Enhancing Crime Specificity: A Target-Specific Classification of Metal Theft

Abstract

This study advances the argument that criminological analysis requires greater crime specificity, emphasizing the need to define and study offenses at finer levels of distinction. We propose a four-level classification framework to organize crime data from broad legal categories to target-specific forms. Using 8,706 metal theft incidents in Louisville, Kentucky (2011–2016), the analysis shows that disaggregating metal theft into target-specific forms (e.g., air conditioners, catalytic converters, copper wiring) reveals temporal and spatial variations invisible in aggregate data analysis. These differences illustrate how greater empirical specificity can sharpen theoretical understanding and promote more prevention. The framework is adaptable to other crime types, offering a scalable model for refining criminological theories, improving data systems, and informing targeted interventions.

Keywords

crime prevention policy quantitative spatial analysis theory

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