Lesson 8: Geographically Weighted Predictive Modelling
Content
- What is Predictive Modelling?
- What is Geospatial Predictive Modelling
- Introducing Recursive Partitioning
- Advanced Recursive Partitioning: Random Forest
- Introducing Geographically Weighted Random Forest
Lesson Slides and Hands-on Notes
Lesson 8 slides.
Handout of Hands-on Exercise 8.
Self-reading Before Meet-up
To read before class:
Stefanos Georganos et. al. (2019) “Geographical Random Forests: A Spatial Extension of the Random Forest Algorithm to Address Spatial Heterogeneity in Remote Sensing and Population Modelling”, Geocarto International, DOI: 10.1080/10106049.2019.1595177.
Georganos, S. and Kalogirou, S. (2022) “A Forest of Forests: A Spatially Weighted and Computationally Efficient Formulation of Geographical Random Forests”. ISPRS, International Journal of Geo-Information, 2022, 11, 471. https://www.mdpi.com/2220-9964/11/9/471
References
George Grekousis et. al. (2022) “Ranking the importance of demographic, socioeconomic, and underlying health factors on US COVID-19 deaths: A geographical random forest approach”, Health and Place, vol. 74, pp. 1-12.
Yaowen Luo, Jianguo Yan & Stephen McClure. (2020) “Distribution of the environmental and socioeconomic risk factors on COVID-19 death rate across continental USA: a spatial nonlinear analysis”, Environmental Science and Pollution Research, 28:6587–6599.
Eun-Hee Koh, Eunhee Lee, & Kang-Kun Lee (2020) “Application of geographically weighted regression models to predict spatial characteristics of nitrate contamination: Implications for an effective groundwater management strategy”, Journal of Environmental Management. Vol. 268