Griffith, Daniel A.
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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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Works in Treasures @ UT Dallas are made available exclusively for educational purposes such as research or instruction. Literary rights, including copyright for published works held by the creator(s) or their heirs, or other third parties may apply. All rights are reserved unless otherwise indicated by the copyright owner(s).
Recent Submissions
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The Importance of Scale in Spatially Varying Coefficient Modeling
(Routledge Journals, Taylor & Francis Ltd, 2018-02)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 ... -
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)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, ... -
Uncertainty and Context in GIScience and Geography: Challenges in the Era of Geospatial Big Data
(Taylor & Francis Ltd, 2019-01-17)No abstract available. -
Eigenvector Spatial Filtering for Large Data Sets: Fixed and Random Effects Approaches
(Wiley, 2018-03-25)Eigenvector spatial filtering (ESF) is a spatial modeling approach, which has been applied in urban and regional studies, ecological studies, and so on. However, it is computationally demanding, and may not be suitable for ... -
Spatial Autocorrelation for Massive Spatial Data: Verification of Efficiency and Statistical Power Asymptotics
Being a hot topic in recent years, many studies have been conducted with spatial data containing massive numbers of observations. Because initial developments for classical spatial autocorrelation statistics are based on ... -
Implementing Moran Eigenvector Spatial Filtering for Massively Large Georeferenced Datasets
Moran eigenvector spatial filtering (MESF) furnishes an alternative method to account for spatial autocorrelation in linear regression specifications describing georeferenced data, although spatial auto-models also are ... -
A Spatial-Filtering Zero-Inflated Approach to the Estimation of the Gravity Model of Trade
Nonlinear estimation of the gravity model with Poisson-type regression methods has become popular for modelling international trade flows, because it permits a better accounting for zero flows and extreme values in the ... -
Geovisualizing Attribute Uncertainty of Interval and Ratio Variables: A Framework and an Implementation for Vector Data
(2017-12-14)This is a prototype implementation for attribute uncertainty visualization based on bivariate. Specifically, the uncertainty visualizations implemented based on three different ways. First, an overlaid symbols on a choropleth ... -
Spatially Simplified Scatterplots for Large Raster Datasets
(Taylor & Francis, 2016-05-24)Scatterplots are essential tools for data exploration. However, this tool poorly scales with data-size, with overplotting and excessive delay being the main problems. Generalization methods in the attribute domain focus ... -
Validation of a Remote Sensing Model to Identify Simulium damnosum s.l. Breeding Sites in Sub-Saharan Africa
(2013-07-25)Background: Recently, most onchocerciasis control programs have begun to focus on elimination. Developing an effective elimination strategy relies upon accurately mapping the extent of endemic foci. In areas of Africa that ...
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