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Open Access Publications from the University of California

Scholarly Works (9 results)

  • Thesis
  • Peer Reviewed
UC Irvine Electronic Theses and Dissertations (2020)

A large body of literature documents that there is a marked increase in incarceration and people on parole in the United States until fairly recently. Empirical research has yet to sufficiently explore how people on parole returning to communities may affect neighborhood crime rates or neighborhood crime in turn influences parolees’ integration into communities. Drawing on recent scholarship on mass incarceration, prisoner reentry, and macrolevel predictors of crime, this study examines the reciprocal relationship between returning parolees and neighborhood crime rates using a large sample of parolees returning to neighborhoods in the five largest cities in Texas (Austin, Dallas, Fort Worth, Houston, and San Antonio) over a nine-year time period (2003 - 2011). I employ cross-lagged regression models using structural equation modeling method to explore whether returning parolees in the former year affect the neighborhood crime

rates the following year and vice versa. To fully capture these reciprocal effects, I propose a more accurate approach for measuring parolees by capturing their exposure in the community – days parolees are present in the neighborhood – and compare it to the most common approach of counting the number of parolees. Results indicate distinctions based on different measures: numbers of parolees do not have significant effect on either violent or property crime, but days of parolees in the neighborhood are significantly associated with reductions in neighborhood crime. The results suggest a strong selection effect of parolees. I highlight the dynamics of parolee reentry and neighborhoods in the era of mass incarceration.

    Cover page: Reciprocal Relationship between Returning Parolees and Neighborhood Crime Rates in Texas: A Longitudinal Study of Prisoner Reentry
    • Article
    • Peer Reviewed
    UC Irvine Previously Published Works (2022)
    Whereas existing ecology of crime research frequently uses a cross-sectional design, an open question is whether theories underlying such studies will operate similarly in longitudinal research. Using latent trajectory models and longitudinal data in half-mile egohoods from the Southern California region over a 10-year period (2000–2010), we explore this question and assess whether the changes in key measures of social disorganization theory are related to changes in violent or property crime through three possible relationships: 1) a monotonic relationship, 2) an asymmetric relationship, and 3) a perturbation relationship in which any change increases crime. We find evidence that measures can exhibit any of these three possible relationships, highlighting the importance of not assuming monotonic relationships. Most frequently observed are asymmetric relationships, which we posit are simultaneously capturing more than one theoretical process of neighborhoods and crime. Specific findings include asymmetric relationships between change in concentrated disadvantage, racial/ethnic minority composition, or population and violent crime, as well as relationships between change in Asian composition or population and property crime. We consider how this strategy opens a needed area of future research assessing how measures for other theories operate as environments change.
      Cover page: Improving or declining: What are the consequences for changes in local crime?*
      • Thesis
      • Peer Reviewed
      UC Irvine Electronic Theses and Dissertations (2024)

      This dissertation seeks to address the fundamental question of how crime changes over time and space, and why certain areas experience higher levels of crime than others. While crime is not randomly distributed and tends to concentrate in specific locations within cities, there is a lack of comprehensive understanding regarding how crime patterns evolve across diverse urban contexts. Existing research primarily focuses on structural neighborhood characteristics, overlooking the significance of longitudinal crime patterns and the impact of changing neighborhood dynamics. Moreover, the literature predominantly examines popular big cities, neglecting the diversity of urban landscapes. To bridge these gaps, this dissertation utilizes multiple data sources to investigate crime changes and spatial patterns from 2010 to 2018 across a wide array of cities in the United States. By exploring how crime evolves at smaller geographic units (i.e., street segments) and examining the reciprocal relationship between neighborhood crime and neighborhood change, this dissertation aims to provide valuable insights into the complexities of crime and place dynamics.This dissertation addresses three main inquiries. Firstly, it investigates crime trajectories at the street segment level across various U.S. cities from 2010 to 2018, exploring the relationship between neighborhood characteristics and crime trajectories. Additionally, it examines how key neighborhood structures, as outlined in social disorganization theory, may shape crime trajectories differently across different city contexts. Notably, a small percentage of street segments is responsible for a significant portion of total violent and property crime. Segments with higher concentrated disadvantage are more likely to exhibit high crime trajectories in medium and large cities, stagnant cities, and low employment cities, while residential stability correlates with a higher likelihood of crime-free or low crime trajectories in small or growing cities. Secondly, the dissertation aims to analyze how changes in neighborhood structures over the decade influence crime changes at the egohoods level across cities. Potential patterns include a monotonic relationship, an asymmetric relationship, or perturbations where alterations in environmental features lead to an increase in crime. Lastly, the third inquiry explores the reciprocal relationship between neighborhood crime and neighborhood changes (housing investment, home values, and vacancies) across diverse city contexts. Higher percent home purchases are associated with lower violent crime in growing and high employment cities. Neighborhood crime has a significant negative impact on home values across all city contexts, leading to decreased demand for housing and increased vacancy rates in small cities but fewer vacancies in medium and large cities. Overall, this research highlights the need for customized crime prevention strategies that align with the distinct requirements and attributes of neighborhoods across diverse urban contexts.

        • Article
        • Peer Reviewed
        UC Irvine Previously Published Works (2024)
        Drawing on recent scholarship on mass incarceration and prisoner re-entry, this study examines the reciprocal relationship between returning parolees and neighbourhood crime rates in five large cities in Texas. Besides the more common approach of counting the number of people on parole in communities (parolee concentration), we propose a novel approach for measuring people on parole by capturing their exposure in the community as parolee embeddedness (i.e., the cumulative number of days that people on parole resided in the neighbourhood). Results show that parolee concentration has a significant positive effect on both violent and property crime, but parolee embeddedness is significantly associated with reductions in violent and property crime. Our findings detect different effects depending on the measurement of people on parole and their community context, illustrating the need to better understand the dynamics of parolee re-entry in the era of mass incarceration.
          Cover page: Parolee concentration, parolee embeddedness, and the reciprocal relationship with crime rates: A longitudinal study of neighbourhoods and re-entryCreative Commons 'BY' version 4.0 license
          • Article
          • Peer Reviewed
          UC Irvine Previously Published Works (2022)
          Using social network data from the American Social Fabric Project (ASFP), this study examines how the distance to social alters may lead to different perceptions of neighborhood and city attachment among urban versus rural residents, and considers which types of relations play influential roles in shaping attachment. Overall, a key finding is that having more local neighborhood ties is positively associated with attachment at both the neighborhood level and city level, holding for any social relationship in our sample and for urban and rural environments. Notably, long distance ties are not irrelevant for attachment; rather, we see that long distance ties are associated with greater neighborhood and city attachment. Among different social relations measured, neighborhood safety ties consistently show the strongest positive relationship with neighborhood and city attachment. Surprisingly, we find that the spatial distribution of social ties appears more consequential for attachment in the rural sample than it does in the urban sample. Further, geographically dispersed ties also matter for urban versus rural settings: physically close and midrange core discussion ties are associated with weaker attachment for urban residents, whereas they do not affect rural residents’ perceptions of attachment.
            Cover page: Does the spatial distribution of social ties impact neighborhood and city attachment? Differentials among urban/rural contexts
            • Article
            • Peer Reviewed
            UC Irvine Previously Published Works (2024)
            Abstract: Research finds that immigration and crime are not related across neighbourhoods, contrary to social disorganization theory and consistent with the immigration revitalization thesis. This research, however, is largely silent as to any possible nonlinear effects. Yet social theory offers sound reasons for why the immigration–crime association may be nonlinear; explanations, including immigrant/ethnic enclave theory and immigrant victimization theory, underscore potential concentration effects—albeit in different ways. Using a novel dataset with information on crime in over 15,000 neighbourhoods across a diverse range of US cities, we examine whether or not the immigration–crime association is nonlinear. We find that for both violent and property crime, a nonlinear relationship best captures the relationship. In additional analyses, we determine the theoretical perspective with which the findings are most consistent.
              Cover page: Immigration and Crime: Is the Relationship Nonlinear?Creative Commons 'BY' version 4.0 license
              • Article
              • Peer Reviewed
              UC Irvine Previously Published Works (2024)
              Geospatial population data are typically organized into nested hierarchies of areal units, in which each unit is a union of units at the next lower level. There is increasing interest in analyses at fine geographic detail, but these lowest rungs of the areal unit hierarchy are often incompletely tabulated because of cost, privacy, or other considerations. Here, the authors introduce a novel algorithm to impute crosstabs of up to three dimensions (e.g., race, ethnicity, and gender) from marginal data combined with data at higher levels of aggregation. This method exactly preserves the observed fine-grained marginals, while approximating higher-order correlations observed in more complete higher level data. The authors show how this approach can be used with U.S. census data via a case study involving differences in exposure to crime across demographic groups, showing that the imputation process introduces very little error into downstream analysis, while depicting social process at the more fine-grained level.
                Cover page: Marginal-Preserving Imputation of Three-Way Array Data in Nested Structures, with Application to Small Areal UnitsCreative Commons 'BY' version 4.0 license
                • Article
                • Peer Reviewed
                UC Irvine Previously Published Works (2020)
                Standard epidemiological models for COVID-19 employ variants of compartment (SIR or susceptible-infectious-recovered) models at local scales, implicitly assuming spatially uniform local mixing. Here, we examine the effect of employing more geographically detailed diffusion models based on known spatial features of interpersonal networks, most particularly the presence of a long-tailed but monotone decline in the probability of interaction with distance, on disease diffusion. Based on simulations of unrestricted COVID-19 diffusion in 19 US cities, we conclude that heterogeneity in population distribution can have large impacts on local pandemic timing and severity, even when aggregate behavior at larger scales mirrors a classic SIR-like pattern. Impacts observed include severe local outbreaks with long lag time relative to the aggregate infection curve, and the presence of numerous areas whose disease trajectories correlate poorly with those of neighboring areas. A simple catchment model for hospital demand illustrates potential implications for health care utilization, with substantial disparities in the timing and extremity of impacts even without distancing interventions. Likewise, analysis of social exposure to others who are morbid or deceased shows considerable variation in how the epidemic can appear to individuals on the ground, potentially affecting risk assessment and compliance with mitigation measures. These results demonstrate the potential for spatial network structure to generate highly nonuniform diffusion behavior even at the scale of cities, and suggest the importance of incorporating such structure when designing models to inform health care planning, predict community outcomes, or identify potential disparities.
                  Cover page: Spatial heterogeneity can lead to substantial local variations in COVID-19 timing and severity
                  • Article
                  UC Irvine Previously Published Works (2020)
                  Standard epidemiological models for COVID-19 employ variants of compartment (SIR) models at local scales, implicitly assuming spatially uniform local mixing. Here, we examine the effect of employing more geographically detailed diffusion models based on known spatial features of interpersonal networks, most particularly the presence of a long-tailed but monotone decline in the probability of interaction with distance, on disease diffusion. Based on simulations of unrestricted COVID-19 diffusion in 19 U.S cities, we conclude that heterogeneity in population distribution can have large impacts on local pandemic timing and severity, even when aggregate behavior at larger scales mirrors a classic SIR-like pattern. Impacts observed include severe local outbreaks with long lag time relative to the aggregate infection curve, and the presence of numerous areas whose disease trajectories correlate poorly with those of neighboring areas. A simple catchment model for hospital demand illustrates potential implications for health care utilization, with substantial disparities in the timing and extremity of impacts even without distancing interventions. Likewise, analysis of social exposure to others who are morbid or deceased shows considerable variation in how the epidemic can appear to individuals on the ground, potentially affecting risk assessment and compliance with mitigation measures. These results demonstrate the potential for spatial network structure to generate highly non-uniform diffusion behavior even at the scale of cities, and suggest the importance of incorporating such structure when designing models to inform healthcare planning, predict community outcomes, or identify potential disparities.
                    Cover page: Spatial Heterogeneity Can Lead to Substantial Local Variations in COVID-19 Timing and SeverityCreative Commons 'BY' version 4.0 license
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