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.
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.
Presents a series of geoinformatic techniques and methodologies to solve real world problems of important societal value
Provides geoinformatic algorithms necessary to extract knowledge from massive amounts of geographically distributed data
Bridges fields together such as GIS, statistics, machine learning, remote sensing, natural hazards, earth and atmospheric sciences