College of Environmental Design

The increase of wildfire disasters globally has highlighted the need to understand and mitigate human vulnerability to wildfire. In response, there has been a substantial uptick in efforts to characterize and quantify wildfire vulnerability. Such efforts have largely focused on quantifying potential wildfire exposure and frequently overlooked the individual and community vulnerability to wildfire. Here, we review the emergent literature on social vulnerability to wildfire by synthesizing factors related to exposure, sensitivity, and adaptive capacity that contribute to a population's or community's overall vulnerability to wildfires. We identify how those factors subsequently affect an individual's or community's agency to enact change, and highlight that many of the current paradigms for reducing wildfire vulnerability fail to acknowledge and address the importance of inequalities that create differential vulnerability. We suggest that paying attention to the systems and conditions that give rise to such vulnerability can ameliorate these shortcomings by centering solutions which address adaptation equity rather than landscape outcomes.

We examined whether neighborhood built environment (BE) and cognition associations in older adults vary by apolipoprotein E (APOE) genotype, a genetic risk factor for Alzheimer's disease (AD). We conducted a cross-sectional analysis of 4091 participants. Neighborhood characteristics included social and walking destination density (SDD, WDD), intersection density, and proportion of land dedicated to retail. Individuals were categorized as APOE ε2 (lower AD risk), APOE ε4 (higher AD risk), or APOE ε3 carriers. Among APOE ε2 carriers, greater proportion of land dedicated to retail was associated with better global cognition, and greater SDD, WDD, intersection density, and proportion of land dedicated to retail was associated with better processing speed. These associations were not observed in APOE ε3 or ε4 carriers. APOE ε2 carriers may be more susceptible to the potentially beneficial effects of denser neighborhood BEs on cognition; however, longitudinal studies are needed.

The consequences of sprawl for travel behavior remain unclear. Theory suggests at least two possible commuting outcomes. As jobs decentralize and central employment areas congest, workers might shorten their commutes in time and distance by relocating to the suburbs. Or, the average commute could grow if residential choice is relatively inelastic with respect to job location, amenity explanations for residential and job location dominate, or as dual-worker households in polycentric labor markets become the norm. Evidence on these questions is surprisingly rare and dated. For data on individual travel behavior, we use the American Housing Survey, a detailed individual-level panel survey for most major metropolitan areas of the U.S. for several years between 1985 and 1997. Commute distance is regressed on a reduced form travel demand model, including U.S. Bureau of Economic Analysis measures of metro-wide employment deconcentration at the one-digit SIC industry level. The model specification conforms to urban form theory, the model estimation uses panel techniques, and the potential endogeneity of wages and land costs -- as compensations for commute costs -- is addressed statistically. We find that the more suburbanized is employment -- that is, the more sprawl -- the shorter the average commute. There are strong differences by industry, however, that may reflect a combination of industry agglomeration effects, differential job location stability by industry, and historical transitions.

We used data from 3227 older adults in the Multi-Ethnic Study of Atherosclerosis (2004-2012) to explore cross-sectional and longitudinal associations between walkability and cardiometabolic risk factors. In cross-sectional analyses, linear regression was used to estimate associations of Street Smart Walk Score® with glucose, triglycerides, HDL and LDL cholesterol, systolic and diastolic blood pressure, and waist circumference, while logistic regression was used to estimate associations with odds of metabolic syndrome. Econometric fixed effects models were used to estimate longitudinal associations of changes in walkability with changes in each risk factor among participants who moved residential locations between 2004 and 2012 (n=583). Most cross-sectional and longitudinal associations were small and statistically non-significant. We found limited evidence that higher walkability was cross-sectionally associated with lower blood pressure but that increases in walkability were associated with increases in triglycerides and blood pressure over time. Further research over longer time periods is needed to understand the potential for built environment interventions to improve cardiometabolic health.