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.

Abstract

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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