Combining Incompatible Spatial Data

TLDR

GPS, GIS, satellite imagery, remote sensing, and internet-enabled data acquisition now allow scientists to collect spatial data at multiple resolutions and scales, but combining such heterogeneous data poses significant statistical challenges. This article reviews the challenges of integrating heterogeneous spatial data and surveys state‑of‑the‑art statistical methods for doing so. It focuses on advanced statistical solutions drawn from geography, ecology, agriculture, geology, and statistics to address the complex integration problem.

Abstract

Global positioning systems (GPSs) and geographical information systems (GISs) have been widely used to collect and synthesize spatial data from a variety of sources. New advances in satellite imagery and remote sensing now permit scientists to access spatial data at several different resolutions. The Internet facilitates fast and easy data acquisition. In any one study, several different types of data may be collected at differing scales and resolutions, at different spatial locations, and in different dimensions. Many statistical issues are associated with combining such data for modeling and inference. This article gives an overview of these issues and the approaches for integrating such disparate data, drawing on work from geography, ecology, agriculture, geology, and statistics. Emphasis is on state-of-the-art statistical solutions to this complex and important problem.