Abstract
Road-kill accidents on highways cause considerable social and economic costs. Therefore, it is critical to minimize the frequency of occurrence through taking appropriate actions such as accident prevention and reduction programs/tools including eco-corridor construction and fence installation. Due to the constraints in allocating available public resource, it may help decision-makers to determine where to put priorities when taking actions The objective of this study is to construct a spatial statistical methodology for pinpointing spatial clusters of road-kill accidents The model is defined based on the assumption that road-kill accident follows Poisson spatial process over geographic space, whereby expected frequency of accident is estimated and then the difference from the actual frequency is calculated. The higher the difference is the higher the degree of spatial clustering is. Interpretively, spatial clustering may be possible outcomes due partly to some environmental factors. This interpretation, which is not readily induced solely form pin-mapping of raw data, makes it possible to identify regions (road-segments) showing higher spatial clustering tendency of road-kill accidents. Analysis results lead to the brief discussions on where to put priorities when taking relevant actions.
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