Hotspots detection in spatial analysis via the extended gustafson-kessel algorithm

Authors:
Ferdinando Di Martino;Salvatore Sessa
Affiliations:
Dipartimento di Architettura, Università degli Studi di Napoli Federico II, Napoli, Italy;Dipartimento di Architettura and Centro Interdipartimentale per l'Analisi e la Progettazione Urbana Luigi Pisciotti, Università degli Studi di Napoli Federico II, Napoli, Italy
Venue:
Advances in Fuzzy Systems - Special issue on Fuzzy Methods and Approximate Reasoning in Geographical Information Systems
Year:
2013

Citing 9
Cited 0

Unsupervised Optimal Fuzzy Clustering

IEEE Transactions on Pattern Analysis and Machine Intelligence
Introduction: Geographic Information Systems in Public Health and Medicine

Journal of Medical Systems
Extended fuzzy C-means clustering algorithm for hotspot events in spatial analysis

International Journal of Hybrid Intelligent Systems
The extended fuzzy C-means algorithm for hotspots in spatio-temporal GIS

Expert Systems with Applications: An International Journal
Clustering algorithms based on volume criteria

IEEE Transactions on Fuzzy Systems
Fuzzy clustering with volume prototypes and adaptive cluster merging

IEEE Transactions on Fuzzy Systems
Application of Data Mining on the Development of a Disease Distribution Map of Screened Community Residents of Taipei County in Taiwan

Journal of Medical Systems
Application of Attribute Weighting Method Based on Clustering Centers to Discrimination of Linearly Non-Separable Medical Datasets

Journal of Medical Systems
Spatiotemporal hotspots analysis for exploring the evolution of diseases: an application to oto-laryngopharyngeal diseases

Advances in Fuzzy Systems - Special issue on Fuzzy Methods and Approximate Reasoning in Geographical Information Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

We show a new approach for detecting hotspots in spatial analysis based on the extended Gustafson-Kessel clustering method encapsulated in a Geographic Information System (GIS) tool. This algorithm gives (in the bidimensional case) ellipses as cluster prototypes to be considered as hotspots on the geographic map and we study their spatiotemporal evolution. The data consist of georeferenced patterns corresponding to positions of Taliban's attacks against civilians and soldiers in Afghanistan that happened during the period 2004-2010. We analyze the formation through time of new hotspots, the movement of the related centroids, the variation of the surface covered, the inclination angle, and the eccentricity of each hotspot.