Crime Mapping and Spatial Data Analysis using R

Juan Medina Ariza, Reka Solymosi

• Publisher: Chapman and Hall/CRC
• Year: 2023
• Print ISBN: 9780367724696
• eBook ISBN: 9781000850796
• Topic: Mathematics
• Subtopic: Probability & Statistics
• Index: Mathematics

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.

Table of contents

• Cover Page
• Half-Title Page
• Series Page
• Title Page
• Copyright Page
• Dedication Page
• Contents
• List of Figures
• List of Tables
• Preface: how to use this book
• Author/editor biographies
• 1 Producing your first crime map
• 2 Basic geospatial operations in R
• 3 Mapping rates and counts
• 4 Variations of thematic mapping
• 5 Basics of cartographic design: elements of a map
• 6 Time matters
• 7 Spatial point patterns of crime events
• 8 Crime along spatial networks
• 9 Spatial dependence and autocorrelation
• 10 Detecting hot spots and repeats
• 11 Spatial regression models
• 12 Spatial heterogeneity and regression
• Appendix A: quick intro to R and RStudio
• Appendix B: Regression analysis (a refresher)
• Appendix C: Sourcing geographical data for crime analysis
• References
• Index