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There is ongoing debate regarding the merits of decriminalization or outright legalization of commercial sex work in the United States. A few municipalities have officially legalized both the selling and purchasing of sex, while others unofficially criminalize purchasing sex but have decriminalized its sale. In addition, there are many other locales with no official guidance on the subject but have unofficially decriminalized sex work by designating specific areas in an urban landscape safe from law enforcement for commercial sex, by quietly ceasing to arrest sex sellers, or by declining to prosecute anyone selling or attempting to sell sex. Despite these efforts, it remains crucial to understand where in an urban area commercial sex exchanges occur—legalization and decriminalization may result in fewer arrests but is likely to increase the overall size of the sex market. This growth could result in an increase in sex trafficking victimization, which makes up the majority of commercial sex sellers in any domestic market. Given the distribution of prostitution activities in most communities, it is possible to use high-fidelity predictive models to identify intervention opportunities related to sex trafficking victimization. In this research, we construct several machine learning models and inform them with a range of known criminogenic factors to predict locations hosting high levels of prostitution. We demonstrate these methods in the city of Chicago, Illinois. The results of this exploratory analysis identified a range of explanatory factors driving prostitution activity throughout Chicago, and the best-performing model correctly predicted prostitution frequency with 94% accuracy. We conclude by exploring specific areas of under- and over-prediction throughout Chicago and discuss the implications of these results for allocating social support efforts. 

The COVID-19 pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continues to impact the United States. While age and comorbid health conditions remain primary concerns in the community-based transmission of the virus, empirical evidence continues to suggest that substantial variability exists in the geographic and geodemographic distribution of COVID-19 infection rates. The purpose of this paper is to provide an alternative, spatiotemporal perspective on the pandemic using the state of Wisconsin as a case study. Specifically, in this paper, we explore the geographic nuances of COVID-19 and its spread in Wisconsin using a suite of spatial statistical approaches. We link detected hot spots of COVID-19 to local geodemographic profiles and the presence of high-risk facilities, including federal and state correctional facilities. The results suggest that the virus disproportionately impacts several communities and geodemographic groups and that proximity to risky facilities correlates to increased community infection rates. 

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.