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The proliferation of internet-based home-sharing platforms like Airbnb has raised heated debates, with many in the general public believing that the presence of Airbnb listings can lead to an increase in crime and disorder in residential neighborhoods. Despite the importance of this debate to residents, policymakers, and other stakeholders, few studies have examined the causal linkage between Airbnb listings and crime in neighborhoods. We conduct the first such empirical test in Boston neighborhoods, focusing on two potential mechanisms: (1) the inflow of tourists might generate or attract crime; and (2) the creation of transient properties undermines local social dynamics. Corresponding to these mechanisms, we examine whether the number of tourists (approximated with reviews) or the prevalence of listings predict more incidents of private conflict, social disorder, and violence both concurrently and in the following year. We find evidence that increases in Airbnb listings–but not reviews–led to more violence in neighborhoods in later years. This result supports the notion that the prevalence of Airbnb listings erodes the natural ability of a neighborhood to prevent crime, but does not support the interpretation that elevated numbers of tourists bring crime with them.

Numerous studies have demonstrated a negative relationship between community violence and youth academic achievement, but they have varied in their geographic definition of “community,” especially as it relates to proximity to students’ residences. We extend this by considering the independent relationships between academic achievement and violent events (from 911 dispatches; e.g., gun shots) at the neighborhood (i.e., census tract) and street-block levels. We use data from standardized Math and English Language Arts (ELA) tests from Boston, MA for 2011–2013. Exposure to community violence was partially independent between streets and tracts, with some students living on low-crime streets in high-crime neighborhoods or high-crime streets in low-crime neighborhoods. Initial regression models found that differences in a neighborhood’s violent crime predicted up to a 3% difference in test scores on both Math and ELA tests. Students living on high-crime streets scored an additional 1% lower than neighbors on safer streets. Subsequent models with student-level fixed effects, however, eliminated these relationships, except for the effect of neighborhood-level violence on Math scores. These findings suggest that future work should consider community violence at both geographic scales, but that in this case the impacts were only consistent at the neighborhood level and associations at the street level were seemingly due to spatial segregation of households.

Disasters often create inequitable consequences along racial and socioeconomic lines, but a pandemic is distinctive in that communities must navigate the ongoing hazards of infection exposure. We examine this for accessing essential needs, specifically groceries. We propose three strategies for mitigating risk when accessing groceries: visit grocery stores less often; prioritize generalist grocery stores; seek out stores whose clientele have lower infection rates. The study uses a unique combination of data to examine racial and socioeconomic inequities in the ability to employ these strategies in the census block groups of greater Boston, MA in April 2020, including cellphone-generated GPS records to observe store visits, a resident survey, localized infection rates, and demographic and infrastructural characteristics. We also present an original quantification of the amount of infection risk exposure when visiting grocery stores using visits, volume of visitors at each store, and infection rates of those visitors’ communities. Each of the three strategies for mitigating exposure were employed in Boston, though differentially by community. Communities with more Black and Latinx residents and lower income made relatively more grocery store visits. This was best explained by differential use of grocery delivery services. Exposure and exposure per visit were higher in communities with more Black and Latinx residents and higher infection rates even when accounting for strategies that diminish exposure. The findings highlight two forms of inequities: using wealth to transfer risk to others through grocery deliveries; and behavioral segregation by race that makes it difficult for marginalized communities to avoid hazards.

ObjectivesMuch recent work has focused on how crime concentrates on particular streets within communities. This is the first study to examine how such concentrations vary across the neighborhoods of a city. The analysis evaluates the extent to which neighborhoods have characteristic levels of crime concentration and then tests two hypotheses for explaining these variations: the compositional hypothesis, which posits that neighborhoods whose streets vary in land usage or demographics have corresponding disparities in levels of crime; and the social control hypothesis, which posits that neighborhoods with higher levels of collective efficacy limit crime to fewer streets.MethodsWe used 911 dispatches from Boston, MA, to map violent crimes across the streets of the city. For each census tract we calculated the concentration of crime across the streets therein using the generalized Gini coefficient and cross-time stability in the locations of hotspots.ResultsNeighborhoods did have characteristic levels of concentration that were best explained by the compositional hypothesis in the form of demographic and land use diversity. In addition, ethnic heterogeneity predicted higher concentrations of crime over and above what would be expected given the characteristics of the individual streets, suggesting it exacerbated disparities in crime.ConclusionsThe extent to which crime concentrates represents an underexamined aspect of how crime manifests in each community. It is driven in part by the diversity of places in the neighborhood, but also can be influenced by neighborhood-level processes. Future work should continue to probe the sources and consequences of these variations.

ObjectivesSome criminologists of place have argued that property owners and place managers are the key actors exerting guardianship over crime and driving differences in crime across places, giving rise to the “Neighborhoods Out of Places Explanation” (NOPE) theory of crime. However, research to date has yet to fully evaluate if crime statistically varies across properties, their owners, or surrounding geographies.MethodsData scraped from Yelp.com is used to identify 1070 land parcels that had at least one business receiving reviews from 2014 to 2020. 911 dispatches for disturbances are linked to parcels and measured as the rate of events per Yelp reviewer in the average year. Hierarchical negative binomial modeling-based variance decomposition techniques are used to evaluate how variation in disturbance rates is distributed across parcels, owners, census blocks, and census tracts. Hierarchical negative binomial models are used to assess the correlates of disturbance rates. Sensitivity analyses assess the correlates of disturbance rates using a single-level negative binomial model with bootstrapped standard errors as well as an alternative outcome measure based on count of 911 events.ResultsCommercial disturbance rates vary across parcels, parcel owners, and blocks. At the parcel level, higher Yelp ratings are associated with lower disturbance rates while parcel square footage and land value are associated with increased disturbance rates. Additionally, parcel-level crime disturbance rates are explained by block features such as poverty, violent crime, and the number of Yelp restaurants on the block.ConclusionsParcel, owner, and block features can all help explain why some restaurants have more crime than others. Future research should build on the place management perspective by investigating the wider breadth of potential actors who may exert guardianship over properties while acknowledging that offenders and targets systematically vary across geographies, making effective guardianship more difficult in some locations than others.

Environmental hazards vary locally and even street to street resulting in microspatial inequities, necessitating climate resilience solutions that respond to specific hyperlocal conditions. This study uses remote sensing data to estimate two environmental hazards that are particularly relevant to community health: land surface temperature (LST; from LandSat) and air pollution (AP; from motor vehicle volume via cell phone records). These data are analyzed in conjunction with land use records in Boston, MA to test (1) the extent to which each hazard concentrates on specific streets within neighborhoods, (2) the infrastructural elements that drive variation in the hazards, and (3) how strongly hazards overlap in space. Though these data rely on proxies, they provide preliminary evidence. Substantial variations in LST and AP existed between streets in the same neighborhood (40% and 70–80% of variance, respectively). The former were driven by canopy, impervious surfaces, and albedo. The latter were associated with main streets and zoning with tall buildings. The correlation between LST and AP was moderate across census tracts (r = 0.4) but modest across streets within census tracts (r = 0.16). The combination of results confirms not only the presence of microspatial inequities for both hazards but also their limited coincidence, indicating that some streets suffer from both hazards, some from neither, and others from only one. There is a need for more precise, temporally-dynamic data tracking environmental hazards (e.g., from environmental sensor networks) and strategies for translating them into community-based solutions.

Objectives Scholars and practitioners have paid increasing attention to problematic properties, but little is known about how they emerge and evolve. We examine four phenomena suggested by life-course theory that reflect stability and change in crime and disorder at properties: onset of issues; persistence of issues; aggravation to more serious types of issues; and desistance of issues. We sought to identify the frequency and dynamics of each. Methods We analyze how residential parcels (similar to properties) in Boston, MA shifted between profiles of crime and disorder from 2011 to 2018. 911 dispatches and 311 requests provided six measures of physical disorder, social disorder, and violence for all parcels. K-means clustering placed each parcel into one of six profiles of crime and disorder for each year. Markov chains quantified how properties moved between profiles year-to-year. Results Onset was relatively infrequent and more often manifested as disorder than violence. Pathways of aggravation led from less serious profiles to a mixture of violence and disorder. Desistance was more likely to occur as de-escalations along these pathways then complete cessation of issues. In neighborhoods with above-average crime, persistence was more prevalent whereas desistance less often culminated in cessation, even relative to local expectations. Conclusions The results offer insights for further research and practice attentive to trends of crime and disorder at problematic properties. It especially speaks to the understanding of stability and change; the role of different types of disorder; and the toolkit needed for problem properties interventions.

Objectives. To examine the relationships among environmental characteristics, temperature, and health outcomes during heat advisories at the geographic scale of street segments. Methods. We combined multiple data sets from Boston, Massachusetts, including remotely sensed measures of temperature and associated environmental characteristics (e.g., canopy cover), 911 dispatches for medical emergencies, daily weather conditions, and demographic and physical context from the American Community Survey and City of Boston Property Assessments. We used multilevel models to analyze the distribution of land surface temperature and elevated vulnerability during heat advisories across streets and neighborhoods. Results. A substantial proportion of variation in land surface temperature existed between streets within census tracts (38%), explained by canopy, impervious surface, and albedo. Streets with higher land surface temperature had a greater likelihood of medical emergencies during heat advisories relative to the frequency of medical emergencies during non–heat advisory periods. There was no independent effect of the average land surface temperature of the census tract. Conclusions. The relationships among environmental characteristics, temperature, and health outcomes operate at the spatial scale of the street segment, calling for more geographically precise analysis and intervention.

Research on collective efficacy in urban neighborhoods has focused predominantly on whether a community can regulate local behavior and spaces and less on how they do so. This study pursues the latter question by examining the social regularities that create collective efficacy, measured as the behavioral composition of a neighborhood (i.e., the extent to which each individual contributes to a social regularity). This perspective is applied to the database of requests for nonemergency government services received by Boston, MA's 311 system in 2011 (>160,000 requests). The analysis categorized custodians who have used the system to combat physical disorder in the public space (e.g., requesting graffiti removal) into two groups-\"typical custodians\" who have made one or two requests in a year, and \"exemplars\" who have made three or more. A neighborhood' s collective efficacy in reporting public issues was identified through audits of sidewalk quality and streetlight outages. Analyses revealed a collaborative model of maintenance in which typical and exemplar custodians were each necessary and non-substitutable. A second analysis found that the two types of custodian were associated with different contextual factors, articulating two different pathways from demographic and social characteristics to collective efficacy, suggesting implications for theory and practice.

A technique that leverages duplicate records in crowdsourcing data could help to mitigate the effects of biases in research and services that are dependent on government records.