MEASURING URBAN SPRAWL AND ENVIRONMENTAL SUSTAINABILITY

Alice Rauber Gonçalves MSc Graduate Student – PROPUR/ UFRGS

alicerauber@hotmail.com

Rômulo Krafta Professor, Phd – UFRGS

krafta@ufrgs.br

Federal University of Rio Grande do Sul/ UFRGS

School of Architecture

Urban and Regional Planning Graduate Program/ PROPUR

Sarmento Leite, 320, 5th

floor – 90050-170 – Porto Alegre – RS – Brazil Phone: (51) 33163145 Fax: (51) 33163145

Purpose: The paper aims to highlight the importance of considering intra urban level on

urban sprawl measuring, as a manner to grasp aspects that have stronger relationship

with sustainability concerns.

Design/ Methodology / Approach: This paper reviews recent methodologies for

measuring sprawl, trying to identify their role in the sustainability debate. This paper

also reviews some concepts of urban configuration systems that can lead to

improvements in methodologies for measuring urban sprawl, since it enable measurements at

intra urban level. Finally, an attempt is made to suggest a different approach to

measurement of urban sprawl.

Findings: Most of current methodologies for measuring sprawl do not consider intra

urban level. In this sense, a network approach, which takes into account the

configuration of streets system and the distribution of inhabitants and activities may

bring new lights into this kind of studies, since it enables more accurate measurements

of distances between activities, such as residences and jobs, which can be used for

verifying configurational issues strongly related to sprawl impacts.

Originality/value: The value of this paper is to contribute to urban form and

sustainability debate and to a deeper understanding of urban sprawl.

Key words: urban sprawl, sprawl measurement, sustainable urban form, network

approach, urban configuration systems

Type of paper: conceptual paper

Abstract

The paper aims to highlight the importance of considering intra urban level on urban

sprawl measuring, as a manner to grasp aspects that have stronger relationship with

sustainability concerns. Recent efforts have been made in order to develop

methodologies for comparing cities about its sprawl degree. This paper reviews these

methodologies, trying to identify their role in the sustainability debate; and then to

suggest a different approach. Most of current methodologies for measuring sprawl do

not consider intra urban level. In this sense, it is suggested here that a systemic/network

approach, which takes into account the configuration of streets system and the

distribution of activities may bring new lights into these kind of studies, since it enables

more accurate measurements about distances between activities, such as residences and

jobs, that can be used for verifying configurational issues strongly related to sprawl

impacts. The value of the insights suggested in this paper is to contribute to a deeper

understanding of urban sprawl, as a different approach suggested here might be able to

demonstrate whether or not current thinking on what constitutes sustainable urban form

is valid when measured in terms of intra urban level.

1. INTRODUCTION

Debate on sustainability has highlighted some issues concerning urban form. In urban

studies, there is general agreement on the idea that urban form is an aspect that can

influence sustainability of cities. Some evidences indicate a strong relationship between

urban form and sustainable urbanization, although it is not always straightforward.

Actually, such relationship is very difficult to demonstrate, since there isn’t a

sustainable form that is applicable in all situations. The concept of sustainable

urbanization we adopt here is related to capability of optimizing occupation of urban

spaces.

Urbanization process in last decades has led to urban sprawl, defined as a condition in

which density is relatively low. The phenomenon has become a subject of particular

interest among planners and policy makers, and has received extensive attention in the

literature over the past 30 years. This urbanization pattern – largely observed in United

States cities, as well as in cities all over the world – is perceived as less sustainable than

a compact pattern and very often is associated with negative environmental impacts.

One of related negative impacts is the rising demand for travel and increasing length of

inner trips, since sprawl is often associated with dispersion of residential and

commercial areas. In terms of environmental sustainability, one of the most concerning

characteristics of sprawl is the intensive use of individual automobile transportation.

The increasing journeys, especially in private vehicles, caused by greater distances

between residence and job location, can lead to more fossil fuel consumption and air

pollution. Advocates of compact city claim that denser urban areas would be more

sustainable since higher densities and more compact forms, supplied with mix of land

uses, would diminish commuting trips and even increase the potential for walking. But

how can these hypotheses actually be proved, or measured? And how those questions

have been treated in academic research?

Some authors suggest that sprawl criticism and compactness praise have no basis. Chin

(2002) complains about the lack of reliable empirical evidence to support the arguments

made either for or against sprawl. Jenks et al (1996) argues that environmental claims

made in support of compact city need to be tested, and supported by empirical research,

if they are to form the basis for urban policy; and that maybe counter-claims that reveal

ways in which the compact city is not environmentally sustainable. Polidori and Krafta

(2005) observed that urban form and sustainability debate usually come out with the

hypothesis that more compact cities are more sustainable, and they suggest that such

hypothesis is not always 100% truth.

Recent efforts have been made by some authors in order to develop methodologies for

measuring sprawl and comparing cities about its sprawl degree through measurements

that can be synthesized in indexes. This paper reviews and discusses these

methodologies, attempting to identify some limitations concerning its relevance to

sustainability debate.

The main purpose here is to highlight the importance of considering intra urban level on

urban sprawl measuring, as a manner to grasp aspects that have stronger relationship

with sustainability concerns. Greater density and compact settlements are widely

accepted principles of sustainable urban form because they are regarded as efficient

urban systems, while urban sprawl is often pointed out as an unsustainable type of

urbanization. In this paper, we suggest that a more detailed analysis, at intra urban level,

is required, because most of current discussions about sprawl measurement use

aggregated data that poorly captures fine-grained pattern and configurational issues.

Intra urban level analysis might grasp some aspects that have stronger relationship with

sustainability concerns, such as distances between urban activities, for instance.

Therefore, the value of this paper, besides contributing to urban form and sustainability

debate, is to produce a review that could lead to improvements in methodologies for

measuring urban sprawl. The insights suggested in this paper seek to contribute to a

deeper understanding of urban sprawl, as a different approach suggested here might be

able to demonstrate whether or not current thinking on what constitutes sustainable

urban form is valid when measured in terms of intra urban level.

The rest of this paper proceeds as follows: the next section reviews some sprawl

measurement methodologies; the thirty section presents recent research in the urban

configuration systems field; and the fourth contains some attempt to bring sprawl

measurement to a network analysis approach. Finally, the paper concludes with some

final considerations.

2. URBAN SPRAWL MEASUREMENT

Sprawl has become an umbrella term, encompassing a wide range of urban forms.

Urban sprawl is regarded as the opposed of the ideal of the compact city, with high

density, centralized development and mix of uses; however what is considered to be

sprawl ranges along a continuum of more compact to completely dispersed

development. Sprawl has been conceptualized in recent studies as a matter of degree,

not an absolute form (Chin, 2002); and as a multidimensional phenomenon that requires

a different set of measures for each dimension (Frenkel and Ashkenazi, 2008).

Some authors (Torrens and Alberti, 2000; Galster et al, 2001; Ewing, 2002; Bertaud and

Malpezzi, 2003; Ojima, 2007; Torres, 2008; Frenkel and Ashkenazi, 2008) have

presented in the past ten years methodologies for measuring sprawl degree and

comparing cities. A great number of quantitative indexes of sprawl/compactness have

been proposed. Those indexes seek to condense multiple aspects of sprawl, since it has

been conceptualized in studies as multidimensional phenomenon. The main question

here is: how sustainability is treated in methodologies for measuring sprawl? Based on

review of the literature, we found that most measures used in sprawl researches do not

concern directly to environmental impacts, but only to aspects that can indirectly

indicate some environmental damage. In this section we will highlight some relevant

aspects of those methodologies for measuring sprawl.

Table 1 comprises a synthesis of main recent sprawl measurements studies. As we can

verify, most of them focus on two main factors, both related to urban form: a)

population distribution pattern within the built-up areas; b) physical expansion pattern

of urban settlements.

als

ter

et a

l (2

001

)

Galster et al. (2001) defined sprawl as a

condition that is

represented by low values on one or

more of eight distinct

dimensions of land use: density,

continuity,

concentration,

clustering, centrality, nuclearity, mixed

use, and proximity.

The authors developed

operational measures

for each of these dimensions.

Density: Average number of residential units per square mile of developable land in a urbanized area

Continuity: Degree to which developable land has been built

upon at urban densities in an unbroken fashion

Concentration: Degree to which development is located

disproportionately over a few patches of the total urbanized area rather than spread evenly throughout

Clustering: Degree to which development has been tightly

bunched to minimize the amount of land in each square mile of developable land occupied by residential or nonresidential uses

Centrality: Degree to which residential or nonresidential

development (or both) is located close to the central business

district (CDB) of an urban area

Nuclearity: Extent to which an urban area is characterized by a mononuclear (as opposed to a polynuclear) pattern of

development

Mixed uses: Degree to which two or more different land uses commonly exist within the same small area

Proximity: Degree to which different land uses are close to

each other

Ber

tau

d a

nd

Ma

lpez

zi (

2003)

The authors have calculated population

density gradients for

almost 50 large cities all over the world

and also constructed

an alternative measure of city

dipersion. They

synthesize densities

and distances from the core into a single

index.

Population Density Gradient: Rate at which population or household density declines in space as a function of

commuting distance from CBD (Central Business District)

Alternative Measure of Dispersion: The ratio between the

average distance per person to the CBD, and the average distance to the center of gravity of a cylindrical city whose

circular base would be equal to the built-up area, and whose

height will be the average population density:

where ρ is the dispersion index,d is the distance of

the ith tract from the CBD, weighted by the tract's

share of the city's population, w; and C is the similar, hypothetical

calculation for a cylindrical city of equivalent population and built

up area.

Oji

ma

(2007)

Ojima (2007) analyses four

Density:

Demographic density (pop./km2)

Household density (dwellings/km2)

dimensions to

determine spatial

distribution processes within the

37 Brazilian urban

agglomerations.

The author calculates a sprawl index from

the average of those

dimensions.

Fragmentation: spatial pattern of settlements

Measurement of the distances between polygons and their

respective standard deviations for each study area (Average

Nearest Neighbor Index)

Proportion of non-urbanized areas of the agglomerations

Orientation/linearity: geographic orientation of cities

measure whether a distribution of polygons follows a certain

directional tendency (directional distribution)

Integration/Commuting:

proportion of commuters to the agglomeration core

proportion of commuters in relation to total population

Torr

ens

(2008)

The author developed an

approach to

diagnosing sprawl,

looking across the full range of its

characteristic

attributes that can be measured. The

analysis is performed

on one American city (Austin) across a

broad range of

sprawl

characteristics. Although inter-urban

comparison is not

focused on in this paper, the

methodology seems

to be sufficient to be

generalized to other cities. The author

devised 42 metrics,

including intra-urban level.

Urban Growth:

Urban footprint of the city; developable land; residential footprint of the city; low-density residential footprint of the

city; total number of urban patches; urban patches by activity.

Density:

Gross population density surface; population density surface

considered over developable land; population density profile as a function of accessibility to the CBD (considered over all land

and developable land); family density profile as a function of

accessibility to the CBD; density gradient by OLS regression; density gradient by spatial regression.

Social:

Owner-occupation profile; renter-occupation profile

Activity-space:

Diversity index; evenness índex

Fragmentation:

Fractal dimension; contagion; interspersion and juxtaposition

index

Decentralization: Gross global spatial autocorrelation; global spatial

autocorrelation over developable land; local spatial

autocorrelation over all land; local spatial autocorrelation over developable land; spatial hotspots and coolspots

Accessibility: Accessibility to the CBD; to major employers; to schools; to

other educational opportunities; to locally-unwanted land-uses

Fre

nk

el a

nd

Ash

ken

azi

(2

00

8) The authors

introduce empirical results obtained by

implementing some

measures of sprawl to 78 Israeli urban

settlements. They

group the metrics

Configuration:

Density: population density

Scatter: irregularity of the shape of the central built-up area

boundary; fragmentation

into two dimensions

(configuration and

composition) and assume that

dimensions of sprawl

are independent, and

are not significantly correlated with each

other.

Composition:

Mixture of land uses: land use composition (percentage of

each land-use category)

Table 1: synthesis of main sprawl measurement studies

Density patterns are the most studied sprawl’s dimension. Modelling the spatial

distribution of urban population densities has been attempted in several ways. Bertaud

and Malpezzi (2003) highlight that “urban economists have studied the spatial

distribution of population since the pioneering work of Alonso (1964), Muth (1969) and

Mills (1972)”. They remember that “this work has a longer history, traceable at least

back to von Thunen (1826), including studies by other social scientists such as Burgess

(1925), Hoyt (1959) and Clark (1951)”.

Sprawl studies often use density gradient. It is a measure of the rate at which population

or household density declines in space as a function of traveling distance from a core.

Mieszkoswski (1989) ascertains that this decline is non-linear, approximately

exponential, which means, absolute densities decline very rapidly as distance from a

core increases. The gradient can be visualized in a graphic, showing the change in

density in an urban area from the center to the periphery. Since Colin Clark (1951)

pioneer study researchers have estimated urban population density functions for an

enormous range of places and times (Anas, et al, 1998). Historically, urban density

gradients have become flatter and cities have become less dense, more descentralized

and more suburbanized (Mieszkoswski, 1989).

According to Torrens (2008), “sprawl is defined as a condition of poor accessibility,

followed by the massive use of private vehicles”. Low accessibility is a very frequently

reported sprawl’s characteristics. It can be considered, from the sustainability point of

view, one of the most undesirable aspects of sprawl, since residences may be far from

out-of-home activities (Ewing, 1997). Little mix of land uses is undesirable too.

Nonetheless such aspects receive little attention in most sprawl studies.

Two urban settlements can have the same population growth rate for the same time, but

one can configure a compact urban form, and another one can configure a sprawled,

extensive pattern of urbanization. But why those patterns challenge sustainable future of

the cities? That`s the main question that should be attempt to be answered in sprawl

studies. Some authors (Chin, 2002; Ewing, 1997; and Ewing et al, 2002) seem to have

an interesting point of view, as they introduce a definition and measurement

methodologies based on impacts, not on urban form. This idea can, probably, lead to a

more objective debate about sustainable urban form, since it’s clear that some impacts

are undesirable to environment.

Limitations on methodologies for measuring sprawl starts on sprawl’s definition itself.

All developments that differ from compact pattern are called sprawl, because no one

knows exactly how to characterize the phenomenon in terms of urban form. Chin (2002)

sees this kind of definition based on form/shape as problematic: all developments that

are not compact are “classified as sprawl, however, the forms and resulting impacts are

vastly different.” It`s difficult to distinguish sprawl from other urban form and “in any

case it is the impacts which make sprawl undesirable not the form itself” (Chin, 2002,

p.5). That`s why the author highlights an alternative way to define sprawl, a definition

based on impacts, an idea first introduced by Ewing (1994), who has indentified poor

accessibility as one of the ways to indentify and define sprawl. He suggests that sprawl

can be regarded “as any development pattern with poor accessibility among related land

uses, resulting from development which is not concentrated and which has homogenous

land uses” (Chin, 2002, p. 5). According to Torrens and Alberti (2000, p. 24), “sprawl

can be characterized by poor accessibility because opportunities are themselves spatially

separated from other opportunities”.

For Ewing (2002) density should not be overemphasized and studies of sprawl have

paid little attention to the impacts of sprawl on daily life. There are other aspects as

important as density, like mixing of land uses, the interconnection of streets, and the

design of structures and spaces at a human scale.

Another problem is that the literature on urban sprawl measurement usually assumes a

monocentric city, but the current pattern of urbanization observed in most cities is not a

monocentric one. So, the critique is that those studies fail to consider multi-centered

employment patterns. Densities are very often verified at density gradient through

mathematical functions that assume the city in study to have only one main center.

Although this approach is very useful for measuring distribution of population densities,

it has some limitations. It doesn`t consider relations and connections between residences

and job location, since it considers a distance from a single core, when most cities have

more than one commercial and employment centre. Studies where sprawl is measured

through density gradients can verify how population or employment is distributed over

a distance from the main core, but it doesn’t consider the existence of another cores.

The problem is that such approach does not allow verifying relationships between

population distribution and employment distribution.

The concerns highlighted here point to a need for more detailed measurement

methodologies, at intra urban level, capable of grasp characteristics more directly

connected to sustainable questions. We need to shift the focus from urban form to

impacts, in order to produce more useful and precise indicators of sprawl. A definition

based on impacts has stronger relationship with sustainability concerning.

Next section will introduce some efforts made on urban configuration systems studies.

The objective is to evidence measurement methodologies developed within this field

that might be helpful for sprawl studies to verify relationship between urban activities,

giving a step further considering the framework designed above.

3. NETWORK AND SYSTEMIC APPROACH

System theory has been applied to urban studies for a long time, at least since mid 20th

century. According to Batty (2007) “systems were conceived of as having subsystems

tied together by interactions, thus invoking the idea of a network” and cities are

extremely suggestive artefacts for such a theory.

Whithin a systemic approach, network studies have contributed a lot for urban spatial

analysis, therefore some concepts will be briefly reviewed here.

Network analysis is a field largely developed by Mathematics. It is based on an

assumption of the importance of relationship among interacting units. The main

difference between a network explanation and a non-network explanation is the

inclusion of concepts and information on relationships among units in a study. In last

decades it’s being applied to other areas. Social network analysis, for instance, has a

huge number of studies. These ideas are already being applied to urban systems too.

Some researchers have been using those concepts to analyses spatial network of streets

and built forms of urban settlements. Some fundamental concepts will be briefly

presented here.

There are many ways to describe network data mathematically. Graph theory, which is a

high developed field of Mathematics, provides the principal mathematical language for

describing properties of networks (Newman et al, 2006). In a graph, nodes represent

units and lines represent ties between units. Nodes are also referred to as vertices or

points, and the lines are also known as edges or arcs. Much of that theory “qualifies as

pure mathematics, and such is concerned principally with the combinatory properties of

artificial constructs”, while applied graph theory is more concerned with real-world

network problems (Newman et al, 2006).

Graphs have been applied to social studies and are used to explore social network data,

since graphs offer a straightforward way to refer to units and relations. According to

Wasserman and Faust (1994), “the graph theoretic notation scheme can be viewed as an

elementary way to represent actors and relations. It is in the basis of the many concepts

of graph theory used since the late 1940’s to study social networks”. Besides social

network, graph theory and network analysis are being applied to other fields, like

computer science and engineering. According to Newman,

“(…) whereas in the past both graph theory and social network

and social network analysis have tended to treat networks as

static structures, recent work has recognize that networks evolve

over time (Barabási and Albert 1999; Watts 1999). Many

networks are the product of dynamical processes that add or

remove vertices or edges” (Newman et al, 2006).

Some authors (Portugali, 1997, Batty 2005 e 2007) suggest that this statement applies to

urban systems growth. As a consequence, graph theory is being applied to urban

studies, in the past few decades. The main objective is to realize spatial differentiation

between elements of a system. Several ways of grasping urban spatial differentiation

have been suggested. Accessibility is one of them. It is the property of an urban location

to be closer to the others. Its measure is the sum of the distances from one point to all

others. The space syntax theory, tailored by Hillier and Hanson (1984), takes

accessibility as topological distance from each space to all others in the same spatial

system. It is one possible way of analyzing spatial network of urban streets.

Krafta (1994, 1997a, 1997b) has extensively studied inner configurational issues and

their possible role within the urban spatial structure, having proposed a set of synthetic

measures of urban morphology based on spatial differentiation measures, mainly

centrality measurements, which may provide the urban designer or policy-maker with

instruments to assess the performance of intra urban spatial systems. Some indicators

developed by Krafta can be seen as the first to bring together built-form attributes, while

accessibility measures just attempt to describe urban streets configuration.

To perform those measures, Krafta (1997a) assumes an urban system to be formed by

public spaces and built form units, and both are related to each other through

adjacencies, so that the system can be expressed by a graph.

4. BRINGING SPRAWL MEASURES TO A NETWORK APPROACH

The possible impacts of sprawl are too numerous to discuss fully. The focus of this

paper will be on some aspects related to increasing distances between households and

jobs location: more automobile trips and more vehicle miles travelled. According to

Chin (2002), there is general agreement that such aspects, related to transportation and

travel costs, are strongly linked to sprawl. Besides, increasing distances are strongly

connected to sustainable debate.

Sprawl studies usually try to define the sprawl degree of an urban settlement, through a

single measure or a set of measures. But can all cities that have similar sprawl degree be

equally associated to negative impacts? More sprawled cities tend to increase journeys,

but not necessarily, since balanced distribution of activities can mitigate a sprawled

configuration of urban streets. We suggest here that measurement of intra urban

distances may be helpful to assess characteristics more directly associated to

environmental impacts of sprawl.

Urban sprawl exhibits poor residential accessibility because residents are often

distanced from opportunities, such as work, shopping and recreation (Torrens and

Alberti, 2000). On that account, the main assumption here is that mismatching between

residences and employment/education activities can lead to a less sustainable

environment, since it deeply impact urban travel patterns. On this way, mix of land uses

and balanced distribution of activities are desirable to an urban system; while

unbalanced distribution – with activities located in such a manner that lead to more

journeys – can be assumed as harmful to urban sustainability. So, one possible way to

compare cities is verifying average distances between residences and job location

Suppose we are interested in verify mismatching between houses and jobs locations. A

traditional approach would define a unit of study (neighborhood or census tract, for

example) and compare absolute number of residences and number of employments or

its respective densities. The key assumption is that a specific unit is independent from

other units. A network perspective, on the other hand, should be helpful to fully

understand and model relationships between households and their jobs, reaching the

intra urban level. In the network analytic framework, the basic unit is a pair of units tied

by some kind of relationship. On this way, one has measurements on interaction

between all possible pairs of units. The ties may be any relationship existing between

units. We can consider the units as being urban built forms and the ties the streets that

connect those built forms, in such a manner that ties can be measured as distances

between urban activities. Cities can be conceived as systems and, therefore, can be

represented as graphs that embody its network properties, and enable to perform some

inner configurational measures. Figure 1 presents an example of an abstract model of an

urban settlement. Here it is described as a graph where nodes represent built forms, and

the lines represent their connection, the streets.

Figure 1. Graph which describes an urban system

In an urban settlement, many kinds of flows can be observed, such as: residence to

residence, job to job, residence to job/school. This last one is the most relevant to

measure, since it is the most frequently to great part of inhabitants.

Considering we have some particular flow of displacement between a residence location

and an employment location, it can be assumed that the higher the population the higher

the flow produced between two locations. If residence and job locations have a

distribution pattern that lead to higher distances, it can be assumed as less sustainable.

So, we propose here to verify intra urban distance between urban activities, as an

alternative way of measuring sprawl.

Accessibility of some point “i” can be calculated by the the sum of distances from point

i to all others points of the system, and can be mathematically expressed as:

Acess i = ∑ d (ij)

In this case, points with high values must be read as low accessibility points. One

possible way to synthesize this measure to the whole system is calculating its average,

which means to calculate the average of all distances within the system. Distances here

are considered as the shortest path between the related points. It is a good measure for

verifying general intra urban distances and it enables comparing different cities. Higher

average distances can indicate longer journeys. Nevertheless it doesn’t consider density

and employments spatial distribution patterns. So we need to aggregate to the model

some attributes such as number of inhabitants and employments, in order to perform a

measure, in such a way that points with poor accessibility combined with high number

of people and employments get higher values. It could be an indicator for measuring the

potential to create negative environmental impacts.

In a certain urban system represented by a graph, suppose we have the nodes associated

with some numeric attribute that expresses the quantity of residences or population and

quantity of employments. The potential to create impacts indicator (Ip) of each node

would be the summation of several components: distance over which the trips must be

transposed (dij); the capacity of an origin to generate trips (ati), measured by the number

of inhabitants; and the ability of activities at a destination to attract those trips (ati),

measured by the number of employments.

Ip i=∑ (dij x ati x atj)

For example, if we have a system with three nodes, with the following attributes: 1=10;

2=5; 3=5, represented by a graph (figure 2)

Figure 2. Graph

The potential to create impacts indicator (Ip) can be calculated as follows in table 2:

node Pair Distance (dij)* Attribute (ati) Attribute (atj) dij x ati x atj

1

1-2 1 10 5 50

1-3 2 10 5 100

Ip 1= ∑= 150

2

2-1 1 5 10 50

2-3 1 5 5 25

Ip 2= ∑= 75

3

3-1 2 5 10 100

3-2 1 5 5 25

Ip 3= ∑= 125

* Topological distance (number of steps)

Table 2 – Example of potential to create impacts indicator (Ip)

At this example it was not considered metric distance, just topological properties. If we

had used Euclidian distances the result would be a little different, but would be closer to

reality. According to this indicator node 1 is the one which have more potential to create

impact to the system, since it has longer distances to the other nodes and higher number

of inhabitants and employments, which means more potential to have people traveling

longer distances.

Therefore, the indicator designed here seems to be a good measure that can be directly

related with jobs-housing spatial mismatching and length of work trips. Furthermore, the relative node-to-node indicator could easily be transformed into a single city-to-city comparative index.

5. FINAL CONSIDERATIONS

The critical approach taken along this paper aimed at emphasizing the significant

aspects of methodologies for measuring sprawl concerning environmental sustainability.

We suggest here to shift the focus from urban form to environmental impacts, in order

to produce more useful and precise indicators of sprawl.

Most studies on measurement of urban sprawl do not consider intra urban distances, a

relevant aspect concerning sustainability. On the other hand, there are some

measurements developed on configuration urban systems, which embody graph theory

and network approach that could be helpful to apprehend intra urban distances.

A network approach, which takes into account the configuration of streets system and

the distribution of activities, may bring new lights into these urban sprawl studies, since

it enable more accurate measurements about distances between residences and jobs, for

instance.

The methodology designed here is still under construction and needs to be worked out

on some computer software that performs the indicator. A real study case must be

performed too, although using this measure is not an easy task, since urban data is

usually acquired in aggregated units, like census tracts or neighborhoods. Despite

difficulties, we see this kind of methodology to be very relevant to sprawl measurement

and sustainability debate.

As a decision-support instrument, measures of sprawl based on intra urban distances

seems to provide indexes more related to sustainability than others measures usually

used, like gradient densities. Verifying distances between residences and out-of-home

opportunities should demonstrate whether or not current thinking on what constitutes

sustainable urban form is valid when measured in terms of intra urban level.

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