LAUSR

Effective measures reducing risk of animal vehicle collisions (AVC) require defining high risk locations on roads where AVCs occur. Previous studies examined factors explaining locations of individual AVCs; however, some AVCs can form hotspots (i.e., clusters of AVCs) that can be explained by local factors. We therefore applied a novel kernel density estimation (KDE) method to AVCs for the Czech Republic from October 2006 to December 2011 to identify AVCs hotspots along roads. Our main goal was to identify local factors and their effect on the non random (clustered) occurrence of AVCs. The remaining solitary AVCs occurred randomly and are likely induced by other human factors on the global scale. The hotspot identification method followed by the selected data mining methods (KDE methods) identified factors causing local clustering of AVCs.