Numerous applications of the KDE+ are possible. If you have line features with points or events along them, you can analyze their pattern with this method to check whether the features are clustering or not. If uncertain whether the KDE+ is suitable for your data,
contact us.
We applied the KDE+ method to traffic accident data on Czech roads. KDE+ was also applied to traffic crash data in the following countries or regions: Sweden, Spain, USA (Idaho, Montana, Texas, California), France, Slovakia, Hungary, Poland, Israel, Switzerland, Italy, Finland, Norway, Germany, Slovenia, Croatia, Belgium, Estonia, Denmark, Lithuania...
KDE+ software is also applied for online data clustering in the following web portals:
www.srazenazver.cz
https://albertawildlifewatch.ca/
Other examples of web map applications with KDE+ clusters
KDE+ software has been applied to data in the following works:
Cunneyworth, P. M. K., Andrášik, R., & Bíl, M., 2023. An effect of canopy bridges on monkey vehicle collision hotspots: Spatial and spatiotemporal analyses. American Journal of Primatology, e23492. https://doi.org/10.1002/ajp.23492
Bíl, M., Andrášik, R., 2020. The effect of wildlife carcass underreporting on KDE+ hotspots identification and importance. Journal of Environmental Management 275, 111254. https://doi.org/10.1016/j.jenvman.2020.111254
Nezval, V., Bíl, M., 2020. Spatial analysis of wildlife-train collisions on the Czech rail network. Applied Geography 125, 102304. https://doi.org/10.1016/j.apgeog.2020.102304
Bíl, M.,
Andrášik, R.,
Sedoník, J., 2019. A detailed spatiotemporal analysis of traffic crash hotspots.
Applied Geography 107, 82-90.
https://www.sciencedirect.com/science/article/pii/S0143622818309081
Bíl, M.,
Andrášik, R.,
Duľa, M.,
Sedoník, J., 2019. On reliable identification of factors influencing wildlife-vehicle collisions along roads.
Journal of Environmental Management 237C, 297-304.
https://www.sciencedirect.com/science/article/pii/S030147971930227
Périquet, S., Roxburgh, L., Le Roux, A., Collinson, W. J., 2018. Testing the Value of Citizen Science for Roadkill Studies: A Case Study from South Africa. Front. Ecol. Evol. 6:15. doi: 10.3389/fevo.2018.00015
https://www.frontiersin.org/articles/10.3389/fevo.2018.00015/full
Favilli, F., Bíl, M., Sedoník, J., Andrášik, R., Kasal, P., Agreiter, A., Streifeneder, T., 2018. Application of KDE+ software to identify collective risk hotspots of ungulate-vehicle collisions in South Tyrol, Northern Italy. European Journal of Wildlife Research 64:59.
https://link.springer.com/article/10.1007/s10344-018-1214-x
Bartonička, T., Andrášik, R., Duľa, M., Sedoník, J., Bíl, M., 2018. Identification of Local Factors Causing Clustering of Animal-Vehicle Collisions on Roads. Journal of Wildlife Management 82, pp. 940-947
https://wildlife.onlinelibrary.wiley.com/doi/full/10.1002/jwmg.21467
Bíl, M., Andrášik, R., Bartonička, T., Křivánková, Z., Sedoník, J., 2018. An Evaluation of Odor Repellent Effectiveness in Prevention of Wildlife-Vehicle Collisions. Journal of Environmental Management 205C pp. 209-214.
https://www.sciencedirect.com/science/article/pii/S0301479717309568
Andrášik, R., 2017. Spatial analysis of traffic crashes by the use of kernel density estimation (Rigorous thesis).
https://library.upol.cz/arl-upol/cs/csg/?repo=upolrepo&key=95918507531
Bíl, M., Kubeček, J., Sedoník, J., Andrášik. R., 2017. Srazenazver.cz: A system for evidence of animal-vehicle collisions along transportation networks.
Biological Conservation 213PA, pp. 167-174.
http://www.sciencedirect.com/science/article/pii/S000632071730263X
Bíl, M., Andrášik. R., Nezval, V., Bílová, M., 2017. Identifying Locations along Railway Networks with the Highest Tree Fall Hazard. Applied Geography 87, 45-53.
https://www.sciencedirect.com/science/article/pii/S0143622817301819
Heigl, F., Horvath, K., Laaha, G., Zaller, J. G., 2017. Amphibian and reptile road-kills on tertiary roads in relation to landscape structure: using a citizen science approach with open-access land cover data. BMC Ecology 17:21
https://bmcecol.biomedcentral.com/articles/10.1186/s12898-017-0134-z
Sørensen, J. B., 2017. Moose-vehicle collisions in Northern Norway: Causes, hotspot detection and mitigation. Master thesis. Norwegian University of Life Sciences, Faculty of Environmental Sciences and Natural Resource Management
https://brage.bibsys.no/xmlui/handle/11250/2459160
Andrášik, R., 2017. Spatial analysis of traffic crashes by the use of kernel density estimation. Rigorous Thesis. Palacký University Olomouc.
https://library.upol.cz/arl-upol/cs/csg/?repo=upolrepo&key=95918507531
Sjölund, Magnus, 2016. Road and landscape features affecting the aggregation of ungulate vehicle collisions in southern Sweden. Second cycle, A2E. Grimsö: SLU, Dept. of Ecology
https://stud.epsilon.slu.se/9868/
Arnold, E., 2016 Spatial, Roadway, and Biotic Factors Associated with Barn Owl (Tyto alba) Mortality and Characteristics of Mortality Hotspots Along Interstates 84 and 86 in Idaho. Master Thesis
https://raptorresearchcenter.boisestate.edu/arnold-erin/
Svoboda, T., 2016. Implementace statistické metody KDE+. Master Thesis. VUT Brno
