Murakami, DaisukeLu, BinbinHarris, PaulBrunsdon, ChrisCharlton, MartinNakaya, TomokiGriffith, Daniel A.2020-08-262020-08-262018-022469-4452http://dx.doi.org/10.1080/24694452.2018.1462691https://hdl.handle.net/10735.1/8814Supplementary material is available on publisher's website. Use the DOI link below.Due to copyright restrictions and/or publisher's policy full text access from Treasures at UT Dallas is limited to current UTD affiliates (use the provided Link to Article).Although spatially varying coefficient (SVC) models have attracted considerable attention in applied science, they have been criticized as being unstable. The objective of this study is to show that capturing the "spatial scale" of each data relationship is crucially important to make SVC modeling more stable and, in doing so, adds flexibility. Here, the analytical properties of six SVC models are summarized in terms of their characterization of scale. Models are examined through a series of Monte Carlo simulation experiments to assess the extent to which spatial scale influences model stability and the accuracy of their SVC estimates. The following models are studied: (1) geographically weighted regression (GWR) with a fixed distance or (2) an adaptive distance bandwidth (GWRa); (3) flexible bandwidth GWR (FB-GWR) with fixed distance or (4) adaptive distance bandwidths (FB-GWRa); (5) eigenvector spatial filtering (ESF); and (6) random effects ESF (RE-ESF). Results reveal that the SVC models designed to capture scale dependencies in local relationships (FB-GWR, FB-GWRa, and RE-ESF) most accurately estimate the simulated SVCs, where RE-ESF is the most computationally efficient. Conversely, GWR and ESF, where SVC estimates are naively assumed to operate at the same spatial scale for each relationship, perform poorly. Results also confirm that the adaptive bandwidth GWR models (GWRa and FB-GWRa) are superior to their fixed bandwidth counterparts (GWR and FB-GWR).en©2018 American Association of GeographersMonte Carlo methodEigenvectorsFilters and filtrationAutocorrelation (Statistics)GeographyarticleMurakami, Daisuke, Binbin Lu, Paul Harris, Chris Brunsdon, et al. 2019. "The Importance of Scale in Spatially Varying Coefficient Modeling." Annals of the American Association of Geographers 109(1): 50-70, doi: 10.1080/24694452.2018.14626911091