This first of a kind book places spatial data within the broader domain of information technology (IT) while providing a comprehensive and coherent explanation of the guiding principles, methods, ...implementation and operational management of spatial databases within the workplace. The text explains the key concepts, issues and processes of spatial data implementation and provides a holistic management perspective that complements the technical aspects of spatial data stressed in other textbooks. In this respect, this book is unique in its coverage of spatial database principles and architecture, database modelling including UML, database and spatial data standards, spatial data infrastructure, database implementation, and workplace-oriented project management including user needs study and end user education. The text first overviews the current state of spatial information technology and it concludes with a speculative account of likely future developments. Cutting edge research and practical workplace needs are defined and explained. Topics covered, among others, include strategies for end user education, current spatial data standards and their importance, legal issues and liabilities in the ownership and use of spatial data, spatial metadata use within distributed databases, the Internet and Web-based solutions to database deployment, quality assurance and quality control in database implementation and use, spatial decision support, and spatial data mining. The book applies equally to senior undergraduate and graduate courses and students, as well as spatial data managers and practitioners already in the workplace. It will enhance their technical and human-resource based understanding of spatial data management. Certification courses that seek to prepare students for careers in the spatial information industry and courses targeted at enhancing needed geospatial workplace knowledge and skills will benefit greatly from its content.
This Open Access volume aims to methodologically improve our understanding of biodiversity by linking disciplines that incorporate remote sensing, and uniting data and perspectives in the fields of ...biology, landscape ecology, and geography. The book provides a framework for how biodiversity can be detected and evaluated—focusing particularly on plants—using proximal and remotely sensed hyperspectral data and other tools such as LiDAR. The volume, whose chapters bring together a large cross-section of the biodiversity community engaged in these methods, attempts to establish a common language across disciplines for understanding and implementing remote sensing of biodiversity across scales. The first part of the book offers a potential basis for remote detection of biodiversity. An overview of the nature of biodiversity is described, along with ways for determining traits of plant biodiversity through spectral analyses across spatial scales and linking spectral data to the tree of life. The second part details what can be detected spectrally and remotely. Specific instrumentation and technologies are described, as well as the technical challenges of detection and data synthesis, collection and processing. The third part discusses spatial resolution and integration across scales and ends with a vision for developing a global biodiversity monitoring system. Topics include spectral and functional variation across habitats and biomes, biodiversity variables for global scale assessment, and the prospects and pitfalls in remote sensing of biodiversity at the global scale.
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•A new “information system” is proposed for mineral exploration targeting.•The system is called “exploration information system” (EIS).•EIS uses “mineral system” concepts in ...conjunction with GIS.•Ore-forming processes are classified into pre-, syn-, and post-mineralization parts.•EIS aims to automated translation of ore-forming processes into spatial proxies.
The advent of modern data collection and storage technologies has brought about a huge increase in data volumes with both traditional and machine learning tools struggling to effectively handle, manage and analyse the very large data quantities that are now available. The mineral exploration industry is by no means immune to this big data issue. Exploration decision-making has become much more complex in the wake of big data, in particular with respect to questions about how to best manage and use the data to obtain information, generate knowledge and gain insight. One of the ways in which the mineral exploration industry works with big data is by using a geographic information system (GIS). For example, GIS platforms are often used for integration, interrogation and interpretation of diverse geoscience and mineral exploration data with the goal of refining and prioritising known and identifying new targets. Here we (i) briefly discuss the importance of carefully translating conceptual ore deposit models into effective exploration targeting maps, (ii) propose and describe what we term exploration information systems (EIS): a new idea for an information system designed to better integrate the conceptual mineral deposit model (i.e., the critical and constituent processes of the targeted mineral system) with data available to support exploration targeting, and (iii) discuss how best to categorise mineral systems in an EIS as scale-dependent subsystems to form mineral deposits. Our vision for the future use of EIS in exploration targeting is one whereby the mappable ingredients of a targeted mineral system are translated and combined into a set of weighted evidence (or proxy) maps automatically, resulting in an auto-generated mineral prospectivity map and a series of ranked exploration targets. We do not envisage the EIS replacing human input and ingenuity; rather we envisage the EIS as an additional tool in the exploration toolbox and as an intelligence amplifying system in which humans are making use of machines to achieve the best possible results.
Maps are well recognized as an effective means of presenting and communicating health data, such as cancer incidence and mortality rates. These data can be linked to geographic features like counties ...or census tracts and their associated attributes for mapping and analysis. Such visualization and analysis provide insights regarding the geographic distribution of cancer and can be important for advancing effective cancer prevention and control programs. Applying a spatial approach allows users to identify location‐based patterns and trends related to risk factors, health outcomes, and population health. Geographic information science (GIScience) is the discipline that applies Geographic Information Systems (GIS) and other spatial concepts and methods in research. This review explores the current state and evolution of GIScience in cancer research by addressing fundamental topics and issues regarding spatial data and analysis that need to be considered. GIScience, along with its health‐specific application in the spatial epidemiology of cancer, incorporates multiple geographic perspectives pertaining to the individual, the health care infrastructure, and the environment. Challenges addressing these perspectives and the synergies among them can be explored through GIScience methods and associated technologies as integral parts of epidemiologic research, analysis efforts, and solutions. The authors suggest GIScience is a powerful tool for cancer research, bringing additional context to cancer data analysis and potentially informing decision‐making and policy, ultimately aimed at reducing the burden of cancer.
The current state and evolution of geographic information science (GIScience) in cancer research is explored by addressing fundamental topics and issues regarding spatial data and analysis that need to be considered. GIScience is a powerful tool for cancer research, bringing additional context to cancer data analysis, potentially informing decision making and policy, and ultimately reducing the burden of cancer.
This open access volume contains the proceedings of the X Hotine-Marussi Symposium on Mathematical Geodesy which was held from 13 to 17 June 2022 at the Politecnico di Milano, Milan, Italy. Since ...2006 the series of the Hotine-Marussi Symposia has been under the responsibility of the Inter-Commission Committee on Theory (ICCT) within the International Association of Geodesy (IAG). The ICCT organized the last five Hotine-Marussi Symposia held in Wuhan (2006), Rome (2009, 2013 and 2018), and Milan (2022). The overall goal of the ICCT and Hotine-Marussi Symposia has always been to advance geodetic theory which is indeed documented by the 22 research articles published in these proceedings. The jubilee X Hotine-Marussi Symposium was organized in 10 topical sessions covering all parts of geodetic theory including reference frames, gravity field modelling, adjustment theory, height systems, time series analysis, or advanced numerical methods. In total, 60 participants attended the Symposium who delivered 62 oral and 18 poster presentations. During a special session, five invited speakers discussed two basic concepts of physical geodesy – geoid and quasigeoid.
Local parks and walkable neighborhoods are commonly cited as elements of the urban environment that promote physical activity and reduce obesity risk. When those vulnerable to obesity-related ...diseases live in neighborhoods without these qualities, it works against environmental justice goals that aim for a fair distribution of amenities. We use geographic information systems (GIS) to evaluate the relationship between the distribution of populations vulnerable to obesity and proximity to parks and walkable street networks in Phoenix, Arizona, USA. Though previous studies have used GIS to assess the distribution of access to opportunities for physical activity, none have analyzed access to both parks and walkable resources at once. Neither have they included data that reflects findings on a smaller scale indicating that perceptions of resource quality, safety, and cultural relevance also affect physical activity levels. We include these safety and quality factors in our study through statistical data on traffic fatalities, crime rates and park size. We find that, counter to predictions, subpopulations generally considered vulnerable to obesity (and environmental injustices more generally) are more likely to live in walkable neighborhoods and have better walking access to neighborhood parks than other groups in Phoenix. However, crime is highest in walkable neighborhoods with large Latino/a and African-American populations and parks are smaller in areas populated by Latino/as. Given the higher prevalence of obesity and related diseases in lower income and minority populations in Phoenix, the results suggest that benefits of built environments may be offset by social characteristics. Our most consistent finding indicates a strong negative relationship between the percentage of the population under 18 years of age living in an area and the likelihood that the structure of the built environment supports physical activity. Children under 18 are significantly underrepresented in regions deemed highly walkable and those with access to parks.
Object retrieval and reconstruction from very-high-resolution (VHR) synthetic aperture radar (SAR) images are of great importance for urban SAR applications, yet highly challenging due to the ...complexity of SAR data. This article addresses the issue of individual building segmentation from a single VHR SAR image in large-scale urban areas. To achieve this, we introduce building footprints from geographic information system (GIS) data as a complementary information and propose a novel conditional GIS-aware network (CG-Net). The proposed model learns multilevel visual features and employs building footprints to normalize the features for predicting building masks in the SAR image. We validate our method using a high-resolution spotlight TerraSAR-X image collected over Berlin. Experimental results show that the proposed CG-Net effectively brings improvements with variant backbones. We further compare two representations of building footprints, namely, complete building footprints and sensor-visible footprint segments, for our task, and conclude that the use of the former leads to better segmentation results. Moreover, we investigate the impact of inaccurate GIS data on our CG-Net, and this study shows that CG-Net is robust against positioning errors in the GIS data. In addition, we propose an approach of ground truth generation of buildings from an accurate digital elevation model (DEM), which can be used to generate large-scale SAR image data sets. The segmentation results can be applied to reconstruct 3-D building models at level-of-detail (LoD) 1, which is demonstrated in our experiments.
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