Despite the growing literature on walkability and health, few guidelines exist for researchers to decide upon proper geographic scales and walkability measures, especially mixed land use measures. In this paper, we examine three geographic scales and four types of mixed land use measures in relationship to BMI. Analyses indicate that the use of the street network buffer generally results in relatively better fitting models.
The advantage of buffer measures is not necessarily consistent across genders or measures of the built environment, however. Further, the best fitting models demonstrate that the performance of models could be improved by choosing an appropriate geographic scale for each measure of the built environment instead of choosing one scale for all variables. We recognize that our results may not generalize to all geographic circumstances. We chose the best scale simply by relying on partial correlations and we limit testing to three levels of geographic scale. This analysis nonetheless illustrates a potential pitfall when any fixed neighborhood definition is used to measure all built environmental features.
Almost all of the alternative measures of mixed use examined in this study are useful in predicting individual BMI. The census proxy of the median housing age and the distance to the closest light rail station are particularly promising, followed by the six land use categories included separately. None of the three statistical summary indices depicted in outperform the six land use categories included separately. In addition, some combinations of the alternative measures demonstrate further improvement in model fit. Together, these findings suggest that no single measure of mixed use will likely fully capture how land use relates to neighborhood BMIs; mixed use is complex and merits complex and multiple measures.
The variable measuring proximity to large grocery stores is notable for its failure to improve model fit. This might happen because common shopping patterns of Americans require motorized transportation to carry multiple, heavy bags. Motorized transportation also allows them to choose stores not in the proximity of their residence. Additionally, grocery stores provide not only healthy food but also less nutritious food and they may be located near unhealthy food outlets such as fast food restaurants. Weight status is determined by physical activity and eating behavior, thus food environments that relate to both components may have more complex associations with BMI than what could be measured by the proximity to grocery stores alone. Another unexpected, notable finding in this study is that mixed use measures at the census tract scale generally resulted in better model fit than those at the block group scale. This is counterintuitive as the smaller block group scale more closely approximates walkable distances. These unexpected findings might result from the fact that walkability in this study is solely restricted to residential neighborhood. Although this is a widely adopted approach in studies concerned with neighborhood effects on human health, individuals travel to many areas for work and shopping, including those outside their residential neighborhoods. Consequently, walkability measures that take into consideration human spatial behaviors such as space-time accessibility developed in time geography (Kwan 1998
; Miller 1999
) might be justified to better capture the built environmental features that are relevant to individuals’ health.
Results also demonstrate gender differences in the association between individual BMI and built environment measures as well as in the preferred geographic scales of analysis for several measures. Consistent with prior research (Rundle et al. 2007
; Brown and Werner 2009
; Brown et al. 2009
), proximity to light rail stations relates to lower female BMI, regardless of the proximity to the CBD. In contrast, proximity to LR relates to lower male BMI only when proximity to the CBD is not controlled. This may reflect gender differences in transit use (Crane 2007
; Pucher and Renne 2003
) and/or private vehicle ownership. In addition, different subsets of the six land use categories show significant associations with male and female BMIs. These results suggest the importance of examining how walkability features are used and perceived differently by men and women.
To the best of our knowledge, self-reported weight and height information on driver license has rarely been used in obesity research. An advantage of driver license data is its extensive coverage of the adult population. The original UPDB driver license data includes almost 90% of Salt Lake County residents between the ages of 25 and 64, which enhances the external validity of the study’s finding.
A possible disadvantage of using driver license data is that they may exclude the most economically disadvantaged individuals who may also have a higher obesity risk. The use of driver license data also imposes potential limitations of self-reported weight and a time lag between the physical environment and weight measures. Past studies find a tendency for individuals to under-report weight and over-report height (Nawaz et al. 2001
; Gorber et al. 2007
). Nevertheless, self-reported weights, such as those in the Behavioral Risk Factor Surveillance System (BRFSS) and the National Health and Nutrition Examination Survey (NHANES), have proved valuable for monitoring obesity trends in the United States (Centers for Disease Control and Prevention 2007
; Ezzati et al. 2006
Individuals’ records in the data correspond to their most recent renewal; in Utah, renewals are required every five years or after address changes, name changes, or loss of license. The data therefore represent the most recent height and weight data from 1995 through 2005, while census and other built environment measures are from different years. In addition, for individuals who changed their residences during this time period, reported BMI measures may not correspond to the built environment of their current residences; we are currently investigating this issue of residential change. Given these potential limitations and the fact that adults 25–64 typically gain weight over time (U.S. Department of Agriculture 2005
), BMI measures in this study are likely to underestimate true BMIs and thus our findings should be viewed as conservative.
There are additional research questions that are beyond the scope of this study. First, the buffer distance of 1km is chosen primarily for compatibility with prior studies; we do not test for optimal buffer distances. Studies that examine multiple buffer distances find differences across distances in terms of the magnitude of associations (Berke et al. 2007
) and the associated built environment features (McCormack, Giles-Corti, and Bulsara 2008
). This implies that optimal distances likely vary for different measures of built environment. Consequently, it is essential to develop theoretical as well as empirical bases to decide upon appropriate buffer distances. Second, the six land use categories used in this study are from Frank et al. (2006)
. They may be classified differently in other municipalities or data sets, which may hamper comparative studies. Third, data were not available that would allow us to control for additional individual-level covariates (e.g., income) and behaviors (e.g., walking). Some studies suggest that women are more likely to use public transit than men (Crane 2007
; Pucher and Renne 2003
), which may explain the gender differences we observed in proximity to LR. However, future research is needed to fully understand the sources of these differences. Fourth, these results are cross sectional and do not consider residential self-selection. Most past studies that relate walkability to physical activity or obesity risk do not explicitly investigate this issue. If more physically active individuals self-select into more walkable neighborhoods, this could introduce a bias into the estimated relationships. Thus, care must be taken so as not to interpret our estimated relationships as causal.
Three variables used in the current study merit special attention in future research. The census proxy measures—median year structure built and the percentage of residents walking to work—are useful measures, given their ease of collection and consistent relations with BMI (Smith et al. 2008
; Brown et al. 2009
; Zick et al. 2009
). However, the question of exactly how these variables affect walkability and BMI deserves further attention. If our findings are replicated elsewhere, census proxy variables might enable researchers and policy makers to identify potential hot spots of obesity risk without requiring detailed land use information. Similarly, the observed health benefit of the proximity to transit stations merits wider study, including investigations of effects that are direct (walking to rail stations) and indirect (walking to other destinations clustered around rail stations). Light rail offers societal benefits of less pollution and oil dependence; if light rail consistently relates to lower BMI, then the health benefits of light rail deserve broader consideration from policy makers and researchers.