Disparities in healthy food access are well documented in cross-sectional studies in communities across the United States. However, longitudinal studies examining changes in food environments within various neighborhood contexts are scarce. In a sample of 142 census tracts in four low-income, high-minority cities in New Jersey, United States, we examined the availability of different types of food stores by census tract characteristics over time (2009–2017). Outlets were classified as supermarkets, small grocery stores, convenience stores, and pharmacies using multiple sources of data and a rigorous protocol. Census tracts were categorized by median household income and race/ethnicity of the population each year. Significant declines were observed in convenience store prevalence in lower- and medium-income and majority black tracts (p for trend: 0.004, 0.031, and 0.006 respectively), while a slight increase was observed in the prevalence of supermarkets in medium-income tracts (p for trend: 0.059). The decline in prevalence of convenience stores in lower-income and minority neighborhoods is likely attributable to declining incomes in these already poor communities. Compared to non-Hispanic neighborhoods, Hispanic communities had a higher prevalence of small groceries and convenience stores. This higher prevalence of smaller stores, coupled with shopping practices of Hispanic consumers, suggests that efforts to upgrade smaller stores in Hispanic communities may be more sustainable.

Food access is a global issue, and for this reason, a wealth of studies are dedicated to understanding the location of food deserts and the benefits of urban gardens. However, few studies have linked these two strands of research together to analyze whether urban gardening activity may be a step forward in addressing issues of access for food desert residents. The Phoenix, Arizona metropolitan area is used as a case to demonstrate the utility of spatial optimization models for siting urban gardens near food deserts and on vacant land. The locations of urban gardens are derived from a list obtained from the Maricopa County Cooperative Extension office at the University of Arizona which were geo located and aggregated to Census tracts. Census tracts were then assigned to one of three categories: tracts that contain a garden, tracts that are immediately adjacent to a tract with a garden, and all other non-garden/non-adjacent census tracts. Analysis of variance is first used to ascertain whether there are statistical differences in the demographic, socio-economic, and land use profiles of these three categories of tracts. A maximal covering spatial optimization model is then used to identify potential locations for future gardening activities. A constraint of these models is that gardens be located on vacant land, which is a growing problem in rapidly urbanizing environments worldwide. The spatial analysis of garden locations reveals that they are centrally located in tracts with good food access. Thus, the current distribution of gardens does not provide an alternative food source to occupants of food deserts. The maximal covering spatial optimization model reveals that gardens could be sited in alternative locations to better serve food desert residents. In fact, 53 gardens may be located to cover 96.4% of all food deserts. This is an improvement over the current distribution of gardens where 68 active garden sites provide coverage to a scant 8.4% of food desert residents. People in rapidly urbanizing environments around the globe suffer from poor food access. Rapid rates of urbanization also present an unused vacant land problem in cities around the globe. This paper highlights how spatial optimization models can be used to improve healthy food access for food desert residents, which is a critical first step in ameliorating the health problems associated with lack of healthy food access including heart disease and obesity.

Given widespread concerns over human-mediated bee declines in abundance and species richness, conservation efforts are increasingly focused on maintaining natural habitats to support bee diversity in otherwise resource-poor environments. However, natural habitat patches can vary in composition, impacting landscape-level heterogeneity and affecting plant-pollinator interactions. Plant-pollinator networks, especially those based on pollen loads, can provide valuable insight into mutualistic relationships, such as revealing the degree of pollination specialization in a community; yet, local and landscape drivers of these network indices remain understudied within urbanizing landscapes. Beyond networks, analyzing pollen collection can reveal key information about species-level pollen preferences, providing plant restoration information for urban ecosystems. Through bee collection, vegetation surveys, and pollen load identification across ~350 km of urban habitat, we studied the impact of local and landscape-level management on plant-pollinator networks. We also quantified pollinator preferences for plants within urban grasslands. Bees exhibited higher foraging specialization with increasing habitat heterogeneity and visited fewer flowering species (decreased generality) with increasing semi-natural habitat cover. We also found strong pollinator species-specific flower foraging preferences, particularly for Asteraceae plants. We posit that maintaining native forbs and supporting landscape-level natural habitat cover and heterogeneity can provide pollinators with critical food resources across urbanizing ecosystems.

We are exploring ways to capture the temporal and spatial dimensions of the use of public transit. Specifically, we are investigating how different land uses affect the spatial and temporal demand for public transit services. Spatially, the availability of new data collection technology in public transit allows us to examine transit demand at the individual stop level. Our hypothesis, however, is that transit users' activity may not be originated from or destined to an individual stop per se; rather, the activity is associated with a specific location in the vicinity of the stop, and this location may be "covered" by several adjacent transit stops. More importantly, understanding the transit demand at this aggregate level (an aggregate "catchment" area) can enhance the ability to define a specific land-use type and the temporal characteristics related to passengers' activities. Temporally, we seek to understand the relationship between the demand for public transit service at specific times of the day and the associated land uses that may strongly influence the timing of that demand. To explore these dimensions, this study: 1) proposes a method of stop aggregation; 2) generates transit service areas based on these aggregated stops; 3) develops a set of metrics to better represent land-use types within these service areas; and 4) examines the spatial and temporal characteristics of transit demand for these service areas. These methods are applied to a case study using land-use and transit demand data from the Minneapolis–St. Paul metropolitan area.

Human-induced climate change is bringing warmer conditions to the Southwestern United States. More extreme urban heat island (UHI) effects are not distributed equally, and often impact socioeconomically vulnerable populations the most. This study aims to quantify how land surface temperature (LST) changes with increasing green vegetation landscapes, identify disparities in urban warming exposure, and provide a method for developing evidence-based mitigation options. ECOSTRESS LST products, detailed land use and land cover (LULC) classes, and socioeconomic variables were used to facilitate the analysis. We examined the relationship between LST and the fractions of LULC and socioeconomic factors in the city of Phoenix, Arizona. A machine learning approach (Random Forest) was used to model LST changes by taking the LULC fractions (scenario-based approaches) as the explanatory variables. We found that vegetation features—trees, grass, and shrubs—were the most important factors mitigating UHI effects during the summer daytime. Trees tended to lower surface temperature more effectively, whereas we observed elevated daytime LST most often near roads. Meanwhile, higher summer daytime temperatures were observed on land with unmanaged soil compared to the built environment. We found that affluent neighborhoods experienced lower temperatures, while low-income communities experienced higher temperatures. Scenario analyses suggest that replacing 50% of unmanaged soil with trees could reduce average summer daytime temperatures by 1.97°C if the intervention was implemented across all of Phoenix and by 1.43°C if implemented within the urban core only. We suggest that native trees requiring little to no additional water other than rainfall should be considered. We quantify mitigation options for urban warming effect under vegetation management interventions, and our results provide some vital insight into existing disparities in UHI impacts. Future UHI mitigation strategies seriously need to consider low-income communities to improve environmental justice. These can be used to guide the development of sustainable and equitable policies for vegetation management to mitigate heat exposure impacts on communities.