A scaled line-based kernel density estimator for the retrieval of utilization distributions and home ranges from GPS movement tracks

Abstract

Utilization distributions (UDs) can be used to describe the intensity with which an animal or human has used a certain geographical location. Within the domain of wildlife ecology, a density distribution model represents one way to describe an animals' home range. Several methods have been developed to derive UDs, and subsequently home ranges. Most of these methods, e.g. kernel density estimation (KDE), and local convex hull methods, have been developed with point-based datasets in mind, and do not utilize additional information that comes with GPS-based tracking data (e.g., temporal information). To employ such additional information we extend the point-based KDE approach to work with sequential GPS-point tracks, the outcome of which is a line-based KDE. We first describe the design criteria for the line-KDE algorithm. Then we introduce the basic modeling approach and its refinement through the introduction of a scaling function. This scaling function modifies the utilization distribution so that a bone-like probability distribution for a single GPS track segment is obtained. Finally we compare the estimated utilization distributions and home ranges for two datasets derived using our line-KDE approach with those obtained using the point-KDE and Brownian Bridge (BB) approaches. Advantages of the line-based KDE by design are (i) a better representation of utilization density near GPS points when compared against the BB approach, and (ii) the ability to model and retain movement corridors when compared against point-KDE.