Lesson 5: Geograpgically Weighted Logistic Regression (GWLR) and Application

Published

December 15, 2022

Overview

In this lesson, you will learn the basic concepts and methods of logistic regression specially designed for geographical data. Upon completion of this lesson, you will able to:

explain the similarities and differences between Logistic Regression (LR) algorithm versus geographical weighted Logistic Regression (GWLR) algorithm.
calibrate predictive models by using appropriate Geographically Weighted Logistic Regression algorithm for geographical data.

Content

Basic concepts and principles of Logistic Regression
Geographically Weighted Logistic Regression methods
- Weighting functions (kernel)
- Weighting schemes
- Bandwidth
- Interpreting and Visualising

Self-reading Before Meet-up

To read before class:

Atkinson PM, German SE, Sear DQ and Clark MJ (2003) “Exploring the relations between riverbank erosion and geomorphological controls using geographically weighted logistic regression”. Geographical Analysis 35(1): 58–82.

References

Schultz, C. et. al. (2016) “Comparison of spatial and aspatial logistic regression models for landmine risk mapping”, Applied Geography 66, pp. 52-63. Available in SMU eLibrary.
Varun Narayan Mishra et. al. (2021) “Geographically Weighted Method Integrated with Logistic Regression for Analyzing Spatially Varying Accuracy Measures of Remote Sensing Image Classification”. Journal of the Indian Society of Remote Sensing, 49(5):1189–1199. Available in SMU eLibrary.
Helen J Mayfield et. al (2018) “Use of geographically weighted logistic regression to quantify spatial variation in the environmental and sociodemographic drivers of leptospirosis in Fiji: a modelling study”. Lancet Planet Health 2: pp. 223–232.
Felix Ndidi Nkeki, Monday Ohi Asikhia (2019) “Geographically weighted logistic regression approach to explore the spatial variability in travel behaviour and built environment interactions: Accounting simultaneously for demographic and socioeconomic characteristics” Applied Geography, Vol. 108, Pp. 47-63. Available in SMU eLibrary.
Han Li, Ye Hua Dennis Wei and Zhiji Huang (2014) “Urban Land Expansion and Spatial Dynamics in Globalizing Shanghai”, Sustainability 6, 8856-8875.

All About R

GWmodel package, especially
- Gollini, I et. al. (2015) “GWmodel: An R Package for Exploring Spatial Heterogeneity Using Geographically Weighted Models”, Journal of Statistical Software, Volume 63, Issue 17 and
- Binbin Lu, Paul Harris, Martin Charlton & Chris Brunsdon (2014) “The GWmodel R package: further topics for exploring spatial heterogeneity using geographically weighted models”, Geo-spatial Information Science, 17:2, 85-101, DOI: 10.1080/10095020.2014.917453.