Spatio-temporal modeling for residential burglary

Spatio-temporal modeling is widely recognized as a promising means for predicting crime patterns. Despite their enormous potential, the available methods are still in their infancy. A lot of research focuses on crime hotspot detection and geographic crime clusters, while a systematic approach to include the temporal component of the underlying crime distributions is still under-researched. In this paper, we gain further insight in predictive crime modeling by including a spatio-temporal interaction component in the prediction of residential burglaries. Based on an extensive dataset, we show that including additive space-time interactions leads to significantly better predictions.

[1]  Carter C. Price,et al.  Predictive Policing: The Role of Crime Forecasting in Law Enforcement Operations , 2013 .

[2]  R. Tibshirani,et al.  Generalized Additive Models , 1991 .

[3]  Timothy Coupe,et al.  DAYLIGHT AND DARKNESS TARGETING STRATEGIES AND THE RISKS OF BEING SEEN AT RESIDENTIAL BURGLARIES , 2006 .

[4]  Jerry H. Ratcliffe.  Intelligence-Led Policing , 2016 .

[5]  Spencer Ch.  The Utility of Hotspot Mapping for Predicting Spatial Patterns of Crime , 2008 .

[6]  Peter Rogerson,et al.  Statistical methods for geography , 2001 .

[7]  Max Kuhn,et al.  Building Predictive Models in R Using the caret Package , 2008 .

[8]  Shane D. Johnson,et al.  Space–Time Patterns of Risk: A Cross National Assessment of Residential Burglary Victimization , 2007 .

[9]  Peter E. Kennedy.  A Guide to Econometrics , 1979 .

[10]  A. Zuur,et al.  Analysing Ecological Data , 2007 .

[11]  Henk Elffers,et al.  Statistical Analysis of Spatial Crime Data , 2010 .

[12]  George E. Tita,et al.  Measuring and Modeling Repeat and Near-Repeat Burglary Effects , 2009 .

[13]  Shane D. Johnson,et al.  Learning where to offend: Effects of past on future burglary locations , 2015 .

[14]  Jane Law,et al.  Bayesian Spatio-Temporal Modeling for Analysing Local Patterns of Crime Over Time at the Small-Area Level , 2014 .

[15]  Elizabeth E. Ebert,et al.  Neighborhood Verification: A Strategy for Rewarding Close Forecasts , 2009 .

[16]  Richard Valliant,et al.  Variance inflation factors in the analysis of complex survey data , 2012 .

[17]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[18]  Alain F. Zuur,et al.  A protocol for data exploration to avoid common statistical problems , 2010 .

[19]  Shane D. Johnson.  Repeat burglary victimisation: a tale of two theories , 2008 .

[20]  W. Bernasco,et al.  How Do Residential Burglars Select Target Areas?: A New Approach to the Analysis of Criminal Location Choice , 2005 .

[21]  J. Ratcliffe.  Crime Mapping: Spatial and Temporal Challenges , 2010 .

[22]  S. Wood.  Generalized Additive Models: An Introduction with R , 2006 .

[23]  N. Roberts.  Assessing the spatial and temporal variation in the skill of precipitation forecasts from an NWP model , 2008 .

[24]  A. Zuur,et al.  Mixed Effects Models and Extensions in Ecology with R , 2009 .

[25]  Xiaofeng Wang,et al.  The spatio-temporal generalized additive model for criminal incidents , 2011, Proceedings of 2011 IEEE International Conference on Intelligence and Security Informatics.

[26]  J. Ratcliffe,et al.  A QUANTITATIVE SPATIAL STUDY OF THE IMPACT OF CANBERRA’S UNIQUE GEOGRAPHY ON RESIDENTIAL BURGLARY OFFENDERS , 2001 .