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Offers New Insight on Uncertainty Modelling Focused 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 emerging developments in the field, the
authors compile various aspects of spatial data quality control.
From multidimensional and multi-scale data integration to
uncertainties in spatial data mining, this book launches into areas
that are rarely addressed. Topics covered include: New developments
of uncertainty modelling, quality control of spatial data, and
related research issues in spatial analysis Spatial statistical
solutions in spatial data quality Eliminating systematic error in
the analytical results of GIS applications A data quality
perspective for GIS function workflow design Data quality in
multi-dimensional integration Research challenges on data quality
in the integration and analysis of data from multiple sources A new
approach for imprecision management in the qualitative data
warehouse A multi-dimensional quality assessment of photogrammetric
and LiDAR datasets based on a vector approach An analysis on the
uncertainty of multi-scale representation for street-block
settlement Uncertainty Modelling and Quality Control for Spatial
Data serves university students, researchers and professionals in
GIS, and investigates the uncertainty modelling and quality control
in multi-dimensional data integration, multi-scale data
representation, national or regional spatial data products, and new
spatial data mining methods.
Describes the State-of-the-Art in Spatial Data Mining, Focuses on
Data Quality
Substantial progress has been made toward developing effective
techniques for spatial information processing in recent years. This
science deals with models of reality in a GIS, however, and not
with reality itself. Therefore, spatial information processes are
often imprecise, allowing for much interpretation of abstract
figures and data. Quality Aspects in Spatial Data Mining introduces
practical and theoretical solutions for making sense of the often
chaotic and overwhelming amount of concrete data available to
researchers.
In this cohesive collection of peer-reviewed chapters, field
authorities present the latest field advancements and cover such
essential areas as data acquisition, geoinformation theory, spatial
statistics, and dissemination. Each chapter debuts with an
editorial preview of each topic from a conceptual, applied, and
methodological point of view, making it easier for researchers to
judge which information is most beneficial to their work.
Chapters Evolve From Error Propagation and Spatial Statistics to
Address Relevant Applications
The book advises the use of granular computing as a means of
circumventing spatial complexities. This counter-application to
traditional computing allows for the calculation of imprecise
probabilities - the kind of information that the spatial
information systems community wrestles with much of the time.
Under the editorial guidance of internationally respected
geoinformatics experts, this indispensable volume addresses quality
aspects in the entire spatial data mining process, from data
acquisition to end user. It also alleviates what is oftenfield
researchers' most daunting task by organizing the wealth of
concrete spatial data available into one convenient source, thereby
advancing the frontiers of spatial information systems.
Offers New Insight on Uncertainty Modelling Focused 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 emerging developments in the field, the
authors compile various aspects of spatial data quality control.
From multidimensional and multi-scale data integration to
uncertainties in spatial data mining, this book launches into areas
that are rarely addressed. Topics covered include: New developments
of uncertainty modelling, quality control of spatial data, and
related research issues in spatial analysis Spatial statistical
solutions in spatial data quality Eliminating systematic error in
the analytical results of GIS applications A data quality
perspective for GIS function workflow design Data quality in
multi-dimensional integration Research challenges on data quality
in the integration and analysis of data from multiple sources A new
approach for imprecision management in the qualitative data
warehouse A multi-dimensional quality assessment of photogrammetric
and LiDAR datasets based on a vector approach An analysis on the
uncertainty of multi-scale representation for street-block
settlement Uncertainty Modelling and Quality Control for Spatial
Data serves university students, researchers and professionals in
GIS, and investigates the uncertainty modelling and quality control
in multi-dimensional data integration, multi-scale data
representation, national or regional spatial data products, and new
spatial data mining methods.
Describes the State-of-the-Art in Spatial Data Mining, Focuses on
Data Quality Substantial progress has been made toward developing
effective techniques for spatial information processing in recent
years. This science deals with models of reality in a GIS, however,
and not with reality itself. Therefore, spatial information
processes are often imprecise, allowing for much interpretation of
abstract figures and data. Quality Aspects in Spatial Data Mining
introduces practical and theoretical solutions for making sense of
the often chaotic and overwhelming amount of concrete data
available to researchers. In this cohesive collection of
peer-reviewed chapters, field authorities present the latest field
advancements and cover such essential areas as data acquisition,
geoinformation theory, spatial statistics, and dissemination. Each
chapter debuts with an editorial preview of each topic from a
conceptual, applied, and methodological point of view, making it
easier for researchers to judge which information is most
beneficial to their work. Chapters Evolve From Error Propagation
and Spatial Statistics to Address Relevant Applications The book
advises the use of granular computing as a means of circumventing
spatial complexities. This counter-application to traditional
computing allows for the calculation of imprecise probabilities -
the kind of information that the spatial information systems
community wrestles with much of the time. Under the editorial
guidance of internationally respected geoinformatics experts, this
indispensable volume addresses quality aspects in the entire
spatial data mining process, from data acquisition to end user. It
also alleviates what is often field researchers' most daunting task
by organizing the wealth of concrete spatial data available into
one convenient source, thereby advancing the frontiers of spatial
inf
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