Quality Aspects in Spatial Data Mining

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 Describes spatial data mining, and focuses on data quality. This book advises the use of granular computing as a means of circumventing spatial complexities. It also addresses quality aspects in the entire spatial data mining process, from data acquisition to end user.