Early Warning System for Temporary Crime Hot Spots

Gorr, Wilpen L.; Lee, YongJei

doi:10.1007/s10940-014-9223-8

Cited by 63 publications

(68 citation statements)

References 64 publications

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“…Weisburd (2015), using crime data across several cities, argued for a law of crime concentration, stating that a large percentage of crime occurs within just a few percent of street segments (lengths of road between two intersections) in a given city. Bolstering this, Gorr and Lee (2015) demonstrated that a policing program based on both chronic hot spots and temporary flare-ups can be more effective than a program based on only one or the other.…”

Section: Crime Forecastingmentioning

confidence: 95%

A Review of Self-Exciting Spatio-Temporal Point Processes and Their Applications

Reinhart¹

2018

Statist. Sci.

124

125

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Self-exciting spatio-temporal point process models predict the rate of events as a function of space, time, and the previous history of events. These models naturally capture triggering and clustering behavior, and have been widely used in fields where spatio-temporal clustering of events is observed, such as earthquake modeling, infectious disease, and crime. In the past several decades, advances have been made in estimation, inference, simulation, and diagnostic tools for self-exciting point process models. In this review, I describe the basic theory, survey related estimation and inference techniques from each field, highlight several key applications, and suggest directions for future research.

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Section: Crime Forecastingmentioning

confidence: 95%

A Review of Self-Exciting Spatio-Temporal Point Processes and Their Applications

Reinhart¹

2018

Statist. Sci.

124

125

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“…Point data are referred to as disaggregate data because each point represents the location of an incident. Various approaches have been developed to analyze crime based on point data, such as kernel density [18][19][20] and Spatial Scan Statistics [20][21][22] for hot spot analysis. In order to investigate the relationship between crime events and the influential factors, crime incidents are usually aggregated into different spatial units, such as census tract or block groups.…”

Section: Introductionmentioning

confidence: 99%

Modeling Spatial Effect in Residential Burglary: A Case Study from ZG City, China

Chen

Liu

Zhou

et al. 2017

IJGI

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Abstract:The relationship between burglary and socio-demographic factors has long been a hot topic in crime research. Spatial dependence and spatial heterogeneity are two issues to be addressed in modeling geographic data. When these two issues arise at the same time, it is difficult to model them simultaneously. A cross-comparison of three models is presented in this study to identify which spatial effect should be addressed first in crime analysis. The negative binominal model (NB), Bayesian hierarchical model (BHM) and the geographically weighted Poisson regression model (GWPR) were implemented based on a three-year residential burglary data set from ZG, China. The modeling result shows that both BHM and GWPR outperform NB as they capture either of the spatial effects. Compared to the NB model, the mean absolute deviation (MAD) of BHM and GWPR was decreased by 83.71% and 49.39%, the mean squared error (MSE) of BHM and GWPR was decreased by 97.88% and 77.15%, and the R 2 d of BHM and GWPR was improved by 26.7% and 19.1%, respectively. In comparison with BHM and GWPR, BHM fits the data better with lower MAD, MSE and higher R 2 d . The empirical analysis indicates that the percentage of renter population, percentage of people from other provinces, bus line density, and bus stop density have a significantly positive impact on the number of residential burglaries. The percentage of residents with a bachelor degree or higher, on the other hand, is negatively associated with the number of residential burglaries.

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“…22 Hot spots can, of course, also be temporary. An excellent accounting of efforts to predict temporary hot spots in Pittsburgh can be found in Gorr and Lee (2015). and Brown 1981).…”

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confidence: 99%

Criminal Deterrence: A Review of the Literature

Chalfin

McCrary

2017

Journal of Economic Literature

509

279

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We review economics research regarding the effect of police, punishments, and work on crime, with a particular focus on papers from the last twenty years. Evidence in favor of deterrence effects is mixed. While there is considerable evidence that crime is responsive to police and to the existence of attractive legitimate labor-market opportunities, there is far less evidence that crime responds to the severity of criminal sanctions. We discuss fruitful directions for future work and implications for public policy. (JEL J64, K42)

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Early Warning System for Temporary Crime Hot Spots

Cited by 63 publications

References 64 publications

A Review of Self-Exciting Spatio-Temporal Point Processes and Their Applications

A Review of Self-Exciting Spatio-Temporal Point Processes and Their Applications

Modeling Spatial Effect in Residential Burglary: A Case Study from ZG City, China

Criminal Deterrence: A Review of the Literature

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