Self-Organizing Map Methodology and Google Maps Services for Geographical Epidemiology Mapping

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Zhang, Jingyuan, Shi, Hao and Zhang, Yanchun (2009) Self-Organizing Map Methodology and Google Maps Services for Geographical Epidemiology Mapping. In: 2009 digital image computing: techniques and applications, DICTA 2009 : 1 - 3 December 2009, Melbourne, Australia. Shi, Hao, Zhang, Yanchun, Bottema, Murk J, Lovell, Brian C and Maeder, Anthony J, eds. IEEE Computer Society, Los Alamitos, California, pp. 229-235.


The Health Geographical Information System (GIS) has been used in many organizations for the management and visualization of public health data. As epidemiology information has become a part of health data repository in the health data management system, many health researchers have dedicated their research areas to geographical epidemiology information analysis and visualization. The Population Health Epidemiology Unit of the Department of Health and Human Services (DHHS) in Tasmania uses the web-based epidemiology system (‘WebEpi’) to conduct monitoring and surveillance of the health of Tasmanian population. In this paper, the epidemiology data Self-Organizing Map (SOM) analysis methodology and Google Maps services techniques of WebEpi are presented. SOM has been used as a tool to recognize patterns with data sets measuring epidemiology data and related geographical information. Google Maps services offer Web GIS Application Programming Interface (API) and GIS views. The integration of SOM and Google Maps facilitates the epidemiology data pattern recognition and geovisualization which enables health research to be conducted in a novel and effective way.

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Additional Information

Item type Book Section
DOI 10.1109/DICTA.2009.46
Official URL
ISBN 9781424452972 (print), 9780769538662 (online)
Subjects Historical > FOR Classification > 0801 Artificial Intelligence and Image Processing
Historical > FOR Classification > 1117 Public Health and Health Services
Historical > SEO Classification > 8903 Information Services
Historical > Faculty/School/Research Centre/Department > School of Engineering and Science
Keywords ResPubID17030, Internet, application program interfaces, data visualisation, geographic information systems, medical information systems, pattern recognition, self-organising feature maps, Google maps services, Web GIS applications, programming interface, epidemiological, geovisualization, health data management system, repositories, pattern recognition, public health data management, artificial neural networks, digital images, diseases, information analysis, pattern recognition, public healthcare, surveillance, epidemiological data, Tasmania
Citations in Scopus 17 - View on Scopus
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