This book illustrates how cutting-edge research explores recent advances in this area, and will serve as a useful point of departure for GIScientists to conceive new approaches and solutions for addressing these challenges in future research.
This book illustrates how cutting-edge research explores recent advances in this area, and will serve as a useful point of departure for GIScientists to conceive new approaches and solutions for addressing these challenges in future research.
Yongwan Chun is Associate Professor of Geospatial Information Sciences (GIS) at the University of Texas at Dallas, USA. His research interests lie in GIS and spatial statistical approaches to solving geographical problems, including geographic flow modeling, space- time modeling, and uncertainty. Mei- Po Kwan is Choh- Ming Li Professor of Geography and Resource Management and Director of the Institute of Space and Earth Information Science at the Chinese University of Hong Kong, China. Her research interests include environmental health, human mobility, sustainable cities, urban, transport and social issues in cities, and GIScience. Daniel A. Griffith is Ashbel Smith Professor of Geospatial Information Sciences at the University of Texas at Dallas, USA, and has authored numerous books and academic articles, garnering him many awards. He pursues research at the interface between geography and mathematics, especially statistics. His current research emphasizes visualization, space- time analysis, and public health.
Inhaltsangabe
Introduction Yongwan Chun, Mei-Po Kwan and Daniel A. Griffith 1. Uncertainty in the effects of the modifiable areal unit problem under different levels of spatial autocorrelation: a simulation study Sang-Il Lee, Monghyeon Lee, Yongwan Chun and Daniel A. Griffith 2. Spatial autocorrelation and data uncertainty in the American Community Survey: a critique Paul H. Jung, Jean-Claude Thill and Michele Issel 3. Uncertainties in the geographic context of health behaviors: a study of substance users' exposure to psychosocial stress using GPS data Mei-Po Kwan, Jue Wang, Matthew Tyburski, David H. Epstein, William J. Kowalczyk and Kenzie L. Preston 4. Exploring the uncertainty of activity zone detection using digital footprints with multi-scaled DBSCAN Xinyi Liu, Qunying Huang and Song Gao 5. Same space - different perspectives: comparative analysis of geographic context through sketch maps and spatial video geonarratives Andrew Curtis, Jacqueline W. Curtis, Jayakrishnan Ajayakumar, Eric Jefferis and Susanne Mitchell 6. Travel impedance agreement among online road network data providers Eric M. Delmelle, Derek M. Marsh, C. Dony and Paul L. Delamater 7. A network approach to the production of geographic context using exponential random graph models Steven M. Radil Concluding Comments Yongwan Chun, Mei-Po Kwan and Daniel A. Griffith
Introduction Yongwan Chun, Mei-Po Kwan and Daniel A. Griffith 1. Uncertainty in the effects of the modifiable areal unit problem under different levels of spatial autocorrelation: a simulation study Sang-Il Lee, Monghyeon Lee, Yongwan Chun and Daniel A. Griffith 2. Spatial autocorrelation and data uncertainty in the American Community Survey: a critique Paul H. Jung, Jean-Claude Thill and Michele Issel 3. Uncertainties in the geographic context of health behaviors: a study of substance users' exposure to psychosocial stress using GPS data Mei-Po Kwan, Jue Wang, Matthew Tyburski, David H. Epstein, William J. Kowalczyk and Kenzie L. Preston 4. Exploring the uncertainty of activity zone detection using digital footprints with multi-scaled DBSCAN Xinyi Liu, Qunying Huang and Song Gao 5. Same space - different perspectives: comparative analysis of geographic context through sketch maps and spatial video geonarratives Andrew Curtis, Jacqueline W. Curtis, Jayakrishnan Ajayakumar, Eric Jefferis and Susanne Mitchell 6. Travel impedance agreement among online road network data providers Eric M. Delmelle, Derek M. Marsh, C. Dony and Paul L. Delamater 7. A network approach to the production of geographic context using exponential random graph models Steven M. Radil Concluding Comments Yongwan Chun, Mei-Po Kwan and Daniel A. Griffith
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