Spatiotemporal interpolation methods for air pollution

Research output: Contribution to book or proceedingChapterpeer-review

2 Scopus citations

Abstract

The increasing amount of air pollution data requires efficient spatiotemporal interpolation methods to handle the demanding computational tasks. Several interpolation methods have been explored in the previous studies. Although many geographic information system applications provide interpolation tools, most current interpolation methods only apply to spatial data. Air pollution data not only have spatial attributes, but also change with time. When interpolating across space and time, the choice of the time scale versus the distance scale is an important issue that affects the accuracy of interpolation. In this chapter, we introduced some efficient deterministic spatiotemporal interpolation methods and search for optimal parameters to achieve good accuracy.

Original languageEnglish
Title of host publicationSpatiotemporal Analysis of Air Pollution and Its Application in Public Health
PublisherElsevier
Pages153-167
Number of pages15
ISBN (Electronic)9780128158227
ISBN (Print)9780128165263
DOIs
StatePublished - Jan 1 2019

Keywords

  • Air pollution
  • Inverse distance weighting
  • Radial basis function
  • Shape function
  • Spatiotemporal interpolation

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