Relevant bibliographies by topics / Spatial Decision Support System

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Spatial Decision Support System'

Author: Grafiati
Published: 4 June 2021
Last updated: 4 March 2023

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Journal articles on the topic "Spatial Decision Support System"

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Armstrong, Marc P., Gerard Rushton, Rex Honey, Brian T. Dalziel, Panos Lolonis, Suranjan De, and Paul J. Densham. "Decision support for regionalization: A spatial decision support system for regionalizing service delivery systems." Computers, Environment and Urban Systems 15, no. 1-2 (January 1991): 37–53. http://dx.doi.org/10.1016/0198-9715(91)90044-e.

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Yatsalo, B., V. Didenko, A. Tkachuk, G. Gritsyuk, O. Mirzeabasov, V. Slipenkaya, A. Babutski, I. Pichugina, T. Sullivan, and I. Linkov. "Multi-Criteria Spatial Decision Support System DECERNS." International Journal of Information Systems and Social Change 1, no. 1 (January 2010): 11–30. http://dx.doi.org/10.4018/jissc.2010092902.

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Land-use planning and environmental management often requires an implementation of both geoyspatial information analysis and value-driven criteria within the decision-making process. DECERNS (Decision Evaluation in Complex Risk Network Systems) is a web-based distributed decision support system for multicriteria analysis of a wide range of spatially-explicit land management alternatives. It integrates mainly basic and some advanced GIS functions and implements several Multi-Criteria Decision Analysis (MCDA) methods and tools. DECERNS can also be integrated with a model server containing generic and site specific models for in-depth analysis of project and environmental risks as well as other decision criteria under consideration. This paper provides an overview of the modeling approaches as well as methods and tools used in DECERNS. Application of the DECERNS WebSDSS (Web-based Spatial Decision Support System) for a housing site selection case study is presented.
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Rao, K. H. V. Durga, and D. Satish Kumar. "Spatial Decision Support System for Watershed Management." Water Resources Management 18, no. 5 (October 2004): 407–23. http://dx.doi.org/10.1023/b:warm.0000049135.79227.f9.

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Sharma, D. K., R. K. Sharma, and D. Ghosh. "A Spatial Decision Support System For Land Management." International Journal of Computers and Applications 28, no. 1 (January 2006): 50–58. http://dx.doi.org/10.1080/1206212x.2006.11441788.

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Sriraj, P. S., Mark Minor, and Piyushimita (Vonu) Thakuriah. "Spatial Decision Support System for Low-Income Families." Transportation Research Record: Journal of the Transportation Research Board 1956, no. 1 (January 2006): 119–26. http://dx.doi.org/10.1177/0361198106195600115.

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Gorr, Wilpen, Michael Johnson, and Stephen Roehrig. "Spatial decision support system for home-delivered services." Journal of Geographical Systems 3, no. 2 (August 2001): 181–97. http://dx.doi.org/10.1007/pl00011474.

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SHARMA, DINESH K., RAKESH K. SHARMA, and DEBASIS GHOSH. "SPATIAL DECISION SUPPORT SYSTEM FOR ZINC CONTENT IN SOIL." International Journal of Information Technology & Decision Making 04, no. 04 (December 2005): 567–80. http://dx.doi.org/10.1142/s021962200500174x.

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Spatial decision support systems can enhance the efficiency and effectiveness of decision-making about the zinc status of soil. In this paper, we present an interactive spatial decision support system (ISDSS), identifying the zinc deficiency zone(s), thus enabling the decision-maker to take appropriate measures for correcting zinc deficiencies in soil. The system can display a contour map of the required Block, District, or State of West Bengal along with information about the quantity of zinc to be added to the soil, if the available zinc is not sufficient for a particular crop. The ISDSS consists of a non-spatial database management system and corresponding spatial database, a model base management system, and a user interface. The ISDSS is developed using the scripting language EASI (Engineering Analysis and Scientific Interface) under the GIS software tool, SPANS (Spatial Analysis System). The system runs under the Microsoft Windows operating system.
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Akabua, Kojo M., Wiktor L. Adamowicz, and Peter C. Boxall. "Spatial non-timber valuation decision support systems." Forestry Chronicle 76, no. 2 (April 1, 2000): 319–27. http://dx.doi.org/10.5558/tfc76319-2.

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Decision support systems (DSS) can be useful tools for facilitating the application of complex economic models. Two examples of DSSs are used to illustrate the application of non-market valuation models that have been developed to estimate the economic value, as well as the level of participation, of recreational moose hunting in Newfoundland and Alberta. DSSs may be useful tools for foresters, biologists, tourist boards and economic development agencies to understand conflicts between industrial operations and other forest uses. Key words: non-timber valuation, decision support system, recreationists, databases.
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Bensalloua, Charef Abdallah, and Djamila Hamdadou. "Spatial OLAP and Multicriteria Integrated Approach for Decision Support System." International Journal of Decision Support System Technology 10, no. 3 (July 2018): 1–26. http://dx.doi.org/10.4018/ijdsst.2018070101.

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This article describes how Spatial On-Line Analytical Processing, such a decisional technology, offers the possibilities of spatial and multidimensional analysis of data stored in multidimensional structure namely spatial data warehouse. However, this technology is limited in the quality aspect of the decision related to the multicriteria consideration. In the current article, the objective is to propose a Spatial Decision Support System namely “Silvicultura” for facilitating decision making in complex situations. This approach is based on integrating multicriteria analysis with SOLAP in order to enrich the spatial and multidimensional analysis with the contribution of MCA tools for mitigating conflict situations. The authors have based their proposal modeling on Unified Modeling language, since it is a well-known standard modeling language and can be easily extended for multidimensional modeling. Finally, in order to validate their proposal, the authors present a case study to show how to use it in the agroforestry management.
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Batsaris, Marios, Dimitris Kavroudakis, Euripides Hatjiparaskevas, and Panagiotis Agouroiannis. "Spatial Decision Support System for Efficient School Location-Allocation." European Journal of Geography 12, no. 4 (December 20, 2021): 31–44. http://dx.doi.org/10.48088/ejg.m.bat.12.4.031.044.

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In Greece, a lack of a planning strategy was identified in the context of allocating students to schools. Particularly, the Secondary Educational Management of Lesvos Prefecture along with school Principals decide upon student allocation based on empirical knowledge and approximation techniques. As a consequence, during the school season of 2018-2019 capacity and proximity limitations were violated. This study introduces a Spatial Decision Support System (SDSS) to assist school location-allocation decisions in future seasons. The objective of the proposed SDSS is to minimize commute-to-school distance concerning capacity and proximity limitations. For this purpose, a capacitated P-median approach is adopted and formulated as a mixed-integer linear problem. The SDSS is further evaluated using actual data for students' transition from primary to secondary education in the city of Mytilene, Greece. Evaluation of current allocation practices carried out and further compared to those obtained by the SDSS. The results indicate a decrease of 8% in total distance whereas proximity and capacity constraints were respected accordingly. The results may be potentially useful for school planners to assist the allocation decisions in the city of Mytilene.
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Dissertations / Theses on the topic "Spatial Decision Support System"

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Sandhu, Raghbir Singh. "Intelligent spatial decision support systems." Thesis, University College London (University of London), 1998. http://discovery.ucl.ac.uk/1317911/.

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This thesis investigates the conceptual and methodological issues for the development of Intelligent Spatial Decision Support Systems (ISDSS). These are spatial decision support systems (SDSS) integrating intelligent systems techniques (Genetic Algorithms, Neural Networks, Expert Systems, Fuzzy Logic and Nonlinear methods) with traditional modelling and statistical methods for the analysis of spatial problems. The principal aim of this work is to verify the feasibility of heterogeneous systems for spatial decision support derived from a combination of traditional numerical techniques and intelligent techniques in order to provide superior performance and functionality to that achieved through the use of traditional methods alone. This thesis is composed of four distinct sections: (i) a taxonomy covering the employment of intelligent systems techniques in specific applications of geographical information systems and SDSS; (ii) the development of a prototype ISDSS; (iii) application of the prototype ISDSS to modelling the spatiotemporal dynamics of high technology industry in the South-East of England; and (iv) the development of ISDSS architectures utilising interapplication communication techniques. Existing approaches for implementing modelling tools within SDSS and GIS generally fall into one of two schemes - loose coupling or tight coupling - both of which involve a tradeoff between generality and speed of data interchange. In addition, these schemes offer little use of distributed processing resources. A prototype ISDSS was developed in collaboration with KPMG Peat Marwick's High Technology Practice as a general purpose spatiotemporal analysis tool with particular regard to modelling high technology industry. The GeoAnalyser system furnishes the user with animation and time plotting tools for observing spatiotemporal dynamics; such tools are typically not found in existing SDSS or GIS. Furthermore, GeoAnalyser employs the client/server model of distributed computing to link the front end client application with the back end modelling component contained within the server application. GeoAnalyser demonstrates a hybrid approach to spatial problem solving - the application utilises a nonlinear model for the temporal evolution of spatial variables and a genetic algorithm for calibrating the model in order to establish a good fit for the dataset under investigation. Several novel architectures are proposed for ISDSS based on existing distributed systems technologies. These architectures are assessed in terms of user interface, data and functional integration. Implementation issues are also discussed. The research contributions of this work are four-fold: (i) it lays the foundation for ISDSS as a distinct type of system for spatial decision support by examining the user interface, performance and methodological requirements of such systems; (ii) it explores a new approach for linking modelling techniques and SDSS; (iii) it investigates the possibility of modelling high technology industry; and (iv) it details novel architectures for ISDSS based on distributed systems.
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Hlaváč, Vít. "Geografický IS jako Decision Support System." Master's thesis, Vysoká škola ekonomická v Praze, 2009. http://www.nusl.cz/ntk/nusl-10377.

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This thesis deals relationship between geographical information systems and decision support systems. Its objective is to verify hypothesis, if it is possible to consider geographical information systems as decision support systems. Opening chapter attends to explanation what is GIS and its usage in practise. Second part is engaged in definition of decision support systems. There are some examples from practise, which help us confirm or refuse the hypothesis, in the last chapter.
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Mansilla, Miguel. "A spatial decision support system framework for rural energy planning." Thesis, University of Reading, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.414556.

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Regmi, Binaya. "Web-enabled Spatial Decision Support System for Interdisciplinary Watershed Management." Thesis, Virginia Tech, 2002. http://hdl.handle.net/10919/35383.

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The development and use of web-enabled tools for watershed modeling and decision-making have gained popularity lately with the increase in internet speed and accessibility. Most of the web-enabled tools available today address the watershed problems related to a narrow discipline like hydrology, or ecology etc. This thesis presents the work done in the development of a web-enabled integrated system, named WebL2W, which can address watershed problems in a more holistic approach.

WebL2W integrates models from hydrology, economics, and biology in a single shell. The integration is performed using GIS as a common platform for database and interface management. A user accesses the system over the web and chooses pre-selected land development patterns to create a 'what if' scenario. The hydrologic model simulates effects of the scenario on annual runoff volume, flood peaks of various return periods, and ground water recharge. The economics model evaluates the changes in land value, tax revenue, and government expenditures as a result of the new land development scenario. The biology model evaluates effects of new land uses to fish habitats in the watershed. The design of the system is based on current software engineering practices such as object oriented programming (OOP) and relational database management system (RDBMS). The implementation uses the Visual Basic programming environment and Active Server Pages.


Master of Science
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Dill, Robert W. "Data warehousing and data quality for a Spatial Decision Support System." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1997. http://handle.dtic.mil/100.2/ADA336886.

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Thesis (M.S. in Information Technology Management) Naval Postgraduate School, Sept. 1997.
Thesis advisors, Daniel R. Dolk, George W. Thomas, and Kathryn Kocher. Includes bibliographical references (p. 203-206). Also available online.
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Barton, John Edward Built Environment Faculty of Built Environment UNSW. "A spatial decision support system for the management of public housing." Awarded by:University of New South Wales, 2007. http://handle.unsw.edu.au/1959.4/35209.

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Canessa, Rosaline Regan. "Towards a coastal spatial decision support system for multiple-use management." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp02/NQ32737.pdf.

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Davey, Faye Elanor. "A GIS based spatial decision support system for landscape character assessment." Thesis, University of Plymouth, 2012. http://hdl.handle.net/10026.1/1168.

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Landscape Character Assessment (LCA) provides a structured approach to identifying the character and distinctiveness about the landscape. It is a tool used to identify what makes a location unique, a set of techniques and procedures used to map differences between landscapes based on their physical, cultural and historical characteristics. Although the UK has committed to assessing all of its landscapes by signing the European Landscape Convention in 2006, only 60% of coverage has been achieved. The majority of LCAs are carried out by professional environment or landscape consultancies rather than ‘in-house’. Geographical Information Systems are increasingly being used to collate and analyse data and produce character maps. This research presents a Spatial Decision Support System (LCA-SDSS) based in ArcGIS 9.3 that can be used to support decision makers in conducting a LCA. The LCA-SDSS provides a method for storing data, a model base for the assessment of Landform, Ground Type, Land Cover & Cultural attributes and a method for the user to interact with the resulting maps. Using the Tamar Valley Area of Outstanding Natural Beauty (AONB) as a study area the SDSS was developed and tested, resulting in character maps for each stage of the modelling and a final characterisation map. These maps were compared to a LCA conducted by a professional environmental consultant and were found to have produced a good quality assessment as verified by the end user at the Tamar Valley AONB Partnership.
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Jones, Kylie. "Application of a spatial decision support system to National Park management." Thesis, Jones, Kylie (2014) Application of a spatial decision support system to National Park management. Masters by Coursework thesis, Murdoch University, 2014. https://researchrepository.murdoch.edu.au/id/eprint/26024/.

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National park managers must balance multiple, potentially conflicting management objectives and criteria, often in the face of limited budgets and patchy data. A spatial decision support system, based on a multi-criteria evaluation, was the option explored for overcoming these difficulties. It was applied to selected national parks in Western Australia with the aim of integrating several quantitative and qualitative datasets with national park managers’ assessments of the relative importance of the multiple indicators of parks’ biodiversity, Aboriginal heritage and recreation values. The outcomes were used to highlight areas within a national park, and national parks within the state, of highest management priority. The results of this research indicate the use of a spatial decision support system can enhance decision processes by making the trade-offs that are inevitable in decision making for national park management more transparent, and can assist managers to produce more effective, evidence-based decision outcomes. With appropriate acknowledgement of limitations in data, these outcomes can be valuable to national park managers in other management or planning decisions.
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Arnette, Andrew Nicholas. "A Spatial Decision Support System for the Development of Multi-Source Renewable Energy Systems." Diss., Virginia Tech, 2010. http://hdl.handle.net/10919/28159.

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This research involves the development of a comprehensive decision support system for energy planning through the increased use of renewable energy sources, while still considering the role of existing electricity generating facilities. This dissertation focuses on energy planning at the regional level, with the Greater Southern Appalachian Mountain region chosen for analysis due to the dependence on coal as the largest source of generation and the availability of wind and solar resources within the region. The first stage of this planning utilizes a geographic information system (GIS) for the discovery of renewable energy sources. This GIS model analyzes not just the availability of wind and solar power based on resource strength, but also considers the geographic, topographic, regulatory, and other constraints that limit the use of these resources. The model determines potential wind and solar sites within the region based on these input constraints, and finally the model calculates the cost and generation characteristics for each site. The results of the GIS model are then input into the second section of the model framework which utilizes a multi-objective linear programming (MOLP) model to determine the optimal mix of new renewable energy sources and existing fossil fuel facilities. In addition to the potential wind and solar resources discovered in the GIS, the MOLP model considers the implementation of solid wood waste biomass for co-fire at coal plants. The model consists of two competing objectives, the minimization of annual generation cost and the minimization of annual greenhouse gas emissions, subject to constraints on electricity demand and capital investment, amongst others. The model uses the MiniMax function in order to find solutions that consider both of the objective functions. The third major section of this dissertation analyzes three potential public policies â renewable portfolio standard, carbon tax, and renewable energy production tax credit - that have been used to foster increased renewable energy usage. These policies require minor modifications to the MOLP model for implementation. The results of these policy cases are then analyzed to determine the impact that these policies have on generation cost and pollution emissions within the region.
Ph. D.
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Books on the topic "Spatial Decision Support System"

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Sugumaran, Ramanathan. Spatial decision support systems. Boca Raton: Taylor & Francis, 2011.

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Leung, Yee. Intelligent Spatial Decision Support Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997.

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Leung, Yee. Intelligent Spatial Decision Support Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/978-3-642-60714-1.

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Leung, Yee. Intelligent spatial decision support systems. Berlin: Springer Verlag, 1997.

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Dill, Robert W. Data warehousing and data quality for a Spatial Decision Support System. Monterey, Calif: Naval Postgraduate School, 1997.

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Hershey, Rachel Riemann. Towards an effective spatial decision support system for landuse management and planning. London: London School of Economics and Political Science. Geographical Information Research Laboratory, 1991.

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Murphy, Mark A. An automated spatial decision support system for the relocation of Army Reserve Units. Monterey, Calif: Naval Postgraduate School, 1997.

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Arentze, Theo. A spatial decision support system for the planning of retail and service facilities. Eindhoven: University of Eindhoven, 1999.

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Falk, Peter R. ARIES: An architectural implementation of a multi-criterion Spatial Decision Support System (SDSS). Monterey, Calif: Naval Postgraduate School, 1997.

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Hootsmans, Rob M. Fuzzy sets and series analysis for visual decision support in spatial data exploration. Utrecht: Koninklijk Nederlands Aardrijkskundig Genootschap, 1996.

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Book chapters on the topic "Spatial Decision Support System"

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Bandyopadhyay, Susmita. "Spatial Decision Support System." In Decision Support System, 217–25. Boca Raton: CRC Press, 2023. http://dx.doi.org/10.1201/9781003307655-12.

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Crossland, Martin D. "Spatial Decision Support System." In Encyclopedia of GIS, 1. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-23519-6_1264-2.

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Crossland, Martin D. "Spatial Decision Support System." In Encyclopedia of GIS, 2011. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-17885-1_1264.

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Crossland, Martin D. "Spatial Decision Support System." In Encyclopedia of GIS, 1095. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-35973-1_1264.

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Shekhar, Sulochana. "Slum-Spatial Decision Support System." In The Urban Book Series, 159–80. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72292-0_8.

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Svoray, Tal. "Spatial Decision Support Systems." In A Geoinformatics Approach to Water Erosion, 305–33. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-91536-0_8.

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Shekhar, Shashi, and Hui Xiong. "Group Spatial Decision Support Systems." In Encyclopedia of GIS, 424. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-35973-1_556.

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Chakhar, Salem, and Vincent Mousseau. "Multicriteria Spatial Decision Support Systems." In Encyclopedia of GIS, 753–58. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-35973-1_840.

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Chakhar, Salem, and Vincent Mousseau. "Multicriteria Spatial Decision Support Systems." In Encyclopedia of GIS, 1–8. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-23519-6_840-2.

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Chakhar, Salem, and Vincent Mousseau. "Multicriteria Spatial Decision Support Systems." In Encyclopedia of GIS, 1404–11. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-17885-1_840.

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Conference papers on the topic "Spatial Decision Support System"

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Nehme, Cláudio Chauke, and Margareth Simões. "Spatial decision support system for land assessment." In the seventh ACM international symposium. New York, New York, USA: ACM Press, 1999. http://dx.doi.org/10.1145/320134.320157.

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Mao, Xi, and Qi Li. "Ontology-based web spatial decision support system." In 2011 19th International Conference on Geoinformatics. IEEE, 2011. http://dx.doi.org/10.1109/geoinformatics.2011.5980936.

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Shan Gao, J. Paynter, and D. Sundaram. "Flexible support for spatial decision-making." In 37th Annual Hawaii International Conference on System Sciences, 2004. Proceedings of the. IEEE, 2004. http://dx.doi.org/10.1109/hicss.2004.1265200.

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Mei, Xin, Junyi Liu, Xuexia Zhang, and Weihong Cui. "Spatial decision support system for tobacco enterprise based on spatial data mining." In Second International Conference on Spatial Information Technology, edited by Cheng Wang, Shan Zhong, and Jiaolong Wei. SPIE, 2007. http://dx.doi.org/10.1117/12.775284.

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Wong, D. W. S., and C. V. Wu. "Spatial metadata and GIS for decision support." In Proceedings of HICSS-29: 29th Hawaii International Conference on System Sciences. IEEE, 1996. http://dx.doi.org/10.1109/hicss.1996.493251.

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Yaheng, Chen, Zhang Junmei, and Men Mingxin. "WebGIS-based farmland ecosystem spatial decision support system." In 2012 First International Conference on Agro-Geoinformatics. IEEE, 2012. http://dx.doi.org/10.1109/agro-geoinformatics.2012.6311654.

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Liu, Shuo, Xiang-tao Fan, Jun-jie Zhu, and Xiao-ping Du. "Study on Web-based Spatial Decision Support System." In 2010 18th International Conference on Geoinformatics. IEEE, 2010. http://dx.doi.org/10.1109/geoinformatics.2010.5567546.

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Su, Ming-Daw, and Tzai-Hung Wen. "Spatial Decision Support System for Irrigation Demand Planning." In World Water and Environmental Resources Congress 2001. Reston, VA: American Society of Civil Engineers, 2001. http://dx.doi.org/10.1061/40569(2001)359.

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Cioca, Marius, Lucian-Ionel Cioca, and Sabin-Corneliu Buraga. "Spatial [Elements] Decision Support System Used in Disaster Management." In 2007 Inaugural IEEE-IES Digital EcoSystems and Technologies Conference. IEEE, 2007. http://dx.doi.org/10.1109/dest.2007.372045.

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Wang, Jianxiong, and Fuxia Zhang. "Research on spatial data mining technology in agricultural spatial decision support system." In International Conference on Transportation Systems and Intelligent Control. Southampton, UK: WIT Press, 2015. http://dx.doi.org/10.2495/ictsic140101.

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Reports on the topic "Spatial Decision Support System"

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Henk, Jordan. Spatial Decision Support Workshop 2011. Fort Belvoir, VA: Defense Technical Information Center, January 2011. http://dx.doi.org/10.21236/ada576992.

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Beck, Tanya, and Ping Wang. Morphodynamics of barrier-inlet systems in the context of regional sediment management, with case studies from West-Central Florida, USA. Engineer Research and Development Center (U.S.), September 2021. http://dx.doi.org/10.21079/11681/41984.

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The temporal and spatial scales controlling the morphodynamics of barrier-inlet systems are critical components of regional sediment management practice. This paper discusses regional sediment management methods employed at multiple barrier-inlet systems, with case studies from West-Central Florida. A decision-support tool is proposed for regional sediment management with discussion of its application to barrier-inlet systems. Connecting multiple barrier islands and inlets at appropriate spatio-temporal scales is critical in developing an appropriately scoped sediment management plan for a barrier-inlet system. Evaluating sediment bypassing capacity and overall inlet morphodynamics can better inform regional sand sharing along barrier-inlet coastlines; particularly where sediment resources are scarce and a close coupling between inlet dredging and beach placement is vital to long-term sustainable management. Continued sea-level rise and anthropogenic activities may intensify the need for investigating longer-term processes and expanding regional planning at a centennial timescale and are acknowledged as challenging tasks for RSM studies. Specifically, we suggested that a regionally focused, multi-inlet study was necessary for management plan of individual inlet for the west-central Florida case studies. Key recommendations based on the case studies are included.
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Callaghan, Caitlin, Matthew Bigl, Brandon Booker, Kyle Elliott, Paulina Lintsai, Marissa Torres, Kathryn Trubac, and Jacqueline Willan. Energy Atlas—mapping energy-related data for DoD lands in Alaska : Phase 1—assembling the data and designing the tool. Engineer Research and Development Center (U.S.), October 2021. http://dx.doi.org/10.21079/11681/42226.

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The U.S. Army is the largest Department of Defense (DoD) land user in Alaska, including remote areas only accessible by air, water, or wintertime ice roads. Understanding where energy resources and related infrastructure exist on and adjacent to DoD installations and training lands can help in-form Army decision-makers, especially in remote locations like Alaska. The Energy Atlas–Alaska provides a value-added resource to support decision-making for investments in infrastructure and diligent energy management, helping Army installations become more resilient and sustainable. The Energy Atlas–Alaska utilizes spatial information and provides a consistent GIS (geographic information system) framework to access and examine energy and related resource data such as energy resource potential, energy corridors, and environmental information. The database can be made accessible to DoD and its partners through an ArcGIS-based user interface that provides effective visualization and functionality to support analysis and to inform DoD decision-makers. The Energy Atlas–Alaska helps DoD account for energy in contingency planning, acquisition, and life-cycle requirements and ensures facilities can maintain operations in the face of disruption.
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4

Callaghan, Caitlin, Matthew Bigl, Brandon Booker, Kyle Elliott, Paulina Lintsai, Marissa Torres, Kathryn Trubac, and Jacqueline Willan. Energy Atlas—mapping energy-related data for DoD lands in Alaska : Phase 1—assembling the data and designing the tool. Engineer Research and Development Center (U.S.), October 2021. http://dx.doi.org/10.21079/11681/42226.

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Abstract:
The U.S. Army is the largest Department of Defense (DoD) land user in Alaska, including remote areas only accessible by air, water, or wintertime ice roads. Understanding where energy resources and related infrastructure exist on and adjacent to DoD installations and training lands can help in-form Army decision-makers, especially in remote locations like Alaska. The Energy Atlas–Alaska provides a value-added resource to support decision-making for investments in infrastructure and diligent energy management, helping Army installations become more resilient and sustainable. The Energy Atlas–Alaska utilizes spatial information and provides a consistent GIS (geographic information system) framework to access and examine energy and related resource data such as energy resource potential, energy corridors, and environmental information. The database can be made accessible to DoD and its partners through an ArcGIS-based user interface that provides effective visualization and functionality to support analysis and to inform DoD decision-makers. The Energy Atlas–Alaska helps DoD account for energy in contingency planning, acquisition, and life-cycle requirements and ensures facilities can maintain operations in the face of disruption.
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5

Luqi. System Engineering and Evolution Decision Support. Fort Belvoir, VA: Defense Technical Information Center, September 2001. http://dx.doi.org/10.21236/ada395539.

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Heacox, N. J., M. L. Quinn, R. T. Kelly, J. W. Gwynne, and R. J. Smille. Decision Support System for Coalition Operations. Fort Belvoir, VA: Defense Technical Information Center, August 2002. http://dx.doi.org/10.21236/ada406338.

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7

Meador, Christopher. Burn Resuscitation Decision Support System (BRDSS). Fort Belvoir, VA: Defense Technical Information Center, November 2014. http://dx.doi.org/10.21236/ada608762.

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8

Luqi. System Engineering and Evolution Decision Support. Fort Belvoir, VA: Defense Technical Information Center, September 2000. http://dx.doi.org/10.21236/ada384684.

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Hibbard, Carol, and Theresa O'Brien. The Source Selection Evaluation Decision Support System. Fort Belvoir, VA: Defense Technical Information Center, September 1993. http://dx.doi.org/10.21236/ada275676.

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10

Bostick, K. V. Decision support system to select cover systems. Office of Scientific and Technical Information (OSTI), February 1995. http://dx.doi.org/10.2172/10116819.

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