Data Mining for Geoinformatics: Methods and Applications

  • 3h 23m
  • Guido Cervone, Jessica Lin, Nigel Waters (eds)
  • Springer
  • 2014

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.

About the Editors

Dr. Guido Cervone is associate professor of geoinformatics in the Department of Geography and Institute for CyberScience at the Pennsylvania State University. He is also affiliate scientist with the Research Application Laboratory (RAL) at the National Center of Atmospheric Research (NCAR). His research expertise is in machine learning and geoinformatics, and his main interest is the mining of spatial and temporal remote sensing, model and social media big data associated with natural, man-made, and technological hazards. He worked on the theoretical development and implementation of symbolic and evolutionary machine learning systems. He developed a new methodology based on non-Darwinian evolution to identify the source characteristics of an unknown toxic atmospheric release.

He sits on the advisory committee of the United Nation Environmental Programme (UNEP), Division of Disasters and Early Warning Assessment (DEWA). His research is currently being funded by the Department of Transportation and by the Office of Naval Research.

Dr. Nigel Waters is a professor in the Department of Geography and Geoinformation Science and director of the Geographic Information Science Center of Excellence at George Mason University. His present research involves the use of GIS techniques and social media data for transportation and health research and is supported by the US Department of Transportation and the National Institutes of Health. He is the editor of Cartographica: The International Journal for Geographic Information and Geovisualization, which is published by the University of Toronto Press. He is a member of the Board of Directors of the University Consortium for Geographic Information Science. He was the 2010 Henrietta Harvey Distinguished Lecturer, at Memorial University, Newfoundland.

Dr. Jessica Lin is an associate professor in the Department of Computer Science at George Mason University (GMU). She received her PhD degree from the University of California, Riverside, in June 2005. Her research interests encompass broad areas of data mining, especially data mining for large temporal and spatiotemporal databases, text, and images. Over the years, she has collaborated with researchers from various domains including medicine, earth sciences, manufacturing, national defense, and astronomy. Her research is partially funded by NSF, US Army, and Intel Corporation. Dr. Lin has been member of the program committee of many international conferences in the area of data mining. She teaches advanced topics on data mining at GMU, concentrating on mining multimedia and high-dimensional data.

In this Book

  • Computation in Hyperspectral Imagery (HSI) Data Analysis: Role and Opportunities
  • Toward Understanding Tornado Formation Through Spatiotemporal Data Mining
  • Source Term Estimation for the 2011 Fukushima Nuclear Accident
  • GIS-Based Traffic Simulation Using OSM
  • Evaluation of Real-Time Traffic Applications Based on Data Stream Mining
  • Geospatial Visual Analytics of Traffic and Weather Data for Better Winter Road Management
  • Exploratory Visualization of Collective Mobile Objects Data Using Temporal Granularity and Spatial Similarity

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