Exploratory Spatial Data Analysis with gwpcorMapper: an Interactive Mapping Tool for Geographically Weighted Correlation and Partial Correlation

摘要

Exploratory spatial data analysis (ESDA) plays a key role in research that includes geographic data. In ESDA, analysts typically need to visualize observations and local relationships on a map. However, software dedicated to visualizing local degrees of association between multiple variables in high-dimensional geospatial datasets remains undeveloped. This paper introduces gwpcorMapper, a newly developed software application for mapping geographically weighted (GW) correlation and partial correlation statistics in high-dimensional datasets. gwpcorMapper facilitates ESDA by giving researchers the ability to interact with map components that describe local correlative relationships. gwpcorMapper is an open source and is built using the R Shiny framework. The software inherits its algorithm logic from GWpcor, an R library for calculating the geographically weighted correlation and partial correlation statistics. We demonstrate the application of gwpcorMapper by using it to investigate census data to find meaningful relationships that describe the work-life environment in the 23 special wards of Tokyo, Japan. gwpcorMapper is useful in both variable selection and parameter tuning for GW statistics.