FIGURES AND TABLES
FROM:
The Utility of Hotspot Mapping for Predicting Spatial Patterns of Crime
Spencer Chainey, Lisa Tompson and Sebastian Uhlig
BACK TO ARTICLEFigure 1.
Common hotspot mapping techniques. (a) Point mapping, (b) standard deviational spatial ellipses, (c) thematic mapping of administrative units, (d) grid thematic mapping and (e) KDE.
Full figure and legend (148K)Figure 2.
The Camden and Islington study area in central/north London.
Full figure and legend (102K)Figure 3.
Hotspots were determined by selecting the uppermost thematic class calculated using the five classes and the default values generated from applying the quantile thematic range method in MapInfo.
Full figure and legend (55K)Figure 4.
Hotspot maps generated from 3 months of residential burglary input data (measurement date of the 1 January 2003) using (a) STAC, (b) thematic mapping of output areas, (c) grid thematic mapping and (d) KDE. Each map is shown with its PAI value, based on 1 month of measurement data.
Full figure and legend (127K)Figure 5.
KDE hotspot maps of (a) residential burglary and (b) street crime, generated from 3 months input data, and where the hotspot area in each is controlled to represent 3 per cent of the total area. Each figure is presented with its PAI value and its hotspot hit rate for predicting where crimes in the next month occurred. The street crime hotspot map is over twice as a good as the residential burglary hotspot map for predicting where crimes of the respective crime type may occur in the future.
Full figure and legend (48K)