Parameterizing Land Use Planning:
Deploying Quantitative Analysis Methods
in The Practice of City Planning
^W*Aft
MASSACHUSETTS 'WBTTE
OF TECHNOLOGY
OCT 0 7 2014
By Talia Kaufmann
B.Arch., Tel Aviv University (2009)
LIBRARIES
Submitted to the Department of Urban Studies and Planning
in partial fulfillment of the requirements for the degree of
Master in City Planning
at the MASSACHUSETTS INSTITUTE OF TECHNOLOGY
September 2014
@ 2014 Talia Kaufmann. All Rights Reserved
The author here by grants to MIT the permission to reproduce and to distribute
publicly pap er and electronic copies of the thesis document in whole or in part
in any medium now known or hereafter created.
Signature redacted
Author
Talia Kaufmann
and Planning
Studies
Urban
Department of
August 29, 2014
iynature IeUdateU
Brent D. Ryan
vPublic Policy
and
Design
Urban
of
Professoj
Associate
Thesis Supervisor
/ A
Signature redacted
!
Certified by
Accepted by
d
Dennis Frenchman
Leventhal Professor of Urban Design and Planning
Chair, MCP Committee
2
Parameterizing Land Use Planning:
Deploying Quantitative Analysis Methods in The Practice of City
Planning
By Talia Kaufmann
Submitted to the Department of Urban Studies and Planning on August 29, 2014
in partial fulfillment of the requirements for the degree of Master in City Planning
Abstract
Planning a city is a complex task. In particular, the practice of land use planning,
which determines the quantities and locations of land uses we find in a city,
is a highly complex process. Planners, developers and citizens involved in this
process need to consider the multiple components of the urban system which
are intertwined and connected in a complex network, and cannot be studied
independently. While cities were extensively studied as complex adaptive
systems over the last 50 years, showing universal patterns across countries,
cultures and times, the practice of land use planning hasn't advanced as much
and still deploys the rigid, macro-scale and local tool of zoning. This thesis will
present a LEGO game planning methodology for urban land use that harnesses
our understanding of cities as interconnected networks to enable a fine-grained,
modular, incremental and universal development tool.
Using a dataset summarizing the fine-grained location of commercial and
public land uses in the 50 largest metropolitan areas in the U.S., this research
will construct a catalog of urban models exploring similar patterns and their
deviations across American cities. Utilizing the emerging patterns, this thesis will
outline a methodology to produce quantitative planning guidelines in two main
aspects: First, a method to assess land use quantities to support population
levels will be demonstrated by implementing the scaling relationships found
in cities from the Bettencourt et al research (2007). Next, a method to evaluate
the spatial organization of cities will be presented by calculating co-location
pairwise distances between amenities within city centers. The research will
show that some co-location patterns are similar across cities, independent
from land use quantities and urban density while others fluctuates between
cities and depend on local characteristics. The LEGO game methodology will
demonstrate an evolutionary iterative process to evaluate the liveliness of each
urban environment, and explore the infinite possible assembly options of urban
building blocks from various types and quantities, to enable a genuine datadriven decision making process for land use planning.
3
Thesis Supervisor:
Brent D. Ryan
Associate Professor of Urban Design and Public Policy
Department of Urban Studies and Planning
Thesis Reader:
Cesar A. Hidalgo
Assistant Professor of Media Arts and Sciences
Program in Media Arts and Sciences
Thesis Reader:
Andres Sevtsuk
Assistant Professor of Architecture and Planning
Singapore University of Technology and Design
4
ACKNOWLEDGEMENTS
Five years ago, when I started my journey towards parametric land
use planning, this moment seemed like a dream. This is a precious
moment, of recognizing the amazing opportunities that have been
opened to me and the world of ideas I entered by joining MIT. Even
more importantly, this is a moment of realizing the privilege I had
of learning from and with inspiring people, and how fortunate I was
when I was given the opportunity to come here.
I owe this moment to the devoted professors who walked me
through the first steps in materializing my vision. First, to Brent
Ryan, for fully supporting my ideas and efforts throughout these
excruciating two years. Second, to Cesar Hidalgo, for giving me an
opportunity that I will forever be thankful for and also, for dedicating
the time to introduce me to a whole new world. Third, to Andres
Sevtsuk, with whom I found a common language, for opening my
mind and guiding me.
This moment was not possible without the help and support of
the dedicated staff of DUSP and the Media Lab, especially Linda
Peterson, Sandy Wellford and Eran Ben-Joseph, head of DUSP I am
incredibility thankful for all your efforts.
And Lastly, to my parents, for allowing me this wonderful opportunity
and supporting me through thick and thin, forever believing in me.
Thank you.
5
"
Patrick Geddes,
Cities is evolution,
1915
"..Idealism and matter of fact are thus not sundered, but
inseparable, as our daily steps are guided by ideals of direction,
themselves unreachably beyond the stars, yet indispensable to
getting anywhere, save indeed downwards. Utopia, then, lies in
the city around us; and it must be planned and realized, here or
nowhere, by us as its citizens each a citizen of both the actual and
the ideal city seen increasingly as one.
6
CONTENTS
I
INTRODUCTION
1.1 The urban LEGO game
1.2 Planning for complexity
1.3 Parameterization of land use planning
1.4 Summary of chapters
9
9
11
14
16
2 THEORY
2.1 Theories and practice of land use planning
2.1.1 The establishment of planning regulations
2.1.2 The practice of land use planning:
The process shaping our cities today
2.1.2.1
Population levels required
to support urban activities
2.1.2.2
Zoning and subdivision ordinance
18
18
18
19
2.2 Alternative planning processes:
the question of how a city should be planned
Jane Jacobs's approach to urban problems
Christopher Alexander's vision
for planning and designing cities
Kevin Lynch's perception of urban theory
and 'good' city form
25
2.3 Modeling the spatial organization of land uses
in economic geography
2.3.1 Planning for economic development
29
21
23
25
26
28
31
2.4 Planning Support Systems: Analysis and assessment tools 32
2.5 Cities as complex systems
3 METHODOLOGY
7
34
3.1 Data sources and structure
38
39
3.2 Choosing a set of building blocks
3.2.1 Analysis method: urban scaling relations
3.2.2 Assessing a set of building blocks
3.2.2.1
Extracting population thresholds
42
42
46
46
3.2.2.2
3.2.3
3.2.4
Assessing levels of land use quantities
Deviations from scaling: exploring urban models
Constructing planning guidelines
3.3 Co-locating the building blocks
3.3.1 Analysis method: Calculating pairwise distances
3.3.2 Deviations from a shared distribution: exploring urban
models
3.3.3 Constructing planning guidelines: examining
the span of pairwise relationships
4 EVALUATION
4.1 Parametric land use planning:
Forming the urban LEGO game
4.2 Methodological limitations of parametric
land use planning
5 BIBLIOGRAPHY
6 APPENDIX
Appendix A: Scaling exponents for all land use
types vs. city size
8
48
50
53
55
56
57
69
73
73
76
78
82
Appendix B: Scaling charts for selected land use types
84
Appendix C: City rankings by scaling residuals
of all land use types
87
Appendix D: Co-location range matrix for
selected land use types
90
Appendix E: Atlas of cities - Parameterizing land use
quantities and co-location patterns
92
1
INTRODUCTION
The Urban LEGO game
Planning a city is a complex task. In particular, the practice of land
use planning, which is the task of determining the quantities and
locations of various land uses we find in a city, is a highly complex
process. Planners, developers and citizens facing this task need to
consider the multiple components of the urban system which are
intertwined and connected in a complex network, and cannot be
studied independently.
1.1
The complexity of this task derives from the planning object itself,
cities. The enormous body of empirical research from over 50
years has shown the nature of cities as complex adaptive systems.
Jane Jacobs was the first to define cities as problems in organized
complexity (Jacobs, 1961), adapting Dr. Warren Weaver's definition
for problems that deal concurrently with many variables that are
interrelated and in constant simultaneous change (Weaver, 1958).
Jacobs' definition marks the start of a quest to understand cities as
complex systems, in which recent studies were successful in proving
that cities manifest universal, quantifiable features, spanning across
time, cultures and nations (Bettencourt et al., 2007). This thesis will
attempt to answer the question of how can planners harness our
understanding of cities as networks of connections to transform the
practice of land use planning.
So how does one go about planning the land uses of a city? Think
of the practice of land use planning as being similar to playing a
Lego game, where players choose a set of building blocks and
than consider the m ultiple options to assemble them together.
However, unlike a regular Lego game, in land use planning the
players' role is to determine the rules and allotments that will limit
the assembly options of the Lego pieces in a way that will benefit as
many interests as possible.
Urban Lego blocks
embeded with
essemmbly game
rules
Source:Talia Kaufmann
The pieces of our urban Lego game are the functional building
blocks we find in every urban environment. These are the amenities
and urban establishments we use everyday as restaurants, banks,
churches, schools, parks and many more. The bank of building
blocks is potentially infinite, but in reality is constrained and
determined as a product of a city's population requirements. Still,
the mix of building blocks in each city is affected by several forces, in
part created naturally by market forces and partly shaped by central
planning efforts. The mixture and combination of building blocks
are key factors affecting the character of every urban environment.
1.1 The urban LEGO
game
9
In the Lego game analogy, when players assemble pieces together,
they usually follow a physical or mental image of the object they
would like to produce. Similarly, when planners face the task of
planning a new area or revitalizing an existing one, they explore
different urban models and choose the one that best serves the
planning purpose. However, the available assessment tools for
planners today are mostly qualitative and limited in their ability
to allow for comparison. Quantitative analysis methods can equip
planners with metrics to assess various urban models by their set of
building blocks and compare the models' spatial patterns by their
combination of blocks.
The Lego game is not just an analogy in this case; it is a
comprehensive methodology for the process of planning land uses
in cities. A Lego game is an open-ended system that does not try
to define a finite product, but rather provides the building blocks
and game rules to enable endless possible outcomes. Researchers
have used the Lego game approach of combinatorial modeling
in the past to model processes such as adaptive evolution and
innovation, and also understand the mechanism that can produce
infinite possibilities (Kauffman and Levin, 1987; Weitzman, 1998). In
the planning realm, a Lego system can introduce modularity and
flexibility to the planning process at the most fine-grain of scales,
for the assembly options of land uses. Here, our understanding of
cities as complex systems to model the relationships between land
use types can be utilized to define the combinatorial rules for the
spatial organization of cities. Combining the open-ended manner
of a game with the quantitative approach of modeling interactions
introduces an opportunity to transform the way we practice land
use planning.
Approaching the practice of land use planning as a game will
enable a kind of development that has been envisioned by
theorists for years (Alexander, 1987; Lynch, 1984; Jacobs, 1961). A
game methodology for planning can enable incremental, flexible
and bottom-up urban development, allowing projects to be
developed in stages and making the building venture accessible
to everyone, from residents to big developers. The kind of
envisioned incremental development could be achieved here by
using methods from complexity science to model the effect and
implications of any additional building block in the system on
the built environment. In this approach, the creation of planning
rules for the distribution and location of land use types will take
the form of planning interactions rather than a final outcomes, as
defined by the words of Michael Batty: "...location is, in effect, a
synthesis of interactions" (Batty, 2013). With assembly rules defined
by planners and the Lego game framework embedded in an open
technological platform, the practice of assembling building blocks
will be available for everyone: a city resident wishing to locate his
home, a city planner aiming to regenerate an area or a developer
wanting to built his high-rise.
1.1 The urban LEGO
game
10
This thesis will explore how quantitative relationships between
urban components can be harnessed in the planning practice
by introducing a set of analysis methods and assessment tools.
These measures will guide the land use planning process and
assist planners in their decisions about the quantities and spatial
distribution of land uses in cities.
While this thesis outlines a comprehensive view of analysis methods
and implementation tools, it only marks the start of an enormous
endeavor. There are still numerous parameters left uncovered and
countless relationships left untangled. Moreover, implementing the
offered methodology in the daily practice of land use planning might
be the biggest effort of all, which will bring enormous challenges
through the process of managing change, a task that for now
remains untouched. The implementation process encompasses
the major transformational potential of this endeavor - which is the
opportunity to change the way we think about and practice land use
planning. But before we move to future ventures, let's first discuss
the problems and challenges in the current planning process.
1.2
Planning for Complexity
"...it is time we start thinking about planning interactions in cities
Batty, 2013
rather than locations, switching from thinking simply as idealized
morphologies to thinking of cities as patterns of communications,
interaction, trade and exchange... thinking of them as networks."
Throughout history, the quest to plan better cities has been framed
in the way we might manipulate urban activities physically and
geographically from the point of view that cities can be studied
and represented in models, maps and pictures of locations. But the
fact of the matter is that plans, which in their nature are based on
moving activities and their land uses into ideal configuration, or
on imposing constrains on what activities can locate where, rarely
deal with the question of what cities are and how they function and
evolve. Locations of urban activities only represent a fraction of
the urban eco system, not revealing the relations and interactions
between population that represents the rational for living and
working in cities and also the dependencies between the various
activities (Ibid).
1.2 Planning for
Complexity
11
As Batty claims, a paradigm shift is long overdue in the practice of
city planning. The current zoning ordinance, a legal devise used
by planners to implement land use plans, has been practiced for
almost a century now; This is a tool that is local in its approach, rigid
and fixed in its nature, and defines a general and macro-scaled set
of rules for the location of land uses. In the highly urbanized, global
and interconnected world we live in, there is an urgent need for a
tool which is: first and foremost modular in its nature, allowing for
maximum flexible open-ended development; a tool that models
land use relationships at the micro-scale and fine-grained level; and
a tool that can serve across nations, cultures and countries. Let's
explore the prevalent zoning tool in light of the complex nature of
cities while envisioning a new methodology for land use planning.
First, a zoning ordinance provides a finite product, dividing the
city into districts with permitted uses, intended to guide city
development for at least a couple of decades into the future. Zoning
presents a naive approach when onlytaking into account the location
of different activities while excluding their dependencies on one
another. Moreover, the nature of the tool poses a huge obstacle
for development in its inability to allow for change, when every
amendment to the zoning ordinance requires multiple bureaucratic
procedures. However, if we analyze a city as a set of interactions
between components, we should strive to plan these interactions,
by modeling the reciprocal effect between different components.
When planning the interactions and multiple assembly options
rather than a finite morphology, change can becomes a value rather
than an obstacle.
&
Second, although zoning is a very inflexible tool in the general
sense, it leaves a high level of flexibility when defining zoning
districts. A district can be zoned to one of five basic categories of
residential, commercial, business, industrial, mixed-use and open
spaces, while the list of permitted uses for each zone clarifies the
fine-grain resolution of the type of business, commercial service,
etc. However, the composition of uses in each zone is not defined,
leaving the character of each zone to be determined predominantly
by market forces. Alexander tackles the macro-scale problem in the
definition of zoning districts in "A New Theory of Urban Design".
He claims that in today's development process the question of what
shall be built in any given place is answered almost exclusively in
economic terms (although written almost 3 decades ago, this claim
still holds true in most planning agencies) (Alexander, 1987). Owing
its origin to protecting uses from one another, zoning has forced
urban planners to think in terms of separation and dispersion of
uses rather than promoting viable urban environments (Duany
Talen, 2001). Nevertheless, if we can equip planners with metrics
to understand the co-location patterns of land uses in the finest of
resolutions, we would enable them to encourage certain types and
qualities of development for their land use mix and relationships
between components.
1.2 Planning for
Complexity
12
Finally, land use plans and consequently zoning ordinances tackles
urban issues in the local level, where every problem is typically
examined and treated as an independent issue. Moreover, the
analysis methods used to identify problems only examine the
city, neighborhood or region level that the plan encompasses.
As a result, remedies for urban issues differ between cities, states
and across nations. Modeling different urban environments by
the relationships between their components while taking into
account the multiple parameters of the urban system and their
dependencies, can assist planners in understanding the cause
and effect of every urban model and deploy desirable models as
remedies in other cities. In addition to harnessing the modularity
that exists between different urban models, variations between city
areas can also be modeled to understand the interplay between
land use types, taking the modularity as a potential urban remedy
to the next level within cities, in a very fine-grained scale.
A quantitative methodology for land use planning can serve the
multiple players involved in the development planning game
which include market players, government officials and advocates
of community and private interests. Contemporary planning
processes are complex and competitive, where all players seek to
achieve the future land use pattern that best suits their needs. A
planning process can be thought of as a community game in which
the values and interests of many players are at stake, where players
are interdependent and rely on each other to forge coalitions in
order to achieve their goals. The planner's role is to facilitate and
balance the competing interests in a planning process that will
result in a win-win outcome for community stakeholders and a
desirable future land use pattern (Hoch, 2000). Quantitative metrics
can assist planners in their role of managing the development
game by equipping all players with measures to understand the
potential implications of land use quantities and location choices on
a city's character. The metrics can serve as a grounds for discussion,
helping players negotiate their interests in a data-driven decision
making process.
However complex the process of land use planning is, its' actual
power to shape land use patterns is somewhat limited. Zoning,
the legal tool for implementing land use plans, has the potential
to correct inefficient land use patterns and guide future growth
to more sustainable directions. Nonetheless, zoning only controls
for the macro-scale development, while economic forces of the
free market shape the fine-grain resolution of land use patterns.
Zoning defines the constrained framework in which developers and
retailers act, while determining the areas they can choose to locate
in or develop their property. However, within these areas, market
players make location choices to maximize their profits and support
successful businesses. The quantitative measures presented
in this thesis can shed light on the land use patterns shaped by
zoning in the macro scale, and by market forces in the micro scale.
Understanding these complex land use patterns is key for paving
the way to a genuine data driven decision-making process for land
use planning.
1.2 Planning for
Complexity
13
Not one solution, but multiple ones
This thesis will follow the definition of planning as an optimumseeking activity rather then as an optimization process. Planning was
first recognized as an optimum-seeking activitywith the emergence
of Planning Support Systems during the 1990s, when it became
clear that planning problems have almost an infinite number of
possible solutions and cannot be optimized and solved sufficiently
with computational methods. A major challenge in optimizing plans
is providing accurate measurements for desirable goals to use as
criteria that need to be met by a given plan. The definition of goals
might fail due to the hidden or undeveloped criteria of choice of all
parties involved in the decision-making process. This situation does
not meet the conditions of optimization methods in general, which
usually seeks one well-defined criterion and presents a single best
solution at the end of the process (Harris, 1989).
Nowadays, after years of attempts to model the built environment
and solve the complexity of a city for <x>, it is clear that no single
mathematical process or automatic aids can fulfill every aspect of
the planning process. However, what we do know is that first, a
complex physical system as a city can be described in an abstract
manner and, second, that empirical analysis has an important
place in guiding and supporting the planning and design of cities.
Alexander articulates the role of quantitative analysis in the planning
practice in the following words:
Alexander, 1966
"Whilst it is impossible with architectural problems to generate
a range of feasible complete and finished solutions by a similar
single uninterrupted predetermined procedure of a mechanical
nature, there is no objection to a stepwise process in which the first
hypothesis is evaluated, the terms of the problem revised and the
second series modified and tested again"
This statement emphasizes the importance of adapting an
evolutionary approach for an iterative planning process, in which
every possible solution is evaluated and tested while modifications
of the parameters and relationships in the analysis method are made
(Martin & March, 1972). An iterative process can be constructed by
deploying a genetic algorithm that will mimic the process of natural
selection. In this process the fitness of every possible solution will
be evaluated versus a set of choice criteria determined by planners,
from which the algorithm will create an improved generation of
solutions from the parameters of chosen solutions (Holland, 1992).
Let us explore how the parameterization of land use planning can
introduce an iterative process with an evaluative nature to the
planning discourse.
1.3
Parameterization of land use planning
Bettencourt & West,
2010
quantitative understanding of cities may well be the choice between
creating a "planet of slums" or finally achieving a sustainable,
creative, prosperous, urbanized world expressing the best of the
human spirit."
1.3 Parameterization of
land use planning
In the quantitative sciences, parameterization is the process of
deciding and defining the parameters necessary for a complete
or relevant specification of a model or geometric object.
Parameterization of a line, surface or volume, for example, implies
identification of a set of coordinates that allows one to uniquely
identify any point (on the line, surface, or volume) with an ordered
list of numbers. This process also defines the degrees of freedom
of the system or model in question and the range of the specific
"... The difference between 'policy as usual' and policy led by a new
14
phenomenon. By borrowing this noun from the quantitative realm,
this thesis will explore a process by which we can first describe an
urban phenomenon across cities in terms of a set of parameters
and then explore the range of this phenomenon using empirical
metrics to assess and compare various urban models.
As mentioned previously, quantitative studies of cities during the
last decay have shown that cities share fundamental properties
that can be observed in similar patterns occurringacross nations,
countries and cultures (Batty, 2008; Bettencourt et al, 2007; Louf et
al., 2013; Louail et al., 2014). Most of these studies found that a city's
population size is an aggregate proxy for a set of general processes
facilitated by the co-location of people and services within cities.
However, these agglomeration laws only provide the expected
average characteristics that a city of a given size should manifest
at the absence of any specific local features. But it is exactly those
local unique features, which can be observed in the deviations of
certain cities from their expected baseline behavior, which are the
most interesting for planners and policy makers (Bettencourt et al,
2010).
Our discussion about shared patterns across cities and their
deployment in the practice of land use planning will be conducted
in two stages: First, we will present an observed similar pattern
and provide recommendations to the extent it can be used to
create planning rules; Second, we will focus on the manifestation
of the same pattern in various cities while emphasizing how the
deviation from this pattern can be used to construct local metrics
for a city, representing its success and failures relative to other cities
(Bettencourt et al, 2010).
This methodology will demonstrate how a set of rules can be
formed, for which every rule has a range of values for planners to
choose from when creating the specific urban model of their desire.
Similar to Alexander's Pattern Language, the sequence of rules will
create a base map from which one can choose the rules that are
most useful for him, leaving them approximately at the same order
they are listed here, and creating a language for his own urban
Lego game (Alexander, 1977).
Let us examine how the Lego methodology can serve the different
stages of the planning process: from the macro scale of choosing a
set of building blocks for a city to the micro scale of co-locating the
blocks within intense urban areas. From the macro scale onwards,
the analysis process would only focus on the most intense of urban
environments, which could be found in city centers of various types.
This will allow the research to first answer the question of what kind
of relationships generates vibrant and lively environments as those
found in city centers.
1.3 Parameterization of
land use planning
15
How many building blocks?
The first step of each land use plan requires estimating future
population growth for a given city. After target population levels
have been set, the proportional number of units and square feet
of each land use type essential to sustain these population levels
is estimated. Here, we will explore an empirical method to assess
the required units using the scaling relations found in the study by
Bettencourt et al. (2007). Deploying the scaling relations between
total population size of a given city to the total amount of units
from each land use type can reveal the expected average of units
required to sustain a predicted population growth. Moreover,
exploring the deviations from the scaling relationships can reveal
the local characteristics of each city that are independent of city
size and can shed light on the impact of different land use types
quantities on the urban environment, resulting in various urban
models.
How to co-locate the building blocks?
The fine-grain scale of the planning process requires thinking about
the spatial organization of land uses within a given city center. This is
a stage that the zoning ordinance lucks and a form of planning that
is not practiced today. Nevertheless, this micro scale of planning
has the potential to transform the way we plan our cities, equipping
planners with a crucial understating of how to plan cities from the
bottom up. Here, we will calculate the pairwise median minimum
distance between various land use types to reveal the co-location
patterns of urban activities and explore an emerging pattern all
cities share. We would explore the co-location patterns in both the
macro scale, using aggregated land use categories as defined by
zoning, and in the micro scale of all land use types in our dataset.
Using both scales of analysis, we would explore how different colocation patterns result in various urban models. Finally, we would
show how planning guidelines could be constructed using the
co-location metric, using the range of every pairwise distance to
form a distance matrix with multiple possibilities to assemble urban
building blocks.
1.4
1.3 Parameterization of
land use planning
16
Summary of chapters
The next chapter will review three major fields of theory and practice
this research encompasses. First, we will review the practice of land
use planning exploring its goals, origins and the implementation
tools of zoning and subdivision. Through reviewing the historical
development of the planning practice, we will highlight previous
attempts to quantify and codify land use planning. Secondly, we will
review the work of three of the great urban theories that offered their
approach for understanding the city and its complexity while also
discussing how and if cities should be planned. Although a lot more
theories have been written throughout the years with an alternative
view about what cities are and how they evolve and function, the
reviewed theories of Jacobs, Lynch and Alexander were chosen
due to their attempt to identify the important parameters of the
urban system and offer their view of how these parameters interact
to form a city, while also articulating principles for planning using
those parameters. Thirdly, we will review models and methods that
attempted to: (1) solve the spatial organization of cities in the field
of economic geography; (2) understand cities using methods from
complexity science; (3) support the planning system by offering
a technological platform for decision-making processes in city
planning. Reviewing this immense body of research will convey
a comprehensive view for the enormous endeavor of envisioning
a new planning methodology while also exploring the tools and
techniques that could assist and support this mission. Chapter three
will first describe the methodological approach and the dataset
analyzed in this research. Thereafter, it will turn to layout the analysis
of the micro and macro scale of the planning process by: analyzing
urban phenomena; observing emerging patterns across cities;
exploring different urban models and how they deviate from the
average patterns and finally reviewing the construction of planning
guidelines using the observed urban behaviors. After introducing
the methodology structure for an alternative planning process, the
final chapter will turn to envision the formation and implementation
of the Lego game methodology as an open platform for the use
of planners, developers and citizens. The discussion section of
the chapter we will review two main topics: (1) methodological
limitations of the research design and future analysis methods for
the spatial organization of cities; (2) offer final remarks regarding
the future development and sustainability of the offered planning
methodology while mentioning the opportunities and risks involved
in the process of implementing it in government.
1.4 Summary of chapters
17
THEORY
The core challenge in city planning has always been determining
the quantities and locations of land uses in the most beneficial
spatial pattern, by developing a strategy to achieve desirable
goals or solve existing problems. While planners focus on problem
solving by a decision making process, emphasizing the desirable
goals and attributes of the produced urban environment, scientists
and economic geographers focus on modeling the interactions
between different urban components, explaining the spatial
patterns they produce and their implications.
Both fields have originated from substantial theories dating back
from the beginning of the 2 0th century and developed separately
until the 1950's when they begun to overlap and support each other
around efforts of planning. Starting from the 1980's, theories from
social sciences has been gradually implemented in city planning
in the form of technical tools, as planning support systems. With
the emergence of complexity science in the last two decades,
the understanding of cities as complex systems had become the
forefront of urban quantitative studies, although their implications
in the practice remains elusive. Although the literature covering the
connections between the practice of planning and the optimization
ideal practiced by the social sciences is limited, this review will
attempt to bridge the two disciplines with the aim of covering all
grounds to create a comprehensive approach for land use planning.
Constructing a methodology for land use planning first requires
understanding the process of planning and its origins while asking
questions like: what k ind of a problem a city is; if and how should
a city be planned and if so, using what theories, analysis processes
and support systems that should take part in the process of land
use planning. To answer these questions and many more we will first
define the process of land use planning and then turn to explore
theories from urban planning, spatial models from economic
geography, assessment and analysis tools to support the planning
process and finally methods from complexity science to reveal
universal laws in the behavior of urban systems.
2.1
2.1.1
2
18
Theory
Theory and practice of land use planning
The establishment of planning regulations
The desire to transform and improve the urban physical fabric in
search of a spatial order marks the start of land use planning. 'The
darkside of urban spatial disorder'was brought into the lighttowards
the end of the 19th century as American cities expanded as places
of production and consumption and simultaneously deteriorated
as places for human life and activity. The over intensive utilization
of land and uneven spatial development of the early 20th century
resulted in high rents but low property values. This phenomenon
gave rise to the regulation argument, claiming that in order "to
preserve the value of land, and the value of buildings it is essential
to regulate type, height, and area of land covered by buildings"
(Purdy, 1916). The 1916 zone plan for New York City marks the official
regulation of zoning in the united stated. The city was divided into
zones with permitted uses, classes of heights were set and design
guidelines were established. In other American cities land was
also being classified into districts and distributed according to use
specifications and height regulations. Slowly the American city was
learning how to secure real estate from destructive congestion and
to discipline nuisance land uses that reduced its land values (Boyer,
1986).
The regulation of zoning ordinance along with the reform planners
of the 1909 plan of Chicago and utopian visions at the turn of
the century gave rise to the foundation of the modern planning
profession. While utopian planning visions of Ebenezer Howard,
Le Corbusier and the City beautiful movement suggested a grand
plan for the ideal city, planning was established as an intervention
with an intention to alter the existing course of events, by replacing
uncertainty of the free market with the logic of a plan. The duality
between planning and the market is a defining framework in
planning theory, where the relationship between the private and
public practice and how much should government intervene are
evaluated (Campbell and Fainstein, 2003).
So what is 'the science of muddling through' of modern planning
versus the ideal model of economic activities and forces that shape
urban spatial pattern? We will now define the goals and process of
land use planning and then introduce the fundamental approaches
in economic geography.
Rem Koolhaas, 1999
The practice of land use planning: the process shaping our cities
today
"The [1916] Zoning Law is not only a legal document; it is also a
design project. In a climate of commercial exhilaration where the
maximum legally allowable is immediately translated into reality,
the "limiting" 3-dimensional parameters of the law suggest a whole
new idea of Metropolis"
2.1 Theory and practice
of land use planning
Land use planning and zoning ordinance are the administrational
procedures that shape our cities. But as Koolhaas wisely defined,
they are not just bureaucratic processes; they are design projects
in which every definition of a limiting parameter has an immense
influence on the final design of our cities. Let us review the every
day practice of land use planning to understand its goals and the
different stages of the process.
2.1.2
19
A land use plan is an outcome of a development planning process
which: (1) translates the community's vision for future growth into
a physical pattern of neighborhoods, commercial and industrial
areas, roads, and public facilities; and (2) includes the policies and
regulations necessary for plan implementation.
Land use planning seeks to influence the location, type, amount,
and timing of future growth. A primary goal of land use planning
is to ensure that all long-term public interests are given adequate
consideration, while helping to mitigate the effects of ad hoc and
nonintervention market decisions. The process of development
planning sets the goals, objectives, policies, and action programs
needed to achieve the long-term vision desired by the community.
This planning process goes by various names as land use planning,
comprehensive planning, or growth management. Regardless
of what it is called, this process revolves around community
development, which is collaborative and aims to balance competing
interests, with a broader focus than simply land subdivision or public
facility provision.
The multiple players involved in the development game all
compete to achieve their desirable land use pattern, while planners
work to facilitate an efficient and equitable development process
that balances stakeholder interests and results in a land use
pattern acceptable by all players. The major participants in the
development process are: market players which include landowners,
developers, builders, financiers, businessmen and others seeking
to profit from development by (1) selling and buying land or (2)
financing, building, and marketing real estate; government officials
which include elected and appointed officials at the federal, state,
regional, and local levels who frame laws, invest public funds,
administer regulations and make decisions on plans and projects
while seeking to maintain their power bases and appointments;
Advocators of community and private interests which include
representatives of neighborhoods, environmental organizations,
economic development organizations, farmers' groups, taxpayers'
organizations, and associations promoting various social and
political goals, all of whom view development on light of their
group's particular values and seek government decisions on
development that will support their aims (Hoch et al., 2000).
2.1 The practice of land
use planning
Facilitating and balancing all competing interest is a complex
planning task. When facing such task, most planners use a rational
decision-making model that adapts versions of a scientific inquiry
as a guide for decision-making. The model follows four basic steps:
1. Identify the problems preventing the fulfillment of
these goals
2. Identify alternative solutions to the problems that
will fulfill the goals
3. Compare the relative advantages of each
alternative as a solution to the problem
While the rational model works well for problems of causal analysis
as in matters of traffic and water, most planning problems are much
more complex than that. Many relationships that contribute to
urban problems do not follow a simple economic model of cause
and effect. In addition, different political interests, social agendas
and theoretical approaches challenge the norms of objectivity and
efficiency on which the rational model relies. In fact, elected officials
rarely use the rational model for public decisions (Ibid).
The rational modelto solve urban problems is the preferred approach
by theorists, but in practice, the successive limited comparisons is
in fact the approach taken by both planners and elected officials.
Although the analytical approach of identifying problems and
performing an in-depth comparison of possible solutions might
seem like the right approach for planning, it only reveals several
aspects that may influence a decision about a planning project but
neglects the values and the non empirical mean-ends routs that
hold great values in the eyes of decision makers. However, due to
the non-empirical nature of the successive limited comparisons
approach, it tends to overlook or consider possible outcomes of a
project, as well as alternative planning policies and other important
values (Lindblom, 1959).
The planning process goals, whether by the rational or
comprehensive approach, are executed by two main planning
stages: firstly, determining the quantities of land use types needed
to support future population growth; and second, spatially arranging
and distributing the quantities across the city, a stage implemented
in the bureaucratic tool of zoning and subdivision ordinance. Let
us explore the problem each planning stage faces and the tools
planners use to tackle these problems.
2.1.2.1
2.1 The practice of land
use planning
21
Population levels required to support urban activities
Estimating future population needs begin from population
projections, which are vital for all elements of planning. Forecasting
for land use planning is a hard task due to the long-range forecast
requiring planners to plan ten or twenty years ahead at a minimum.
In addition, land use planners sometimes focus on small urban
areas, which make the projection task much more complicated
due to volatile and unpredictable economic and demographic
dynamics. The role of population forecasting is to provide a reality
check for vision planning because it defines the limits within which
key ratios can be altered.
Planning agencies use population projections as an index for future
needs in functional areas. Simple ratios are often used to convert
population projections into other future impacts. For example,
the number of future housing units can be derived from the future
population divided by the ratio of people per household. These
calculations can be improved when taking into account the different
population subgroups and their specific needs. For example, home
ownership, employment rates and transportation behaviors all vary
substantially among different demographic groups.
Some analysis of past trends is involved in the assessment process
for future needs but there is also valuable weight for judgment calls
of planners and citizens involved in the planning process. A vision
for a city or sub-area plays an important role in setting quantities
of urban activities. When a city is interested in attracting young
families for example, planners may enlarge the quantities of public
facilities as schools, parks and health services to facilitate this future
growing population group (Hoch et al., 2000).
Throughout planning history, there have been two comprehensive
attempts to quantify the total area or units required of each urban
activity for any given future population growth. The first attempt
appears at the first addition of Time-saver standards for housing
and residential development from 1984, but actually originated at
the rapid urban expansion at the beginning of the last century. At
that period, developers were acquiring vast land areas for large
housing development projects at the outskirts of major cities.
These developments were funded by loans from private banks
across the country. Due to the scale of the projects and the large
sums of money invested, bankers wanted to make sure that the
developers were expending the funds wisely. Thus, the banks
put together a set of standards for population levels required to
support urban activities. This set of standards defined population
thresholds for various urban activities of education, institutional,
health, employment, transpiration, recreation and commercial
facilities (De Chiara et al., 1995).
2.1 The practice of land
use planning
22
The second known attempt to create a method for estimating
required land areas for urban activities was of Harland Bartholomew
in his book from 1932 Urban Land Uses amounts of land used and
needed for various purposes by typical American cities; an aid
to scientific zoning practice (Bartholomew, 1932). Bartholomew
conducted research to provide a method for estimating the total
area required for each particular urban use for any given future
population in the range between 5,000 and 300,000 people. He
aimed to provide planners with a quantitative estimation method,
as an aid for a scientific zoning practice, that will allow the practice
to avoid the speculation in real estate market and decisions based
on conjectures. The research was based on land use data from 22
American cities that Bartholomew collected in his years as a planner
and studied them with respect to population data from the census
of 1920 and 1930. The research classified cities to 4 population
levels and divided the cities to self-contained and satellite cities.
The method used in the research examined the ratios between land
use types quantities and population size in each city, examining how
many acres of a specific land use type existed per 100 people and
the range these rations change between cities. The results of the
study were astonishing - fitting the population size of the examined
cities to the area in acres of each land use type, Bartholomew was
able to show a high correlation between the factors, discovering a
trend and normative values to implement as land use multipliers.
The research revealed that there are definite laws of absorptions or
norms for single-family dwelling, multi-family dwelling, commercial
uses, and combined industrial and railroad property. Moreover, the
study remarkably showed that the proportions of total developed
area increase with respect to city population size, in a linear fit.
As for the implications of this vast study, Bartholomew emphasized
the need for a detailed survey of present city development in the
start of any zoning plan. The survey will be used to assess the
current situation and will be compared to the norms revealed in
the study in order to estimate the required additional quantities of
each land use type. Bartholomew aimed for this research to provide
a guideline of norms for land use quantities as a comparison to the
survey of a given city. The comparison will then be used to arrive at
a satisfactory norm for future growth of a city. It was Bartholomew's
belief that zoning plans that are rooted in actual requirements
will fully realize their purpose as comprehensively conceived and
economically organized cities (lbid).
2.1.2.2
Zoning and subdivision ordinance
When a local government adopts a comprehensive plan, the
two most common legal devices to carry it out are the zoning
ordinance and subdivision regulations. A zoning ordinance divides
a community into zones and regulates the permitted land uses of
each zone, the density of each use, and the dimensions of buildings
on lots. Subdivision regulations on the other hand, govern both
the division of land into lots, parcels, or sites for buildings and
the location, design, and installation of supporting infrastructure.
Together, these regulatory devices unsure that (1) the goals for land
use patterns set in the comprehensive plan are achieved; (2) specific
land uses as homes are shielded from incompatible land uses
such as heavy industry; (3) development is adequately served by
infrastructure and public facilities; and (4) environmentally sensitive
areas such as floodplains are protected from development.
Zoning ordinance separate land uses into 5 basic categories:
residential, commercial, business, industrial, mixed-use and open
spaces. Minimum lot sizes are applied to regulate residential density,
which is the number of dwelling units per acre. In commercial and
industrial areas, intensity is controlled by limiting the number of
square feet or floor area that can be built for each square foot
of land in the building lot. Zoning also enforces building height
limitations, lot coverage restrictions and building setback or yard
requirements.
2.1 The practice of land
use planning
23
A number of governmental entities are responsible for establishing,
authorizing and emending zoning ordinances and subdivision
regulations. These entities include the state legislature, the local
governing body, the planning commission, the board of zoning
appeals, the zoning hearing examiner and planning staff. A zoning
ordinance is usually drafted by the planning staff and brought to
approval bythe planning commission. Every petition for site-specific
modifications are referred to the board of zoning appeals (BZA)
which is authorized to grant variances for an individual property or
use variances for uses not listed in the zoning ordinance.
A zoning ordinance document is comprised of a map showing the
zoning districts and a report that includes:
" Definitions: listing the terms used throughout the report
" General provisions: describing the purposes of the
ordinance
" Zoning district regulations: a table listing permitted uses
- Special development standards: describing provisions for
building dimensions
The list of permitted uses for each zone differentiates between
single-, two-, and multifamily homes. The permitted types of
commercial, business, industrial and public facilities uses are listed,
giving each zone the flexibility (which can also be interoperated
as indefinite) to develop multiple uses. Although the uses are
mentioned in the fine-grain scale of food establishments, religious
institutions, schools, type of industries and much more, the
composition of uses is not defined leaving each zone to develop
its own character based on market forces and developers interests.
However, due the detailed level of permitted uses listed, each
change or revision to the ordinance is a bureaucratic hassle, which
demands multiple hearings and administrational procedures. The
zoning district regulations also include the floor area ratio (FAR),
which is the ratio of permitted floor area of a building in relation to
the size of the lot.
Some criticisms of zoning and particularly the rigid framework
of conventional zoning have prompted the need for more
flexibility in land use regulations. A number of specialized zoning
techniques have evolved that permit more creative approaches for
development as planned unit development (PUDs), overlay zones
and inclusionary zoning (Hoch et al., 2000).
Critics of zoning claim that the pursuit of divergent land use goals is
imbedded in a capitalistic system with distinctive historical features,
when individual and corporate investors aggressively pursue their
capital investments across rural and urban space. The history of
successful and unsuccessful attempts at implementing land use
regulations in the United States in one of conflict, which has taken
various forms, including racial conflict, interclass conflict, and
intraclass conflict. The need to seek the "public good" has been
a source of much discussion in planning, law and policy literature.
Under capitalism, what is called the public good or the generalized
public interest is a community interest, since socio-economic and
racial differences do not allow for a general consensus on most
issues of urban development and land use controls (Haar and
Kayden, 1989). As a consequence of this constant conflict, concerns
regarding class and racial separation which started to arise after
world war I eventually gave rise to the idea of Inclusionary zoning.
2.1 The practice of land
use planning
24
Amongst the critics of zoning, the economists are among the
severest. They claim that through its basic operation, zoning
interferes with the workings of the marketplace of land. For
economists whose disciplinary orientation depends on the utility of
the marketplace to measure and fulfill the needs of people, zoning
is problematic because of its substitution of government judgment
for that of the market (Ibid).
Zoning has been called the doctrine of the statistically ordered city.
It can be described as a planning survey in which uses are quantified,
sorted out and zoned into particular areas; population densities are
assessed and growth and change predicted (Martin & March, 1972).
Let's explore planning theories that offered an alternative to the
systematic process of zoning while also thinking about the nature
of cities and the problems they pose.
2.2
Alternative planning processes: the question of how a city
should be planned
This thesis represents the constant stress between the two main
assumptions lying at the base of the planning profession. This is
the tension between the idea that a city needs to grow organically
without central planning versus the idea that a city is visually ordered
and should be controlled by artificial plans. Three of the great
urban planning theorists of the twentieth century Jane Jacobs,
Christopher Alexander and Kevin Lynch discuss this extreme
tension and share their view of how cities should be planned. The
three theorists take on the question of what makes vital, lively and
holistic cities, quantifying their components in a search for analysis
methods to understand functional cities and offer principles to plan
them. They all shared the belief that planning is an act that should
be practiced by all people, not just qualified planners, but each
theorist gave his unique explanation to the nature of cities and the
source of their complexity.
"Vital cities have marvelous innate abilities for understanding,
communicating, contriving and inventing what is required to
combat their difficulties. ... Lively, diverse, intense cities contain the
Jacobs, 1961
2.1 The practice of land
use planning
25
seed of their own regeneration, with energy enough to carry over
for problems and needs outside themselves."
Jane Jacobs transformed the way we think about and understand
cities in her revolutionary book "The Death and Life of Great
American Cities". Her critique of grand urban visions from the turn
of the century shook the planning world from its core, but it was her
methodological systematic thinking about the nature of cities and
the problem they pose that was the most influential for years to
come. At the last chapter of her book, Jacob raised the question of
'what kind of problem a city is?' in a quest to develop new strategies
for thinking about cities. She turns to scientific thinking methods
and adapts Weaver's (1958) definition of organized complexity to
cities, claiming that cities happen to be problems of such type. This
statement and its importance in the development of a science of
cities will be discussed later on in this review, when we'll turn to
review how complexity science has been applied in the studies of
cities.
Jacobs' life quest was dedicated to decoding vital, vibrant and
diverse cities. She believed that diversity is a dominant and crucial
feature in creating successful cities, and looked for analysis methods
to understand their components and develop thinking tactics that
can be practiced by both planners and ordinary citizens. She saw
the clues for how to plan for such diversity as embedded in the
urban environment around us, just waiting to be noticed as the right
clues. Jacobs laid out the unique features and advantages of big
cities while offering four major needs of a city to create diversity: a
mixture of land uses in each city quarter; short and walkable blocks;
old historic buildings; and urban density of diverse population.
When discussing how should one think about cities, Jacobs offered
three important habits of thought: (1) to think about processes,
their temporal dimension and search for their catalysts; (2) to
work inductively, from the bottom up, reasoning from interactions
among unique combinations of particulars to the general; (3) to
seek for 'unaverage' clues involving very small quantities, which
reveal the way larger and more 'average' quantities operate. The
three offered thinking tactics are dedicated to help urban dwellers
understand the complexity of their environment and shed light on
the important generators of diversity in them, making these cities
successful and vibrant.
"... The city is not, cannot, and must not be a tree. The city is a
Alexander, 1964
2.2 Alternative planning
processes
26
receptacle for life. If the receptacle severs the overlap of the strands
of life within it, because it is a tree, it will be like a bowl full of razor
blades on edge, ready to cut up whatever is entrusted to it... If we
make cities which are trees, they will cut our life within to pieces."
Christopher Alexander made his life's work about decoding the
elements and development process that created old towns and cities
and gave them life, with the aim of utilizing them to heal modern
cities. In his groundbreaking paper "A city is not a tree" from 1965,
Alexander made his first attempt to characterize the complexity of
vibrant old cities by making a clear distinction between 'natural' and
'artificial' cities and the abstract ordering principle behind them. He
argued that all cities are structures of sets, which are collections of
elements that can be combined together in sets in various ways.
Alexander showed that while 'natural' cities, which developed
incrementally over many years have an ordering principle of a semilattice, allowing endless possibilities for combinations of sets and
creating a potentially more complex and subtle structures, 'artificial'
central-planned cities have an ordering principle of a tree, limiting
the number of possible combinations and creating simple and
rigid structures, where no element can be distinctly connected to
another except through the set of elements as a whole.
The most important difference between the two structures is
defined by the possibility of overlap between sets of elements.
While semi-lattices contain overlapping sets, tree structures does
not allow for an overlap to occur. When referring to cities with
tree structures, Alexander gives the example of pre-planned cities
that are organized in segregated neighborhoods, were land uses
as work and housing are totally separated by zoning and even
recreation is disconnected from everything else in the form of
fenced playgrounds. According to Alexander, the humanity and
richness of the living city are embedded in its complex semi-lattice
structure, which will be destructed by the compartmentalization
and dissociation of tree-like modern plans for cities.
2
d
4
4
3
2
123454
2
3
6
6
The structure
illustrated in diagrams
a and b is a semilattice while c and d is
a tree
Source: Alexander,
1964
Alexander, 1987
2.2
27
Alternative planning
processes
It is only in a later work from 1977 where Alexander laid out the
semi-lattice idea of how a city should be planned, in the form of
"A Pattern Language". This book is essentially a 'cookbook' for
the most successful patterns of urbanism that together form a new
design language. The patterns are written as recipes instructions
for ordinary people to be able to design their own homes and
neighborhoods for themselves. This innovated approach to design
originated in the idea that most of the wonderful places of the
world were not made by architects but by the people that used
them. Each society will have its own unique pattern language and
so will every individual in that society, partly similar and shared with
others. In this sense, the language presented in the book is the
archetypal core of all possible pattern languages, which can make
people feel alive and human.
The elements of the language are patterns, each describing a
problem of our environment and its core solution in such an abstract
way that can be used a million times without ever doing the same
thing twice. All patterns are connected in a network, but every
pattern should always be used in a sequence, moving from the larger
patterns in which it is embedded in, to the smaller patterns that are
embedded in it, still allowing for infinite variety of combinations.
The language represent a holistic approach, demonstrating that
nothing is ever built in the world in total isolation and any act of
building must also repair the world around it and within it, so that
the larger world becomes more coherent and more whole.
"An urban process can only generate wholeness, when the structure
of the city comes from the individual building projects and the life
they contain, rather than being imposed from above.
"
6
Alexander presented his complete methodology to the process of
urban design and planning in "A New Theory of Urban Design".
Here, he explored the idea of growing a whole in the urban context
and suggested seven detailed rules of a growth process that creates
wholeness in a city. The overriding rule in the process suggests that
every increment of construction must be made in such a way as to
heal the city. This includes not only the repair of existing wholes
already there but also the creation of new wholes. In addition,
Alexander defined that every new act of construction must create a
continuous structure of whole around itself. The seven intermediate
rules elaborate the multiple layers of creating wholeness in a city.
As we review Alexander's work throughout the years, we can
observe that he started his journey with abstractly defining ordering
urban structures, laying out fundamental thinking principles for
understanding the complexity of the urban system from the bottom
up. As his work progressed over time, he presented more and more
efforts to define the particular components of vibrant and rich
cities while specifying deterministic values and quantities, rather
than identifying parameters to measure and generic principles
to follow. Alexander's vision originated from a general analysis
method for the spatial structure of the city and overall concept
of how cities should be planned. It evolved into the specification
of urban patterns and a possible language of instructions for how
the patterns should be used to plan. Finally, his work resulted in a
deterministic theory for urban design, naming mainly the features
of the desired environment rather than the general elements that
comprises it.
Lynch, 1984
"The fundamental good (in a city) is the continuous development
of the individual or the small group and their culture: a process of
becoming more complex, more richly connected, more competent,
acquiring and realizing new powers - intellectual, emotional, social
and physical."
Kevin Lynch posed the question of "what makes a good city?"
towards developing his vision of measuring city form. Though
an abstract question, Lynch explored it by connecting city form
to human values and the process of development to objective
relationships. He believed that if we could only articulate whywe feel
a certain place is less than satisfactory, "then we might be prepared
to make effective changes". Unlike Jacobs and Alexander, Lynch
reframed himself from making general claims about what cities
are and what makes them 'good' or 'bad', as he saw these claims
as specific in time and place, totally embedded in the culture and
values of that place. Therefore, his quest was to create a general
normative theory of city form, composed from a set of performance
dimensions to evaluate any given city or development plan and
locate it on the dimension scale, whether by number or estimation.
The dimensions offered are performance characteristics deriving
mainly from the spatial features of a city and set a measurable scale
for any community, individual or planner to prefer a satisfactory
position on.
2.2 Alternative planning
processes
28
Lynch offered five major performance dimensions, each referring to
a cluster of qualities for the spatial form of cities, share a common
basis and can be measured in a similar way. The five dimensions
to measure the quality of goodness in an urban environment are
the following: (1) Vitality - the degree to which the form of the
settlement supports the vital functions, the biological requirements
and capabilities of human being, protecting the survival of the
human species; (2) Sense - the degree to which the environment,
our sensory and mental capabilities, and our cultural perceptions
match each other; (3) Fit - the degree to which the behavioral
settings of a settlement sufficiently support the quantity of actions
its' people want to engage in including the adaptability to future
actions; (4) Access - the ability to reach other people, activities,
resources, services and information, which are many and diverse; (5)
Control - the degree to which people using services in settlement
are controlling the use, access and modification of these services.
In addition to the five performance dimensions, Lynch added two
meta-criteria that are totally dependent on the prior five dimensions:
(6) Efficiency of cost for any level of the listed dimensions and (7)
Justice in the distribution of resources. Together, the seven criteria
are the comprehensive measures of settlement quality by Lynch.
Each society can prioritize the measures and by applying them,
can evaluate the relative goodness of its environment, thus having
some indications for how to improve or maintain that goodness.
Throughout constructing his theory, Lynch raises various questions
and doubts regarding the level of sufficiency, objectivity and
interdependency of his suggested measures. Moreover, although
his measures offer a method to quantify social and cultural values,
he himself notes that their integration should be left to personal
and social judgment.
2.3
L6sch, 1954
Modeling the spatial organization of land uses in economic
geography
"The real duty of the economist is not to explain our sorry reality,
but to improve it. The question of the best location is far more
dignified than determination of the actual one".
The question of how to determine the spatial organization of lands
uses in cities has long been the interest of planners and economic
geographers. The idea of modeling the urban environment, whether
qualitatively or quantitatively, with the goal of finding the ideal
location for land uses given a set of constraints appeared as the
solution for optimizing the performance of the built environment in
multiple aspects.
2.2 Alternative planning
processes
2?
The fundamental approach of economic geography is the bid rent
functional approach, which was first introduced into an agricultural
land use model by Von Thinen (1826) and later extended to an
urban context by Alonso (1964). This approach focuses on land as
a commodity that is completely immobile and therefore associated
with a unique location in geographical space. Urban Economists
employ the bid rent function approach to determine the equilibrium
location of each household in the city as well as the equilibrium
approach and optimal land use patterns of the city. A bid rent
function essentially describes a particular household's ability to
pay for land at each location under fixed utility level. This approach
enables economists to graphically analyze the competition for
land among different agents in the urban space (Fujita, 1989). In
the central business district (CBD) of the city, the bid rent function
considers all land uses of retail, office and residential as competing
for the most accessible location in the city, when the highest bidder
for rent per square feet will locate at the most central spot and all
other uses will create concentric rings around the CBD with rent
decreasing as a function of distance from the central core of the
city.
Relationship between
location rent and the
spatial organization
of land uses within an
urban center
Source: Lloyd & Dicken, 1977
Thus, according to the bid rent model, we would expect to find
offices and commercial services at the heart of the CBD, which are
establishments that would benefit the most from locating in the
most accessible area for customers along with maximizing the
proximity to other similar establishments to benefit from positive
externalities. At the outer core, we would expect to find industrial
land uses of manufactories, willing to pay enough to be close to
central transportation arteries and marketplaces, but still requires
larger lots, which are available at the outer core. The farthest we move
from the CBD's inner core, the attractiveness of land for industry
and retail establishments decreases, leaving rent prices cheaper
and appealing for residential uses, which are less dependent on
transportation linkage and proximity to marketplaces.
RAN"
HOL
The spatial distribution of retailers across the city was formulated
in the work of Walter Christaller (1933) and August L6sch (1954)
on Central Place Theory (CPT). Centralistic order was defined by
Christaller as "the crystallization of mass around a nucleus...
the elementary form of order of things which belong together"
(Christaller, 1933). In CPT, locations are determined by range, which
is the maximum distance a consumer will travel to purchase a good
and threshold, which is the minimum demand necessary for a store
to stay in business. CPT defines the catchment area for amenities
included in central places, categorizing them to either higher
order or lower order. Higher order central places include central
functions serving larger regions while lower order central places
include functions of local central importance to the immediate
surrounding. The combination of range and threshold leads to a
regular hexagonal pattern of retail locations, where the maximum
range consumers will travel and the minimum threshold for a retail
activity determines the size of the hexagon.
-
THRE
Market ares of
identical stores in
Central Place Theory
Source: Sevtsuk, 2010
A
^
Overlapping market
areas of hierarchical
centers in Central
Place Theory
Source: Sevtsuk, 2010
2.3 Modeling spatial
organization
30
The two approaches we mentioned explain land use patterns
observed in central locations by describing location choices as
expressing the tradeoff between land and travel. Both models
simplify the analysis of spatial patterns by assuming a homogenous
environment while eliminating the role of transportation networks
and urban form. However, the reality of the built environment is
much more complex. The irregularity of the urban street network
and the variety of three-dimensional buildings in lot sizes, heights
and shapes carries an important effect on the spatial distribution
of centers in the intra-urban settings. Another example for such
simplified model is the one-dimensional model of DiPasquale and
Wheaton for store location from 1996, which assumes that retailers
distribute evenly along a straight line in an identical distance from
each other as a function of retail facility, transportation costs,
purchase frequency and buyer density. However, if we take into
account the complexity of the urban environment, noticing that the
density of customers is affected by building heights, we will find that
the relationship between retail and customer density is not linear.
Therefore, doubling the population density of an area reduces the
distance between retailers by less than half (Sevtsuk, 2010).
2.3.1
Planning for economic development
Models of economic geography equip planners with the ability
to understand spatial patterns in order to repair markets failures
and predict future growth. Spatial patterns are the product of
the interaction of economies of scale and transportation costs.
Economies of scale are the savings achieved in per-unit cost as the
level of output increases. This means that the larger the size of the
output necessary to achieve production efficiencies, the greater the
concentration of activity in few places. Moreover, transportation
costs can cause economic activities to cluster or disperse,
depending on the circumstances. That is to say, the higher the costs
of transporting goods, the more likely that the activity will serve a
small geographic area.
Another challenge planners face is economic growth. The economic
base model uses a multiplier to link changes in regional economy
to (1) changes in export activity, as employment or income and (2)
changes in demand for more local activity. The model assumes
that money earned through exports generates demand for more
local activity. This base model allows planners to identify the
industries that are over or under represented in an area. While
overrepresentation of one industry may make a region vulnerable
to nationwide changes in employment patterns for that industry,
the presence of an underrepresented industry creates the potential
for growth. Location quotients (LQs) assess the local distribution
of national economic activity by comparing an industry's share of
the local economy with that same industry's share of the national
economy. This measure allows for comparison of the performance
of different urban areas in respect to the national average. However,
it does not investigate how and why economic variations occur,
a question that requires other analytical tools. One such analysis
tool is an input/output analysis, understanding the links between
the flow of incoming money to a region versus the flow leaving
the same region. This tool also sheds light on the dependencies
between industries and enables planners to identify the industries
that are more likely to strengthen inter-industry linkages, and hence
create more substantial clusters of related economic activity (Hoch
et al., 2000).
2.3 Modeling spatial
organization
31
2.4
Planning Support Systems: analysis and assessment tools
&
The term Planning Support Systems (PSS) encompass a wide range
of technology-based solutions that aim to facilitate a 'new' planning
practice, by implementing the diversity of methods, techniques and
models from academic research in the analysis and decision making
processes of urban planners. Most available geo-information tools
do not fit the changing needs of the planning practice and are far
too general, complex and rigid to facilitate planning tasks, oriented
towards technology and theory rather than the planning problems
themselves. This mismatch between demands of practitioners and
the supply of methods and techniques gave rise to the diversity of
planning support systems related to geo-information technology
and are primarily developed to support different stages of the
planning process including: problem diagnosis, data collection,
mining and extraction, spatial and temporal analysis, data modeling,
visualization and display, scenario-building and projection,
plan formulation and evaluation, report preparation, enhanced
participation and collaborative decision-making (Geertman
Stillwell, 2004).
Before the development of PSS, the rational comprehensive model
of problem solving and decision-making emerged in the fields of
planning theory and economic geography during the late 1950s
and 1960s. This model was based on the idea that one ultimate
formula could be produced to solve the spatial organization
problem of the city in the form of a 'What if' simulation modeling.
During this period, idealistic models were developed with the goal
of optimizing city plans to redefine the urban reality for the better.
When city data became available, it was clear that all models suffered
from limited empirical validation and could not be calibrated to
represent the reality of the city. However, optimization remained
a viable goal when PSS started to emerge during the late 1990s
but planning had to be recognized as an optimum-seeking activity
and the optimization argument had to be shifted to a concrete
consideration of optimizing methods (Harris, 1989). The theoretical
quest was scaled down to the level of identifying spatial patterns
and understanding the process that produces such patterns in an
attempt to predict future ones.
2.4 Planning Support
Systems
32
Britton Harris coined the term Planning Support Systems in his
landmark paper "Beyond Geographic Information Systems:
Computers and the Planning Professional" where he argued
that standardization optimization methods could not work in the
context of city planning, but offered ways by which the study of
optimization can help guide the planning process. Harris showed
that most large planning problems are "NP-complete", which are
problems that have enormous number of possible solutions (Cook,
1971) as in the case of the spatial organization of only one city block,
and cannot be solved in a satisfactory manner by computational
methods. However, he claimed that optimization methods could
be useful when applied to certain sub-systems in the metropolitan
region by using algorithmic resources to solve distinct problems as
Harris, 1989
finding the shortest path between two urban activities. Moreover,
it is the nature of NP-complete problems to have many local
optima and Harris outlined how by comparing such local solutions
which reveal certain urban patterns, the planning process can
be supported by computational methods to achieve a satisfying
plan. The comparison and evolution process can take the form of
an evolutionary approach, where both the computer algorithm
and planners offer minor improvements to every alternative and
test its performance by measures of spatial interactions such as
congestion, land rents, accessibility, density and amenities service.
In Harris's words:
"a true planning support system must have the capability to
employ locational and spatial interaction models, both to produce
parts of plans constructively and to provide diverse measures of
planning effectiveness. Such a capability goes beyond the analysis
of coincidence, contiguity, and proximity supported by standard
forms of GIS"
The development of PSS gained momentum towards the end of
the 1990s and today the field includes a diversity of PSS that differ
in aims, capabilities, content, structure and the technology they use
(Geertman & Stillwell, 2004). Types of PSS include: retail planning
support systems that deploy spatial-interaction models to predict
retail turnover in shopping centers and shifting market shares of
existing centers; transportation models of travel demand that
traditionally have been used to assess investments in new highway
development, but more recently are also utilized in the context
of transportation demand management. The four step model of
transport demand first predicts the number of trips generated for
a series of traffic zones, allocates the demand to transportation
modes and destination zones, inputting the origin-destination
result table to a simulation model to understand traffic intensities
in different parts of the city; cellular automata models which for
an array of cells, characterize different agents and by enforcing
transition rules, letting the process run iteratively resulting in spatial
patterns that emerging showing how cities are likely to evolve over
time; integrated land use transportation models that assess the
quantities of population and employment corresponding to land
use quantities and then turns to allocate the activities to zones
according to their potential (Timmermans, 2008).
2.4 Planning Support
Systems
33
The application of PPS in planning practice has been the subject
of much debate and there seem to be evidence to substation
frustration about an assumed lack of dissemination of models to
the planning practice in literature on the subject. The application
of models in every practice is a process that requires sufficient
resources of time, money and qualified personal. The dissemination
of models in the planning practice is a process that needs to be
managed in itself. It requires cooperation between academics and
practitioners and an incorporation of a given model in the working
habits and routine procedures of planners. Though the reasons for
a government agency to adapt a planning model are diverse and
numerous, a dominant reason is that existing tools and practices
fail to give answers to new policy questions leaving the planners in
need for new decision-making tools. Moreover, the dissemination
process over a wide range of planning agencies requires some
leading players that will create a positive wave for other agencies
to follow, sometimes taking a change in generation (Ibid).
Nowadays, there is much less consensus than there was 50 years
ago about how cities grow and evolve. Fragmented theories
have made technique dominant in the field of PSS and thus
developments in computational technologies drive the field rather
than innovative large-scale models (Batty, 2007). Nonetheless,
nowadays complexity science emerges as the key approach for
understanding and modeling the development of cities. Modeling
cities as complex systems is leading the forefront of urban studies
while used to develop comprehensive theories about how cities
grow and evolve.
2.5
Cities as Complex Systems
Simon, 1962
"Roughly, by a complex system I mean one made up of a large
number of parts that interact in a nonsimple way. In such systems,
the whole is more than the sum of the parts, not in an ultimate,
metaphysical sense, but in the important pragmatic sense that,
given the properties of the parts and the laws of their interaction, it
is not a trivial matter to infer the properties of the whole."
This quote by Herbert Simon implies that complex systems are more
than what they seem by the naked eye. This idea is long-standing
in science and traditional systems theory. The unique definition
of complex systems is the idea that to understand the whole, it is
the dynamics of how the systems' parts behave in relation to one
another that is important.
Weaver first defined problems of organized complexity in the 1958
"Annual Report of the Rockefeller Foundation", a definition that
was later adapted by Jacobs (1961) to describe cities as problems
of organized complexity. Modeling cities as complex systems is
grounded in the thesis that these systems have many variables
that are interconnected and in constant simultaneous change.
According to Jacobs, understanding how interactions in a network
of individuals shape their surrounding urban environment is key to
plan better solutions for urban problems (Batty, 2005).
2.4 Planning Support
Systems
34
At her time, Jacobs was actually arguing against the theories that
were becoming the contemporary wisdom. The contemporary
theories of the social sciences of the 1950s and 1960s were essentially
positivist and assumed that cities were systems that moved to
equilibrium rather quickly and thus can be modeled with one
comprehensive formula. These idealistic models were developed
in an attempt to change the planning paradigm completely and
redefine the urban reality for the better, dealing with urgent
problems of massive urban growth and new transportation systems
that were transforming cities. When city data became available,
it was clear that the models all suffered from limited empirical
validation and could not be calibrated to represent the reality of
the city. Starting from the 1970s, the social scientists and economic
geographers redefined the theoretical study and scaled it down to
identify the spatial patterns in the city in addition to understanding
the process which produces such patterns in an attempt to predict
future patterns, while leaving the notion of temporal dynamics
almost entirely absent.
The need to think about urban dynamics to provide more than a
descriptive explanation of how various economic and social forces
"could" work, gave rise to the concern for nonsmooth dynamics
explaining discontinuities around 1980s. But it was the conception
of thinking about cities in terms of actions of individuals, agents,
stemming from ideas about disaggregation and heterogeneity,
that actually change the face of modeling urban systems, opening
the gate for agent based modeling and simulation models around
1990s. It took development not in theory but in computing and
data to propel these models forward. The major transformation of
modern computing and available data in the last forty years was the
dominant factor in enabling processing for urban systems models,
allowing the feasibility of models based on a large number of units
in the micro level of disaggregation in space, time and location of
urban activities (Ibid).
Nowadays, we stand on the verge of a cultural shift in the way
we perceive data and its ability to help us solve the fundamental
problems of the city. The rise and development of the sciences
of complexity in recent years have changed the direction of
studying systems theory from top down to bottom up, treating
systems as a product of an evolutionary process, rather than that
of a grad design. This approach enables us to study cities as not
being centrally ordered, but as systems that evolve mainly from
the bottom up as the product of millions of individuals and group
decisions, with only occasional top-down centralized actions (Batty,
2013). The result is a set of ordered patterns that emerge from the
actions of countless individuals. The patterns observed in complex
systems as cities, which self-organize into clusters from the bottom
up, manifests themselves by a set of rules, which are established at
the lowest level and repeat themselves at larger or higher scales.
PlEAILE CITES
.11
."
...
Data from 360 US
metro areas show that
metrics as wages and
35
crime scale similarly
with population size
&
Source: Bettencourt
west, 2010
2.5 Cities as Complex
Systems
Recent studies attempted to establish a predictive quantitative
theory of urban organization and development using the vast
amount of accessible data while applying methods from complexity
science. Most of these inductive studies set population size as
a proxy for multiple variables in the urban context. A study by
Bettencourt et al. (2007) was the first to present empirical evidence
showing that important demographic, socio-economic, and
behavioral urban indicators are, on average, scaling function of a
city's population that are quantitatively consistent across different
nations and times. The urban indicators are divided into three
H
0
0CA
Scale independent
ranking of residuals
for US metro areas by
patenting (red denotes above average
performance, blue
below)
Source: Bettencourt et
al., 2010
-gas
..
.
...
-..
-- --.
-I
The temporal
evolution of scale
independent
indicators for patents
displays long-term
memory over time
(1975-2006). Shaded
grey areas indicate
periods of national
economic recession
Source: Bettencourt et
al., 2010
2.5 Cities as Complex
Systems
36
trends: quantities reflecting wealth creation and innovation show
increasing returns to scale, scaling superlinearly with city size;
quantities accounting for infrastructure demonstrates economies of
scale while scaling sublinearly with city size; and individual human
needs as jobs, housing and household water consumption show
linear scaling with city size. These findings suggest that there is a
universal social dynamic at play that underlies all characteristics of
urban life and which can be used to predict growth and the pace of
life in cities. Moreover, in a study from 2010, Bettencourt et al. show
how deviations of various cities from the universal average behavior
can be used to understand the strengths and weaknesses of every
city, showing the local characteristics of each urban environment.
Another approach for modeling the temporal and spatial dynamics
of cities to understand the networks of interactions between
millions of individuals is by looking at universal patterns in human
urban mobility. Studies analyzing pedestrian mobility flows from
recent years were able to prove that human mobility flows in urban
environments share a similar power low distribution as physical
distance decreases between two locations in a city (Noulas et al.,
2012; Gonza'lez et al., 2008). The dependency of human mobility
on distance shown in gravity models is inspired by Newton's law of
gravity and validates the idea that density of people and services
in urban environments generates movement. Moreover, a study
modeling the transition between monocentric to polycentric cities
found that the number of subcenters and total commuting distance
within a city scale sublinearly with its population, when measuring
the traffic congestion in cities (Louf et al., 2013).
All these studies and many more using substantial datasets to
model the complex networks of cities has the potential to assist
city planners in developing operational planning tools grounded
in extensive empirical data. A new science for city planning can
inform questions connecting city size to scale and shape through
information, material, and social networksthat constitutethe essential
functioning of cities (Batty, 2008). In their piece "A unified theory of
urban living" Geoffrey West and Luis Bettencourt (2010) emphasize
the need for an integrated, quantitative, predicative, science-based
understanding of the dynamics, growth and organization of cities.
They claim a 'grand unified theory of sustainability' of cities and
urbanization must be developed, requiring collaboration across
science, economics and technology, including business leaders,
scientists and practitioners to work together to create a new science
of performance-based planning: "by coupling general goals to
actionable policies and measurable indicators of social satisfaction,
successes and failures can be assessed and corrected for, guiding
development of theory and creating better solutions ".
Paving the way to develop a grand unified theory of urban living
and scientific tools to plan cities requires developing urban metrics
to compare and contrast the performance and functionality of
cities in various dimensions. Such metrics can help us identify
the connections between dominant factors at play in each city,
the diversity of factors and their combined effect on the overall
performance as captured by the metrics. This analysis process is
similar to identifying the building blocks or Lego pieces available in
a city and the urban model their combination produces.
SINGAPORE
HOUSTON
xSAN FRANOISCO
LL
0.24(.)-o
2
Iff'
..
-.
.......
io'
102
]02
101
RANK
Human movments
driven by the density
of the of the geographical environment
showing a trend of
decreasing movements as a function
of increasing ranks
(defined as the no. of
closer loc ations in a
given path)
Source: Noulas et al.,
2012
The Lego game methodology or combinatorial model approach
has been used to capture measures of complexity in various
fields as economics, theoretical biology and mathematics. In
mathematics, Kauffman and Levin (1987) developed the NK model,
a general theory to understand the process of adaptive evolution
and the optimization process it involves, measuring the fitness of
variables in incremental steps, the complexity of the system and
optimizing the overall size and characteristics of the system by
adjusting its' parameters. The NK model has found application in a
wide variety of fields, including the theoretical study of evolutionary
biology, immunology, optimization and complex systems. In
economics, Weitzman (1998) developed a recombinant growth
model to understand innovation through the way old ideas can be
reconfigured to make new ideas, providing a production function
for the creation of new knowledge that depends on the various
new ways to recombine old knowledge. Another example for a
Lego game methodology in economics is the work of Hidalgo and
Hausmann (2009) that provides measures to capture the components
of economic complexity, by looking at growth and development
and connecting products to the countries that export them. The
model of economic complexity developed in this research captures
the capabilities of a country to develop a particular product by
measuring the diversity of capabilities and their dependencies in
one another to understand the country's level of productivity.
The combinatorial models reviewed here have major implications in
various practices and can be deployed to advance the productivity
of multiple entities ranging from countries and their economic
development to firms and economic models. The planning
methodology outlined in this thesis will attempt to bridge the gap
between academic research and the planning practice, by providing
a Lego game methodology to develop the efficiency of land use
patterns and flexibility of the planning process. This thesis follows the
approach of modeling cities as problems in organized complexity
to reveal universal patterns in the quantities and co-location of
land uses in cities while also untangling dependencies between
land use types. Moreover, using the combinatorial approach of a
Lego game, the modeled interactions and dependencies between
land uses will enable planners to create game rules for the infinite
number of assembly options of land uses while understanding the
implications of each combination.
2.5 Cities
as Complex
Systems
37
METHODOLOGY
This chapter will introduce a quantitative methodology for land
use planning. We will review stages of the planning process in the
scale and scope of the problem they face while offering an analysis
method to reveal quantitative relationships. These relationships will
then be used to construct a set of planning guidelines. Implementing
quantitative methods in the practice of land use planning will allow
us to introduce a dimension of modularity to the field by offering
planning guidelines that enable multiple solutions for choosing
and assembling a set of urban building blocks, in the form of a
Lego game methodology.
The idea behind creating a Lego game methodology for planning
is driven by the quest to find the right mixture of components and
their spatial organization that defines a successful urban model.
Analyzing the mixture and arrangements of urban components is a
thinking process we all exercise when we choose the city we want
to live in, the neighborhood we want to raise our children at or the
urban area we enjoy for leisure. Think about how one will describe
his favorite area of a city: he'll talk about the vibrancy and liveliness
of the area, the leisure activities he find there as restaurants and
shops or the recreation activities as parks and zoos. A parent
trying to find the best neighborhood to relocate with his family will
describe the quality of schools, the handful of open spaces and
the health and public facilities a neighborhood has to offers. These
are all descriptions for the right set of components that together
compose a desirable urban model.
Urban planners engage in the same thinking process in their daily
practice, in an attempt to analyze good urban models and extract
the planning guidelines that will recreate them. But how does one
define a good urban model? Kevin Lynch defined a model as: "an
adjective meaning 'worthy of emulation'... a picture of how the
environmentought to be made, a descriptionofa form or a process
which is a prototype to follow"(Lynch, 1984). Lynch argued for the
necessity of models in the planning process and characterized
how one should construct planning guidelines from desirable
models. He defined performance statements as the right method
to construct guidelines as such that describe the underlying effect
of a desirable product, while leaving the means flexible and open
for innovation. While the flexibility of performance statements are
the quality making them suitable for a general planning theory,
Lynch claimed that connecting the statements to specific mental
pictures of an environment and the method by which they should
be implemented are key to their success.
3
38
Methodology
We will attempt to follow Lynch's definition for planning guidelines
in constructing the Lego game methodology. We will offer flexible
guidelines that define a range of possible values, while also exploring
closely particular urban models as mental images to follow. We will
examine various urban models to understand what makes one city
differ from another, asking questions like how does Boston differ
from New York and does Los Angeles resembles Washington. Here,
analyzing urban environments as complex systems will enable us
to characterize cities by a set of parameters and the relationships
between them, identifying key parameters in preferable urban
models.
The construction of the Lego game methodology will explore the
practice of land use planning with a set of several assumptions
in mind: (1) Cities as complex adaptive systems manifest similar
patterns of behavior; (2) These patterns can serve as guidelines
for the expected average urban behavior; (3) Planning guidelines
would be drafted as relationships between components, offering
a range of possible values; (4) Consolidating a set of parametric
planning guidelines to model an urban environment offer multiple
assembly options for the spatial organization of land uses.
To establish the Lego game methodology, we will review each
planning stage and the problem it faces following four main steps:
1. Analyze an urban phenomenon from a wide perspective: to
reveal similar patterns across cities
2. Observe an emerging pattern: to define the average
behavior a city should manifest
3. Explore different urban models: to identify unique urban
characteristics as manifested by the deviations from the
baseline average behavior
4. Construct a set of planning rules: by choosing a single
value or range of possible values for each urban relationship
We will begin by outlining the offered methodology from the
macro scale of choosing a set of building blocks for a whole city
and then zoom-in to the micro scale of distributing and co-locating
the building blocks in intense urban environments as city centers.
The findings from each analysis stage will allow us to explore the
modularity in choosing a set of building blocks to create various
spatial organizations for a city.
3.1
3
39
Methodology
Data sources and structure
To demonstrate the quantitative planning methodology we needed
to analyze a dataset that is consistent across cities with a unified
index of land use categories in a very fine-grain scale. This kind of
dataset is challenging to collect for several reasons. First, although
the general structure of land use data is similar across cities, there
are multiple variations between cities in the indexing of particular
land use types. Second, there are numerous differences in zoning
ordinance indexes due to specific definitions of zones and special
overlay districts defined by each city. Third, land use data is collected
and organized by an aggregated index of top land use categories
for necessary simplifications. This index typically includes six to ten
categories, providing a very low-level resolution of the quantities
and distribution of land uses in cities. Our analysis required a
high-level resolution dataset with a disaggregated index of uses,
in order to have the ability to describe the realistic, fine-grained
land use patterns we observe in cities. Due to these reasons, we
chose to use the Google Places API dataset that presents a unique
opportunity of indexed categories, which are consistent across the
world and includes a high number of land use types in a very finegrain scale.
Each data point in our Google API dataset marks the location and
type of amenities in the 50 largest metropolitan areas in the US,
ranked by population size. The criterion of largest metropolitan
areas was adapted for data availability reasons, attempting to
include indexed cities that contain as many points as possible.
The availability of data presents a challenge since the data points
included in Google maps are mostly user generated and thus, the
most populated cities in the US have a better chance of including
more data points with better accuracy.
The original index of amenity types included 96 different categories.
We cleaned the dataset and filtered out the irrelevant categories
for land use analysis: establishment, route, point of interest,
locality, sub-locality, intersection, colloquial area, neighborhood,
and natural feature. We also filtered out categories that were not
well represented in the data and had less than a 100 points overall,
in the entire dataset. The cleaned dataset we generated have an
index of 78 unique categories, including all land use types, except
residential and industrial uses (see full list in table 2).
3.1 Data sources and
structure
40
The unique Google Places dataset also has its limitations. First,
land use data is generally collected in area units as square meters
or feet, describing the total land area allotted or occupied by a
particular use. Data in total land area provides a measurement
for the volume of each particular use in a city, even more so when
the data is measured in floor area, providing a genuine threedimensional volume of land uses. However, the Google Places
dataset presents land uses as points, providing a different measure
of volume for land uses, possibly less accurate for practical planning
purposes. Second, since the data included in the dataset is partly
user generated, there are potential accuracy issues, shortcomings
and biases of the data that might create a misrepresentation of
reality versus the true situation on the ground. User-generated
data can potentially be more accurate in larger cities in comparison
to smaller cities, where more users have access and are aware of
the technological platform of Google maps. In addition to usergenerated data on Google maps, Google also incorporates data
collected from location information that appears on Internet pages,
which are unverified sources that can also potentially skew the data.
Moreover, Google maps also serves as an advertising platform in
addition to being a navigation tool. This fact can also potentially
skew the data in favor of businesses wishing to promote themselves
through this platform. However, since the data is mostly provided by
licensed businesses, it is more authoritative and reliable compared
to data collected from private contributors. Also, Google has
moderators who try to verify the accuracy of data provided from
and changed by contributing users (Helft, 2009).
Our chosen unit of analysis is the metropolitan area of a city, a
concept known as the "functional urban region": a flow of people,
goods, energy, information and capital connected by ground
transportation paths. In the U.S., this definition corresponds with
the Metropolitan Statistical Area (MSA). The United States Office
of Management and Budget (OMB) defined the MSA as one or
more adjacent counties or county equivalents that have at least
one urban core area of at least 50,000 population, plus adjacent
territory that has a high degree of social and economic integration
with the core as measured by commuting ties. Population data for
metropolitan areas was collected from the US census bureau, from
the 2010 American community survey.
Let us now demonstrate how this dataset can be used to analyze
urban environments by their land use composition and spatial
distribution, and use the results for the purposes of constructing
planning guidelines.
3.1 Data sources and
structure
41
3.2
Hoch et al., 2000
Choosing a set of building blocks
"Planning efforts revolves around people: people create the need
for planning functions, and they experience the effects - for better
or for worse - of these efforts. The anticipation ofpopulation change
is essential to planning that can effectively meet future needs."
The first stage of drafting a land use plan is crucial for meeting
future needs as it involves estimating future population growth
or change of a city and the corresponding functions required to
serve this population. In this estimation process, the amounts of
units or square feet from each land use type required to sustain
the predicted population growth are assessed and quantified. This
estimation process is similar to choosing a set of building blocks
that together make up the addition part of the city. This set has
immense influence in determining the effects of the planning effort
- the volume of every land use type has substantial implications on
the functionality and character of the planned area. Hence, the first
planning stage can benefit significantly from the ability to assess
the implications of a given set of blocks on an urban environment.
The assessment process of land use quantities tends to vary
between nations, states and cities. The variations occur due to an
assessment process that heavily relies on economic development
models and analysis of past trends that are studied in light of local
context. These local assessment methods produce coefficients or
simple ratios for people per required urban activity and are later
implemented as guidelines for planning by governmental and local
planning agencies.
Let us explore a quantitative method for assessing population
requirements and their future implications that can be consistent
across cities worldwide. We will consider Bettencourt el al. claim
that all city indicators scales with population size (2007, 2010) as an
analysis method to: extract population thresholds; assess required
units of land use types; and review possible resultant implications
of land use quantities on the urban environment by examining the
deviation from the scaling behavior in different urban models.
3.2.1
3.2 Choosing a set of
building blocks
42
Analysis method: urban scaling relations
Bettencourt el al. presented empirical evidence showing that
important demographic, socio-economic, and behavioral urban
indicators are, on average, scaling function of city size that are
quantitatively consistent across different nations and times. These
findings suggest that given a population size of a city, we will be
able to predict the quantities required from all urban indictors.
Bettencourt et al. identified three main trends for scaling relations
from which land uses, the topic at hand, showed sub-linear scaling
with city size. This kind of sublinear scaling suggests an economy
of scale in urban activities - for any additional population growth
in city size, a city requires less urban activities as stores, banks,
libraries, etc. per person.
To validate this claim by Bettencourt el al. with our dataset, we
transformed all types of land use data to logarithmic form and fitted
them using Ordinary Least Squares to the logarithm of population
for all 50 metro areas in our dataset. Using population size, N, as the
measure of city size, the power law scaling equation can be written
as:
Y(N) = YN#
Where Y is the intercept; # is the scaling exponent reflecting the
different trends at play across cities; and Y(N) defines the quantities
of various urban activities for a given population size.
Our results revealed that most quantities of land use types do show
a good fit to population size (see figure 1), demonstrating a high
value of adjusted R2, which evaluates the goodness of fit. However,
while the majority of land use quantities in our dataset show sublinear
scaling (fl < 1) as found by Bettencourt et al., quite a few land use
quantities as parks (see figure 2), schools, supermarkets, restaurants
and many more show linear scaling (f ~~1) with population size,
with a scaling exponent ranging from 1.00 for dentists to 1.09 for
art galleries (see full scaling table and in Appendix A). Our results
reveal that while about 60% of all land use types included in our
dataset show sublinear scaling with population size, 25% show linear
scaling and 15% show superlinear scaling. These scaling exponents
demonstrate the average behavior a city should manifest, if it were
to follow the common pattern shown in urban systems around the
world (see Scaling charts for land use types in Appendix B).
#= 0.94 R2= 0.75
1066
IN-_y-k
Los angeles
10W
TMiaa
Las
Figure 1:
Sublinear
Scaling of all
land use types
vs. American
metropolitan
population
43
.m-.
10
IndiaSenod
ILouivia
iMOMPhIS
,Nihv-le
Virginis be-0
:HWttord
10'-
3.2 Choosing a set of
building blocks
m veghinas
IV~
le,
loll
Poilo size
0""
1072
adjusted
intercept
Table 1:
Scaling
exponents for
land use types
vs. city size
3.2 Choosing a set of
building blocks
44
Land use type
cemetery
airport
courthouse
bank
church
funeralhome
lawyer
shopping-mall
storage
localgovernmentoffice
roofing-contractor
cardealer
museum
stadium
accounting
carwash
police
doctor
library
moving-company
firestation
store
carrepair
nightsclub
dentist
school
furniturestore
post-office
park
hospital
plumber
restaurant
clothingstore
art.gallery
bakery
liquor-store
meal_deliveryjtakeaway
totalamenities
(YO)
-2.00
-2.61
-2.75
-2.03
-1.48
-3.05
-1.81
-3.06
-2.92
-2.50
-2.77
-2.63
-3.92
-3.97
-2.85
-3.90
-4.10
-2.10
-3.87
-3.28
-4.47
-2.49
-3.22
-4.32
-3.32
-3.39
-3.95
-4.62
-3.79
-4.70
-4.28
-3.35
-4.09
-4.81
-4.98
-5.24
-5.91
-1.41
slope (P)
0.59
0.65
0.66
0.71
0.72
0.78
0.79
0.79
0.79
0.80
0.80
0.81
0.86
0.86
0.90
0.90
0.90
0.90
0.91
0.93
0.96
0.96
0.98
1.00
1.00
1.01
1.01
1.02
1.02
1.04
1.05
1.06
1.08
1.09
1.14
1.17
1.25
0.94
95% CI
[0.28,0.89]
[0.42,0.88]
[0.41,0.90]
[0.53,0.89]
[0.55,0.89]
[0.53,1.03]
[0.64,0.93]
[0.60,0.98]
[0.56,1.02]
[0.65,0.95]
[0.59,1.01]
[0.61,1.011
[0.67,1.04]
[0.69,1.03]
[0.73,1.07]
[0.69,1.11]
[0.63,1.18]
[0.74,1.07]
[0.71,1.10]
[0.74,1.11]
[0.66,1.26]
[0.78,1.14]
[0.80,1.17]
[0.79,1.20]
[0.80,1.20]
[0.84,1.17]
[0.82,1.19]
[0.85,1.18]
[0.79,1.25]
[0.87,1.22]
[0.86,1.23]
[0.90,1.21]
[0.92,1.25]
[0.86,1.32]
[0.95,1.33]
[0.92,1.41]
[1.08,1.43]
[0.79,1.09]
k2
0.22
0.39
0.36
0.55
0.59
0.45
0.70
0.58
0.49
0.69
0.53
0.57
0.63
0.68
0.70
0.61
0.47
0.72
0.64
0.67
0.45
0.71
0.71
0.66
0.67
0.75
0.71
0.75
0.62
0.74
0.72
0.79
0.78
0.64
0.75
0.65
0.81
0.75
P= 1.01 R 2= 0.62
10
10'
I
f
~10
*~dnoa
Ilenespo
0"'
.*..v._
Figure 2:
Linear
Scaling of parks
vs. American
metropolitan
population
.Los-ngwbs
***of
ed-m
10'
. .NanOA.Tans
6"
lit
to,
lo"
4-8"
10"
Poplaon iz
10'
-
#= 1.08 R 2 = 0.78
.Lok_sngel-
1o~-
I
.poue e.*89
.
.am
.o--rd
.
""
.
o"*"'"
MW.ask.
....
.0
Oeft
1op
.
10"
''''0'
il:
to'
-
1o'~
3.2 Choosing a set of
building blocks
45
,.&Uj--
10'
-
Figure 3:
Superlinear
Scaling of
clothing stores
vs. American
metropolitan
population
-
a
log Of total Population
1Dr,2
3.2.2
Assessing a set of building blocks
As an expected baseline behavior, the power law scaling equation
can be used to calculate average population thresholds, which
are the minimum number of people in a city required to support
various urban activities. The scaling equation can also be used to
assess the rate of development in growing cities. These assessment
measures together can serve as a unified and consistent planning
guideline for new development projects and also in the construction
of new cities. Considering the accelerated urbanization process
that our world is experiencing today, when new cities are planned
and constructed in a rapid pace (Watson, 2013) and existing cities
are expanding in unprecedented volumes (Un-habitat, 2010), this
method hold great potential to help guide development around
the world.
3.2.2.1
Extracting population thresholds
When planners and developers engage in the process of
constructing a new city or a large housing development project,
they estimate the levels of population required to support various
urban activities in order to choose which land use types will be
constructed in which development stage. Consider a scenario
when a new city is constructed in a location where there are no
accessible services or commercial establishments essential for
everyday life as supermarkets, banks, gas stations or hospitals. All
these urban establishments need a sufficient amount of potential
customers to support their financial activity in a sustainable
manner for the developers to justify their initial investment. In this
scenario, developers need a recommended measure of population
thresholds to assess how many people their development project
has to house in order for it to sustain a type of urban activity.
The thresholds for land uses in a city can be extracted when
applying the scaling equation on the collected data. By inversing
the logarithmic binning of the scaling equation and solving it the
for Y(1), the required number of people, N,, to observe the first
activity of type x in a city, can be calculated using the following
equation:
Nx=10
3.2 Choosing a set of
building blocks
46
With this equation we found that for example, a city needs
approximately 210 people to observe the first doctor, 640 people
to observe the first convenience store and 720 people to observe
the first bank branch, demonstrating possibly the most essential
activities for everysmall developing project. To observe the possibly
less essential daily activities, the thresholds levels are much higher
as for example 1,500 people to observe the first restaurant, 2,000
people to observe the first beauty salon, 2,300 people to observe
the first school, 7,200 people to observe the first gas station and
32,100 people to observe the first hospital. Possibly the most
'luxurious' of urban activities are observed when a town or city is
well established and has a sufficient clientele to sustain land use
types as a museums (38,000 people), amusement parks (43,600
people) and take-away and delivery food establishments (51,800
people) (see full thresholds list in table 2).
Land use type
church
lawyer
doctor
store
insurance-agency
generalcontractor
conveniencestore
bank
health
finance
local-governmentoffice
accounting
restaurant
pharmacy
car_dealer
car-repair
beauty.salonspa
dentist
school
veterinary-care
cemetery
realestateagency
physiotherapist
roofing-contractor
movingcompany
atm
departmentstore
lodging
storage
park
clothing..store
home.goodsstore
bar
Table 2:
.p i
painter
electronicsstore
for
ershlids
Therhold for
hardwarestore
gas~station
shopin
all land use
typesshopping-maill
types
groceryorsupermarket
3.2
47
Choosing a set of
building blocks
Population
Threshold
110
200
210
390
590
590
640
720
900
1,400
1,400
1,500
1,500
1,700
1,800
1,900
2,000
2,100
2,300
2,400
2,500
2,600
2,800
2,900
3,400
3,400
3,600
3,900
4,900
5,100
6,000
6,100
6,200
6,500
6,500
6,900
7,200
7,400
7,500
Land use type
funeralhome
electrician
furniturestore
hair_care
airport
laundry
plumber
university
courthouse
library
florist
jewelrystore
car_wash
night_club
bakery
movietheater
bookstore
art-gallery
shoestore
rvpark
liquor_store
hospital
police
postoffice
carrental
parking
museum
cafe
stadium
amusement.park
firestation
meal_delivery.takeaway
gym
travel-agency
pet~store
movie_rental
bicyclestore
synagogue
embassy
Population
Thresholds
8,000
8,200
8,200
8,700
10,100
10,600
12,300
12,700
15,700
18,600
20,500
21,600
21,700
21,800
23,300
23,500
23,600
26,100
26,500
30,800
31,000
32,100
35,200
35,200
36,000
36,700
38,000
39,000
40,200
43,600
45,100
51,800
54,800
58,400
65,700
132,300
192,300
195,100
412,500
3.2.2.2
Assessing levels of land use quantities
In a similar manner to calculating thresholds, we can use the power
law equation to also assess levels of land use quantities required to
support population growth. This is a method that can be essential
for every master plan drafted for an existing city or for assessing
the current growth in rapidly expanding cities as cities of the global
south (Un-habitat, 2010). The observed scaling pattern in cities with
respect to their population size can provide an aggregate proxy
to assess how well a city is supporting its growing population with
required services.
City officials or developers engaged in managing growth can
estimate their city growing rates using the power law scaling
equation. By placing their city's population target of 10z in the
scaling equation, the estimated quantity of Y for any given land
use type x, can be calculated using the following equation:
Y=
010"IX
Where Z is the exponent of 10 for a target city size and f, is the
scaling exponent of the estimated land use type. For demonstrating
purposes, we calculated the required average land use quantities
for a target population of one million people (106). Our c alculations
showed that one million people require, by average, 700 churches,
1,000 restaurants, 220 parks, 140 supermarkets, 30 post office
braches and 43 coffee shops (see full estimations list in table 3).
The presented method of assessment can equip policy makers
and developers with a quick and simple understanding of whether
the master plan of their city or the actual pace of development in
the field is enough to sustain the expected population growth and
allow them to make changes in exiting plans or promote further
regulations in order to meet future needs.
3.2 Choosing a set of
building blocks
48
It is important to mention that because our dataset only provides
data about number of units of each land use type and not total
amount of square feet, the thresholds and assessments numbers
need to be taken with a pinch of salt. A more detailed dataset
with measured square feet quantities using the presented
method can potentially help provide a more accurate measure
of population thresholds and required building blocks needed
to meet population demand. However, the presented method of
assessment only provides a measure for the expected average of
land use quantities given a certain population size. It can be used to
understand a general direction of development and to this purpose
it holds great potential, but it cannot be used to give a detailed
and accurate measure of required land use quantities to meet the
needs of a certain type of city and its population. To understand
how specific cities deviate from expected average with respect to
their particular population requirements, cultural differences, urban
environment and much more, we shell now take a closer look at
the deviations from the scaling relations and explore what types of
urban models they produce.
Land use type
embassy
bicyclestore
synagogue
rv-park
mrovie_rental
courthouse
airport
firestation
police
amusement~park
pet-store
museum
stadium
cemetery
carwash
movietheater
parking
carrental
post~office
Table 3:
Estimation of
required land
use quantities
for a population size of 1
milion
3.2
49
gym
funeralhome
gasstation
library
cafe
bookstore
hospital
travel-agency
meal_deliverytakeaway
shopping-mall
art-gallery
night_club
shoe_store
university
departmentstore
liquorstore
jewelry-store
florist
storage
hardware_store
Choosing a set of
building blocks
No. of Land
use units for 1
million people
3
7
8
10
10
20
20
20
20
20
20
20
20
30
30
30
30
30
30
30
40
40
40
40
40
40
40
40
50
50
50
50
50
60
60
60
60
70
70
Land use type
bakery
veterinary-care
haircare
roofing_contractor
bar
lodging
painter
electrician
homegoods-store
furniture_store
plumber
grocery-or-supermarket
bank
conveniencestore
physiotherapist
car_dealer
pharmacy
park
moving-company
local-governmentoffice
electronics_store
laundry
accounting
atm
clothing_store
finance
realestate-agency
school
insurance-agency
dentist
carrepair
beauty-salon-spa
church
health
general_contractor
lawyer
restaurant
store
doctor
No. of Land
use units for 1
million people
70
90
90
110
110
130
110
110
140
130
100
140
170
200
160
170
230
220
190
190
200
160
340
260
250
440
410
440
540
500
480
530
700
710
780
790
980
1,900
2,100
3.2.3
Deviations from Scaling: Exploring urban models
So far we have explored scaling relationships in cities and discussed
the potential planning implications of the observed trends, allowing
us to calculate the average behavior that a city of a given size is
expected to manifest. However, while observing the scaling charts
of each land use type, one can clearly notice that while some cities
appear as points on the regression line, other cities appear above
or below this line. These are cities that deviate from their expected
baseline behavior and by that deviation reveal some of their 'true
colors'.
Analyzing how specific cities deviate from the expected pattern can
illuminate some of the local characteristics and dynamics of those
cities and allow for a meaningful comparison between cities that rely
on relative quantities and is independent from city size (Bettencourt
et al., 2010). These deviations enable us to parameterize the
characteristics of each individual city and consider the implications
different quantities of land use have on a city.
Deviations from the regression line are quantified by the residuals
from the logarithmic fit and are expressed as:
K
Y(N,)
Where Y represents the observed land use quantity for each city
and Y(N,) represents the expected average behavior. Ranking cities
by the magnitude of their deviations reveals how cities perform
in terms of their land use quantities and can demonstrate the
possible implications of more dominant urban activities. Plotting
the residuals by category show no population bias when 7 from
the top 10 cities in rankings for banks, airports and schools are
small metropolitan areas, ranking for services that heavily depend
on population size. Moreover, compared to per capita indicators,
15 of the top 20 cities in the arts and culture index by Forbes are
amongst the 20 biggest metropolitan areas by population, while
our ranking for museums and art galleries show less than 10 of
the biggest metro areas ranked in the top 20. To understand the
impact of one dominant category on a city's atmosphere, we can
examine cities with positive deviations from the base line behavior
for that category. For example, vibrant cities have more than the
l=log
average amount of restaurants as Las Vegas (ranked 3th), Salt Lake
City (ranked 4 th), New York (ranked 6 th), San Francisco (ranked 1 th)
and Miami (ranked 11 th) (see figure 4). However, also smaller cities
are ranked high for restaurants, a fact that might suggest that these
cities have more restaurants then their population actually needs
as Portland and Buffalo (ranked 1 th and 2th, respectively), or might
point to bias in the data.
3.2 Chosing set of
50
Another method for plotting the deviations is by city, showing the
unique patterns of the city's functioning. This kind of plot can be
referred to as presenting the measures of the city, similar to a blood
test results showing how a human is functioning relatively to the
norm. Examining these deviation plots by city, one can observe
that for example, Boston shows an equal number of positive and
negative deviations from the average, and has more land uses as
parks, museums, libraries, universities and cemeteries (see figure 5);
In contrast, a declining city as Detroit shows positive deviations from
the average behavior in almost all land use types except universities,
stadiums, movie theaters, art galleries and bike stores (see figure
6), possibly providing a snapshot of the problematic situation of
having more supply than demand for services in a declining city.
An example for a possibly struggling city that is underperforming is
Tampa, which shows less quantities of every land use type except
RV parks, a measure that might point to having more demand than
supply for services in the city (see figure 7).
The observed deviations present a visualization of the underlying
dynamics at play in every city, but it is hard to distill the impact of
a specific land use deviation on a city's character, due to the high
levels of complexity and interconnectivity of different components
in the urban system. However, the measure of adjusted R2 can shed
light on how sensitive are different land use types to local variations,
by explaining the percentage of variance in a land use quantity that
is predicted by population size. A high level of adjusted R2 as found
in restaurants (0.79), food delivery and take away establishments
(0.81) and universities (0.81) suggests that these land use types
are less sensitive to local factors while multiple public facilities
as cemeteries (0.22), courthouses (0.36) and embassies (0.46) are
exposed to stronger local influences and show a wider distribution
of residuals (see full list in Appendix A).
Above.
average
Below
average
Figure 4:
Scaling
Residuals of
Restaurants
for all 50 US
Metro areas
3.2 Choosing a set of
building blocks
51
0
l
i2
Rank
Above
average
0.50-
0.25Below
average
0.00
-0.25
4.0
4.75-
............................
.
Figure 5:
Residuals for all
land use types
for Boston
Metro area
0.5-
0.4-
0.3-
0.2-
0.10.0-
Figure 6:
Residuals for all
land use types
for Detroit
Metro area
amenity
I
0.00-
-. 25-
-0.50-
Figure 7:
Residuals for all
-00-
land use types
for Tampa
Metro area
3.2 Choosing a set of
building blocks
52
a"-*
3.2.4
Constructing a set of planning rules
Now that we have analyzed the observed scaling behavior across
cities and understood how each particular city deviates from the
baseline behavior, we have created a range of possible values for
each land use type, together forming a catalog of urban behaviors
and their potential implications (see figure 8).
The catalog of urban models demonstrates the possible implications
of a deviation from a baseline behavior of a particular urban activity
on a city's local flavor. Moreover, observing all the deviations of
one particular city together reveals the possible implications of a
particular land use composition on a city. For example, if we examine
cities we know as vibrant and lively as New York, San Francisco and
Boston, we'll find that most of them have more than the expected
average of land use quantities that play a major part in the liveliness
of a city as cafes, restaurants, bars, night clubs, museums and art
galleries. In contrast, in declining cities as Baltimore and St. Louis
we'll find the same categories with a negative deviation from the
expected behavior.
The relative range of values we defined for each land use type
presents an opportunity for planners to define the character of a
city they wish to create or regenerate, inspired by other cities, and
than pick the appropriate value of each land use quantity to support
this desirable local flavor. When planners face the task of creating a
new master plan for a specific city, they can first assess the land use
quantities their city contains in its current condition with respect to
the city's population size, understanding whether the city follows
the average scaling behavior or deviates from it. After setting a
target population level for future growth, planners can then use the
scaling equation to predict how many additional units of each land
use type is essential to serve the expected population. Given that
the first analysis found that the city presents deviations in several
land use quantities, planners can decide whether or not they would
like to adjust this deviation by adding more units of a certain type
or subtracting from the additional units. Finally, when considering
the master plans goals for their city, planners can use the catalog of
urban behaviors to choose which quantities of land use type they
would like to enhance and which to reduce, in order to give their
city it's desirable flavor. For example, If a city like Washington would
decide to create a new master plan with the aim of regenerating
the urban street life in the city, its' planners can use the catalog
of various urban models to match the city's land use quantities
as restaurants, bars, stores, parks and night clubs demonstrating
deficiencies to the levels observed in San Francisco or New York,
relative to the population of Washington.
3.2 Choosing a set of
building blocks
53
To conclude, understating the expected baseline urban behavior is
a powerful tool that planners can adapt to their daily practice. The
possible implementations of this analysis tool are much greater than
the ones discussed in this thesis and call for an in-depth research
of this tool in comparison to the available metrics we have today
to measure performance of cities, mostly by economic activity as
Forbes and The Economist. In addition, considering that our findings
suggests that most land use quantities are sensitive to local
fluctuations and are not fully explained by their population size, it is
important to investigate further what are the local dynamics at play
influencing these land use quantities other than population. We
can hypothesize that cultural differences, daily commuters to the
city and climate or geographic conditions play a crucial part in the
dynamics effecting land use compositions. Finally, and maybe most
importantly is to study how deviations from scaling distribute across
the city in relation to different population groups of age, income
and race and understand the level by which these communities are
served by various land use types.
Figure 8:
Boxplot of residuals for all land
use types for all
The scalability of all urban indicators with respect to population size
holds true even when land uses are distributed randomly in the city
and contain no information about the relative position of each land
use type. However, urban activities often cluster, co-locating to
form patterns that may contain information about the dependence
of activities on each other, shared demand, or similar infrastructure
and transportation requirements. We will now present a method
of analysis that can shed light on the clustering and co-location of
urban activities, holding great potential to transform the practice of
land use planning.
Metro areas
Residuals
3.2
54
Choosing a set of
building blocks
3.3
Co-locating the building blocks
The question of land use location might be the hardest and most
influential of all in the process of city planning. Location is everything
when it comes to cities, even in the globalization era (Fujita &Thisse,
2013). Though a lot have been studied about location theory, the
question of how to co-locate different urban activities in cities as
a tool to regenerate urban areas still remains open. Nevertheless,
urban planners still make daily location decisions using zoning as
their primary tool to distribute different land use types across the
city. However, zoning does not allow for the fine-grain placing of
various urban activities, rather only for district allocations, leaving
location choices entirely at the hands of market forces (Alexander,
1987). Moreover, since planners are not equipped with metrics
to understand the implications of location choices, they are left
exposed to intense political pressures, without having the tools to
react to them (Altshuler, 1965; Duany & Talen, 2001).
Zoning was first introduced to the field of urban planning as a
tool to shield residential areas from incompatible land uses and
guarantee that development is served by the proper public services
and infrastructure. Since then, zoning became the primary tool for
implementing revitalization initiatives, plans that set their goal to
regenerate urban areas experiencing decline, and also growth
master plans, when all these plans usually include urban vibrancy
and active street life as one of their primary goals.
But what do we really know about the factors that generate active
and lively street life? Jane Jacobs was the first to articulate the
relationship between mixed land uses and social interactions as a
generator for lively and active urban environments (Jacobs, 1961).
Jacobs claimed that when housing is co-located with places to work,
shop or recreate, social interactions of different incomes, races or
ages are encouraged since people will tend to walk more and drive
less. The mixture of residential and commercial land uses creates
a multipurpose environment in which lingering is encouraged,
intensity of human interactions is generated and liveliness in the
public realm emerges (Talen, 1999).
New Urbanism has tried to quantify and research the meaning
of proximity in an urban environment, as facilitating closeness by
arranging space appropriately. The movement introduced the
concept of the 'Transect' to characterize how the built environment
is transformed on the range between urban to rural as a function of
distance from the city center. Every human habitat along the Transect
is captured by the integrity and coherence of the combination
of its elements, creating a method to allocate elements along a
geographic cross-section (Duany & Talen, 2001). Attempts to test
the claims of New Urbanist showed some support to placing parks
and retail activity in a walking distance from homes as a generator
to increase levels of pedestrian activity (Lund, 2003).
3.3 Co-locating the
building blocks
55
Social science studies from the past decade were able to detect
universality in human movement using widely available geo sensor
data to understand mobility patterns across cities. These studies
were able to show that the flow of individuals decreases with physical
distance between two locations, while factoring in the accessibility
of resources along the chose path, verifying that urban density is a
driving force of urban movement (Noulas et al., 2012; Gonza'lez et
al., 2008). Detecting universality in mobility patterns can educate
planners of how people move around cities. However, they still do
not supply an answer to the important question of what are the
dominant urban activities effecting human movement, making a
particular urban environment more desirable than others.
Let us introduce a method to quantify the distances between urban
activities as a measure for the intensity of an urban environment. We
will examine an emerging pattern across cities, showing similarities
in the co-location patterns of US cities. We would also examine
different urban models to understand what are the relationships
between dominant components in every urban environment that
can generate liveliness. Finally, we will show how planners can use
the co-location analysis findings to construct planning guidelines
that are modular and deploy them to plan the spatial organization
of cities in a fine-grain resolution.
3.3.1
Analysis method: Calculating pairwise distances
To explore the co-location patterns of urban activities we calculated
the minimum pairwise distances between all land use types for all
cities in our dataset. A minimum pairwise distance is measured from
every land use point to the closest point from the same or different
land use type. Using this method we calculated for example the
minimum distance from a bar to the closest night club, from a night
club to the closest convenience store and so forth. Geographic
distances were computed in meters as great-circle distances
representing the shortest path between two points on the surface
of a sphere. Latitude and longitude coordinates for every point
were converted into spherical coordinates, taking into account
the radius of the earth (r= 6371 km). Given two points in spherical
coordinates p, (0,, ,) and p2 (02, v2), the surface distance between
them, S1 , can be can be calculated using the following equation
(Gade, 2010):
SL2
Calculating minimum
pairwise distances
from every point to
the closest point from
the same or different
land use type
3.3 Co-locating the
building blocks
= arccos(sin p, sin p2 + cos(p, cosqv 2 cos(01 -02))* r
After computing all minimum pairwise distances for a given city,
we calculated the median pairwise distance for each pair of land
use types in a city and formed an asymmetric distance matrix for
each city. The asymmetry manner of the matrix stems from the
asymmetrical spatial organization of cities: the median closest
restaurant from a store is not equal to the median closest store from
a restaurant. The distance calculations revealed that for example,
the median minimum distance from a restaurant to a clothing store
is 38 meters in New York, 52 meters in Boston, 76 meters in Los
Angeles and 103 meters in Houston, possible depicting the level
of compactness in each city (see matrices for cities in Appendix D).
Plotting the probability density function (PDF) of all calculated
pairwise distances for each city as a function of distance (transformed
to log binning) revealed that surprisingly, all cities share a similar
distance distribution, regardless of population size, density or land
area (see figure 9). The Gaussian distribution showed that most
distances in all cities range from 100 meters to 10,000 meters, with
a mean of 1,270 meters and a standard deviation of 1,280 meters.
Given that the measured average walking speed of humans is 1.4
meters per second (Levine & Norenzayan, 1999), we can understand
from our findings that the closest urban activities will be located in
approximately 1 or 2 minutes of walking distance from each other
and the majority of activities will be located in about 25 minutes of
walking distance. Considering our results in light of the half-mile (=
800 m) radius established for pedestrian walkability limit in transitoriented development (TOD) in the United States (Calthorpe, 1993),
we can argue that people are willing to walk greater distances
for certain urban activities and support the claim that fluctuating
boundaries for city centers should be considered in the practice of
land use planning (Canepa, 2007).
The observed pattern of unified pairwise distance distribution
across cities can serve as an average baseline behavior that cities
are expected to manifest regardless of local characteristics. This
measure can help guide developments of expanding cities around
the world and specifically in developing countries to assess their
spatial organization and aim to stabilize their pairwise distance
distribution to align with the emerging pattern of American cities.
Unified distance distributions across cities are observed when we
plot all pairwise distances in all land use types. However, when
we take a closer look at the distribution of the six aggregated
categories as defined in land use planning, our data reveals that
while commercial and service establishments demonstrate a similar
distribution in all cities, offices and public facilities demonstrate
fluctuations between cities. Moreover, open spaces and parking
facilities do not share a similar distribution and while exhibiting
a similar range of distances, distributions vary from city to city
(see figure 10). The four categories showing variations present an
opportunity to explore more closely the co-location patterns in
different cities and understand the dominant components affecting
the liveliness of urban environments.
3.3.2
3.3 Co-locating the
building blocks
57
Deviations from a shared distribution: exploring urban models
The emerging pattern of unified distance distribution serves as
a baseline behavior for all cities, regardless of size or density.
Nonetheless, every urban spectator can observe with his naked eye
that US cities differ in their spatial organizations. To understand
how different cities deviate from the common pattern we will first
take a closer look at non-unified distributions of some land use
types in our dataset and then analyze how these variations result in
different co-location distance matrices for different cities.
When observing the various distributions of land uses in the
fine-grain resolution of all 78 land use types, one can detect the
dissimilarities across cities in multiple types. While services as
banks, beauty salons, doctors and insurance agencies demonstrate
similar pairwise distance distributions across cities, public facilities
as cemeteries, courthouses, embassies and museums show extreme
fluctuations in distributions between cities (see figure 11).
Distanc iilbsions for al ois
1.0Ch.l
7-
0.5-
-
Figure 9:
Distributions
of calculated
median
minimum pairwise distances
between all
land use types,
by city
Ph-
"I'llp
so-fts
Jtm-M
3M..f.
0.0-
10'
10,
0
'St
to'
Log o
Diice(M)
I A~n.
NnhMe
Now owo
Odisea
506001
WWiMe CRY
5.0-
Figure 10:
Distributions
of calculated
median
minimum
pairwise distances by city,
aggregated by
top land use
category
3.3 Co-locating the
building blocks
58
C*
PhNWWi
MIMI)s
""T,-
T'._YZ'7 1*
Hodo
Miluot-
Dabs
.ladcISCOWl
SmdagaO
Smrlc~co
(WMM
SmJoa
[LBYear
50
lop
iot
lee
~(M)
t'
Log of owtmte ftro ut
Se
lop
o,
IS
o'
ic
ice
SKWRk
S--P ityo
LOILMO
seam00.6
Losissl
StLois
MllwktU
I
yagsfta
PDF
J-
1
CL
f
Ilk
LA
E
Figure 11:
Distributions
of calculated
median
minimum pairwise distances
by city, for each
land use type
3.3 Co-locating the
building blocks
59
R
MF7
CL
G0
From the full list of 78 unique land use types, only 22 percent show
similar distributions across cities, which are wide distributions with
a smaller mean, compared to the general distribution, ranging
between 100m and 1,000m. Analyzing the list of types showing
similar distributions reveals that the majority are types classified as
services, expect from 5 commercial establishments. On the other
hand, most land use types that demonstrate extreme variations
between cities are public facilities, with a large range of distances
starting from 1,000m and ending at 10,000m, deviating to the
right from the majority of distributions. This behavior possibly
indicates that public facilities are often large establishments with
substantial presence in the urban environment and hence their
location is usually determined by central planning agencies and not
effected by market forces as other land use types. The fluctuating
distributions of public institutions demand for an in-depth analysis
of their co-location patterns when exploring the pairwise heat maps
of different urban models.
To construct planning guidelines, we first need to examine the
co-location patterns of different cities in the aggregated level,
as defined by land use planning, to shed light on which types of
activities tend to cluster. Examining the clustering behavior in the
macro scale will allow us to interpret the more dominant land use
types, which tend to co-locate closely with other types and hence
carry an influence on the particular character of each city.
To generate aggregated pairwise distance matrices for each city,
we calculated the median distance of each land use category,
producing a six by six heat map for each city. Comparing the various
cities, we can observe that overall, heat maps of denser cities (U.S.
Census Bureau, 2010) as New York and Boston present smaller
distance values and 'warmer' heat maps, while car dependent
sprawled cities as Houston and Dallas show higher distance values
and 'cooler' heat maps (see figure 12). As we first observed when
plotting the distributions of top land use categories, offices, public
facilities, open spaces and parking facilities differ the most when
comparing heat maps.
3.3 Co-locating the
building blocks
Exploring closely the co-location patterns of public facilities with
all other land use categories, we can observe that in New York,
San Francisco, Boston and Chicago public facilities co-locate with
other public facilities and also offices, while in Atlanta, Houston,
Dallas and Los Angeles the same matrix cells show the lowest
distance values, suggesting that these functions do not co-locate.
When observing the co-locations patterns of open spaces, we learn
that they demonstrate smaller distance values to all other land use
types in cities as New York, Boston, Atlanta, Washington, Chicago
and Dallas while in cities as Houston, Miami, San Francisco and Los
Angeles open spaces are furthest apart from services, commercial
establishments and public facilities. One might think that this
finding suggests that cities where open spaces co-locate with all
other uses simply have more parks and open spaces. Yet, when
considering the deviations of these cities from the scaling behavior,
0
(D
0
ro
3
'5
i
t, V
.0
M
HO
(D 0D
10
(A
Mt
D
ow
0
.1
'2*
()
P.
.0 P
0
a)
w
w
(D
0
(D
La
()
H
(D
En
0
C
fn
offices
U
services
z
CM
I
i
74
public
facilities
commercial
law
open spaces
parking
3
(5
I
CLC
____________________
offices
services
public
facilities
K0
commercial
open spaces
parking
offices
EM
services
0
public
facilities
commercial
open spaces
K0
parking
IA
offices
z
V),
services
public
facilities
z
commercial
open spaces
0
Jparking
Z
Figure 12:
pairwise
distance heat
maps at the
aggregat-
ed level of
top land use
catagories
3.3 Co-locating the
building blocks
61
of fices
c0
services
01
I
public
facilities
commercial
open
spaces
parking
we can determine that this pattern does not stem from high
quantities of parks in cities as Washington and Atlanta. Moreover,
cities where open spaces do not co-locate with other uses as San
Francisco and Miami have more quantities of parks compared to
the average scaling behavior, suggesting that parks are dominant
and well distributed in some cities regardless of their quantities and
density index.
After examining the co-location patterns in the macro scale, we will
now turn to explore the micro scale heat maps, showing all land use
types in our dataset. Planners can use the process of scaling down
gradually in land use resolution to first understand which land use
types tend to co-locate and what environments these co-location
patterns create in order to distribute the types to different areas of
the city. For example, if planners aim to follow the patterns of San
Francisco (see figure 13) as a vibrant and lively city, they can choose
to locate offices in zones with other offices and open spaces, and
also cluster public facilities together. Second, to understand which
particular types of offices tend to co-locate and in what manner,
planners can use the disaggregated heat map of San Francisco
to observe that accounting, finance and law firms co-locate with a
minimum median distance ranging from 90 meters to 160 meters
apart.
3.3 Co-locating the
building blocks
62
Taking a closer look atthe microscale heatmaps of Boston (seefigure
14), New York (see figure 15), San Francisco (see figure 13), Houston
(see figure 16), Los Angeles (see figure 17) and Washington (see
figure 18) we can observe that in all cities land uses as restaurants,
stores, beauty salons, health services and accounting firms tend
to co-locate closely with all other land uses, while publics facilities
as stadiums, synagogues, courthouses and airports tend to locate
further apart from all other land uses. These heat maps reveal a
similarity in the co-location patterns of six cities that are generally
perceived as very different from each other: while Boston, New
York and San Francisco are cities that are characterized by a dense
core, narrow streets and efficient public transportation systems; Los
Angeles, Houston and Washington on the other hand, are sparse
cities, characterized by big lot sizes, wide streets and are heavily car
depended. These surprising similarities may suggest that in the finegrain scale of co-location in the most intense urban environments,
the dependencies and proximities between amenities are similar
across cities, regardless of density or sprawl indexes. Moreover,
these co-location patterns are not affected by quantities of land
uses, whether closely or distantly located with respect to all other
types. Examining the land use quantities by their deviations from
the average scaling behavior reveals that even when cities have
more units of a certain land use type as in the case of courthouses
in New York (ranked 1th) or synagogues in Boston (ranked 7*), the
extra quantities do not effect the co-location patterns when these
land use type do not locate in close proximity, on average, to all
other urban activities. The independence of co-location from land
use quantities is also demonstrated when cities have low quantities
of a particular land use type with respect to the average behavior,
as in the case of restaurants in Washington (ranked 4 5 th), Los
Angeles (ranked 3 9 th) and Houston (ranked 38 th) or accounting firms
in Boston (ranked 3 9 th), San Francisco (ranked 3 0th) and Washington
(ranked 4 7 th) (see full ranking table in Appendix C).
The presented findings show that some co-location patterns are
not simply expressions of variations in land use quantities or rather
the compactness or sprawling nature of a city, but represent the
unique co-location behavior of a land use and its significance in
urban environments. This is a valuable finding for urban planners,
providing a performance measure for the importance of some
urban activities.
Figure 13:
pairwise
distance heat
map of all land
use types in
San Francisco
r1st-rant
trane IgenCY
r ,afiIo
rt r many,
11 iI
l dniur
tor
entalt
doi
shpig
l
rr curare
gramr
1g
oark
51 11
R i ra yer
armerts tore
a funnour
t sallspwo store
haDwet-store
sfoageu
movieretal
prOracy
coneenc
sorie
'a dging
bank
arterallerym
unive 111ty
ebakssy
gas0statoy
el1office
Cal wash
poClice
packing
cemetery
MEN
ONE.
m M
mum
M 0 ME
ommoo m on ESSN MENow"a
ImNmmummm
n
MEEK
N
................
sMBmagoon
a MNo NE 'NE
MMuE
S EKE UEEE
;icrstation
bicycle store
MnE mP mmo * m I Am na m a
mmmm mmm OsMm muso
e mNmE Iiaf01e
mmmm
mma oU
u
won@ m
&um ummmmom mm
mmmmnmmmmamm
EENISM
MMO
X
E
aI
2
amk
W
t
M
mEo
?bE
MOORE
M
3.3 Co-locating the
building blocks
63
amenity B
m
LOW
amenity A
io
HIGH
aa
a
y 4uOWe
HDIH
m
M01
flW
I] 4uawe
t9
S Ofq 6upnq
ay 6ui~ecDo1-o0:S
moons
EEEEEE EEEEEEMEEEEUENEEEE~uEEEEE ,onmsomonmaE...U..
EmmEmoUE
Em
MAN
0 0
amM
m
E
ammam MEoE *UwagonuEanEESE
EUM
mnyE* E,am amua.,a.v,.A 0,.mu.,
MoEns
EWEmossuld
EEW MEE
E9 eME U EuMomm em Umomm m EmanU
MMNO.
*uuuumumSm *m
on mit a M*EE urn manME woma
mu... EEEEEEEEEUEEEUEEEEUEEEEEEEUEEEEEEEUEEUEUEUESEEUEEUEEEEEUEEEEEUUEEEEE
Eu....MBMM EEEEEUUEEUEEEEEEEEUMMM
MMEEEEUMEEEEEEEUEEEEEEEEEEEEEE..........
u.siuumm
MEMNO
Uu*MENNEME
mmmu m
** *
mm,,,
VINE MEMOMMn
B iME ESEMEW
U MEU U ME ENE MMEREEK
USUREMER
U
ERRMMMMME
EEE
EU
E '121111 aEall a ano . mU sE UA
Emu..EuEEEEEEEEEMEMMMMEMMMEEEEEEEEEmE...uu..uuuuuuMumMuEu...mumuumuuuuE
MEMEMEEEEEEEEEEEEEEEEEEEEEEENEEEEEEEEEEEEEEEEE
a M.
RMN
at lm
agion
M ssM an BRmou mM was a
a
a 0 a Z
a
M vnp"
UEEEMEMEE*EEEEEEEEEEEEE*EEEEEflE*EEEEEMEMMEEEMMMMMEMEEEEEEEEEEEEEEEE
BmuuMuuuuuuuuEuuuuuEuuuuuMEM..EEuMu.MMEumuuuMuuEuE.EuuuuuuuMum.......M
nEE
slm
sE
u..Euuurnuu E
EEEuEEEEmmEEEEEEEEEE
MMMMMAEuuu.EMM
ME ua.uMMMENuu..uauHEEEE
E
EanE
EUEu EEE U IllfIml*m 3 m3 *nmmomm MflEMMflEMEMMUM
MMMEN
mommonwsmmossm NOMEEEMMO MME ONMOSENUNEMON N MEEMMMENNEMMEM
uuMu....uMuuu.....u...u..u..u...EuEEEMEEuuuMuuMiiMiiiiiiEEUEEEEMBEEEEEME
...MEiEE.EE.EiIIIIIMiiMiuuiiiuiIImiiEiuiEIBMuI*ME***EMEIEII.........EM
a
MMMMMMMMEMMMsoomumomME
sommoom
aMMwassaEMMMEMMMEMEMMEMMMmomMONM
Eu....EEEEMEMMEEE
MEuuuiEEEMMEMMEiEEuEEuumMMEMuMEuu.uuUEE..
AA
EAMA
A
lmolmmom m
ONME MMMMMEEE M IN
EMMEE
Eu...
uuEuMMuuuuMu
sMMMMME mommiloonsm iMM
Euuuuuu MMEMMMEUEEUMEE
EEEuE
a IMMMMMwsm
EMMEBE
E
.uuu.I~m....u~uuumuus
u...u...u....u..EMEEMEEEEE
MMMMMMMMMMMMMEMMMMEMMMEMMMMMEMMMMMEMMMEMMMBMEEUEEEEEEU.EEE
EUuuIuIIIuEUUUEEEIuS
uMuuuUUMuEEEEEUEEMMEuMEMflEE.U.mMuuu.U..uEM
-
tAAPA
4AonA
EEUE*BEMMEE** 3 **UEUUUMMEUEE*E**EEEu.
EEEEMMBEMMBMMEMEEEEMMuE
uuuuuuu
N
OMEN 0
EmmommmmEEEEEEnrnuurnumummuEU..EUUEmaEUMMMMMBEUUE:E
aMEMEaME"EEM EE
wilooMEMMMEMMMEMMMMMMMBB mB moommEB
almomemsomonMM ME
omEmaEEESMMEEE MonUEEE EEUu
onuEusEEUEEEounuuu..uuMuu
EmE EE EUE AAB AYANA
Bom
ON
MMSuEEmuouuons
UunuEMEuuuiMEu
auuuuuumuusmEMuM
M
EaIMM IAIAaEMM
asmmosoBmmums
osoRmEmmummEmEmamom
MEMMMM
RmoomMmEmmm
MMEASURMEmmsE
I
EMOE
a
8iiA
A A-
EEEEUEUUEEMBME.MMEEMUEUEUEEEuEEEEEEEEMMMuE..EMMEEEMMEBBMEEuEE.EEEUEEEEUE.
smannem MMMRMEMMMEMEMMMBMMMEMBMEN 0 mmommnsMMMM MMEM
MMEMNE
MEEMEEUMEuuEuuMMMEuEEUEMuEuuEuuuuuuuuuuuuuEuEEEuu..U...Uuuuuuuuuu
EEEUEEUEEUUEEEEEEEUEUEUEEEEEEMuEEEEEEEEEEEEEEUEEMMEMMMMMEEEEUUMMEMMEEE
A
AM
I I A A A A A A AAAA AA A AMAEM N m o m-AmmA A A A AuAAsAA A A A A A A
AEEE
MME
A
on
0
MMEM
Ao A
MEESam
ON A A A
A EAMA AMEA
so S s a
moommommemnsensmm~:V
!dMMNM
asiMu!ed
Figure 15:
pairwise
distance heat
map of all land
use types in
New York
emMM~nneM
s.
-9T 2
L,
2
E
2
w-
r
> anmeMMM m
I
*UEEEUUEEUEEEUEEUEEEUUEEUEmEMEEEUEUUEEEUUUEEUUUEUUmmEUEUE
I
I L, G
LMMMM
.E..............
... mu....EEEEnamMME....u.EEE..Enu.E...EEMu..Emeu............sME.KamuEEE...E..E
U
mE
EEiEEEEEU
*EEEEEE *EUEEEEEE
EUEEEiMMME MME...
EEEEEEEEEEE.E
g :PEsEEEE
EEEEEEEEE
EE
EE-maneEnMs=EEEEEEEEEE-EE-E-U-EEEEEEUEEEEE
moin-cwpn"
O NE N so
w
nn
a M an
nM a win M ~M MNu MON s EEE
~
~ EmmEmmommEmEmmE
n a n UE M UK
EEMEEEEEEEEEEEEEEEEEUKEEEMMMamMaMaEEE
EEEEEEEEEEE EEEEEEEEKEEEEEEEEKEEKEE
EaEEEuEEs a an a
EEEEEEEnEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEan
IIIIIIIII!IIIIsoon a !m am I IIIIIIIIIIIIIIIIINNUMMEMMINill
MIMEMMEMMMMMMEMM
............:..
....
E.........E
tnier
9 mannommoussMBM MORON
......
.... NoMEMEMEMMMMM EE..........
MMEMMMMMMMMM
K1MMEMMMMS1nI n MENSM
careerMMMMMMMMMM
ch oo mnmomu eM MOim
h
EOmom
.....E......................E.E~...E....,E..........
......
Paka
an
a
*EKEEEEEEUEEEEn EE
UM
EEEEEEM
M *EEEEEEEE EEEEEU
K E EMM *UMEE
'n Juce
EU
EEUon
msmutm ana
EEEUEUEUEI4D
humdwarm
store
r-rntaloa
E UEE
1
E Eu U
mmT a
aEE
u au
OES
oUEEEEUEEEE
EU~
INSE
t
SN M
U
hateoon~U
gy am=
SE SNUEE man so EMMOES
MEMEEEUMMEME *MMMEN
mi mum moo
e M u a
UE
EUE
EUEM
"Ier Emm 0MMMMMMMMMMMMMMM
EEmma EUEEE
Smuuuuurnuu EKE
noi
uW NEEU
a
m
an mommam-o aam MAIIIII Nonan 0 aman ON N
U ENEN
E E *UE*mmEE EM*EOMEM SEN MUEUNO NE
M WMENM No MN NUMM MMMMMMM
EEE
E EEM
EuO
ouuummm
EUE... EMMONS
EEUEEEEUEEMEEMEE
0
no a
M
EU
05
ME
osms
boksor NESEN SmoanM MMO
aUEEUE
RUU
aEUE*.
No M
0. Imen ISO
a an mBmmmommomm amEmmmoomEmmEmoss
a YAM
Ma
MS
vt1rrEEEE
amusement_park
U m n M Is EUEm EU o E EmoEn
E
ME
EM
biyl-t
K EE
& E
esynag sE
ogEENEE
3.3 Co-ocating the
building blocks
U
S,
poi
ce
ENo 1E
Mn
othouse
E EEEEEEon
OE I
kngMONK
an N MEr
E
M ISM N
U
librarm
65
M
L
mo E
a a
a s
2-
EEEmEEM
NE UE EE u
o
on
L'
NE
UEEU mE oE 8 0E EEEEEE MEENENESEEs
Edg
mg
99
"NO
0 Eu
a
,
EE EEEEMEEEK
EEUEE
a
o
EE EE ME
on U
E -E
a
7
EEEEEN
oaM
UUEEEE
**SMEONUEE
MsEEEE
EEEEM
Sm mf 0Um
Wg
o E NonaEMMEM
EEEEE EEEEuEEE
"
0M omm
a 0 an 0 MEMNON
stadiEEmU. noE
E
EEEEEEE mm a E
EEE Eno
MmmaE aE EE
MEN
MuNuEFF
E u E .EmEEEM
iiE**
* E*
100
EEMEMEE..IMMIEEE
EEE. E..
EEU
EEMMMMEMMEE MMEMM
EE.EEEEEEMMEEME
university
EU
numswoons
USamon
mam uu o MOuN
au
a RMMMMMMMMEEMMMMMMMMEMMMEEMMMMMMMMMM
ENOW
masns
B BM~unsnos MEE
manmENEEEn 9111
En
EEUKEMEEE
a~a~nn
nMM aM annnseMM
E on 0 MMa MM
mElmEEEEEm
EMSSEON SIM
EMEMMM
NEou
E
a hg
s E
e
0
a
a
MENNEN
oE EE
U1,
a
a,
a K 9 mEEETAMM
ONO .Mlp;
El mAEm
. Em E
u..E*EEUEE
amenity 8UEUEU
LOW
E EEm K
ESEE
HIGH
Ns
EMEN
amenity A
EEE
an a rnmmo o
Figure 16:
pairwise
distance heat
map of all land
use types in
Houston
ta
ste
-|||||
||||||||||||||E|||EE|||EEEEEEEEEEEEEEEEUEEEEElEEEEEEEiEE
||E
2e t
ME
a
f
-m SEUEEEEUEU UEEEUEEmumEmomEEEEEEEEE EEUEouEEEEEmm UEEE E*E essomo
resturnt
00M OE no
MBEME M nMMM .E!...E.............
EMMM M MMM
ncyMMM
.... !8!.....!......8.8.8i!...i.......
MMM
MM MM
mosngcoepnyMEN aNN
Maso wMMMMEMM~ne Moas
an eMMMM~ Man
Me MMMMMMMMM
uu
Euuu,.uMEMEEE.EEuEMMB
EMEME...EMEMEMME...
...
u
.E,*ufl*EEEEE.MM
33
belagty
a EEMEME MEEMMEEMMEEE E EENE E ENEE E EEEE - E E EEEE
0,iatummnMs
a
"wnBesm an MMEmmunosmM
MMaoB~nNEMMEMEEM EmomENOEEE
g i-D..E.....EEoErs....EEE.EE.EMnsonsEEmoE
onEEEnEEEEEo an EEmEm
mom 1011M
EEEE EEUEEEEEE
o
E E Eou EME
E
EMEENBUE
EEE
EEEEU
E EU
EU UUR
'EEEE M -,NoEEE E
u
mu MEMMEMMBMMBRMEBME
u
, uuuuu...u
uuu..u UUEMEonoMMM
EuM
EEEEEEE
pakamingooll
ERNWOUNN
RmeM EEE E
-- ore
NE
01manommug
uE.Emun..uuumsM
Emammas
EE.EEEEM
EEEEEE.EEEEEEEIMMEMMMMMmUMEIUMEII!IIEEEEE.EEEEEEEEEEEEEMMMEEMEEEUUEEE.E
AREiumb-MEMBMEE~am annomunon
0mansmMEmEmsma.E OMENS
MEMNONM 5
M a EN
igh
paUUEEE
clb11
a Plv
EEEEEE S EE
E
EEEEEEEEUEEEEEEE
EEEEE
EEM
MM
EEBMBBMMEEEEEEEEEEEEEEE
.uuuuu.
EEEE No
anny EE
EE E EEEEEEEEEmos
o
n m
n
w
i'im
m
m1-M
M
HE MEMEMEEE M MEMEMEEEE
MEE
EEEEEEXEINNSaamm
E
KEEE
EEMMuEEEEu
uEMEMEEuMEMEEuuuuuuuuuuuuuuuuEE~EEMMMME
MMMEMR
MMENS)V X Mi;C&VA SWUMMM SiONEMNMRM 00009 EEEEEE
M 0 11ta EEMuM
MMUN Now 0M
a
uuuuuuuuuuuwuuuuuuuuu 0 .uu uuumuu umuu a a a kn M u. 'I OWNER ON u
Flrs
Ifoo npi
MMMM
MMMEXMM EMNONE MJMMg MOMMOR
M
-immims
Fa
E M R a 47 EU a Emom UEmEUEEE oE.0o
M m aUU
E a o mmu
j MQ;y-%o
M isNWMENun MMBRMMBMBMMMMEMMMMEMEMNO
faaosmmsmosmmoEm
mmusm
MEE
NNE MM
EEMu EEEE Eu E MM E
uuuuu uu u uuu 8 uu EEME
Eu E M
~
hijar
sonommussaws
a mams
gyma0
we
w
aMttE3EEE
EE3
EEuuuu
E-amEuuuuuuuuuu
V
0
0 uI
CM MONSOO
s33
EE.EME
EEE
EEEE
EuuumM
EE ono
muu
baa e EWEIM EEEEE e E E EEa a E E
uEu
4 SM3fM im a CKv a w
t
ass
lEm if mamma 7c
ae
an
a
Mom TEMEMEM
a
uuE EEEER E
R
EMENEUEM EMNEE
MN
U
MEU M EU
.UEU M3EESEEE
EulIE Nnw
aUE
E
gas M
14 offmMuasoanam
ol:
K EE oEm
aR m mus
uuuuuuIa uuuuuuEuu
uuuu
EE
E
N UME E
uuuuuuuuuuuuuuuu
11UE1131E *MEERMEEEM
EEm
MEEEE
m -- on
EE
UEWI
U EUe. EVEEUEUU
EE M MED s
Min
M
-VM
a
so
ME
UE
oU
o
UUEEUEEEE EUa
ag
a
M
ve M aUU
E,,
UWS
UEUU
U
UEM
E
DUN
EEEuEEUEEMEMBEMuMMuMEEEUEUEEEEEEUUuEEEEEE.EEEMEMuEEMomEMgaMMEMMo
i
t
I
u
0
E
E
EE
E
E
is
u
u
M
ca,"ie-'
ting a~~th EmuuEuy
0 go'MMM _ & MMMMMMMMMMMMEEMMMBMENMEMM
a
SOM aMMM 0a E
asm
a-
tt
biding O.NaaaMN
b acks
ama
MM
Emm a
uns
y UEE
aU
6E
41,
na
ga
3.3 Co-locating the
building blocks
66
EUEMED
0 E
s
U
' EENE
uNE
0ESEENEsoEE
U
UEE
UMMEE
u
E Kou
U
U
M MMM
LW
ON Eu
MEUM
EKE
M
EKKEt
MUE
S a
onE
0umu
EmeniE
Mj M
O" E
NE
MRU 0yUMA Eu
r EW E M
HG How N%
ME
SME
MEYE
6E
E
PP
P
aS
EE EE
a Elm
%
movie
ME
U
E.
O
yum u.
amenity B
LOW
HIGH
amenity A
U
UEE E
Figure 17:
pairwise
distance heat
map of all land
use types in
Los Angeles
oEE.EEEEEEE.MEEEEEEEE.E.EE.E.EEEEEEE..E...E.EEEEEE..EEE.EE...EEEEEEEE.MMM3E
.EEE
EEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEMEEEEMiiiiiiiiiMiiiiiiiiiiiiiiiiiiiiii
ium smmon
mossummossim men mining, in
MMMEMBMMMMBMM BOEMMUNNNMME ftwffx 9
E*EEE.E.EEEE..EEEEE.EE.E..EME...MEMEEEEE.E.mmmmMOONSEEamosEE mmEEmE
EparEkEEE.EuE..EEEEE.EEEE.....................
Epm
mommoom oMMmmummoismmu MM M M mmsEMMMMMMEMMMMEMMMMMMM
EEEEr*E*EE*E.EEEEEEEE.EEE.EEEEEEEEEEEE.EE..EE..EE...WE..........u.............
WE*E*
EE DREW WUEEEEEEEWEWWEEEDEUEEEU EMEMMMEMEUUEUE
MMEWEEU
*EEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEENun
EU
EEsooEonsmmoEmm
Esummm
so UEE
MEMMEMMEMEMMMM
MMMEMMMMEEMEMMMMBEMMEMEMEMMMMENMMMEN
b
EEEEEEEEEEEEUEEEEWUEWEUEEEEEEUEUEEMWEINEEaEEEEEEEWEEEaEEaEUaEEEgas
aE
EEmanEEU
fMMcM
M UW *EEWE3EEDDD
MI
*******ua**W**.E............................E.
.WWD..M.W.
candey
EEWEWDWDDEDEWDDDEEWDEWEDEDEDDEEEgDDWEDWDDDEDWDEWEDEDEEEDEEEEEEDDDEEWWg
EE
EonDWEEWDEWDEDEEWWDEWDEDW
.. D...W..W...WE.E...W...DD..EW........
.**WW..WWDMMME...W...W....E....WW.W..
enera~~Roan 0a moms! NOMMMMMMMEMMMMMMMMMMONOMENIMMMMMMNWA
U
ntDDED
DED
moe tre
comties
EE
U
DRUERDEDE
EEWyE
EW EE
WEW
DEE
DREDEED
N
U
DEDEDDEDDEE
WEE
WE
ED
U
U
EU
D WDEEEEEMM M MM E M ME M M MM M EEEE EEE EEEE UEEM M M EEEEEWEEE EEWWDEEE
EE
8,00
No a
a 4 a a NMMM
mon'so
no
flung on an anman
pokre
-eMoe
U
E V11MONNOMMEN#0MENENM
If ngggggggggon on
2 111 maggggggggggg
asnownsoEmmONE smax
as MM
MMEMMMMMMMMMMMEEMMMMMMMMM,.Mn
Urvrst
1
U
U
Ea E
la
WEE
a
a angg
IIIMMM
aET
M MM MM
m ommi
EMR t DEW
ao nammm in9I ola m m 6 los
DsNEE
Msgoujffffi
an
vtrnrcare
oAMURRMMUSMEMogME
l
EEEEr
an aDyEan10111111EX1as
E EDIBM
was&E EEE
E
Monosson
a P111 smene ssaMmanMEDEson
Ea
E
9 aonaoNulls
E EEmEmmE
EEMO
oo m gi
IB
QIM
a IF onMM
Mn
coiarhaa
cey
EEWEEEEEEWEEEEEEEDEEEEEEEWEEEEEEEEEEEEEEEEEEEEWEEEEEEWEEEDDEEDEEEEEEEWEEEEEEEEuaa o
cemetryON am NONE EnumoWom
nE
U
WEE EWEM EEEEEEE
policoe
EE
NAM g
E
4wmga D asgg
-,ENE E ,-,gv
irorte
EEEMaMEEM
MONOSSON
arlot~ EDW EE E EEMom
W E aEgEoEEMEDESOND
WE
E
EM E WM
RM GEEU
E
WE WE
WEEE EDEEE WEED WE DDEM UW
E
corhue
a
poofc
mnn
paint
16
0
Go
Maomuseum
Makn
Ma T
In 0
e s m
A
3. o-oatin thMeRMMMMMMMMMMM
building blocks
67
s
0
noM
6 i li0w
ao m omonon
6
ix
as a
n a
RifnBIN M
M
a a
-M
an P
IBMM MMM M M " M rm
A ausg MA iglo E04",
'MAN 'M
BE.
ameam
MEAN
LOW
;go
mmnit
amnogm on ew
HIGH
a
W,111111
som ao
go RMNL 1-
mnnm
N on
" ma
no
amenity
Figure 18:
pairwise
distance heat
map of all land
use types in
Washington
MMMMMMMMMMMMMEMMMMuMEMMMMMMMMMMMMMMEEMMEMMM00MM
1 isstoreaithingst*re
** MEN** 0
sh''ppg
phy,,
a NM
mus uu=num so ammuuuamosommummuu
aggg~gg~gg agg
t-erast
ag
now
nogMM 7
anM
anM onE
MEg~gg onMM
INEEIIIM MNIMUMIEIIEIRHMflHUNINIHIU
doctor
muuu EKE EU
Eu..
*UE
*EUUUEEEUEEUEUEMMMMEEEUEUUU
EEUE EEEUMuEEEEEEUEEEUEEUEEUEEEEEEEEEEEEEEUEEEEEEEEEEUEEEEUEEEEEEEEEEUUEE
smonumum
iliiu uuumluuuM
inu.Ulmuuuuuuu
........EE.u
m E EE E EEu1u
i a i aINiI niii i
1111111Uommo
N MESMEMOMMMMMEMENS
phi
mo
g itactor
33
*
EU
*E
E
u ruu1u
u
sonaEMmMMMMEEMMMMMMBBB
EUNEEEUUUE
E
UEEor
uE11E
EUME
U
pa
Mno E
RE EUN
N a
painterEu.,,
a
0
a
1111Ia
umuuuuuuuumu~euMEMOunouuuuuuu.uuisuuuuuuumumuuuuuEomuumuun
Vwl a, * an SMONNNNo 19
omiu on MMM
on 'mi
niglitM
clu 7 M 7
aMM a UEnowE 1111,MMM
m IM B aM P M 9 M aVa mono
M1RmMPMM1M 1 a aM 33
NAEE on ME*EE
aE
on ,Nmg w
a EEm
flr~n
man",MMMM "ME MMNMM ER BMRMBM M
.. i...i....
=INSMMMM
MMM
*
storage
,qoieuentz1 L& .......
MEM11110i
.
.
.... n. OREMME
.:....
.. u
.....
coen ar
M MEM
MMMEMMMMEMMMMMMMMMMMMMEM
uuurnuuuuuunm
11111111111V
BM M MM anno Eanman
EZE
EMN SEEmE
agoEU
a RO MINEMINO 0 8 a Wo Mai MEM on
E 0*uON..E.uM
NEKEPEE
m m MMMMEEEUEEEME
mu.. aa1ma
u m a a No ONm
M ONNO MM MMMM M INa M
uMmuuuuuuuu EE
00 No 3
Mu*c
uurnuuu u
urn. EMMMmu. rnm urnM
3 E smEUEUU ONEU ENSE
ONESmNMMuENMMMMNm
*uuuum
u MEmuuuuuuuuumuuuumuuummuERmmmuEammumuuumuuuuuu
MEmNEM
a
SEEEn *muEMuuMENS
uu MEmmuuumuuuumuuumuuE
L * M I a an aa m an waEs *umium muuuu s u * mu a .w
uuuuuuuuuuuuuomunuumnuuammuuanumm uuuamumuuume*
mmmumummummB
mumuRAmNmmm u
oeM,.*,
pta
MEMO
*mj MOEN u me u s . ......... umuummmmurnu. a 0 so P EMMONS
..uuMumEE
- Wila. a mu
s
ao
so
E
uEmm
a
muew mu~uu
n o vU~ u m u
M a
aomm
EEMM Men mKm mU
m **EM um
Um
* a~
am
U U
mum *U
4 0 m m:E m m mu u u u
aM a
0a mo wNU M EE o
K
USE EE EE
EM..
*ummuu
so
a
UoEUEUUEUUEEE
a , a s s o o IMImM u u u u
E- 0 asma 0
U EM
E MSEMSES
ummE *uum mm umum m
MMa w.
.mum.ummmusum
*muomommum
smmammu
S ang
a.uu...~mummmMuM
uNE
a MNawal
mm qkiiiim'
m a
pNg
No a
No
OEM
we
Na
wm, m
4g
Noa
soon
EUon' ,,a Oi iU oT a MWjI
U
UA
Uiv,
1);,,,kl-ng
MMis Lan2S 0
Mmn m no mEmom I o sMMMM~eMmanM
5
111111
Oa 'X On
L ..11
M
M
MM
o
r
u
mmL, - M uM LL' "n- _C
M M
a
M No
MM
wasMMAMM MMMMMM
MM
MMMMMMMMEM~~~~~a
om-yaaN
o
MM
NNo*uu1
MEN mu
Inau aN Imol
U *g,
u
s
oe
wons
S MMEMOEEN OM E
M M MM M M MM eMM~M MO
uuU mr
M t0 MEN
0,ag no
MNLE
IN_0 an ?, , 'oma i Noa .' f S n 0A11110a 10
EN NONE
M
M
aS ONEa
on No MEN a -aMaM MMMM
a-
3.3 Co-locating the
amenity B Mn
building blocks
LOW
68
SmM
MEN
gUsa umumm
MM
aI
HIGH
amenityA
Nonetheless, some co-location patterns do depend on land
use quantities. Fitting the residuals from our scaling equation to
the mean of the matching row type in our co-location matrices,
transformed into logarithmic form, we found that some co-location
patterns correlate with respect to land use quantities (see table
4). Our calculations showed that the mean distance of types as
bars, cemeteries, fire stations, embassies, liquor stores and cafes
from all other types decreases as the quantity of the same land
use type increases. In other words, when a city has more of these
land use types, they tend to co-locate closely with all other types.
On the other hand, land uses with similar co-location behaviors
across cities, as restaurants, hospitaIs and churches indeed show
no correlation to land use quantities. However, the correlations we
found show low values of adjusted R2 , implying that there are other
dynamics at play in cities affecting land use patterns.
We will now take a closer look at the fluctuations in the co-location
patterns as demonstrated in different cities and the range they
create for possible positioning of urban activities, considering their
implications on an urban environment.
3.3.3
Constructing planning guidelines: examining the span of pairwise
relationships
Now that we have revealed an emerging pattern in the spatial
organization of cities and reviewed dominant urban components
and their implications on different city models, we have created
a catalog of urban behaviors and their possible implications. We
were able to quantify some of the relationships that hold a possible
effect on creating a vibrant urban environment and thus, created a
catalog of models 'worthy of emulation', as defined by Kevin Lynch
(Lynch, 1984). We will now turn to describe how the urban catalog
can be used to form modular planning guidelines and explain the
process by which the guidelines can be deployed in the practice of
land use planning.
When we plotted the probability density function for each land
use type separately, we observed that although cities vary in
their distribution behavior, all cities share a common range for
every particular land use type. Hence, although the variations in
distributions between cities are manifested by means of different
standard deviations, mean values and skew of the curve, the range
of distance values for each land use type is similar across cities. This
finding is crucial for our ability to construct planning guidelines for
co-location of urban building blocks.
Clocating the
69
Using the observed range of median distance values, we can
construct a matrix summarizing the span of possible values for each
pairwise distance in a city (see full matrix in Appendix D). This matrix
includes the minimum and maximum values for each relationship
between two land use types, for which possible effect on an urban
environment can be found in the catalog of urban models. Together,
Table 4:
Regression
results of land
use quantities
vs. co-location
patterns
adjusted
adjusted
intercept
Land use typ
2.91
liiquor..store
3.46
museum
3.05
cafe
3.13
library
courthouse
3.54
2.89
bar
bicycle -store
3.46
parking
3.32
3.02
travel-agency
2.86
park
movie_rental
3.38
cemetery
3.35
funeralhome
3.18
nightclub
3.15
synagogue
3.55
rv.park
3.60
stadium
3.48
fire_station
3.30
3.24
petstore
police
3.30
jewelrystore
3.02
art-gallery
3.11
2.77
grocery-or-supermarket
3.76
embassy
gas_station
3.15
amusement-park
3.34
2.87
bakery
meal delivery-takeaway
3.00
3.10
gym
2.91
physiotherapist
2.96
lodging
florist
2.97
3.11
bookstore
lawyer
2.65
shoe_store
3.09
airport
3.45
post_office
3.10
painter
2.90
storage
3.06
3.3
70
Co-locating the
building blocks
slope
-0.51
-0.47
-0.47
-0.46
-0.43
-0.42
-0.41
-0.38
-0.37
-0.37
-0.37
-0.36
-0.36
-0.35
-0.35
-0.34
-0.34
-0.34
-0.34
-0.32
-0.31
-0.30
-0.30
-0.29
-0.27
-0.27
-0.23
-0.22
-0.21
-0.20
-0.20
-0.20
-0.19
-0.18
-0.18
-0.17
-0.16
-0.15
-0.15
R2
0.40
0.31
0.36
0.33
0.43
0.49
0.38
0.17
0.23
0.32
0.29
0.48
0.43
0.27
0.33
0.68
0.20
0.43
0.32
0.43
0.20
0.19
0.14
0.43
0.40
0.22
0.09
0.06
0.07
0.13
0.13
0.05
0.06
0.03
0.05
0.14
0.03
0.09
0.16
adjusted
Land use typ
veterinary-care
university
roofing-contractor
local-governmentoffice
electrician
hospital
laundry
hardwarestore
doctor
realestate.agency
carrental
movietheater
plumber
shopping-mall
conveniencestore
health
restaurant
dentist
home-goodsstore
carwash
clothing--store
car_repair
bank
finance
school
cardealer
furniturestore
pharmacy
church
general_contractor
electronicsstore
departmentstore
beauty-.salonspa
atm
accounting
store
moving-company
insurance-agency
hair_care
intercept
slope
3.06
-0.15
-0.15
-0.14
-0.14
-0.14
-0.14
-0.14
-0.14
-0.14
3.19
2.94
2.90
2.90
3.18
2.68
3.02
2.35
2.55
3.20
3.18
2.91
3.10
2.73
2.39
2.24
2.66
2.80
3.16
2.66
2.54
2.76
2.53
2.55
2.92
2.82
2.76
2.56
2.43
2.66
3.07
2.43
2.58
2.59
2.07
2.80
2.52
2.89
-0.13
-0.13
-0.13
-0.13
-0.12
-0.12
-0.12
-0.11
-0.11
-0.11
-0.11
-0.10
-0.10
-0.10
-0.10
-0.09
-0.09
-0.09
-0.09
-0.09
-0.07
-0.06
-0.05
-0.04
-0.04
-0.03
-0.03
-0.03
-0.03
-0.01
0.17
0.01
0.13
0.02
0.06
0.03
0.02
0.06
0.03
0.04
0.02
0.06
0.03
0.05
0.06
0.02
0.01
0.02
0.02
0.06
0.01
0.05
0.04
0.03
0.01
0.02
0.01
0.01
0.01
0.01
-0.01
0.00
-0.02
-0.02
-0.02
-0.02
-0.02
-0.01
-0.02
the two documents can be used as a manual for planning the colocation of urban building blocks. For example, if planners wish to
create a culture district in a city, including art galleries, museums,
cafes and shops, they can use the summarizing matrix and examine
the range of possible distances between a coffee shop and an art
gallery (135 meters to 1071 meters) and then turn to the catalog
of urban models to understand how these values play out in an
urban environment (see figure 19). The minimum median distance
between a caf6 and an art gallery is observed in New Orleans,
followed by San Francisco, Portland, Washington and Chicago with
distance values of under 300 meters, while the upper range include
cities as Orlando and Dallas with values above 900 meters. Hence, if
planners aim to induce a dense and vibrant district they will choose
a value from the 25 percentile, but they can also choose multiple
distance values that together produce an average below 300
meters. Moreover, since art galleries and cafes show fluctuations in
the co-locations patterns with respect to all other land use types,
planners can use these patterns to explore the dominance of art
galleries and caf6s in cities. For instance, San Francisco and New
York have more art galleries and caf6s that tend to co-locate closely
with all other land use types, which in turn affect the vibrancy of
the city, while in Washington these establishments do not co-locate
closely with all other activities but the city in general has less of
them, suggesting that adding more quantities of these types can
enhance the liveliness of cultural districts.
To conclude, exploring the span of pairwise distances between
multiple land uses introduces a dimension of flexibility to the
practice of land use planning by allowing for versatile assembly
options of urban components. This level of modularity signifies
a transformation for the objective of the planning process from
a finite product of one solution to an open process that aims to
plan the relationships between components rather than their final
juxtaposition. Enabling this level of flexibility in planning guidelines
calls for a transformation of the whole planning process: from a
closed practice that aims to envision and control the final outcome
of a city by means of central planning to an open platform for
which planners set land use quantities and ground rules for their
assembly while opening the final location choices to all citizens and
developers in a controlled manner. Moreover, an open platform
with game rules will allow for a kind of incremental and some what
organic development process, where small developments can
occur in a gradual and slow process, enabling the urban building
blocks to assemble in stages while also controlling for their possible
implications.
3.3 Co-locating the
building blocks
71
Our method of calculating the minimum pairwise distances allow
for an in-depth examination of the spatial organizations of cities,
a measure that is independent from predetermined parameters
that can effect results, as in clustering analysis and other analysis
methods for spatial datasets. Hence, it reveals the patterns created
naturally in cities, by market forces or central planning, enabling
a novel comparative method. However, since this is a method to
measure the average behavior across a city by calculating the median
minimum distance rather than the precise measure of distances, it
is important to study the method in predefined urban areas, as a
comparative method of compactness or intensity between different
areas in a city. Comparing the minimum median pairwise distances
from a restaurant to a store in several centers of the same city will
allow planners to understand what is the performance measure that
meets their planning goal and follow this measure to locate the
next store in a given area with the aim of regenerating it. Moreover,
comparing the minimum median distance between different areas
of the city, as an inner-city neighborhood versus a suburb of a
city, will create a new measure of density or compactness of an
area, while not relying on average density across a large area but
quantifying the performance of an environment by the relationship
of its own urban building blocks.
Another important point to emphasize is that the land use patterns
we revealed characterize American cities and might not be
immediately applicable in other parts of the world. Land use patterns
are rooted in the history and culture of the urban environment,
revealing the development process the city has experienced
throughout the years. Although we have found similarities in colocation patterns between American cities that vary greatly in their
population density, age of the city, climate conditions and car
dependencies, we also found variations between them that might
be explained by the variation in the physical, historical and cultural
conditions of those cities. Moreover, the high level of economic
development in American cities and consumption habits might be
similar to those in western cities around the world but very different
compared to cities of the global south, South America or Africa. In
addition, American cities are relatively 'new' compared to cities in
Europe and some big cities in the global south, a dominant factor
in the physical morphology and driving habits of these cities that
also affect land use patterns. It would be interesting to compare
the co-location patterns found in US cities to patterns of cities in
Europe, the global south and South America, a study that is very
much applicable given the availability of Google maps data from
those regions.
Figure 19:
/l~l
The range of
possible minimum distance
values from a
cafe to an art
galley
3.3 Co-locating the
building blocks
72
(in)
4
4.1
EVALUATION
Parametric land use planning: Forming the urban LEGO game
Understanding how to deploy quantitative analysis methods in the
process of land use planning is a complex task, which has ignited
the minds of scientists, urban planners and economic geographers
for most of the last century. This is the task of modeling the urban
environment given a set of constrains, with the goal of finding the
ideal location for land uses in cities. Our view is that modeling cities
as problems in organized complexity, by unraveling the connections
and dependencies between multiple urban components, is key to
forming a quantitative performance-based science for planning,
as argued by Jacobs (1961) and also recently by Batty (2013) and
Bettencourt and West (2010). This thesis has offered a LEGO game
planning methodology for urban land use that harnesses our
understanding of cities as interconnected networks to enable a
fine-grained, modular, incremental and universal development tool.
The LEGO game analogy helped us form the parametric argument
for identifying what are the city's building blocks in a fine-grained
scale; choosing which and how many blocks will be included in
a set for development and than constructing game rules to limit
the infinite number of possible co-location assembly options. We
offered a methodological approach to analyze emerging similar
patterns across cities, examine the deviations from these patterns
to construct a catalog of urban models and by discussing the
variations between them - formulate flexible planning guidelines
using the range of possible values for each emerging phenomenon.
Using this approach we structured a planning process that allows
for flexibility and incremental development while adapting change
as a value, by planning the interactions between land use types
rather than a finite and rigid morphology.
4.1 Parametric land use
planning
73
Parametricland use planning provides an alternative methodological
process to the prevalent zoning and subdivision ordinance. This
methodology presents assessment and characterization metrics to
evaluate cities and utilizes them to construct planning guidelines
for the development and regeneration of urban environments.
Using an evolutionary approach for an iterative planning process,
we offer a method to evaluate the liveliness and vibrancy of each
urban environment versus a set of choice criteria determined by
planners, to create improved solutions for urban environments.
Here, we preferred not to expand on the set of criteria for choosing
'good' urban models but rather leave this choice at the hands of
urban planners. We created a catalog of urban models characterized
by our metrics, enabling planners to understand various urban
environments by their parameters and their effect on a city. Our
developed measures offer planners the ability to parameterize their
desirable urban models with the aim of developing or regenerating
their city by emulating quantities and co-location patterns that
compose a desirable urban model. The metrics also offer all players
involved in the development game tools to identify the problems
of urban environments while establishing grounds to facilitate a
data driven decision making process.
This research did not articulate the choice criteria for vibrant,
diversified and lively urban environments, or in the Kevin Lynch's
(1984) terms -the performance measures to evaluate the 'goodness'
of an urban atmosphere. Although choice criteria are fundamental
to the evolutionary approach since they provide a measure to
determine the fitness of a model, they are extremely hard to define.
Thus, we adapted Harris'(1989) definition of planning as an optimumseeking activity and tried to find multiple local optimums of land
use patterns who satisfy various conditions rather than a limited
set of best characteristics. Moreover, the adaptability of an urban
model that satisfy specific choice criteria is not solely dependent on
a particular set of parameters and the relationships between them.
As in biological systems, adaptability is measured with respect to
local surroundings, when the same gene set can survive in one
environment while go extinct in another. Similarly, the success of a
particular set of building blocks is first and foremost dependent on
the population groups it services. Surely, socio-economic factors
along with cultural values and environmental conditions are key to
understanding why some sets of building blocks flourish in one city
and fail in another. We would explore these points further when we
come to discuss the limitations of the LEGO methodology.
4.1 Parametric land use
planning
74
The planning methodology takes the form of an evolutionary
iterative process characterized by a network structure, where
multiple parameters are intertwined and in constant simultaneous
change. Hence, our metrics of land use quantities and co-location
work simultaneously, affecting one another with respect to
dependencies and sequence of steps, when a decision regarding
land use quantities can affect co-location patterns and vice versa. The
methodology process follows a strict sequence of planning stages,
using estimated future population growth as an input parameter to
capture: (1) the current level of urban service - its deficiencies and
surpluses; and (2) the estimated average level of services needed to
sufficiently support future population growth. These two measures
are estimated as a function of universal scaling laws and are used
as a base map for decision-making regarding which particular land
use quantities to increase, preserve or decrease with the aim of
creating a desirable urban environment. At this point of the process,
planners will turn to the catalog of urban models to browse through
the range of possible values for every land use type and pick the
value most suitable to induce their planning goal in regards to the
city's character. The use of the catalog can be regarded as a nonautomated version of a genetic algorithm, where every possible
land use quantity is evaluated for its effect on a city's atmosphere
versus subjectively defined choice criteria that meets the goals of a
land use plan, while only the most suitable quantities are chosen to
form possible combinations for a given city.
After a set of building blocks are chosen and assessed, the next
step is to plan their distribution across the city. Here, once again
planners will work according to their own choice criteria to emulate
particular co-location patterns. While some patterns depend on the
quantity of a land use type (when more common types co-locate
closely with all other types), some patterns are similar across cities
regardless of land use quantities or a city's density. Therefore, the
decision to choose particular co-location patterns can imply in
some cases that the corresponding land use quantities should be
increased or decreased. For every given city, planners will first need
to consider the land use types that co-locate similarly across cities.
If those types demonstrate quantities that are under or over the
average behavior for the city they are considering, the decision to
increase or decrease a quantity will depend on a desirable urban
model to emulate. For example, if restaurants co-locate similarly
with all other land use types across cities, increasing the number
of restaurants in a city showing low quantities can potentially have
a positive effect on the city's liveliness. However, for co-location
patterns that varies across cities, planners will use the scaling
exponent from our quantities versus mean distance equation to
calculate how many units of a land use type should be added to
increase its' co-location distance with all other urban activities in
order to vitalize the city.
4.1 Parametric land use
planning
75
Let us demonstrate the use of our parametric land use methodology
to create a regeneration plan for a city using our developed metrics
and urban catalog in an evaluative planning process. As we observed
in chapter 3, the metropolitan area of Washington demonstrates
deficiencies in almost all of its land use quantities (see appendix
6.E.3). If the city of Washington will decide to create a new master
plan to guide city development for the next 20 years, the parametric
land use tool can be deployed to assess land use quantities and colocation distances with the aim of revitalizing central Washington.
Let's hypothesize that the city expects to have a positive population
growth estimated at a 10 percent rate change, expressed by an
additional 600,000 people moving into the city. Using our scaling
equation, planners can first estimate how many additional land use
quantities are needed to support this population growth. Next, to
decide which land use quantities should be increased to induce the
liveliness of central Washington planners can turn to the catalog
of co-location models. For example, New York and San Francisco
can be used as models to emulate, as cities that are famous for
their bustling streets, vibrant cultural and recreational city life . In
the case of land use showing similar co-location patterns across
cities as restaurants and stores, planners can choose to match the
quantities of Washington to the high quantities observed in both
cities, which are significantly higher than the quantities estimated
by the scaling equation. In the case of land uses showing variations
in co-location patterns and are correlated with land use quantities
as cafes (scaling exponent of -0.45) and bars (scaling exponent
of -0.4), planners can use the low co-location values these types
show in San Francisco and slightly less in New York to induce an
environment of vibrant streets in Washington.
To conclude, we have shown how our metrics can equip planners
with quantitative methods to understand the components of
urban environments and utilize them to make decisions regarding
land use quantities and co-location distances in cities. Future
research and analysis can add additional land use metrics to the
methodology capturing important land use patterns as clustering
behavior and land use distribution in relation to socio-economic
groups' distribution, to name a few. These'future metrics will follow
the important features the methodology process offers of fine-grain
measures and modularity for planning, using a range of possible
values for every land use pattern. The LEGO game methodology
offers a unique approach for land use planning, holding great
potential to transform the practice to an open, flexible and datadriven process for planning cities.
4.2
Lynch, 1984
Methodological limitations of parametric land use planning
Before outlying his theory of good city form, Kevin Lynch (1981)
discusses the limitations and possible objections to creating a
general normative theory of city form. By doing so, he creates a
discussion over written pages with his opponents, reminding
himself and his audience the problems in a general theory while also
grounding the needs for a theory in spite of these objections. I shell
use these general objections to evaluate the offered methodology,
its limitations and shortcomings.
Objection 3: "Physical patterns may have predictable effects in a
single culture, with its stable structure of institutions and values.
But it is not possible to cons truct a cross-cultural theory. It is even
dangerous, since it will inevitably be used to impose the value of
one culture on another."
This objection emphasized the subjective nature of a physical
urban pattern, which is highly dependent on local characteristics as
social and cultural values, environmental conditions and economic
structures. Surely, the fact that all metro areas in our dataset are
large global cities, all rooted in American culture and values played
a crucial part in the common land use patterns we observed. To
establish our findings of universal patterns as purely objective, local
features at play must be analyzed and compared.
Lynch, 1984
Objection 4: "Regardless of any influence it may or may not have,
physical form is not the key variable whose manipulation will induce
change."
Lynch emphasizes the objective insignificance of physical change
4.1 Parametric land use
planning
76
while also stating that it might help induce social change or more
accurately, support it. Social change is a gradual process, as Jane
Jacobs (1961) claimed in offering her first thinking tactic, motivating
urban dwellers to think about processes and the temporal dimension
of urban phenomena. Our current analysis only offers a snapshot
into the present reality of cities in the United States. For us to have
the ability to isolate the generators of urban patterns and distill the
catalyst parameters responsible for creating vibrant environments,
we must analyze land use patterns over a long time period, to find
their starting point and articulate their development over time.
Lynch, 1984
Objection 6: "...There is no such thing as the "public interest", even
within a single culture and a single settlement. There are a plurality
of interests, all in conflict. The only proper role for a planner is to
help clarify the course of that conflict by presenting information on
the present form and function of the city, predicting future changes
and explaining the impact of various possible actions."
This statement by Lynch was later supported by Harris' (1989)
claim regarding hidden choice criteria and conflicting interests of
players involved in decision-making. We offered a methodology
that follows Lynch's description for the proper role of the planner.
By using the structure of an open-ended LEGO game, defining
only rules for assembly options rather than a finite plan, we tried to
tackle the issue of multiple conflicting interests in shaping land use
patterns. Nonetheless, the difficult role of the planner as balancing
conflicting agendas still exists in our methodological process.
However, we do believe that by parameterizing as much as possible
of the cause and effect of urban phenomena, we allow planners to
be as objective and knowledgeable as possible in order for them to
manage urban change successfully.
Objection 8: "... city form is intricate and complex, and so is the
Lynch, 1984
4.2 Methodological
limitations
77
system of human values. The linkages between them are probably
unfathomable. Not only that, cities are so complicated that, while
you can design a house, you can never design a city. And should
not."
Adapting Lynch's approach, we did not offer a methodology
to design cities. On the contrary, we only offered rules to guide
the development of cities, suggesting flexible ranges for every
parameter at play in the network of land uses. However, the complex
linkages Lynch describes between land use types and the effect they
might have on human values are key for developing a substantial
parametric approach for land use planning. Our methodology did
not tackle this issue of dependencies between land use types in
both quantities and co-location patterns. Although connecting
human values to land use patterns of vibrant environment is indeed
a complex task, the possibility of modeling these dependencies
hold great potential for explaining the parameters effecting land use
patterns. This type of modeling can be pursued using methods as
clustering analysis and is, without a doubt, the next most important
step for future research and the advancement of a truly parametric
approach for land use planning.
5
BIBLIOGRAPHY
Alexander, C. (1964). A city is not a tree. 1965.
Alexander, C. (Ed.). (1987). A new theory of urban design (Vol. 6).
Oxford University Press.
Alexander, C., & Ishikawa, S., & Silverstein, M. (1977). A pattern
language: towns, buildings, construction (Vol. 2). Oxford University
Press.
Altshuler,A. (1965). Rationality and influence in publicservice. Public
Administration Review, 226-233.
Amindarbari, R., & Sevtsuk, A. Measuring Growth and Change in
Metropolitan Form. Sciences, 104(17), 7301-7306.
Bartholomew, H. (1932). Urban land uses, amounts of land used and
needed for variable purposes by typical American cities: an aid to
scientific zoning practice (Vol. 4). Harvard University Press.
Batty, M. (2005). Cities and complexity: understanding cities with
cellular automata, agent-based models, and fractals. The MIT press.
Batty, M. (2007). Planning support systems: progress, predictions,
and speculations on the shape of things to come. Planning support
systems for cities and regions. Lincoln Institute of Land Policy. 3-30
Batty, M. (2008). The size, scale, and shape of cities. science,
319(5864), 769-771.
Batty, M. (2013). The New Science of Cities. MIT Press.
Bettencourt, L. M., Lobo, J., Helbing, D., K~hnert, C., & West,
G. B. (2007). Growth, innovation, scaling, and the pace of life in
cities. Proceedings of the National Academy of Sciences, 104(17),
7301-7306
Bettencourt, L. M., Lobo, J., Strumsky, D., & West, G. B. (2010).
Urban scaling and its deviations: Revealing the structure of wealth,
innovation and crime across cities. PloS one, 5(11), el 3541.
Bettencourt, L., & West, G. (2010). A unified theory of urban living.
Nature,467(7318), 912-913.
5
78
Bibliography
Boyer, M. C. (1986). Dreaming the rational city: the myth ofAmerican
city planning. MIT Press.
Calthorpe, P. (1993). The next American metropolis: Ecology,
community, and the American dream. Princeton Architectural Press.
Campbell, S., & Fainstein, S. S. (Eds.). (2003). Readings in planning
theory. Malden, MA: Blackwell.
Canepa, B. (2007). Bursting the bubble: Determining the transitoriented development's walkable limits. Transportation Research
Record: Journal of the Transportation Research Board, 1992(1), 2834.
Christaller, W., & BASKIN, C. W. (1966). Die Zentralen Orte in
Sddeutschland. Central Places in Southern Germany; Translated
by Carlisle W Baskin. Prentice-Hall.
De Chiara, J., Panero, J., & Zelnik, M. (1995). Time-saver standards
for housing and residential development. McGraw-Hill Companies.
Duany, A., &Talen, E. (2001). Making the good easy: the smart code
alternative. Fordham Urb. LJ, 29, 1445.
Fujita, M. (1989). Urban economic theory: land use and city size.
Cambridge university press.
Fujita, M., &Thisse, J. F. (2013). Economics ofAgglomeration: Cities,
Industrial Location, and Globalization. Cambridge university press.
Gade, K. (2010). A non-singular horizontal
representation. Journal of Navigation, 63(03), 395-417.
position
&
Geddes, S. P. (1915). Cities in evolution: an introduction to the town
planning movement and to the study of civics. London: Williams
Norgate.
Geertman, S., & Stillwell, J. (2004). Planning support systems: an
inventory of current practice. Computers, Environment and Urban
Systems, 28(4), 291-310.
Glaeser, E. (2011). Triumph of the city: How our greatest invention
makes US richer, smarter, greener, healthier and happier. Pan
Macmillan.
Gonzalez, M.C., Hidalgo, C.A.,&Barabasi,A. L.(2008). Understanding
individual human mobility patterns. Nature, 453(7196), 779-782.
Haar, C. M., & Kayden, J. S. (1989). Zoning and the American dream:
promises still to keep. American Planning Association.
Harris, B. (1989). Beyond geographic information systems. Journal
of the American Planning Association, 55(1), 85-90.
5
79
Bibliography
Helft, M (2009). Online Maps: Everyman Offers New Directions. New
York Times. Retrieved from http://www.nytimes.com/2009/11/17/
technology/internet/1 7maps.html?
Hidalgo, C. A., & Hausmann, R. (2009). The building blocks of
economic complexity. Proceedings of the National Academy of
Sciences, 106(26), 10570-10575.
Hoch, C., Dalton, L., So, F. (Eds.). (2000). The practice of local
government planning. International City Management Association.
Holland, J. H. (1992). Genetic algorithms. Scientific American, 267(1),
66-72.
http://www.forbes.com/pictures/mhj45jded/introduction-25/.
Accessed August 2, 2014
Jacobs, J. (1961). The death and life of great American cities.
Random House LLC.
Kauffman, S., & Levin, S. (1987). Towards a general theory of adaptive
walks on rugged landscapes. Journal of theoretical Biology, 128(1),
11-45.
Koolhaas, R. (1999). Delirious New York: a retroactive manifesto for
Manhattan. New York: Monacelli Press.
Levine, R. V., & Norenzayan, A. (1999). The pace of life in 31 countries.
Journal of cross-cultural psychology, 30(2), 178-205.
Lindblom, C. E. (1959). The science of" muddling through". Public
administration review, 79-88.
Lloyd, P. E., & Dicken, P. (1977). Location in space: a theoretical
approach to economic geography. New York: Harper & Row.
Louail, T., Lenormand, M., Cantu', 0. G., Picornell, M., Herranz, R.,
Frias-Martinez, E., ... & Barthelemy, M. (2014). From mobile phone
data to the spatial structure of cities. arXiv preprint arXiv:1401.4540.
Louf, R., & Barthelemy, M. (2013). Modeling the polycentric transition
of cities.Physical review letters, 111(19), 198702.
Lund, H. (2003). Testing the claims of new urbanism: Local access,
pedestrian travel, and neighboring behaviors. Journal of the
American Planning Association, 69(4), 414-429.
Lynch, K. (1984). Good city form. MIT press.
Martin, L., & March, L. (Eds.). (1972). Urban space and structures
(Vol. 1). Cambridge University Press.
5
Bibliography
Noulas, A., Scellato, S., Lambiotte, R., Pontil, M., & Mascolo, C.
(2012). A tale of many cities: universal patterns in human urban
mobility. PloS one, 7(5), e37027.
Purdy, L. (1916). The districting of cities. Proceeding of the national
housing association.
Sevtsuk, A. (2010). Path and place: a study of urban geometry
and retail activity in Cambridge and Somerville, MA (Doctoral
dissertation, Massachusetts Institute of Technology).
Talen, E. (1999). Sense of community and neighborhood form:
An assessment of the social doctrine of new urbanism. Urban
Studies, 36(8), 1361-1379.
Timmermans, H. (2008). Disseminating spatial decision support
systems in urban planning. Planning support systems for cities and
regions, 31-43.
U.S. Census Bureau. (2010). A national list of all 2010 urbanized
areas (including Puerto Rico and the Island Areas) sorted by UACE
code. Retrieved from http://www.census.gov/geo/reference/ua/
urban-rural-201 0.html
Un-habitat. (2010). State of the world's cities 2010/2011: bridging
the urban divide. Routledge.
Watson, V. (2013). African urban fantasies: dreams or nightmares?.
Environment and Urbanization, 0956247813513705.
Weitzman, M. L. (1998). Recombinant growth. Quarterly Journal of
Economics, 331-360.
5
81
Bibliography
6.A
APPENDIX A: SCALING EXPONENTS FOR ALL
LAND USE TYPES VS. CITY SIZE
adjusted
intercept
6.A Appendix A:Scaling
exponents table
82
Land use type
cemetery
airport
courthouse
bank
conveniencestore
church
rvpark
departmentstore
veterinary-care
gasstation
funeralhome
lawyer
shopping-mall
storage
local-governmentoffice
roofing-contractor
cardealer
insurance-agency
pharmacy
museum
physiotherapist
stadium
hardwarestore
lodging
movietheater
university
general_contractor
accounting
amusement-park
carwash
police
doctor
library
bar
finance
moving-company
health
painter
haircare
(YO)
slope (f)
-2.00
-2.61
-2.75
-2.03
-2.02
-1.48
-3.28
-2.60
-2.56
-2.96
-3.05
-1.81
-3.06
-2.92
-2.50
-2.77
-2.63
-2.34
-2.77
-3.92
-2.96
-3.97
-3.31
-3.18
-3.88
-3.67
-2.48
-2.85
-4.17
-3.90
-4.10
-2.10
-3.87
-3.49
-2.90
-3.28
-2.76
-3.57
-3.70
0.59
0.65
0.66
0.71
0.72
0.72
0.73
0.73
0.76
0.77
0.78
0.79
0.79
0.79
0.80
0.80
0.81
0.85
0.86
0.86
0.86
0.86
0.86
0.88
0.89
0.89
0.89
0.90
0.90
0.90
0.90
0.90
0.91
0.92
0.93
0.93
0.94
0.94
0.94
95% Cl1
[0.28,0.89.]
[0.42,0.88]
[0.41,0.90]
[0.53,0.89]
[0.53,0.90]
[0.55,0.89]
[0.26,1.20]
[0.55,0.92]
[0.53,0.98]
[0.53,1.00]
[0.53,1.03]
[0.64,0.93]
[0.60,0.98]
[0.56,1.02]
[0.65,0.95]
[0.59,1.01]
[0.61,1.01]
[0.65,1.04]
[0.65,1.07]
[0.67,1.04]
[0.65,1.07]
[0.69,1.03]
[0.66,1.07]
[0.67,1.10]
[0.70,1.07]
[0.77,1.02]
[0.70,1.09]
[0.73,1.07]
[0.68,1.12]
[0.69,1.11]
[0.63,1.18]
[0.74,1.07]
[0.71,1.10]
[0.68,1.16]
[0.74,1.11]
[0.74,1.11]
[0.77,1.01]
[0.74,1.14]
[0.81,1.07]
k2
0.22
0.39
0.36
0.55
0.55
0.59
0.15
0.56
0.48
0.46
0.45
0.70
0.58
0.49
0.69
0.53
0.57
0.60
0.58
0.63
0.57
0.68
0.59
0.59
0.65
0.81
0.63
0.70
0.57
0.61
0.47
0.72
0.64
0.54
0.66
0.67
0.72
0.64
0.82
adjusted
intercept
6.A Appendix A:Scaling
exponents table
83
Land use type
firestation
store
bookstore
homegoodsstore
atm
electrician
car.repair
night-club
dentist
school
furniturestore
grocery-or-supermarket
beauty salon-spa
realestate-agency
post-office
park
carrental
hospital
plumber
electronicsstore
florist
restaurant
jewelrystore
parking
clothing..store
art-gallery
shoestore
pet-store
laundry
bakery
embassy
movierental
cafe
liquor-store
gym
bicyclestore
synagogue
mealdelivery.takeaway
travelagency
totalamenities
(YO)
-4.47
-2.49
-4.21
-3.67
-3.44
-3.84
-3.22
-4.32
-3.32
-3.39
-3.95
-3.91
-3.34
-3.45
-4.62
-3.79
-4.72
-4.70
-4.28
-3.99
-4.54
-3.35
-4.62
-4.90
-4.09
-4.81
-4.82
-5.30
-4.48
-4.98
-6.45
-5.90
-5.31
-5.24
-5.62
-6.44
-6.54
-5.91
-6.28
-1.41
slope (P) 95% C
0.96
0.96
0.96
0.97
0.98
0.98
0.98
1.00
1.00
1.01
1.01
1.01
1.01
1.01
1.02
1.02
1.04
1.04
1.05
1.05
1.05
1.06
1.07
1.07
1.08
1.09
1.09
1.10
1.11
1.14
1.15
1.15
1.16
1.17
1.19
1.22
1.24
1.25
1.32
0.94
[0.66,1.26]
[0.78,1.14
[0.80,1.13]
[0.78,1.15]
[0.81,1.14]
[0.78,1.18]
10.80,1.17]
[0.79,1.20]
[0.80,1.20]
[0.84,1.17]
[0.82,1.19]
[0.84,1.18]
[0.86,1.16]
[0.82,1.21]
[0.85,1.18]
[0.79,1.25]
[0.86,1.21]
[0.87,1.22]
[0.86,1.23]
[0.86,1.23]
[0.88,1.22]
[0.90,1.21]
[0.87,1.26]
[0.88,1.26]
[0.92,1.25]
[0.86,1.32]
[0.91,1.27]
[0.85,1.35]
[0.94,1.29]
[0.95,1.331
[0.79,1.50]
[0.94,1.36]
[0.93,1.39]
[0.92,1.41]
[1.01,1.37]
[0.96,1.48]
[0.95,1.52]
[1.08,1.431
[1.10,1.54]
[0.79,1.09]
R2
0.45
0.71
0.74
0.69
0.73
0.66
0.71
0.66
0.67
0.75
0.71
0.74
0.78
0.69
0.75
0.62
0.74
0.74
0.72
0.72
0.75
0.79
0.71
0.72
0.78
0.64
0.75
0.61
0.77
0.75
0.46
0.72
0.67
0.65
0.78
0.64
0.60
0.81
0.75
0.75
6.B
APPENDIX B: SCALING CHARTS FOR SELECTED
LAND USE TYPES
o. y--IA+O.9-A, A=O.80U
*Nowyook
*Cftw
"Aftrft
.84sn'-dia"
03W_11&k*_c-1y
Figure 6.B.1:
Scaling of
Restaurants vs.
American
metropolitan
population
M
10*P
'6'm
-V-Zd
4TOMB
j8dwonv"
AMiftft
"MM10
WgVfkt-h
,Mwfwod
010
t0-
10l
10"l
log of tow pop
10"
10'
10"
10
,0l
*won
y--1.6+0.72-x, ?=O.AS
10
10-
io-
PhM1Md90Nhd
W.*Q
w- 111111OCNWAM 081firo"
Figure 6.B.2:
Scaling of
Churches vs.
American
metropolitan
population
6.B Appendix B:Scaling
Charts
84
*M
T
Ohwwaunorooldmon
0Aaw,
~so
10'-
10o0
-ovdenc
Har#H~
1000
10"
lap,
I" of bftfl pcpiiioo
100~
y -+01.7
-x,
?=O67
10'
30
9NBWymlt
Figure 6.B.3:
Scaling of
Banks vs.
American
metropolitan
population
jo".
,P'Losrloell
*Pm*
,'
10"
loll
y-4i.+Q*6-z.
10"
10"
log of total
10'
popula0on
r2=O*SS
*N-wYoft
Figure 6.B.4:
Scaling of
Museums vs.
American
metropolitan
population
"''ar"e*
-ahogo
10'
,WPt
'"
l
-s'"'
Ban-"
~i-de
---
#Rn$d
tr~ar*Some
10' 7
I0
lop,
,0'-6
y --2.1+0.9-z.
10'
loll
log of toW population
2
r = 0.22
ONOWymok
1
-1-.L"
-e
.oston
n
-
loll
.
12 1. *
-H-for
-
Figure 6.B.5:
Scaling of
Doctors vs.
American
metropolitan
population
*Mgonnlad
.N-h.
10'-
6.B Appendix B:Scaling
Charts
85
10"
10"
log oftotal population
10"
10'
10"
y--43+1-x. ?=0.706
10.
,.,..*k
.--.....
10'
Figure 6.B.6:
Scaling of
Beauty Salons
vs. American
metropolitan
population
.M.
.... ,A0--po
acwhrmlbu-C
&AAA~l~d
IOU-
I0
le,
10'-'
log of
le,
toal popuon
y-4.3+1-x,?=0.725
10'7
Newyork
-
10
LOS
ange"e
10"*-'0-I--
Figure 6.B.7:
Scaling of
Churches vs.
American
metropolitan
population
6.B Appendix B:Scaling
Charts
86
Memphtio
10'
^
maea
NaWDlle
10
10"
log of Mat popuhion
10'-'
10'
9L
0
3
'-
m8
6u!leos Aq s6upluei
'Al]o::) xipueddV :)9
3; 6
a0
a-i
0
.
C
L
)Q
t.t t t
U'.
1
X,*
0
-,r
2I-
0
00
i; t t
W
(~
W4
.~~~
~.gi -~ ~
8:
t8: t
1
13:~
NJJ
_
Lrn
o-.
N3
wo 14 -t
VIc
L
I I:
4: t&:8
14 c
LI)
NJ
0-
co
N
J
trn
~Cj
N
NJ
..
J--
)
-
NJ8
t: P. w
12 (A
,
:j:
Ln
Ln
CAI .40
4
-0
N3
W:
*
~ ~
88
6uile:)s Aq s6upjuei
4!::) xqpuaddV :)9
Q0.
0
0()-(3
bi
'4
t
Nrl
t
-N8o;8W
(3Jz
N)33
0
-NJ
)
0
W 4-
93
C3)
(33333
C3)-
W1 ea
N) NJ3
J -0NJ-
J
JO
NJ--
0.
~J
8ZZ;Z
0
0
--.-
(n
t-
Z3z! aZ W
8 (3
-,o
~Q
JJ(
)
~
- N
0
(3)
0,-
(3,
" --
-
8
IL.
o9
wIN
W3)~
ON
V 9
L,
z
(1^O1
Wa N
-
N- t
J
w
It
(;;
8
%N
C)
-0--lw
3
s
;: ;
ZN
(,
wJ
0.
-
4-
(3)
;!
'
Z
-3w)
ww wN
------o U)
oe~comes-------'N M s
s
e
ssN
i04Ra
fu
It3N- U
't m
10
;
N
C
rl 1
-
9, ::i A 2F Z 8A
R
V LQ 2
0
m' -d- N0 Nf
--
xamman
at
It
'oe
t2
Nn NM
ossa0,
'0-
E
O
eu
'0u
Re2
~
r
-
a
'
)
g
C
IN IR-4
m
mm
NLn4r
V
g
.-
R')
!2
)N '0
R
mL
!5 Qj F R P! ?2
A
Imo se
-
RRP
R) nA sA tX
;;;
ss
N
0,ag
n
ga
s
aNC'n
C') -- C-) NeMa
eC')
K L0
9 2
OR
R
con
10
-
es
CN
:-
2 ::
0,
?QQ
t
NC
gesO4
04A
01,
5
,CQ
mmassm
I
N 2
ma
U'
me
tM4 ! 4 R
s
0,
4
-MO
LMO
P;
!-- 1
Rssse
*e
"
C-
0.
?!
~,
1am eg m r
L2
---
N
om
C14 l-ge e Im
-
gI
2
mR A K 0,
S
e
a
as
e
a
vq)
R
M
If; 44
4
w t : OAsN
4v L 8z
Co
0 '0 N-- o-
UNom
qM* CNA 4v
v!
0r
MOLrA
R
! -m t 4wo
W Id
4R
A
o-2
O-M
NO
a
a
q &Lfo)%
:2
) 4A i!!
4 ns z W
s
-
0,
C')
:
W
mm
n:
RsP4"eo4
-;2I ,t N
I. -t
tQ - o,
E
0
A
I
6.C Appendix C: City
rankings by scaling
89
Lt
m
- "f
tem~-a-a
F
2
q
n
o
"accounting
0
UY- 12U4
)
r+
3
mn
airport art gallery bakery
:3 0bank
8
church
store
343 - 13i)1
418 - 25O2
doctor
56-259
427- 2637
1883 - 5664 84 - 4238
471 - 2674 881 - 3927
316-2112
241 - 3282
40- 227
142-517
146- 2075
234-1397
253-2373
291 -3414
-
1818
290-1789
1227- 8377 1174-7173
308 - 2688 285 - 2448
303-1497 321 -1375
709-4773 543-5251
45- 139
27 - 154
159- 484
165-465
691 - 2542 460-2656
281
426 - 2026
25 - 115
142 - 428
156-1773
258- 1220
209- 4584
226-1865
561 - 5078
168-536
178-558
135-361
154-436
1125-8504 1797-5269 1813-5284 1361 -6940
55-1167
262-2032 316- 1835 149- 1864
1YI - 12Y4
1YI- 1152
165 - IUb4 152 - 1lU1
151-754
168- 710
257 - 878
132 - 571
112- 815
152-1047 236 - 1752 237-1434
92 - 539
81 - 249
49-253
63-236
135- 1071 160- 2273 160-2250 114- 1619
162-641
199- 598
212- 595
168 - 556
75-673
147 - 530
103- 557
130- 617
91 - 354
77 - 234
93-310
98-245
197 - 1001
191-846
159-936
162- 1010
563- 2308 718-3042 668 - 2689 543-2014
clothing
150-383
194-467
318-741
196-462
189-559
1886-5066 1128-6330 1894-4576 1556-5813 1524-5047
342-2664 210-2079 551 -4523
235-2141 380-2098
433
2298
174 - 929
364 - 1236
150 - -1120
9
259-812
203-634 632 - 1260 229- 806
312- 710
265 - 1762
146- 917
514-2606 182-1143 403-1208
26 - 122
67 -244
197 -603
111 - 405
52- 194
201 -3006
157- 1882 620-3148 137 - 2235 289 - 2089
72-319
152- 543
182-569
206-680
208 - 500
108-505
373 - 945
129- 476
40- 216
225 -659
80- 223
154-619
63- 209
78-286
0
176 - 826
166- 978
259-1067 169- 1096 262-1096
754-2631 453-2146 970- 3933 523 - 3080 506-1770
301 - 1677 251 -1793
621 - 2394 224- 1735 507 - 1983
1474-7224 798-6971 1407-5882 871 -7375 1098- 7099
403- 2328 213-2229 733-2634 282 - 2548 523 - 2357
341 - 1431
307 - 1405 312- 1580 351 -1380
253 - 1351
972 - 5744 266-4705 1326-4832 393-7136 833- 4656
29- 109
32-102
148-504
33 - 107
96- 279
142-511
163 - 435
153-471
161 -578
171-494
751 - 2530 409 - 2060 971 - 3147 559 - 2556 718 - 2420
beauty_salo
* Summarizing the range (min-max) of the closest pairwise distances in meters) from 50 US cities
bank
295-736
374- 2285
bar
431 - 1889 529-3569
beautsalon-spa
95-316
358-2091
cafe
323-2818 421 -4387
church
206- 509
391 -1194
clothing_.store
305- 1638
225 - 632
doctor
173- 1916
76- 238
grocery_.orsupermarket 252-1092 565- 1956
hospital
926-3142 855 - 5383
meal_deliveryjakeaway 422 - 1804 630- 3063
museum
1515-7629 608-10678
nightsclub
460-2774 332 - 3646
park
350-1413 506 - 3888
parking
924 - 5807 101 -5739
restaurant
71-230
100-1131
school
368-1111
180- 470
university
798- 2695 860-6146
bakery
%40
bar P~
n.spa cafe'
accounting
airport
art-gallery
6.D APPENDIX D: CO-LOCATION RANGE MATRIX FOR SELECTED LAND USE TYPES*
b
} p'
466-2221
262-1681
524-4424
98-351
178-481
477-2071
318- 1649
1045- 4564
182-478
822-2651
47 - 204
123 - 496
155 - 829
419-1065
416- 2048
89- 359
350-2816
129-490
246- 753
253 - 1793
1851 -4772
442-3258
501 - 2378
hospital
836- 3662
425-1821
1340-6830
3 0
210- 723
441 - 4948
394-2335
279- 1407
227 - 860
373-1432
152 - 447
335 - 1538
192- 595
234-819
1- 47
271 -1175
88-1088
436-2346
933-7717
246 - 554
ar-+
accounting
airport
art-gallery
bakery
bank
bar
beautysalon spa
cafe
church
clothing_store
doctor
groceryor-supermarket
hospital
meal_deliverytakeaway
museum
night-club
park
parking
restaurant
school
university
grocery or-su
permarket
01.
5>
296 - 883
348 - 1828
115 - 448
315-2814
210- 622
189- 795
115 - 369
263-1145
817-3084
378 - 1728
1317-8053
522 - 2577
360-1615
883-3717
52 - 255
180- 547
785 -2342
261 -1265
1607 - 4907
307 - 2680
mealdelive
rytakeawa
y
196-759
museum
148- 782
667-6391
232- 1821
337-1695
193- 1024
244- 1195
172 - 650
237 - 3261
141 -580
228-1381
124- 585
321 -1385
588 - 2694
363-2130
63- 2858
227 - 2342
164-778
265 - 3027
70-627
179-616
350 - 2208
155- 566
801 -8512
155-2641
144- 1230
175 - 806
63 - 1183
69- 329
120-3161
153 - 584
81-599
77 - 355
132 - 832
436 - 2629
208- 1790
333 - 4409
76-2084
255-1691
170- 5034
25 - 155
130- 545
331 -2060
night club
park
parking
restaurant
484 - 828
179-934
184-461
1806 -4934 524- 12057 1706-4768
670-2468 181 -2624
274 - 2052
619-1586 200- 1049 185- 1033
694 - 1238 173- 1049
246-760
649- 1863 157 - 1187 210- 1419
406-684
126- 794
64- 199
648- 2574 150-1450 150-2272
261-605
193 -792
189 - 559
528 - 978
152 - 1113
131-445
272 - 609
91-690
102-244
485 - 1150 225 - 1158
151 -897
1119 - 3037 355 - 3092 619-2697
762 - 1914 269 - 2646 320- 1724
1537-7065 301 - 3755 1126- 7175
898-3001
185 - 3535 291 -2191
32-241
203- 1653 289 - 1536
1160-4957 101 -1311
513-5056
279 - 604
59- 380
27 - 111
290-516
148 - 540
169-434
1022 - 2808 275- 1625 588 - 2280
school
341-725
2012- 4994
549 - 3594
386-2132
577-1171
580 - 2232
257 - 588
538-3434
175-468
372-881
177-512
343-1097
957 - 3575
551 -1988
1537-7815
709-2640
324 - 1534
1336-4758
166 - 505
114 - 336
1028-2559
-
1055
89-318
291 - 1208
459-2344
407 - 2642
242-6195
430-2133
232 - 1289
211 -3702
110- 368
158-650
80 - 938
261
245-2120
337- 1658
228 - 798
276 - 1339
212- 619
236- 1766
182 - 656
683-6842
university
309-925
6.E
APPENDIX E: ATLAS OF CITIES
Deviations from scaling & Co-location heat maps
6.E Appendix E:
Atlas of cities
92
6.E.1
New York
Residuals and
pairwise distance heat
maps for all
land use types
0.4-
I
-OA-
Above
average
Below
average
amenity
nI
s*ummmmmummmu mmm mmmmmmmmmmuiumm mu mmuImuloummm a em s
...
E..E .... ...
....
.U.............
UE.E.E .
m*** .m.umm.
... ..
I~..................mmm....mm....*.....m...*
....................
...
........................E.............EEUEEEUEEEEUEEUEEE.EENEUEEEEEEEUEU
m............. m.. mm ...m... ... .mu......
.. u...........
i!!!!!!!a Noun, a mann!!Iissammsmi!!!M!liniIi!I!!!i!
a memr mm fle
m u
mu
m
m lmom nsu
m
mum
umm
a
umUarU*mmmu.
muaammm *umuumummmaEma
............. ammamumem *3...............................fl*3...3*.3........E..
.U.....
.umm
*mmmmu
,EUEmEU~UUUEE*
*mmmummmmmmmummmmr ~
MmmI m
'"IumMuINOR1:110m onmmmmuumum
mmns
1minumumum
*umuu
ago s masms
Eum
~aMONEDNO m ou m a onsm umm lmm nowuu
.nNoau.. M
.mmumuummumm Mami a .one
.um .....m
..umm
ua.
mm..u..................mmm..
I.........m
..............................
.mm..mmmmmuummumuSOmfl~uMuuuE~EEE*E
a mommmm::~mumu
; mu.ity,
m yMO
ApedimxEmm
6.E
HGHmm
ummum
Atlms fcitmmmmm
ONmmuumm mm mOR mu
IN mm A m a u mm
on im an m
amu
N6 _ m mAPN on mW N m mmmNm
iinofa Omamaa iua
ma 80maof
m muXME m mumumamu
IN an m * umm ami mosmmos m
ausu owns
OMMON mm
umm
mu
.u....u..................umu....mm..m.u.....m.u....u......m..u.u.u.uuu.
Umum.e~ *anmmm
mumpi umu
mms
uummmum
m 0 m% inu pom
ummummufmwm RMmmu s a
mm 0mummmummmu
mum
mmmmmummum
mNm mmmu
amumooUAON0
a
ama
aem
on manm
mon2m,
muns
mOU mo
mnm
a asgmmumivmuaummmmmmumummmmmmns omanou
mmoon Rus m unn mu
Immmuum
mass manamgm
ORONmmOsa
wom
Nomi san
mmo ammun
mmumuuuNEW
uuMN
a auumumuu
a
4
am"
a auumumm
MEE
U umgmosmm
u mmamms a a
muIna
mam
oil
n so um
Mmmuffo
6.EApendn
Ata oacame
93
Ewn
LO
aHoan"wn osHl
a
aEM
1
mumsnmumuumonmsmmummu
'mmSmMaum ME&0 w0'lpummu
um
ame
0m
mlgn I
ma
in X mMEN
U1
uMason
m M summon
amnit
A
6.E.2
Boston
0.W0
0.26-
Residuals and
pairwise dis-
tance heat
maps for all
land use types
00-
w uUIiEU
I2
or
-0.50-
Above
average
Below
average
I.%Ini..ii!!....!!."""*...* ".iinhIliiiuiiiii
=use~u-:mnmea===s
mn:
ilan
ill
*.......,.m..
*Eiiimii
in=umin
EU
.
J
a11
ou..uE
soe
BEjjuu1
.
!|iiiNE
----
n
UmU
auEI.
No
.
..... nwa
m*UUEUUE
uEEE
nininini
sa
.
u....*...:.
..
IiIlIllI
--. 0:18Eii-"---.SON"".
moomm""jliOMMOggs
MU
ao1E ilfl
miuss
.
******-***:
-aa
.I...m....
-No.mu- ms
-E
g.
o
=====|=========-n
L
ElE
man11111
*....m ..qp
nq"Pn
I -IN Ume-oo.
nslliiisi
na
*****
i-nm
m-um:ms
.-
s: U...
mnumumu
.....:E............ nr:. -u.
*
..
**
i.uuy.nrn..I..igru.r.
-*:.:I. *.!....:...'..uiu'.rr:-!ii
==D=U ..
I
UUUE
UEmum
mm1
.A!r
IIIUIIIIHIIIIIEU!!IEUUUUEii
.k
-z
6.E Appendix E:
Atlas of cities
94
.a
UENunm
.
*.33.*mm***nnlin"***
IiU
IEEE.UU...uE**..:::.......*......*.
*.-A*
i"................. .***" a...m..
nanu!ci iiEEEnvuui:E:Ii:m:
amenity
B. . amenity A
LOW
HIGH
6.E.3
Washington
Residuals and
pairwise distance heat
05
32
di
III'M'
maps for all
land use types
ii. iini
0
-0
Above
average
Below
average
amenity
E...E.....E..
.....................
.mu.....................................
u******** WON*******mu*
IiIiIIIIIII~lIIIIImE~IIhImIIiII,I gI3I.EIIIIIiIiihuihuI
*msmmmungummmmmauummumnuuummmnm
........... u.I ... .rMrE
:uu
La
ma
siool
S. :::=.:
.. ..a --Nm 0- 1
==mq=u
-=:s-a
Imamm
So
Nm
-IMm.
==...mm.muu....um::
unrn=nu anaam
man mamFamPa
mmamma.
m-m a ammmmaammmm
m
m
m.m I.En*..qI+pu
ummIajml
BRaEamEN
Emmmm
as
*q m
ma*
ammmm
amaul...rm.II:-:** II.
-rUUUrEUUEEE..:
wUH.E-EE
1
10111110
1mily..
a a a so m m .e
umm~umma
L
*I
a
mm
n
m
a
m
unn
mav n mm, X
Mnna m Emman
o 111,11aln
MaU mm
a . ..
u. a 1111,o n ail a a
.
.. nm.mm...............
.....
............. r.............ma mmuurmInin
MEN
as
ma
-tmake on0i~i1w l Ein .mim
annmmmms
0
i
osmmE eEUEE mmmmm
111:m
oN* mmmmmm
.ii.
mosi igMaawsmannnw
IMB11L MIIIIIIIIItI,:
U EMMM a
U
Nun U
a mm ******m
m
r :.BlownamA
r
s..
..n
..
n
urn.
..-.
m
.nmuamo
mu: -:..
a
amamaa
amNn
ME
a.,mu mm ma
asmEmuu nionum mmnmonummmmm
pmEE
E
a
NJ
an
E
U
.
U
a I Imn
a V1i p a "a
m
u
mma
aniammmmmUmEEmm mmm 1Basinmoi
mm
am a -- -~im
a Ia a a
a . mME asa
ENNOMMMUNNOmmm
mu aam
&===UEEU
a
m
mmaum
uu
nnam
un
u~
m
m mm u ma
m m m n m m n m a~~
- ~r m
Monm
0..If=
an
so
~ r
ara r urn m o u a Ima una m. u
a
uum
0
Uunm.usn
u u m u nuu m n n m m annua mlammum.V
MENNE:N=
'ano mNun
::* ma
maE-==rJu :--.a *mma
=r-rmau:L:n-m:=n.
nmmmimrnrnmm
anmum
a~aaaman a mmmmmm
*mmuammmnmuunm
am .al.l...ow::.S
mass am msm 0
a
Vu aU
on
2:ing
a a mmon sams0n aa mEso=K11"1"!!i":
son man omem
a os
IN* *iNumenmmmmn
mummama.
RONnmmuaNMNmuNNN
rt~ram
NUNas mm.asn
mooaan
Atlasa
go
.e:t~
.
.r ann
am. NUNm111m
ho.Eta Ap en i
Atlasof ciies
95
LOWHIG
E:an
a
-a,:
:11c.it1es
HBai:manHOno
T,ommi
ty TAF a W .1
:1." .r0..fl:.1: a a U
nu "H
H 0.mu
"
EUU
T*No=*ON
mem.Na.mem
a ma
fmoomlesso
EU m a
a
U
.,..
KEHEE
non mey
V /Aiuawe
H01H
sna
AA01
*om
a AjIuawe
9 UEa*
a
.U.U
a.
Mm
E.
~ U ~ ~E~E~E
U
sa!lp pO
96
selivi
:] xipuaddv 3-9
U
U
U4
*mm0m
.......
Eai.,E*UEM.EEaEEEUEEEEUUE
. a . ..... .......... ... ummo . .
. . a . u .ma... .n
Ua
N U&MI MEEEUE E MOUEEEE$E
'
Eini"
"UE!!"PI
!i
'
nniiii
MUEEEE!!
ni
........
a
GONE
n low ..!MR
anfalon
EENE
EN E.... *.sa il .VA ...........................
Eu...,.. M
o
01111811111
waa
0Mlna
bi[.
.............
... I....
.............. .......
...
.. ......
8...... . . _
OCing
noHmensw oI IliiiiIIII
.. m
-
.
-n-
............
__ _i ian Min a M!
._
...
-
E
..
......
uu......... ....
WEEMENMI
IHIII
-- nmioum
5f....l
.... U.....
..
..
sEsoEa E
ONSE
a
~no
E
OMENammommemo
......
.n.......... m..
IIIIII!I
.Mimu
mI" liMi
-11jull
.E ......
W
OVA .II n
a . ..
mumNnI B an.uIuI so nn om
K us. om
..............
................... ..
..........
... LN---... i ..... I:dI
.....
IIII
0P
a
q ulHIInil
IHII is
no
a
111;
T in
neiAM
amm~PI: .. Hn no u..111 INE
8,-:11
nmno ON
an offlouIIIl ago!
a IIIIIIN -- ----- on a=
E
isBI
BORROWDl ll
AaIUOW
e6eeAe
Mole
5
e6eleAe
eJoAR
M
000
!
EW77UU
sedki. esn puel
lie ioj sdew
jeaq q:)uej
-sip eS!ied
pue slenp sad
oospuej ues
V9
6.E.5
Los Angeles
0.3-
Residuals and
pairwise distance heat
maps for all
land use types
~ait3hJ
0.0
-0.3
IAbove
-0,6
average
Below
average
amenity
lil
|| IA wti, _;3 .1 .1 Z' z Z Pllllil
liilil
muiniiliinin ininiiiniiiniiiiiniminin uuuum!muuiuinu***u!!
beaua
iiii iiiII~nilii.I"II.linliii....iiiin... imilili. .i iiii ill
lsonsommon IHiliHHIRilow
6 HIHIH lil 4 H~~iiiii
ME
...............
mu.o..ss
U...........
8.:....;:.....uggn......-.-........
Il"jjjjllj""lEME liR
mimiiiininiiniin~inininin.Iainiin
sm.Im...m
.m...........N...
!20
is!!ln
lii|
||!1
............
Masnu
uuuuuumuummmmuuuuuuu
soon...
mmuummuumumummm.m..m.mm..
a.....Nosum........
mummmmmmmamuummm
...
................
uuuuuuuuuuuumuummmn!ummmmmmuin
smumomumu
m uuumuuuuumm mum mm mu a
Imumurn u m muminlilinimmm*mmmm
m.....u-u...*.a..*..uim..uuu.Uummul.u..uumu
a
a
a.muum...u.en
ama
mmmmmmum mmumm * mm~m.
.....................
umumT mmsen'
At
s GHm
m..mummmm..muuuuu
was*
anuIBMuumuaui
*u...u
omuumumm uuum:o
......
.
.......
.
.ummm
..
.
.
m..
....
Oma
SIMMS*
manmu~~ ~u
.a
On
mma
RES u
*u~u~mm uuummmmummumm
muum
"1
ImuONOI uamu Nua...mu
urn....os
MmmmunMM ummmumuMnMmMMM
ar_"'
MEf
~~~~~Nwa
M
OitOes
lqcstegm mu
9mono
0a
uu
a muin!
a a ols mmmsm
baNEaaa a
TlUenae
mu ammmmmuumm .ummuuuuumanbu u man
mmmmuuann mum
u mum u uu u
uon mms
I
*.1..
auuuummm..
uu*mmm m
ummum ....
o
sio au mu mmm mummumum
*uuuso smmo
ao mummmwumuumoo~mu~m.mmuuuuuu...EummuummmEEomm
H
LO
l -imm.a
um
a
Blo
Y.. t
o3n
S
rwas
mu
*mmu
m inE
m
am ma Imum emeom
Emmon
a a um
IIIIIII'
ui ma
ss E
*WIN
'Ir'rmetr umn Niluuwe aummmuum u MENuu~uummmmmuum
*runuuuuuum X* sao* B i o 1
.~_t mmi wmuu mo la *uumsts.."aummm. was
u
*umommon
1mmons
Lt -mas annmutnw II9
a
umu
soo lmu N
*
mua a m mumwa
ban
MEO
hoptl6.Ess
Atlas of cities
97
no
mmumuuuummmummluau
a
musbsa
Apeni E11111
ummmum
mmoammmnum*u~um
''noonI
a anm um onanou *uu m m
amnt B11
mE m
uomonms
asHiGHNNOE naamuaome' oo o8n
LOW
uu
amenity
6.E.6
Houston
I
0.2-
Residuals and
pairwise dis-
48
tance heat
V
0
..
N
maps for all
land use types
Above
average
Below
average
..................
;uuu"T
m
.........
..~.........
. ...~ ~ ............
...... saallsp
~
~
M,...................
M o..mon ........ ......
.. .....
KErk ...
... ~mu......
Numnn
7K
mm******
miim..min.
.......m m m No.. ..mmnf":Wf
arI..
..........
.
m..... uu.um..umamuMIMS..umm
.
.O..l....
IM IIIIIEIP!EHIEFII.
1'''P'rIIIII'I'IIIIIIIihEi!!!
mm...
sitmmummu muummmm
a.
m
....
m~MonsomMummmmmmmmumuon Eu~mmmuum~mmmmma N"==n= 0a Emmmmmssm
a~
a MNEaum
am . .i
aum-ea
. mm
.
n~ml..
omea
.
a
0
. smo=~mmm
.
nu mS...m .
.m EME.
U
.a .. a ea
.. a
... . ...0 m.m
u669.n.
n~r
k asong.w
eeso
am
..
.o
.mK. .1
m ... *u*~*
i. mm.
e
m.. .m .. a .
.. E........
......
~~ U...EU
a..a..............E
..
ia
wwry2.a~moen .==a...
.
m ...... m...m.........mu...m...................UE.....
m
. .U.D
......
mmmu .. m.......
.. u ..
........
~
....
.
l .m aa
a.... m.. a*aa. .auom..ua
eam
naim.
U.
. E.
W.@%
.
4
E.. um.. .........
EUEUE..EEUU. ...........
mu l
.
...
"t
m.... .9.w..
m.m..m....u....................,.........u....................
.... uu.........uu.~iii~uuum.m..m......m..... ....u.... ...........
.
beauty
a
ON***lf*
.. *..............
.... *.....
pe.ilI
IIIII III oil
"I'li"HRI" muIIIIIIIIIII
.I I It... il
,...
mm.m...................
.....
............
3
IK
M 2 amWuu
mtuheU1Er
...
4....
.
0
I
omt
.........
....
..
*.m..
m...m.
..
m..
m.umpmm
...
.
.ma...n....n.....um............moan
am..
EME
E
m.....m
..
s
lm.. .l m
0..m
.a.m
a uam ..
.. 10 .m
.M.LE.S
ra.
.
du
LOWmG
m
.
.I
a .
mm.
.
.me....
.n
B
.
.
umnity
A
..
m
.. m.H . .
..
UUU .oU. m...
ameno.n.
..
.
m
d,.
..
of
.
citesco .
a9unma
.ELM
m. mm
a ... ... mm.. *.u...
ama ...u mumm.
mu m .. U .EUUN~f S .KK . ..
norun
a
a
mwmowal .... a 6.Eje
.
:
1a
Ap
en imsnmE:
menit 1, .. _ s mm
mmmusmmemamuamkmumm;umummmummagmmmmmmmammummummummmmumagjamm
mmmumm au
,f
0109M
ma
.
.
Ata
.
a.. a m
e. mommoo oux.n umu.. u. !0
mm.ne.z El mfl m
ihiIII~ mf m
.a
m. a .1110.0
e.
ME
*IOUUEUE
U U.mm
sm .lrmwu
. ... .uav
m
....
6 A
amr.
en.Emum.mrnssm.uu.m
..
.. EK
. ... . K. U.. ... ..U.. . EE.E EE . U.. Un
aeaauu" n .
Atlacities
..
m...
.
ai.
1
a
U
U
tr................
a
um
m