Data Mining for Geoinformatics: Methods and Applications

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Data Mining for Geoinformatics: Methods and Applications Editors: Guido Cervone, Jessica Lin, Nigel Waters Format: Paperback / softback First Published: Published By: Springer-Verlag New York Inc. Pages: 166 Illustrations and other contents: XI, 166 p. Language: English ISBN: 9781489985743 Category:

The rate at which geospatial data is being generated exceeds our computational capabilities to extract patterns for the understanding of a dynamically changing world. Geoinformatics and data mining focuses on the development and implementation of computational algorithms to solve these problems. This unique volume contains a collection of chapters on state-of-the-art data mining techniques applied to geoinformatic problems of high complexity and important societal value. Data Mining for Geoinformatics addresses current concerns and developments relating to spatio-temporal data mining issues in remotely-sensed data, problems in meteorological data such as tornado formation, estimation of radiation from the Fukushima nuclear power plant, simulations of traffic data using OpenStreetMap, real time traffic applications of data stream mining, visual analytics of traffic and weather data and the exploratory visualization of collective, mobile objects such as the flocking behavior of wild chickens. This book is designed for researchers and advanced-level students focused on computer science, earth science and geography as a reference or secondary text book. Practitioners working in the areas of data mining and geoscience will also find this book to be a valuable reference.

Weight0.3241825 kg

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From the book reviews: "This hardcover book is a technically accurate production. ... chapters can be used in advanced undergraduate and graduate courses for various purposes ... . I recommend it to anyone who is interested in GIS applications. As a nonspecialist in this area, I found the editiors' introduction very informative and enjoyable reading. ... this expertly edited book addresses application areas to be used for research and specialty teaching purposes." (M. M. Tanik, Computing Reviews, June, 2014)