[PDF] Visualizing And Modeling Spatial Data Uncertainty eBook

Author : Hyeongmo Koo
Publisher :
Page : pages
File Size : 22,89 MB
Release : 2018
Category : Autocorrelation (Statistics)
ISBN :

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This dissertation extends the understanding of spatial data uncertainty, which inevitably exists in any process of Geographic Information Sciences involving measuring, representing, and modeling the world. This dissertation consists of three specific sub-topics in visualizing and modeling spatial data uncertainty. First, a framework for attribute uncertainty visualization is suggested based on bivariate mapping techniques, and this framework is implemented in a popular GIS environment. The framework and implementation support many visual variables that have been investigated in the literature. This research outcome can provide flexibility to enhance communication and visualization effectiveness for uncertainty visualization. The second sub-topic is a development of optimal map classification methods by simultaneously considering attribute estimates and their uncertainty. This study expands the discussion of constructing an optimal map classification result in which data uncertainty is incorporated in a map classification process. This method utilizes a shortest path problem in an acyclic network based on dissimilarity measures with various cost and objective functions. Finally, modeling positional uncertainty acquired through street geocoding is investigated to understand potential factors of the uncertainty and then to identify impacts of the uncertainty on spatial analysis results. This study accounts for spatial autocorrelation among geocoded points in a modeling process, which has been barely included in this type of modeling. This research has contributions to increasing explanation and to extending geocoding uncertainty modeling by suggesting additional covariates and considering spatial autocorrelation.

Author : Wenzhong Shi
Publisher : CRC Press
Page : 456 pages
File Size : 35,41 MB
Release : 2009-09-30
Category : Mathematics
ISBN : 1420059289

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When compared to classical sciences such as math, with roots in prehistory, and physics, with roots in antiquity, geographical information science (GISci) is the new kid on the block. Its theoretical foundations are therefore still developing and data quality and uncertainty modeling for spatial data and spatial analysis is an important branch of t

Author : Shi Wenzhong
Publisher : CRC Press
Page : 312 pages
File Size : 11,86 MB
Release : 2015-11-04
Category : Mathematics
ISBN : 1498733344

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Offers New Insight on Uncertainty ModellingFocused on major research relative to spatial information, Uncertainty Modelling and Quality Control for Spatial Data introduces methods for managing uncertainties-such as data of questionable quality-in geographic information science (GIS) applications. By using original research, current advancement, and

Author : Jean-Paul Chilès
Publisher : John Wiley & Sons
Page : 718 pages
File Size : 32,55 MB
Release : 2009-09-25
Category : Mathematics
ISBN : 0470317833

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A novel, practical approach to modeling spatial uncertainty. This book deals with statistical models used to describe natural variables distributed in space or in time and space. It takes a practical, unified approach to geostatistics-integrating statistical data with physical equations and geological concepts while stressing the importance of an objective description based on empirical evidence. This unique approach facilitates realistic modeling that accounts for the complexity of natural phenomena and helps solve economic and development problems-in mining, oil exploration, environmental engineering, and other real-world situations involving spatial uncertainty. Up-to-date, comprehensive, and well-written, Geostatistics: Modeling Spatial Uncertainty explains both theory and applications, covers many useful topics, and offers a wealth of new insights for nonstatisticians and seasoned professionals alike. This volume: * Reviews the most up-to-date geostatistical methods and the types of problems they address. * Emphasizes the statistical methodologies employed in spatial estimation. * Presents simulation techniques and digital models of uncertainty. * Features more than 150 figures and many concrete examples throughout the text. * Includes extensive footnoting as well as a thorough bibliography. Geostatistics: Modeling Spatial Uncertainty is the only geostatistical book to address a broad audience in both industry and academia. An invaluable resource for geostatisticians, physicists, mining engineers, and earth science professionals such as petroleum geologists, geophysicists, and hydrogeologists, it is also an excellent supplementary text for graduate-level courses in related subjects.

Author : Paula Moraga
Publisher : CRC Press
Page : 217 pages
File Size : 11,72 MB
Release : 2019-11-26
Category : Medical
ISBN : 1000732150

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Geospatial health data are essential to inform public health and policy. These data can be used to quantify disease burden, understand geographic and temporal patterns, identify risk factors, and measure inequalities. Geospatial Health Data: Modeling and Visualization with R-INLA and Shiny describes spatial and spatio-temporal statistical methods and visualization techniques to analyze georeferenced health data in R. The book covers the following topics: Manipulate and transform point, areal, and raster data, Bayesian hierarchical models for disease mapping using areal and geostatistical data, Fit and interpret spatial and spatio-temporal models with the Integrated Nested Laplace Approximations (INLA) and the Stochastic Partial Differential Equation (SPDE) approaches, Create interactive and static visualizations such as disease maps and time plots, Reproducible R Markdown reports, interactive dashboards, and Shiny web applications that facilitate the communication of insights to collaborators and policy makers. The book features fully reproducible examples of several disease and environmental applications using real-world data such as malaria in The Gambia, cancer in Scotland and USA, and air pollution in Spain. Examples in the book focus on health applications, but the approaches covered are also applicable to other fields that use georeferenced data including epidemiology, ecology, demography or criminology. The book provides clear descriptions of the R code for data importing, manipulation, modeling and visualization, as well as the interpretation of the results. This ensures contents are fully reproducible and accessible for students, researchers and practitioners.

Author : Yongwan Chun
Publisher : Routledge
Page : 0 pages
File Size : 23,91 MB
Release : 2023-09-25
Category :
ISBN : 9780367643003

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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.

Author : Jef Caers
Publisher : John Wiley & Sons
Page : 294 pages
File Size : 19,23 MB
Release : 2011-05-25
Category : Science
ISBN : 1119998719

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Modeling Uncertainty in the Earth Sciences highlights the various issues, techniques and practical modeling tools available for modeling the uncertainty of complex Earth systems and the impact that it has on practical situations. The aim of the book is to provide an introductory overview which covers a broad range of tried-and-tested tools. Descriptions of concepts, philosophies, challenges, methodologies and workflows give the reader an understanding of the best way to make decisions under uncertainty for Earth Science problems. The book covers key issues such as: Spatial and time aspect; large complexity and dimensionality; computation power; costs of 'engineering' the Earth; uncertainty in the modeling and decision process. Focusing on reliable and practical methods this book provides an invaluable primer for the complex area of decision making with uncertainty in the Earth Sciences.

Author : Wenzhong Shi
Publisher : CRC Press
Page : 340 pages
File Size : 23,85 MB
Release : 2019-08-30
Category :
ISBN : 9780367395858

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As research in the geosciences and social sciences becomes increasingly dependent on computers, applications such as geographical information systems are becoming indispensable tools. But the digital representations of phenomena that these systems require are often of poor quality, leading to inaccurate results, uncertainty, error propagation, and potentially legal liability. Spatial data quality has become an essential research topic within geographical information science. This book covers many of the cutting-edge research issues related to spatial data quality, including measurement in GIS and geostatistics, the modeling of spatial objects that have inherent uncertainty, spatial data quality control, quality management, communicating uncertainty and resolution, reasoning and decision-making, visualization of uncertainty and error metadata. Spatial Data Quality will be of interest to anyone undertaking research using GIS and related technologies.

Author : Julian Kardos
Publisher :
Page : 88 pages
File Size : 39,19 MB
Release : 2006
Category :
ISBN :

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This thesis defines the various techniques of visual representations of uncertainty in the systems of geographical information. It enables to choose effective techniques of display, the uncertainty of spatial borders and the temporal uncertainty of Choropleth. This study rests upon the 2001 census in New Zealand, dwells upon internet surveys, explanatory maps and the achievement of a software Trust and suggests to develop and test new methods to represent uncertainty.