Готові списки джерел за темами / 090903 Geospatial Information Systems

Добірка наукової літератури з теми "090903 Geospatial Information Systems"

Автор: Grafiati
Опубліковано: 10 грудня 2022
Оновлено: 29 січня 2023

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Статті в журналах з теми "090903 Geospatial Information Systems"

1

Meeks, W. Lee, and Subhasish Dasgupta. "Geospatial information utility: an estimation of the relevance of geospatial information to users." Decision Support Systems 38, no. 1 (October 2004): 47–63. http://dx.doi.org/10.1016/s0167-9236(03)00076-9.

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2

POKORSKI, Grzegorz, and płk w. st spocz prof dr hab inż PIOTR ZASKÓRSKI. "Geospatial information systems in business process management." Nowoczesne Systemy Zarządzania 13, no. 2 (June 28, 2018): 107–27. http://dx.doi.org/10.37055/nsz/129514.

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Анотація:
Systemy Informacji Geoprzestrzennej (GIS), zazwyczaj kojarzone z zastosowaniami kartograficznymi, rozszerzają swój zakres wykorzystania. Internet Rzeczy w połączeniu z formułą otwartych danych zwiększa powszechność i dostępność danych geograficznych. Możliwe więc staje się zwiększenie zakresu zasobów informacyjnych utrzymywanych dotychczas w postaci znakowej. Narzędzia GIS wzbogacają funkcjonalność rozwiązań informatycznych w obszarze zarządzania poprzez coraz szersze wykorzystanie zarówno analiz geoprzestrzennych, jak również własności samych systemów GIS. Artykuł jest próbą pokazania możliwości wykorzystania wymiaru geograficznego dla potrzeb analiz biznesowych.
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3

Sboui, Tarek, Mehrdad Salehi, and Yvan Bédard. "A Systematic Approach for Managing the Risk Related to Semantic Interoperability between Geospatial Datacubes." International Journal of Agricultural and Environmental Information Systems 1, no. 2 (July 2010): 20–41. http://dx.doi.org/10.4018/jaeis.2010070102.

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Geospatial datacubes are the database backend of novel types of spatiotemporal decision-support systems employed in large organizations. These datacubes extend the datacube concept underlying the field of Business Intelligence (BI) into the realm of geospatial decision-support and geographic knowledge discovery. The interoperability between geospatial datacubes facilitates the reuse of their content. Such interoperability, however, faces risks of data misinterpretation related to the heterogeneity of geospatial datacubes. Although the interoperability of transactional databases has been the subject of several research works, no research dealing with the interoperability of geospatial datacubes exists. In this paper, the authors support the semantic interoperability between geospatial datacubes and propose a categorization of semantic heterogeneity problems that may occur in geospatial datacubes. Additionally, the authors propose an approach to deal with the related risks of data misinterpretation, which consists of evaluating the fitness-for-use of datacubes models, and a general framework that facilitates making appropriate decisions about such risks. The framework is based on a hierarchical top-down structure going from the most general level to the most detailed level, showing the usefulness of the proposed approach in environmental applications.
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4

Jung, Hyung-Sup, Saro Lee, and Biswajeet Pradhan. "Sustainable Applications of Remote Sensing and Geospatial Information Systems to Earth Observations." Sustainability 12, no. 6 (March 19, 2020): 2390. http://dx.doi.org/10.3390/su12062390.

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Анотація:
The Special Issue on “Sustainable Applications of Remote Sensing and Geospatial Information Systems to Earth Observations” is published. A total of 20 qualified papers are published in this Special Issue. The topics of the papers are the application of remote sensing and geospatial information systems to Earth observations in various fields such as (1) object change detection, (2) air pollution, (3) earthquakes, (4) landslides, (5) mining, (6) biomass, (7) groundwater, and (8) urban development using the techniques of remote sensing and geospatial information systems. More than 100 researchers have participated in this Special Issue. We hope that this Special Issue is helpful for sustainable applications.
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5

Mohan, M. "GEOSPATIAL INFORMATION FROM SATELLITE IMAGERY FOR GEOVISUALISATION OF SMART CITIES IN INDIA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B8 (June 24, 2016): 979–85. http://dx.doi.org/10.5194/isprs-archives-xli-b8-979-2016.

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Анотація:
In the recent past, there have been large emphasis on extraction of geospatial information from satellite imagery. The Geospatial information are being processed through geospatial technologies which are playing important roles in developing of smart cities, particularly in developing countries of the world like India. The study is based on the latest geospatial satellite imagery available for the multi-date, multi-stage, multi-sensor, and multi-resolution. In addition to this, the latest geospatial technologies have been used for digital image processing of remote sensing satellite imagery and the latest geographic information systems as 3-D GeoVisualisation, geospatial digital mapping and geospatial analysis for developing of smart cities in India. The Geospatial information obtained from RS and GPS systems have complex structure involving space, time and presentation. Such information helps in 3-Dimensional digital modelling for smart cities which involves of spatial and non-spatial information integration for geographic visualisation of smart cites in context to the real world. In other words, the geospatial database provides platform for the information visualisation which is also known as geovisualisation. So, as a result there have been an increasing research interest which are being directed to geospatial analysis, digital mapping, geovisualisation, monitoring and developing of smart cities using geospatial technologies. However, the present research has made an attempt for development of cities in real world scenario particulary to help local, regional and state level planners and policy makers to better understand and address issues attributed to cities using the geospatial information from satellite imagery for geovisualisation of Smart Cities in emerging and developing country, India.
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6

Mohan, M. "GEOSPATIAL INFORMATION FROM SATELLITE IMAGERY FOR GEOVISUALISATION OF SMART CITIES IN INDIA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B8 (June 24, 2016): 979–85. http://dx.doi.org/10.5194/isprsarchives-xli-b8-979-2016.

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Анотація:
In the recent past, there have been large emphasis on extraction of geospatial information from satellite imagery. The Geospatial information are being processed through geospatial technologies which are playing important roles in developing of smart cities, particularly in developing countries of the world like India. The study is based on the latest geospatial satellite imagery available for the multi-date, multi-stage, multi-sensor, and multi-resolution. In addition to this, the latest geospatial technologies have been used for digital image processing of remote sensing satellite imagery and the latest geographic information systems as 3-D GeoVisualisation, geospatial digital mapping and geospatial analysis for developing of smart cities in India. The Geospatial information obtained from RS and GPS systems have complex structure involving space, time and presentation. Such information helps in 3-Dimensional digital modelling for smart cities which involves of spatial and non-spatial information integration for geographic visualisation of smart cites in context to the real world. In other words, the geospatial database provides platform for the information visualisation which is also known as geovisualisation. So, as a result there have been an increasing research interest which are being directed to geospatial analysis, digital mapping, geovisualisation, monitoring and developing of smart cities using geospatial technologies. However, the present research has made an attempt for development of cities in real world scenario particulary to help local, regional and state level planners and policy makers to better understand and address issues attributed to cities using the geospatial information from satellite imagery for geovisualisation of Smart Cities in emerging and developing country, India.
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7

PAUL, MANOJ, and S. K. GHOSH. "A SERVICE-ORIENTED APPROACH FOR INTEGRATING HETEROGENEOUS SPATIAL DATA SOURCES REALIZATION OF A VIRTUAL GEO-DATA REPOSITORY." International Journal of Cooperative Information Systems 17, no. 01 (March 2008): 111–53. http://dx.doi.org/10.1142/s0218843008001774.

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Анотація:
Searching and accessing geospatial information in the open and distributed environments of geospatial information systems poses several challenges due to the heterogeneity in geospatial data. Geospatial data is highly heterogeneous — both at the syntactic and semantic level. The requirement for an integration architecture for seamless access of geospatial data has been raised over the past decades. The paper proposes a service-based model for geospatial integration where each geospatial data provider is interfaced on the web as services. The interface for these services has been described with Open Geospatial Consortium (OGC) specified service standards. Catalog service provides service descriptions for the services to be discovered. The semantic of each service description is captured in the form of ontology. The similarity assessment method of request service with candidate services proposed in this paper is aimed at resolving the heterogeneity in semantics of locational terms of service descriptions. In a way, we have proposed an architecture for enterprise geographic information system (E-GIS), which is an organization-wide approach to GIS integration, operation, and management. A query processing mechanism for accessing geospatial information in the service-based distributed environment has also been discussed with the help of a case study.
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8

Göbel, Stefan, and Uwe Jasnoch. "Visualization techniques in metadata information systems for geospatial data." Advances in Environmental Research 5, no. 4 (November 2001): 415–24. http://dx.doi.org/10.1016/s1093-0191(01)00093-4.

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9

Pierce, Marlon E. "Special Issue Editorial Introduction: Grids and Geospatial Information Systems." Concurrency and Computation: Practice and Experience 20, no. 14 (September 25, 2008): 1611–15. http://dx.doi.org/10.1002/cpe.1381.

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10

Sa’ad Alhefafi, Abdullah, and Adnan Yahya Almutawakle. "A Review on Geospatial Information Systems in Cloud Computing." International Journal of Computer Science and Mobile Computing 11, no. 11 (November 30, 2022): 205–8. http://dx.doi.org/10.47760/ijcsmc.2022.v11i11.017.

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Анотація:
Cloud computing technology is one of the most important technology in information systems (IS) today. It is an important alternative that ensures powerful data processing, storage and exchange. And as a result of the emergence of multiple sources of spatial data and their tremendous growth and the expansion of areas of use of Geospatial Information Systems (GIS), it became necessary to integrate these tow technologies to achieve the optimum benefit of these technologies to make Geospatial Information Systems capable of accommodating the large, rapid, and diverse amounts of spatial data available nowadays. Cloud computing is a new paradigm in Geographic Information Systems. Although there are many publications related to cloud computing in Geographic Information System, there is no systematic review of current research taxonomies .The purpose of this paper is to conduct a survey on GIS Cloud Computing. It addresses the different GIS Cloud frameworks or architectures. The search for articles carried out in general academic databases including the Scopus database, Web of Science Core Collection, and Google Scholar Citations. Retried articles are analysed according to inclusion and exclusion criteria, finally articles selected for review.
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Дисертації з теми "090903 Geospatial Information Systems"

1

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

Hafernik, Carolyn T. "Automatic methods to disambiguate geospatial queries /." Connect to online version, 2007. http://ada.mtholyoke.edu/setr/websrc/pdfs/www/2007/246.pdf.

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3

Paulsson, Hampus. "Geospatial Processing in the Cloud." Thesis, Uppsala universitet, Avdelningen för beräkningsvetenskap, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-281275.

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Cloud computing is an ubiquitous term that encompasses on-demand computing and related services over the Internet. This thesis work aims to develop a parallel program for processing geographical raster data and use it on the Amazon Web Services cloud platform for parallel, distributed processing. We will also investigate the program’s performance on the cloud platform and investigate different instances to see how they affect the program performance and network capabilities. We found that Amazon Web Services could be used for parallel processing using Message Passing Interface and that the program scales with an increased number of workers used, but that the scaling is limited by the time it takes to transfer data between nodes in the cluser. We also saw how network performance as shown by bandwidth tests varies depending on what instance is used.
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4

Alesheikh, Ali Asghar. "Modeling and managing uncertainty in object-based geospatial information systems." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk2/tape17/PQDD_0005/NQ34653.pdf.

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5

Guo, Man. "Large imagery handling in relational databases for geospatial information systems." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape8/PQDD_0019/MQ48368.pdf.

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6

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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7

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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8

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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9

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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Анотація:
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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10

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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Книги з теми "090903 Geospatial Information Systems"

1

Sample, John T., and Elias Ioup. Tile-Based Geospatial Information Systems. Boston, MA: Springer US, 2010. http://dx.doi.org/10.1007/978-1-4419-7631-4.

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Murayama, Yūji. Progress in Geospatial Analysis. Tokyo: Springer Japan, 2012.

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3

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

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4

Haklay, Mordechai. Interacting with geospatial technologies. Chichester, West Sussex, UK: John Wiley, 2010.

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5

Haklay, Muki. Interacting with geospatial technologies. Chichester, West Sussex: J. Wiley & Sons, 2010.

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6

Haklay, Muki. Interacting with geospatial technologies. Chichester, West Sussex: J. Wiley & Sons, 2010.

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7

Haklay, Muki. Interacting with geospatial technologies. Chichester, West Sussex: J. Wiley & Sons, 2010.

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8

National Research Council (U.S.). Committee on Intersections Between Geospatial Information and Information Technology., ed. IT roadmap to a geospatial future. Washington, D.C: National Academies Press, 2003.

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9

Siyka, Zlatanova, and Li Jonathan, eds. Geospatial information technology for emergency response. London: Taylor & Francis, 2008.

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10

Zlatanova, Siyka. Geospatial information technology for emergency response. London: Taylor & Francis, 2008.

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Частини книг з теми "090903 Geospatial Information Systems"

1

Johnson, Noel D. "Geospatial Information Systems." In An Economist’s Guide to Economic History, 425–32. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96568-0_49.

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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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3

Voisard, Agnès, and Marcus Jürgens. "Geospatial Information Extraction: Querying or Quarrying?" In Interoperating Geographic Information Systems, 165–79. Boston, MA: Springer US, 1999. http://dx.doi.org/10.1007/978-1-4615-5189-8_14.

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4

Wiegand, Nancy. "Ontology for the Engineering of Geospatial Systems." In Geographic Information Science, 270–83. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33024-7_20.

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Sample, John T., and Elias Ioup. "Introduction." In Tile-Based Geospatial Information Systems, 1–3. Boston, MA: Springer US, 2010. http://dx.doi.org/10.1007/978-1-4419-7631-4_1.

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Sample, John T., and Elias Ioup. "Map Projections." In Tile-Based Geospatial Information Systems, 165–91. Boston, MA: Springer US, 2010. http://dx.doi.org/10.1007/978-1-4419-7631-4_10.

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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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Sample, John T., and Elias Ioup. "Case Study: Tiles from Blue Marble Imagery." In Tile-Based Geospatial Information Systems, 205–20. Boston, MA: Springer US, 2010. http://dx.doi.org/10.1007/978-1-4419-7631-4_12.

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Sample, John T., and Elias Ioup. "Case Study: Supporting Multiple Tile Clients." In Tile-Based Geospatial Information Systems, 221–33. Boston, MA: Springer US, 2010. http://dx.doi.org/10.1007/978-1-4419-7631-4_13.

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Sample, John T., and Elias Ioup. "Logical Tile Schemes." In Tile-Based Geospatial Information Systems, 5–15. Boston, MA: Springer US, 2010. http://dx.doi.org/10.1007/978-1-4419-7631-4_2.

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Тези доповідей конференцій з теми "090903 Geospatial Information Systems"

1

Proctor, Alissa, and Chhiu-Linn Kaing. "NetCentric geospatial SmartCache - Caching geospatial information, infrastructure and services." In 2011 Military Communications and Information Systems Conference (MilCIS). IEEE, 2011. http://dx.doi.org/10.1109/milcis.2011.6470394.

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"ON GEOSPATIAL AGENTS." In 1st International Conference on Web Information Systems and Technologies. SciTePress - Science and and Technology Publications, 2005. http://dx.doi.org/10.5220/0001230402100213.

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Blasch, Erik P. "Characterizing the semantic information loss between geospatial sensors and geospatial information systems (GIS)." In SPIE Defense, Security, and Sensing, edited by Matthew F. Pellechia, Richard Sorensen, Shiloh L. Dockstader, Rudy G. Benz II, and Bernard V. Brower. SPIE, 2011. http://dx.doi.org/10.1117/12.883733.

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4

Ben, Jin, Xiao-chong Tong, Yong-sheng Zhang, and Heng Zhang. "Discrete global grid systems: generating algorithm and software model." In Geoinformatics 2006: Geospatial Information Technology, edited by Huayi Wu and Qing Zhu. SPIE, 2006. http://dx.doi.org/10.1117/12.712679.

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Ge, Ying, Ye Wu, Qi Liang, and Weibiao Yan. "Simulation of self-organizing urban systems based on object-oriented GIS." In Geoinformatics 2006: Geospatial Information Science, edited by Jianya Gong and Jingxiong Zhang. SPIE, 2006. http://dx.doi.org/10.1117/12.713017.

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Agostini, Caterina, and Carrie Beneš. "A Geospatial La Sfera." In SIGSPATIAL '21: 29th International Conference on Advances in Geographic Information Systems. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3486187.3490207.

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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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Zhang, Mei, Jing-Hua Wen, Zu-Xuan Zhang, and Jian-Qing Zhang. "Modeling and simulation of virtual human's coordination based on multi-agent systems." In Geoinformatics 2006: Geospatial Information Technology, edited by Huayi Wu and Qing Zhu. SPIE, 2006. http://dx.doi.org/10.1117/12.712939.

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de las Heras, Alejandro, and Yong Hu. "Predicting spatio-temporal failure in large scale observational and micro scale experimental systems." In Geoinformatics 2006: Geospatial Information Science, edited by Jianya Gong and Jingxiong Zhang. SPIE, 2006. http://dx.doi.org/10.1117/12.712994.

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Tabrizian, Payam, Anna Petrasova, Brendan Harmon, Vaclav Petras, Helena Mitasova, and Ross Meentemeyer. "Immersive tangible geospatial modeling." In SIGSPATIAL'16: 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2996913.2996950.

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Звіти організацій з теми "090903 Geospatial Information Systems"

1

Walker, H., R. Chou, K. Chubb, and J. Schek. Evaluation of Open Geospatial Consortium Standards fur Use In LLNL Geographic Information Systems (GIS). Office of Scientific and Technical Information (OSTI), September 2005. http://dx.doi.org/10.2172/885372.

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2

Madera, Jose M. Civil Information Management in Support of Counterinsurgency Operations: A Case for the Use of Geospatial Information Systems in Colombia. Fort Belvoir, VA: Defense Technical Information Center, May 2006. http://dx.doi.org/10.21236/ada450461.

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3

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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Анотація:
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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4

Al Hosain, Nourah, and Alma Alhussaini. Evaluating Access to Riyadh’s Planned Public Transport System Using Geospatial Analysis. King Abdullah Petroleum Studies and Research Center, June 2021. http://dx.doi.org/10.30573/ks--2021-dp10.

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The King Abdulaziz Project for Public Transport in Riyadh city is one of the world’s largest urban transit systems being developed. The project aims to meet the demands of the city’s growing urban population while reducing traffic congestion, heavy private car dependence and air pollution. The performance of any public transport system largely depends on its accessibility. Therefore, this study evaluates the populations’ access to Riyadh’s public transport stations using network analysis tools based on geographic information systems.
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5

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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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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Анотація:
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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