Web geospatial visualisation for clustering analysis of epidemiological data

Zhang, Jingyuan (2014) Web geospatial visualisation for clustering analysis of epidemiological data. PhD thesis, Victoria University.


Public health is a major factor that in reducing of disease round the world. Today, most governments recognise the importance of public health surveillance in monitoring and clarifying the epidemiology of health problems. As part of public health surveillance, public health professionals utilise the results of epidemiological analysis to reform health care policy and health service plans. There are many health reports on epidemiological analysis within government departments, but the public are not authorised to access these reports because of commercial software restrictions. Although governments publish many reports of epidemiological analysis, the reports are coded in epidemiology terminology and are almost impossible for the public to fully understand. In order to improve public awareness, there is an urgent need for government to produce a more easily understandable epidemiological analysis and to provide an open access reporting system with minimum cost. Inevitably, it poses challenges to IT professionals to develop a simple, easily understandable and freely accessible system for public use. It is not only required to identify a data analysis algorithm which can make epidemiological analysis reports easily understood but also to choose a platform which can facilitate the visualisation of epidemiological analysis reports with minimum cost. In this thesis, there were two major research objectives: the clustering analysis of epidemiological data and the geospatial visualisation of the results of the clustering analysis. SOM, FCM and k-means, the three commonly used clustering algorithms for health data analysis, were investigated. After a number of experiments, k-means has been identified, based on Davies-Bouldin index validation, as the best clustering algorithm for epidemiological data. The geospatial visualisation requires a Geo-Mashups engine and geospatial layer customisation. Because of the capacity and many successful applications of free geospatial web services, Google Maps has been chosen as the geospatial visualisation platform for epidemiological reporting.

Item type Thesis (PhD thesis)
URI https://vuir.vu.edu.au/id/eprint/25917
Subjects Historical > FOR Classification > 0801 Artificial Intelligence and Image Processing
Historical > FOR Classification > 1117 Public Health and Health Services
Current > Division/Research > College of Science and Engineering
Keywords WebGISs, WebEpi, geospatial processing, geographic information systems, health information systems, epidemiology, algorithms, epidemiological reporting systems, DHHS, Department of Health and Human Services, Tasmania, Australia
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