Spatial Data Mining for Highlighting Hotspots in Personal Navigation Routes

Rapid developments in the availability and access to spatially referenced information in a variety of areas have induced the need for better analytical techniques to understand the various phenomena. In particular, the authors’ analysis is an insight into a wealth of geographical data collected by individuals as activity dairy data. The attention is drawn on point datasets corresponding to GPS traces driven along a same route in different days. In this paper, the authors explore the presence of clusters along the route, trying to understand the origins and motivations behind that to better understand the road network structure in terms of ‘dense’ spaces along the network. Therefore, the attention is focused on methods to highlight such clusters and see their impact on the network structure. Spatial clustering algorithms are examined (DBSCAN) and a comparison with other non-parametric density based algorithm (Kernel Density Estimation) is performed. Different tests are performed over the urban area of Trieste (Italy), considering both multiple users and different origin/destination journeys.