The Effect of Commuting on City-Level Crime Rates

Stults, Brian J.; Hasbrouck, Matthew

doi:10.1007/s10940-015-9251-z

Cited by 49 publications

(55 citation statements)

References 32 publications

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“…Thus, the larger number of potential targets and offenders in an area does indeed seem related to greater increases in crime in the cities within that area (Glaeser and Sacerdote 1999;Glaeser, Sacerdote, and Scheinkman 1996). This implies that a broader population beyond that of just the city impacts city crime rates (Stults and Hasbrouck 2015). On the other hand, increasing population in the city-either during the current decade or over a much longer period of time-is actually associated with crime decreases.…”

Section: Discussionmentioning

confidence: 99%

“…Whereas much of this research has focused on the relationship between key socio-demographic characteristics and levels of crime at a single point in time in cities (Chamlin and Cochran 1997;Sampson 1987), a smaller body of research has explored how the economic environment and levels of crime can co-evolve (Ousey and Kubrin 2009;Stults and Hasbrouck 2015). Nonetheless, a consistent feature of this literature is an almost exclusive focus on very large geographic units such as MSAs, counties, or large cities, typically with at least 100,000 population.…”

Section: Changing Crime Levels In Citiesmentioning

confidence: 99%

“…We also take into account the characteristics of the county in which these cities are located, and how that context is changing. Furthermore, we account for possible endogeneity between some of these socio-demographic measures and crime rates by estimating models in which the covariates at a point in time are used to predict the change in crime rates over the subsequent decade (Stults and Hasbrouck 2015). Finally, to account for possible macro structural shifts over this long period of time, our analytic technique allows us to assess if these parameters have changed over this time period.…”

Section: Changing Crime Levels In Citiesmentioning

confidence: 99%

See 2 more Smart Citations

Cities and the larger context: What explains changing levels of crime?

Hipp

Kane

2017

Journal of Criminal Justice

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This study explores whether the broader context in which a city is located impacts the change in crime levels over the subsequent decade. This study uses a wide range of cities (those with a population of at least 10,000), over a long period of time (from 1970 to 2010). We test and find that although cities with larger population and those surrounded by a county with a larger population typically experience larger increases in crime over the subsequent decade, cities experiencing an increase in population during the current decade experience crime decreases.The study finds that cities with higher average income experience greater subsequent crime decreases, and those surrounded by counties with larger unemployment increases experience crime increases. Higher levels of income inequality and racial/ethnic heterogeneity are associated with increasing crime rates, and increasing inequality and racial/ethnic heterogeneity in the surrounding county are associated with further increases. Furthermore, this relationship has strengthened since 1970, suggesting that both scales of inequality are even more important from a public safety perspective. Finally, we tested the time invariance of these relationships, and showed that the magnitude of the relationship between city-level inequality and increasing crime has increased over the study period.Keywords: cities, crime, longitudinal, inequality, racial/ethnic heterogeneity. Changing crime levels in cities BioJohn R. Hipp is a Professor in the departments of Criminology, Law and Society, and Sociology, at the University of California 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 an outcome measure -in the form of changes to the built environment, shifting patterns of employment, or the socioeconomic composition of places -and links these to drivers of change including policy, structural economic shifts, or preferences for how we use and travel across urban space. An important characteristic of existing macro-level studies is that they have almost never focused on how the broader metropolitan context in which cities are located might impact changes in crime. That is to say, the focus is often placed either on variation in crime across counties or MSAs, or variation in crime across a set of large cities -in both cases omitting the simultaneous relationship between a city's crime rate and the characteristics of both the city and the region in which it is situated. This suggests a needed area of research given that the larger context of a region, and how it is changing, likely has consequences for the level of crime in cities within that region.In raising the question of the relationship between socio-demographic characteristics and crime rates of cities, and how the larg...

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Section: Discussionmentioning

confidence: 99%

Section: Changing Crime Levels In Citiesmentioning

confidence: 99%

Section: Changing Crime Levels In Citiesmentioning

confidence: 99%

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Cities and the larger context: What explains changing levels of crime?

Hipp

Kane

2017

Journal of Criminal Justice

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“…We did not take into account daytime changes in population size due to commuting. This may influence the risk estimations and bias the assessment of contextual influences on crime risks in ecological crime studies as indicated by recent research [66][67][68][69]. Third, the measures used to represent socioeconomic status (i.e., the unemployment rate and percentage of highly educated) may not be powerful enough, as other related variables, such as average household income [27], percentage of individuals below the poverty line [70] and percentage of high occupational status [14], were not included.…”

Section: Discussionmentioning

confidence: 99%

Joint Modeling of Multiple Crimes: A Bayesian Spatial Approach

Liu

Zhu

2017

IJGI

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A multivariate Bayesian spatial modeling approach was used to jointly model the counts of two types of crime, i.e., burglary and non-motor vehicle theft, and explore the geographic pattern of crime risks and relevant risk factors. In contrast to the univariate model, which assumes independence across outcomes, the multivariate approach takes into account potential correlations between crimes. Six independent variables are included in the model as potential risk factors. In order to fully present this method, both the multivariate model and its univariate counterpart are examined. We fitted the two models to the data and assessed them using the deviance information criterion. A comparison of the results from the two models indicates that the multivariate model was superior to the univariate model. Our results show that population density and bar density are clearly associated with both burglary and non-motor vehicle theft risks and indicate a close relationship between these two types of crime. The posterior means and 2.5% percentile of type-specific crime risks estimated by the multivariate model were mapped to uncover the geographic patterns. The implications, limitations and future work of the study are discussed in the concluding section.

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“…This line of investigation pursues the area-based socioeconomic and land-use factors which are likely to promote or hinder the development of crime [2]. Sociological studies have investigated links between burglary and concentrated disadvantage [22,25], high population density [29,30], disadvantage [1,20,25], and residential instability [31][32][33], and many significant associations have been found. In an extensive study of 352 U.S. cities over 30 years, Hipp [34] discovered that the level of economic stratification had significant implications for burglary.…”

Section: Area-based Burglary Influencesmentioning

confidence: 99%

Modeling Spatial Interactions between Areas to Assess the Burglary Risk

Mburu

Bakillah

2016

IJGI

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It is generally acknowledged that the urban environment presents different types of risk factors, but how the structural effects of areas influence the risk levels in neighboring areas has been less widely investigated. This research assesses the local effects of burglary contributory factors on burglary over small areas in a large metropolitan region. A comparative framework is developed for analyzing the effects of geographic dependence on burglary rates, and for assessing how such dependence conditions the community context and the urban land use. A local indicators spatial autocorrelation analysis assesses burglaries over five years (2011)(2012)(2013)(2014)(2015) to identify risk clusters. Thereafter, effects of different variables (e.g., unemployment, building density) on burglary frequency are estimated in a series of regression models while controlling for changes in the risk levels of nearby surrounding areas. Results uncover strong evidence that the configuration of the surroundings influences risk. After controlling for area-based interaction, patterns are identified that contrast with the previous literature, such as lower burglary frequency in areas with higher tenancy in social housing units. Together the findings demonstrate that the spatial arrangement of areas is as crucial as contextual crime factors, particularly when assessing the risk for small areas.

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The Effect of Commuting on City-Level Crime Rates

Cited by 49 publications

References 32 publications

Cities and the larger context: What explains changing levels of crime?

Cities and the larger context: What explains changing levels of crime?

Joint Modeling of Multiple Crimes: A Bayesian Spatial Approach

Modeling Spatial Interactions between Areas to Assess the Burglary Risk

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