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

  • Gabriella Schoier
  • Giuseppe Borruso
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6782)


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).


DBSCAN Kernel Density Estimation GPS traces activity dairy data 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Bailey, T.C., Gatrell, A.C.: Interactive Spatial Data Analysis. Addison Wesley Longman, Edinburgh (1996)Google Scholar
  2. 2.
    Borruso, G.: Network Density Estimation: Analysis of Point Patterns over a Network. In: Gervasi, O., Gavrilova, M.L., Kumar, V., Laganá, A., Lee, H.P., Mun, Y., Taniar, D., Tan, C.J.K., et al. (eds.) ICCSA 2005. LNCS, vol. 3482, pp. 126–132. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  3. 3.
    Borruso, G.: Network Density Estimation: A GIS Approach for Analysing Point Patterns in a Network Space. Transactions in GIS 12(3), 377–402 (2008)CrossRefGoogle Scholar
  4. 4.
    Brunsdon, C.: Analysis of Univariate Census Data. In: Openshaw, S. (ed.) Census Users Handbook. GeoInformation International, Cambridge, pp. 213–238 (1995)Google Scholar
  5. 5.
    Chainey, S., Reid, S., Stuart, N.: When is a hotspot a hotspot? A procedure for creating statistically robust hotspot maps of crime. In: Kidner, D., Higgs, G., White, S. (eds.) Socio-Economic Applications of Geographic Information Science Innovations in GIS 9, Taylor & Francis, Abington (2002)Google Scholar
  6. 6.
    Cressie, N.A.C.: Statistics for spatial data. John Wiley & Sons, London (1993)zbMATHGoogle Scholar
  7. 7.
    Danese, M., Lazzari, M., Murgante, B.: Kernel Density Estimation Methods for a Geostatistical Approach in Seismic Risk Analysis: the Case Study of Potenza Hilltop Town (southern Italy). In: Gervasi, O., Murgante, B., Laganà, A., Taniar, D., Mun, Y., Gavrilova, M.L. (eds.) ICCSA 2008, Part I. LNCS, vol. 5072, pp. 415–429. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  8. 8.
    El-Sonbaty, Y., Ismail, M.A., Farouk, M.: An Efficient Density-Based Clustering Algorithm for large Databases. In: Proceedings of the 16th IEEE International Conference on Tods with Artificial Intelligence, ICTAI (2004)Google Scholar
  9. 9.
    Epanechnikov, V.A.: Nonparametric estimation of a multivariate probability density. Theory of probability and its applications 14, 153–158 (1969)MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    Ester, M., Kriegel, H.P., Sander, J., Xiaowei, X.: A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. In: Proceeding of the 2nd International Confererence on Knowledge Discovery and Data Mining, pp. 94–99 (1996)Google Scholar
  11. 11.
    Gatrell, A., Bailey, T., Diggle, P., Rowlingson, B.: Spatial Point Pattern Analysis and its Application in Geographical Epidemiology. Transactions of the Institute of British Geographers 2, 1256–1274 (1996)Google Scholar
  12. 12.
    Gatrell, A.: Density Estimation and the Visualisation of Point Patterns. In: Hearnshaw, H.M., Unwin, D. (eds.) Visualisation in Geographical Information Systems, pp. 65–75. John Wiley, Chichester & Sons (1994)Google Scholar
  13. 13.
    Han, J., Kamber, M., Tung, A.K.H.: Spatial Clutering Methods in Data Mining: A Survey (2001),
  14. 14.
    Koperski, K., Han, J., Adhikary, J.: Mining Knowledge in Geographical Data (1998),
  15. 15.
    Okabe, A., Satoh, T.: Uniform Transformation for Points Pattern Analysis on a Non-uniform Network. Journal of Geographical Systems 8, 25–37 (2006)CrossRefGoogle Scholar
  16. 16.
    O’Sullivan, D., Unwin, P.J.: Geographic Information Analysis. Wiley, Chichester (2003)Google Scholar
  17. 17.
    Sander, J., Ester, M., Kriegel, H.P., Xiaowei, X.: Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and its applications (1999),
  18. 18.
    Schoier, G., Bato, B.: A modification of the DBSCAN Algorithm in a Spatial Data Mining Approach. In: Meeting of the Classification and Data Analysis Group of the SIS: CLADAG 2007, Macerata, pp. 395–398 (2007)Google Scholar
  19. 19.
    Schoier, G., Borruso, G.: A Clustering Method for Large Spatial Databases. In: Laganá, A., Gavrilova, M.L., Kumar, V., Mun, Y., Tan, C.J.K., Gervasi, O. (eds.) ICCSA 2004. LNCS, vol. 3044, pp. 1089–1095. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  20. 20.
    Silverman, B.W.: Density Estimation for Statistics and Data Analysis. Chapman Hall, London (1986)CrossRefzbMATHGoogle Scholar
  21. 21.
    Yamada, I., Thill, J.: Comparison of Planar and Network K-functions in Traffic Accident analysis. Journal of Transport Geography 12, 149–158 (2004)CrossRefGoogle Scholar
  22. 22.
    Yu, X., Zhou, D., Zhou, Y.: A New Clustering Algorithm Based on Distance and Density, pp. 1016–1021. IEEE, Los Alamitos (2005)Google Scholar
  23. 23.
    Yue, S., Li, P., Gou, J., Zhou, S.: Using greedy Algorithm: DBSCAN revisited II. Journal of Zhejiang University SCIENCE, 1404–1412 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

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

Personalised recommendations