Data Mining for Geoinformatics

1. Front Matter

   Pages i-xi

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2. Computation in Hyperspectral Imagery (HSI) Data Analysis: Role and Opportunities

   • Mark Salvador, Ron Resmini

   Pages 1-27

3. Toward Understanding Tornado Formation Through Spatiotemporal Data Mining

   • Amy McGovern, Derek H. Rosendahl, Rodger A. Brown

   Pages 29-47

4. Source Term Estimation for the 2011 Fukushima Nuclear Accident

   • Guido Cervone, Pasquale Franzese

   Pages 49-64

5. GIS-Based Traffic Simulation Using OSM

   • Jörg Dallmeyer, Andreas D. Lattner, Ingo J. Timm

   Pages 65-82

6. Evaluation of Real-Time Traffic Applications Based on Data Stream Mining

   • Sandra Geisler, Christoph Quix

   Pages 83-103

7. Geospatial Visual Analytics of Traffic and Weather Data for Better Winter Road Management

   • Yuzuru Tanaka, Jonas Sjöbergh, Pavel Moiseets, Micke Kuwahara, Hajime Imura, Tetsuya Yoshida

   Pages 105-126

8. Exploratory Visualization of Collective Mobile Objects Data Using Temporal Granularity and Spatial Similarity

   • Tetsuo Kobayashi, Harvey Miller

   Pages 127-154

9. Back Matter

   Pages 155-166

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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.

• Computational Methods
• Data Mining
• Earth Science
• GIS
• Geoinformatics
• Geoscience
• Machine Learning
• Natural Hazards
• Regression
• Remote Sensing

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)

• Department of Geography and Geoinformati, George Mason University, Fairfax, USA

  Guido Cervone, Nigel Waters

• Department of Computer Science, George Mason University, Fairfax, USA

  Jessica Lin

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