Abstract
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.
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References
Alani H, Jones C, Tudhope D (2001) Voroni-based region approximation for geographical information retrieval with gazetteers. Int J Geogr Inf Sci 15:278–306
Agosto E, Ajmar A, Boccardo P, Tonolo F, Lingua A (2008) Crime scene reconstruction using a fully geomatic approach. Sensors 8:6280–6302
Bailey T, Gatrell A (1995) Interactive spatial data analysis. Longman Scientific and Technical, Essex
Burt WH (1943) Territoriality and home range as applied to mammals. J Mammal 24:346–352
Cross V, Firat A (2000) Fuzzy objects for geographical information systems. Fuzzy Set Syst 113:19–36
de Berg M, Schwarzkopf O, van Kreveld M, Overmars M (2000) Computational geometry: Algorithms and applications, 2nd edn. Springer, Berlin
Downs J, Horner M (2009) A characteristic-hull based method for home range estimation. Trans GIS 13:527–537
Downs J, Horner W (2007) Effects of point pattern shape on home range estimates. J Wildl Manag 72:1813–1818
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 41:3224–3236
Dupenois M, Galton A (2009) Assigning footprints to dot sets: an analytical survey. COSIT 2009, LNCS 5756, pp. 227–244
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 Alamitos
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. 2226–231
Estivill-Castro V (2002) Why so many clustering algorithms: a position paper. ACM SIGKDD Explor Newslett 4(1):65–75
Galton A, Duckham M (2006) What is the region occupied by a set of points? GIScience 2006, LNCS, 4197:91–98
Getz WM, Wilmers CC (2004) A local nearest-neighbor convex-hull construction of home ranges and utilization distributions. Ecography 27:489–505
Gold C (1999) Crust and anti-crust: a one-step boundary and skeleton extraction algorithm. Proceedings of the ACM Conference on Computational Geometry, pp. 189–196
Grothe C, Schaab J (2009) Automated footprint generation from geotags with kernel density estimation and support vector machines. Spat Cogn Comput 9:195–211
Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH (2009) The WEKA data mining software: an update; SIGKDD Explorations, 11(1)
Harris RJ, Johnston RJ (2008) Primary schools, markets and choice: studying polarization and the core catchment areas of schools. Appl Spat Anal Policy 1:59–84
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 74:455–463
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. 58–65
Jain A, Murty M, Flynn P (1999) Data clustering: a review. ACM Comput Surv 31:264–323
Jiang H, Eastman J (2000) Application of fuzzy measures in multi-criteria evaluation in GIS. Int J Geogr Inf Sci 14:173–184
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 125–166
Mitchell MS, Powell RA (2008) Estimated home ranges can misrepresent habitat relationships on patchy landscapes. Ecol Model 216:409–414
Mohr CO (1947) Table of equivalent populations of North American small mammals. Am Midl Nat 37:223–249
Nasibov E, Ulutagay G. Robustness of density-based clustering methods with various neighborhood relations. Fuzzy Sets and Systems, in press
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):169–194
Silverman BW (1986) Density estimation for statistics and data analysis. Chapman Hall, London
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. 288–301
Wang F, Hall G (1996) Fuzzy representation of geographical boundaries in GIS. Int J Geogr Inf Sci 10:573–590
White GC, Garrott RA (1990) Analysis of wildlife radio-tracking data. Academic, San Diego
Zadeh L (1965) Fuzzy sets. Inf Control 8:338–353
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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.
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Parker, J.K., Downs, J.A. Footprint generation using fuzzy-neighborhood clustering. Geoinformatica 17, 285–299 (2013). https://doi.org/10.1007/s10707-012-0152-0
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DOI: https://doi.org/10.1007/s10707-012-0152-0