Mapping the results of local statistics

Stephen Matthews and Tse-Chuan Yang
Additional contact information
Stephen Matthews: Pennsylvania State University
Tse-Chuan Yang: State University of New York at Albany

Demographic Research, 2012, vol. 26, issue 6, 151-166

Abstract: Background: The application of geographically weighted regression (GWR) - a local spatial statistical technique used to test for spatial nonstationarity - has grown rapidly in the social, health, and demographic sciences. GWR is a useful exploratory analytical tool that generates a set of location-specific parameter estimates which can be mapped and analysed to provide information on spatial nonstationarity in the relationships between predictors and the outcome variable. Objective: A major challenge to users of GWR methods is how best to present and synthesize the large number of mappable results, specifically the local parameter parameter estimates and local t-values, generated from local GWR models. We offer an elegant solution. Methods: This paper introduces a mapping technique to simultaneously display local parameter estimates and local t-values on one map based on the use of data selection and transparency techniques. We integrate GWR software and GIS software package (ArcGIS) and adapt earlier work in cartography on bivariate mapping. We compare traditional mapping strategies (i.e., side-by-side comparison and isoline overlay maps) with our method using an illustration focusing on US county infant mortality data. Conclusions: The resultant map design is more elegant than methods used to date. This type of map presentation can facilitate the exploration and interpretation of nonstationarity, focusing map reader attention on the areas of primary interest.

Keywords: mapping; geographically weighted regression; nonstationarity; local statistics (search for similar items in EconPapers)
JEL-codes: J1 Z0 (search for similar items in EconPapers)
Date: 2012
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (22)

Downloads: (external link)
https://www.demographic-research.org/volumes/vol26/6/26-6.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:dem:demres:v:26:y:2012:i:6

DOI: 10.4054/DemRes.2012.26.6

Access Statistics for this article

More articles in Demographic Research from Max Planck Institute for Demographic Research, Rostock, Germany
Bibliographic data for series maintained by Editorial Office ().