Contributions to Spatial Uncertainty Modelling in GIS - Guo, Danni
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Environmental data is very costly and difficult to collect and are often vague or imprecise in nature. Fuzziness and small datasets leads to uncertainty, which is addressed by the research objective of this book: To model spatial environmental data with fuzzy uncertainty, and to explore the use of small sample data in spatial modelling predictions, within Geographic Information System (GIS). The methodologies underlying the theoretical foundations for spatial modelling are examined, such as geostatistics, fuzzy mathematics Grey System Theory, and Credibility Measure Theory. Fifteen papers…mehr

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Produktbeschreibung
Environmental data is very costly and difficult to collect and are often vague or imprecise in nature. Fuzziness and small datasets leads to uncertainty, which is addressed by the research objective of this book: To model spatial environmental data with fuzzy uncertainty, and to explore the use of small sample data in spatial modelling predictions, within Geographic Information System (GIS). The methodologies underlying the theoretical foundations for spatial modelling are examined, such as geostatistics, fuzzy mathematics Grey System Theory, and Credibility Measure Theory. Fifteen papers including three journal papers were written in contribution to the developments of spatial fuzzy and grey uncertainty modelling. The book is a particularly useful tool to postgraduate students and researchers, who work on uncertainty modelling, and with small sample spatial or GIS data.
Autorenporträt
Danni Guo, PhD: Studied Spatial Statistics at the Department of Statistical Sciences, University of Cape Town, South Africa. Specialist Scientist at the Climate Change and Bioadaptation Division, South African National Biodiversity Institute, South Africa.