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.
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