Crime Mapping and Spatial Data Analysis using R
2022-09-08
Chapter 1 Preamble: how to use this book
This book aims to provide the reader with an introduction to Crime Mapping and Spatial Data Analysis, using R as an engine for spatial data analysis and visualisation. Based on teaching materials developed by the authors, the book is a practical guide for those interested in crime analysis and criminology.
We imagine this to be useful for those teaching (or enrolled in) upper level undergraduate and graduate courses in higher education, as well as analysts in professional roles embarking on further training, or looking for a guide for their work, as well as criminologists interested in crime mapping. The material has been successful in our teaching both in higher education settings, and when training crime analysts working for the police or other law enforcement agencies.
Given the source material, this book may be used as a companion text for similar course units in crime mapping, the geography of crime, environmental criminology, or crime analysis. Equally, it can be used by students, practitioners, and academics alike interested in learning more about R and its GIS and spatial analysis capacity. It is not an advanced statistics textbook, but rather an applied textbook. Someone “self teaching” R for these purposes will find it helpful and, thus, unlike reference books, it is better to read and practice each chapter in sequence.
Crime mapping and analysis sits at the intersection of geocomputation, data visualisation and cartography, spatial statistics, environmental criminology, and crime analysis. In this book, environmental criminology, which focuses on the analysis of the spatial and geographical distribution of crime, provides the substantive background.
This text cannot make justice to each of these bodies of inquiry, professional practice, and literature. We cannot offer a comprehensive and systematic treatment of each of these areas. What we do is provide a helpful introduction to R as a way to bring together these specialties and offer adequate references to our readers so that they can deepen their understanding of each of them. For a grounding in Environmental Criminology we recommend Bruinsma and Johnson (2018), for debates and issues in spatial criminology/crime analysis and crime and place research we suggest Weisburd, Bernasco, and Bruinsma (2008), and for geocomputation and spatial data science Lovelace, Nowosad, and Muenchow (2019) and M. D. Smith, Goodchild, and Longley (2007) provide great foundations. For further topics for crime analysts Chainey and Ratcliffe (2005) and more recently Chainey (2021) are excellent as well. We also provide further resources at the end of each chapter for those who wish to delve deeper into each individual topic introduced.
Although all the examples we use concern the study of crime, there is fairly limited substantive criminology in the text. In fact, and despite the title, the volume could also be used, more generally, as a companion text for courses on social science spatial data analysis, since the techniques we cover are transferable to other substantive domains.
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 and techniques that are relevant to study spatial homogeneity and dependence of crime data. 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 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. Geostatistics have fewer applications in crime analysis and research and, therefore, we do not cover this topic here.
To follow along with the exercises we provide the data we use in a zip file. The best approach is to download this zip file into the working directory of your R project, and unzip it there. This way, you can follow our code. If you run the below code, this should do this for you automatically:
download.file(url = "https://osf.io/5u42g/download",
destfile = "data.zip")
unzip("data.zip", exdir = "data")From then on, you can read all files in from this downloaded data directory. In our code, we use the convention that all data are stored in a "data/" folder within our working directory.
We hope you enjoy this book and find it useful in your journey into crime mapping. The world of R and spatial analysis is ever-evolving, and we will try to stay updated. If you have ever suggestions, comments, concerns, or requests, do not hesitate to get in touch, or raise this as an issue in our GitHub repository: www.github.com/maczokni/crime_mapping.