, Volume 17, Issue 2, pp 285–299 | Cite as

Footprint generation using fuzzy-neighborhood clustering

  • Jonathon K. Parker
  • Joni A. DownsEmail author


Geometric footprints, which delineate the region occupied by a spatial point pattern, serve a variety of functions in GIScience. This research explores the use of two density-based clustering algorithms for footprint generation. First, the Density-Based Spatial Clustering with Noise (DBSCAN) algorithm is used to classify points as core points, non-core points, or statistical noise; then a footprint is created from the core and non-core points in each cluster using convex hulls. Second, a Fuzzy-Neighborhood (FN)-DBSCAN algorithm, which incorporates fuzzy set theory, is used to assign points to clusters based on membership values. Then, two methods are presented for delineating footprints with FN-DBSCAN: (1) hull-based techniques and (2) contouring methods based on interpolated membership values. The latter approach offers increased flexibility for footprint generation, as it provides a continuous surface of membership values from which precise contours can be delineated. Then, a heuristic parameter selection method is described for FN-DBSCAN, and the approach is demonstrated in the context of wildlife home range estimation, where the goal is to a generate footprint of an animal’s movements from tracking data. Additionally, FN-DBSCAN is applied to produce crime footprints for a county in Florida. The results are used to guide a discussion of the relative merits of the new techniques. In summary, the fuzzy clustering approach offers a novel method of footprint generation that can be applied to characterize a variety of point patterns in GIScience.


Point pattern analysis Clustering algorithms Geometric footprints Fuzzy logic 



The authors would like to thank Dave Onorato, Florida Fish and Wildlife Conservation Commission, for providing the panther locational data used in this paper and Mr. John Chaffin, Hillsborough County Sheriff’s Office, for his assistance in acquiring the arrest data.


  1. 1.
    Alani H, Jones C, Tudhope D (2001) Voroni-based region approximation for geographical information retrieval with gazetteers. Int J Geogr Inf Sci 15:278–306CrossRefGoogle Scholar
  2. 2.
    Agosto E, Ajmar A, Boccardo P, Tonolo F, Lingua A (2008) Crime scene reconstruction using a fully geomatic approach. Sensors 8:6280–6302CrossRefGoogle Scholar
  3. 3.
    Bailey T, Gatrell A (1995) Interactive spatial data analysis. Longman Scientific and Technical, EssexGoogle Scholar
  4. 4.
    Burt WH (1943) Territoriality and home range as applied to mammals. J Mammal 24:346–352CrossRefGoogle Scholar
  5. 5.
    Cross V, Firat A (2000) Fuzzy objects for geographical information systems. Fuzzy Set Syst 113:19–36CrossRefGoogle Scholar
  6. 6.
    de Berg M, Schwarzkopf O, van Kreveld M, Overmars M (2000) Computational geometry: Algorithms and applications, 2nd edn. Springer, BerlinGoogle Scholar
  7. 7.
    Downs J, Horner M (2009) A characteristic-hull based method for home range estimation. Trans GIS 13:527–537CrossRefGoogle Scholar
  8. 8.
    Downs J, Horner W (2007) Effects of point pattern shape on home range estimates. J Wildl Manag 72:1813–1818Google Scholar
  9. 9.
    Duckham M, Kulik L, Worboys M, Galton A (2008) Efficient generation of simple polygons for characterizing the shape of a set of points in the plane. Pattern Recogn 41:3224–3236CrossRefGoogle Scholar
  10. 10.
    Dupenois M, Galton A (2009) Assigning footprints to dot sets: an analytical survey. COSIT 2009, LNCS 5756, pp. 227–244Google Scholar
  11. 11.
    Edelsbrunner H, Kirkpatrick DG, Seidel R (1983) On the shape of a set of points in the plane. In: Computer Vision and Image Understanding, vol. IT-29, pp. 551–559. IEEE, Los AlamitosGoogle Scholar
  12. 12.
    Ester M, Kriegel H, Sander J, Xu X (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, pp. 2226–231Google Scholar
  13. 13.
    Estivill-Castro V (2002) Why so many clustering algorithms: a position paper. ACM SIGKDD Explor Newslett 4(1):65–75CrossRefGoogle Scholar
  14. 14.
    Galton A, Duckham M (2006) What is the region occupied by a set of points? GIScience 2006, LNCS, 4197:91–98Google Scholar
  15. 15.
    Getz WM, Wilmers CC (2004) A local nearest-neighbor convex-hull construction of home ranges and utilization distributions. Ecography 27:489–505CrossRefGoogle Scholar
  16. 16.
    Gold C (1999) Crust and anti-crust: a one-step boundary and skeleton extraction algorithm. Proceedings of the ACM Conference on Computational Geometry, pp. 189–196Google Scholar
  17. 17.
    Grothe C, Schaab J (2009) Automated footprint generation from geotags with kernel density estimation and support vector machines. Spat Cogn Comput 9:195–211Google Scholar
  18. 18.
    Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH (2009) The WEKA data mining software: an update; SIGKDD Explorations, 11(1)Google Scholar
  19. 19.
    Harris RJ, Johnston RJ (2008) Primary schools, markets and choice: studying polarization and the core catchment areas of schools. Appl Spat Anal Policy 1:59–84CrossRefGoogle Scholar
  20. 20.
    Hemson G, Johnson P, South A, Kenward R, Ripley R, Macdonald D (2005) Are kernels the mustard? Data from global positioning system (GPS) collars suggests problems for kernel home-range analyses with least-squares cross-validation. J Anim Ecol 74:455–463CrossRefGoogle Scholar
  21. 21.
    Hinneberg A, Keim D (1998) An efficient approach to clustering in large multimedia databases with noise, Proc. 4th Int. Conf. on Knowledge Discovery & Data Mining, New York City, NY, pp. 58–65Google Scholar
  22. 22.
    Jain A, Murty M, Flynn P (1999) Data clustering: a review. ACM Comput Surv 31:264–323CrossRefGoogle Scholar
  23. 23.
    Jiang H, Eastman J (2000) Application of fuzzy measures in multi-criteria evaluation in GIS. Int J Geogr Inf Sci 14:173–184CrossRefGoogle Scholar
  24. 24.
    Kernohan BJ, Gitzen RA, Millspaugh JJ (2001) Analysis of animal space use and movements. In: Millspaugh JJ, Marzluff JM (eds) Radio tracking animals and populations. Academic, San Diego, pp 125–166CrossRefGoogle Scholar
  25. 25.
    Mitchell MS, Powell RA (2008) Estimated home ranges can misrepresent habitat relationships on patchy landscapes. Ecol Model 216:409–414CrossRefGoogle Scholar
  26. 26.
    Mohr CO (1947) Table of equivalent populations of North American small mammals. Am Midl Nat 37:223–249CrossRefGoogle Scholar
  27. 27.
    Nasibov E, Ulutagay G. Robustness of density-based clustering methods with various neighborhood relations. Fuzzy Sets and Systems, in pressGoogle Scholar
  28. 28.
    Sander J, Ester M, Kriegel H, Xu X (1998) Density-based clustering in spatial databases: the algorithm GDBSCAN and its applications. Data Min Knowl Disc 2(2):169–194CrossRefGoogle Scholar
  29. 29.
    Silverman BW (1986) Density estimation for statistics and data analysis. Chapman Hall, LondonGoogle Scholar
  30. 30.
    Tang M, Zhou Y, Cui P, Wang W, Li J, Zhang H, Hou Y, Yan B (2009) Discovery of migration habitats and routes of wild bird species by clustering and association analysis. Proceedings of the 5th International Conference on Advanced Data Mining and Applications, pp. 288–301Google Scholar
  31. 31.
    Wang F, Hall G (1996) Fuzzy representation of geographical boundaries in GIS. Int J Geogr Inf Sci 10:573–590Google Scholar
  32. 32.
    White GC, Garrott RA (1990) Analysis of wildlife radio-tracking data. Academic, San DiegoGoogle Scholar
  33. 33.
    Zadeh L (1965) Fuzzy sets. Inf Control 8:338–353CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of South FloridaTampaUSA
  2. 2.Department of Geography, Environment, and PlanningUniversity of South FloridaTampaUSA

Personalised recommendations