Description of KDE+

- theoretical background
Bíl, M., Andrášik, R., Janoška, Z., 2013. Identification of Hazardous Road Locations of Traffic Accidents by means of Kernel Density Estimation and Cluster Significance Evaluation. Accident Analysis and Prevention 55, 265–273.

Andrášik, R., 2017. Spatial analysis of traffic crashes by the use of kernel density estimation (Rigorous thesis). 

Bíl, M., Andrášik, R., Sedoník, J., 2019. A detailed spatiotemporal analysis of traffic crash hotspots. Applied Geography 107, 82-90

- software description
Bíl, M., Andrášik, R., Svoboda, T., Sedoník, J., 2016. The KDE+ software: a tool for effective identification and ranking of animal-vehicle collision hotspots along networks. Landscape Ecology 31, 231–237.

In summary, the KDE+ method allows users to analyze their data with a method which is an improvement of the standard Kernel Density Estimation (KDE). The main contributions are a statistical significance test and ranking of resulting significant clusters which is based on their strength. Therefore, this approach is objective and does not depend on users' subjective view.