Carolina Transportation Program White Paper Series
The Measurement of the Level of Mixed Land Uses: A Synthetic Approach Yan Song* and Daniel A. Rodríguez**
Department of City and Regional Planning New East Bldg, CB#3140 University of North Carolina Chapel Hill, NC 27599-3140; USA * Email:
[email protected] ** Email:
[email protected]
Preparation of this White Paper was supported by a grant from the Robert Wood Johnson Foundation Active Living Research program.
The Measurement of the Level of Mixed Land Uses: A Synthetic Approach Abstract Despite the burgeoning interests in studying mixed land uses and their relationship with individual and community outcomes in disciplines such as landscape ecology and the environment, transportation, health outcomes, and housing markets, there is a paucity of research on the measurement of such mixed land use. In this paper we provided a synthetic examination of an array of land use mix measures which would tap various dimensions of the urban land use mixture. We classified existing indices as measures of accessibility, intensity and pattern. With the purpose of evaluating the measures, we also applied selected measures in an empirical case study. Our review and the empirical application provide insights for researchers and practitioners regarding the appropriateness of particular measures for particular purposes. We propose three criteria for choosing the measures: the extent to which a measure captures the presence or configuration of land uses, practical considerations including data collection, amount of computation and ease of communicability, and connection between the measures and the purpose of the investigation.
1
1. Introduction The separation of land uses has been the cornerstone of conventional land use planning in the U.S. Partly as a response to a set of complex problems brought on by urban sprawl that have beset most U.S. metropolitan areas, planners and researchers have begun advocating for the mixing of certain types of land uses. For example, the Smart Growth Network, established under the auspices of the U.S. Environmental Protection Agency, promotes the mixing of residential and commercial uses as one of the ten principles of Smart Growth. The Congress of New Urbanism (CNU) also calls for: “Neighbourhoods [to] contain a mix of shops, offices, apartments, and homes; land uses are mixed-use within neighbourhoods, within blocks, and within buildings” (CNU, 2002).
In
addition, the US Centres for Disease Control and Prevention has identified mixing land uses as a strategy to promote active community environments (Centres for Disease Control and Prevention, 2005).
The interest in mixing certain land uses stems from emerging empirical evidence suggesting that greater mixture of complementary land use types, which may include housing, retail, offices, commercial services, industrial and civic uses, is related to people’s propensity to walk and thus to be physically active, transit use, and property values. Mixed land uses also have been
2
associated to lower automobile ownership, use and emissions. Although not tested empirically, mixed land uses also are thought to reinforce streets as public spaces, create a sense of community and local investment, assist in achieving local housing and employment mixes, and promote transitsupportive development among others (American Planning Association, 1998).
Despite the practical interest and the mushrooming empirical research, there have been few substantive analyses devoted to the measurement of land use mixtures. In this paper we: a) provide a synthetic examination of land use mix measures used in prior research; and b) we adapt and test related measures used in other disciplines (ecology, sociology, business, micro-economics). By providing insights regarding the strengths and weaknesses of various land use measures, we contribute to clarifying existing evidence and provide suggestions for future researchers. Four sections follow in this paper. In the next section we summarize recent research on land use mixtures and outcomes of interest to planners and policy-makers. The second section presents our approach to categorizing, developing and implementing land use mix measures and discusses the strengths and weaknesses of the measures. In the third section we summarize an empirical application of various measures to a case study in Hillsboro, Portland metropolitan area (OR). We use the empirical
3
example to further the discussion on how the measures can facilitate and advance our understanding of land use distribution across the urban landscape. In the last section we draw conclusions and provide recommendations for researchers interested in the measurement of mixed land uses.
2. Land use mixtures and community outcomes Since the mid 20th century, zoning ordinances throughout the United States have had the effect of isolating employment, shopping and services from residential housing. Partly motivated by exclusionary practices and by the need to mitigate perceived negative externalities, residential neighbourhoods have been developed separated, and often at substantial distances, from jobs and services. Advocates for mixed land uses have argued that the practice of separating land uses has led to excessive commuting times, traffic congestion, air pollution, inefficient energy consumption, loss of open space and habitat, inequitable distribution of economic resources, job housing imbalance, and loss of sense of community (Smart Communities Network, 2002).
These
thematic concerns are mirrored by existing research examining the relationship between land use mixtures and individual and community outcomes. Outcomes of interest include landscape ecology and environmental outcomes (air quality, water quality), transportation (auto ownership, travel behaviour), health
4
outcomes (physical activity behaviour, obesity), and housing markets (property values).
Aided by the increasing abundance of micro-level data that provide a rich empirical basis, the relationship between land use mix and transportation outcomes has received a flurry of attention over the last decade. Having higher mixes of land uses nearby has been positively related to frequency of trips by pedestrian and bicycle modes (Cervero, 1996; Greenwald and Boarnet, 2001; Handy, 1996; Khattak and Rodriguez, In press; Kitamura et al., 1997) and negatively related to frequency of auto trips (Cervero and Kockelman, 1997). Discrete choice models of travel mode also have shown that high levels of land use mixing in one’s home or work neighbourhood are related to higher walking, bicycling and transit shares
(Cervero, 1996; Srinivasan, 2002),
although the effect size has been qualified as “fairly marginal” (Cervero and Kockelman, 1997) and “modest” (Cervero and Duncan, 2003).
Shorter
commuting distances (Cervero, 1996) and lower commuting times (Ewing et al., 2003) have been positive related to mixed land uses. Finally, evidence of associations between mixed land uses and auto ownership is less consistent, with Ewing et al (2003) finding no relationship and others finding a negative relationship (Cervero, 1996; Hess and Ong, 2002).
5
In landscape ecology, land uses are often the starting point of modelling approaches (O’Neill et al., 1988; Turner, 1990).
Because land uses are
intimately associated with ecological consequences, there is an interest in quantifying land uses and potential changes. Furthermore, environmental consequences often vary depending on the pattern of uses, the remaining habitat, and the size and proximity of disturbances to sensitive areas (Geoghegan et al 1997). Thus, quantifying uses relative to each other, their pattern, is essential for monitoring and assessing ecological outcomes. In this vein, studies have attempted to examine the relationship between land use mix, emissions and air quality.
Although some studies have found a positive
relationship between mixed land uses and emissions (Frank et al., 2000), others have detected an opposite, negative relationship (Ewing et al., 2003).
For health-related disciplines, the emergence of ecologic models (Stokols, 1992) has underscored the levels at which multiple factors (personal, interpersonal, community, environment and policy) can influence individual behaviour and health outcomes. As a result, an expanded set of factors, such as neighbourhood land use mix, are hypothesized to influence individual behaviour (Sallis et al., 1997). Although land use mixing has been positively
6
associated with physical activity time (Frank et al., 2005; Hoehner et al., 2005), the emerging evidence with respect to obesity is equivocal, with studies finding conflicting associations (Frank et al., 2004; Rutt and Coleman, Forthcoming). By contrast, the evidence regarding the relationship between physical activity and the mixing of residential and recreational land uses (like parks and community centres) more consistently shows a positive association (Giles-Corti et al., 2005; Giles-Corti and Donovan, 2002a; Giles-Corti and Donovan, 2002b).
Land use mixes also have been related to housing markets and individuals’ preferences for housing types. Measures of land-use mix between residential and commercial uses generally correlate with high residential land prices (Cervero and Duncan, 2004; Geoghegan et al., 1997; Song and Knaap, 2004) and in related studies land prices and the mix between residences and open space are also positively related (Geoghegan, 2002; Irwin, 2002; Irwin and Bockstael, 2001).
In summary, empirical ambiguities remain regarding the relationship between land use mixtures and community and individual outcomes. The presence of
7
mixed commercial and residential uses of land appears to support nonmotorized modes of travel, higher physical activity time, and higher property values. By contrast, the evidence regarding land use mix and auto ownership, obesity, and air quality is equivocal. Although these variations are likely due to differences in the context of each study, the type of behaviour being observed and the data used, differences in measurement, scale, and refinement of land use mixture also contribute to the distinct outcomes.
It is thus
necessary to scrutinize and evaluate various measures of land use mix used in various fields. In the next section we turn to summarizing existing measures of land use mix and adapting new measures that were developed in other fields, while discussing their strengths and weaknesses and suggesting potential refinements.
3. Measures of land use mix Urban planners have developed numerous ways to study the level of land use mixture. Researchers from other fields have also developed loads of measures in studying the distributional characteristics of various phenomena.
For
example, economists have examined market share of firms; sociologists have observed residential segregation patterns, and landscape ecologists have monitored land covers in relations to each other. Many of these measures can
8
be adjusted to serve our purpose of assessing land use mixture. For ease of summarizing, we categorize various measures based on their different approaches to conceptualizing land use mixture: to appraise mixture based on the concept of accessibility (or proximity), of intensity (or magnitude), and of distribution pattern. Accessibility is the degree to which mixed land activities are easy to reach by residents; intensity is the volume or magnitude of mixed land uses present in an area; and pattern is the way in which different types of land uses are organized in an area. Our discussion on measures of land use mix below revolves around these three concepts.
For the purpose of demonstration, we divide land use into different types: single family residential (residential hereafter) and non-single family residential (non-residential hereafter). Non-residential land further includes: commercial stores, multi-family residential units, light industrial sites, public institutions, and public parks.
The geographic units of analysis of
measurement, depending on the measures, are either individual land parcel units, or neighbourhoods. In this study, neighbourhoods can be defined by census boundaries such as zip codes, census tract, blockgroups, or Traffic
9
Analysis Zones (TAZs) 1 .
Neighbourhoods can also be defined by user-
determined boundaries such as individual buffers of user-defined sizes drawn around land parcels, or square grids of user-defined sizes.
Figure 1
demonstrates the organization chart of the measures included in this study. For each of the measures presented next, we include a detailed example of at least one (and in many cases more than one) implementation of the specific measure in the Appendix, including references to studies that have used such measures.
--insert Figure 1 here--
3.1. Accessibility-based land use mix measures
3.1.1. Distance Definition: The linear or street network distance between residential land use and another given non-residential land uses. Unit of analysis: Individual land parcels or neighbourhoods.
1
A Traffic Analysis Zone (TAZ) is a special area delineated by state and/or local transportation officials for tabulating traffic related data—especially journey-to-work and place-of-work statistics.
10
This measure takes adjacent non-residential land uses into consideration by calculating the nearest distances between pairs of observations.
It also
accounts for individual variances in proximity to other land uses. However, the measure offers little information on the broader context of the proximity by paying no heed to land uses other than the closest one. In addition, the measure takes no notice of the size of the nearest non-residential land use.
3.1.2. Gravity Definition: The simplest gravity-based measure of land use mix can be defined as the sum of accessibility of residential land use to all other given type of nonresidential land uses, discounted by the distance decay function between these two points. Unit of analysis: Individual land parcels or neighbourhoods. This approach generates a relatively comprehensive measure of accessibility from a residential land use to a given type of non-residential land uses by including distances to all other non-residential units. A major challenge with this straightforward approach is to fine-tune the impedance function to reflect the true impedance at that point, since as urban structures change, the distance decay or impedance function also changes. Another limitation of this measure is that it overlooks the scale or the size of non-residential land use activities.
11
3.1.3. Gravity with competition Definition: The sum of accessibility of residential land use to all other given type of non-residential land uses, discounted by the distance decay function between these two points, and extended by considering both the supply side of non-residential land uses (i.e., the attractiveness of the non-residential land use) and the demand side of non-residential land uses (i.e., the competition for consuming the functions provided by the non-residential land use). Unit of analysis: Individual residential units or neighbourhoods. This measure provides information on accessibility to non-residential land use in a more thorough way than the previous measures by considering both the scale (the attraction) of and the competition for the services. However, it assumes that accessibility is based only on the distance between various competitors and the destinations, and their relative attractiveness, for example as dictated by floorspace or number of employees.
3.1.4. Denominator of destination choice model Definition: The denominator of a discrete model of destination choice can be interpreted as a generalized measure of accessibility to destinations.
12
Unit of analysis: Individuals. This measure has theoretical appeal because it is rooted in consumer choice theory and thus can be linked directly to consumer surplus calculations of accessibility. The main drawback is that it requires substantial attribute data on all destinations or on a sample of likely destinations. To this end, data on the preferred destination and non-preferred (but available) destinations for a representative sample of individuals in the study area are necessary. Another limitation is that the comparability of this measure across samples or across individuals is limited because the utility function is not measured in a consistent scale.
3.2. Intensity-based land use mix measures
3.2.1. Counts Definition: Number of non-residential activities in the neighbourhood. Unit of analysis: Neighbourhoods.
3.2.2.
Area proportions
13
Definition: Proportions of different types of land uses within a user-defined neighbourhood. Unit of analysis: Neighbourhoods. The measures in this and the previous category are easy to compute and offer practical information on the intensity of a particular type of land use in a userdefined neighbourhood.
Unfortunately, there are several limitation of the
analyses based at the neighbourhood level. First, as the counts or proportion of land uses are conventionally aggregated by areal units such as census boundaries and TAZs, fine variations at smaller-unit level are averaged out and smoothed over during successive levels of aggregation, effectively disappearing with each higher level of aggregation (the modifiable areal unit problem, MAUP).2
2
Researchers have attempted to manage MAUP by computing land use measures at the parcel level, identifying homogenous buffers around individual land parcels (e.g., residential housing units) as the parcels’ immediate neighbourhoods and thus avoiding the aggregation problem. Although this approach is well-founded in presenting a uniform comparison on land use mixture across the immediate neighbourhoods of land parcels, the appropriate size of the buffers remain in debate. If the purpose of examining land use mixture is to evaluate the availability of activities within walking distance of households, it is then generally recommended to use 1/4-mile as buffer radius since pedestrian access is generally accepted as 1/4-mile network distance (Duany & Plater-Zyberk, 1992). One criticism of this uniform buffer-drawing approach is that it assumes people with different characteristics (e.g., teens vs. adults) and at different locations (e.g., urban core vs. exurban) perceive their neighbourhoods to be of equivalent sizes. However, it is more likely that neighbourhood sizes deviate from each other within the urban landscape and between different population groups. It should be noted that there is a paucity of research quantifying the size of relevant catchment areas as immediate neighbourhoods, particularly for the purpose of studying behaviour.
14
Second, it is well understood that results are determined by the oftentimes arbitrary location of neighbourhood boundaries and therefore might be misleading. It is also necessary to consider how different levels of aggregation can affect results. For example, a larger neighbourhood is simply prone to more land use types. If the results change with the selection of different sizes of areal units, the reliability of results is called into question.
Third, there is concern with using larger neighbourhoods (e.g., census tracts) is that the units of analysis are too large to have an intrinsic meaning with respect to the underlying land use distribution. The issue – the non-uniformity of space – has to do with the fact that the physical environmental conditions need to be taken into account as contexts for confirm or refute calculated distribution patterns. For example, the observed concentration of residential and non-residential uses can be less significant than originally thought because the other part of the city has a large lake.
3.3. Land-use mix pattern measures
15
Compositional pattern of land use mixture, as manifested through spatial assimilation of land development, is another important aspect of studying mixed land uses. We now present the measures of pattern, which can be further classified into three dimensions: evenness, exposure, and clustering (Figure 1).
3.3.1. Evenness and Diversity Evenness and diversity measures of land use mixture compare the distributions of different land uses. We include the following measures: the Balance index, the Herfindahl-Hirschman index, the Dissimilarity index, the Gini coefficient, entropy, and the Atkinson index. •
Balance index
Definition: The degree to which two different types of land uses (e.g., housing units and employees, or residential and non-residential land parcels) exist in balance to each other within a neighbourhood. If the two land use types are distributed evenly, the index is 1. The smaller the value, the greater the unevenness. If there is only one type of land uses in the neighbourhood, the index is 0. Unit of analysis: Neighbourhoods.
16
The measure’s merit is its computational ease.
However, MAUP is present
because of the measure is based on aggregated units. For example, larger neighbourhoods will tend to have a higher jobs-to-residents balance.
•
Herfindahl-Hirschman index (HHI)
Definition: The Herfindahl-Hirschman Index (HHI index), a commonly accepted measure of market concentration used to detect market monopoly, can be used to assess the level of land use mixture. The HHI index is the sum of squares of the percentages of each type of land uses in the user-defined neighbourhoods. If there is only one land use type in the neighbourhood, HHI index will equal 10,000. The higher the value of HHI Index, the lower the level of land use mixture. Unit of analysis: Neighbourhoods. The main virtue of the HHI is its simplicity. However, it shares the same set of drawbacks with the measures of intensity as they all rely on the aggregated areal units for calculation.
•
Dissimilarity Index
17
Definition: The degree to which different land uses exist within the smaller unit of analysis (e.g., grids or neighbourhood) and this distribution pattern is typical throughout the larger unit of analysis (e.g., neighbourhood or the city). The value of the index ranges between 0 and 1. A value of 0 means perfect similarity; a value of 1 means perfect dissimilarity. This measure is borrowed from Duncan and Duncan (1955) who employed it to reflect the level of residential segregation and has received strong endorsement from recent findings that it is effective to capture the evenness dimension of residential segregation (Massey and Denton, 1988). The measure is implemented in the Appendix for two land uses (e.g., residential and non-residential land uses) and for various land use types simultaneously. Unit of analysis: Different levels of neighbourhoods (e.g., grids, census boundaries, or metropolitan areas). Although the D index is easy to compute and is relatively effective for evaluating the level of evenness in the distribution of land uses, it has several limitations. The first limitation of this index (illustrated in Figure 2a) is that it does not consider the spatial arrangement of land uses. The index can not detect if the areal units with dominant one type of land uses are spatially clustered together or dispersed into the neighbourhood. The second major drawback of the index is that it reveals no information on the magnitude of
18
each type of land uses within the neighbourhood.
Figure 2b shows that
different amount of areal units within the study area that are dominated by only one type of land uses exclusively would always return a “1.” We also see that the D index is not sensitive to relative sizes of land uses within areal units (illustrated in Figure 2c). Thirdly, in order to implement the index, both types of the land uses must be present in the study area. In our example, the index would not be computed for the neighbourhoods with either only residential or only non-residential land uses.
And finally, the D index is not a very
discriminating indicator. Two very different distributions – one having more residential land uses, the other having more non-residential land uses (illustrated in Figure 2d) – can return the exact same value.
--insert Figure 2 here --
• Gini index Definition: The Gini coefficient is a measure of inequality developed by Gini (1912). The Gini index is the area between the Lorenz curve and the line of perfect equality. It ranges from 0 to 1, where 0 means perfect equality (or even distribution) and 1 means perfect inequality (or uneven distribution).
19
Unit of analysis: Different levels of neighbourhoods (e.g., grids, census boundaries, or metropolitan areas). The Gini index is useful both to measure changes in distribution over time and for cross-sectional comparisons across neighbourhoods or metropolitan areas. As the D index, the Gini index is not a very discriminating indicator. Two very different distributions can have exactly the same Gini index. To report the Gini index for only one neighbourhood, by and large, is not sufficient to have a complete picture of the situation. It would be necessary to compare this value with the values obtained from the other neighbourhoods.
•
Entropy measures
Definition: The entropy index is a measure of variation, dispersion or diversity (Turner et al., 2001).
It measures the degree to which land uses are
heterogeneously distributed within a neighbourhood. A value of 0 indicates homogeneity, wherein all land uses are of one single type; a value of 1 means heterogeneity, wherein area is evenly distributed among all land use categories. Unit of analysis: Neighbourhoods. The entropy index incorporates more than two land use types in a single calculation, very conveniently aggregating a measure of land use diversity at
20
various levels. Although other indices (e.g., dissimilarity) can be implemented to capture the integration of various land use types, the simplicity in computations of the entropy index makes it highly desirable.
•
Atkinson index
Definition: The Atkinson index (Atkinson, 1970), one of the few inequality measures
that
explicitly
incorporates
normative
judgments
about
heterogeneous distribution, allows for the differential weights assigned to subunits (e.g., grid cells within neighbourhoods) and thus enables grids where non-residential land uses are under- or over-represented to contribute more or less heavily to the overall index. The Atkinson index ranges between 0 and 1, with a score of 1 indicating the highest level of homogeneous land use distribution (or maximum segregation of land use types). Unit of analysis: Neighbourhoods. The Atkinson Index provides a practical opportunity for assigning weights to various land use distributions and making normative adjustments.
For
example, in a situation where some neighbourhoods have a large proportion of commercial land use areas due to the presence of strip malls, while some other neighbourhoods might only have small neighbourhood corner stores,
21
researchers might value more the land use composition pattern in the latter neighbourhoods. Using the Atkinson index with an appropriate value for the inequality aversion parameter can accommodate these value judgements.
3.3.2. Exposure •
Interaction Index
Definitions:
Exposure, originated in the field of measuring residential
segregation, measures “the degree of potential contact or possibility of interaction” between two subject groups (Massey and Denton, 1988: 287). The interaction index measures the “publicity” of non-residential land uses to residential uses. Lower values of interaction indicate lower exposure. Unit of analysis: Neighbourhoods. Exposure and evenness (or diversity) measure different things: exposure measures depend on the relative sizes of the two groups being compared, while evenness measures do not (Massey and Denton, 1988). Exposure measures can thus correct for the problem (as we illustrated in Figure 2c) that evenness measures have.
3.3.3. Clustering
22
Clustering, originated in the studies of residential and income segregation, measures the extent to which areal units with different subjects “adjoin one other, or cluster, in space” (Massey and Denton, 1988: 293). We import one clustering measure to study the degree of spatial clustering of one type of land uses.
•
Absolute Clustering
Definition: Absolute Clustering summarizes the degree to which nonresidential land uses are found in nearby as opposed to spatially distance areal units. The index ranges from 0 to 1, with higher values indicating a clustering of non-residential land uses. Unit of analysis: Neighbourhoods. Clustering considers the spatial arrangement of land uses within the neighbourhoods.
Absolute Clustering corrects for the problem (as we
illustrated in Figure 2a) that evenness measures have and can thus detect if the areal units with dominant one type of land uses are spatially clustered together.
4. Empirical analysis of land use mixture measures
23
In order to test the effectiveness of the measures on land use mix described in the previous section, we chose the City of Hillsboro which lies in the western portion of the Portland metropolitan area (See Figure 3) for an empirical study. We computed one measure of accessibility at the individual parcel level and ten measures of pattern at the neighbourhood level. We define neighbourhood by census blockgroup boundaries or by square gridcells ¼-mile high and wide. We obtain the following GIS data:3 (1) Parcel-based (tax lot) property data; The parcel-based property data includes attributes for each parcel such as lot size, floor space, and information on land use type; (2) jurisdiction and census blockgroup boundaries; (3) Street networks, and (4) Parks, open space and other recreational land uses. As demonstrated in Figure 3, the larger scale of mixed activities including commercial strip, light-industrial (office), and multifamily residential land uses that were developed from after the World War II to the present day are agglomerated in the northeast corner of the city or along the arterial road, and most of the small-scale commercial enterprises, offices, and customary retail establishments that were developed prior to the war are in the downtown area of the city.
--insert Figure 3 here-3
These data are from Portland Metro’s Regional Land Information System (RLIS).
24
The measure AG which is used to compute the accessibility of individual households to commercial stores replicates the actuality well (Figure 4). We see that housing units that are in the northeast corner of the city, closer to the downtown area, or along the major arterial roads, have higher accessibility.
--insert Figure 4 here--
For the measures of land use mixture between two groups (i.e., residential vs. non-residential land uses) at the blockgroup level, we compute the Dissimilarity index (DN), the Gini index, (GN), a set of Atkinson indices (A0.1, A0.5, and A0.9), the exposure indices (INT and ISO), and the Cluster index (CLUSTER). We provide a visual representation of the measures in Figure 5 and the correlations among them in Table 1. The generalization of the indices suggests that the neighbourhoods in the northeast corner of the city, at downtown area, or along major arterial roads are more mixed than the other neighbourhoods.
25
The evenness suite of measures (including DN , GN, A0.1, A0.5, and A0.9), not surprisingly, are highly correlated to each other (see Table 1). A0.5 is more similar to DN and GN since A0.5 does not make adjustments to the under- or over-represented areal units. A0.1 and A0.9 modify the evenness by allowing the areal units where non-residential land uses are below- or above-average of the neighbourhood’s proportion contribute more or less heavily to the overall indices. The exposure measure (INT) is correlated with the evenness measures. However, exposure measures are sensitive to the relative sizes of the two groups (i.e., residential vs. non-residential land uses) being compared and are thus able to detect that neighbourhoods a and b (see Figure 5), although score the same in the dimension of evenness since the distributions of land uses in the sub-units are comparable in relation to the larger blockgroups, do differ in the dimension of exposure. Since Neighbourhood b has a larger proportion of non-residential land uses compared to neighbourhood a, the non-residential uses in neighbourhood b are less likely to interact with residential uses, as well as less likely to be isolated from other non-residential uses.
The Clustering index taps into the spatial properties of adjacency or contiguity of non-residential land uses. For example, a smaller value of the CLUSTER index for Neighbourhood c than for Neighbourhood d (see Figure 5) reveals
26
that the non-residential land uses in Neighbourhood c are found in distant subunits as opposed to nearby, while non-residential land uses in Neighbourhood d are more clustered.
--insert Figure 5 and Table 1 here--
To discover land use mixture between two groups (i.e., residential vs. nonresidential land uses) within blockgroups, we experiment with two evenness measures at the ¼-mile by ¼-mile square level: DG and GG (see Figure 6). These two measures, within expectation, are performing alike and having a correlation of 0.81.
A closer examination by comparing Figure 3 and 6
suggests that, although the indices are effective in capturing intra-blockgroup variation in land use mixture, the outcomes are sensitive to the spatial position of the imposed grids.
--insert Figure 6 here--
For the measures of land use mixture among multiple groups at the blockgroup level, we compute the Dissimilarity index (D(m)), the Entropy index, (E2), the
27
Herfindahl-Hirschman Index (HHI), and a set of Atkinson indices (A(m)0.25, A(m)0.5, and A(m)0.75). We present the results of this set of indices in the lower panels of Figure 6 and the correlations among the indices in Table 2. An overview of the indices suggests that they correspond to the findings of the two-group measures: the neighbourhoods in the northeast corner of the city, at downtown area, or along major arterial roads are more mixed than the other neighbourhoods. The values of the Entropy, HHI, and Atkinson family of indices which highly correlate with each other point to the same generalization.
--insert Table 2 here--
5. Discussion and Conclusions Measures of land use mix are useful for understanding the patterns of land use distribution. They also enable researchers to evaluate their relationship with individual and community outcomes in disciplines such as including landscape ecology and the environment (air quality, water quality), transportation (auto ownership, travel behaviour), health outcomes (physical activity behaviour, obesity), and housing markets (property values).
Despite the burgeoning
interests in studying mixed land uses and their consequence there is a paucity
28
of research on the measurement of such mixed land use. In this paper we provided a synthetic examination of an array of land use mix measures which would tap various dimensions of the urban land use mixture. We classified existing indices as measures of accessibility, intensity and pattern. With the purpose of evaluating the measures, we also applied selected measures in an empirical case study.
Measures of accessibility are valuable for directly incorporating geographic distance into the measure. The distance measures involve unsophisticated computation and provide convenient information on individual units’ or neighbourhoods’ accessibility to mixed land activities.
They range in
sophistication and computational burden from simple measures (e.g., distance) to measures requiring parcel-level data and calibration of the parameters (e.g., gravity with competition and destination choice measures). Their conceptual simplicity, coupled with the requisite disaggregate-level data make these measures comprehensive and suitable for studies focusing on individual (as opposed to community) outcomes.
29
Measures of intensity can only be implemented at aggregated unit level and entail the least amount of computation and data requirements. Because these measures ignore information on the spatial configuration of land uses, they can be considered aspatial. Their major strength, relative to all other measures, is the conceptual and computational simplicity.
This translates into ease of
communicability. Their strength, however, also is their major weakness. Our review highlighted concerns related to the reliance on an aggregate analysis unit (such as the modifiable aerial unit problem, edge effects, and issues with the scale of analysis).
Measures of pattern are more adequate for capturing the diversity, isolation, and the clustering of land uses. Our correlational analysis demonstrates a high degree of interrelatedness among our diversity measures within pattern. Among the measures of evenness, the two-land use type Dissimilarity index is valuable for its ease of interpretation and computation. It correlates highly with other measures (e.g., the Gini and the Atkinson indices) but requires less computational and data management burden.
The Dissimilarity index’s
usefulness, however, is limited to the evaluation of evenness between two groups of land uses. The data management complexity of our multi-group implementation of the dissimilarity index limits its usefulness for practitioners.
30
Because the Entropy and HHI indices can handle multiple land use types using relatively simple calculations, we consider them as convenient measures of land use diversity. The empirical comparison of these indices suggests that the HHI may be easier to communicate to a non-technical audience, although the Entropy index has deeper roots in the literature and can have a behavioural interpretation.
Finally, the Interaction index and the Clustering index are
complements to measures of evenness and diversity. They contribute information about clustering and thus are valuable for providing a richer depiction of the land use distribution in a given area. The effectiveness of these two measures is, however, constrained to two land use types.
Our review of the land use mix measures, and the empirical application, offer an improved understanding of the measures’ properties for researchers and practitioners. It is tempting to ask which measure is the most appropriate one in evaluating land use mixture. Obviously, there is no single best measure of land use mixture, since each measure captures different dimensions of how land uses are distributed in space. However, our review and the empirical
31
application can provide insights for researchers and practitioners regarding the appropriateness of particular measures for particular purposes.4
First, the choice of measure is depended on the extent to which a measure captures the presence and configuration of land uses in space. For example, is the pattern of several land uses more or less of interest than the mere presence of those uses in the study area? Should the measure account for more than two land use types? Will the index measure what the researcher or practitioner wants to measure?
Second, practical considerations should also influence the choice of measure. These include data collection and management, computational burden, and ease of communicability. While some measures require data manipulations that require database programming, others result naturally from a land use cover map. The technical appendix containing the various implementations of measures confirms the importance of practical considerations in deciding which measures to use. By most accounts, relatively simple measures have been implemented more frequently than complex measures. Of course, this
4
Others have relied on the mathematical properties of selected measures discussed here, but in the context of racial segregation (James and Taeuber, 1985).
32
simplicity has tradeoffs and may contribute to explain divergent results in various disciplines with respect to the relevance of land use mixtures for community and individual outcomes.
Finally, and perhaps most importantly, the connection between the measures and the purpose of the investigation should drive the measures selected. In other words, measures should be selected based on the substantive questions driving the inquiry. If the question being asked is about non-motorized travel behaviour then the location of commercial and office land uses relative to residential uses is of paramount interest. A two-land use type measure may suffice. By contrast, if the question motivating the research is the impact of non-residential land uses on property values, then the location of at least parks, industrial and commercial uses relative to residential units should be of concern.
There is undoubtedly a need to acknowledge that the land use mixture is only one, perhaps modest, influence on the individual, neighbourhood, and societal outcomes. Nevertheless, the measures of mixed land uses are useful for quantifying the distribution of land uses that can be used across disciplines to
33
investigate, through empirical research, the new inquiry on the importance of the impact of the land use mixture on a variety of outcomes. Our exercise is a contribution to the investigation, and an attempt to begin a long-term process of refinement and advancement in this field.
34
References
Atkinson, A B, 1970, "On the Measurement of Income Inequality" Journal of Economic Theory 2: 244-263 Brown, M, 1994, "Using Gini-style indices to evaluate the spatial patterns of health practitioners; theoretical considerations and an application based on the Alberta data" Social Science and Medicine 38 9:1243-1256 Centres for Disease Control and Prevention (2005) Active Community Environments http://www.cdc.gov/nccdphp/dnpa/aces.htm, 2005 [cited February 28 2005] Cervero, R, 1996, "Mixed land-uses and commuting: evidence from the American Housing Survey" Transportation Research A 30 5:361-377 Cervero, R and Duncan, M, 2003, "Walking, bicycling, and urban landscapes: Evidence from the San Francisco Bay area" American Journal of Public Health 93 9:1478-1483 Cervero, R and Duncan, M, 2004, "Neighbourhood composition and residential land prices: Does exclusion raise or lower values?" Urban Studies 41 2:299-315 Cervero, R and Kockelman, K, 1997, "Travel demand and the 3Ds: Density, diversity and design" Transportation Research D 2 3:199-219 Duany, A and Plater-Zyberk, E, 1992, May, "The second coming of the American small town" Plan Canada: 6–13. Duncan, O D and Duncan, B, 1955, "A methodological analysis of segregation indexes" American Sociological Review 20: 210-217 Ewing, R, Pendall, R and Chen, D, 2003, "Measuring sprawl and its transportation impacts" Transportation Research Record 1831:175-183 Frank, L D, Andresen, M A and Schmid, T L, 2004, "Obesity relationships with community design, physical activity, and time spent in cars" American Journal of Preventive Medicine 27 2:87-96
35
Frank, L D, Schmid, T L, Sallis, J F, Chapman, J and Saelens, B E, 2005, "Linking objectively measured physical activity with objectively measured urban form" American Journal of Preventive Medicine 28 2S2:117-125 Frank, L D, Stone, J, Brian and Bachman, W, 2000, "Linking land use with household vehicle emissions in the central puget sound: methodological framework and findings" Transportation Research Part D: Transport and Environment 5 3:173-196 Geoghegan, J, 2002, "The value of open spaces in residential land use" Land Use Policy 19 1:91-98 Geoghegan, J, Wainger, L A and Bockstael, N E, 1997, "Spatial landscape indices in a hedonic framework: an ecological economics analysis using GIS" Ecological Economics 23 3:251-264 Giles-Corti, B, Broomhall, M H, Knuiman, M, Collins, C, Douglas, K, Ng, K, Lange, A and Donovan, R J, 2005, "Increasing walking: How important is distance to, attractiveness, and size of public open space?" American Journal of Preventive Medicine 28 2, Supplement 2:169-176 Giles-Corti, B and Donovan, R J, 2002a, "The relative influence of individual, social and physical environment determinants of physical activity" Social Science & Medicine 54 12:1793-1812 Giles-Corti, B and Donovan, R J, 2002b, "Socioeconomic Status Differences in Recreational Physical Activity Levels and Real and Perceived Access to a Supportive Physical Environment" Preventive Medicine 35 6:601611 Gini, C, 1912, "Variabilitá e mutabilita" reprinted in Memorie di metodologica statistica (Ed. Pizetti E, Salvemini, T). Rome: Libreria Eredi Virgilio Veschi 1955. Greenwald, M J and Boarnet, M G, 2001, "Built environment as determinant of walking behaviour: Analyzing nonwork pedestrian travel in Portland, Oregon" Transportation Research Record 1780:33-42 Handy, S, 1996, "Urban form and pedestrian choices: study of Austin neighbourhoods" Transportation Research Record 1552:135-144
36
Hess, D B and Ong, P M, 2002, "Traditional neighbourhoods and automobile ownership" Transportation Research Record 1805:35-44 Hoehner, C M, Ramirez, L K, Elliott, M B, Handy, S L and Brownson, R C, 2005, "Perceived and objective environmental measures and physical activity among urban adults" American Journal of Preventive Medicine 28 2, Supplement 2:105-116 Irwin, E G, 2002, "The effects of open space on residential property values" Land Economics 78 4:465-480 Irwin, E G and Bockstael, N E, 2001, "The problem of identifying land use spillovers: Measuring the effects of open space on residential property values" American Journal of Agricultural Economics 83 3:698-704 James, D R and Taeuber, K E, 1985, "Measures of segregation" Sociological Methodology 15: 1-32 Khattak, A and Rodriguez, D, In press, "Travel behaviour in neo-traditional developments: A case study from the U.S.A" Transportation Research A Kitamura, R, Laidet, L and Mokhtarian, P, 1997, "A micro-analysis of land use and travel in five neighbourhoods in the San Francisco Bay Area" Transportation 24:125-158 Massey, D S and Denton, N A, 1988, "The dimension of residential segregation" Social Forces, 67: 281-315 O’Neill, R V, Krummel, J R, Gardner, R H, Sugihara, G, Jackson, B, DeAngelis, D L, Mime, B T, Turner, M G, Zygmut, B, Christensen, S, Dale, V H and Graham, R L, 1988, "Indices of landscape pattern" Landscape Ecology 1 3:153-162 Rutt, C D and Coleman, K J, In press, "Examining the relationships among built environment, physical activity, and body mass index in El Paso, TX" Preventive Medicine Sakoda, J M, 1981, "A generalized index of dissimilarity" Demography 18 2: 245-250
37
Sallis, J, Johnson, M, Calfas, K, Caparosa, S and Nichols, J, 1997, "Assessing perceived physical environmental variables that may influence physical activity" Research Quarterly for Exercise and Sport 68 4:345-351 Shen, Q, 1998, "Location characteristics of inner-city neighborhoods and employment accessibility of low-income workers" Environment and Planning B 25: 345 – 365 Song, Y and Knaap, G J, 2004, "Measuring the effects of mixed land uses on housing values" Regional Science and Urban Economics 34 6:663-680 Song, Y and Sohn, J, In press, " Valuing spatial accessibility to retailing: A case study of the single family housing market in Hillsboro, Oregon" Journal of Retailing and Consumer Service Srinivasan, S, 2002, "Quantifying spatial characteristics of cities" Urban Studies 39 11:2005 -- 2028 Stokols, D, 1992, "Establishing and maintaining healthy environments: Toward a social ecology of health promotion" American Psychologist 47 1:6-22 Turner, M G, 1990, "Spatial and temporal analysis of landscape patterns" Landscape Ecology 4 1:21-30 Turner, M G, Gardner, R H, and O’Neill, R V, 2001. Landscape Ecology in Theory and Practice: Pattern and Process. Springer Verlag, New York. Weber, J and Kwan, M, 2003, "Evaluating the effects of geographic contexts on individual accessibility: A multilevel approach" Urban Geography 24 8: 647–671 Weibull, J W, 1976, "An axiomatic approach to the measurement of accessibility" Regional Science and Urban Economics 6: 357 – 379 Wong, D W S, 2003, "Implementing spatial segregation measures in GIS" Computers, Environment and Urban Systems 27: 53-70
38
