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.

· This book is an updated version of a well-received book previously published in Chinese by Science Press of China (the first edition in 2006 and the second in 2013). It offers a systematic and practical overview of spatial data mining, which combines computer science and geo-spatial information science, allowing each field to profit from the knowledge and techniques of the other. To address the spatiotemporal specialties of spatial data, the authors introduce the key concepts and algorithms of the data field, cloud model, mining view, and Deren Li methods. The data field method captures the interactions between spatial objects by diffusing the data contribution from a universe of samples to a universe of population, thereby bridging the gap between the data model and the recognition model. The cloud model is a qualitative method that utilizes quantitative numerical characters to bridge the gap between pure data and linguistic concepts. The mining view method discriminates the different requirements by using scale, hierarchy, and granularity in order to uncover the anisotropy of spatial data mining. The Deren Li method performs data preprocessing to prepare it for further knowledge discovery by selecting a weight for iteration in order to clean the observed spatial data as much as possible. In addition to the essential algorithms and techniques, the book provides application examples of spatial data mining in geographic information science and remote sensing. The practical projects include spatiotemporal video data mining for protecting public security, serial image mining on nighttime lights for assessing the severity of the Syrian Crisis, and the applications in the government project ‘the Belt and Road Initiatives’.

This edited volume assesses capabilities of data mining algorithms for spatial modeling of natural hazards in different countries based on a collection of essays written by experts in the field. The book is organized on different hazards including landslides, flood, forest fire, land subsidence, earthquake, and gully erosion. Chapters were peer-reviewed by recognized scholars in the field of natural hazards research. Each chapter provides an overview on the topic, methods applied, and discusses examples used. The concepts and methods are explained at a level that allows undergraduates to understand and other readers learn through examples. This edited volume is shaped and structured to provide the reader with a comprehensive overview of all covered topics. It serves as a reference for researchers from different fields including land surveying, remote sensing, cartography, GIS, geophysics, geology, natural resources, and geography. It also serves as a guide for researchers, students, organizations, and decision makers active in land use planning and hazard management.

Advances in automated data collection are creating massive databases and a whole new field, Knowledge Discovery Databases (KDD), has emerged to develop new methods of managing and exploiting them. Geographic Data Mining and Knowledge Discovery is the interrogation of large databases using efficient computational methods. The unique challenges brought about by the storing of massive geographical databases - from high resolution satellite-based systems to data from intelligent transportation systems, for example - has led to the field of Geographical Knowledge Discovery (GKD). Geographic or spatial data mining is the exploration of these geographical information databases. Developed out of contributions to the highly-respected Varenius Project in 1999, this collection will be the definitive volume focusing on GKD and addresses the special challenges to be found in knowledge discovery and data mining from geographic databases.

Co-location pattern mining detects sets of features frequently located in close proximity to each other. This book focuses on data mining for co-location pattern, a valid method for identifying patterns from all types of data and applying them in business intelligence and analytics. It explains the fundamentals of co-location pattern mining, co-location decision tree, and maximal instance co-location pattern mining along with an in-depth overview of data mining, machine learning, and statistics. This arrangement of chapters helps readers understand the methods of co-location pattern mining step-by-step and their applications in pavement management, image classification, geospatial buffer analysis, etc.

"The science of informatics in the broadest sense has been several thousands of years in the making. With the recent emergence of large storage devices and high-speed processing of data, it has become possible to organize vast amounts of data as digital products with ontologic tags and concepts for smart queries. Coupling this computational capability with earth science data defines the emerging field of geoinformatics. Since the science of geology was established several centuries ago, observations led to conclusions that were integrative in concept and clearly had profound implications for the birth of geology. As disciplinary information about Earth becomes more voluminous, the use of geoinformatics will lead to integrative, science-based discoveries of new knowledge about planetary systems. Twenty one research papers, co-authored by 96 researchers from both earth and computer sciences, provide the first-ever organized presentation of the science of informatics as it relates to geology. Readers will readily recognize the vast intellectual content represented by these papers as they seek to address the core research goals of geoinformatics."--Publisher's website.

Big data has always been a major challenge in geoinformatics as geospatial data come in various types and formats, new geospatial data are acquired very fast, and geospatial databases are inherently very large. And while there have been advances in hardware and software for handling big data, they often fall short of handling geospatial big data efficiently and effectively. Big Data: Techniques and Technologies in Geoinformatics tackles these challenges head on, integrating coverage of techniques and technologies for storing, managing, and computing geospatial big data. Providing a perspective based on analysis of time, applications, and resources, this book familiarizes readers with geospatial applications that fall under the category of big data. It explores new trends in geospatial data collection, such as geo-crowdsourcing and advanced data collection technologies such as LiDAR point clouds. The book features a range of topics on big data techniques and technologies in geoinformatics including distributed computing, geospatial data analytics, social media, and volunteered geographic information. With chapters contributed by experts in geoinformatics and in domains such as computing and engineering, the book provides an understanding of the challenges and issues of big data in geoinformatics applications. The book is a single collection of current and emerging techniques, technologies, and tools that are needed to collect, analyze, manage, process, and visualize geospatial big data.

A grand challenge for science is to understand the human implications of global environmental change and to help society cope with those changes. Virtually all the scientific questions associated with this challenge depend on geospatial information (geoinformation) and on the ability of scientists, working individually and in groups, to interact with that information in flexible and increasingly complex ways. Another grand challenge is how to respond to calamities-terrorist activities, other human-induced crises, and natural disasters. Much of the information that underpins emergency preparedness, response, recovery, and mitigation is geospatial in nature. In terrorist situations, for example, origins and destinations of phone calls and e-mail messages, travel patterns of individuals, dispersal patterns of airborne chemicals, assessment of places at risk, and the allocation of resources all involve geospatial information. Much of the work addressing environment- and emergency-related concerns will depend on how productively humans are able to integrate, distill, and correlate a wide range of seemingly unrelated information. In addition to critical advances in location-aware computing, databases, and data mining methods, advances in the human-computer interface will couple new computational capabilities with human cognitive capabilities. This report outlines an interdisciplinary research roadmap at the intersection of computer science and geospatial information science. The report was developed by a committee convened by the Computer Science and Telecommunications Board of the National Research Council.

Mobile communications and ubiquitous computing generate large volumes of data. Mining this data can produce useful knowledge, yet individual privacy is at risk. This book investigates the various scientific and technological issues of mobility data, open problems, and roadmap. The editors manage a research project called GeoPKDD, Geographic Privacy-Aware Knowledge Discovery and Delivery, and this book relates their findings in 13 chapters covering all related subjects.

In this cohesive collection of peer-reviewed chapters, field authorities present the latest field advancements and cover essential areas such as data acquisition, geoinformation theory, spatial statistics, and dissemination. Each chapter opens with an editorial preview of each topic from a conceptual, applied, and methodological point of view, making it easier for researchers to judge which information is most beneficial to their work. Under the editorial guidance of internationally respected geoinformatics experts, the volume addresses quality aspects in the entire spatial data mining process, from data acquisition to end user.