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Principles of Modeling Uncertainties in Spatial Data and Spatial Analyses (Paperback)
Loot Price: R1,478
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Principles of Modeling Uncertainties in Spatial Data and Spatial Analyses (Paperback)
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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 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.
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