Space–time variability in burglary risk: A Bayesian spatio-temporal modelling approach

Li, Guangquan; Haining, Robert; Richardson, Sylvia; Best, Nicky

doi:10.1016/j.spasta.2014.03.006

“…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%

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

Liesenfeld

¹

,

Richard

²

,

Vogler

³

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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“…It is possible that the elephant poaching data that we used for this study only represent a proportion of the total poaching that occurred between 2002 and 2012. However, we mitigated this problem by utilizing a spatio-temporal Bayesian model, which provides a flexible framework for borrowing information over space and time from adjacent blocks by using spatial and temporal random effects (Li et al 2014). For example, the recorded elephant poaching incidences (Figure 2) and estimated poaching trend over time (Figure 3) are very different in blocks no.…”

Section: Discussionmentioning

confidence: 99%

“…For all models, we used Poisson regression with the log link function, as this function accounts for rare incidences (McCullagh and Nelder 1989, Torabi and Rosychuk 2012, Li et al 2014. Bayesian approaches combine observed data (i.e.…”

Section: Modeling Strategy and Analysismentioning

confidence: 99%

“…The prior for the regional time trend (ƴ) was a vague prior normal distribution with mean equal to zero and variance of 1000 (Law et al 2014). An Intrinsic Conditional Autoregressive Gaussian distribution (ICAR) was used to specify priors for spatial random effect s i and spatio-temporal interaction δ i (Torabi and Rosychuk 2012, Law et al 2014, 2015, Li et al 2014, Luan et al 2015. Under the ICAR specification, the means of s i and δ i for one block depend on the s i and δ i , respectively, of the neighboring blocks' distribution, where adjacency is defined as areas that have common borders (Torabi andRosychuk 2012, Luan et al 2015).…”

Section: Computational Detailsmentioning

confidence: 99%

“…Under the ICAR specification, the means of s i and δ i for one block depend on the s i and δ i , respectively, of the neighboring blocks' distribution, where adjacency is defined as areas that have common borders (Torabi andRosychuk 2012, Luan et al 2015). The amount of variation of s i and δ i is controlled by hyperparameters σ s 2 and σ δ 2 , respectively, and is contrariwise related to the number of neighbors of the ith block (Torabi and Rosychuk 2012, Law et al 2014, 2015, Li et al 2014, Luan et al 2015. In Bayesian approaches, priors assigned to hyperparameters (i.e.…”

Section: Computational Detailsmentioning

confidence: 99%

See 2 more Smart Citations

Assessing trends and seasonal changes in elephant poaching risk at the small area level using spatio-temporal Bayesian modeling

Rashidi

¹

,

Skidmore

²

,

Wang

³

et al. 2017

International Journal of Geographical Information Science

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Knowledge about changes in wildlife poaching risk at fine spatial scale can provide essential background intelligence for law enforcement and crime prevention. We assessed interannual trends and seasonal changes in elephant poaching risk for Kenya's Greater Tsavo ecosystem for 2002 to 2012 using spatio-temporal Bayesian modeling. Poaching data were obtained from the Kenya Wildlife Service's database on elephant mortality. The novelty of our paper is (1) combining space and time when defining poaching risk for elephant; (2) the inclusion of environmental risk factors to improve the accuracy of the spatio-temporal Bayesian model; and (3) the separate analysis of dry and wet seasons to understand seasondependent poaching patterns. Although Tsavo's overall poaching level increased over time, the risk of poaching differed significantly across space. Three of the 34 spatial units had a consistently high poaching risk regardless of whether models included environmental risk factors. Adding risk factors enhanced the model's predictive power. We found that highest poaching risk areas differed between the wet and dry season. The findings improve our understanding of elephant poaching and highlight high risk areas within Tsavo where action to reduce elephant poaching is required. ARTICLE HISTORY

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

Liesenfeld¹,

Richard

²

,

Vogler³

2016

J of Applied Econometrics

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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.

show abstract

Space–time variability in burglary risk: A Bayesian spatio-temporal modelling approach

Cited by 103 publications

References 42 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

Assessing trends and seasonal changes in elephant poaching risk at the small area level using spatio-temporal Bayesian modeling

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

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