Correlates of homicide: new space/time interaction tests for spatiotemporal point processes

Statistical inference on spatiotemporal data often proceeds by focusing on the temporal aspect of the data, ignoring space, or the spatial aspect, ignoring time. In this paper, we explicitly focus on the interaction between space and time. Using a geocoded, time-stamped dataset from Chicago of almost 9 millions calls to 911 between 2007 and 2010, we ask whether any of these call types are associated with shootings or homicides. Standard correlation techniques do not produce meaningful results in the spatiotemporal setting because of two confounds: purely spatial e ff ects (i.e. “bad” neighborhoods) and purely temporal e ff ects (i.e. more crimes in the summer) could introduce spurious correlations. To address this issue, a handful of statistical tests for space-time interaction have been proposed, which explicitly control for separable spatial and temporal dependencies. Yet these classical tests each have limitations. We propose a new test for space-time interaction, using a Mercer kernel-based statistic for measuring the distance between probability distributions. We compare our new test to existing tests on simulated and real data, where it performs comparably to or better than the classical tests. For the application we consider, we find a number of interesting and significant space-time interactions between 911 call types and shootings / homicides.

[1]  Ingrid Gould Ellen,et al.  Do foreclosures cause crime , 2013 .

[2]  Sivaraman Balakrishnan,et al.  Optimal kernel choice for large-scale two-sample tests , 2012, NIPS.

[3]  Lawrence Carin,et al.  Nonparametric Bayesian Segmentation of a Multivariate Inhomogeneous Space-Time Poisson Process. , 2012, Bayesian analysis.

[4]  Bernhard Schölkopf,et al.  A Kernel Two-Sample Test , 2012, J. Mach. Learn. Res..

[5]  Nick Tilley,et al.  Policing Problem Places: Crime Hot Spots and Effective Prevention , 2012 .

[6]  Robert J. Sampson,et al.  Great American City: Chicago and the Enduring Neighborhood Effect , 2012 .

[7]  P. Cochat,et al.  Et al , 2008, Archives de pediatrie : organe officiel de la Societe francaise de pediatrie.

[8]  Bernhard Schölkopf,et al.  Kernel-based Conditional Independence Test and Application in Causal Discovery , 2011, UAI.

[9]  George E. Tita,et al.  Self-Exciting Point Process Modeling of Crime , 2011 .

[10]  Matt Taddy Autoregressive Mixture Models for Dynamic Spatial Poisson Processes: Application to Tracking Intensity of Violent Crime , 2010 .

[11]  Randall Walsh,et al.  Foreclosure, Vacancy and Crime , 2010 .

[12]  M. Fortin,et al.  Comparison of the Mantel test and alternative approaches for detecting complex multivariate relationships in the spatial analysis of genetic data , 2010, Molecular ecology resources.

[13]  Trevor Hastie,et al.  Regularization Paths for Generalized Linear Models via Coordinate Descent. , 2010, Journal of statistical software.

[14]  Bernhard Schölkopf,et al.  Hilbert Space Embeddings and Metrics on Probability Measures , 2009, J. Mach. Learn. Res..

[15]  Heather J. Lynch,et al.  A spatiotemporal Ripley's K-function to analyze interactions between spruce budworm and fire in British Columbia, Canada , 2008 .

[16]  Elizabeth A. Mack,et al.  Spatio-Temporal Interaction of Urban Crime , 2008 .

[17]  Shashi Shekhar,et al.  Environmental Criminology , 2008, Encyclopedia of GIS.

[18]  Le Song,et al.  A Kernel Statistical Test of Independence , 2007, NIPS.

[19]  Wilpen L. Gorr,et al.  Leading Indicators and Spatial Interactions: A Crime‐Forecasting Model for Proactive Police Deployment , 2007 .

[20]  P. Guttorp,et al.  Geostatistical Space-Time Models, Stationarity, Separability, and Full Symmetry , 2007 .

[21]  M. Fuentes Testing for separability of spatial–temporal covariance functions , 2006 .

[22]  Anthony Widjaja,et al.  Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond , 2003, IEEE Transactions on Neural Networks.

[23]  M. Kulldorff,et al.  The Knox Method and Other Tests for Space‐Time Interaction , 1999, Biometrics.

[24]  J. Møller,et al.  Log Gaussian Cox Processes , 1998 .

[25]  W. McWhirter,et al.  Spatial temporal patterns in childhood leukaemia: further evidence for an infectious origin. EUROCLUS project. , 1998, British Journal of Cancer.

[26]  R. Baker Testing for space-time clusters of unknown size , 1996 .

[27]  G. Jacquez A k nearest neighbour test for space-time interaction. , 1996, Statistics in medicine.

[28]  R. Tibshirani Regression Shrinkage and Selection via the Lasso , 1996 .

[29]  R. Häggkvist,et al.  Second-order analysis of space-time clustering , 1995, Statistical methods in medical research.

[30]  N. H. Anderson,et al.  Two-sample test statistics for measuring discrepancies between two multivariate probability density functions using kernel-based density estimates , 1994 .

[31]  Dudley,et al.  Real Analysis and Probability: Measurability: Borel Isomorphism and Analytic Sets , 2002 .

[32]  C. Micchelli Interpolation of scattered data: Distance matrices and conditionally positive definite functions , 1986 .

[33]  M. Klauber,et al.  TWO-SAMPLE RANDOMIZATION TESTS FOR SPACE-TIME CLUSTERING , 1971 .

[34]  N. Mantel The detection of disease clustering and a generalized regression approach. , 1967, Cancer research.

[35]  E G Knox,et al.  The Detection of Space‐Time Interactions , 1964 .