gwpcorMapper: an interactive mapping tool for exploring geographically
weighted correlation and partial correlation in high-dimensional geospatial
datasets
Exploratory spatial data analysis (ESDA) plays a key role in research that
includes geographic data. In ESDA, analysts often want to be able to visualize
observations and local relationships on a map. However, software dedicated to
visualizing local spatial relations be-tween multiple variables in high
dimensional datasets remains undeveloped. This paper introduces gwpcorMapper, a
newly developed software application for mapping geographically weighted
correlation and partial correlation in large multivariate datasets.
gwpcorMap-per facilitates ESDA by giving researchers the ability to interact
with map components that describe local correlative relationships. We built
gwpcorMapper using the R Shiny framework. The software inherits its core
algorithm 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 explore census data in order to find meaningful
relationships that describe the work-life environment in the 23 special wards
of Tokyo, Japan. We show that gwpcorMapper is useful in both variable selection
and parameter tuning for geographically weighted statistics. gwpcorMapper
highlights that there are strong statistically clear local variations in the
relationship between the number of commuters and the total number of hours
worked when considering the total population in each district across the 23
special wards of Tokyo. Our application demonstrates that the ESDA process with
high-dimensional geospatial data using gwpcorMapper has applications across
multiple fields.Comment: 18 pages, 8 figures, 2 table