A promising example of smart policing: A cross-national study of the effectiveness of a data-driven approach to crime and traffic safety

Abstract

Smart policing emphasizes the combination of existing interdisciplinary datasets, improvement in analysis procedures, and design of more efficient policing strategies. One promising example, the Data-Driven Approach to Crime and Traffic Safety (DDACTS), integrates traffic crash and crime data into the design of more efficient patrol routes, ensuring higher visibility traffic enforcement. This new method allows the police to more effectively allocate their limited resources. Although the DDACTS model has significantly reduced crime and crash rates in the United States, it is necessary to thoroughly study its effects before applying it in other parts of the world; the factors that influence crime, crashes, and police patrol systems in the United States may differ significantly from those in, for instance, Asia. In the present research, Taiwan was chosen as an initial area of study because of the nation’s open data policy and good quality of the data available. This study focused on two key differences between the United States and Taiwan: (1) the cluster distributions of crash and crime events, and (2) possible effectiveness of DDACTS in these two regions. ArcGIS was used to calculate point cluster patterns and identify hotspots. Although the point patterns for crimes and crashes varied greatly between Texas and Taiwan, all pairs of crash and crime hotspots were in close proximity to one another. Thus, DDACTS may be effective for improving patrol efficiency in Taiwan, despite the nation’s significant socioeconomic differences with the United States Consequently, the results show that DDACTS may be efficient in various regions with different socioeconomic structures than the United States, such as countries in Asia. In the future, researchers from other nations may be able to use these results to revise and adjust their own DDACTS patrol plans.