Paper Info
Title
Self-Organizing Map Methodology and Google Maps Services for Geographical Epidemiology Mapping
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.
Year
DOI
Venue
2009
10.1109/DICTA.2009.46
DICTA
Keywords
Field
DocType
public health data visualization,medical information systems,public health data management,google maps services,health data repository,application program interfaces,pattern recognition,geographic information systems,self-organizing map methodology,geographical epidemiology mapping,web-based epidemiology system,web gis application programming interface,geovisualization,self-organizing map,health data management system,data visualisation,internet,geographical epidemiology information analysis,health researcher,health geographical information system,self-organising feature maps,epidemiology information,epidemiology data,epidemiology data pattern recognition,public health data,health research,data mining,mashups,data visualization,self organizing map,public health,geographic information system,clustering algorithms,information analysis,application program interface
Data science,Information system,Geovisualization,Population,Geographic information system,World Wide Web,Data visualization,Computer science,Information repository,Population health,Data management
Conference
ISBN
Citations 
PageRank 
978-0-7695-3866-2
6
0.70
References 
Authors
5
3
Name
Order
Citations
PageRank
Jingyuan Zhang165360.53
Hao Shi2121.64
Yanchun Zhang33059284.90