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A significant portion of government agencies' activities require mobile data collection and analysis in the field. To accommodate such tasks, various data sources are involved to provide the supporting information. The data integration, especially spatial data integration, in these applications turns out to be a big issue due to the multitude and heterogeneity of possible data sources. An aim of our research was to develop a flexible and extensible infrastructure to facilitate the field applications with integrated data sources. New designs have been proposed to treat heterogeneous data sources as a set of object views. This object view approach helps to integrate multiple data sources into an existent object-oriented view system. This research also goes a step further from the previous version of infrastructure in terms of extending data processing and communication capabilities. The oracle spatial database has been included as a new type of spatial data source. The CORBA-based client-server model is also implemented as another communication resort for the infrastructure to interact with the data access component.
This book is a valuable resource for the increasing body of researchers and practitioners in the field of geospatial technologies. Written by leading researchers and experts it is designed in such a way that technical achievements and challenges of geospatial computing applications are followed by various applications developed for society. As such, they serve as a bridge between technologists and solution developers, which is critical in the context of developing countries. There have been significant advances in geospatial technologies in India in the last decade, including advances in spatial data infrastructures, geocomputation and spatial databases, and innovative applications in natural resource development. Ranging from LIDAR standards, to data integration using ontologies, and mobile computing, such progress enhances the utility of the technology for both urban and rural development. This book discusses these achievements and considers the way forward.
Science is increasingly driven by data, and spatial data underpin the science directions laid out in the 2007 U.S. Geological Survey (USGS) Science Strategy. A robust framework of spatial data, metadata, tools, and a user community that is interactively connected to use spatial data in an efficient and flexible way-known as a spatial data infrastructure (SDI)-must be available for scientists and managers to find, use, and share spatial data both within and beyond the USGS. Over the last decade, the USGS has conducted breakthrough research that has overcome some of the challenges associated with implementing a large SDI. Advancing Strategic Science: A Spatial Data Infrastructure Roadmap for the U.S. Geological Survey is intended to ground those efforts by providing a practical roadmap to full implementation of an SDI to enable the USGS to conduct strategic science.
The convergence of big data and geospatial computing has brought forth challenges and opportunities to Geographic Information Science with regard to geospatial data management, processing, analysis, modeling, and visualization. This book highlights recent advancements in integrating new computing approaches, spatial methods, and data management strategies to tackle geospatial big data challenges and meanwhile demonstrates opportunities for using big data for geospatial applications. Crucial to the advancements highlighted in this book is the integration of computational thinking and spatial thinking and the transformation of abstract ideas and models to concrete data structures and algorithms.
This book introduces the latest research findings in cloud, edge, fog, and mist computing and their applications in various fields using geospatial data. It solves a number of problems of cloud computing and big data, such as scheduling, security issues using different techniques, which researchers from industry and academia have been attempting to solve in virtual environments. Some of these problems are of an intractable nature and so efficient technologies like fog, edge and mist computing play an important role in addressing these issues. By exploring emerging advances in cloud computing and big data analytics and their engineering applications, the book enables researchers to understand the mechanisms needed to implement cloud, edge, fog, and mist computing in their own endeavours, and motivates them to examine their own research findings and developments.
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 year 2005 sparked a geographic revolution through the release of Google Maps, arguably the first geographic tool to capture public interest and act as a catalyst for neogeography (i.e. the community of non-geographers who built tools and technologies without formal training in geography). A few years later, in 2008, the scientific community witnessed another major turning point through open access to the Landsat satellite archive, which had been collecting earth observation data since 1972. These moments were critical starting points of an explosion in geographic tools and data that today remains on a rapid upward trajectory. In more recent years, new additions in data and tools have come from the Free and Open Source Software (FOSS), open and volunteered data movements, new data collection methods (such as unmanned aerial vehicles, micro-satellites, real-time sensors), and advances in computational technologies such as cloud and high performance computing (HPC). However, within the broader Data Science community, specific attention was often not given to the unique characteristics (e.g. spatial dependence) and evolutions in geospatial data (e.g. increasing temporal/spatial resolutions and extents). Beginning in 2015, researchers such as Luc Anselin as well as others who had been developing geospatial cyber-infrastructure (CyberGIS) since 2008 began to call for a Spatial Data Science, a field that could leverage the advances from Data Science, such as data mining, machine learning, and other statistical and visualization ‘big’ data techniques, for geospatial data. New challenges have emerged from this rapid expansion in data and tool options: how to scale analyses for ‘big’ data; deal with uncertainty and quality for data synthesis; evaluate options and choose the right data or tool; integrate options when only one will not suffice; and use emerging tools to effectively collaborate on increasingly more multi-disciplinary and multi-dimensional research that aims to address our current societal and environmental challenges, such as climate change, loss of biodiversity and natural areas, and wildfire management. This dissertation addresses in part these challenges by applying emerging methods and tools in Spatial Data Science (such as cloud-computing, cluster analysis and machine learning) to develop new frameworks for evaluating geospatial tools based on collaborative potential and for evaluating and integrating competing remotely-sensed map products of vegetation change and disturbance. In Chapter One, I discuss in further detail the historical trajectory toward a Spatial Data Science and provide a new working definition of the field that recognizes its interdisciplinary and collaborative potential and that serves as the guiding conceptual foundation of this dissertation. In Chapter Two, I identify the key components of a collaborative Spatial Data Science workflow to develop a framework for evaluating the various functional aspects of multi-user geospatial tools. Using this framework, I then score thirty-one existing tools and apply a cluster analysis to create a typology of these tools. I present this typology as the first map of the emergent ecosystem and functional niches of collaborative geospatial tools. I identify three primary clusters of tools composed of eight secondary clusters across which divergence is driven by required infrastructure and user involvement. I use my results to highlight how environmental collaborations have benefited from these tools and propose key areas of future tool development for continued support of collaborative geospatial efforts. In Chapters Three and Four, I apply Spatial Data Science within a case study of California fire to compare the differences as well as explore the synergies between the three remotely-sensed map products of vegetation disturbance for 2001-2010: Hansen Global Forest Change (GFC); North American Forest Dynamics (NAFD); and Landscape Fire and Resource Management Planning Tools (LANDFIRE). Specifically, Chapter Three identifies the implications of the differing creation methods of these products on their representations of disturbance and fire. I identify that LANDFIRE (the traditional created product that integrates field data and public data on disturbance events with remote sensing) reported the highest amount of vegetation disturbance across all years and habitat types, as compared to GFC and NAFD, which are both produced from automated remote sensing analyses. I also find that these differences in reported disturbance are driven by differential inclusion of reference data on fire (rather than differences in environmental conditions) and identify the widest range in reported disturbance (i.e. more uncertainty) in years with more fire incidence and in scrub/shrub habitat. In Chapter Four, I use spatial agreement among the competing products as a measure of uncertainty. I identify low uncertainty in disturbance (i.e. where all products agree) across only 15% of the total area of California that was reported as disturbed by at least one product between 2001 and 2010. Specifically, I find that scrub/shrub habitat had a lower uncertainty of disturbance than forest, particularly for fire, and that uncertainty was universally high across all bioregions. I also identify that LANDFIRE was solely responsible for approximately 50% of the total area reported as disturbed and find large differences between the burned areas reported by the reference data and the areas with low uncertainty of disturbance, indicating potential overestimation of disturbance by both LANDFIRE and the reference data on fire. Last, in Chapter Five, I conclude by highlighting how unresolved key challenges for Spatial Data Science can serve as new opportunities to guide the scaling of methods for “big” data, increased spatial-temporal integration, as well as promote new curriculum to better prepare future Spatial Data Scientists. In all, this dissertation explores the opportunities and challenges posed by Spatial Data Science and serves as a guiding reference for professionals and practitioners to successfully navigate the changing world of geospatial data and tools.
The Encyclopedia of Big Data Technologies provides researchers, educators, students and industry professionals with a comprehensive authority over the most relevant Big Data Technology concepts. With over 300 articles written by worldwide subject matter experts from both industry and academia, the encyclopedia covers topics such as big data storage systems, NoSQL database, cloud computing, distributed systems, data processing, data management, machine learning and social technologies, data science. Each peer-reviewed, highly structured entry provides the reader with basic terminology, subject overviews, key research results, application examples, future directions, cross references and a bibliography. The entries are expository and tutorial, making this reference a practical resource for students, academics, or professionals. In addition, the distinguished, international editorial board of the encyclopedia consists of well-respected scholars, each developing topics based upon their expertise.
This book elucidates how cyberGIS (that is, new-generation geographic information science and systems (GIS) based on advanced computing and cyberinfrastructure) transforms computation- and data-intensive geospatial discovery and innovation. It comprehensively addresses opportunities and challenges, roadmaps for research and development, and major progress, trends, and impacts of cyberGIS in the era of big data. The book serves as an authoritative source of information to fill the void of introducing this exciting and growing field. By providing a set of representative applications and science drivers of cyberGIS, this book demonstrates how cyberGIS has been advanced to enable cutting-edge scientific research and innovative geospatial application development. Such cyberGIS advances are contextualized as diverse but interrelated science and technology frontiers. The book also emphasizes several important social dimensions of cyberGIS such as for empowering deliberative civic engagement and enabling collaborative problem solving through structured participation. In sum, this book will be a great resource to students, academics, and geospatial professionals for leaning cutting-edge cyberGIS, geospatial data science, high-performance computing, and related applications and sciences.
This book focuses on the modeling and management of spatial data in distributed systems. The authors have structured the contributions from internationally renowned researchers into four parts. The book offers researchers an excellent overview of the state-of-the-art in modeling and management of spatial data in distributed environments, while it may also be the basis of specialized courses on Web-based geographical information systems.