Individual Movements and Geographical Data Mining. Clustering Algorithms for Highlighting Hotspots in Personal Navigation Routes

  • Gabriella Schoier
  • Giuseppe Borruso
Conference paper

DOI: 10.1007/978-3-642-21928-3_32

Part of the Lecture Notes in Computer Science book series (LNCS, volume 6782)
Cite this paper as:
Schoier G., Borruso G. (2011) Individual Movements and Geographical Data Mining. Clustering Algorithms for Highlighting Hotspots in Personal Navigation Routes. In: Murgante B., Gervasi O., Iglesias A., Taniar D., Apduhan B.O. (eds) Computational Science and Its Applications - ICCSA 2011. ICCSA 2011. Lecture Notes in Computer Science, vol 6782. Springer, Berlin, Heidelberg

Abstract

The rapid developments in the availability and access to spatially referenced information in a variety of areas, has induced the need for better analysis techniques to understand the various phenomena. In particular our analysis represents a first 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. Our aim here is to explore the presence of clusters along the route, trying to understand the origins and motivations behind that in order to better understand the road network structure in terms of ’dense’ spaces along the network. In this paper the attention is therefore 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. A test is performed over the urban area of Trieste (Italy).

Keywords

DBSCAN Kernel Density Estimation GPS traces activity dairy data 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Gabriella Schoier
    • 1
  • Giuseppe Borruso
    • 1
  1. 1.DEAMSUniversity of TriesteTriesteItaly

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