measuring urban sprawl and environmental ...

MEASURING URBAN SPRAWL AND ENVIRONMENTAL SUSTAINABILITY Alice Rauber Gonçalves

MSc Graduate Student – PROPUR/ UFRGS [email protected]

Rômulo Krafta

Professor, Phd – UFRGS [email protected]

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 methodolo gies 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.

alster et al (2001)

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

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

Ojima (2007)

Bertaud and Malpezzi (2003)

Proximity: Degree to which different land uses are close to each other

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.

Ojima (2007) analyses four

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.

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)

Frenkel and Ashkenazi (2008)

Torrens (2008)

Integration/Commuting:  proportion of commuters to the agglomeration core  proportion of commuters in relation to total population 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.

The authors introduce empirical results obtained by implementing some measures of sprawl to 78 Israeli urban settlements. They group the metrics

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

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 Composition: dimensions of sprawl  Mixture of land uses: land use composition (percentage of are independent, and each land-use category) are not significantly correlated with each other. 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 1-3

1 2

10 10

5 5 Ip 1= ∑=

50 100 150

2

2-1 2-3

1 1

5 5

10 5 Ip 2= ∑=

50 25 75

3

3-1 3-2

2 1

5 5

10 5 Ip 3= ∑=

100 25 125

* 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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