Spatial data mining for highlighting hotspots in personal navigation routes

International Journal of Data Warehousing and Mining

Volumn 8, Issue 3, 2012, Pages 45-61

  Schoier, Gabriella ^a   Borruso, Giuseppe ^a  

^a UNIVERSITY OF TRIESTE   (Italy)

Author keywords

Activity dairy data; Clustering algorithms; Density based spatial clustering of applications with noise (DBSCAN); GPS traces; Kernel density estimation; Spatial data mining

Indexed keywords

DENSITY-BASED ALGORITHM; DENSITY-BASED SPATIAL CLUSTERING OF APPLICATIONS WITH NOISE; DIFFERENT ORIGINS; GEOGRAPHICAL DATA; HOTSPOTS; KERNEL DENSITY ESTIMATION; MULTIPLE USER; NETWORK STRUCTURES; NON-PARAMETRIC; PERSONAL NAVIGATION; POINT DATASETS; ROAD NETWORK; SPATIAL DATA MINING; TRIESTE; URBAN AREAS;

DIGITAL STORAGE; STATISTICS;

CLUSTERING ALGORITHMS;

EID: 84870283445     PISSN: 15483924     EISSN: 15483932     Source Type: Journal    
DOI: 10.4018/jdwm.2012070103     Document Type: Article

References (28)

1. Bailey, T. C, & Gatrell, A. C. (1996). Interactive spatial data analysis. Reading, MA: Addison-Wesley.
2. Borruso, G. (2005). Network density estimation: Analysis of point patterns over a network. In O. Gervasi, M. L. Gavrilova, V. Kumar, A. Laganà, H. P. Lee, Y. Mun, D. Taniar, & C. J. Tan (Eds.), Proceedings of the International Conference on Computational Science and its Applications (LNCS 3482, pp. 126-132). (Pubitemid 41313550)
3. Borruso, G. (2008). Network density estimation: A GIS approach for analysing point patterns in a network space. Transactions in GIS, 12(3), 377-402. doi:10.1111/j.1467-9671.2008.01107.x
4. Brunsdon, C. (1995). Analysis of univariate census data. In Openshaw, S. (Ed.), Census users handbook (pp. 213-238). London, UK: GeoInformation International.
5. Chainey, S., Reid, S., & Stuart, N. (2002). 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. Boca Raton, FL: Taylor and Francis. doi:10.4324/9780203301074-chapter-3
6. Cressie, N. A. C. (1993). Statistics for spatial data. Chichester, UK: John Wiley & Sons.
7. Danese, M., Lazzari, M., & Murgante, B. (2008). Kernel density estimation methods for a geostatistical approach in seismic risk analysis: The case study of Potenza Hilltop Town (southern Italy). In O. Gervasi, B. Murgante, A. Laganà, D. Taniar, Y. Mun, & M. L. Gavrilova (Eds.), Proceedings of the International Conference on Computational Science and its Applications, Part I (LNCS 5072, pp. 415-429).
8. *: A constraint-based algorithm for mining temporal relational patterns. International Journal of Data Warehousing and Mining, 4(4), 42-61. doi:10.4018/jdwm.2008100103
9. El-Sonbaty, Y, Ismail, M. A., & Farouk, M. (2004). An efficient density-based clustering algorithm for large databases. In Proceedings of the 16th IEEE International Conference on Tods with Artificial Intelligence (pp. 673-677). (Pubitemid 40455079)
10. Engle, K. M., & Gangopadhyay, A. (2010). An efficient method for discretizing continuous attributes. International Journal of Data Warehousing and Mining, 6(2), 1-21. doi:10.4018/jdwm.2010040101
11. Epanechnikov, V. A. (1969). Nonparametric estimation of a multivariate probability density. Theory of Probability and Its Applications, 14, 153-158. doi:10.1137/1114019
12. nd International Conference on Knowledge Discovery and Data Mining (pp. 94-99).
13. Gatrell, A. (1994). Density estimation and the visualisation of point patterns. In Hearnshaw, H. M., & Unwin, D. J. (Eds.), Visualisation in geographical information systems (pp. 65-75). Chichester, UK: John Wiley & Sons.
14. Gatrell, A., Bailey, T., Diggle, P., & Rowlingson, B. (1996). Spatial point pattern analysis and its application in geographical epidemiology. Transactions of the Institute of British Geographers, 2, 1256-1274.
15. Han, J., Kamber, M., & Tung, A. K. H. (2001). Spatial clustering methods in data mining: A survey. Retrieved from http://www.cs.uiuc.edu/homes/ hanj/pdf/gkdbk01.pdf
16. Koperski, K., Han, J., & Adhikary, J. (1998). Mining knowledge in geographical data. Retrieved from http://www.cs.uiuc.edu/homes/hanj/pdf/geo- survey98.pdf
17. Miller, H. (2005). A measurement theory for time geography. Geographical Analysis, 37, 17-45. doi:10.1111/j.1538-4632.2005.00575.x
18. Miller, H., & Bridwell, S. A. (2009). A field-based theory for time geography. Annals of the Association of American Geographers. Association of American Geographers, 99, 49-75. doi:10.1080/00045600802471049
19. O'Sullivan, D., & Unwin, D. J. (2003). Geographic information analysis. Chichester, UK: John Wiley & Sons.
20. Okabe, A., & Satoh, T. (2006). Uniform transformation for points pattern analysis on a non-uniform network. Journal of Geographical Systems, 8, 25-37. doi:10.1007/s10109-005-0009-2 (Pubitemid 43280267)
21. Sander, J., Ester, M., Kriegel, H. P., & Xiaowei, X. (1999). Density-based clustering in spatial databases: The algorithm GDBSCAN and its applications. Retrieved from http://www.dbs.informatik.uni-muenchen.de/ Publikationen/
22. Schoier, G., & Bato, B. (2007). A modification of the DBSCAN algorithm in a spatial data mining approach. In Proceedings of the Meeting of the Classification and Data Analysis Group of the SIS: CLADAG (pp. 395-398).
23. Schoier, G., & Borruso, G. (2004). A clustering method for spatial databases. In A. Laganá, M. L. Gavrilova, V. Kumar, Y. Mun, C. J. K. Tan, & O. Gervasi (Eds.), Proceedings of the International Conference on Computational Science and its Applications (LNCS 3044, pp. 1089-1995).
24. Silverman, B. W. (1986). Density estimation for statistics and data analysis. London, UK: Chapman Hall.
25. Trasarti, R., Giannotti, F., Nanni, M., Pedreschi, D., & Renso, C. (2011). A query language for mobility data mining. International Journal of Data Warehousing and Mining, 7(1), 24-45. doi: 10.4018/jdwm.2011010102
26. Yamada, I., & Thill, J. (2004). Comparison of planar and network k-functions in traffic accident analysis. Journal of Transport Geography, 12, 149-158. doi:10.1016/j.jtrangeo.2003.10.006 (Pubitemid 38522410)
27. Yu, X., Zhou, D., & Zhou, Y (2005). A new clustering algorithm based on distance and density. In Proceedings of the IEEE International Conference on Service Systems and Services Management (pp. 1016-1021).
28. Yue, S., Li, P., Gou, J., & Zhou, S. (2004). Using greedy algorithm: DBSCAN revisited II. Journal of Zhejiang University. Science, 5(11), 1404-1412. doi:10.1631/jzus.2004.1405 (Pubitemid 40017149).