Spatial eigenvector filtering for spatiotemporal crime mapping and spatial crime analysis

• Verfasst von: Helbich, Marco [VerfasserIn]
• Verfasst von: Jokar Arsanjani, Jamal [VerfasserIn]
• Titel: Spatial eigenvector filtering for spatiotemporal crime mapping and spatial crime analysis
• Verf.angabe: Marco Helbich, Jamal Jokar Arsanjani
• Jahr: 2015
• Jahr des Originals: 2014
• Umfang: 15 S.
• Fussnoten: Published online: 05 Mar 2014 ; Gesehen am 17.08.2020
• Titel Quelle: Enthalten in: Cartography and geographic information science
• Ort Quelle: Abingdon : Taylor & Francis, 1999
• Jahr Quelle: 2015
• Band/Heft Quelle: 42(2015), 2, Seite 134-148
• ISSN Quelle: 1545-0465
• DOI: doi:10.1080/15230406.2014.893839
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: generalized additive model
• Sach-SW: negative binomial regression
• Sach-SW: Poisson regression
• Sach-SW: spatial autocorrelation
• Sach-SW: spatial filtering
• Sach-SW: spatiotemporal crime mapping
• K10plus-PPN: 1727125436

Abstract

Spatial and spatiotemporal analyses are exceedingly relevant to determine criminogenic factors. The estimation of Poisson and negative binomial models (NBM) is complicated by spatial autocorrelation. Therefore, first, eigenvector spatial filtering (ESF) is introduced as a method for spatiotemporal mapping to uncover time-invariant crime patterns. Second, it is demonstrated how ESF is effectively used in criminology to invalidate model misspecification, i.e., residual spatial autocorrelation, using a nonviolent crime dataset for the metropolitan area of Houston, Texas, over the period 2005-2010. The results suggest that local and regional geography significantly contributes to the explanation of crime patterns. Furthermore, common space-time eigenvectors selected on an annual basis indicate striking spatiotemporal patterns persisting over time. The findings about the driving forces behind Houston’s crime show that linear and nonlinear, spatially filtered, NBMs successfully absorb latent autocorrelation and, therefore, prevent parameter estimation bias. The consideration of a spatial filter also increases the explanatory power of the regressions. It is concluded that ESF can be highly recommended for the integration in spatial and spatiotemporal modeling toolboxes of law enforcement agencies.