Mixed Land Uses White Paper

8/11/2019 Mixed Land Uses White Paper

1/39

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: [email protected]

** Email: [email protected]

Preparation of this White Paper was supported by a grant from the Robert WoodJohnson Foundation Active Living Research program.


2/39

1

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.


3/39

2

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


4/39

3

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


5/39


6/39

5

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).


7/39

6

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


8/39

7

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


9/39

8

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


10/39

9

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


11/39

10

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.


12/39

11

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.


13/39

12

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.


14/39

13

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


15/39

14

Definition: Proportions of different types of land uses within a user-defined

neighbourhood.


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.


16/39

15

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


17/39

16

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.



18/39

17

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.


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


19/39


20/39


21/39

20

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.


The entropy index incorporates more than two land use types in a single

calculation, very conveniently aggregating a measure of land use diversity at


22/39

21

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).


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,


23/39

22

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.


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


24/39

23

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.


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


25/39

24

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.


3These data are from Portland Metros Regional Land Information System (RLIS).


26/39

25

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.


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.


27/39

26

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


28/39

27

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.


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


29/39

28

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


30/39

29

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.


31/39

30

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.


32/39

31

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


33/39

32

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).


34/39

33

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


35/39

34

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.


36/39

35

References

Atkinson, A B, 1970, "On the Measurement of Income Inequality"Journal ofEconomic Theory2: 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 Medicine389:1243-1256

Centres for Disease Control and Prevention (2005) Active CommunityEnvironments 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 A305:361-377

Cervero, R and Duncan, M, 2003, "Walking, bicycling, and urban landscapes:Evidence from the San Francisco Bay area" American Journal of

Public Health939:1478-1483

Cervero, R and Duncan, M, 2004, "Neighbourhood composition and

residential land prices: Does exclusion raise or lower values?" Urban

Studies412:299-315

Cervero, R and Kockelman, K, 1997, "Travel demand and the 3Ds: Density,

diversity and design" Transportation Research D23:199-219

Duany, A and Plater-Zyberk, E, 1992, May, "The second coming of theAmerican small town" Plan Canada: 613.

Duncan, O D and Duncan, B, 1955, "A methodological analysis of segregationindexes"American Sociological Review20: 210-217

Ewing, R, Pendall, R and Chen, D, 2003, "Measuring sprawl and its

transportation impacts" Transportation Research Record1831:175-183

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 Medicine272:87-96


37/39

36

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 282S2:117-125

Frank, L D, Stone, J, Brian and Bachman, W, 2000, "Linking land use withhousehold vehicle emissions in the central puget sound: methodological

framework and findings" Transportation Research Part D: Transport

and Environment53:173-196

Geoghegan, J, 2002, "The value of open spaces in residential land use" Land

Use Policy191:91-98

Geoghegan, J, Wainger, L A and Bockstael, N E, 1997, "Spatial landscapeindices in a hedonic framework: an ecological economics analysis

using GIS"Ecological Economics233:251-264

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 Medicine282, 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 & Medicine5412:1793-1812

Giles-Corti, B and Donovan, R J, 2002b, "Socioeconomic Status Differences in

Recreational Physical Activity Levels and Real and Perceived Accessto a Supportive Physical Environment" Preventive Medicine356:601-

611

Gini, C, 1912, "Variabilit e mutabilita" reprinted inMemorie di metodologica

statistica (Ed. Pizetti E, Salvemini, T). Rome: Libreria Eredi VirgilioVeschi 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 Record1780:33-42

Handy, S, 1996, "Urban form and pedestrian choices: study of Austinneighbourhoods" Transportation Research Record1552:135-144


38/39


39/39

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 Sport684:345-351

Shen, Q, 1998, "Location characteristics of inner-city neighborhoods and

employment accessibility of low-income workers" Environment andPlanning B25: 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" UrbanStudies 39 11:2005 -- 2028

Stokols, D, 1992, "Establishing and maintaining healthy environments:Toward a social ecology of health promotion" American Psychologist

471:6-22

Turner, M G, 1990, "Spatial and temporal analysis of landscape patterns"

Landscape Ecology41:21-30

Turner, M G, Gardner, R H, and ONeill, 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

248: 647671

Weibull, J W, 1976, "An axiomatic approach to the measurement ofaccessibility"Regional Science and Urban Economics6: 357 379

Wong, D W S, 2003, "Implementing spatial segregation measures in GIS"

Computers, Environment and Urban Systems27: 53-70

Mixed Land Uses White Paper

Documents

Transcript of Mixed Land Uses White Paper