Case08B-Detecting Colocation between Crime Incidents and Facilities

Chapter 8 Spatial Statistics and Applications

Spatial statistics analyzes the pattern, process and relationship in spatial (geographic) data. This chapter implements some fundamental tasks in spatial statistics: measuring geographic distributions, spatial cluster analysis, and spatial regression models. Section 8.1 uses a case study on place names in Yunnan, China, to illustrate the implementations of various centrographic measures and point-based spatial clustering. Section 8.2 employs a case study to demonstrate the methods for detecting global and local colocation patterns of two types of points (here crimes and facili ties). Section 8.3 applies spatial cluster analysis and spatial regression in a case study of homicide patterns in Chicago.

Case Study 8B: Detecting Colocation between Crime Incidents and Facilities

This case study is based on the work reported in Wang, Hu et al. (2017) and detects colocation between motorcycle thefts and three types of facilities, namely entertainment entities, retail shops and schools, in a city in Jiangsu Province, China. It implements the global and local colocation quotients and explores various ways of defining spatial weights.

Data for the case study is organized in the folder Jiangsu and includes:

1. A reference layer District.zip for the study area boundary and administrative districts,

2. A network feature dataset Cityroad.zip for the road network of the city and

3. A point layer MotorTheft.zip with a field Category, whose values = Motorcycle_ theft , Retail_shop , School , or Entertainment , corresponding to four types of points.

Computational Methods and GIS Applications in Social Science - KNIME Lab Manual
Lingbo Liu, Fahui Wang

External resources

• Github for Geospatial Analytics
• Github for Workbook Issue Report