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GeoInformatica

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

Footprint generation using fuzzy-neighborhood clustering

  • Jonathon K. Parker
  • Joni A. DownsEmail author
Article

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.

Keywords

Point pattern analysis Clustering algorithms Geometric footprints Fuzzy logic 

Notes

Acknowledgments

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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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

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