Multiscale Geographically-Weighted Modeling of Breast Cancer Incidence with Environmental Variables

This is my portion of the 2021 Johns Hopkins GIS Day Lightning Talk. In this original research, I combined the techniques I presented at conferences in 2019 and 2020, to complete this novel Python-callable MGWR analysis. County-level data were abstracted from SEER and other datasets. Similar geospatial analyses can be run for any chronic condition. MGWR is useful in constructing scalable community-level interventions. If you would like to view my presentation for MGWR analysis on Lung Cancer from 2020, please visit:

   • Geographic Regression for Lung Cancer...  

If you want more information, you can visit the open-source book by the authors of the program:

https://gistbok.ucgis.org/bok-topics/...

For the stand-alone program and manual please visit:
https://sgsup.asu.edu/sparc/mgwr

For the python version of MGWR please visit:
https://github.com/pysal/mgwr

Below is a good reference for applying MGWR:
Oshan, T. M., Smith, J. P., & Fotheringham, A. S. (2020). Targeting the spatial context of obesity determinants via multiscale geographically weighted regression. International Journal of Health Geographics, 19(1), 1-17.

For the full version of the 2021 Johns Hopkins Lightning talks please visit:
Featured Speakers session:    • JHU GIS Day 2021: Featured Speakers  
Lightning Talks session:    • JHU GIS Day 2021: Lightning Talks