ABSTRACT

Crime mapping and analysis sit at the intersection of geocomputation, data visualisation and cartography, spatial statistics, environmental criminology, and crime analysis. This book brings together relevant knowledge from these fields into a practical, hands-on guide, providing a useful introduction and reference material for topics in crime mapping, the geography of crime, environmental criminology, and crime analysis. It can be used by students, practitioners, and academics alike, whether to develop a university course, to support further training and development, or to hone skills in self-teaching R and crime mapping and spatial data analysis. It is not an advanced statistics textbook, but rather an applied guide and later useful reference books, intended to be read and for readers to practice the learnings from each chapter in sequence.

In the first part of this volume we introduce key concepts for geographic analysis and representation and provide the reader with the foundations needed to visualise spatial crime data. We then introduce a series of tools to study spatial homogeneity and dependence. A key focus in this section is how to visualise and detect local clusters of crime and repeat victimisation. The final chapters introduce the use of basic spatial models, which account for the distribution of crime across space. In terms of spatial data analysis the focus of the book is on spatial point pattern analysis and lattice or area data analysis. 

 

chapter 1|32 pages

Producing your first crime map

chapter 2|36 pages

Basic geospatial operations in R

chapter 3|32 pages

Mapping rates and counts

chapter 4|22 pages

Variations of thematic mapping

chapter 6|22 pages

Time matters

chapter 7|26 pages

Spatial point patterns of crime events

chapter 8|34 pages

Crime along spatial networks

chapter 9|32 pages

Spatial dependence and autocorrelation

chapter 10|28 pages

Detecting hot spots and repeats

chapter 11|28 pages

Spatial regression models

chapter 12|18 pages

Spatial heterogeneity and regression