mixed land uses white paper
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Carolina Transportation Program
White Paper Series
The Measurement of the Level of Mixed Land Uses:
A Synthetic Approach
Yan Song* and Daniel A. Rodrguez**
Department of City and Regional Planning
New East Bldg, CB#3140
University of North Carolina
Chapel Hill, NC 27599-3140; USA
* Email: ys@email.unc.edu
** Email: danrod@email.unc.edu
Preparation of this White Paper was supported by a grant from the Robert WoodJohnson Foundation Active Living Research program.
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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.
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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 peoples propensity to walk and thus to be physically
active, transit use, and property values. Mixed land uses also have been
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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 transit-
supportive 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
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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 ones 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).
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In landscape ecology, land uses are often the starting point of modelling
approaches (ONeill 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
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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 mixbetween 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
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mixed commercial and residential uses of land appears to support non-
motorized 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
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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
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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.
1A Traffic Analysis Zone (TAZ) is a special area delineated by state and/or local
transportation officials for tabulating traffic related dataespecially journey-to-work and
place-of-work statistics.
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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 non-
residential 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.
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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.
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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
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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 user-
defined 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
2Researchers 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 usemixture 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 generallyrecommended 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 neighbourhoodsto be of equivalent sizes. However, it is more likely that neighbourhood sizes deviate fromeach 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.
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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
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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.
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The measures 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
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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
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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 sub-
units (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,
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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
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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 non-
residential 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
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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 -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 multi-
family 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--
3These data are from Portland Metros Regional Land Information System (RLIS).
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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.
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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 A0.5does not make adjustments to the under- or
over-represented areal units. A0.1andA0.9modify the evenness by allowing the
areal units where non-residential land uses are below- or above-average of the
neighbourhoods 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 aand 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 bhas a larger proportion of
non-residential land uses compared to neighbourhood a, the non-residential
uses in neighbourhood bare less likely to interact with residential uses, as well
as less likely to be isolated from other non-residential uses.
TheClustering 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
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that the non-residential land uses in Neighbourhood care found in distant sub-
units as opposed to nearby, while non-residential land uses in Neighbourhood
dare more clustered.
--insert Figure 5 and Table 1 here--
To discover land use mixture between two groups (i.e., residential vs. non-
residential land uses) within blockgroups, we experiment with two evenness
measures at the -mile by -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
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Herfindahl-Hirschman Index (HHI), and a set of Atkinson indices (A(m)0.25,
A(m)0.5, andA(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
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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.
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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 indexs
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.
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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
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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
thepatternof 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
4Others have relied on the mathematical properties of selected measures discussed here, but in
the context of racial segregation (James and Taeuber, 1985).
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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
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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.
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