Comparing measures of urban land use mix

Highlights

• Reviews mathematical formulas and conceptual underpinnings for a wide range of measures of urban land use mix.

• Conducts a Monte Carlo simulation to discover statistical relationships between various mixed use measures.

• Compares results from the Monte Carlo simulation to empirical data from Hillsboro, Oregon.

• Identifies crucial limitations of mixed use measures and discusses techniques for adapting to those limitations.

• Provides recommendations about which mixed use measure to use based upon available contextual information.

Abstract

We review a variety of common measures of urban land use mix in order to understand their differences and to identify their strengths and limitations. We then apply these measures to data from a Monte Carlo simulation to ascertain statistical relationships among them, finding that they can be placed into four groups where measures within each group produce highly consistent results: Percentage and Exposure Index; all varieties of the Atkinson Index; Balance, Entropy, and Herfindahl–Hirschman indices; and the Dissimilarity and Gini indices. We find that when analyzing two dimensions of land use, generally both the Balance Index and the Dissimilarity Index should be used, and that the Dissimilarity Index should be tested at multiple scales. We provide a number of other practical recommendations about which mixed use measure to apply given the contextual information available to a researcher or analyst.

Introduction

The need for greater land use mix, and in particular the closer integration of residential development with commercial, civic, and recreational uses, has been adopted as the conventional wisdom among urban planners as well as public health professionals. Indeed, it is interesting to note that the separation of land uses was not always the custom in the United States and resulted from one of planning’s early “successes.” Zoning, as embodied in the famous case of Euclid v. Ambler, was one of the great contributions of early 20th century urban planning to the formation of US cities and the widespread adoption of zoning over time resulted in not just the separation of incompatible land uses, but increasingly the separation of urban land uses into large, homogenous districts. However the over-strict separation of land uses through zoning has over time had a number of unintended consequences that planners and other policy makers are currently attempting to undo through a shift towards promoting mixed use.

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 ten principles of Smart Growth (Smart Growth Network, 2006). Likewise, the Congress of New Urbanism’ Charter also argues that: “Neighborhoods should be compact, pedestrian-friendly, and mixed-use” (Congress for the New Urbanism, 2001). In addition, the US Centers for Disease Control and Prevention has identified mixing land uses as a strategy to promote healthy community environments (Centers for Disease Control and Prevention, 2011, Keener et al., 2009). The potential benefits of mixed use include promoting active travel, increasing the viability of transportation alternatives, reducing private vehicle use and its associated impacts, raising property values and helping to build a sense of place for local communities.

The conceptual basis for mixing urban land uses is that a range of land uses or activities within close proximity can potentially serve complementary functions, with each use enhancing the utility of its partners (Jacobs, 1961). For example, having small commercial establishments in a residential neighborhood provides a convenient location for residents seeking regular services or social activity nearby (Duany, Plater-Zyberk, & Speck, 2001). Likewise for heavily commercial districts, increasing the amount of their residential population can activate businesses and public spaces during off-peak hours, potentially contributing to these districts’ long term success. Although the explicit geographic range for measures of urban land use mix is often not specified, typically a local scale is intended, in the range of 1-mile radius or less (Ewing et al., 2003, Krizek, 2003).

Despite growing research interest in the impacts of urban land use mix, there have been few methodological analyses of how best to measure urban mixed use environments. In this paper we: (a) Review various land use mix measures, including selected measures adapted from other disciplines, (b) Compare the various measures on a conceptual and a mathematical basis, and (c) Conduct a Monte Carlo simulation on a pseudo-population of randomly generated land use patterns to explore to what degree these measures are statistically related. In addition, we apply these same mixed use measures to data from a real-world city to verify the results from the simulation. We hope that this paper will better equip researchers and analysts in deciding which mixed use measures are most appropriate for their context.

In the next section we summarize recent research on the impacts of urban land use mix from three fields: Transportation, public health, and urban economics. The third section presents our review of various land use mix measures, including their mathematical formulae, and discusses their conceptual and computational differences. In the forth section we conduct a Monte Carlo simulation with two land use types and a large number of randomly generated land use patterns, calculating a variety of mixed use measures and discovering statistical relationships between them. This section also includes a brief comparison of results from the simulation with data from Hillsboro, Oregon. In the last section we discuss our results, draw conclusions, and provide recommendations for researchers interested in selecting appropriate mixed use measures.

The benefits of urban land use mix have been studied in several fields, most notably in transportation, public health, and urban economics. From a transportation point of view, the benefits of mixed uses come primarily from bringing a variety of origins and destinations closer together, therefore enabling shifts to non-motorized modes and/or shorter travel distances. From a public health point of view, bringing a variety of interesting destinations near residential areas is a means to encourage active travel modes. And from an urban economics point of view, the appropriate mixture of complementary urban land uses has the potential to raise land values and encourage higher density development through the provision of urban amenities (Song & Knaap, 2004).

According to theories of travel demand, mixing land uses places trip origins (homes) and trip destinations (jobs, shopping, services) closer together, and therefore may result in reducing average trip lengths, promoting active travel modes (walking and cycling), and reducing vehicle dependence. Recent transportation research indicates that land use mix is positively associated with the frequency of active travel trips (Cervero, 1996, Ewing and Cervero, 2010, Greenwald and Boarnet, 2001, Handy, 1996, Khattak and Rodriguez, 2005, Kitamura et al., 1997) and negatively related to frequency and the distance of auto trips (Cervero and Duncan, 2006, Cervero and Kockleman, 1997, Ewing and Cervero, 2010, Krizek, 2003). Discrete choice models of travel modes also have shown that high levels of land use mix in one’s home or work neighborhood is related to higher walking, bicycling and transit shares (Cervero, 1996, Srinivasan, 2002), although the effect size has been qualified as “fairly marginal” (Cervero & Kockleman, 1997) and “modest” (Cervero & Duncan, 2003). Shorter commuting distances (Cervero, 1996) and lower commuting times (Ewing et al., 2003, Miller, 2011) have also been related to mixed land uses. Mixed use in residential areas may also promote greater levels of internal trip capture for a particular neighborhood or development (Ewing et al., 2011, Greenwald and Boarnet, 2001). On the other hand, evidence of associations between mixed land uses and auto ownership is less consistent, with Ewing (Ewing et al., 2003) finding no relationship and others finding a negative relationship (Cervero, 1996, Hess and Ong, 2002).

For health-related disciplines, the emergence of ecological models (Stokols, 1992) has led to an increased focus on how the built environment, as one of many influential environmental features, may be able to help shape individual health behaviors. As a result, a cadre of researchers has begun to investigate whether neighborhood land use patterns can influence levels of physical activity (Calfas et al., 1997, Giles-Corti et al., 2005, Wendel-Vos et al., 2007). Neighborhood land use patterns have been studied with regard to the accessibility, intensity, and diversity of non-residential land uses (McConville, Rodríguez, Clifton, Cho, & Fleischhacker, 2010), and studies have examined how the built environment influences levels of physical activity such as walking for transportation, walking for exercise, and total physical activity (Forsyth et al., 2008, Hoehner et al., 2005, McConville et al., 2010). The presence of non-residential land uses in residential neighborhoods has been positively associated with both walking for transportation and walking for exercise (Frank et al., 2005, Hoehner et al., 2005, McConville et al., 2010). However the link between local land use mix and total physical activity time or obesity has been less clear, with studies finding conflicting associations (Forsyth et al., 2008, Rodriguez et al., 2006, Rutt and Coleman, 2005). By contrast, the evidence regarding the relationship between physical activity and the mixing of residential and recreational land uses (such as parks and community centers) has more consistently shown a positive association (Giles-Corti, 2002, Giles-Corti et al., 2005, Giles-Corti and Donovan, 2002).

Land use mix has also been related to housing markets and individuals’ preferences for housing in particular neighborhood types. Conceptually, the presence of complementary land uses makes it more convenient for residents to engage in desired activities, such as socializing or convenience shopping. Also, mixed use places may foster greater street activity and make a neighborhood feel livelier, creating a positive neighborhood amenity. Measures of land-use mix between residential and commercial uses generally correlate with higher residential land prices (Cervero and Duncan, 2004, Geoghegan et al., 1997, Song and Knaap, 2004); however results also indicate that some households have a preference for neighborhoods that are primarily single family in nature (Song & Knaap, 2004).

In summary, ambiguities remain regarding the relationship between land use mix and transportation, health, and land value outcomes. Urban land use mix appears to support reduced automobile use, higher amounts of active travel, and higher property values. By contrast, the evidence regarding land use mix and auto ownership and obesity is more equivocal. Although some of the variability in the results of empirical research may be due to differences in context, differences in the approach to measuring land use mix also unintentionally contribute to varying outcomes. A more consistent approach to measuring land use mix may help yield more consistent and more generalizable results. In the next section we turn to summarizing and comparing measures of urban land use mix, as well as adapting related measures from other fields.