Effectiveness of the Data-Driven Approach to Crime and Traffic Safety (DDACTS): A Cross-National Study

Data-Driven Approaches to Crime and Traffic Safety (DDACTS) integrate crash and crime data to design more efficient patrol routes for high visibility traffic enforcement. These new policies allow police to more effectively allocate their limited resources. Although the DDACTS model seems to work well in the United States, it is necessary to thoroughly study its effects before applying it in other parts of the world. Factors influencing crime, crashes, and police patrol systems in the United States may differ significantly from those in Asia. We have chosen Taiwan as our first area of study because of its open data policy. Our research focused on two differences between the United States and Taiwan: (1) the cluster distributions of crash and crime events, and (2) the possible effectiveness of DDACTS in these two areas. ArcGIS was used to calculate point cluster patterns and identify hotspots. The results showed that although the cluster patterns for crimes and crashes varied greatly between Texas and Taiwan, DDACTS may still be effective for improving patrol efficiency in this country. In the future, researchers in other countries will be able to use our results to revise and adjust their current DDACTS patrol plans.