Comparison of planar and network K-functions in traffic accident analysis

The network and planar K-function methods are applied to traffic accident data to illustrate the risk of false positive detection associated with the use of a statistic designed for a planar space to analyze a network-constrained phenomenon.

Abstract

The network and planar K-function methods are applied to traffic accident data to illustrate the risk of false positive detection associated with the use of a statistic designed for a planar space to analyze a network-constrained phenomenon. We also demonstrate the benefits of using a method specifically designed for a network space. The results clearly indicate that the planar K-function analysis is problematic since it entails a significant chance of over-detecting clustered patterns. Analyses are implemented based on Monte Carlo simulation and applied to 1997 traffic accident data in the Buffalo, NY area.