Exploring the spatio-temporal dynamics of geographical processes with geographically weighted regression and geovisual analytics

Authors:
Urška Demšar;A. Stewart Fotheringham;Martin Charlton
Affiliations:
National Centre for Geocomputation, National University of Ireland, Maynooth, Co. Kildare, Ireland;National Centre for Geocomputation, National University of Ireland, Maynooth, Co. Kildare, Ireland;National Centre for Geocomputation, National University of Ireland, Maynooth, Co. Kildare, Ireland
Venue:
Information Visualization
Year:
2008

Citing 8
Cited 3

Self-organizing maps

Self-organizing maps
GeoVISTA studio: a codeless visual programming environment for geoscientific data analysis and visualization

Computers & Geosciences
Visualization

Intelligent data analysis
The parallel coordinate plot in action: design and use for geographic visualization

Computational Statistics & Data Analysis - Data visualization
Coordinating computational and visual approaches for interactive feature selection and multivariate clustering

Information Visualization - Special issue on coordinated and multiple views in exploratory visualization
Visualizing Demographic Trajectories with Self-Organizing Maps

Geoinformatica
Exploratory Analysis of Spatial and Temporal Data: A Systematic Approach

Exploratory Analysis of Spatial and Temporal Data: A Systematic Approach
The self-organizing map, the Geo-SOM, and relevant variants for geosciences

Computers & Geosciences

Towards personal high-performance geospatial computing (HPC-G): perspectives and a case study

Proceedings of the ACM SIGSPATIAL International Workshop on High Performance and Distributed Geographic Information Systems
A visual analytics framework for spatio-temporal analysis and modelling

Data Mining and Knowledge Discovery
Opening up the "black box" of medical image segmentation with statistical shape models

The Visual Computer: International Journal of Computer Graphics

Quantified Score

Hi-index	0.00

Visualization

Abstract

The paper examines the potential for combining a spatial statistical methodology - Geographically Weighted Regression (GWR) - with geovisual analytical exploration to help understand complex spatio-temporal processes. This is done by applying the combined statistical - exploratory methodology to a simulated data set in which the behaviour of regression parameters was controlled across space and time. A variety of complex spatio-temporal processes was captured through space-time (i.e. as spatio-temporal) varying parameters whose values were known. The task was to see if the proposed methodology could uncover these complex processes from the data alone. The results of the experiment confirm that the combined methodology can successfully identify spatio-temporal patterns in the local GWR parameter estimates that correspond to the controlled behaviour of the original parameters.