Estimation of environmental exposure: interpolation, kernel density estimation or snapshotting

Abstract

In environmental health researches and practices, spatial analysis became an important approach to estimation of environmental exposure of human subjects under concern. A typical situation in this kind of application is that the data of pollution are available only at certain locations, and thus, inference is needed to convert a limited number of values at discrete locations into a continuous surface. This paper intends to clarify the distinction among three methods that can be used to achieve this conversion, namely interpolation, kernel density estimation and snapshotting. Due to the apparent similarity of the three, they may cause confusions that lead to misuses. We compare and contrast the three methods, in terms of nature of the input data, mathematical process of the inference, and essential meaning of the output. For each method, we suggest appropriate applications within the context of estimation of environmental exposure.