Lesson07
IS415 GeoSpatial Analytics and Applications
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Modelling Spatial Varying Relationship with Geographically Weighted Regression
Contents
Content
- Granddaddy of All Models: Multiple Regression
- Basic concepts of Spatial Non-stationary
- Geographically Weighted Regression (gwr) Methods
- Basic principles and concepts
- Distance matrix, kernel and bandwidth
- Basic grw
- Beyond basic grw
- GW regression and addressing local collinearity
References
Methods
Assuncao, R. M., Neves, M.C., Camara, G. and Costa Freitas, C.D. 2006. “Efficient Regionalization Techniques for Socio-Economic Geographical Units Using Minimum Spanning Trees.” International Journal of Geographical Information Science 20: 797–811.
Chavent, M., Kuentz-Simonet, V., Labenne,A. and Saracco, J. 2018. “ClustGeo: an R package for hierarchical clustering with spatial constraints” Computational Statistics. 33: 1799-1822.
Guo, D. 2008. “Regionalization with Dynamically Constrained Agglomerative Clustering and Partitioning (Redcap).” International Journal of Geographical Information Science, 22(7): 801-823.
Applications
R Packages
AMOEBA: A Multidirectional Optimum Ecotope-Based Algorithm [1]
ClustGeo: Hierarchical Clustering with Spatial Constraints [2] and Introduction to Clustgeo [3]
skater: A function from spdep package that implements a SKATER procedure for spatial clustering analysis.[4]
spatialcluster: An R package for spatially-constrained clustering using either distance or covariance matrices. [5]
