Recent ethnographic research reveals that gang dynamics in Chicago's Southside have evolved with decentralized micro-gang "set" factions and cross-gang interpersonal networks marking the contemporary landscape. However, standard police datasets lack the depth to analyze gang violence with such granularity. To address this, we employed a natural language processing strategy to analyze text from a Chicago gangs message board. By identifying proper nouns, probabilistically linking them to gang sets, and assuming social connections among names mentioned together, we created a social network dataset of 271 individuals across 11 gang sets. Using Louvain community detection, we found that these individuals often connect with gang-affiliated peers from various gang sets that are physically proximal. Hierarchical logistic regression revealed that individuals with ties to homicide victims and central positions in the overall gang network were at increased risk of victimization, regardless of gang affiliation. This research demonstrates that utilizing crowd-sourced information online can enable the study of otherwise inaccessible topics and populations.
In the early 20th century, Chicago School scholars proposed social disorganization theory, which posited that some neighborhoods experience more crime due to disadvantage, residential instability, and racial heterogeneity. However, contemporary studies using annual demographic data have found mixed evidence about the role of racial heterogeneity. We consider that, instead, racial heterogeneity among people in public spaces may act in real time to impact people’s willingness to exercise guardianship over crime. To test this possibility, a machine learning strategy was used to train and test a race prediction algorithm and estimate the race of 7,069 Twitter users who sent at least one geotagged ‘tweet’ from Boston in 2018. By measuring the locations of users’ tweets, we create a census tract-level measure representing ambient racial heterogeneity in the average week. Validation exercises indicated that inferred race of individual Twitter users was 90.7% accurate and produced a sample with comparable demographics to the Boston residential population. Results from negative binomial regression analyses suggested that ambient heterogeneity is a strong positive correlate of multiple indicators of violent and non-violent crime. Future research should develop theory and methodological strategies to further investigate how individual characteristics aggregate within ambient populations to explain crime differences across places.
Objectives: Some 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.
Methods: Data scraped from Yelp.com is used to identify 1,071 land parcels that had at least one business receiving reviews from 2014-2020. 911 dispatches for disturbances are linked to parcels and measured as the rate of events per Yelp reviewer in the average year. Hierarchical linear modeling-based variance decomposition techniques are used to evaluate how variation in disturbance rates is distributed across parcels, owners, census blocks, and census tracts. Negative binomial hierarchical linear 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.
Results: Commercial 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 is associated with increased disturbance rates. Additionally, parcel-level crime disturbance rates are explained by block features such as poverty and violent crime
Conclusions: Parcel, 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 for some guardians than others.
Prospect-refuge theory suggests that people prefer environments that offer both prospect, the ability to scan for resources, and refuge, a safe place to hide. Urban planners, architects and researchers alike have had a tendency to use prospect-refuge theory research on natural scenes to inform on the design of urban environments. Despite the large body of prospect-refuge theory research, the degree to which prospect and refuge impact preference in urban environments remain unclear. Here, we aim to first evaluate the relationship between prospect, refuge and preference for urban scene images. Secondly, we aim to evaluate the contributions of visual features and streetscape quality ratings to subjective ratings of prospect and refuge in order to create proxy values of prospect and refuge. Finally, we aim to understand how the proxy values impact preference for urban scenes, and if the proxy values created replicate the relationship between subjective measures of prospect, refuge and preference. First, we used participant ratings of prospect and refuge to predict participants' preference for 552 images of urban street scenes. Higher ratings of both prospect and refuge predicted greater image preference. We next used principal components analysis to summarize these images' low- and high-level visual features as well as participant ratings of streetscape qualities, such as walkability and disorder. Visual feature and streetscape quality principal components predicted prospect and refuge ratings in this first image set, providing “proxy measures' for prospect and refuge. In an independent set of 1119 images from Talen et al. (2022) for which prospect and refuge ratings were not available, we asked whether these proxies for prospect and refuge predicted preference. Findings replicated the effect that more refuge in an image predicts more preference. However, the proxy measure of prospect did not predict preference. In summary, our results show that refuge ratings do relate to preferences in urban environments, which extends prospect-refuge theory to more urban environments. Future work is needed to understand if prospect has different implications in more urban environments.
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: 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.
Research examining the effects of physical environments on individual behaviors often focuses on self-reported exposures or spatially-derived aggregate exposures. However, to gain a more comprehensive understanding of the environmental and economic drivers of behavior, a holistic approach encompassing multiple scales of analysis is needed. To better understand the effects of physical environmental factors on impulsivity and aggression across scales, we leveraged an experience sampling dataset collected in May-September 2022 (N=382) and public data aggregated at the neighborhood level in Chicago. Analysis of participants' trait impulsivity and aggression were collected, along with economic hardship and environmental exposures using both participant-reported and neighborhood-level factors. Significant positive associations were found between impulsivity and exposure to heat stress at home and via urban heat island effects. Aggression was related to greater economic hardship and less tree canopy at the neighborhood level, though positively related to having urban parks. Exploratory analyses of the subtypes of impulsivity and aggression demonstrated that while home heat stress was predictive of all three subtypes of impulsivity, financial hardship was only associated with lack of planning ahead, but not inattentiveness or the tendency to act without thinking. Additionally, exposure to the urban heat island effect was associated with more verbal aggression and trait anger, and economic hardship was only predictive of physical aggression (not verbal, anger, or hostility). While correlational in nature, these results demonstrate that environmental exposures are associated with individual differences in impulsivity and aggression across multiple levels of analysis.
Certain places generate inordinate amounts of crime and disorder. We examine how places differ in their nature of crime and disorder, with three objectives: (1) identifying a typology of profiles of crime and disorder; (2) assessing whether different forms of crime and disorder co-locate at parcels; and (3) determining whether problematic parcels explain crime and disorder across neighborhoods. The study uses 911 and 311 records to quantify physical and social disorder and violent crime at residential parcels in Boston, MA (n = 81,673). K-means cluster analyses identified the typology of problematic parcels and how those types were distributed across census block groups. Cluster analysis identified five types of problematic parcels, four specializing in one form of crime or disorder and one that combined all issues. The second cluster analysis found that the distribution of problematic parcels described the spectrum from low- to high-crime neighborhoods, plus commercial districts with many parcels with public physical disorder. Problematic parcels modestly explained levels of crime across neighborhoods. The results suggest a need for diverse intervention strategies to support different types of problematic parcels; and that neighborhood dynamics pertaining to crime are greater than problematic properties alone.
Research on relevance of race and community context for police shooting mortality is underdeveloped. We collected data on 623 police-involved shootings in the state of Texas to examine which incident- and county-level factors are associated with shooting outcomes. In doing so, we incorporate a novel incident-level variable: distance from incident to trauma hospitals. Results from hierarchical linear models suggest that the lethality of police-involved shootings is positively correlated with the number of officers and citizen possession a deadly weapon. Additional models indicate that community violent crime rates are the only contextual factor associated with both fatal and non-fatal shootings.
A pandemic, like other disasters, changes how systems work. In order to support research on how the COVID-19 pandemic impacted the dynamics of a single metropolitan area and the communities therein, we developed and made publicly available a “data-support system” for the city of Boston. We actively gathered data from multiple administrative (e.g., 911 and 311 dispatches, building permits) and internet sources (e.g., Yelp, Craigslist), capturing aspects of housing and land use, crime and disorder, and commercial activity and institutions. All the data were linked spatially through BARI’s Geographical Infrastructure, enabling conjoint analysis. We curated the base records and aggregated them to construct ecometric measures (i.e., descriptors of a place) at various geographic scales, all of which were also published as part of the database. The datasets were published in an open repository, each accompanied by a detailed documentation of methods and variables. We anticipate updating the database annually to maintain the tracking of the records and associated measures.
This is the abstract of publication 3.
Objectives: Test the reliability of geotagged Twitter data for estimating block-level population metrics across place types. Evaluate whether the proportion of Twitter users on a block at a given time who are local residents, inter-metro commuters, or tourists is correlated with incidences of public violence and private conflict for four different time periods: weekday days, weekday nights, weekend days, and weekend nights.
Methods: DBSCAN* machine learning technique is used to estimate the home clusters of 54,249 Twitter users who sent at least one geotagged tweet in Boston. Public violence and private conflict are measured using geocoded 911 dispatches. ANOVA models are used to evaluate how the presence of our three groups of interests varies across three types of block-level land usage. Hierarchical linear regression models are used to evaluate whether the proportion of commuters and tourists at census tract- and block-levels are predictive of crime events across the four time periods of interest.
Results: We find evidence that Twitter data has limited reliability across residential blocks due to data sparseness. For non-residential blocks, we find that commuter and tourist presence at the block-level are positively associated with both public violence and private conflict, but that these effects are not stable across time periods. Commuters and tourists only effect violence during weekday days, and the effects of commuters and tourists on private conflict are only statistically significant during weekday days and weekend days.
Conclusions: Consistent with routine activities and crime pattern theories, the influx of outsiders in a given location impacts the likelihood of crime occurring there. While we find that data from Twitter users can be valuable for measuring block-level ambient populations, it appears this is not true for residential blocks. Future research may further consider how the characteristics of Twitter users may inform spatial patterns in crime.
This is the abstract of publication 3.
Purpose: Despite the rich history of empirical research on neighborhood collective efficacy, studies considering the factors that contribute to collective efficacy formation at the individual level have yet to account for family members’ perceptions of collective efficacy. This study examines whether individuals form perceptions of neighborhood collective efficacy through knowledge of their geographic locales or via the intra-familial transmission of perceptions of collective efficacy.
Methods: This study appends information from the following three distinct samples of adults in the Project on Human Development in Chicago Neighborhoods (PHDCN): 349 young adult respondents, their primary caregivers (n = 349), and an independent sample of over 8000 adults distributed across 146 neighborhoods. Interviews with respondents (average age 20 years; 53.87% female) and their primary caregivers were conducted from 2000 to 2002. Regression analysis adjusting for clustering and mediation macros was utilized to examine the research questions.
Results: At baseline, neighborhood collective efficacy was associated with respondents’ perceptions of collective efficacy. The impact of neighborhood collective efficacy, however, was mediated completely by parents’ perceptions of collective efficacy. Parents’ perceptions of collective efficacy, family support, and concentrated disadvantage were the strongest predictors of respondents’ perceptions of collective efficacy.
Conclusion: Findings suggest that neighborhood collective efficacy can be altered through family processes as well as by changing the structural characteristics of broader social settings. Increasing collective efficacy—and social capital more generally—may be best facilitated intergenerationally from parents to their children.
Objectives: To examine the extent to which adults’ and youths’ perceptions of collective efficacy align, the shared and unique correlates of adults’ and youths’ perceptions, and the effects of adults’ and youths’ perceptions on youths’ violence.
Method: Descriptive analysis, hierarchical linear modeling, and spatial analysis analyze 1,636 youths from the 2008 Boston Youth Survey and 1,677 adults distributed across 85 neighborhoods from the 2008 Boston Neighborhood Study.
Results: Descriptive analysis indicates that Boston adults’ and youths’ perceptions are largely divergent. Spatial analysis indicates that there is not clustering of either adults’ or youths’ perceptions across Boston neighborhoods. Multilevel models indicate that adults’ and youths’ perceptions exhibit divergent etiology: Adults’ perceptions align closely with neighborhood collective efficacy and with their own neighborhood perceptions, while youths’ perceptions are largely a function of individual differences (race and age) and sociobehavioral factors (social support and educational expectations). Youths’ perceptions of collective efficacy, rather than aggregated adults’ perceptions of collective efficacy, are inversely associated with youths’ violence.
Conclusions: Adults’ and youths’ perceptions of collective efficacy represent distinct constructs. Research should focus on the divergent processes through which adults’ and youths’ perceptions are generated and the differential effects of adults’ and youths’ perceptions on youths’ behaviors.