A Methodology for Studying Crash Dependence on Demographic and Socioeconomic Data

Many agencies use traffic crash data to identify problems, establish goals and performance measures, measure progress of specific programs, and support development and evaluation of highway and vehicle safety countermeasures. Traditionally, efforts have considered only crash data and roadway network attributes and have not taken adjacent demographics, socioeconomics, land use, and other non-roadway variables into consideration. The evaluation of non-roadway variables may support two related types of safety management efforts: identification of additional causal factors for roadway crashes and identification of empirical relationships between crashes and non-roadway factors. The second may provide improved estimates of the impact of future changes in land use, demographics, and socioeconomics. Recent efforts use Geographic Information Systems (GIS) or non-spatial relational databases to combine crash and other data to assess correlation and causation. The variety of data available, both within the traditional approach and with the addition of demographic, socioeconomic, and land use data, creates a complex analytical environment. The complexity of these analyses warrants development of a typology to structure an assessment of the best approach in a given situation. This paper presents a concept typology to organize the use of GIS, along with statistical techniques, to explore the relationship between crash incidence and underlying demographic, socioeconomic, and land use data.