Academic journal article Geographical Analysis

Applying Geostatistical Analysis to Crime Data: Car-Related Thefts in the Baltic States

Academic journal article Geographical Analysis

Applying Geostatistical Analysis to Crime Data: Car-Related Thefts in the Baltic States

Article excerpt

Geostatistical methods have rarely been applied to area-level offense data. This article demonstrates their potential for improving the interpretation and understanding of crime patterns using previously analyzed data about car-related thefts for Estonia, Latvia, and Lithuania in 2000. The variogram is used to inform about the scales of variation in offense, social, and economic data. Area-to-area and area-to-point Poisson kriging are used to filter the noise caused by the small number problem. The latter is also used to produce continuous maps of the estimated crime risk (expected number of crimes per 10,000 habitants), thereby reducing the visual bias of large spatial units. In seeking to detect the most likely crime clusters, the uncertainty attached to crime risk estimates is handled through a local cluster analysis using stochastic simulation. Factorial kriging analysis is used to estimate the local- and regional-scale spatial components of the crime risk and explanatory variables. Then regression modeling is used to determine which factors are associated with the risk of car-related theft at different scales.

Introduction

Quantitative analyses of area crime data often focus on the identification of areas of extreme criminality, such as areas with high rates or counts of offenses (crime hot spots). Hot spot detection is often undertaken using any one of a number of ad hoc techniques (e.g., Sherman, Gartin, and Buerger 1989) or statistical cluster detection methods drawn either from spatial epidemiology (e.g., Kulldorff 1997) or quantitative geography (e.g., Messner et al. 1999; Anselin et al. 2000; Haining 2003). Ecological modeling to explain spatial variation in counts or rates is usually undertaken using regression (e.g., Ceccato and Haining 2008; Haining, Law, and Griffith 2009).

Several authors suggest the use of geostatistical methods for the investigation of crime data (Anselin et al. 2000; Krivoruchko and Gotway 2003; Krivoruchko, Got-way, and Zhigimont 2003; Getis 2004), but we are aware of only one application. Camara et al. (2004) use ordinary kriging based on centroids of administrative units to produce a surface of homicide rates in Brazil and to identify clusters. However, recent advances in geostatistical methodology, such as area-to-area (ATA) and area-to-point (ATP) kriging (Kyriakidis 2004) and Poisson kriging (Goo-vaerts 2005; Monestiez et al. 2005), have opened up new opportunities.

We demonstrate in this article the application of geostatistical methods for analyzing the geography of offenses and for identifying significant clusters of crimes. Data about car-related thefts in the Baltic states (Estonia, Latvia, and Lithuania) in 2000, a data set previously analyzed by Ceccato and Haining (2008), are used. This article contrasts the insights obtained using geostatistical methodology with those reported by Ceccato and Haining.

Acquisitive crime in the Baltic states: an earlier study

A conceptual framework

Since the collapse of the Soviet Union in 1991, the three Baltic states of Estonia, Latvia, and Lithuania have undergone profound political change and associated social and economic change as their economies have become more market oriented. The conceptual framework developed by Ceccato and Haining (2008, p. 216) to explain the geography of acquisitive crime emphasizes the role of both medium- and short-term dynamics. In the medium term, citizens of countries experiencing profound socioeconomic change are subject to uncertainty and instability that create anomic conditions leading to increased rates of crime and violence (Durkheim 1897). However, effects are moderated where strong social institutions exist (Messner and Rosenfeld 1997; Kim and Pridemore 2005). Ceccato and Haining (2008) measure medium-term effects using "economic, social and welfare change" over the period 1993-2000. All the change variables were calculated so that more change (e. …

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