DocumentCode :
3179772
Title :
Self-Organizing Map Methodology and Google Maps Services for Geographical Epidemiology Mapping
Author :
Zhang, Jingyuan ; Shi, Hao ; Zhang, Yanchun
Author_Institution :
Sch. of Eng. & Sci., Victoria Univ., Melbourne, VIC, Australia
fYear :
2009
fDate :
1-3 Dec. 2009
Firstpage :
229
Lastpage :
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 geo-visualization which enables health research to be conducted in a novel and effective way.
Keywords :
Internet; application program interfaces; data visualisation; geographic information systems; medical information systems; pattern recognition; self-organising feature maps; Google maps services; Web GIS application programming interface; Web-based epidemiology system; geographical epidemiology mapping; geovisualization; health data management system; health data repository; health geographical information system; pattern recognition; public health data management; public health data visualization; self-organizing map; Artificial neural networks; Data visualization; Digital images; Diseases; Geographic Information Systems; Humans; Information analysis; Pattern recognition; Public healthcare; Surveillance;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Digital Image Computing: Techniques and Applications, 2009. DICTA '09.
Conference_Location :
Melbourne, VIC
Print_ISBN :
978-1-4244-5297-2
Electronic_ISBN :
978-0-7695-3866-2
Type :
conf
DOI :
10.1109/DICTA.2009.46
Filename :
5384978
Link To Document :
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