Spatiotemporal interpolation methods for air pollution

Authors

• Lixin Li, Georgia Southern University
• Weitian Tong, Eastern Michigan University
• Reinhard Piltner, Georgia Southern University

Document Type

Book Chapter

Publication Date

2020

Department/School

Computer Science

Publication Title

Spatiotemporal Analysis of Air Pollution and Its Application in Public Health

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.

Comments

W. Tong is a faculty member in EMU's Department of Computer Science.

Recommended Citation

Li, L., Tong, W., & Piltner, R. (2020). Spatiotemporal interpolation methods for air pollution. In L. Li, X. Zhou, & W. Tong (Eds.), Spatiotemporal analysis of air pollution and its application in public health (pp. 153–167). Elsevier. https://doi.org/10.1016/B978-0-12-815822-7.00007-8