Chapter 7 - Spatiotemporal interpolation methods for air pollution

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.