This book is a collection of seminal position essays by leading researchers on new development in Geographic Information Sciences (GIScience), covering a wide range of topics and representing a variety of perspectives. The authors propose enrichments and extensions to the conceptual framework of GIScience; discuss a series of transformational methodologies and technologies for analysis and modeling; elaborate on key issues in innovative approaches to data acquisition and integration, across earth sensing to social sensing; and outline frontiers in application domains, spanning from natural science to humanities and social science, e.g., urban science, land use and planning, social governance, transportation, crime, and public health, just name a few. The book provides an overview of the strategic directions on GIScience research and development. It will benefit researchers and practitioners in the field who are seeking a high-level reference regarding those directions.

Targeting those charged with launching or implementing a geographic information system for their organization, this book details a practical method for planning a GIS proven successful in public and private sector organizations.

Includes an entirely new chapter on AI and GIS, including ontologies and the Semantic Web, knowledge representation (KR) and spatial reasoning, machine learning and spatial analysis, and neural networks and deep learning. Presents new material reflecting the advances made in cloud computing, stream computing, and sensor networks, as well as extensively revised and updated content on cartography, visualization, and interaction design. Connects the technology to the social aspects and implications of GIS, including privacy and fair information practices, FATE (fairness, accountability, transparency, and ethics), and codes of conduct for responsible use of GIS. Integrates the necessary background to foundational areas, such as databases and data structures, algorithms and indexes, system architecture and AI, provided in context so readers new to those topics can still understand the concepts being discussed. Incorporates over 20 carefully explained spatial algorithms; over 60 inset boxes with in-depth material that enriches the central topics; and more than 300 color figures to support the reader in mastering key concepts. Welcomes a new coauthor, Qian (Chayn) Sun, to the third edition who brings her expertise in topics such as web mapping, cloud computing, critical geography, and machine learning with big spatial data.

This textbook integrates GIS, spatial analysis, and computational methods for solving real-world problems in various policy-relevant social science applications. Thoroughly updated, the third edition showcases the best practices of computational spatial social science and includes numerous case studies with step-by-step instructions in ArcGIS Pro and open-source platform KNIME. Readers sharpen their GIS skills by applying GIS techniques in detecting crime hotspots, measuring accessibility of primary care physicians, forecasting the impact of hospital closures on local community, or siting the best locations for business. FEATURES Fully updated using the latest version of ArcGIS Pro and open-source platform KNIME Features two brand-new chapters on agent-based modeling and big data analytics Provides newly automated tools for regionalization, functional region delineation, accessibility measures, planning for maximum equality in accessibility, and agent-based crime simulation Includes many compelling examples and real-world case studies related to social science, urban planning, and public policy Provides a website for downloading data and programs for implementing all case studies included in the book and the KNIME lab manual Intended for students taking upper-level undergraduate and graduate-level courses in quantitative geography, spatial analysis, and GIS applications, as well as researchers and professionals in fields such as geography, city and regional planning, crime analysis, public health, and public administration.

Early exposure to geography, spatial thinking, and geographic information systems (GIS) helps students gain an understanding of the world around them. This work helps students improve their basic map-reading skills and extend those skills by analyzing and thinking critically about the data. Thinking Spatially Using GIS contains teacher materials, lesson plans, a resource CD with exercises, and ArcExplorer Java Edition for Education software.

This nine-volume set LNCS 14104 – 14112 constitutes the refereed workshop proceedings of the 23rd International Conference on Computational Science and Its Applications, ICCSA 2023, held at Athens, Greece, during July 3–6, 2023. The 350 full papers and 29 short papers and 2 PHD showcase papers included in this volume were carefully reviewed and selected from a total of 876 submissions. These nine-volumes includes the proceedings of the following workshops: Advances in Artificial Intelligence Learning Technologies: Blended Learning, STEM, Computational Thinking and Coding (AAILT 2023); Advanced Processes of Mathematics and Computing Models in Complex Computational Systems (ACMC 2023); Artificial Intelligence supported Medical data examination (AIM 2023); Advanced and Innovative web Apps (AIWA 2023); Assessing Urban Sustainability (ASUS 2023); Advanced Data Science Techniques with applications in Industry and Environmental Sustainability (ATELIERS 2023); Advances in Web Based Learning (AWBL 2023); Blockchain and Distributed Ledgers: Technologies and Applications (BDLTA 2023); Bio and Neuro inspired Computing and Applications (BIONCA 2023); Choices and Actions for Human Scale Cities: Decision Support Systems (CAHSC-DSS 2023); and Computational and Applied Mathematics (CAM 2023).

Derived from presentations made at the third annual UK National Conference on GIS Research, this work consists of contributions by leading experts in: geography, mathematics, computing science, surveying, archaeology, planning and medicine.

This comprehensive handbook covers Geospatial Artificial Intelligence (GeoAI), which is the integration of geospatial studies and AI machine (deep) learning and knowledge graph technologies. It explains key fundamental concepts, methods, models, and technologies of GeoAI, and discusses the recent advances, research tools, and applications that range from environmental observation and social sensing to natural disaster responses. As the first single volume on this fast-emerging domain, Handbook of Geospatial Artificial Intelligence is an excellent resource for educators, students, researchers, and practitioners utilizing GeoAI in fields such as information science, environment and natural resources, geosciences, and geography. Features Provides systematic introductions and discussions of GeoAI theory, methods, technologies, applications, and future perspectives Covers a wide range of GeoAI applications and case studies in practice Offers supplementary materials such as data, programming code, tools, and case studies Discusses the recent developments of GeoAI methods and tools Includes contributions written by top experts in cutting-edge GeoAI topics This book is intended for upper-level undergraduate and graduate students from different disciplines and those taking GIS courses in geography or computer sciences as well as software engineers, geospatial industry engineers, GIS professionals in non-governmental organizations, and federal/state agencies who use GIS and want to learn more about GeoAI advances and applications.

* Provides case studies in each chapter illustrating how principles work in practice. * Compares strengths and weaknesses of off-the-shelf software packages.

Through the results of a developed case study of information system for low temperature geothermal energy, GIS to Support Cost-effective Decisions on Renewable Sources addresses the issue of the use of Geographic Information Systems (GIS) in evaluating cost-effectiveness of renewable resource exploitation on a regional scale. Focusing on the design of a Decision Support System, a process is presented aimed to transform geographic data into knowledge useful for analysis and decision-making on the economic exploitation of geothermal energy. This detailed description includes a literature review and technical issues related to data collection, data mining, decision analysis for the informative system developed for the case study. A multi-disciplinary approach to GIS design is presented which is also an innovative example of fusion of georeferenced data acquired from multiple sources including remote sensing, networks of sensors and socio-economic censuses. GIS to Support Cost-effective Decisions on Renewable Sources is a useful, practical reference for engineers, managers and researchers involved in the design of GIS, decision support systems, investment planning/strategy in renewable energy and ICT innovation in this field.