ROADWAY TRAFFIC CRASH MAPPING: A SPACE-TIME MODELING APPROACH

Abstract

Mapping transform spatial data into a visual form, enhancing the ability of users to observe, conceptualize, validate, and communicate information. Research efforts in the visualization of traffic safety data, which are usually stored in large and complex databases, are quite limited at this time. This paper shows how hierarchial Bayes models, which are being vigorously researched for use in disease mapping, can also be used to build model-based risk maps for area-based traffic crashes. County-level vehicle crash records and roadway data from Texas are used to illustrate the method. A potential extension that uses hierarchial models to develop network-based risk maps is also discussed.