Identifying a spatial scale for the analysis of residential burglary: An empirical framework based on point pattern analysis

Identifying a spatial scale for the analysis of residential burglary: An empirical framework based on point pattern analysis

Blog Article

A key issue in the spatial and temporal analysis of residential burglary is the choice of scale: spatial patterns might differ appreciably for different time periods and vary across geographic units of analysis.Based on point pattern analysis of burglary incidents in Columbus, Ohio during a 9-year period, this study develops an empirical framework to identify a useful spatial scale and its dependence on temporal animed aniflex complete aggregation.Our analysis reveals that residential burglary in Columbus clusters at a characteristic scale of 2.2 km.An ANOVA test shows no significant impact of temporal aggregation on spatial scale of clustering.

This study demonstrates the value of point pattern analysis in identifying a scale for the analysis of crime patterns.Furthermore, the characteristic scale of clustering determined using our method has great potential applications: (1) it can reflect the spatial environment of criminogenic processes and thus be used to define the spatial boundary for place-based policing; (2) it can serve as a candidate for the bandwidth (search radius) for hot spot policing; (3) its independence better waters xl7000 of temporal aggregation implies that police officials need not be concerned about the shifting sizes of risk-areas depending on the time of the year.