Statistical visualization for data exploration: a case study on Sydney Olympic Park

Due to the powerful graphic interface in modern computers, visualization techniques have become more and more popular for data exploration. As a preliminary investigation, visualization is a greatly useful tool to lead to further statistical analysis and modelling. It is likely that historical envir...

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Bibliographic Details
Published in:	Chemosphere (Oxford) Vol. 52; no. 9; pp. 1601 - 1614
Main Authors:	Shao, Q., Li, Y., Campbell, E., De Boer, E.S., Laginestra, E., Statzenko, A.
Format:	Journal Article Conference Proceeding
Language:	English
Published:	Oxford Elsevier Ltd 01-09-2003 Elsevier
Subjects:	Applied sciences Computer Graphics Data Display Data Interpretation, Statistical Databases, Factual Environmental Monitoring - methods Environmental Monitoring - statistics & numerical data Environmental Pollutants - analysis Exact sciences and technology Global environmental pollution Magnoliopsida - chemistry Models, Statistical Multi-panel scatterplot New South Wales One-to-all scatterplot Pollution Spatial coverage Spatial variation Sydney Olympic park Temporal coverage Temporal variation Zinc - analysis Australia Oceania Sydney Olympic park Temporal variation One-to-all scatterplot Temporal coverage Multi-panel scatterplot Spatial coverage Spatial variation Case study Statistical method Cartography Pollution Surveillance Environmental management Time variation Concentration distribution Environmental protection
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Summary:	Due to the powerful graphic interface in modern computers, visualization techniques have become more and more popular for data exploration. As a preliminary investigation, visualization is a greatly useful tool to lead to further statistical analysis and modelling. It is likely that historical environmental data from a range of different studies may contain useful information that should be assessed for determining management actions. However, these historical data are collected from many different studies, which have different space and/or time scales making the visualization process complicated. In this paper we present several ways of visualizing historical data based on a study at Sydney Olympic Park. The tools include spatial coverage and variation for different spatial resolutions, temporal coverage and time series plots for different time scales, multi-panel scatterplots for a small number of variables and “one-to-all” scatterplots for a large number of variables.
ISSN:	0045-6535 1879-1298
DOI:	10.1016/S0045-6535(03)00500-9