Book Review: Opportunity, Environmental Characteristics, and Crime: An Analysis of Auto Theft Patterns

• Author: Sarah Becker
• DOI: 10.1177/0734016811400362
• Published date: 01 September 2011
• Date: 01 September 2011
• Subject Matter: Book Reviews

theoretical underpinnings, is necessary for replicating this multidimensional methodology in cross-

national research. Kutateladze’s blueprint should be welcomed by researchers who wish to develop

and test the theories that these results tantalizingly suggest.

M. P. Levy

Opportunity, Environmental Characteristics, and Crime: An Analysis of Auto

Theft Patterns El Paso, TX: LFB Scholarly Publishing, LLC, 2009. 222 pp. $65.00. ISBN 978-1-593-32327-1

Reviewed by: Sarah Becker, Louisiana State University, Baton Rouge, LA, USA

DOI: 10.1177/0734016811400362

In Opportunity, Environmental Characteristics, and Crime, author Marissa P. Levy draws together

previously divergent research perspectives to analyze and develop a predictive model for one type

of street crime—auto theft. Levy, who earned her PhD from Rutgers University and is currently an

Associate Professor of Criminal Justice and Program Coordinator at The Richard Stockton College

of New Jersey, makes important theoretical, methodological, and substantive contributions to the field

of environmental criminology with this book. It will appeal to both scholars and practitioners who seek

to develop better models for predicting the spatial distribution of specific types of street crime.

In the first half of the book, Levy outlines the theoretical traditions that inform her multimethod

study of auto theft in Lexington, Kentucky: routine activities theory, situational crime prevention

research, and crimepattern theory. She pulls together research findings fromthe disciplines of sociol-

ogy, human ecology, criminology, and criminal justice to make the argument that it is important to

look at environmental-level characteristics and offenders’ interpretations of those environmental fac-

tors/cues when developing models to understand and predict local opportunity structures for criminal

involvement.

Readers are introduced to past research linking community-level variables like streets and major

roadways, residential/commercial areas, public housing and apartment complexes, parking lots, con-

venience stories and gas stations, transportation hubs, schools, hotels and motels, restaurants and

bars, and automobile service establishments to geographical patterns in crime. In Chapters 3 and

4, Levy moves on to research findings for an even narrower set of geographical areas: sites within

specific neighborhoods and how crime is unevenly distributed between them. Levy examines

research on active offenders, their decision-making processes, and factors like capable guardians/

watchers, concentrated areas of social activity, lighting, and security and how these variables con-

tribute to the emergence of ‘‘hot spots’’ for criminal activity.

The studies reviewed in first half of the book support Levy’s argument for how important it is to

carefully examine environmental and site-level factors conducive to crime. This approach, she con-

tends, allows scholars to develop an understanding of local opportunity structures for criminal

offending that goes beyond simple crime mapping. Levy’s study of auto theft in Lexington effec-

tively illustrates the strengths of such a research-informed model for predicting concentrations of

criminal activity within neighborhoods and specific sites within neighborhoods. The study centers

on one primary question: Can a model for auto theft opportunity structures that is informed by the

cumulative findings of research literature on the subject predict actual hot spots of criminal activity

in a city? Levy uses multiple methodologies and data sources to analyze the patterning of auto theft

in Lexington from 2000 to 2001. She compares her own models for predicting criminal activity to

actual auto theft data from the police department.

Levy develops models for predicting auto theft at two levels. In the community-level analysis,

she creates a base model that includes variables past studies have revealed are correlated to auto

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