Conjunctive Analysis of Case Configurations (CACC)

This chapter introduces Conjunctive Analysis of Case Configurations (CACC) for the study of crime and place. After introducing CACC as an alternative approach to variable-oriented thinking, we present its conceptual underpinnings and limitations. We then provide a step-by-step tutorial on how to conduct a CACC, accompanied by an example using public data and R code. The chapter contains many references to other publications that developed the CACC method and applied it to different crime contexts.

Open Street Map for crime and place

This chapter provides a framework to approach and meaningfully interpret open data. The chapter also offers a practical hands-on guide to demonstrate how to access, wrangle, and analyse different sources of open data in R in order to draw conclusions about crime and place.

Using Crowdsourced Data to Study Crime and Place

Crowdsourcing refers to the practise of enlisting the knowledge, experience or skills of a large number of people (the crowd) through some digital platform to collect data towards a collaborative project. Crowdsourcing can generate large volumes of data in relatively little time at a very small cost, and can be useful for research, strategic police management and many other purposes. To make effective use of crowdsourced data, it is important to understand its key strengths to emphasize, and limitations to mitigate. In this chapter we highlight the main strengths and weaknesses of crowdsourcing, and illustrate how to acquire, make sense of, and critically evaluate crowdsourced data to study crime and place. We present a step-by-step exemplar study using crowdsourced data from a platform called Place Pulse, where people rate their feelings of safety between different areas. Taking the case study of Atlanta, Georgia, we work through analyzing and interpreting these data while highlighting how to emphasize and evaluate the strengths and limitations of crowdsourcing. Exercises are presented using R software.

The Influence of Street Network Features on Robberies Around Public Housing Communities

Previous research has consistently identified a crime spillover effect from public housing communities. However, variation in spillover between communities has also been observed. Drawing upon environmental criminology and recent advancements in crime and place research, we examine whether street block betweenness conditions the relationship between street block distance to public housing communities and robbery levels. Negative binomial regression results indicate that street block distance is inversely related to robberies and betweenness is positively associated with robbery levels. The interaction between distance to the nearest public housing community and betweenness suggests robberies decrease more slowly farther from public housing communities on street blocks with higher betweenness. Findings demonstrate the importance of considering the context in which potentially criminogenic facilities are situated.

Studying Crime and Place with the Crime Open Database

The study of spatial and temporal crime patterns is important for both academic understanding of crime-generating processes and for policies aimed at reducing crime. However, studying crime and place is often made more difficult by restrictions on access to appropriate crime data. This means understanding of many spatio-temporal crime patterns are limited to data from a single geographic setting, and there are few attempts at replication. This article introduces the Crime Open Database (code), a database of 16 million offenses from 10 of the largest United States cities over 11 years and more than 60 offense types. Open crime data were obtained from each city, having been published in multiple incompatible formats. The data were processed to harmonize geographic co-ordinates, dates and times, offense categories and location types, as well as adding census and other geographic identifiers. The resulting database allows the wider study of spatio-temporal patterns of crime across multiple US cities, allowing greater understanding of variations in the relationships between crime and place across different settings, as well as facilitating replication of research.

Studying crime and place with the Crime Open Database

The study of spatial and temporal crime patterns is important for both academic understanding of crime-generating processes and for policies aimed at reducing crime. However, studying crime and place is often made more difficult by restrictions on access to appropriate crime data. This means understanding of many spatio-temporal crime patterns are limited to data from a single geographic setting, and there are few attempts at replication. This article introduces the Crime Open Database (CODE), a database of 16 million offenses from 10 of the largest United States cities over 11 years and more than 60 offense types. Open crime data were obtained from each city, having been published in multiple incompatible formats. The data were processed to harmonize geographic co-ordinates, dates and times, offense categories and location types, as well as adding census and other geographic identifiers. The resulting database allows the wider study of spatio-temporal patterns of crime across multiple US cities, allowing greater understanding of variations in the relationships between crime and place across different settings, as well as facilitating replication of research.

Organized Crime and Places

This chapter begins by discussing the three types of organized crime—racketeering, transit crime, and the local provision of illegal goods and services—and the significance of place. It then considers the role of places and the (built) environment for organized crime. The main message is that place has a different meaning for these three types of organized crime and raises several theoretical challenges. As these types of organized crime often require a higher degree of social organization than opportunistic street-level crime, the chapter elaborates on two theoretical concepts that should be included in the study of organized crime and place: social opportunity structure and offender convergence settings.