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Fast Geographically Weighted Regression (FastGWR): a scalable algorithm to investigate spatial process heterogeneity in millions of observations.

, , , and . Int. J. Geogr. Inf. Sci., 33 (1): 155-175 (2019)

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arcos and arcospy: R and Python packages for accessing the DEA ARCOS database from 2006 - 2014., , , , , and . J. Open Source Softw., 5 (53): 2450 (2020)Fast Geographically Weighted Regression (FastGWR): a scalable algorithm to investigate spatial process heterogeneity in millions of observations., , , and . Int. J. Geogr. Inf. Sci., 33 (1): 155-175 (2019)Geographically weighted regression and multicollinearity: dispelling the myth., and . J. Geogr. Syst., 18 (4): 303-329 (2016)splot - visual analytics for spatial statistics., , , , , , , , and . J. Open Source Softw., 5 (47): 1882 (2020)A comment on geographically weighted regression with parameter-specific distance metrics., , , , , and . Int. J. Geogr. Inf. Sci., 33 (7): 1289-1299 (2019)mgwr: A Python Implementation of Multiscale Geographically Weighted Regression for Investigating Process Spatial Heterogeneity and Scale., , , , and . ISPRS Int. J. Geo Inf., 8 (6): 269 (2019)Comparing spatial interaction models and flow interpolation techniques for predicting "cold start" bike-share trip demand., and . Trans. GIS, 26 (4): 2081-2098 (2022)Are we breaking bubbles as we move? Using a large sample to explore the relationship between urban mobility and segregation., , , and . Comput. Environ. Urban Syst., (2021)On the notion of 'bandwidth' in geographically weighted regression models of spatially varying processes., , , , and . Int. J. Geogr. Inf. Sci., 36 (8): 1485-1502 (2022)A scoping review on the multiplicity of scale in spatial analysis., , , , and . J. Geogr. Syst., 24 (3): 293-324 (2022)