leased out to generate income
for the primary household (Poulsen and Silverman 2005). This potential income is incredibly
important because Tacoma homeowners, such as the 25% that are at cost burdened, will be able
to construct ADUs on their properties to be leased out. Further, MH designs under the GMA can
be modular as well, designed to fit together like building blocks to form multi-unit buildings
rather than trying to find a unique solution for every housing project.
In the Angola study, there was also an exploration of micro loans to help finance these
expansions. Developing MDH does not have to be restricted to commercial builders. Anyone
with property and resources can contribute to the local housing stock. The researchers found a
high rate of success with projects funded through micro loans, indicating that the personal
investment from the homeowner was a highly motivating factor (Mitchell 2020). With the right
standards and supportive structures, those who have struggled to obtain or stay in
homeownership in Tacoma may be able to do that and even provide housing for others by
constructing ADUs. This can all become more efficient by utilizing bulk materials and similar
design templates where possible, reducing costs and planning time to produce housing.

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2.3 Barriers to MDH Development
Broadly speaking, there are two categories of barriers that would inhibit the development
of MDH. The first is government regulations, such as building codes and environmental safety
practices, while the latter is pushback from members of the public (Mallach 2009, Schill 2005).
The government regulations can be further subdivided into necessary regulations and
regulations that add inefficiencies and unnecessary costs. Necessary regulations are ones that
encourage health and safety, such as less flammable or non-toxic building materials, zoning
policies separating residential and industrial areas and environmental protections for natural
spaces (Schill 2005). While these all may add additional costs to homes, this is simply a residual
byproduct as the primary intent of each regulation is some form of protection for human or
ecological life.
Unnecessary regulations add additional costs for minimal return. Regarding building
materials, some housing codes require materials of far higher quality than what could still meet
minimum safety standards. These higher quality materials come at higher price points that are
passed to the final customer. Other regulations may require a developer to consistently meet with
the local government to get approval for certain plans or review environmental studies and the
like. Every interaction with the American government is another interaction with a notoriously
slow bureaucracy (Schill 2005). The regulations and more could be enough to dissuade any
number of real estate developers from initiating a construction project, let alone low-profit public
or affordable housing, which would come with even more regulatory hoops to jump through.
Additionally, interfacing with the government often means dealing with members of the public,
especially those with their own conceptions of how their communities should look and operate.

20
When wide-scale changes are proposed, such as the recent amendments to the GMA,
municipal government spaces can be sources of friction for members of the public. Many people
have found social and economic stability in their lives through existing systems that have not
served everyone equitably. They may see government actions such as housing densification as
threats to their own stability, even if the intention of the new policies is to increase stability for
everyone.
In looking at dense housing perception in London, England, Navarrette et al. (2021)
found that residents living in lower density neighborhoods had a lower tolerance for medium to
high density developments while residents of higher density neighborhoods had a higher
tolerance overall. The researchers found that tolerance increases were connected to residents’
perceptions of London’s housing crisis and design elements of housing listed in their survey. The
results indicated that those that understood or experienced the housing crisis had a greater
acceptance of increased density (Navarrette et al. 2021). Certain design elements increased
acceptance by making more dense housing appear more aesthetically compatible with less dense
housing in the same neighborhood, though the researchers note that design choices did not
significantly sway survey participants This study alone shows how steep of an uphill battle
simply discussing dense housing with residents of a low-density city may be.
2.4 MCDA and Site Selection
Increasing the density of Tacoma does not mean placing MDH throughout low density
neighborhoods simply because that is where they are lacking. As the research by Navarrate et al.
(2021) showed, acceptance of denser levels of housing was tied to the existing density of
neighborhoods of participants. In order to increase density equitably while also encouraging
local residents to be more tolerant of the new housing type, MDH should be made a common

21
sight across the city being placed in all neighborhoods. The housing type should be placed such
that it does not severely inhibit anyone else’s ability to live their life.
Searching for optimal locations for specific uses is called site suitability analysis, where
divisions of land are scored based on a variety of factors (Al-Shalabi et al. 2006). Some factors
are ubiquitous to many searches, such as proximity to food options, crime rate, and local school
performance. Others can be more unique to one’s lifestyle or needs, such as proximity to a
certain school campus, workplace, or house of worship. Factors can align with one another while
others may be conflicting, requiring compromise. In regard to housing, each household will have
their own list of needs and priorities they refer to as they search for a new home. It is a complex
series of decision making and ranking that can grow increasingly complex with many possible
outcomes like the tens of thousands of parcels of Tacoma. This project will represent this
complex decision making in a virtual environment in order to assess Tacoma’s suitability for
MDH through MCDA.
MCDA is a set of analytical techniques that can balance these factors to inform decisions
of best compromise or other priority. This range of techniques is often used in site selection for a
variety of industries, locating optimal locations for businesses, infrastructure, services and much
more in a GIS (Al-Shalabi et al. 2006). MCDA models are often flexible with many user-defined
parameters, allowing the analyst to create custom suitability models with relative ease.
Parameters can be adjusted to produce multiple scenarios depending upon the needs of the
project. Since the 1980s, there have been over 100 published works utilizing a form of MCDA,
with a majority in supply chain management and environmental studies (Razmak and Belaid
2015). The range of works and applications of MCDA highlights how suitable of a family of
methods it is for this project.

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Pursuing a topic similar to this project, Maciel-Cervantes (2016) performed site selection
for high density affordable housing developments in Los Angeles, highlighting the strengths of
MCDA for housing research (Maciel-Cervantes 2016). Maciel-Cervantes integrated a variety of
variables: land use, zoning, cost of land, fair share, employment, public transit, public parks,
public schools, public libraries, healthcare centers, grocery stores, and farmers’ markets. The
project combined these into a set of criteria to represent the various needs of potential residents
more simply. These criteria were weighted and then integrated into site suitability analysis.
Maciel-Cervantes did not identify individual properties but identified multiple regions of varying
suitability and identified restrictions or lack of amenities that limited potential suitability across
Los Angeles. Across multiple iterations of her analysis, Maciel-Cervantes consistently identified
cost of land, job availability and public transit access as among the most important criteria.
These identifications assisted in narrowing the list of criteria for this project following the
compilation of factors from a review of MCDA research. Further, Maciel-Cervantes’
methodology was referenced in the development of workflow of this project, providing essential
guidance in the application of MCDA for housing.
Wei and Ding (2015), utilize MCDA to help locate appropriate sites for housing
developments in a fast-growing urban region in China. In weighting their criteria, the researchers
used the analytical hierarchy process (AHP). AHP is a pairwise weighting method that compares
the relative importance of every possible pair of criteria on a scale, typically 1-9 (Saaty 1987). Its
strengths lie in that it allows people to rank relative importance of criteria without having to
consider every single variable all at once. All of these relative scores are combined and
processed to obtain the final weights. With the large number of criteria this project will attempt

23
to integrate, AHP will be an incredibly useful method to determine and improve the weighting
scheme.
2.5 Criteria Relevant to MDH Site Selection
With an understanding of the function and strength of MCDA regarding studying
housing, research literature was reviewed to develop a comprehensive list of relevant criteria.
These sources primarily described the analysis of housing data while some sources described
best practices in urban planning. Seventy-eight criteria were extracted from twenty-two sources.
Appendix A lists sources by reference ID, which correspond to a table of sources in Appendix B.
Given the number of sources, the criteria are wide reaching, with some describing the
neighborhood level infrastructure, aesthetics of housing and even satisfaction of the project team
building the housing. While representing all 78 of these criteria could assist an extremely indepth investigation of medium density housing suitability, that is far outside the scope and
technical limitations of this project. Including all criteria would also be inappropriate, as not
every criterion is relevant to every location. Further, the large number of factors may obscure the
influence of individual criterion upon the final results.
2.5.1 Access Criteria
Measuring the proximity to a location is a very simple measure of access. Generally, the
further away a destination is from the starting location, the greater the travel cost in terms of
energy and time. With increasing distance, multiple modes of transport may be needed on top of
more travel time. In an urban environment, essential services and businesses may be common,
but unequally distributed across the city. This distribution leads to a landscape where not every
residence has the same access to healthcare, fresh food, and other essential services. In order to
maximize the success of MDH developments, convenient access to these amenities is essential

24
(Chohan et al. 2015). Measuring access to a number of key criteria locations can be thought of as
a proxy indicator to quality of life, where if essential needs like healthcare and education have a
high travel cost requirement, the likelihood of a lower quality of life rises.
For those unable to obtain a personal vehicle, public transportation can be a priceless
lifeline. It can reduce travel barriers to essential needs like healthcare, food, and job
opportunities without the expense of owning and maintaining a vehicle. When considering less
advantaged communities, placing transportation options near housing can act as a means to uplift
households through increasing access to the greater world (Van Atta 2013). An isolated
community will not provide much opportunity for its residents. The relationship between public
transit and dense housing goes the other way as well. Placing denser housing near public transit
encourages use of the system by making the neighborhood much more walkable, increasing
ridership and overall cost efficiency (Maciel-Cervantes 2016). A well-designed system would
only become increasingly efficient with rising ridership, with benefits being passed to riders
through reduced fares and frequent service, among other perks.
Just as important as access to public transportation is proximity to high-capacity roads.
For this project, high-capacity roadways will refer to highways and major and minor arterial
roads. Highways handle massive volumes of traffic and connect distant municipalities while
arterials connect subregions of a city, such as downtown to an outlying residential area. Arterial
roads are usually major thoroughfares through an area, hosting more moderate levels of traffic
alongside public transportation options. Placing housing developments nearer to higher capacity
roadways can mitigate the impacts of increased density by directing traffic down thoroughfares
rather than through low-capacity residential streets. Further, an affordable housing development
in a remote location may not be truly affordable if there is a significant travel cost (Adabre and

25
Chan 2019). For example, a study looking at public housing developments in the 1980s in
Bangkok, Thailand found that among 40,000 plots of land allocated for affordable housing on the
periphery of the city, a majority were left vacant through the end of the millennium. The vacancy
was because the local government did not have the resources to expand road infrastructure,
leading to no plan to build out the road network to support these plots (Thomson and Hardin
2000). While non-paved roads have been informally constructed on some plots of land, these are
insufficient for what was originally intended. The plots of land could have been highly
affordable for low-income households, but the additional travel cost simply outweighed the
discount.
While road infrastructure can connect communities, outdoor recreational spaces can help
bring communities together. Communal spaces such as public parks and community centers
serve as focal points in the neighborhood, where residents can enjoy outdoor activities and gather
with their friends and neighbors (Maciel-Cervantes 2016). Being local landmarks can contribute
to a neighborhood's sense of place, where memories and experiences contribute to how
meaningful a place is to people. For example, an annual summer event at a local park could be
something a neighborhood looks forward to each year. This idea feeds into the concept of
socially sustainable communities, where neighborhoods are designed such that they integrate
economic, environmental, and social objectives (Saleh and Setyowati 2020). A strong sense of
place can contribute to community connectivity and strengthen social capital between residents.
Aside from the health of communities, the actual health of residents is of utmost
importance as well. Medical facilities are not equally distributed across cities and not every
resident is easily able to be seen by a medical professional. Even the availability and level of
health insurance varies wildly among communities, and low-income residents may not be able to

26
have all of their medical problems addressed. While health insurance is beyond the scope of this
project, locating denser housing developments nearer to existing medical facilities will at least
reduce a barrier of transportation in accessing medical services for those residents. Regardless,
every citizen should have reasonable access to healthcare, as sustained poverty and poor living
conditions significantly increase the chances for mental and physical health issues across
generations. These difficulties can create significant barriers for gaining employment,
professional training, education and exiting public welfare programs (Van Atta 2013).
The locations of schools can also impact the success of nearby affordable housing
developments. Multiple articles note access to quality public schools as one of the top factors in
ensuring subsidized housing uplifts communities rather than perpetuating cycles of poverty
(Shargi et al 2021; Saleh and Setywati 2020). While obtaining education does not guarantee a
high quality of life due to greater societal issues, studies have shown a direct relationship
between education, quality neighborhoods and decent housing (Van Atta 2013). People that can
obtain a basic education gain more of a fighting chance in rising out of poverty. Further, public
schools can provide free childcare and meals for low-income households that may struggle to
obtain either. After school programs and socialization with peers can help keep students out of
trouble and build other skills outside the classroom setting. Additionally, school functions as a
form of free childcare for some households, allowing guardians the freedom to work and gain
income for their household. These facts together show the multifaceted public benefit of schools
and their relevance in assessing MDH suitability.
Like schools, nearby childcare facilities are extremely important as they provide a means
for guardians to be able to work while helping provide a foundation for a quality upbringing.
Frequently, low-income households are headed by single parents and have more children than

27
their affluent counterparts (Van Atta 2013). These guardians often have jobs that require them to
be in-person. At the same time, guardians that work from home can easily be distracted by their
young children. If a guardian cannot work, the household may struggle to gain income and the
cycle of poverty will be perpetuated through their children. Further, quality care is essential for
the development and socialization of young children. While providing a quality upbringing is
certainly possible for single parents, it is an extremely difficult challenge to overcome for many
individuals. Van Atta (2013) notes that quality childcare is linked to decreasing generational
poverty and reduced dependence on public housing options. This is not to say that childcare
options do not assist higher income households as well, most households would benefit from
quality childcare, but lower income households stand to benefit the most from this type of
resource. Thus, it is clear that childcare facilities do more than watch children while guardians
are away, they have the potential to change generations.
Another generation-changing criterion is access to food, particularly grocery stores with
fresh food options. Food is essential for life and unfortunately low-income neighborhoods are
often located in food deserts, where affordable fresh food options like grocery stores are eclipsed
by fast food restaurants and convenience stores filled with highly processed foods. As stated
above, these communities may not have many transportation options and will have to pick the
most convenient option for their next meal. Long term impacts of living in food deserts can lead
to significant adverse health outcomes for residents, further compounded by traditionally low
access to medical care and low education around nutrition (Van Atta 2013). This is not to say
that a household living in MDH in a food desert cannot be successful, but they are being dealt a
very poor hand in terms of resources. To set a community up for success, its residents need to be

28
able to fuel and maintain their physical bodies through fresh, affordable, and convenient food
options.
Neighborhood-level business districts (NBDs) can contain all of the public amenities
listed above. They contribute to the walkability of communities by placing a number of shops
and services along higher traffic corridors. Often, these districts occur in the middle of residential
areas, with hundreds or thousands of residences within a short walk. These corridors are welltraveled by cars, but also often possess multiple transit stops, allowing both modes of transport to
be viable. These shops can include grocery stores, corner stores, fast food, boutiques, mechanics,
restaurants, childcare, and many other services. Nearby, parks and community centers can serve
as gathering spots for local events like farmer’s markets. Local residents will be able to more
easily access these businesses to fulfill basic needs rather than having to travel across town to go
to the single location of a big box store, reducing congestion and travel time (Hall and Andrews
2019). Additionally, popular NBDs can attract visitors from other parts of the city or region,
contributing to the flow of money into the local economy. Thus, siting denser developments
nearer NBDs will not only provide access for those residents, but also contribute to local
community investment.
2.5.2 Socioeconomic Criteria
One of the primary drivers of the popularity of MDH is affordability. A household is
considered in poverty when the monthly household income is below the federal poverty
threshold, which is dependent upon household size and number of children (US Census Bureau
2023). For example, a family of four with an annual income below $30,000 would be considered
in poverty (US Department of Health and Human Services 2023). High rates of poverty can be
cyclical, destroying the futures of multiple generations by limiting their ability to get out of

29
public housing or even leave their neighborhoods. Further, the historical placement of public
housing in primarily low-income areas has led to the concentration of poverty in some urban
regions, contributing to an endless negative feedback cycle for those communities (Van Atta
2013).
With the increasing unaffordability of housing, communities can become more deprived
of resources and their residents may turn to criminal means to meet their needs. This economic
and social deprivation can push a narrative of blight, that an area is wholly unsafe because of the
resident criminals. But crime ridden neighborhoods do not necessarily have high numbers of
criminals, just greater proportions of desperate, hungry, poor, and uneducated individuals (Van
Atta 2013). In addition, the presence of crime can lead to a home that does not feel safe and is
not a place a household can rest and thrive in. The physical and emotional strain of living in an
area of common and visible crime cannot be understated. High degrees of crime can contribute to
poor health, low education, and dysfunctional family units throughout a community. On the
neighborhood level scale, social cohesion and worker productivity can break down, driving
disinvestment (Esruq-Labin et al. 2014). This together shows how examining incidences of crime
can be indicative of the deprivation of a neighborhood. Like with the poverty criterion above,
including incidences of crime will inform the prioritization of areas for MDH development
within Tacoma. For this project, areas with lower numbers of crime incidents will be deemed
more suitable for MDH in order to build affordable units in more desirable neighborhoods. This
prioritization is intended to limit the concentration of crime in high-crime areas by shifting high
population density away towards other neighborhoods.
Employment is the key to gaining the resources needed to obtain and hold onto housing.
Higher rates of unemployment can indicate a lack of accessible job opportunities. Given that

30
residents in high poverty areas are typically lower income, the travel barrier of getting work will
feel even larger from their perspective. For example, the cost of a bus pass or to fill a fuel tank
would be a greater proportion of their regular income compared to a higher income individual.
By being denied the ability to work with relative convenience, the social mobility of residents is
reduced and their opportunity to rise out of poverty and exit social services is truncated (Van
Atta 2013). Further, their ability to pay for healthcare, food and even entertainment are severely
reduced, impacting their quality of life. Any gains of income would go towards paying for these
expenses first before even considering looking for better housing. Similar to poverty, both lower
and higher values of unemployment are desirable for this project. Areas with low degrees of
unemployment may have more local job opportunities or be nearer to options to get to job sites.
Conversely, areas of high unemployment will have reduced income, thus building affordable
housing would assist households in obtaining stable housing while searching for work. In both
cases, affordable, dense housing would serve to benefit both types of neighborhoods by
providing more stability for residents and allow them to look for work.
The above socioeconomic criteria all contribute greatly to land value. Generally
speaking, areas of high poverty will be assessed at a lower dollar value than areas of lower
poverty. This evaluation is because of the perception of blight for low-income areas, making
them less desirable to investors and less profitable to sell as property (Thomson and Hardin
2000). At the same time, low value properties can be good locations for MDH because the initial
discount on land can be passed onto residents through rent reductions. Rent reductions can ease
financial burdens for any household to free up money for other expenses. Additionally,
developments with lower total costs may have an easier time getting funding from investors or
through bank loans (Esruq-Labin et al. 2014). This is not to say that MDH cannot be developed

31
in areas with high land value, but those developments may have a more difficult time being
funded or passing any savings onto their future residents. That housing could be dense, but not
necessarily affordable in nature. Thus, for this project, lower land values are more desirable for
developing affordable, dense housing.
The final socioeconomic criteria being considered is air pollution. While this criterion is
part of environmental health, which is broad in nature as it includes air and water pollution,
temperature, tree canopy presence and many other factors, it is also a proxy measure for local
industrial and commercial activity. For this project, PM2.5 data will represent air pollution.
PM2.5 refers to particulate matter 2.5 microns or smaller. These particulates are often the result
of industrial practices, motor emissions and more increasingly, climate change driven wildfires.
By examining PM2.5 readings, a sense of economic traffic and activity can be obtained to
understand areas of higher health risk for residents (Saleh and Setyowati 2020). Ideally, residents
live in areas of low or no health risk while being near to amenities that serve their needs.
2.5.3 Administrative Criteria
The final and the only constraint administrative is Zoning. Zoning is the allowed use for a
particular area or plot of land. This use typically means what type of building is allowed on that
land but may also include activities such as resource extraction or public gathering. A single
zone may be composed of a single parcel or building up to multiple city blocks. For example, a
neighborhood of SFHs would be low density residential. An area with a shopping mall and
assorted businesses would be mixed use commercial. A shopping mall would not be allowed in
the former and a single-family home not allowed in the latter. Zoning can also indicate the
allowed intensity of development, ranging from low density up to high density. Thus, with a map
of zoned districts of Tacoma, areas where residential developments are not allowed can be

32
excluded from the final suitability maps to allow focus on residentially zoned areas.
Additionally, non-residential areas with high suitability scores in the final maps will be briefly
discussed as potential candidates for rezoning in Chapter 5.

33
Chapter 3 Methods and Data
In order to estimate the potential impact of the updated GMA on the city of Tacoma’s housing
stock, MCDA was applied to locate suitable locations for MH. Site suitability analysis using
MCDA has become increasingly significant to urban and regional planning in recent decades,
assisting decision makers with integrating various physical, social, and environmental criteria to
guide policy implementation and decision making. The MCDA family of methods’ ability to
balance these disparate characteristics through the weighting of criteria make it well suited to
represent intricacies of our built environment. This chapter describes how MCDA and related
techniques were used to identify highly suitable locations for MDH in Tacoma.
This project built parallel sets of service area layers to compare walking and driving
access. These layers were supported by AHP, a method of criteria weighting that uses pairwise
comparisons to rank criteria, as opposed to deriving individual weights from literature or
subjectively deciding them. The analysis was completed with weighted overlay, a type of data
combination that integrates the above weighting and data layers to produce composite scores. All
of these methods are overviewed in the general workflow diagram shown in Figure 3.

34
Figure 3. The general workflow for this project
The following sections describe the criteria of interest and the reasoning behind their
inclusion in this study. Then the datasets used to prepare the analytical layers are described.
Section 3.3 shows the workflow for preparing the analytical layers for the weighted sum analysis
with descriptions of the mapped criteria of interest. Following data preparation, the usage of
AHP in devising a weighting scheme for the criteria is included. Finally, how the Weighted Sum
tool integrates the criteria and weighting scheme is described.
3.1 Variable Selection
A comprehensive list of criteria was determined through a literature review of sources
related to the site selection of housing. In total, 78 criteria were extracted from 22 papers

35
(Appendix A and B). A count of papers referencing each individual criteria was noted and used
to reduce the total number of criteria count to fourteen (Table 2). These fourteen criteria
represented factors that would be relevant in most cases of housing MCDA. As shown in the
table, no criterion was noted in a majority of sources, highlighting how difficult it was to pin
down a common set of criteria for housing site selection. A study’s priority of criteria is highly
dependent upon the locality and its people, indicated by the huge number of criteria initially
derived from the literature. It should not be expected that Tacoma, Washington would have
identical priorities to other cities of a similar size in other nations. As such, the criteria noted
below were not necessarily the factors that had the most sources overall, they were the most
relevant to the study area among the more cited factors. Sections 3.1.1 through 3.1.4 briefly
describe how portions of the list of the 78 criteria were reduced to highlight only the most
relevant criteria for this study.

36
Table 2. Selected criteria for analysis
Criterion Category Sources Criterion Category Sources
Public
Transit Access 11 Food Socioeconomic 3
Recreational
Spaces Access 10 Poverty Socioeconomic 6
Schools Access 10 Safety Socioeconomic 7
Healthcare Access 8 Employment Socioeconomic 3
Retail Access 7 Land Cost Socioeconomic 5
Road
Network Access 4 Air Pollution Socioeconomic 4
Childcare Access 3 Zoning Constraint 5
3.1.1 Infrastructure and Amenities
Access to infrastructure or amenities appears to be the most common type of criteria
among literature sources. This group of factors includes criteria such as proximity to retail shops,
availability of garbage collection services and proximity to city center. Consideration of
infrastructure and local amenities is essential to any site suitability project, as the physical and
economic supports afforded by these concepts can have a massive impact on the barrier to
success for a development. However, localities are unique and what is relevant for one location
may not be relevant for others. For example, Wei and Ding (2015), note that proximity to
drinking water and proximity to a water body as very important for the suitability of high-density
housing. While these are important factors to keep in mind regarding the habitability of housing,
they are not at all relevant for this project. As mentioned in chapter 1, Tacoma sits on a peninsula
and thus much of the city is close to the Puget Sound in addition to nonexistent water quality

37
issues. In narrowing the list of criteria, the presence of specific forms of infrastructure or services
in Tacoma determined whether a criterion was relevant or not.
3.1.2 Property Characteristics
Another common type of criteria found in the literature was characteristics of the
physical property. This grouping includes criteria like maintainability of facility, structure age
and land use designation. Unlike infrastructure and amenities above, characteristics of the
property is more limited by the availability of data rather than the presence of a phenomenon.
Adabre and Chan (2019) highlight a number of interesting criteria: maintainability of facility,
project team satisfaction, applicant waiting time for rental units and reduced occurrence of
disputes and litigation and other criteria, totaling 21 for the study. It would be interesting to
include these minute factors to replicate the experiences of builders, renters and owners in the
housing development and selection process, but the lack of publicly available resources makes
that level of exploration of housing highly difficult to accomplish. It could be possible to obtain
or even develop the datasets from the ground up, but it would have likely taken more time and
resources than available for this project. Therefore, availability of datasets helped exclude a
number of criteria.
3.1.3 Population Characteristics
Many criteria also describe characteristics of the local populations. Criteria in this
category include economic diversity, education rates and rates of poverty. In order to develop the
best solution for a locality, the local population needs to be understood and characterized to
determine local needs and possible paths of progress. Across populations, there will likely be
certain commonalities that are ubiquitous to housing, such the local poverty rates. But others,
such as economic diversity of the population, may be more unique to specific localities or

38
questions. For example, Sharghi et al. (2021) was the only study to include economic diversity as
a criterion. In their study, the researchers group their factors into four categories, one of which
being ‘inclusiveness.’ This grouping highlights a specific priority of the researchers to build
housing that is inclusive and accessible to all residents. However, this being the only study to
mention economic diversity indicates that it may not be as important to housing MCDA as
Sharghi et al. believe it to be. With this understanding, the primary qualifier in excluding certain
criteria were the number of sources mentioning them. This helped pare down the list of
population-related criteria to those most commonly accepted as important.
3.1.4 Local Economy
The last general category of the mentioned criteria describes aspects of the local
economy. These factors are all important because current economic conditions can inform what
is possible now and what the future could look like. For example, the availability of
homeownership financial products and local median income can serve as indicators of the
economic health of a locality. A lack of financial products and low incomes would indicate a
high barrier constructing new housing. That high barrier could lead to a shortage of housing in
the future, if not already occurring. Like with the other categories above, all of these criteria are
important, but not all are entirely relevant to this project. The relevance to this project is
determined by a combination of the number of sources mentioning each criterion in addition to
the availability of datasets. Homeownership financial products does sound like something that
would be important for site selection for new housing, but it only mentioned by one source siting
condominiums for low-income households, Esruq-Labin et al. (2014). Condominiums, while
related to MH, are slightly outside the scope of this project and thus would not be relevant.

39
Further, a dataset containing all possible financial products that support homeownership does not
appear to be available, adding difficulty to including that factor in analysis.
3.2 Data
The following section describes the geospatial data layers used in this project. In total,
thirteen datasets were used with most derived from government databases, leaving no question to
their validity and authority (Table 3). The table also notes the purpose of each dataset, with the
importance of each related criteria described in the previous chapter. Two datasets, Grocery
Stores and Daycares, were user created, being derived from Google Maps address listings. These
layer descriptions are of the raw data, before any analytical preparation
Table 3. Project Data
Data Layer Source Data
Type
Spatial
Granularity
Purpose Variables of Interest
City of Tacoma
Boundaries
City of
Tacoma
Polygon City Study Area
Extent
n/a
Tacoma City
Council Districts
City of
Tacoma
Polygon City District Subdivision of
final maps
n/a
Pierce County
Parcels
Pierce
County
Polygon Parcel of Land Parcels to be
analyzed
Land_Cost
Tacoma Zoning
Districts
City of
Tacoma
Polygon Subdivisions of
City Districts
Identification of
residential areas
Zoning
Tacoma Equity
Index 2022
City of
Tacoma
Polygon Census Block
Groups
Source layer for
criteria
% Of households in
poverty, PM2.5
particulates,
Personal Crime,
Property Crime
Pierce Transit - Bus
Stops
Pierce
Transit
Points Point locations County Transit
Locations
Lat, long

40
Pierce County
Road Management
& Hierarchy Class
Pierce
County
Polylines County lines Classifications
for all roads
RoadClass
Tacoma
Neighborhood
Business Districts
City of
Tacoma
Polygons Subdivisions of
Zoning
Districts
Designated
neighborhoodlevel
commercial
areas
NAME
Tacoma Public
Parks &
Community
Centers
City of
Tacoma
Points Point locations City public
parks and
community
centers
Address
Pierce County
Public Schools
Pierce
County
Points Point locations Public schools
county-wide
Address
Pierce County
Public Healthcare
Centers
Pierce
County
Points Point locations Public
healthcare center
county-wide
Address
Tacoma Grocery
Stores
User
created
.csv,
Geocoded
Points
Point locations Tacoma fullservice grocery
stores
Address
Tacoma Childcare
& Daycare
User
created
.csv,
Geocoded
Points
Point locations Tacoma
childcare and
daycare
locations
Address
The City of Tacoma Boundary layer contained the administrative boundaries for the City
of Tacoma. It was used to restrict the extent of any layers with data outside of the city to retain
only pertinent information for the study area. Another layer that delineated portions of the city is
the Tacoma Zoning Districts layer. The dataset contained 46 classifications of allowed uses for
Tacoma, including residential, commercial, and industrial and various combinations thereof. The
classifications also included a secondary class that indicate intensity of development such as high
density residential or medium density mixed use. For this project, the most relevant field was
Base_Zone, which indicated the primary classification of the zone: Residential, Commercial, or

41
Industrial. As the changes to the GMA will apply to all residential zones, there was not a need to
delineate the intensity of development for this analysis.
The Pierce County Parcels layer contained the polygon geometry and records for every
single parcel in the county. Of the variables contained within, the most relevant to this project
was land_value, indicating the parcel’s value not including the value of any structures on it. The
land values ranged from $0 up to $130,349,500, spread across over 300,000 parcels.
Tacoma’s Equity Index was a result of a study ordered by the city to develop metrics to
guide the city’s comprehensive planning process (City of Tacoma 2020). It contained 32
different variables related to general quality of life to build an index score for each census block
group in the city. These index scores indicated the degree to which residents have access to the
resources to satisfy their essential needs. The purpose of the Equity Index is similar to the
purpose of this project, its scoring indicates candidate areas that could benefit from investment
but does not focus wholly on housing as this project does. The holistic nature of the Equity Index
has the benefit of containing data of multiple phenomena within a single dataset.
For this project, the most relevant variables within the Equity dataset were % of
households below 200% poverty level, PM2.5 particulates, property crime count, personal crime
count and unemployment rate. The values for the rate of poverty ranged from 12 up to 91%. The
PM2.5 values ranged from 7.79-8.27 ppb, with unsafe levels ranging from 12-15. The personal
and property crime counts ranged from 23-2404 counts and 0-176 counts, respectively. The final
relevant variable in this dataset was the unemployment rate with values ranging from 0-23%,
though it is important to note that many school age teenagers were included in this count and
thus the unemployment rate of legal adults may be different.

42
The public transportation criterion was represented by a dataset from the county
transportation authority, Pierce Transit. Pierce Transit Bus Stops showed the locations of bus
stops managed by the county transportation agency. Contained within were 1958 bus stops
located across the county. Additional layers from the regional transportation authority and Pierce
Transit were considered for inclusion in this analysis, but the locations of those facilities often
coincided with the locations of Pierce Transit bus stops and were deemed redundant in the
calculation of access to services. The bus dataset contained a field for the address of each stop
location, which was used to geocode the point locations in ArcGIS Pro 3.1.3.
The Road Maintenance and Functional Classifications layer contained essential
information for maintaining the road network in addition to classifications that described the
local hierarchy of roads. This layer covered the full extent of the county with the most relevant
field being FunctionalClass. Within FunctionalClass, the classifications of interest were urban
interstate, urban principal arterial and urban major collector, all higher capacity roads that would
be less negatively impacted by increased population density in comparison to roads lower in the
hierarchy, such as small residential streets and alleys.
Another layer that contained districts of Tacoma was the Neighborhood Business District
Layer. In total, there were fifteen NBDs designated throughout the city. For this dataset, there
were not any values used for calculations, but the names of the districts were important in
discussing neighborhood level in Chapter 5.
The Public Parks and Community Centers, Public Schools, Public Healthcare Centers,
Grocery Stores and Daycare layers contained the point locations for their respective facility
types. With Public Parks and Community Centers, there were 83 locations spread across
Tacoma. For public K-12 schools, there were 57 facilities. 24 hospitals and clinics compose the

43
Public Healthcare dataset. There were 35 full-service grocery stores in that dataset with 99
locations contained in the daycare dataset.
3.3 Data Preparation
The following sections outline the methodology for this site selection project. This
section describes the process of acquiring and preparing the data for analysis, including the
preparation of service area layers to measure access to locations of amenities. The next section
notes the procedure of using pairwise comparison under AHP to devise a criterion weighting
scheme. The final subsection of this chapter briefly mentions the use of the weighted sum tool to
compose the final suitability maps for MDH in Tacoma. Chapter 4 will describe and discuss the
results of the site suitability analysis.
The environment settings within ArcGIS are able to control the outputs of all tools to
ensure consistency of projected coordinate system, extent and raster cell size, among other
settings as needed for a project. The following selections for the environment settings are
summarized in Figure 4. Based on the sources of the datasets from government portals, all data
layers were transformed to NAD 1983 HARN StatePlane Washington South FIPS 4602 (US
Feet) prior to analysis. The snap raster option forces a tool output to align to its extent, ensuring
that outputs line up with one another. A rasterized version of the city limits polygon layer was
used as the snap raster. Finally, the raster cell size determines the resolution of any raster
outputs, with larger numbers being coarser in resolution and aggregating values more and
smaller values being finer in resolution and not aggregating values as much. 90ft was determined
to be an appropriate size, based on the 30m digital elevation model cell size. On the ground, a
90ft cell is approximately 1-2 parcels in size, making it well suited to represent the suitability
scores for parcels or clusters of parcels. On a larger scale, a 90ft cell size would allow for

44
differences within neighborhoods to be observed, more precisely showing suitability across the
city.
Figure 4. The modified environment settings for this project
The relevant datasets were first downloaded from the government GIS data portals noted
in Table 3. Grocery Store and Childcare location datasets were not readily available and needed
to be custom made. Addresses were obtained from Google listings and input into a .csv
spreadsheet. With all data layers obtained, they were imported into ArcGIS Pro.
Before the data can be used in any analysis, it must be cleaned before being prepared for
a specific analytical technique (Figure 5). First, the Grocery Store and Childcare spreadsheets
were geocoded using the Geocode Addresses tool, backed by Esri’s ArcGIS World Geocoding
Service, which is a database of all street addresses of participating nations. These data sets
contained 35 and 99 locations, respectively. Next, each dataset was closely examined for missing
or null values, removing any occurrences of each. For the roads layer, higher capacity roads
classified as highways or major arterials were isolated to a new layer. The boundaries of NBDs
were converted into points at the center of each district to more simply note the locations of the
retail centers. Regarding public transportation, the majority of transit options around Tacoma are

45
related to the county bus whereas regional rail and commuter lot options number in the area in
single digits, not including stations outside the county. Many regional transit locations within
Pierce County are coincident with county bus stops. With the gap in the number of locations
between transit authorities in Tacoma, it did not appear that differentiating the transit location
types would be particularly meaningful. Thus, only county bus stops were used as a measure of
access to public transportation. Finally, all of the datasets were restricted to the spatial extent of
Tacoma using the city boundaries to eliminate all extraneous data.
Figure 5. Workflow diagram of dataset cleaning
The layers were then prepared such that their spatial layouts aligned with one another
when input into the Weighted Sum tool. First, separate service area layers were created for each

46
of the eight access criteria: Public Transit, Recreation Spaces, Schools, High-Capacity Roads,
Medical Care, Retail, Childcare, Food Access (Figure 6). Each of the point layers were imported
into their respective service area layers as facility locations. One copy was then made of each
service area layer in order to later create separate sets of walksheds and drivesheds that would
eventually represent travel time by walking and driving. Next, the poverty rate, personal crime
counts, property crime counts, unemployment rate and PM2.5 fields within the Tacoma Equity
Index 2022 data layer were individually selected and then the Selection to New Layer function
used to copy each field to new individual layers containing census block groups. These copies
needed to be made as the data table of the original Equity Index layer was locked against
modification. At this point, the data layers were cleaned and ready for processing to be combined
in weighted overlay.

47
Figure 6. Preparation of cleaned data layers for MCDA
The two sets of crime counts were further modified to build a composite metric indicating
Safety. At this stage, both layers were in vector polygon format and needed to be rasterized in
order to be compatible with the Weighted Sum tool. The Weighted Sum tool is simple but
powerful, it multiplies the values of the input rasters by their respective weight values and then
sums all layers to produce an output raster. Once rasterized with the polygon to raster tool, they
were reclassified using the scheme noted in Table 4. With MCDA, reclassification of values to a
common scale is essential as different data layers often represent different phenomena and the
combination of their raw values may be entirely meaningless. For example, summing
temperature data in Fahrenheit with literacy rates in % would not produce values that mean

48
anything. Additionally, desirable values across different phenomena may be different, with lower
values preferable for one and higher values preferable for another. Reclassification helps
integrate these different factors in a coherent fashion.
Table 4. Reclassification scheme for counts of crime incidences
Dataset Property
Crime
Personal
Crime
Range 23-2404 0-176
5 (most suitable) 0-150 0-20
4 150-300 20-40
3 300-450 40-60
2 450-600 60-80
1 (least suitable) 600-800 80-100
0 (not suitable) 800+ 100+
The reclassified crime rasters were then input into the Weighted Sum tool with equal
weighting. Equal weighting for crime was selected because while personal crime could be
considered more important due to bodily harm compared to theft or property damage, the
number of incidences of property crime were an order of magnitude larger than personal crime.
To represent both types of crime without devaluing either, equal weighting was deemed most
appropriate. The output of this Weighted Sum of the crime data is a raster layer that is already
ready for the final Weighted Sum analysis with the other criteria layers.
Returning to the two sets of service area layers, the Network Analyst Extension in
ArcGIS Pro was used to build walksheds and drivesheds to prepare for the final combination of
layers Weighted Sum (Figure 7). The Network Analyst Extension is a powerful tool in routing

49
problems as it is able to utilize the real-world road network in calculating travel time and
distance to and from locations. For this project, the towards facilities parameter was selected to
create polygons indicating how long it would take to reach a facility location. A total of 60
minutes of travel time was designated to create large service areas. Large polygons are needed
because the Weighted Sum tool output is the intersection of the composite layers. If that
intersection were to be small in area, the output map would reflect that small intersection and
possibly not cover much of the city.
Figure 7. Preparing the criteria layers for input into the Weighted Sum tool
The first fifteen minutes of travel time were broken up in increments of three minutes,
with decreasing suitability as time increased. Anything beyond fifteen minutes would be

50
considered unsuitable, but still included in calculations to build an as complete map as possible.
Fifteen minutes was selected as the suitability cutoff to support the idea of the fifteen-minute
city, an urban planning concept where most essential resources in a neighborhood are generally
within a short radius of a home by walking or biking. This was not saying that all resources in a
city are no more than 15 minutes away, just the day-to-day resources such as laundromats, corner
stores and pharmacies. By intentionally placing these frequently used resources within walking
distance, reliance on motor vehicles, and thus associated traffic, could be reduced. In a
discussion of policies enabling fifteen-minute cities, Loader (2023) writes that these
neighborhoods become places of dwelling rather than thoroughfares to the next destination. As
mentioned in the history of sprawl in Chapter 2, the suburbanization of America has turned
residential areas into places one just sleeps in, not necessarily where one spends their time
interacts with people around them. The concepts of home and community have become reduced
in strength, which MDH can try to address. Finally, walking and driving were designated as the
modes of transport for their respective sets of service area layers. The Network Analyst tool was
run for each service area layer, leading to two sets of layers containing walksheds or drivesheds
around each facility that indicated how long it would take to arrive at that particular facility.
The final step of data preparation was the rasterization and reclassification of the
remaining criteria polygon layers. The two sets of service areas in addition to the poverty,
unemployment, PM2.5 and land value layers were rasterized using the Polygon to Raster tool. As
the snap raster and raster cell size environment settings were previously set, all resulting outputs
automatically aligned with one another. These data layers were then reclassified using the
schemes noted in Table 5. Values reclassified to four or five indicated higher suitability and
values reclassified to one or two indicate lower suitability. At this point of the methodology,

51
there were two sets of rasterized and reclassified service area layers in addition to Poverty,
Unemployment, Air Pollution, Land Cost and Crime rasters that were ready for the Weighted
Sum tool. (Navarrette et al. 2021).
Table 5. Reclassification schemes for the criteria layers before Weighted Sum input
Criteria Access
(8)
Poverty Unemployment PM 2.5 Land Cost
Value Range 0-60 min 0-63% 0-23% 7.79-8.27
ppb
USD$0-
130,349,500
Suitability
Score
5 (more
suitable)
0-3 0-
10%,
>50%
0-5%,
>20%
0-6 0-200,000
4 3-6 10-
30%
5-10%,
15-20%
6-7 200,000- 400,000
3 6-9 30-
50%
10-15% 7-8 $400,000-
1,000,000
2 9-12 n/a n/a 8-9 1,000,000-
4,000,000
1 (least suitable) 12-15 n/a n/a 9-10 4,000,000
0 (not suitable) >15 n/a n/a >10 >50,000,000
3.4 Analytical Hierarchy Process
With the data layers fully prepared for input into the weighted overlay analysis, a
weighting scheme was needed to score parcels of MDH suitability. Weighting determines a
variable’s influence on final calculations, with higher weights indicating greater influence and
vice versa. This project determined weighting using the analytical hierarchy process, a structured
technique of organizing complex decisions developed by mathematician Thomas Saaty (1987).
AHP is a general technique that can be applied in any field where multiple criteria, decisions and

52
outcomes are being considered: business, sports, organizational management, event planning and
especially in MCDA GIS problems.
AHP works by performing pairwise comparisons of every possible pair of criteria.
Between each pair, the more important criterion in the context of the research question is
determined with a value between 1 and 9 indicating the intensity of that importance (Saaty
1987). A value of one indicates equal importance of both criteria. Values of three, five, seven
and nine indicate increasing magnitudes of dominance of one criterion over the other, with two,
four, six and eight representing interim levels of dominance (Table 6). The strength of this
method was in focusing only on one pair at a time rather than trying to consider every criterion
simultaneously, which only grows more difficult with greater numbers of criteria. In the case of
this project, ranking the thirteen criteria, their possible interactions with one another and all
possible outcomes for all of Tacoma would be far too tedious and difficult to interpret. AHP
provided a consistent and repeatable methodology that could easily be scaled up or down to fit
the needs of the project.
Table 6. The AHP scale of importance for pairwise comparisons
Intensity of
Importance from
Absolute Scale
Definition Explanation
1 Equal importance Two activities contribute equally
to the objective
3 Moderate importance of
one over another
Experience and judgment favor
one activity over another
5 Essential or strong
importance
Experience the judgment strongly
favor one activity over another
7 Very strong importance An activity is strongly favored, and
its dominance demonstrated in
practice

53
9 Extreme importance The evidence factoring one activity
over another is of the highest order
of magnitude
2, 4, 6, 8 Intermediate values
between the two
adjacent judgements
When compromise is needed
Source: Saaty, 1987
To perform the pairwise comparisons, all of the criteria except for Zoning were input into
a 13x13 comparison matrix (Figure 8). An example comparison matrix is provided in Appendix
C. Zoning would be used to exclude any non-residential areas from the weighted sum outputs to
produce the final suitability maps. A total of 78 comparisons were completed with the matrix.
One way to check the weighting scheme is through the calculation of the consistency ratio (CR).
CR indicates how random the ratings appear to be, with high values indicating more randomness
and inconsistency. Inconsistency indicates the stability of ratings. For example, if the initial
ratings of the user indicate A>B and B>C, then by the transitive property one would think A>C.
But if the later ratings indicate that B>A and C>B, then the ratings are inconsistent because the
logic is no longer compatible. In other words, high degrees of consistency indicate there is a
cohesive logic that runs through all of the ratings. Ratings need to follow the same flow of logic,
otherwise their combination or comparison would be meaningless. Non-intuitively, CR values
range from 0.00 to 1.00, with higher values indicating inconsistency and lower values indicating
consistency.

54
Figure 8. Production of a weighting scheme using AHP
Saaty does state some inconsistency is acceptable and even desired because we should
not always assume all existing knowledge is itself consistent and complete. A minor amount of
inconsistency would allow for new knowledge and perspectives to be introduced and ideas
adjusted rather than assuming a totally static understanding of the world. In other words, we
cannot assume we know everything that there is to know. Saaty (1987) writes that the ideal CR
value is equal to or less than 0.10, indicating 10% or less inconsistency.
To calculate the CR, the geometric mean of each row from the completed matrix was
calculated to produce a single column of thirteen values. The geometric mean values were
normalized by dividing each value by the sum of all the means. The normalized means were then
matrix multiplied against each row of the comparison matrix to produce another column of 13
values. These thirteen values were then normalized by their corresponding priority value cells to
produce a 13x1 matrix called the eigen vector. the values of the eigen vector are averaged to
produce a single value called the principal eigen value. the principal eigen value is used to

55
calculate the consistency index, which itself is used to calculate the CR. The formula to calculate
the consistency index is:
�� = (�� − �)
(� − 1)
where CI is the consistency index, PEV is the principal eigen value and n the dimension
of the comparison matrix. The formula to calculate the consistency ratio is:
�� = ��
�!
where CR is the consistency ratio, CI is the consistency index and nr is a corresponding
value in a randomly generated matrix. For this project, nr was the thirteenth value in a random
matrix. The above AHP procedure and calculations were repeated until the CR value dropped
below 0.10 indicating adequate consistency of the ratings.
With the weighted scheme checked for consistency, the final weighting step was
validation by subject matter experts familiar with the local housing market for Tacoma. Building
developers, city and county officials, real estate experts and housing providers were contacted
with a table of ranked criteria for review and feedback. A total of sixteen experts were contacted
for validation with three responses from a university professor, city of Tacoma planner and
residential realtor. These responses were considered when adjusting weight values. Table 7shows
the weighting scheme used for this project.
Table 7. Weighting scheme for Weighted Sum
Criterion Weight Criterion Weight
Public Transit 0.0849 Food 0.1471
Recreational Spaces 0.0233 Poverty 0.1094
Schools 0.0325 Safety 0.1209

56
Healthcare 0.0623 Unemployment 0.1030
Retail 0.0377 Land Cost 0.0503
Road Network 0.1581 Air Pollution 0.0446
Childcare 0.0257 Zoning N/A
3.5 Weighted Overlay
At this point, weighted overlay analysis could be conducted. As mentioned above, the
ArcGIS Pro Weighted Sum tool takes the input rasters and weighting scheme to produce a raster
with each cell indicating the summed values of all input, overlain raster cells. First, the eight
service area layers with walksheds were input with the five layers of socioeconomic criteria and
the criteria weights into the Weighted Sum tool (Figure 9). Figure 9. Final combination of data
layers to produce the MDH suitability resultsThe process was repeated with the driveshed layers.
This step produced two suitability rasters differentiating modes of transport. Using the zoning
layer and the extract by mask tool, any non-residential zones were subtracted from the map,
leaving behind only residential areas. The non-residential zones were assigned a deep red color
to clearly communicate their lack of suitability on both outputs. This produced the two final
suitability maps, which contained scores of MDH suitability for all residential areas of Tacoma.
The labels of the NBD layer were overlayed over these suitability maps, providing landmarks to
point to in the discussion of neighborhood level trends. Finally, these suitability maps were
spatially joined to the parcel layer in order to assign scores to specific properties and produce
housing unit counts.

57
Figure 9. Final combination of data layers to produce the MDH suitability results

58
Chapter 4 Results
This chapter contains the findings for the site suitability analysis for MDH for the city of
Tacoma, Washington. Section 4.1 establishes the spatial landscape of Tacoma through the
descriptions of the mapped criteria of interest. Then, section 4.2 contains assessments of the
access-based criteria through the description of service areas representing travel time by driving
and walking. Then, the outputs of the weighted sum analysis are described. These findings are
more deeply interrogated in Sections 4.4 and 4.5, describing trends and limitations of the laters
results of this project.
4.1 Spatial Qualities of Criteria of Interest
This section will overview the fourteen mapped criteria layers in order to communicate
the current landscape of Tacoma in regard to MDH suitability. The original data values are
mapped and described to highlight the ranges of values for each dataset that are not as apparent
when mapping reclassified values as shown in Table 7. Zoning will first be described, followed
by the socioeconomic criteria and lastly the access-based criteria.
Figure 10 shows Tacoma’s zoning structure, being color coded with Residential and
Mixed Residential in blue and all others in red. Approximately three quarters of the city appears
to be zoned for residential use with most occurring west and south of the Port of Tacoma as part
of the main city. The outlying suburb of Brown’s Point comprises the residential areas to the
northeast. The Port of Tacoma is the large red area partitioning the eastern part of the city,
containing various industrial and commercial activities. Downtown Tacoma is just to the west of
the Port, with a mix of residential, commercial, and municipal government facilities. At the city’s
geographic center and southwestward along Interstate 5 are the Tacoma Mall and South Tacoma

59
Way areas that contain a massive amount of commercial activity including retail, car dealerships,
restaurants, and shopping centers. Interspersed throughout all of the residential neighborhoods
are NBDs, retail centers and businesses of varying sizes. It should be noted that certain
amenities, such as schools and public parks are categorized as residential zones and not their own
categories. Thus, for Figure 10, these amenities are hidden in the vast residential areas.
Figure 10. Tacoma’s residentially zoned areas
This map very clearly communicates what areas of the city would be impacted as a result
of the GMA’s changes to housing policies. As is shown, every neighborhood has the potential to
host MDH while being in some proximity to the businesses and services that occur nearby. The

60
final suitability maps will reveal to what degree MDH may be successful in different
neighborhoods.
In any sort of construction project, costs must be considered, particularly in land value.
The availability and affordability of land parcels could impact the final price to the end user of
whatever structure is built. This is highly relevant in considering where to build denser,
affordable housing types. Figure 11 maps the land values of every parcel in Tacoma alongside
counts of the number of parcels within each price range. Very obviously, most individual land
parcels are under $1 million, occurring throughout neighborhoods of primarily SFH. The higher
value parcels tend to be industrial or commercial in nature or facilities managed by the city, such
as Point Defiance Park on the northwest peninsula of Tacoma. At this scale, it is difficult to
perceive patterns in the residential parcels under $1 million, but it can be said that a majority of
residential parcels are relatively affordable for a commercial builder and that there is no shortage
of options in most neighborhoods.

61
Figure 11. Land values for all Tacoma parcels.
Figure 12 shows the unemployment rate for individuals sixteen and older within each of
the census block groups in Tacoma. This unemployment data was derived from 2018 counts,
where the annual average unemployment was 5.2% (City of Tacoma 2023). Generally, many of
the census block groups fall between 0 and 5%, aligning with the annual average. The northern
half of the city appears to have a greater proportion of employed residents than the southern half,
as evidenced by the larger cluster of census block groups with 0-2% unemployment. The
southern half of Tacoma, while containing some census block groups with 0-2%, possesses more
block groups with 13-23% unemployment. Further, the low unemployment areas are broken up
by higher unemployment areas in the south whereas they appear to be more consistently low in

62
the north. Both regions possess the full range of possible values, indicating that there may be
underlying reasons for underemployment among the population. This may be because of a large
number of seniors unable to work or teenagers sixteen and older who are not working, but this is
unclear at this current stage.
Figure 12. Unemployment rate by census block group
Property crime incidences are mapped in Figure 13. What is immediately obvious is that
from the Port of Tacoma extending westward through downtown to South Tacoma Way is a
dividing line of crime for the city. This dividing zone makes sense because the Port and South
Tacoma Way are areas of heavy commercial and industrial activity and thus there is a greater
concentration of goods and equipment criminals can try to seize. As distance increases going

63
north or south, crime incidences decrease. The northern portion of the city sees a more
significant decrease in incidences with distance compared to the southern portion. The southern
portion does see a slight decrease, but the surface levels off rather than continuing to decline.
Figure 13. Property crime counts by census block group
Personal crime incidents follow a similar pattern to property crime, where increasing
distance from the dividing lines trends towards lower crime counts (Figure 14). Minor deviations
include slightly elevated personal crime counts around the downtown area with a much more
rapid drop in incidences going northward. To the south, incidences do decrease relatively slowly
before increasing as the southern border of the city is approached. This indicates the influence of
adjoining municipalities on occurrences of crime. These crime incidences are relevant to the

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suitability for MDH because increasing population density usually results in increased potential
for crime, which would be particularly concerning for areas already experiencing elevated crime
rates. Shifting population density to lower crime areas will not necessarily solve crime but can
mitigate the growth of crime across neighborhoods.
Figure 14. Personal Crime counts for Tacoma.
Figure 15 shows the proportion of the population of each census block group considered
in poverty. Similar to the crime incidences, the high poverty areas of the Port of Tacoma to
South Tacoma Way split up the city. To the north, there is a gradual decrease in poverty to the
lowest tier. To the south, there is a minor decrease before a slight increase as the southern and
eastern city limits are approached. Considering the 2022 unemployment rate of 5.2%, the
significant number of block groups with over 24% poverty indicates many employed residents

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are not earning enough money to cover expenses, particularly in the central and southern
portions of Tacoma. Like with crime incidences above, increasing population density in high
poverty areas may result in the concentration of poverty, mitigating the positive impact of
affordable housing developments. Building these affordable units in low poverty, low density
areas can contribute to the economic diversity of localities.
Figure 15. Proportion of census block groups earning below the poverty level
Air is inescapable and studying air pollution is one of many measures in understanding if
living in a location is hazardous to one’s health or not. Whether siting housing or a new office
building, if humans spend significant time there, hazards need to be accounted for. For Tacoma,
air pollution is represented by 2.5mm Particulate Matter concentrations (Figure 16). The EPA

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states the threshold to unsafe concentrations of particulates is 12ppb, thus the city is entirely safe
in this aspect as the highest levels only reach 8.27ppb. The map displays a very simple trend
where the Port of Tacoma has the highest concentration levels with values decreasing with
distance. The exception to this trend is around the South Tacoma Way area, which has slightly
elevated levels compared to its surroundings. Given the known industrial and commercial
activities of both areas, this is an unsurprising pattern. Further, car emissions can contribute to
PM2.5 readings, thus high traffic areas may see higher readings. The decrease in concentration is
more significant to the northwest as the Puget Sound may play a role in dispersing the
particulates. The smaller decrease to the south is likely due to the activities of adjoining
municipalities.

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Figure 16. Airborne particulate levels for Tacoma
Figure 17 displays the hundreds of Pierce Transit bus stops throughout Tacoma. These
bus routes occur primarily along local arterial roads with many converging on downtown, where
transit depots allow riders to access light rail, train, regional bus services and other county routes
that connect the entire Puget Sound region. With the increased density provided by MDH, these
routes will connect residents to the greater world without the need for a motor vehicle. Gaps in
coverage appear to be in the Port of Tacoma and along the northwestern coast, where there are
stretches of undeveloped, forested land. Brown’s Point north of the Port also has a relatively
small number of bus stops in comparison to other neighborhoods in the city, where multiple bus
routes cross through neighborhoods. It is likely residents of these bus stop-poor areas will have a

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higher barrier to access the public transportation system. Regardless, a significant portion of
Tacoma is in proximity to transit options.
Figure 17. Local bus stops in Tacoma
The Tacoma school district manages dozens of public schools throughout the city, shown
in Figure 18. Most residential parcels appear to be within a few miles of one or more public
schools. This high level of access is compounded by the district’s policy of allowing students to
attend any school at their grade level, with bus services being provided for any students in need.
There do appear to be areas with gaps of coverage with no schools, but these occur in industrial
and commercial districts and thus would be inappropriate to host public schools. This map does
not account for any universities or private institutions.

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Figure 18. Locations of public K-12 schools
Locations of daycares for young children are mapped in Figure 19. Dozens of these
facilities occur all throughout the city. There does not seem to be a discernable pattern in their
locations, especially given that the facilities could be commercial daycare centers or hosted in
private residences. There may be some clustering near retail and office locations, such as in
downtown, but the relationship does not appear strong nor consistent across the entire dataset.
Overall, this indicates a decent level of access for much of the city, with multiple options within
a reasonable distance of most homes. Capacities, fees, and specialties were not publicly available
for all facilities, so it is unclear the level of access for specific neighborhoods or socioeconomic
groups.

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Figure 19. Daycare facilities throughout Tacoma
High-capacity roads in Tacoma are noted in Figure 20, alongside generated points at onemile intervals along each road. The points were used as a simplistic representation of the roads in
the service area analysis of the access criteria due to software limitations. A noticeably denser
network of high-capacity roads is evident in the Port of Tacoma and the southern half of the city
compared to the northern half. This distribution of the road network is unsurprising as the North
End is the oldest area of the city and thus the infrastructure was designed for different types and
levels of traffic compared to the more modern infrastructure to the south. The more robust road
network in the south puts significantly more residential parcels within reasonable access to a
high-capacity roadway. Placing MDH units nearer to these types of roadways will help ensure

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access to the public transit that occurs along these roads alongside directing motor vehicles away
from low-capacity streets.
Figure 20. Network of Tacoma's higher capacity roadways
In denser living environments where private outdoor space is limited, having public
spaces to recreate is essential to both the mental and physical health of a community. Figure 21
displays the locations of all public parks and community centers managed by Tacoma’s Parks
Department. It is immediately apparent that there are dozens of facilities throughout the city.
There does appear to be some clustering of facilities nearer to downtown, which may support the
higher density communities in the area. There also does seem to be coincidence of some facilities
throughout Tacoma, where known community centers occur adjacent to public parks. A strong

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trend in the distribution of these facilities is not readily apparent, but it does seem that most
residential areas have at least one option nearby. Further, this map does not note other types of
spaces that the public may access, such as a public school field when school is not in session or
communal spaces in housing developments. Given the high number of schools described
previously, there are likely an abundance of options of residents to enjoy outdoor recreation.
Figure 21. Tacoma's parks and community centers
The City of Tacoma has fifteen designated locations as business districts for their
surrounding neighborhoods, shown in Figure 22. These areas contain numerous retail businesses,
services and food options for nearby residents and visitors. Also mapped are the center points of
each NBD, used as their primary locations when conducting the service area analysis. With the

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increased density of MDH, these businesses can be supported by larger populations while the
residents enjoy the benefits of having a variety of services extremely close to them. Some NBDs
such as Proctor and Stadium already possess multiple large apartment buildings with dozens of
active storefronts. The NBDs appear to be closer in proximity to one another closer to downtown
and spread out with distance. Additionally, many NBDs occur along major roadways, providing
access to both drivers and transit riders. Some NBDs even extend along these roadways,
adopting somewhat rectangular forms, such as Pacific and 6th Ave.
Figure 22. Designated neighborhood business districts in Tacoma
Figure 23 shows the public medical facilities in Tacoma. Immediately, it is clear that this
type of facility is sparse in the city. Facilities are primarily concentrated near downtown and

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closer to the Port of Tacoma. Outside of these clusters, some individual facilities exist, but they
appear isolated from one another. There is a complete lack of facilities in the southwestern
portion of the city. While this map indicates healthcare access may be low, this map does not
include private practices or specialist offices and so actual access may look different.
Figure 23. Public medical facilities in Tacoma
Access to fresh produce and groceries is one aspect of ensuring residents get the nutrients
they need to maintain their mental and physical health. Tacoma’s full-service grocery stores are
mapped in Figure 24. Grocery stores occur in every neighborhood, but they appear to be less
frequent to the northeast and southwest. They tend to occur in proximity to local highways and
arterials while completely avoiding downtown and the Port of Tacoma. It is difficult to discern

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any differences in distribution between the northern and southern halves of the city; both areas
display patterns of both clustering and dispersion of their grocery store locations. A number of
these stores do appear coincident with the locations of the NBDs noted above. Something to note
is that this map includes American, International and specialty groceries and thus the nearest
store may not serve one’s dietary needs. For example, one Halal grocery is included on this map.
It may serve well for those nearby with that diet, but less so for those without food restrictions.
At the same time, this Halal grocery may be the only option for residents with that diet in other
areas of the city. Thus, actual food access may look different than just the locations of grocery
stores.
Figure 24. Full-service grocery stores in Tacoma

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4.2 Service Area Results
This subsection describes the sixteen service area maps created for the eight criteria
related to access to amenities. Both walking and driving modes of transportation were used to
understand how those modalities impact the final suitability scores for MDH. Each criterion is
described with a pair of maps, the former being the walkshed while the latter is the driveshed.
Overall, the walksheds are much more variable than their counterparts, with clear peaks and
valleys in coverage across most maps. Access to these multiple amenities by driving is much
greater, often with multiple options available for much of the city. Descriptions of the results of
the weighted sum analysis follow this section.
Figure 25 shows the walksheds for daycare locations while Figure 26 shows the
drivesheds. On the walking map, it does appear that most residential areas in Tacoma are within
a fifteen-minute walk of at least one daycare location. There are clear dips in access in areas
where daycares are more sparse and closer to the city limits. With Figure 26, there is a clear
difference in access, with a majority of the city being within a three-minute drive of multiple
daycare occasions. Among residential areas, the highest travel time by driving only reaches
about nine minutes. Among both maps, areas with poor or lack of access include the Port and
known parks and wooded areas.

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Figure 25. Walksheds to daycare facilities
Figure 26. Drivesheds to daycare facilities

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The service area maps for the full-service grocery stores reveal significant differences in
access to fresh produce for walkers and drivers (Figure 27Figure 28). Figure 27 shows large
portions of multiple neighborhoods being further than a fifteen-minute walk from the nearest
grocery store. Clustering of stores in the southern half of the city shows a significant area of
decent access, but the lack of stores closer to city limits contributes to poor access at the city’s
physical periphery. Further, it is very clear that fresh food access is poor for much of the
downtown area, indicating residents must leave their neighborhoods to obtain produce. Access is
particularly poor to the northeast in Browns Point, where only two grocery stores serve the entire
suburb, though there may be additional stores in the adjacent municipality. The driveshed map
shows a similar, but less intense trend to Figure 27, where the geographic core of the city has
very good access with travel time increasing towards the city boundaries (Figure 28).
Figure 27. Walksheds to grocery stores

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Figure 28. Drivesheds to grocery stores
Differences in the transportation modalities are further highlighted in Figure 29 and
Figure 30, which regard access to public medical facilities. The few facilities in Tacoma lead to
extremely poor access for walkers in all neighborhoods. Most areas are greater than fifteen
minutes walking to the nearest facility. In creating the service areas, the upper travel time cutoff
was set to 60 minutes and thus any unfilled areas within city limits are more than an hour’s walk
to medical care. Access is improved when examining Figure 30. Most residential areas are within
a fifteen-minute drive to a medical center. Access is relatively poor on both maps to the
southwest due to the lack of facilities.

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Figure 29. Walksheds to public clinics
Figure 30. Drivesheds to public clinics

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The high-capacity road service areas look very different from one another but do display
some similarities when looking at the least suitable areas for both maps. The surface shown in
Figure 31 appears highly variable and erratic with many rises and falls in travel time in short
spans of space. There is a clear increase in travel time closer to Old Town. It is also evident that
significant portions of most residential areas are within fifteen minutes walking to the nearest
high-capacity roadway. When examining the overall footprint of the fifteen-minute service areas
for walking, it is observed that they are very similar to the footprint of the three-minute service
areas for driving (Figure 32). The driveshed map also shows increasing travel time nearer to Old
Town and along the city peripheries, though not as dramatic an increase. Thus, both modalities
indicate moderate or high access for much of the city and agree with areas of low access. But it is
clear driving has a significant advantage over walking.
Figure 31. Walksheds to high-capacity roads

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Figure 32. Drivesheds to high-capacity roads
With the large number of public schools spread through Tacoma, a clear majority of
residences are within a fifteen-minute walk to one or more neighborhood schools (Figure 33).
There are areas of increased travel time beyond a fifteen-minute walk, but these locations are
nearer to commercial or industrial facilities with few or no residences. As with the above criteria,
the driveshed map reveals somewhat similar patterns to its walking counterpart while
communicating a high level of access with low calculated travel times to schools for most
residential areas Figure 34). Both maps show an increasing travel time toward the city
peripheries, an expected pattern as schools would not be placed near city limits.

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Figure 33. Walksheds to public schools
Figure 34. Drivesheds to public schools

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As noted in the previous section, there are hundreds of bus stops in Tacoma. Creating
multilevel service areas for every Tacoma bus stop would have exceeded available resources and
so only 15 and 60 minutes were used as cutoffs. This leads to maps that sacrifice finer detail
while still noting areas no more than fifteen minutes away from a stop. This tradeoff was deemed
acceptable because public transit is meant to be used as a system, thus access to one stop
indicates access to the entire transit network. Figure 35 shows the walksheds, where most of the
city is in reasonable proximity to at least one bus stop. Lower access primarily occurs near city
boundaries and the Port of Tacoma. The driveshed map shows that the entire city is close to bus
stops, aside from waterways and wooded areas (Figure 36). While these figures appear binary in
nature, that is not the true identity of the data. The data is being represented more simply to
accommodate technical limitations.
Figure 35. Walksheds to bus stops

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Figure 36. Drivesheds to bus stops
Overall, in comparing the service areas of the two transportation modalities, it is obvious
that access by walking in Tacoma is highly variable and inconsistent, with clear peaks and
valleys in the surfaces of the service areas. Though, there are some criteria that stand out with
higher levels of walking access compared to their peers, such as with Schools and Recreation
spaces. Conversely, travel by driving has an incredibly high level of access for most of the city
across all criteria. These patterns suggest that the spatial landscape of Tacoma is more wellsuited for drivers. Weighted overlay will show how these transportation modalities impact the
suitability for MDH among Tacoma’s residential areas.
4.3 Site Suitability Results
The following section describes the results of the weighted sum analysis and details the
final suitability maps for MDH in Tacoma. These maps will inform policymakers and builders of
where the suitable locations are for MDH and to what degree. Policies could then be adjusted or

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taken advantage of in order to mass produce MDH throughout Tacoma and meet anticipated
population growth needs. This section concludes this chapter and will lead into a deeper
assessment of the results of the project.
Figure 37 and Figure 38 show the initial outputs of the weighted sum tool, differentiated
by transportation modality. In both figures, greens indicate higher suitability and reds indicate
lower. In Figure 37, in the main portion of Tacoma west of the Port, higher suitability appears to
be occurring along transit corridors where there are both high-capacity roadways and transit
options. However, suitability appears to lower closer to downtown and the city boundaries. This
is particularly concerning for downtown, as the most population dense area of the city scores low
in walkability. To the northeast in Brown’s Point, significant portions of the suburb are
unsuitable for walkers, likely due to the neighborhood’s distance from the rest of the city. Figure
38 shows a different store where most of the city scores relatively high in access by driving.
Most residential areas have a moderate to deep green color, with poor access areas occurring in
wooded areas or deep within industrial districts like the Port. Even Brown’s Point has
moderately high access, with its lowest access portion occurring near the Port. When comparing
the two figures, it is undeniable that drivers can more easily access more of the City of Tacoma
than walkers.

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Figure 37. Weighted Sum output with the walksheds
Figure 38. Weighted Sum output with the drivesheds

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The final suitability maps for MDH are shown in Figure 39 and Figure 40. The weighted
sum outputs were reclassified such that any commercial and industrial zones were qualified as
completely unsuitable, and the range of suitability scores normalized to a five-point scale for
clarity. Labels of NBDs were added to enhance later discussion. The raster cell size for both
maps is 90ft, representing one or two parcels per pixel. Across both maps, the impact of NBDs
can be observed as multiple districts like Pacific and 6th Ave form the cores of highly suitable
areas. Additionally, both maps show at least moderate suitability for MDH in all neighborhoods.
Looking at the MDH suitability map regarding walking, it appears that the southern
portion of the city has slightly more highly suitable areas than the northern portion (Figure 39).
This is supported by the fact that the southern portion of the city was built more recently and thus
has more modern infrastructure that can support larger and denser populations. As Tacoma was
founded in 1872, much of the older portions of the city’s infrastructure reflect the needs and
trends of the time. The larger and higher scoring areas to the south include the Lincoln and
Pacific districts.
Like with Figure 38, the MDH suitability map for driving shows a moderate to high
suitability across most neighborhoods (Figure 40). Interestingly, the relationship between
suitability and NBDs is somewhat maintained in the north but breaks down to the south. The
Proctor district forms the corner of a large, highly suitable area whereas Lincoln and Pacific still
have highly suitable areas nearby but are much smaller in size.

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Figure 39. MDH Walking Suitability map

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Figure 40. MDH Driving Suitability
Regardless of the differences between these two results, both indicate large areas that
would be highly suitable for MDH, equivalent to thousands of parcels. These results were joined
with the parcel layer in order to assign scores to parcels to determine exact counts for each level
of suitability for each modality. Figure 41 shows the parcel scores under walking while Figure 42

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shows the scores under driving. The counts of scored parcels reveal that driving has more parcels
scoring four or five while walking has more parcels scoring three, these values indicating
moderate to high suitability. In total, walking has approximately 48,000 moderate to high scoring
parcels while driving possesses approximately 58,000 parcels with the same scores. As
mentioned in Chapter 1, Tacoma is approximately 55,000 housing units short of 2040 population
growth estimates. The next chapter will discuss if this goal can be accomplished with the higher
scoring parcels.
Figure 41. Tacoma parcels with assigned MDH suitability scores for walking

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Figure 42. Tacoma parcels with assigned MDH suitability scores for driving
4.4 Service Area Spatial Trends
The delineation of service areas to compare walking and driving transportation modalities
is a critical component of this project, measuring access to different essential amenities to live a
full and healthy life. These service areas reveal that significant portions of neighborhoods are
within proximity of many amenities for both modalities. However, the walksheds are much more
variable in nature, leading to irregular rolling raster surfaces across those maps.
Focusing on the walksheds, it is observed that the footprints, or outlines, of the walksheds
are very distinct. It is very easy to see the decreasing suitability with distance from facility

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locations through the darkening of the symbology shades. The greater degree of detail indicates
how limiting the spatial landscape can be for walkers. Fifteen minutes of walking may get some
individual to their destination, but it also may not be enough to overcome hindrances such as the
large distances between amenities or lack of options to cross highways.
Tacoma’s sprawl of neighborhoods outside of the downtown core leads to the dispersion
of amenities. This scattering leads to large distances between some amenities, distances that
become a relatively larger barrier for walkers over drivers. For some amenities, such as outdoor
recreational spaces, their quantity counters some of this dispersion. But for others like healthcare,
what few public clinics exist are far outside a reasonable proximity to most residential areas.
Further, roadways such as Interstate 5 and State Route 16 have few, distant options for walkers
to cross over. These highways partition Tacoma such that motor vehicles seem like the only
reasonable option to get to other neighborhoods.
On the other hand, these barriers are practically nonexistent for drivers. The relatively
low travel times lead to indistinct driveshed maps where most residential areas are a single
shade. There is some variation of increased travel time, particularly toward the city peripheries,
but these increases are not significant, indicating only an extra few minutes of travel. Further,
areas of increased travel time constitute a minority of the area of the driveshed maps. The layout
of the city is not inhibitive to the ability of drivers to get around. Thus, Tacoma’s spatial
landscape is highly driver centric. At the same time, the city’s layout is not entirely anti-walker,
as the dispersion of some resources alleviates some of the travel barriers of large distances.
However, it is obvious that drivers are more supported by the design and layout of Tacoma’s
infrastructure and amenities than walkers are.

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The service areas reveal significant patterns of access in Tacoma, but these patterns
should be recognized as limited in their application due to choices in representing the
phenomena. It is highly difficult to represent reality in a digital environment, especially with
such a complex system like a mid-sized city. In completing this analysis, sacrifices were made to
allow the software to comprehend the unique composition of Tacoma. For example, the polylines
of the road network layer are incompatible with the tool to create facility service areas. Points
were generated along these road lines to build a simplistic, limited representation of the road
infrastructure that the software could accept as an input. While the service areas maps are
significant in revealing underlying patterns in the distribution of Tacoma amenities, there are
limitations in these abilities when looking at the maps as a whole.
Comparing the road network service areas to the final suitability maps, it is difficult to
ignore the similarities of high suitability areas to the service areas built around the generated
road points. Figure 31 shows the walkshed for high-capacity roads. Note the regular pattern of
similarly sized service areas in the north-south direction. This pattern is evident in Figure 39
which shows the MDH suitability for walking. While the footprints do not perfectly match, the
influence of the service areas for roads is clear.
These similarities acknowledge a possible disproportionate relationship between the
high-capacity road criteria and suitability scores, where the former’s high weight inflates final
suitability scores. This is evident in seeing the footprints of the high-capacity road service areas
manifest as outlines in the final suitability maps rather than aggregating with the other criteria
values. It is also possible that these observations are simply noise in the data that would be
reduced with improvements to the methodology of this project.

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Similar to the issue of the road network, the bus stop system could not be fully
represented in the service area analysis. Due to the massive number of stops in Tacoma, creating
multilevel service areas would have consumed more software resources than available to the
principal researcher. A dual level structure for bus stop service areas was deemed an acceptable
sacrifice, trading the fidelity of the resulting service areas to ensure enough remaining resources
for the other criteria of interest. This was justifiable because a transit network is meant to be used
holistically as an interconnected system, as opposed to daycare centers, which could be
independently run or be part of a small network. Access to one transit stop would presume access
to the entire system. Meanwhile, access to one independent daycare center does not mean access
to all independent centers. However, while the dual level structure is justifiable, it manifests as
service areas that score most of Tacoma equally in terms of transit, which is not at all the case in
reality. It would have been preferable to not make this sacrifice in order to achieve greater
fidelity and confidence in the final results with respect to transit access.
Another limitation is that these service areas only recognize one mode of travel for each
variation. The Network Analyst extension only allows for a single travel mode to be designated,
in this case only walking or driving. While public transit is included as a parameter, the tool does
not actually simulate using any transit system. Thus, ArcGIS Pro views the transit stops simply
as destinations rather than facilitators of travel. Certain transit stations have nearby parking lots,
opening up their usage to both walkers and drivers. A future iteration of this project would
incorporate a mixed transportation modality structure to represent the access to and usage of
public transit when building service areas for criteria. Additional modalities such as biking or use
of rideshare services could be explored as well.

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4.5 Site Suitability Spatial Trends
As mentioned in the previous chapter, the walksheds are highly variable in nature
compared to the relatively smooth surfaces of the drivesheds. Despite these differences, the
resulting suitability maps agree in many aspects, both noting tens of thousands of parcels with
suitability scores of at least three. Given the overall higher access to amenities by driving, the
driving suitability map shows larger highly suitable regions compared to its counterpart, shown
in FiguresFigure 39 and Figure 40. Looking to the north, the driving suitability map is
dramatically higher scoring, most notably around the Proctor district, indicating high suitability.
The walking suitability map, on the other hand, sees a greater degree of light shading, indicating
moderate to low suitability. This figure does possess significant high suitability areas in red, but
they are a lower proportion of the overall area shown in the map. This trend continues through
most areas of Tacoma, where driving suitability maps generally show larger, more suitable areas.
Counts of all the scored parcels are summarized in Table 8. While both maps contain the
same total number of parcels, the driving map sees nearly 10,000 more highly scoring parcels
than its counterpart. The greater number of high scoring parcels under driving continues to
underscore the advantage driving has over walking in Tacoma due to driver centric design of the
city. Approximately 50-60,000 additional housing units will be needed for the anticipated
population growth through 2040. How MDH types will impact these counts are explored in the
next subsection.

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Table 8. Suitable parcels scores and counts
Modality 0 1 2 3 4 5 Sum (3-5)
Walk 8041 2727 16264 15256 24331 8103 48,690
Drive 7936 340 8823 10743 36204 11676 58,623
Across both MDH suitability maps shown in the previous chapter, the Point Defiance,
Proctor, 6th Ave, Stadium, Hilltop, Lincoln, and Pacific NBDs are identified as the cores of
highly suitable areas. While the NBDs themselves are designated unsuitable due to their
commercial zoning, they are surrounded by large swaths of high scoring residential zones. All of
these districts are popular destinations for Tacoma residents, each hosting their own events,
specialty stores and other amenities. All but 6th Ave host at least one full-service grocery. Thus,
not only are these communities well connected and well supplied, but they also serve to maintain
the social fabric of the community.
All of this together indicates that these specific NBDS can serve as the focus regions for
early and mass MDH development. Stadium and Hilltop, being adjacent to the high density of
downtown, already contain a large number of subdivided homes and apartment buildings, with
even more under construction through 2023. Thus, medium or even high-density housing in
these localities would not contrast with existing buildings as starkly as they would in SFH
neighborhoods.
The intensification of development would be supported by the existing amenities. While
investment into the local businesses and services would ideally increase with population density,
what is already existing could serve the population growth in the relative short term, say ten
years. This would allow time for the proliferation of MDH with future commercial investments
adding new and enhancing existing amenities.

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NBDs that appear relatively unsuitable for MDH development include the Old Town,
Oakland, and Dome districts. The Dome district is a heavily industrial and commercial area that
hosts the city’s largest transit hub in addition to the Tacoma Dome, a large concert and
convention venue (Figure 43). While these features make the area a destination, the lack of
residential zoning removes this area as a candidate for MDH development. The nearest
residential zones are across one of two interstates bounding the area. Oakland faces a similar
issue, hosting many commercial amenities with minimal residential development nearby. Old
Town, while hosting residential zones, is far from most other amenities in addition to the
premium placed on land values due to the historic nature of the area. While it would be desirable
to make these areas more suitable for MDH, given the high degree of suitable spaces near other
NBDs, focus should be placed on where the largest gains in the number of units can be
completed the quickest to address the most pressing issues around housing. However, the priority
given to other areas does not discount these localities from MDH development. These areas do
possess certain amenities, services and even aesthetics that would be attractive to particular
residents and should be an option for them in the future.

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Figure 43. Zoning for central/southern Tacoma
Research ordered by the City of Tacoma estimates that approximately 125,000 new
residents will move to Tacoma through 2040 (Tacoma 2015). Using the 2022 Pierce County
average household size of 2.63, this translates to approximately 50-60,000 housing units needed
(US Census n.d.). This study shows that sufficient suitable locations for MDH development in
Tacoma exist to meet this goal. Below is a table that summarizes two scenarios of MDH
development, the first where all parcels scoring three or higher are only developed into duplexes
and the second where the higher suitability scores are related to higher tiers of MDH (Table 9).
Scenario one assumes the GMA is the only policy requiring increased density and scenario two
integrates aspects of Home in Tacoma where additional density is added in areas of relatively

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higher suitability. These calculations presumed all residential parcels were SFH to simplify
calculations.
Table 9. Net unit gain based on two scenarios of MDH development
3 4 5 Sum (3-5) Net Unit Gain
MDH Type Duplex Duplex Duplex
Walk (1) 30512 50662 16206 97380 48,690
Drive (1) 21486 72408 23352 117246 58,623
MDH Type Duplex Triplex Fourplex
Walk (2) 30512 75993 32412 138917 90,227
Drive (2) 21486 108612 46704 176802 118,179
In the first scenario, the metrics demonstrate a case where the GMA’s mandate of simply
doubling density on all SFH is the only housing legislation being administered. The walking
suitability map sees a net gain in housing units of 48,690 while driving sees a gain of 58,623
units. Both of these gains in units are surprising, it was unexpected how much of a net gain
would occur simply by doubling density. While these gains are significant, they may not be as
impactful as their values may suggest as this calculation assumes the conversion of every
counted parcel. It is unlikely for every single suitable parcel to be converted to MDH in the near
future. The updated GMA includes the ability for municipalities to apply for exemptions to the
increased density. For example, a city may designate 75% of its residential area as high density
and leave the remaining 25% as low density, resulting in an overall net increase in housing
supply to comply with the mandate while preserving specific neighborhoods. It is entirely
possible that certain neighborhoods in Tacoma, such as the historic Old Town, could receive

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these exemptions and remain low density areas. Furthermore, there is not a clear designation of
the responsibility of enforcement or punishments for noncompliance. Will agents of the state
patrol neighborhoods? Will homeowners unable to afford renovation be punished for
noncompliance? While the law mandates that cities and municipalities update their
comprehensive plans to reflect the GMA by July 2024, one year after the passage of its
amendment, it is unclear how the doubling of density will be measured and enforced. Finally,
building materials, funds and even labor are finite resources, only so much can be obtained and
deployed at once. Given that the GMA is a statewide mandate, Tacoma property owners
interested in MDH development may be competing with other municipalities for resources and
workers. While 100% conversion of SFH to MDH is desirable, the reality of the various existing
buildings, property ownership structures, in demand resources will complicate MDH
development.
The other scenario incorporates some of the proposed policies of Home in Tacoma,
Tacoma’s homegrown rezoning effort aiming to increase density beyond what the GMA
mandates. In particular, the proposed policy of additional density nearer to transit stops is
conceptually applied by increasing the MDH type tier with increasing suitability. Parcels scoring
three get a duplex, those scoring four get a triplex and five get a fourplex. As parcels scoring four
or five primarily occur near NBDs or high-capacity roads, denser developments in these
locations would maintain high access and low barriers to many amenities. With net gains of
90,277 and 118,179 units, this scenario far exceeds the estimated goal of 55,000 units. Like with
the first scenario above, there should not be an expectation that every single parcel will be
converted to MDH. This in mind, it is evident that the conversion of 50-60% of Tacoma’s
parcels to MDH under this second scenario could be just enough to meet the 55,000-unit

102
threshold, potentially exceeding it. Of course, this is highly dependent upon the variety of MDH
constructed.
This second scenario is also more realistic than the first. While converting SFH to
duplexes seems simpler than converting them to duplexes, triplexes or fourplexes, it is highly
unlikely to achieve 100% adoption and compliance with the GMA. It is much more likely that
adoption will be closer to a proportion that is a simple majority of the parcels with a mix of
MDH types depending upon the landowner. For example, commercial landlords may be highly
interested in constructing fourplexes or larger because it will allow for a greater degree of rent
extraction from the same property. Meanwhile, a small-time landlord or even a typical
homeowner may be more interested in renovating their property to a duplex due to lower initial
overhead costs.
4.6 Limitations
There are limitations to the results discussed above. Foremost is the questioning of the
soundness of the final suitability maps, a concern rooted in the weighting scheme of the criteria.
In survey responses from subject matter experts familiar with housing in Tacoma, all responses
disagreed with the notion of high-capacity roads being assigned the highest weight value.
Respondents pointed at public transit and safety as more important to the development of MDH.
These criticisms, coupled with the concerns of suitability scores being inflated due to
overweighting of the high-capacity road criteria bring many questions to the suitability maps.
This issue is highly concerning because correcting it may lower the degree of highly suitable
areas throughout Tacoma. However, it is still prudent to understand the influences of every
criterion on the final result and sacrifice of the smaller potential area for MDH may be worth the
clarity. In order to address this concern, multiple different weighting schemes could be applied to

103
prioritize the needs of different lifestyles. With multiple schemes and results to compare,
statistical analysis could help understand the underlying relationships.
These results also could not accommodate the current and changing landscape of
Tacoma. In the calculations of net units gained, the assumption was that every residential parcel
counted was SFH in nature. This is not truly represented in reality, while large apartment
buildings are a minority of overall residential properties in Tacoma, they still exist and are much
higher density than any SFH property. There are also quite a number of duplexes and triplexes
dotted throughout the city. Finally, there are a number of ongoing apartment building
construction projects not yet represented in the data. These evolving characteristics were set
aside to simplify the representation of the city, diverging further from reality.

104
Chapter 5 Conclusions
Accessible, safe homes should be a human right but many households throughout the United
States struggle with finding adequate and affordable housing. The city of Tacoma, Washington is
no stranger to this, with a significant gap in its current housing stock compounding with an
anticipated 50% population growth through 2040. Unfortunately, this trend is apparent in other
cities in the region, being severe enough to push the state government to pass legislation
mandating increased housing density in all Washington cities in order to meet housing demand
universally. This research has utilized GIS software to perform multi-criteria decision analysis
with the objective of identifying suitable sites for medium density housing among Tacoma’s
residential areas. The findings of this research indicate that significant portions of Tacoma are at
least moderately suitable for denser degrees of housing. There is high potential in building a
variety of housing types in a variety of neighborhoods within Tacoma. However, calculated
projections of added housing units indicate that these areas alone may not be sufficient, either
missing or barely exceeding the estimated threshold for needed units. The following sections
provide recommendations for enhancing the GMA to better support affordable MDH in
Washington State.
5.1 Policy Implications
The results of this analysis have shown that thousands of parcels are suitable for the
construction of MDH in Tacoma . However, the actual number of constructed units is highly
dependent upon property owners being willing and able to expend resources of renovating or
building entirely new MDH properties. The administration of the GMA needs to be expanded to

105
provide more guidance and tools for building MDH. These expansions include financial support
to back property development and the addition of new amenities to support the future population.
While the GMA aims to provide more housing for all residents of Washington, it appears
to put the onus of the responsibility of construction back on these same residents. Unfortunately,
not every property owner has the abundance of resources needed to renovate or construct MDH
properties. If there was language in the bill that directed state resources towards a fund or loan
program for MDH development, there would likely be a greater degree of MDH uptake as the
barrier to entry would be lowered. This is similar to a study described in Section 2.4, where
home renovations funded by microloans and conducted by the property owners found a high rate
of success, leading to greater social and economic stability for these communities (Mitchell
2020). The researchers noted that the owners’ personal investment in their properties contributed
to the high degree of success as well. Thus, lowering the financial barrier to entry may be enough
to motivate individual homeowners to build additional units on their properties where they
otherwise would not have been able to.
The service areas shown in Section 4.2 highlight the uneven distribution of the access to
different amenities throughout Tacoma. An further enhancement to the GMA could be the
mandated support of resource deprived areas that lack nearby amenities such as grocery stores.
By building out amenities in these areas, they would likely become more suitable for MDH,
expanding the total area of suitable location for MDH development. Of course, each city is
different in its amenities and layout and so how this amenity expansion would occur manifest
differently. For Tacoma, much of the expansion of amenities would occur in the southern portion
of the city, which scores low in terms of access across all service area maps in Section 4.2. By
requiring an expansion of the amenities and services to support denser populations, the GMA can

106
much better support denser housing developments rather than relying on current infrastructure
and services to support future populations.
5.2 Future Work
This analysis builds upon existing spatial analysis research for housing, identifying a
number of suitable sites for MDH development in Tacoma, Washington. However, the
development of this project revealed points of inquiry and areas of improvement in the
methodology, not all of which could be addressed in this research. These updates include more
criteria of interest, widening of the study area and incorporation of the sidewalk network in the
analysis. In regard to the criteria, the list of fourteen was pared down from an initial list of
seventy-eight to focus on establishing and completing the workflow rather than one that was as
comprehensive as possible. Expert opinions noted criteria such as tree canopy coverage, lot
characteristics and housing costs like rent or mortgage as potentially important to the success of
MDH. Inclusion of these criteria in future iterations could help identify further disparities in the
infrastructure of Tacoma or specific characteristics that make a parcel more attractive to potential
developers.
While this analysis highlights the population growth of Tacoma, it is really the entire
Puget Sound region that is growing. Future work on this project would widen the study area to
include the city of Lakewood south of Tacoma up to Everett, north of Seattle. The methodology
of this project can easily be scaled up and supported with ArcGIS automation tools like
ModelBuilder to manipulate and combine the greater number and size of datasets from every
municipality. Even if a cohesive map of the region cannot be created due to data limitations,
MDH suitability maps for the major cities of the region can help understand where to focus
MDH development for future residents. These maps could also help with the refinement of the

107
GMA and the comprehensive plans for population centers. Further, these maps could help
understand the local propagation of urban sprawl and minimize it into the future.
An additional path of future work is the inclusion of the sidewalk network. ArcGIS uses a
network dataset of the road network in order to calculate the service areas around facilities. In
this representation, any mode of travel can go anywhere in the network. While this is handy in
quickly assessing different modes of travel, it ignores some of the spatial qualities that could
impact travel. In particular, the sidewalk infrastructure is not a part of the network dataset. This
is important because not every neighborhood has sidewalks or crossings and where these features
are, quality can vary widely. Some neighborhoods have cleaned, wide sidewalks whereas others
are constantly covered in litter or plant growth. This uneven infrastructure quality applies to bike
lanes as well, where Tacoma’s network is somewhat fractured with noncontinuous bike lanes,
unmaintained paint, and other issues. By incorporating these qualities into the analysis, specific
barriers and facilitators for non-driving modes of travel can be identified.
5.3 Conclusions
The results of this analysis are surprising. It was expected that significant portions of
residential areas would not be suitable for MDH. But through the identification of suitable sites,
it is estimated that the GMA alone could be able to meet the lower threshold for units
constructed in the first scenario described in Error! Reference source not found.. While the
housing gap would not be entirely filled in this scenario, it would definitely be eased. The
housing gap is further addressed by including a mix of different MH types in a second scenario,
far exceeding the upper estimated threshold of units. As these scenarios both under and overshot
the number of housing units needed, a combination of both could be a solution to Tacoma’s
housing needs. This scenario combination could see a variety of MH types throughout Tacoma

108
where a significant proportion are duplexes or triplexes. Not only would there be enough housing
units to go around, but there would also be a variety of unit types and sizes to fit a number of
lifestyles and household needs.
These scenarios together show that Tacoma does have suitable land available to meet
future housing needs, at least through 2040, the development of MH just needs to be more
broadly supported by the government to initiate the builds. The GMA could be enhanced to
specify enforcement policies and responsibilities among local municipalities to ensure as many
parcels as possible are redeveloped into a variety of MH types in the near future. Further,
legislators may work to provide subsidies or financial assistance to those interested in developing
MH, lowering barriers and risks for those individuals. Finally, local infrastructure and amenities
need to be expanded to support future, denser population rather than expect those populations to
rely on infrastructure designed for smaller populations.

109
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Appendix A
Criterion Source RefID
arrival time to city center 10
building density 10, 18, 22
building fees 10
business center proximity 6
characteristics of construction 7, 8, 10, 12, 16, 18, 19
climate 1
college education rate 11
commercial service facility 6
community cost to public facilities 13
construction cost performance of housing 13
daycare 3, 4, 12
drinking water source proximity 5
economic diversity 2
employment 1, 3, 12
end user's satisfaction 13
environmental health 6, 7, 12, 14
evacuation gathering site proximity 6
existing building proximity 5
fair share 1
food access 1, 3, 4
garbage collection service 7, 12
heating fuel type 10
home sale price 19
homeownership financial products 12
housing aesthetics 7, 8, 13
housing type 8, 10, 16
industrial center proximity 6
job proximity 2, 8, 14
land cost 1, 2, 9, 11, 14
land ownership 9, 12
land use 1, 9, 13, 14, 15, 16, 18, 19, 21, 22
low-income housing availability 16
maintainability of facility 13
mean income 20
median income 19

114
Criterion Source RefID
medical care 1, 2, 3, 4, 6, 7, 12, 21
medium income household neighborhood 14
minimum commute cost 20
minimum rent 20
monthly rent 10, 14
neighborhood level infrastructure 10, 17, 18
policy support 6
population change 14
population density 3, 9, 19, 21
poverty 3, 10, 11, 12, 13, 14
presence of public housing 11, 16
private green space 10
project team satisfaction 13
proximity to city center 10, 20
public libraries 1, 4
public transportation proximity 1, 3, 4, 6, 7, 8, 9, 11, 12, 14, 19
quality of neighborhood 7
quality performance 13
racial composition 14, 16
recreational spaces 1, 2, 4, 6, 7, 8, 10, 12, 14, 18, 22
reduced life cycle cost of facility 13
reduced occurrence of disputes and litigation 13
reduced public funds of housing management 13
rental housing availability 12, 16
rental housing rate 2, 11
retail shopping 2, 3, 7, 12, 14, 19, 22
road network proximity 2, 4, 5, 15, 21
safety & security 2, 3, 8, 10, 12, 17, 22
safety performance 13
satellite access 10
schools 1, 2, 3, 4, 6, 7, 12, 14, 21,22
sense of "place" 7, 22
social capital 18
society constitution 6
spatial agglomeration 6
structure age 11, 16, 19
take up rate of housing facility 13
timely completion of project 13
topography 5, 10, 14, 15, 21

115
Criterion Source RefID
total population 20
waiting time of public housing 13
water body proximity 5
zoning 1, 9, 13, 19, 21

116
Appendix B
RefID Title Citation
1
Site Selection for Higher Density Affordable Rental Housing
Development: Applying the Weighted Linear Combination
(WLC) Method in the City of Los Angeles, California Maciel-Cervantes 2017
2
Low-Income Housing Location Based on Affordable Criteria
Using AHP Model and GIS Technique (Case Study: Babolsar
City) Sharghi et al. 2021
3
Constricted Urban Planning: Investigating the Site and
Suitability of Low-Income Housing in Fairfax County, Virginia Van Atta 2013
4
Site Suitability Analysis: Mid-Density Low-Income Housing for
SE Portland Way and Miller 2016
5
Selecting Housing Development Sites Using Multi-Criteria
Decision Analysis (MCDA) A Case Study of Guangzhou, China Wei and Ding 2015
6
Site Selection of Affordable Housing in Direct Management Area
Under Jiangbei’s New District in Nanjing Fang et al. 2022
7
A Model of Housing Quality Determinants (HQD) for Affordable
Housing Chohan et al. 2015
8
The Basic Criteria for The Provision of Affordable Housing in
Melaka Osman et al. 2018
9
Remote Sensing/GIS Integration to Identify Potential LowIncome Housing Sites Thomson and Hardin 2000
10
A New Hybrid Decision Making Approach for Housing
Suitability Mapping of an Urban Area Zeydan, Bostanci and Oralhan 2018
11
Land-Based Interests and the Spatial Distribution of Affordable
Housing Development: The Case of Beijing, China Dang, Liu and Zhang 2014
12
Criteria for Affordable Housing Performance Measurement: A
Review Esruq-Labin et al. 2014
13
Bridging the Gap Between Sustainable Housing and Affordable
Housing: The Required Critical Success Criteria (CSC) Adabre and Chan 2019
14
GIS for Planning a Sustainable and Inclusive Community: MultiCriteria Suitability Analysis for Siting Low-Income Housing in a
Sustainable Community and Suitable Neighborhood in Buffalo
Metropolitan Area, New York Saleh and Setyowati 2020
15
GIS Based Multicriteria Approaches to Housing Site Suitability
Assessment Al-Shalabi et al. 2006
16
Density, Housing Types and Mixed Land Use: Smart Tools for
Affordable Housing? Aurand, 2010
17
Site Selection by Using the Multi-Criteria Technique—A Case
Study of Bafra, Turkey Kilicoglu et al. 2020
18
The Relationship of Urban Design to Human Health and
Condition Jackson, 2003
19 Zoning, Density, and Rising Housing Prices Dong and Hansz 2019
20
City Affordability and Residential Location Choice: A
Demonstration Using Agent-Based Model Marwal and Silva 2023
21
GIS Based Analysis for Suitability Location Finding in The
Residential Development Areas of Greater Matara Region Madurika and Hemakumara 2017
22
Designing High-Density Neighbourhoods to Promote Social
Health in Australia Hall and Andrews 2019

117
Appendix C
Public
Transit
Recreational
Spaces
Schools Medical
Care
Retail
Shop-ping
HighCapacity
Road
network
Daycare Grocery Poverty Safety &
Security
Unemployment
Air Pollution
Land
Cost
Public
Transit 1 8 5 3 4 1 6 1 3 1 7 3 8
Recreational Spaces 0.125 1 0.333 0.143 1 0.333 3 0.111 0.2 0.143 1.5 1 1
Schools 0.2 3 1 1 1 0.333 1 0.25 1 1 3 5 5
Medical
Care 0.333 7 1 1 3 1 5 1 0.25 1 3 1 5
Retail
Shopping 0.25 1 1 0.333 1 1 4 0.25 0.167 0.2 0.2 5 3
HighCapacity
Road
Network
1 7 3 1 1 1 5 1 3 0.2 3 5 7
Daycare 0.167 0.333 1 0.2 0.25 0.2 1 0.143 0.2 0.2 0.2 0.2 0.2
Grocery 1 9 4 1 4 1 7 1 1 3 1 3 1
Poverty 0.333 5 1 4 6 0.333 5 1 1 1 1 5 3
Safety &
security 1 7 1 1 5 5 5 0.333 1 1 1 5 5
Unemployment 0.143 5 0.333 0.333 5 0.333 5 1 1 1 1 4 0.333
Air Pollution 0.333 1 0.2 1 0.2 0.2 5 0.333 0.2 0.2 0.25 1 0.333
Land Cost 0.125 1 0.2 0.2 0.333 0.143 5 1 0.333 0.2 3 3 1

Abstract (if available)

Abstract The Puget Sound region in the Pacific Northwest has failed to provide adequate housing for its residents for decades, leaving behind a trail of displacement and inequity throughout many of its cities. The city of Tacoma, the second largest city in the region, is expected to grow in population by nearly 50% between 2020 and 2040, highlighting the urgency to build sufficient housing to home both current and future residents. The state legislature responded in 2023 with the passage of legislation mandating increased minimum density regulations in all low-density residential zones and allow a greater variety of housing type where they were once restricted. This project identified tens of thousands of parcels in Tacoma that would be suitable for denser housing within the context of this new legislation. Service areas were created to measure access to a variety of amenities and resources such as schools, public clinics, and grocery stores, deemed important for the success of denser housing. Weighted overlay methods combined these service areas with other layers to build suitability maps scoring the entire city of Tacoma for medium density housing. The results indicate that significant portions of Tacoma would be moderately or highly suitable for increased housing density. The methods of this project highlight how geographic information science and technology can support housing policy to ensure accessible, affordable housing for the state of Washington.

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Maui’s wildland-urban interface: enhancements for the unique vegetative and agricultural landscape

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A site suitability analysis for an inland port to service the ports of Los Angeles and Long Beach

Asset Metadata

Creator Le, Kevin C. (author)

Core Title Site selection of medium density housing in Tacoma, Washington: where to put “missing middle” housing

School College of Letters, Arts and Sciences

Degree Master of Science

Degree Program Geographic Information Science and Technology

Degree Conferral Date 2023-12

Publication Date 12/14/2023

Defense Date 11/28/2023

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

Tag affordable housing,analytical hierarchy process,equity index,housing policy,medium density housing,middle housing,missing middle housing,multicriteria decision analysis,OAI-PMH Harvest,residential zoning,service area analysis,site suitability selection,weighted overlay,zoning

Format theses (aat)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Sedano, Elisabeth (committee chair), Wang, Siqin (committee member), (Wu, An-Min)

Creator Email kevin.ct.le@gmail.com

Permanent Link (DOI) https://doi.org/10.25549/usctheses-oUC113792526

Unique identifier UC113792526

Identifier etd-LeKevinC-12556.pdf (filename)

Legacy Identifier etd-LeKevinC-12556

Document Type Thesis

Format theses (aat)

Rights Le, Kevin C.

Internet Media Type application/pdf

Type texts

Source 20231214-usctheses-batch-1115 (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.

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