This document summarizes the Extensible Web-based Geovisual Analytics Toolkit (EWGAT), which allows users to perform Geographically Weighted Regression (GWR) analysis through a web interface. EWGAT uses a three-tier architecture with JavaScript libraries for the client interface, R for computation on the backend server, and JSON format for data exchange. It implements various geovisualization functions to enable users to calibrate, present, and synthesize large numbers of GWR results through interactive graphical interfaces. Future work may include additional case studies, interface improvements, and incorporating more spatial statistical analytics capabilities.

1. Main Features
Reference
Technical Architecture
EWGAT： Extensible Web-based Geovisual
Analytics Toolkit
TIAN XIANCAI
Motivation
GWR is an important and useful exploratory tool for studying spatial
nonstationarity of relationship strength in regression models. However, in real
life practice, the use of GWR was constrained by some valid concerns:
• GWR analysis is not readily available in both conventional GIS and web
GIS
• Most statistical packages we use, such as SAS and SPSS, lack support for
geospatial analysis
• Although R language has specialized packages for GWR, it requires users to
be familiar with inherent scripting syntax
• There is a lack of appropriate visualization methods to calibrate, present and
synthesize a large number of GWR results
EWGAT was extended from existing framework, using a three-tier architecture,
specifically the client-end interface, the backend server and the data layer.
• Javascript libraries, such as D3.js, leaflet.js, are used for building dynamic
and interactive maps and graphics
• R is used for all computation run in the backend
• Files are saved in a flat file system, json file is the exchange data format
between client end and server
Data Layer
Web Client Server
Convert Format
Via Java
R-Serve
JSON
Read / Write
Upload (csv, SHP)
Download (.zip)
Conclusion and Future Work
In this project, a web-based geovisual analytics toolkit was developed, which
allows people to perform GWR using their own collected data. Various
geovisual analytics functions have been implemented to enable users to
calibrate, present and synthesize a large number of GWR results through
visually appealing graphical interfaces. A case study has been conducted to
demonstrate how EWGAT can be used and help users to obtain some
meaningful insights.
In the future, case studies could be done to test the usability of EWGAT in
other research fields. Graphic interfaces could be further improved according to
users’ feedback. Finally, more features and capabilities could be built up to
make EWGAT a more comprehensive spatial statistical analytics toolkit.
Model Calibration
 Interactive Scatter Plot Matrix
 Geographical Brushing
 Ordinary Least Squares Modelling
 Residual Choropleth Map
 Statistics Values
 Estimates
 t Value
 p Value
 VIF Value
 Global Moran’s I
 R Squared
 Adjusted R Squared
GWR Algorithms
 Basic GWR Regression
 Semiparametric GWR
 Kernel Function
 Bisquare
 Gaussian
 Exponential
 Tricube
 Boxcar
 Weighting Scheme
 Adaptive
 Fixed
 Bandwidth Method
 AICc
 CV
Results Interpretation
 Multiple Views
 Synchronized Maps
 Parallel Coordinates Plot
 Local Statistics
 Estimates Summary
 Residual
 p Value
 t Value
 Global Statistics
 Bandwidth
 R Squared
 Adjusted R Squared
 Residual Squares
 AICc
Fotheringham, A. S., Brunsdon, C. and Charlton, M. E. Geographically weighted
regression: a natural evolution of the expansion method for spatial data
analysis. Environment and Planning A, 30, 1905–1927, 1998.
Gollini I, Lu B, Charlton M, et al. GWmodel: an R package for exploring spatial
heterogeneity using geographically weighted models [J]. arXiv preprint
arXiv: 1306.0413, 2013.
Teng, Aladdin. PyRe: Web-based Python Regionalization for Contiguously
Constrained Classification. Singapore Management University MITB Analytics,
2014.
Acknowledge
Foremost, I would like to express my deepest thanks to my supervisor,
Professor KAM Tin Seong, for the patient guidance, encouragement and advice
he has provided throughout the developing of the project.
Introduction
EWGAT is a web-based geovisual analytics toolkit, which aims to provide
general users and researchers with readily available access to calibrate, present
and synthesize tremendous Geographically Weighted Regression(GWR) results.