Public and private institutions are committing resources and making important long-term decisions concerning the collection, management, and use of spatial data. Although these actions are influenced by current pressures, priorities, and opportunities, their ultimate success depends on how these spatial data activities will be relevant to future needs and demands. The Mapping Science Committee, in cooperation with the Federal Geographic Data Committee, convened a workshop in April 1996 to examine societal and technological changes that might occur within the next 15 years. The purpose was to consider within the context of spatial data activities a series of long-term visions and to identify societal forces and changes that would make those visions more or less likely. The workshop provided a framework for thinking about the future of U.S. spatial data activities.
This book draws on author’s wealth of knowledge working on numerous projects across many countries. It provides a clear overview of the development of the SDI concept and SDI worldwide implementation and brings a logical chronological approach to the linkage of GIS technology with SDI enabling data. The theory and practice approach help understand that SDI development and implementation is very much a social process of learning by doing. The author masterfully selects main historical developments and updates them with an analytical perspective promoting informed and responsible use of geographic information and geospatial technologies for the benefit of society from local to global scales. Features Subject matter spans thirty years of the development of GIS and SDI. Brings a social science perspective into GIS and SDI debates that have been largely dominated by technical considerations. Based on a world-wide perspective as a result of the author's experience and research in the USA, Australia, Canada, Brazil, Peru, China, India, Korea, Malaysia, and Japan as well as most European countries. Draws upon professional and academic experience relating to pioneering UK and European GIS research initiatives. Includes updated historical material with an analytical perspective explaining what was done right, and what didn't work.
The National Spatial Data Infrastructure (NSDI) was envisioned as a way of enhancing the accessibility, communication, and use of geospatial data to support a wide variety of decisions at all levels of society. The goals of the NSDI are to reduce redundancy in geospatial data creation and maintenance, reduce the costs of geospatial data creation and maintenance, improve access to geospatial data, and improve the accuracy of geospatial data used by the broader community. At the core of the NSDI is the concept of partnerships, or collaborations, between different agencies, corporations, institutions, and levels of government. In a previous report, the Mapping Science Committee (MSC) defined a partnership as "...a joint activity of federal and state agencies, involving one or more agencies as joint principals focusing on geographic information." The concept of partnerships was built on the foundation of shared responsibilities, shared costs, shared benefits, and shared control. Partnerships are designed to share the costs of creation and maintenance of geospatial data, seeking to avoid unnecessary duplication, and to make it possible for data collected by one agency at a high level of spatial detail to be used by another agency in more generalized form. Over the past seven years, a series of funding programs administered by the Federal Geographic Data Committee (FGDC) has stimulated the creation of such partnerships, and thereby promoted the objectives of the NSDI, by raising awareness of the need for a coordinated national approach to geospatial data creation, maintenance, and use. They include the NSDI Cooperative Agreements Program, the Framework Demonstration Projects Program, the Community Demonstration Projects, and the Community-Federal Information Partnerships proposal. This report assesses the success of the FGDC partnership programs that have been established between the federal government and state and local government, industry, and academic communities in promoting the objectives of the National Spatial Data Infrastructure.
Applied Spatial Data Analysis with R, second edition, is divided into two basic parts, the first presenting R packages, functions, classes and methods for handling spatial data. This part is of interest to users who need to access and visualise spatial data. Data import and export for many file formats for spatial data are covered in detail, as is the interface between R and the open source GRASS GIS and the handling of spatio-temporal data. The second part showcases more specialised kinds of spatial data analysis, including spatial point pattern analysis, interpolation and geostatistics, areal data analysis and disease mapping. The coverage of methods of spatial data analysis ranges from standard techniques to new developments, and the examples used are largely taken from the spatial statistics literature. All the examples can be run using R contributed packages available from the CRAN website, with code and additional data sets from the book's own website. Compared to the first edition, the second edition covers the more systematic approach towards handling spatial data in R, as well as a number of important and widely used CRAN packages that have appeared since the first edition. This book will be of interest to researchers who intend to use R to handle, visualise, and analyse spatial data. It will also be of interest to spatial data analysts who do not use R, but who are interested in practical aspects of implementing software for spatial data analysis. It is a suitable companion book for introductory spatial statistics courses and for applied methods courses in a wide range of subjects using spatial data, including human and physical geography, geographical information science and geoinformatics, the environmental sciences, ecology, public health and disease control, economics, public administration and political science. The book has a website where complete code examples, data sets, and other support material may be found: http://www.asdar-book.org. The authors have taken part in writing and maintaining software for spatial data handling and analysis with R in concert since 2003.
The book introduces a variety of latest techniques designed to represent, enhance, and empower multi-disciplinary approaches of geographic information system (GIS), artificial intelligence (AI), deep learning (DL), machine learning, and cloud computing research in healthcare. It provides a unique compendium of the current and emerging use of geospatial data for healthcare and reflects the diversity, complexity, and depth and breadth of this multi-disciplinary area. This book addresses various aspects of how smart healthcare devices can be used to detect and analyze diseases. Further, it describes various tools and techniques to evaluate the efficacy, suitability, and efficiency of geospatial data for health-related applications. It features illustrative case studies, including future applications and healthcare challenges. This book is beneficial for computer science and engineering students and researchers, medical professionals, and anyone interested in using geospatial data in healthcare. It is also intended for experts, offering them a valuable retrospective and a global vision for the future, as well as for non-experts who are curious to learn about this important subject. The book presents an effort to draw how we can build health-related applications using geospatial big data and their subsequent analysis.
This book highlights advanced applications of geospatial data analytics to address real-world issues in urban society. With a connected world, we are generating spatial at unprecedented rates which can be harnessed for insightful analytics which define the way we analyze past events and define the future directions. This book is an anthology of applications of spatial data and analytics performed on them for gaining insights which can be used for problem solving in an urban setting. Each chapter is contributed by spatially aware data scientists in the making who present spatial perspectives drawn on spatial big data. The book shall benefit mature researchers and student alike to discourse a variety of urban applications which display the use of machine learning algorithms on spatial big data for real-world problem solving.
Geocomputation with R is for people who want to analyze, visualize and model geographic data with open source software. It is based on R, a statistical programming language that has powerful data processing, visualization, and geospatial capabilities. The book equips you with the knowledge and skills to tackle a wide range of issues manifested in geographic data, including those with scientific, societal, and environmental implications. This book will interest people from many backgrounds, especially Geographic Information Systems (GIS) users interested in applying their domain-specific knowledge in a powerful open source language for data science, and R users interested in extending their skills to handle spatial data. The book is divided into three parts: (I) Foundations, aimed at getting you up-to-speed with geographic data in R, (II) extensions, which covers advanced techniques, and (III) applications to real-world problems. The chapters cover progressively more advanced topics, with early chapters providing strong foundations on which the later chapters build. Part I describes the nature of spatial datasets in R and methods for manipulating them. It also covers geographic data import/export and transforming coordinate reference systems. Part II represents methods that build on these foundations. It covers advanced map making (including web mapping), "bridges" to GIS, sharing reproducible code, and how to do cross-validation in the presence of spatial autocorrelation. Part III applies the knowledge gained to tackle real-world problems, including representing and modeling transport systems, finding optimal locations for stores or services, and ecological modeling. Exercises at the end of each chapter give you the skills needed to tackle a range of geospatial problems. Solutions for each chapter and supplementary materials providing extended examples are available at https://geocompr.github.io/geocompkg/articles/. Dr. Robin Lovelace is a University Academic Fellow at the University of Leeds, where he has taught R for geographic research over many years, with a focus on transport systems. Dr. Jakub Nowosad is an Assistant Professor in the Department of Geoinformation at the Adam Mickiewicz University in Poznan, where his focus is on the analysis of large datasets to understand environmental processes. Dr. Jannes Muenchow is a Postdoctoral Researcher in the GIScience Department at the University of Jena, where he develops and teaches a range of geographic methods, with a focus on ecological modeling, statistical geocomputing, and predictive mapping. All three are active developers and work on a number of R packages, including stplanr, sabre, and RQGIS.
