Geo-visualization and Clustering to Support Epidemiology Surveillance Exploration

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Zhang, Jingyuan and Shi, Hao (2010) Geo-visualization and Clustering to Support Epidemiology Surveillance Exploration. In: 2010 Digital Image Computing : Techniques and Applications (DICTA 2010) : 1-3 December 2010, Sydney, Australia : proceedings. Zhang, Jian, Shen, Chunhua, Geers, Glenn and Wu, Qiang, eds. IEEE Computer Society, Los Alamitos, California, pp. 381-386.


WebEpi is an epidemiological WebGIS service developed for the Population Health Epidemiology Unit of the Tasmania Department of Health and Human Services (DHHS). Epidemiological geographical studies help analyze public health surveillance and medical situations. It is still a challenge to conduct large-scale geographical information exploration of epidemiology surveillance based on patterns and relationships. Generally, there are two crucial stages for GIS mapping of epidemiological data: one precisely clusters areas according to their health rate, the other efficiently presents the clustering result on GIS map which aims to help health researchers plan health resources for disease prevention and control. There are two major cluster algorithms for health data exploration, namely Self Organizing Maps (SOM) and K-means. In this paper, the clustering based on SOM and K-means are presented and their clustering results are compared by their clustering process and mapping results. It is concluded from experimental results that K-means produces a more promising mapping result for visualizing the highest mortality rate municipalities.

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Item type Book Section
DOI 10.1109/DICTA.2010.71
Official URL
ISBN 9781424488162 (print), 9780769542713 (online)
Subjects Historical > FOR Classification > 0801 Artificial Intelligence and Image Processing
Historical > FOR Classification > 1117 Public Health and Health Services
Historical > SEO Classification > 9204 Public Health (excl. Specific Population Health)
Historical > Faculty/School/Research Centre/Department > School of Engineering and Science
Keywords ResPubID19670, geo-visualisation, Internet, data visualisation, diseases, epidemics, geographic information systems, health care, medical computing, pattern clustering, self-organising feature maps, surveillance, GIS mapping, K-means, Tasmania Department of Health and Human Services, WebEpi, cluster algorithms, disease control, diseases, disease prevention, epidemiological WebGIS service, epidemiological geographical study, epidemiology surveillance exploration, geographical information exploration, health data exploration, health rate, medical situation, mortality rate, population health epidemiology unit, public health surveillance, self-organizing maps, Google Maps, SOM, algorithm design and analysis, classification algorithms, clustering algorithms
Citations in Scopus 3 - View on Scopus
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