Spatial Regression Models in Criminology: Modeling Social Processes in the Spatial Weights Matrix

Tita, George E.; Radil, Steven M.

doi:10.1007/978-0-387-77650-7_6

Cited by 39 publications

(36 citation statements)

References 54 publications

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“…If criminal activities are solely determined by the structural factors included in a regression model there should be no spatial dependence beyond that generated by structural similarities of regions that are in close geographic proximity (Baller et al, 2001). However, spatial clustering typically cannot be completely explained by common measures of structural similarity between geographical regions (see, e.g., Moreno and Sampson, 1997, Moreno et al, 2001, Baller, et al, 2001, and Tita and Radil, 2013.…”

Section: Spatial Dependencementioning

confidence: 99%

“…In Table 3 (panel 1 to 4), we report the results of four auxiliary regressions for the estimated random eectsτ i as obtained from the baseline model by Equations (18) typically occur in disadvantaged communities and regions with a large population size (see, e.g., Baller et al, 2001, Helbich and Arsanjani, 2014, Kubrin, 2003, and Tita and Radil, 2013). …”

Section: Random Eects Covariatesmentioning

confidence: 99%

“…See, e.g., Ratclie (2013), Bernasco and Elers (2013), Tita and Radil (2013), Roth et al (2013) or Li et al (2014) for recent contributions, surveys and extensive lists of references.…”

Section: Introductionmentioning

confidence: 99%

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Likelihood Based Inference and Prediction in Spatio-Temporal Panel Count Models for Urban Crimes

2015

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. recently developed ecient importance sampling techniques applicable to high-dimensional spatial models with sparse precision matrices. Our estimation results conrm socio-economic explanations for crime and, foremost, the broken-windows hypothesis, whereby less severe crimes in a region is a leading indicator for severe crimes. In addition to ML parameter estimates, we compute several other statistics of interest for law enforcement such as elasticities (idiosyncratic, total, short-term as well as long-term) of severe crimes w.r.t. less severe crimes, one-month-ahead out-of-sample forecasts, predictive cumulative distribution functions and validation test statistics based on these cdf's. Terms of use: Documents inJEL classication: C15; C23; C25; C51; C53; K42; R15.

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Section: Spatial Dependencementioning

confidence: 99%

Section: Random Eects Covariatesmentioning

confidence: 99%

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Likelihood Based Inference and Prediction in Spatio-Temporal Panel Count Models for Urban Crimes

2015

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“…This lack of independence makes the use of OLS regression techniques inappropriate. While there are a variety of methods to address spatially autocorrelated data, simultaneous autoregressive (SAR) models have become the most popular, especially spatial error models and spatial lag (or ''dependence'') models (see Tita and Radil 2010a).…”

Section: Ecological Studies Of Crime: the Use Of Spatial Regression Mmentioning

confidence: 99%

Making Space for Theory: The Challenges of Theorizing Space and Place for Spatial Analysis in Criminology

Tita

Radil

2010

J Quant Criminol

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“…HELBICH et al 2013a). To receive unbiased and correct inference results, spatial autocorrelation must be explicitly considered in statistical analysis (TITA & RADIL 2011). This is non-trivial in count regressions, where the response variable used is the number of crimes within a spatial unit (e.g.…”

Section: Introductionmentioning

confidence: 99%

Driving Forces of Non-Violent Crime in Houston, TX: A Spatially Filtered Negative Binomial Model

Helbich

Arsanjani

Leitner³

2013

GI_Forum 2013 – Creating the GISociety

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The analysis and understanding of spatial crime patterns is crucial for law enforcements to improve strategic and tactical decision-making. In this context, generalized linear models, such as count regressions, are commonly applied. These non-spatial models are challenged by spatial autocorrelation effects, contradicting fundamental model assumptions. Therefore, the purpose of this research is to present a spatially explicit approach, which combines a negative binomial model and spatial filtering to explain the spatial distribution of nonviolent offences in Houston, TX, for the year 2010. The results provide evidence that the non-spatial negative binomial model is biased while the supplementary consideration of a spatial filter is capable to absorb these undesirable spatial effects and results in a wellspecified regression model. Moreover, besides the significant importance of space in the explanation of the non-violent crime patterns, only the percentage of renter-occupied housing units and the percentage of Asian population are significantly related to the crime. The former covariate has a stimulating effect while the latter has an inhibiting effect.

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Spatial Regression Models in Criminology: Modeling Social Processes in the Spatial Weights Matrix

Cited by 39 publications

References 54 publications

Likelihood Based Inference and Prediction in Spatio-Temporal Panel Count Models for Urban Crimes

Likelihood Based Inference and Prediction in Spatio-Temporal Panel Count Models for Urban Crimes

Making Space for Theory: The Challenges of Theorizing Space and Place for Spatial Analysis in Criminology

Driving Forces of Non-Violent Crime in Houston, TX: A Spatially Filtered Negative Binomial Model

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