Paper Info
Title
Geo-visualization and Clustering to Support Epidemiology Surveillance Exploration
Abstract
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.
Year
DOI
Venue
2010
10.1109/DICTA.2010.71
DICTA
Keywords
Field
DocType
webepi,clustering result,health care,k-means,pattern clustering,geo-visualization,google maps,geographical information exploration,diseases,mortality rate,clustering process,disease control,geographic information systems,health data exploration,medical situation,tasmania department of health and human services,gis mapping,epidemiological webgis service,promising mapping result,disease prevention,som,health rate,surveillance,mapping result,epidemiological geographical study,epidemics,data visualisation,internet,health researchers plan health,cluster algorithm,epidemiology surveillance exploration,medical computing,self-organising feature maps,population health epidemiology unit,public health surveillance,support epidemiology surveillance exploration,self-organizing maps,classification algorithms,self organizing maps,algorithm design and analysis,data visualization,clustering algorithms,k means
Data science,Health care,k-means clustering,Geographic information system,Data visualization,Public health surveillance,Computer science,Self-organizing map,Population health,Cluster analysis
Conference
ISBN
Citations 
PageRank 
978-0-7695-4271-3
0
0.34
References 
Authors
4
2
Name
Order
Citations
PageRank
Jingyuan Zhang165360.53
Hao Shi25622.60