Geographically weighted regression (GWR) is an inherently exploratory technique for examining process non-stationarity in data relationships. This paper develops and applies a hyper-local GWR which extends such investigations further. The hyper-local GWR simultaneously optimizes both local model selection (which covariates to include in each local regression) and local kernel bandwidth specification (how much data should be included locally). These are evaluated using a measure of model fit. The hyper-local GWR approach evaluates different kernel bandwidths at each location and selects the most parsimonious local regression model. By allowing models and bandwidths to vary locally, this approach extends and refines the one-size-fits-all whole map model" and "constant bandwidth calibration" under standard GWR. The results provide an alternative
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Comber, Alexis; Wang, Yunqiang; Lü, Yihe; Zhang, Xingchang; and Harris, Paul
"Hyper-local geographically weighted regression: extending GWR through local model selection and local bandwidth optimization,"
Journal of Spatial Information Science:
Available at: https://digitalcommons.library.umaine.edu/josis/vol2018/iss17/3