Researchers often choose mixed use measures for studies of weight and other health-related outcomes without knowing how alternative measures relate to weight in the same data set. This study uses county-wide data to provide comparisons and best combinations of alternative measures. We examined three entropy measures with varying degrees of detail in the classification of land uses, three sets of the specific categories used in the computation of entropy scores, and three destination-based measures (bus stops, rail stops, and parks). Generalized estimating equations demonstrated that entropy measures with 3 and 6 land use categories, the 6 categories constituting the 6-category entropy measure, and the distance to the closest light rail stop had significant, if not consistent, associations with weight outcomes, especially BMI.
The superior performance of the BMI model with six categories over the entropy score suggests that it is the presence of walkable land uses, not the entropy score representation of land use mixture, that might improve walkability of a neighborhood. For example, one might walk to a neighborhood grocery store even if it does not comprise an equal one-sixth of the land along with five other equal amounts of different types of walkable land uses.
Not only were all approaches to measuring mixed land useful in predicting some outcomes, but the 6 land use categories and rail stop proximity were sufficiently unique that they did not cancel out each others' effects in the final combined model for this data set. We should note that retaining several land use categories in a model raises the possibility that the contribution of single categories may be obscured. For example, the highest correlation among the categories was between office and retail (r
= .54), which means their individual coefficients may not show significance, although their combined presence may improve model fit. Consistent with Smith et al. (2008)
, older housing was related to lower risks of overweight and obesity in 5 of 6 final models (and was marginally significant for the sixth model of male overweight), demonstrating a fairly robust effect in relation to weight outcomes. Older housing age likely indexes a broad array of neighborhood properties that support walking for pleasure, including aesthetic qualities such as tree cover and narrow streets, and walking to destinations, such as a fine-grained mix of attractive destinations (Handy 1996a
; Handy 1996b
) and merits replication efforts in other communities.
Results also demonstrated differences in the association between neighborhood walkability and the weight status of males and females. Although office space relates to lower weight outcomes (lower BMI for women, lower overweight risk for men), no other land use category predicts lower weight for both genders, net of other predictors. Residing in a block group with more educational institution space is not directly related to weight for either gender (), but in combination with other predictors, has positive relations to female obesity and negative to male overweight (). Among adults, proximity to schools has been associated with more transportation walking (McCormack et al. 2008
) and direct routes to school associated with more general walking in other studies (Moudon et al. 2007
), consistent with findings for males in this study. Among females, and consistent with prior research, proximity to entertainment spaces (Cohen et al. 2007
; Giles-Corti et al. 2003
) and especially light rail stops (Rundle et al. 2007
) relates to lower BMI and obesity risk . Perhaps women walk more when nearby destinations generate other foot traffic throughout the day and evening; women feel more comfortable when socially safe others provide “eyes on the street” (Jacobs 1961
; Loukaitou-Sideris and Fink 2009
). Alternatively, perhaps women who prefer walking move to areas of town where walking can be a more useful means of transportation.
This evaluation yields several suggestions for future researchers. First, entropy measures should be chosen carefully, with an understanding of how similar entropy scores can represent very different walking environments. In addition to finding superior empirical results for the land use categories over the entropy scores, our earlier review highlighted several conceptual limitations in the ability of entropy scores to serve as ideal indicators of mixed use. The uncounted land, the interconnections between types of land, and the unused categories within a mix equation can all make a difference. Thus, researchers may want to consider examining carefully their components of entropy scores, scrutinizing unscored land, and complementing entropy scores with other mixed use and walkability measures, including population density, street connectivity, and housing age and distances to transit. In addition, future researchers are encouraged to provide more comprehensive comparisons using a wide range of mix measures, which have not been used in BMI-related outcomes but have been reviewed by other researchers who focus on walking and other outcomes (Brownson et al. 2009
; Forsyth 2007
; Song and Rodriguez 2004
Second, the components of entropy scores have not been examined in past research that uses entropy measures, yet they often provided a superior model fit in our tests. Researchers may want to consider reporting the effects of the underlying components, even when they also use entropy scores. Third, directions and magnitudes of associations with BMI varied across the six land use categories. This implies that the simple dichotomy between residential and non-residential walkable uses implemented by a 2-category entropy score may be incapable of capturing the complexities of neighborhood land use relationships to residents' weight outcomes. The weaker performance of the 2-category entropy score in this study also supports this finding. Fourth, destination-based measures may become unwieldy, so researchers may need to adapt methods (e.g., use GPS indicators of individual destinations) or use careful conceptualizations to select destinations most likely to draw pedestrians, such as light rail stops. Fifth, we found no clear statistically preferred measure of land use mix when the outcomes were overweight or obesity, perhaps due to the reduced sample size or a less sensitive categorical outcome measure of obesity or overweight compared to a continuous BMI outcome; researchers who test for effects only on obesity and/or overweight may want to examine BMI outcomes as well.
Several important issues not investigated in this study should also be noted. First, our destination-based measures only considered public transportation facilities and parks when a broader variety of destinations are likely to promote walking, especially transportation walking; similarly, food-related destinations may also affect BMI. Second, we chose to examine the 1-km street network buffer for compatibility with prior research, but other geographic scales might provide better, or at least different, predictors of weight outcomes. Third, we used land use data from one county and it is not clear how comparable land use codes are to those used in other geographic areas, although we used multiple raters to classify land as closely as possible to the categories used by Frank and colleagues. Although many municipalities classify land uses for tax purposes, future work is needed to determine whether there is an optimal way to classify land uses for health outcomes. A more optimal solution might depend on some combination of careful comparisons across tax codes, business listings, and even remote sensing. Finally, the role of resident selection into neighborhoods was not assessed in this study or in most cross sectional studies that relate land use to health outcomes, but may affect BMI. Selection should be examined in future studies using longitudinal designs, statistical models that address selection (i.e., propensity score models or instrumental variable models), and through direct measurement and control of neighborhood preference self-reports.
For policy makers, by retaining the separate types of land uses that are often combined into entropy indices, the implications for land use recommendations may become clearer. For example, the presence of multifamily dwellings was associated with better weight outcomes in some analyses, but single family detached housing was not. Given how controversial it can be to add multifamily housing to neighborhoods (Basolo and Hastings 2003
; Pendall 1999
), it is useful to know they have positive associations as well. The distance to light rail stops was another measure that may be of importance to policy makers and was the most powerful destination-based measure among women. Transit oriented development often combines multiple factors, such as the encouragement of higher densities, less convenience for cars, and special consideration for pedestrians, that may provide for better walking conditions overall. Rail stop permanence and rail's ability to serve many riders may encourage business development in addition to supporting healthy walkability and weight. A recent cost benefit analysis estimated that rail stop users can accrue 8.3 minutes of walking per day walking to transit, which over time may prevent weight gain and prevent estimated expenditures of $5,500 per person in additional health costs (Edwards 2008
). As countries consider ways to reduce reliance on oil and automotive travel, the positive correlates of living near transit may make the transition to rail-serviced neighborhoods more attractive.