Griffith, Daniel A.
Permanent URI for this collectionhttps://hdl.handle.net/10735.1/3736
Daniel Griffith is an Ashbel Smith Professor of Geospatial Information Sciences. His primary areas of research are in spatial statistics, quantitative urban and economic geography, and applied statistics.
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Browsing Griffith, Daniel A. by Subject "Geography"
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Item The Importance of Scale in Spatially Varying Coefficient Modeling(Routledge Journals, Taylor & Francis Ltd, 2018-02) Murakami, Daisuke; Lu, Binbin; Harris, Paul; Brunsdon, Chris; Charlton, Martin; Nakaya, Tomoki; Griffith, Daniel A.; 0000-0001-5125-6450 (Griffith, DA); 14855602 (Griffith, DA); Griffith, Daniel A.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).Item Uncertainty and Context in GIScience and Geography: Challenges in the Era of Geospatial Big Data(Taylor & Francis Ltd, 2019-01-17) Chun, Yongwan; Kwan, Mei-Po; Griffith, Daniel A.; 0000-0002-4957-1379 (Chun, Y); 0000-0001-5125-6450 (Griffith, DA); 297769863 (Chun, Y); 14855602 (Griffith, DA); Chun, Yongwan; Griffith, Daniel A.No abstract available.Item Uncertainty in the Effects of the Modifiable Areal Unit Problem under Different Levels of Spatial Autocorrelation: A Simulation Study(Taylor & Francis Ltd, 2018-11-13) Lee, Sang-Il; Lee, Monghyeon; Chun, Yongwan; Griffith, Daniel A.; 0000-0002-4957-1379 (Chun, Y); 0000-0001-5125-6450 (Griffith, DA); 297769863 (Chun, Y); 14855602 (Griffith, DA); Chun, Yongwan; Griffith, Daniel A.The objective of this paper is to investigate uncertainties surrounding relationships between spatial autocorrelation (SA) and the modifiable areal unit problem (MAUP) with an extensive simulation experiment. Especially, this paper aims to explore how differently the MAUP behaves for the level of SA focusing on how the initial level of SA at the finest spatial scale makes a significant difference to the MAUP effects on the sample statistics such as means, variances, and Moran coefficients (MCs). The simulation experiment utilizes a random spatial aggregation (RSA) procedure and adopts Moran spatial eigenvectors to simulate different SA levels. The main findings are as follows. First, there are no substantive MAUP effects for means. However, the initial level of SA plays a role for the zoning effect, especially when extreme positive SA is present. Second, there is a clear and strong scale effect for the variances. However, the initial SA level plays a non-negligible role in how this scale effect deploys. Third, the initial SA level plays a crucial role in the nature and extent of the MAUP effects on MCs. A regression analysis confirms that the initial SA level makes a substantial difference to the variability of the MAUP effects.