title: On geodetic distance computations in spatial modeling.
abstract: Statisticians analyzing spatial data often need to detect and model associations based upon distances on the Earth's surface. Accurate computation of distances are sought for exploratory and interpretation purposes, as well as for developing numerically stable estimation algorithms. When the data come from locations on the spherical Earth, application of Euclidean or planar metrics for computing distances is not straightforward. Yet, planar metrics are desirable because of their easier interpretability, easy availability in software packages, and well-established theoretical properties. While distance computations are indispensable in spatial modeling, their importance and impact upon statistical estimation and prediction have gone largely unaddressed. This article explores the different options in using planar metrics and investigates their impact upon spatial modeling.
authors: Division of Biostatistics, School of Public Health, University of Minneapolis, Minnesota 55455, USA. sudiptob@biostat.umn.edu
authors: Sudipto
authors: S
authors: Banerjee
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D016000:Cluster Analysis; D003627:Data Interpretation, Statistical; D005843:Geography; D006297:Health Services Accessibility; D008377:Maps as Topic; D008957:Models, Genetic; D015233:Models, Statistical; D008962:Models, Theoretical; D012044:Regression Analysis; D012984:Software