Case03C-Detecting Spatiotemporal Crime Hotspots in Baton Rouge

Chapter 3 Spatial Smoothing and Spatial Interpolation

This chapter covers two generic tasks in GIS-based spatial analysis: spatial smoothing and spatial interpolation. Both are useful to visualize spatial patterns and highlight spatial trends. Spatial smoothing computes the average values of a variable in a larger spatial window to smooth its variability across space. Spatial interpolation uses known (observed) values at some locations to estimate (interpolate) unknown values at any given locations.

There are three case studies. The first case study of place names in southern China illustrates some basic spatial smoothing and interpolation methods. The second illustrates how to use area-based spatial interpolation methods to transform population data between different census areal units. The third demonstrates how to use the spatio-temporal kernel density estimation (STKDE) method for detecting spatiotemporal crime hotspots.

Case Study 3C:Detecting Spatiotemporal Crime Hotspots in Baton Rouge

This case study explores the spatiotemporal patterns of residential burglary crimes in Baton Rouge, Louisiana in 2011, as reported in Hu et al. (2018). Data needed for the project is provided in a sub-folder BRcrime under the folder BatonRouge . It contains a zipped Shapefile BRcrime.zip for residential burglary crimes with XY coordinates and time label, and an R file stkde.r for implementing the STKDE model.

Computational Methods and GIS Applications in Social Science - KNIME Lab Manual

Lingbo Liu, Fahui Wang

External resources

• Github for Workbook Issue Report
• Github for Geospatial Analytics