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Abstract Crime is a costly societal issue. While many factors influence urban crime, one less-studied but potentially important factor is neighborhood greenspace. Research has shown that greenspace is often negatively associated with crime. Measuring residents’ use of greenspace, as opposed to mere physical presence, is critical to understanding this association. Here, we used cell phone mobility data to quantify local street activity and park visits in Chicago and New York City. We found that both factors were negatively associated with crime, while controlling for socio-demographic factors. Each factor explained unique variance, suggesting multiple pathways for the influence of street activity and greenspace on crime. Physical tree canopy had a smaller association with crime, and was only a significant predictor in Chicago. These findings were further supported by exploratory directed acyclic graph modeling, which found separate direct paths for both park visits and street activity to crime. 

The digital divide—and, in particular, the homework gap— have been exacerbated by the COVID-19 pandemic, laying bare not only the inequities in broadband Internet access but also how these inequities ultimately affect citizens’ ability to learn, work, and play. Addressing these inequities ultimately requires having holistic, “full stack” data on the nature of the gaps in infrastructure and uptake—from the physical infrastructure (e.g., fiber, cable) to speed and application performance to affordability and neighborhood effects that ultimately affect whether a technology is adopted. This paper surveys how various existing datasets can (and cannot) shed light on these gaps, the limitations of these datasets, what we know from existing data about how the Internet responded to shifts in traffic during COVID-19, and—importantly for the future—what data we need to better understand these problems moving forward and how the research community, policymakers, and the public might gain access to various data. Keywords: digital divide,iInternet, mapping, performance