Spatial Data Analysis Example

Spatial Data Analysis by Example: Categorical and directional data

Spatial Data Management

Spatial database management deals with the storage, indexing, and querying of data with spatial features, such as location and geometric extent. Many applications require the efficient management of spatial data, including Geographic Information Systems, Computer Aided Design, and Location Based Services. The goal of this book is to provide the reader with an overview of spatial data management technology, with an emphasis on indexing and search techniques. It first introduces spatial data models and queries and discusses the main issues of extending a database system to support spatial data. It presents indexing approaches for spatial data, with a focus on the R-tree. Query evaluation and optimization techniques for the most popular spatial query types (selections, nearest neighbor search, and spatial joins) are portrayed for data in Euclidean spaces and spatial networks. The book concludes by demonstrating the ample application of spatial data management technology on a wide range of related application domains: management of spatio-temporal data and high-dimensional feature vectors, multi-criteria ranking, data mining and OLAP, privacy-preserving data publishing, and spatial keyword search. Table of Contents: Introduction / Spatial Data / Indexing / Spatial Query Evaluation / Spatial Networks / Applications of Spatial Data Management Technology

Emerging Trends, Techniques, and Applications in Geospatial Data Science

With the emergence of smart technology and automated systems in today’s world, big data is being incorporated into many applications. Trends in data can be detected and objects can be tracked based on the real-time data that is utilized in everyday life. These connected sensor devices and objects will provide a large amount of data that is to be analyzed quickly, as it can accelerate the transformation of smart technology. The accuracy of prediction of artificial intelligence (AI) systems is drastically increasing by using machine learning and other probability and statistical approaches. Big data and geospatial data help to solve complex issues and play a vital role in future applications. Emerging Trends, Techniques, and Applications in Geospatial Data Science provides an overview of the basic concepts of data science, related tools and technologies, and algorithms for managing the relevant challenges in real-time application domains. The book covers a detailed description for readers with practical ideas using AI, the internet of things (IoT), and machine learning to deal with the analysis, modeling, and predictions from big data. Covering topics such as field spectra, high-resolution sensing imagery, and spatiotemporal data engineering, this premier reference source is an excellent resource for data scientists, computer and IT professionals, managers, mathematicians and statisticians, health professionals, technology developers, students and educators of higher education, librarians, researchers, and academicians.