A network-based scan statistic for detecting the exact location and extent of hotspots along urban streets

Socio-economic activities and incidents such as crimes and traffic accidents have a negative impact on our society, and their reduction has been a priority in our social-science endeavour. These events are not uniform in their occurrences but, rather, manifest a distinct set of concentrations, commonly known as hotspots. Detecting the exact extent, shape and changes in these hotspots can lead to deeper understanding of their cause and help reduce the volume of incidents, yet accuracy of the analytical outcomes using existing methods are often hampered by their reliance on Euclidean distance. This paper proposes a new type of cluster detection method for identifying significant concentration of urban and social-science activities recorded at the individual street-address level. It extends Scan Statistic—a regular hotspot detection method originally developed in the field of epidemiology—by introducing flexible search windows that adapt to and sweep across a street network. Using a set of synthetic data of crime incidents as an example, performance of the proposed method is measured against that of its conventional counterparts. Results from the performance tests confirm that the proposed method is more accurate in detecting the exact locations of hotspots without over- or under-representing them, thus offering an effective means to identify problem places at the individual street-address level. The simulation also demonstrates how well the proposed method captures changes in the intensity of hotspots, which is also something existing methods have struggled with. An empirical analysis is carried out with data on drug, burglary, robbery, as well as thefts from vehicles in Chicago. The study demonstrates the capacity of the proposed method to extract the detailed profile of the concentration of each crime type, which offers interesting insights into their micro-scale patterns which were previously not available at such a fine spatial granularity.