Готові списки джерел за темами / Geospatial information systems and geospatial data modelling

Добірка наукової літератури з теми "Geospatial information systems and geospatial data modelling"

Автор: Grafiati
Опубліковано: 10 грудня 2022
Оновлено: 28 січня 2023

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Статті в журналах з теми "Geospatial information systems and geospatial data modelling"

1

Breunig, Martin, Patrick Erik Bradley, Markus Jahn, Paul Kuper, Nima Mazroob, Norbert Rösch, Mulhim Al-Doori, Emmanuel Stefanakis, and Mojgan Jadidi. "Geospatial Data Management Research: Progress and Future Directions." ISPRS International Journal of Geo-Information 9, no. 2 (February 4, 2020): 95. http://dx.doi.org/10.3390/ijgi9020095.

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Анотація:
Without geospatial data management, today’s challenges in big data applications such as earth observation, geographic information system/building information modeling (GIS/BIM) integration, and 3D/4D city planning cannot be solved. Furthermore, geospatial data management plays a connecting role between data acquisition, data modelling, data visualization, and data analysis. It enables the continuous availability of geospatial data and the replicability of geospatial data analysis. In the first part of this article, five milestones of geospatial data management research are presented that were achieved during the last decade. The first one reflects advancements in BIM/GIS integration at data, process, and application levels. The second milestone presents theoretical progress by introducing topology as a key concept of geospatial data management. In the third milestone, 3D/4D geospatial data management is described as a key concept for city modelling, including subsurface models. Progress in modelling and visualization of massive geospatial features on web platforms is the fourth milestone which includes discrete global grid systems as an alternative geospatial reference framework. The intensive use of geosensor data sources is the fifth milestone which opens the way to parallel data storage platforms supporting data analysis on geosensors. In the second part of this article, five future directions of geospatial data management research are presented that have the potential to become key research fields of geospatial data management in the next decade. Geo-data science will have the task to extract knowledge from unstructured and structured geospatial data and to bridge the gap between modern information technology concepts and the geo-related sciences. Topology is presented as a powerful and general concept to analyze GIS and BIM data structures and spatial relations that will be of great importance in emerging applications such as smart cities and digital twins. Data-streaming libraries and “in-situ” geo-computing on objects executed directly on the sensors will revolutionize geo-information science and bridge geo-computing with geospatial data management. Advanced geospatial data visualization on web platforms will enable the representation of dynamically changing geospatial features or moving objects’ trajectories. Finally, geospatial data management will support big geospatial data analysis, and graph databases are expected to experience a revival on top of parallel and distributed data stores supporting big geospatial data analysis.
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2

Mazroob Semnani, N., P. V. Kuper, M. Breunig, and M. Al-Doori. "TOWARDS AN INTELLIGENT PLATFORM FOR BIG 3D GEOSPATIAL DATA MANAGEMENT." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-4 (September 19, 2018): 133–40. http://dx.doi.org/10.5194/isprs-annals-iv-4-133-2018.

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<p><strong>Abstract.</strong> The use of intelligent technologies within 3D geospatial data analysis and management will decidedly open the door towards efficiency, cost transparency, and on-time schedules in planning processes. Furthermore, the mission of smart cities as a future option of urban development can lead to an environment that provides high-quality life along stable structures. However, neither geospatial information systems nor building information modelling systems seem to be well prepared for this new development. After a review of current approaches and a discussion of their limitations we present our approach on the way to an intelligent platform for the management and analysis of big 3D geospatial data focusing on infrastructure projects such as metro or railway tracks planning. three challenges are presented focusing on the management of big geospatial data with existing geo-database management systems, the integration of heterogeneous data, and the 3D visualization for database query formulation and query results. The approach for the development of a platform for big geospatial data analysis is discussed. Finally, we give an outlook on our future research supporting intelligent 3D city applications in the United Arab Emirates.</p>
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3

Jetlund, Onstein, and Huang. "Adapted Rules for UML Modelling of Geospatial Information for Model-Driven Implementation as OWL Ontologies." ISPRS International Journal of Geo-Information 8, no. 9 (August 22, 2019): 365. http://dx.doi.org/10.3390/ijgi8090365.

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Анотація:
This study aims to improve the implementation of models of geospatial information in Web Ontology Language (OWL). Large amounts of geospatial information are maintained in Geographic Information Systems (GIS) based on models according to the Unified Modeling Language (UML) and standards from ISO/TC 211 and the Open Geospatial Consortium (OGC). Sharing models and geospatial information in the Semantic Web will increase the usability and value of models and information, as well as enable linking with spatial and non-spatial information from other domains. Methods for conversion from UML to OWL for basic concepts used in models of geospatial information have been studied and evaluated. Primary conversion challenges have been identified with specific attention to whether adapted rules for UML modelling could contribute to improved conversions. Results indicated that restrictions related to abstract classes, unions, compositions and code lists in UML are challenging in the Open World Assumption (OWA) on which OWL is based. Two conversion challenges are addressed by adding more semantics to UML models: global properties and reuse of external concepts. The proposed solution is formalized in a UML profile supported by rules and recommendations and demonstrated with a UML model based on the Intelligent Transport Systems (ITS) standard ISO 14825 Geographic Data Files (GDF). The scope of the resulting ontology will determine to what degree the restrictions shall be maintained in OWL, and different conversion methods are needed for different scopes.
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4

Anderson, Taylor, and Suzana Dragićević. "Representing Complex Evolving Spatial Networks: Geographic Network Automata." ISPRS International Journal of Geo-Information 9, no. 4 (April 20, 2020): 270. http://dx.doi.org/10.3390/ijgi9040270.

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Анотація:
Many real-world spatial systems can be conceptualized as networks. In these conceptualizations, nodes and links represent system components and their interactions, respectively. Traditional network analysis applies graph theory measures to static network datasets. However, recent interest lies in the representation and analysis of evolving networks. Existing network automata approaches simulate evolving network structures, but do not consider the representation of evolving networks embedded in geographic space nor integrating actual geospatial data. Therefore, the objective of this study is to integrate network automata with geographic information systems (GIS) to develop a novel modelling framework, Geographic Network Automata (GNA), for representing and analyzing complex dynamic spatial systems as evolving geospatial networks. The GNA framework is implemented and presented for two case studies including a spatial network representation of (1) Conway’s Game of Life model and (2) Schelling’s model of segregation. The simulated evolving spatial network structures are measured using graph theory. Obtained results demonstrate that the integration of concepts from geographic information science, complex systems, and network theory offers new means to represent and analyze complex spatial systems. The presented GNA modelling framework is both general and flexible, useful for modelling a variety of real geospatial phenomena and characterizing and exploring network structure, dynamics, and evolution of real spatial systems. The proposed GNA modelling framework fits within the larger framework of geographic automata systems (GAS) alongside cellular automata and agent-based modelling.
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5

Kim, K. S., and R. M. Beresford. "Use of geographic information systems and satellite data for assessing climatic risk of establishment of plant pathogens." New Zealand Plant Protection 62 (August 1, 2009): 109–13. http://dx.doi.org/10.30843/nzpp.2009.62.4779.

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Geographic information systems (GIS) have been used for geospatial data management and analysis map production and spatial modelling They also have the potential to incorporate climate and satellite data that could provide a spatial perspective on risk of establishment of plant pathogens In the present case study the climatic suitability for establishment of dwarf bunt a disease that can cause market access restrictions on wheat or other grass hosts in New Zealand was analysed using a GIS approach Establishment risk for dwarf bunt in New Zealand was found to be very low GIS was found to be a more versatile tool for modelling potential geographic distribution of organisms than conventional climate matching tools such as CLIMEX
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6

Jetlund, Knut, Erling Onstein, and Lizhen Huang. "Information Exchange between GIS and Geospatial ITS Databases Based on a Generic Model." ISPRS International Journal of Geo-Information 8, no. 3 (March 14, 2019): 141. http://dx.doi.org/10.3390/ijgi8030141.

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This study aims to improve interoperability between Geographic Information Systems (GIS) and geospatial databases for Intelligent Transport Systems (ITS). Road authorities maintain authoritative information for legal and safe navigation in GIS databases. This information needs to be shared with ITS databases for route planning and navigation, and for use in combination with local knowledge from vehicle sensors. Current solutions for modelling and exchanging geospatial information in the domains of GIS and ITS have been studied and evaluated. Limitations have been pointed out related to usability in the GIS domain and flexibility for representing an evolving real world. A prototype for an improved information exchange model has been developed, based on ISO/TC 211 standards, Model Driven Architecture (MDA), and concepts from the studied solutions. The prototype contains generic models for feature catalogues and features, with implementation schemas in the Geography Markup Language (GML). Results from a case study indicated that the models could be implemented with feature catalogues from the ITS standard ISO 14825 Geographic Data Files (GDF) and the INSPIRE Transport Networks specification. The prototype can be a candidate solution for improved information exchange from GIS databases to ITS databases that are based on the Navigation Data Standard.
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7

Bacher, U. "HYBRID AERIAL SENSOR DATA AS BASIS FOR A GEOSPATIAL DIGITAL TWIN." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B4-2022 (June 2, 2022): 653–59. http://dx.doi.org/10.5194/isprs-archives-xliii-b4-2022-653-2022.

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Abstract. More and more cities declare themselves to be a smart city or plan to be the same. Smart cities require a solid data source as basis for all further actions and the urban digital twin is the basis on which all information is collected and analysed. The urban digital twin is much more than just a 3D city model, but often this together with GIS data is the starting point for the urban digital twin. The basis of the urban digital twin is formed by geospatial data in the form of the geospatial digital twin. The digital twin hereby acts as a kind of hub into which all relevant and available information is included and analysed. To generate a geospatial digital twin aerial sensors that collect multiple data simultaneously, hybrid sensors, are perfectly suited for this task. In aerial data acquisition a new era started with the introduction of the first real hybrid sensor systems, like the Leica CityMapper-2. Hybrid in this context means the combination of an (oblique) camera system with a topographic LiDAR into an integrated aerial mapping system. By combining these complimentary sub-systems into one system the weaknesses of the one system could be compensated by using the alternative data source. An example is the mapping of low-light urban canyons, where image-based systems mostly produce unreliable results. For an LiDAR sensor the geometrical reconstruction of these areas is straight forward and leads to accurate results. The paper gives a detailed overview over the development and technical characteristics of hybrid sensor systems. The process of data acquisition is discussed and strategies for hybrid urban mapping are proposed. Furthermore, the paper provides insights into the advantage of LiDAR data for the 3D Mesh generation for urban modelling and on the possibilities to generate new products from the combination of the single products with the help of GeoAI. Finally, the use and some use cases of the hybrid sensor data and the derived products in the context of the urban digital twin is discussed and with the infinite loop of data, analysis, and action it is shown, that all data from the urban digital twin can only be a snapshot at a given point in time and the data recording and analysis is a permanent loop.
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8

Hor, A.-H., A. Jadidi, and G. Sohn. "BIM-GIS INTEGRATED GEOSPATIAL INFORMATION MODEL USING SEMANTIC WEB AND RDF GRAPHS." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-4 (June 3, 2016): 73–79. http://dx.doi.org/10.5194/isprsannals-iii-4-73-2016.

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In recent years, 3D virtual indoor/outdoor urban modelling becomes a key spatial information framework for many civil and engineering applications such as evacuation planning, emergency and facility management. For accomplishing such sophisticate decision tasks, there is a large demands for building multi-scale and multi-sourced 3D urban models. Currently, Building Information Model (BIM) and Geographical Information Systems (GIS) are broadly used as the modelling sources. However, data sharing and exchanging information between two modelling domains is still a huge challenge; while the syntactic or semantic approaches do not fully provide exchanging of rich semantic and geometric information of BIM into GIS or vice-versa. This paper proposes a novel approach for integrating BIM and GIS using semantic web technologies and Resources Description Framework (RDF) graphs. The novelty of the proposed solution comes from the benefits of integrating BIM and GIS technologies into one unified model, so-called Integrated Geospatial Information Model (IGIM). The proposed approach consists of three main modules: BIM-RDF and GIS-RDF graphs construction, integrating of two RDF graphs, and query of information through IGIM-RDF graph using SPARQL. The IGIM generates queries from both the BIM and GIS RDF graphs resulting a semantically integrated model with entities representing both BIM classes and GIS feature objects with respect to the target-client application. The linkage between BIM-RDF and GIS-RDF is achieved through SPARQL endpoints and defined by a query using set of datasets and entity classes with complementary properties, relationships and geometries. To validate the proposed approach and its performance, a case study was also tested using IGIM system design.
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9

Hor, A.-H., A. Jadidi, and G. Sohn. "BIM-GIS INTEGRATED GEOSPATIAL INFORMATION MODEL USING SEMANTIC WEB AND RDF GRAPHS." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-4 (June 3, 2016): 73–79. http://dx.doi.org/10.5194/isprs-annals-iii-4-73-2016.

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Анотація:
In recent years, 3D virtual indoor/outdoor urban modelling becomes a key spatial information framework for many civil and engineering applications such as evacuation planning, emergency and facility management. For accomplishing such sophisticate decision tasks, there is a large demands for building multi-scale and multi-sourced 3D urban models. Currently, Building Information Model (BIM) and Geographical Information Systems (GIS) are broadly used as the modelling sources. However, data sharing and exchanging information between two modelling domains is still a huge challenge; while the syntactic or semantic approaches do not fully provide exchanging of rich semantic and geometric information of BIM into GIS or vice-versa. This paper proposes a novel approach for integrating BIM and GIS using semantic web technologies and Resources Description Framework (RDF) graphs. The novelty of the proposed solution comes from the benefits of integrating BIM and GIS technologies into one unified model, so-called Integrated Geospatial Information Model (IGIM). The proposed approach consists of three main modules: BIM-RDF and GIS-RDF graphs construction, integrating of two RDF graphs, and query of information through IGIM-RDF graph using SPARQL. The IGIM generates queries from both the BIM and GIS RDF graphs resulting a semantically integrated model with entities representing both BIM classes and GIS feature objects with respect to the target-client application. The linkage between BIM-RDF and GIS-RDF is achieved through SPARQL endpoints and defined by a query using set of datasets and entity classes with complementary properties, relationships and geometries. To validate the proposed approach and its performance, a case study was also tested using IGIM system design.
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10

Aleksandrov, M., A. Diakité, J. Yan, W. Li, and S. Zlatanova. "SYSTEMS ARCHITECTURE FOR MANAGEMENT OF BIM, 3D GIS AND SENSORS DATA." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-4/W9 (September 30, 2019): 3–10. http://dx.doi.org/10.5194/isprs-annals-iv-4-w9-3-2019.

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Abstract. This paper presents a system architecture for structuring and manipulation of Building Information Models (BIM), three-dimensional (3D) geospatial information, point clouds and time series data obtained from sensors. The system consists of four layers including data pre-processing, data structuring and storage, system interface and front-end data manipulation. To enable the integration of different data, a unified UML model is developed. The paper explains all steps of 3D reconstruction, BIM geo-referencing, storage of spatial data and visualisation. Special attention is given to the integration of sensors data. The data model and the system architecture are tested for a university campus. The results demonstrate an approach for BIM-GIS-Sensor integration as part of Precinct Information Modelling (PIM). The system architecture allows for a flexible structuring and manipulation of different spatial data towards managing various 3D spatial and non-spatial data.
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Дисертації з теми "Geospatial information systems and geospatial data modelling"

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Sharad, Chakravarthy Namindi. "Public Commons for Geospatial Data: A Conceptual Model." Fogler Library, University of Maine, 2003. http://www.library.umaine.edu/theses/pdf/SharadCN2003.pdf.

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2

Joshi, Kripa. "Combining Geospatial and Temporal Ontologies." Fogler Library, University of Maine, 2007. http://www.library.umaine.edu/theses/pdf/JoshiK2007.pdf.

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3

Yang, Zhao. "Spatial Data Mining Analytical Environment for Large Scale Geospatial Data." ScholarWorks@UNO, 2016. http://scholarworks.uno.edu/td/2284.

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Nowadays, many applications are continuously generating large-scale geospatial data. Vehicle GPS tracking data, aerial surveillance drones, LiDAR (Light Detection and Ranging), world-wide spatial networks, and high resolution optical or Synthetic Aperture Radar imagery data all generate a huge amount of geospatial data. However, as data collection increases our ability to process this large-scale geospatial data in a flexible fashion is still limited. We propose a framework for processing and analyzing large-scale geospatial and environmental data using a “Big Data” infrastructure. Existing Big Data solutions do not include a specific mechanism to analyze large-scale geospatial data. In this work, we extend HBase with Spatial Index(R-Tree) and HDFS to support geospatial data and demonstrate its analytical use with some common geospatial data types and data mining technology provided by the R language. The resulting framework has a robust capability to analyze large-scale geospatial data using spatial data mining and making its outputs available to end users.
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4

Ngo, Duc Khanh. "Relief Planning Management Systems - Investigation of the Geospatial Components." Thesis, KTH, Geodesi och geoinformatik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-118373.

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5

Sahr, Kevin Michael. "Discrete global grid systems : a new class of geospatial data structures /." view abstract or download file of text, 2005. http://wwwlib.umi.com/cr/uoregon/fullcit?p3190547.

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Thesis (Ph. D.)--University of Oregon, 2005.
Typescript. Includes vita and abstract. Includes bibliographical references (leaves 109-115). Also available for download via the World Wide Web; free to University of Oregon users.
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6

Wylie, Austin. "Geospatial Data Modeling to Support Energy Pipeline Integrity Management." DigitalCommons@CalPoly, 2015. https://digitalcommons.calpoly.edu/theses/1447.

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Several hundred thousand miles of energy pipelines span the whole of North America -- responsible for carrying the natural gas and liquid petroleum that power the continent's homes and economies. These pipelines, so crucial to everyday goings-on, are closely monitored by various operating companies to ensure they perform safely and smoothly. Happenings like earthquakes, erosion, and extreme weather, however -- and human factors like vehicle traffic and construction -- all pose threats to pipeline integrity. As such, there is a tremendous need to measure and indicate useful, actionable data for each region of interest, and operators often use computer-based decision support systems (DSS) to analyze and allocate resources for active and potential hazards. We designed and implemented a geospatial data service, REST API for Pipeline Integrity Data (RAPID) to improve the amount and quality of data available to DSS. More specifically, RAPID -- built with a spatial database and the Django web framework -- allows third-party software to manage and query an arbitrary number of geographic data sources through one centralized REST API. Here, we focus on the process and peculiarities of creating RAPID's model and query interface for pipeline integrity management; this contribution describes the design, implementation, and validation of that model, which builds on existing geospatial standards.
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7

Farrugia, James A. "Semantic Interoperability of Geospatial Ontologies: A Model-theoretic Analysis." Fogler Library, University of Maine, 2007. http://www.library.umaine.edu/theses/pdf/FarrugiaJA2007.pdf.

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8

Comer, Bryan. "Sustainable intermodal freight transportation : applying the geospatial intermodal freight transport model /." Online version of thesis, 2009. http://hdl.handle.net/1850/10887.

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9

Gordon, Josef. "Comparative Geospatial Analysis of Twitter Sentiment Data during the 2008 and 2012 U.S. Presidential Elections." Thesis, University of Oregon, 2013. http://hdl.handle.net/1794/13424.

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The goal of this thesis is to assess and characterize the representativeness of sampled data that is voluntarily submitted through social media. The case study vehicle used is Twitter data associated with the 2012 Presidential election, which were in turn compared to similarly collected 2008 Presidential election Twitter data in order to ascertain the representative statewide changes in the pro-Democrat bias of sentiment-derived Twitter data mentioning either of the Republican or Democrat Presidential candidates. The results of the comparative analysis show that the MAE lessened by nearly half - from 13.1% in 2008 to 7.23% in 2012 - which would initially suggest a less biased sample. However, the increase in the strength of the positive correlation between tweets per county and population density actually suggests a much more geographically biased sample.
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Toups, Matthew A. "A study of three paradigms for storing geospatial data: distributed-cloud model, relational database, and indexed flat file." ScholarWorks@UNO, 2016. http://scholarworks.uno.edu/td/2196.

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Geographic Information Systems (GIS) and related applications of geospatial data were once a small software niche; today nearly all Internet and mobile users utilize some sort of mapping or location-aware software. This widespread use reaches beyond mere consumption of geodata; projects like OpenStreetMap (OSM) represent a new source of geodata production, sometimes dubbed “Volunteered Geographic Information.” The volume of geodata produced and the user demand for geodata will surely continue to grow, so the storage and query techniques for geospatial data must evolve accordingly. This thesis compares three paradigms for systems that manage vector data. Over the past few decades these methodologies have fallen in and out of favor. Today, some are considered new and experimental (distributed), others nearly forgotten (flat file), and others are the workhorse of present-day GIS (relational database). Each is well-suited to some use cases, and poorly-suited to others. This thesis investigates exemplars of each paradigm.
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Книги з теми "Geospatial information systems and geospatial data modelling"

1

Introduction to geospatial technologies. New York, NY: W.H. Freeman and Co., 2012.

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2

Shakarian, Paulo. Geospatial abduction: Principles and practice. New York, NY: Springer, 2011.

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3

Richard, Groot, and McLaughlin John D, eds. Geospatial data infrastructure: Concepts, cases, and good practice. Oxford: Oxford University Press, 2000.

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4

Hoalst-Pullen, Nancy, and Mark W. Patterson. Geospatial technologies in environmental management. Dordrecht: Springer, 2010.

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5

Shen, Zhenjiang. Geospatial Techniques in Urban Planning. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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6

CoastGIS 2005 Symposium (2005 Aberdeen, Scotland). Coastal and marine geospatial technologies. Dordrecht: Springer, 2010.

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7

Maantay, Juliana. Geospatial Analysis of Environmental Health. Dordrecht: Springer Science+Business Media B.V., 2011.

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8

Leitner, Michael. Crime modeling and mapping using geospatial technologies. Dordrecht: Springer, 2013.

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9

Komor, S. C. Sources of geospatial data for central and western New York, 1998. Ithaca, N.Y: U.S. Dept. of the Interior, U.S. Geological Survey, 1999.

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10

Association, Information Resources Management. Geographic information systems: Concepts, methodologies, tools, and applications. Hershey, PA: Information Science Reference, 2013.

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Частини книг з теми "Geospatial information systems and geospatial data modelling"

1

Horak, Petr, Karel Charvat, and Martin Vlk. "Web Tools for Geospatial Data Management." In Information Systems Development, 793–800. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/b137171_83.

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2

Sample, John T., and Elias Ioup. "Tile Creation using Vector Data." In Tile-Based Geospatial Information Systems, 193–203. Boston, MA: Springer US, 2010. http://dx.doi.org/10.1007/978-1-4419-7631-4_11.

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3

Keller, Stefan F., and Hugo Thalmann. "Modeling and Sharing Graphic Presentations of Geospatial Data." In Interoperating Geographic Information Systems, 151–62. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/10703121_13.

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4

Schade, Sven, Carlos Granell, Glenn Vancauwenberghe, Carsten Keßler, Danny Vandenbroucke, Ian Masser, and Michael Gould. "Geospatial Information Infrastructures." In Manual of Digital Earth, 161–90. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-32-9915-3_5.

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Abstract Geospatial information infrastructures (GIIs) provide the technological, semantic, organizational and legal structure that allow for the discovery, sharing, and use of geospatial information (GI). In this chapter, we introduce the overall concept and surrounding notions such as geographic information systems (GIS) and spatial data infrastructures (SDI). We outline the history of GIIs in terms of the organizational and technological developments as well as the current state-of-art, and reflect on some of the central challenges and possible future trajectories. We focus on the tension between increased needs for standardization and the ever-accelerating technological changes. We conclude that GIIs evolved as a strong underpinning contribution to implementation of the Digital Earth vision. In the future, these infrastructures are challenged to become flexible and robust enough to absorb and embrace technological transformations and the accompanying societal and organizational implications. With this contribution, we present the reader a comprehensive overview of the field and a solid basis for reflections about future developments.
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Homburg, Timo, and Frank Boochs. "Situation-Dependent Data Quality Analysis for Geospatial Data Using Semantic Technologies." In Business Information Systems Workshops, 566–78. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-04849-5_49.

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Nikolaou, Charalampos, Kallirroi Dogani, Kostis Kyzirakos, and Manolis Koubarakis. "Sextant: Browsing and Mapping the Ocean of Linked Geospatial Data." In Advanced Information Systems Engineering, 209–13. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41242-4_26.

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Kulawiak, Marcin, and Marek Moszynski. "Integration of Geographic Information Systems for Monitoring and Dissemination of Marine Environment Data." In Geospatial Techniques for Managing Environmental Resources, 33–52. Dordrecht: Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-94-007-1858-6_3.

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Ram, Sudha, Vijay Khatri, Limin Zhang, and Daniel Dajun Zeng. "GeoCosm: A Semantics-Based Approach for Information Integration of Geospatial Data." In Conceptual Modeling for New Information Systems Technologies, 152–65. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-46140-x_13.

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Crooks, Andrew T., and Christian J. E. Castle. "The Integration of Agent-Based Modelling and Geographical Information for Geospatial Simulation." In Agent-Based Models of Geographical Systems, 219–51. Dordrecht: Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-90-481-8927-4_12.

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Makhortykh, Mykola. "Geospatial Data Analysis in Russia’s Geoweb." In The Palgrave Handbook of Digital Russia Studies, 585–604. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-42855-6_32.

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AbstractThe chapter examines the role of geospatial data in Russia’s online ecosystem. Facilitated by the rise of geographic information systems and user-generated content, the distribution of geospatial data has blurred the line between physical spaces and their virtual representations. The chapter discusses different sources of these data available for Digital Russian Studies (e.g., social data and crowdsourced databases) together with the novel techniques for extracting geolocation from various data formats (e.g., textual documents and images). It also scrutinizes different ways of using these data, varying from mapping the spatial distribution of social and political phenomena to investigating the use of geotag data for cultural practices’ digitization to exploring the use of geoweb for narrating individual and collective identities online.
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Тези доповідей конференцій з теми "Geospatial information systems and geospatial data modelling"

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Patroumpas, Kostas, Giorgos Giannopoulos, and Spiros Athanasiou. "Towards GeoSpatial semantic data management." In SIGSPATIAL '14: 22nd SIGSPATIAL International Conference on Advances in Geographic Information Systems. New York, NY, USA: ACM, 2014. http://dx.doi.org/10.1145/2666310.2666410.

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"Implementing a Semantic Catalogue of Geospatial Data." In International Conference on Web Information Systems and Technologies. SCITEPRESS - Science and and Technology Publications, 2014. http://dx.doi.org/10.5220/0004820101520159.

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Wang, Xin, and Howard Hamilton. "Using clustering methods in geospatial information systems." In Geoinformatics 2008 and Joint Conference on GIS and Built environment: Advanced Spatial Data Models and Analyses, edited by Lin Liu, Xia Li, Kai Liu, and Xinchang Zhang. SPIE, 2009. http://dx.doi.org/10.1117/12.813150.

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Devarakonda, Ranjeet, Kavya Guntupally, Michele Thornton, Yaxing Wei, Debjani Singh, and Dalton Lunga. "FAIR Interfaces for Geospatial Scientific Data Searches." In SIGSPATIAL '21: 29th International Conference on Advances in Geographic Information Systems. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3486640.3491391.

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Ponciano, Claire, Markus Schaffert, Falk Würriehausen, and Jean-Jacques Ponciano. "Publish and Enrich Geospatial Data as Linked Open Data." In 18th International Conference on Web Information Systems and Technologies. SCITEPRESS - Science and Technology Publications, 2022. http://dx.doi.org/10.5220/0011550600003318.

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Wang, Shaohua, Yang Zhong, Hao Lu, Erqi Wang, Weiying Yun, and Wenwen Cai. "Geospatial Big Data Analytics Engine for Spark." In SIGSPATIAL'17: 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3150919.3150923.

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Trajcevski, Goce, Booma Sowkarthiga Balasubramani, Isabel F. Cruz, Roberto Tamassia, and Xu Teng. "Semantically Augmented Range Queries over Heterogeneous Geospatial Data." In SIGSPATIAL '20: 28th International Conference on Advances in Geographic Information Systems. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3397536.3422271.

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Golodoniuc, Pavel, and Simon Cox. "Geospatial Information Modelling for Interoperable Data Exchange - Application Schema Modelling: From Concept to Implementation." In 2010 IEEE 6th International Conference on E Science (e-Science). IEEE, 2010. http://dx.doi.org/10.1109/escience.2010.36.

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Liu, Kuien, Yandong Yao, and Danhuai Guo. "On managing geospatial big-data in emergency management." In SIGSPATIAL'15: 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2835596.2835614.

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Punjani, D., K. Singh, A. Both, M. Koubarakis, I. Angelidis, K. Bereta, T. Beris, et al. "Template-Based Question Answering over Linked Geospatial Data." In SIGSPATIAL '18: 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3281354.3281362.

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Звіти організацій з теми "Geospatial information systems and geospatial data modelling"

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Johnson, Eric M., Robert Urquhart, and Maggie O'Neil. The Importance of Geospatial Data to Labor Market Information. RTI Press, June 2018. http://dx.doi.org/10.3768/rtipress.2018.pb.0017.1806.

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School-to-work transition data are an important component of labor market information systems (LMIS). Policy makers, researchers, and education providers benefit from knowing how long it takes work-seekers to find employment, how and where they search for employment, the quality of employment obtained, and how steady it is over time. In less-developed countries, these data are poorly collected, or not collected at all, a situation the International Labour Organization and other donors have attempted to change. However, LMIS reform efforts typically miss a critical part of the picture—the geospatial aspects of these transitions. Few LMIS systems fully consider or integrate geospatial school-to-work transition information, ignoring data critical to understanding and supporting successful and sustainable employment: employer locations; transportation infrastructure; commute time, distance, and cost; location of employment services; and other geographic barriers to employment. We provide recently collected geospatial school-to-work transition data from South Africa and Kenya to demonstrate the importance of these data and their implications for labor market and urban development policy.
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Garton, Timothy. Data enrichment and enhanced accessibility of waterborne commerce numerical data : spatially depicting the National Waterway Network. Engineer Research and Development Center (U.S.), December 2020. http://dx.doi.org/10.21079/11681/39223.

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This report provides methodologies and processes of data enrichment and enhanced accessibility of Waterborne Commerce and Statistics Center (WCSC) maintained databases. These databases house tabular and statistical data that reports on The U.S. Army Corps of Engineers (USACE) Civil Works Division National Waterway Network (NWN), which geospatially represents approximately 1,000 harbors and 25,000 miles of channels and waterways. WCSC is a division of The Institute for Water Resources (IWR). They have been tasked with the international collection, maintenance, and archival of all records involving commercial movements and commerce that occur on federal waterways. The current records structure is a large, tabular dataset and limited to the systems and processes put in place prior to the computing standards and capabilities available today. Methods have been tested and utilized to bring the tabular datasets into an optimized, modern geospatial network and expanded upon to create a higher resolution than previously maintained by the WCSC. This report will expand upon the applied methodologies to optimize data queries and the overall enhancement of the data system to allow for linkages to various other sources of information for commerce data enhancement for decision support assistance.
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Blundell, S. User guide : the DEM Breakline and Differencing Analysis Tool—gridded elevation model analysis with a convenient graphical user interface. Engineer Research and Development Center (U.S.), August 2022. http://dx.doi.org/10.21079/11681/45040.

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Gridded elevation models of the earth’s surface derived from airborne lidar data or other sources can provide qualitative and quantitative information about the terrain and its surface features through analysis of the local spatial variation in elevation. The DEM Breakline and Differencing Analysis Tool was developed to extract and display micro-terrain features and vegetative cover based on the numerical modeling of elevation discontinuities or breaklines (breaks-in-slope), slope, terrain ruggedness, local surface optima, and the local elevation difference between first surface and bare earth input models. Using numerical algorithms developed in-house at the U.S. Army Engineer Research and Development Center, Geospatial Research Laboratory, various parameters are calculated for each cell in the model matrix in an initial processing phase. The results are combined and thresholded by the user in different ways for display and analysis. A graphical user interface provides control of input models, processing, and display as color-mapped overlays. Output displays can be saved as images, and the overlay data can be saved as raster layers for input into geographic information systems for further analysis.
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