Modeling the Social and Spatial Proximity of Crime: Domestic and Sexual Violence Across Neighborhoods

Kelling, Claire; Graif, Corina; Korkmaz, Gizem; Haran, Murali

doi:10.1007/s10940-020-09454-w

Cited by 18 publications

(13 citation statements)

References 125 publications

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“…In doing so, we capture the economic structure in which people on parole are embedded, and how this structure has consequences for finding a job. Accordingly, commuting flows between neighborhoods represent economic links between neighborhoods (see also Graif et al 2017, Kelling et al 2020, 2 and by examining the structure of these links, we capture how the neighborhood of a person on parole is (or is not) integrated into the labor market. Somewhat similarly, another related line of research focuses on the activity space of people whereby their activity and commuting patterns result in ties between neighborhoods (Browning et al 2017;, suggesting that these activity patterns may have consequences for joblessness (e.g., awareness of job openings).…”

Section: Implications For Joblessness While On Parolementioning

confidence: 99%

“…People on parole who live in neighborhoods with greater outdegree have more economic engagement and integration within the structure of the network of neighborhoods. These communities with stronger engagement and outreach may have more institutional resources (e.g., see Kelling et al 2020), as well as access to opportunities through transportation channels. When people on parole live in a more advantageous structural position within the network of neighborhoods, they may also have more access to resources and information about the labor market (e.g., see Bellair 1997, Hunter 1985.…”

Section: The Network Of Neighborhoods and Joblessness For People On Parolementioning

confidence: 99%

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The Network of Neighborhoods and Geographic Space: Implications for Joblessness While on Parole

Boessen

Hipp

2021

J Quant Criminol

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Irvine. His research interests focus on how neighborhoods change over time, how that change both affects and is affected by neighborhood crime, and the role networks and institutions play in that change. He approaches these questions using quantitative methods as well as social network analysis. He has published substantive work in such journals as American Sociological Review, Criminology, Social Forces, Social Problems, Social Networks, City & Community, Urban Studies and Journal of Urban Affairs. He has published methodological work in such journals as Sociological Methodology, Psychological Methods, and Structural Equation Modeling. Acknowledgements: We thank Marisa Omori for her input on an earlier draft of this paper.

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Section: Implications For Joblessness While On Parolementioning

confidence: 99%

Section: The Network Of Neighborhoods and Joblessness For People On Parolementioning

confidence: 99%

The Network of Neighborhoods and Geographic Space: Implications for Joblessness While on Parole

Boessen

Hipp

2021

J Quant Criminol

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“…It is well known that in spatial disease mapping, the effect of a covariate may be confounded with the spatial random effect leading to biased estimates of the fixed effects and to variance inflation (Reich et al 2006;Hodges and Reich 2010). Consequently, if a risk factor is included in the model, the estimation may not be valid (see for example Kelling et al 2020). This is even worse in the spatio-temporal setting where confounding may be present due to the spatial, temporal, and the interaction random effects.…”

Section: Discussionmentioning

confidence: 99%

Bayesian inference in multivariate spatio-temporal areal models using INLA: analysis of gender-based violence in small areas

Vicente

Goicoa

Ugarte

2020

Stoch Environ Res Risk Assess

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Multivariate models for spatial count data are currently receiving attention in disease mapping to model two or more diseases jointly. They have been thoroughly studied from a theoretical point of view, but their use in practice is still limited because they are computationally expensive and, in general, they are not implemented in standard software to be used routinely. Here, a new multivariate proposal, based on the recently derived M models for spatial data, is developed for spatio-temporal areal data. The model takes account of the correlation between the spatial and temporal patterns of the phenomena being studied, and it also includes spatio-temporal interactions. Though multivariate models have been traditionally fitted using Markov chain Monte Carlo techniques, here we propose to adopt integrated nested Laplace approximations to speed up computations as results obtained using both fitting techniques were nearly identical. The techniques are used to analyse two forms of crimes against women in India. In particular, we focus on the joint analysis of rapes and dowry deaths in Uttar Pradesh, the most populated Indian state, during the years 2001–2014.

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“…Spatial dependence and spatial heterogeneity violate the assumptions underlying classical regression models, and thus, these spatial effects should be addressed when modeling geographic data. In crime analysis, conventional spatial regression models dealing with spatial autocorrelation are the spatial lag and spatial error models [39,40]. A less common approach to deal with it is the Cliff-Ord spatial autoregressive model with spatial autoregressive disturbances [41].…”

Section: Literature On Geography Of Crime Analysismentioning

confidence: 99%

Spatial Modeling and Analysis of the Determinants of Property Crime in Portugal

Tavares

Costa

2021

IJGI

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Many researchers have unraveled innovative ways of examining geographic information to better understand the determinants of crime, thus contributing to an improved understanding of the phenomenon. Property crimes represent more than half of the crimes reported in Portugal. This study investigates the spatial distribution of crimes against property in mainland Portugal with the primary goal of determining which demographic and socioeconomic factors may be associated with crime incidence in each municipality. For this purpose, Geographic Information System (GIS) tools were used to analyze spatial patterns, and different Poisson-based regression models were investigated, namely global models, local Geographically Weighted Poisson Regression (GWPR) models, and semi-parametric GWPR models. The GWPR model with eight independent variables outperformed the others. Its independent variables were the young resident population, retention and dropout rates in basic education, gross enrollment rate, conventional dwellings, Guaranteed Minimum Income and Social Integration Benefit, purchasing power per capita, unemployment rate, and foreign population. The model presents a better fit in the metropolitan areas of Lisbon and Porto and their neighboring municipalities. The association of each independent variable with crime varies significantly across municipalities. Consequently, these particularities should be considered in the design of policies to reduce the rate of property crimes.

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Modeling the Social and Spatial Proximity of Crime: Domestic and Sexual Violence Across Neighborhoods

Cited by 18 publications

References 125 publications

The Network of Neighborhoods and Geographic Space: Implications for Joblessness While on Parole

The Network of Neighborhoods and Geographic Space: Implications for Joblessness While on Parole

Bayesian inference in multivariate spatio-temporal areal models using INLA: analysis of gender-based violence in small areas

Spatial Modeling and Analysis of the Determinants of Property Crime in Portugal

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