Estimation of Environmental Exposure: Interpolation, Kernel Density Estimation, or Snapshotting

Authors: Xun Shi*, Dartmouth College, Meifang Li, Sun Yat-sen University; Dartmouth College
Topics: Spatial Analysis & Modeling, Geographic Information Science and Systems, Medical and Health Geography
Keywords: Environmental exposure, interpolation, kernel density estimation, snapshotting, health
Session Type: Paper
Day: 4/6/2019
Start / End Time: 3:05 PM / 4:45 PM
Room: Tyler, Marriott, Mezzanine Level
Presentation File: No File Uploaded


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 (KDE), 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.

Abstract Information

This abstract is already part of a session. View the session here.

To access contact information login