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 that theory. Principles of Modeling Uncertainties in Spatial Data and Spatial Analyses outlines the foundational principles and supplies a firm grasp of the disciplines’ theoretical underpinnings. Comprehensive, Systematic Review of Methods for Handling Uncertainties The book summarizes the principles of modeling uncertainty of spatial data and spatial analysis, and then introduces the developed methods for handling uncertainties in spatial data and modeling uncertainties in spatial models. Building on this foundation, the book goes on to explore modeling uncertainties in spatial analyses and describe methods for presentation of data as quality information. Progressing from basic to advanced topics, the organization of the contents reflects the four major theoretical breakthroughs in uncertainty modeling: advances in spatial object representation, uncertainty modeling for static spatial data to dynamic spatial analyses, uncertainty modeling for spatial data to spatial models, and error description of spatial data to spatial data quality control. Determine Fitness-of-Use for Your Applications Modeling uncertainties is essential for the development of geographic information science. Uncertainties always exist in GIS and are then propagated in the results of any spatial analysis. The book delineates how GIS can be a better tool for decision-making and demonstrates how the methods covered can be used to control the data quality of GIS products.
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 ...
Under examination: multilevel models, geography and health research. Progress in Human Geography, 40(3), 94–412. https://doi.org/10.1177/0309132515580814 Robson, K. and Pevalin, D. (2015). Multilevel Modeling in Plain Language.
A Casebook for Spatial Statistical Data Analysis: A Compilation of Analyses of Different Thematic Datasets, New York: Oxford University Press. Griffith, D., and Y. Chun. 2014. An eigenvector spatial filtering contribution to short range ...
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Dealing with Doubt in Natural Resource Management Bruce G. Marcot. charger that might catch fire or explode during flight, not being detected through an individual check point: With n check-points, the total joint probability of not ...
... analysis of techniques for spatialinterpolation of precipitation. Water Resources Bulletin, 21(3): 365–380. Tan JQ, Ding MZ. 2004. An Evaluation of Spatial Data Interpolation Methods. Geomatics and Spatial Information Technology, 27(4): ...
This book is a collection of papers reflecting the latest advances in geographic research on health, disease, and well-being.
This 3-volumes reference provides an up-to date account of this growing discipline through in-depth reviews authored by leading experts in the field.
SPIE Conference Intelligent Robots and Computer Vision XXVIII: Algorithms and Techniques, vol. 7878 (2011), doi:10.1117/12.876663 Dios, M.-D., Ollero, A.: Automatic detection of windows thermal heat losses in buildings using UAVs.