Spatio-Temporal Data Mining for Air Pollution Problems

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Author(s): Seoung Bum Kim (The University of Texas at Arlington, USA), Chivalai Temiyasathit (The University of Texas at Arlington, USA), Sun-Kyoung Park (North Central Texas Council of Governments, USA)and Victoria C.P. Chen (The University of Texas at Arlington, USA)
Copyright: 2009
Pages: 8
Source title: Encyclopedia of Data Warehousing and Mining, Second Edition
Source Author(s)/Editor(s): John Wang (Montclair State University, USA)
DOI: 10.4018/978-1-60566-010-3.ch277

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Abstract

Vast amounts of data are being generated to extract implicit patterns of ambient air pollution. Because air pollution data are generally collected in a wide area of interest over a relatively long period, such analyses should take into account both temporal and spatial characteristics. Furthermore, combinations of observations from multiple monitoring stations, each with a large number of serially correlated values, lead to a situation that poses a great challenge to analytical and computational capabilities. Data mining methods are efficient for analyzing such large and complicated data. Despite the great potential of applying data mining methods to such complicated air pollution data, the appropriate methods remain premature and insufficient. The major aim of this chapter is to present some data mining methods, along with the real data, as a tool for analyzing the complex behavior of ambient air pollutants.

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