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Journal articles on the topic 'Spatial Decision Support System'

Author: Grafiati

Published: 4 June 2021

Last updated: 4 March 2023

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1

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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De Silva, F. Nisha. "Designing a Spatial Decision Support System for Evacuation Planning." International Journal of Mass Emergencies & Disasters 20, no. 1 (March 2002): 51–68. http://dx.doi.org/10.1177/028072700202000104.

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Emergency planning and management profoundly depend on computer aided decision support tools which gather and analyze data on damage assessment, meteorology, demography, geography, etc., and provide decision support for prevention/mitigation, response, and recovery. Various technologies are amalgamated to provide useful functions to aid this decision-making process. Complexities arise when attempting to link two streams of technology to achieve a realistic, usable tool that emergency planners can rely on. This discussion aims to identify and analyze the challenging issues faced in using two technologies, simulation modeling and Geographical Information Systems, to design decision support tools for the evacuation planning process. The issues investigated arise from experiences in designing the prototype decision support system CEMPS. The discussion primarily focuses on issues related to the. behavioral and decision-making processes of the various players in the evacuation system, logistics, generating realistic scenarios for testing out contingency plans, the. validation of such decision support tools, and the future trends in technology and the emergency planning and management processes.

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Platz, M., J. Rapp, M. Groessler, E. Niehaus, A. Babu, and B. Soman. "Mathematical Modeling of spatial disease variables by Spatial Fuzzy Logic for Spatial Decision Support Systems." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-8 (November 27, 2014): 213–20. http://dx.doi.org/10.5194/isprsarchives-xl-8-213-2014.

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A Spatial Decision Support System (SDSS) provides support for decision makers and should not be viewed as replacing human intelligence with machines. Therefore it is reasonable that decision makers are able to use a feature to analyze the provided spatial decision support in detail to crosscheck the digital support of the SDSS with their own expertise. Spatial decision support is based on risk and resource maps in a Geographic Information System (GIS) with relevant layers e.g. environmental, health and socio-economic data. Spatial fuzzy logic allows the representation of spatial properties with a value of truth in the range between 0 and 1. Decision makers can refer to the visualization of the spatial truth of single risk variables of a disease. Spatial fuzzy logic rules that support the allocation of limited resources according to risk can be evaluated with measure theory on topological spaces, which allows to visualize the applicability of this rules as well in a map. Our paper is based on the concept of a spatial fuzzy logic on topological spaces that contributes to the development of an adaptive Early Warning And Response System (EWARS) providing decision support for the current or future spatial distribution of a disease. It supports the decision maker in testing interventions based on available resources and apply risk mitigation strategies and provide guidance tailored to the geo-location of the user via mobile devices. The software component of the system would be based on open source software and the software developed during this project will also be in the open source domain, so that an open community can build on the results and tailor further work to regional or international requirements and constraints. A freely available <i>EWARS Spatial Fuzzy Logic Demo</i> was developed wich enables a user to visualize risk and resource maps based on individual data in several data formats.

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Ashraf, Ather, Muhammad Akram, and Mansoor Sarwar. "Type-II Fuzzy Decision Support System for Fertilizer." Scientific World Journal 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/695815.

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Type-II fuzzy sets are used to convey the uncertainties in the membership function of type-I fuzzy sets. Linguistic information in expert rules does not give any information about the geometry of the membership functions. These membership functions are mostly constructed through numerical data or range of classes. But there exists an uncertainty about the shape of the membership, that is, whether to go for a triangle membership function or a trapezoidal membership function. In this paper we use a type-II fuzzy set to overcome this uncertainty, and develop a fuzzy decision support system of fertilizers based on a type-II fuzzy set. This type-II fuzzy system takes cropping time and soil nutrients in the form of spatial surfaces as input, fuzzifies it using a type-II fuzzy membership function, and implies fuzzy rules on it in the fuzzy inference engine. The output of the fuzzy inference engine, which is in the form of interval value type-II fuzzy sets, reduced to an interval type-I fuzzy set, defuzzifies it to a crisp value and generates a spatial surface of fertilizers. This spatial surface shows the spatial trend of the required amount of fertilizer needed to cultivate a specific crop. The complexity of our algorithm isO(mnr), wheremis the height of the raster,nis the width of the raster, andris the number of expert rules.

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Wen, Richard, and Songnian Li. "Spatial Decision Support Systems with Automated Machine Learning: A Review." ISPRS International Journal of Geo-Information 12, no. 1 (December 30, 2022): 12. http://dx.doi.org/10.3390/ijgi12010012.

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Many spatial decision support systems suffer from user adoption issues in practice due to lack of trust, technical expertise, and resources. Automated machine learning has recently allowed non-experts to explore and apply machine-learning models in the industry without requiring abundant expert knowledge and resources. This paper reviews recent literature from 136 papers, and proposes a general framework for integrating spatial decision support systems with automated machine learning as an opportunity to lower major user adoption barriers. Challenges of data quality, model interpretability, and practical usefulness are discussed as general considerations for system implementation. Research opportunities related to spatially explicit models in AutoML, and resource-aware, collaborative/connected, and human-centered systems are also discussed to address these challenges. This paper argues that integrating automated machine learning into spatial decision support systems can not only potentially encourage user adoption, but also mutually benefit research in both fields—bridging human-related and technical advancements for fostering future developments in spatial decision support systems and automated machine learning.

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Alçada-Almeida, Luís, João Coutinho-Rodrigues, and José-Paulo de Almeida. "Interactive multicriteria decision support system for spatial planning analysis." Proceedings of the Institution of Civil Engineers - Municipal Engineer 166, no. 1 (March 2013): 3–15. http://dx.doi.org/10.1680/muen.12.00010.

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16

Marinoni, Oswald. "A stochastic spatial decision support system based on PROMETHEE." International Journal of Geographical Information Science 19, no. 1 (January 2005): 51–68. http://dx.doi.org/10.1080/13658810412331280176.

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17

Frank, William C., Jean-Claude Thill, and Rajan Batta. "Spatial decision support system for hazardous material truck routing." Transportation Research Part C: Emerging Technologies 8, no. 1-6 (February 2000): 337–59. http://dx.doi.org/10.1016/s0968-090x(00)00007-3.

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Seffino, Laura A., Claudia Bauzer Medeiros, Jansle V. Rocha, and Bei Yi. "woodss — a spatial decision support system based on workflows." Decision Support Systems 27, no. 1-2 (November 1999): 105–23. http://dx.doi.org/10.1016/s0167-9236(99)00039-1.

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Jain, Dharmesh K., Udoyara S. Tim, and Robert Jolly. "Spatial decision support system for planning sustainable livestock production." Computers, Environment and Urban Systems 19, no. 1 (January 1995): 57–75. http://dx.doi.org/10.1016/0198-9715(94)00031-x.

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20

Cohen, Y., A. Cohen, A. Hetzroni, V. Alchanatis, D. Broday, Y. Gazit, and D. Timar. "Spatial decision support system for Medfly control in citrus." Computers and Electronics in Agriculture 62, no. 2 (July 2008): 107–17. http://dx.doi.org/10.1016/j.compag.2007.12.005.

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21

Linchong, Kang, Jiang Xiaoyi, Zhao Longfei, Wang Yi, He Long, Guo Xue, and Huang Haiyan. "A Decision Support System for Marine Regulation." IOP Conference Series: Earth and Environmental Science 1004, no. 1 (March 1, 2022): 012009. http://dx.doi.org/10.1088/1755-1315/1004/1/012009.

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Abstract The implementation of marine spatial data management and decision support based on GIS provides a powerful tool for marine regulation. This study constructed a three-dimensional spatiotemporal data model of marine resources that is hierarchical, partitioned, and classified according to the management principle of using an underground resource layer, surface matrix layer, surface overburden layer, and management layer, taking the marine resource entity as the unit. Key research topics include marine multi-source information integration, visual expression, and spatiotemporal topology analysis for typical application scenarios such as sea area use, island development and utilization, and marine environmental monitoring and protection. The study developed a marine information decision support system with comprehensive integration and visual analysis of information on maritime islands, early warning and monitoring, the marine economy, and the marine environment. The system was applied to dynamic regulation of sea area use, spatiotemporal characteristic analysis of marine resources, the regulation of human activities in marine protected areas, and the monitoring of sea level rise. Practice has proved that a three-dimensional spatiotemporal data model of marine resources can meet the application requirements of “One Map”, the basic land and spatial information platform.

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Jelonek, Dorota, Ilona Pawełoszek, Cezary Stępniak, and Tomasz Turek. "The Role of Spatial Decision Support System in Regional Business Spatial Community." Applied Mechanics and Materials 795 (October 2015): 107–14. http://dx.doi.org/10.4028/www.scientific.net/amm.795.107.

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Decision support systems are tools that extend the functionality of management information systems. Similarly, spatial decision support systems (SDSS) enhance the capabilities of geographic information systems (GIS) with analytical modelling capabilities and support processes of spatial decision making. SDSS offer many kinds of spatial analyses, from processing data (as well spatial as other data residing in databases), do intelligent maps and models of different types of phenomena. Regional Busines Spatial Community (RBSC) is the concept of community gathering units responsible for building infrastructure networks and supplying utilities for citizens of a region.The authors try to create the community on the basis of companies and institutions from the territory of the City of Częstochowa. A commonality of all the aforementioned stakeholders is to be the possibility to participate in the shared SDSS with the purpose of planning infrastructure investments on the territory of the city. This paper aims to examine the functionality of accessible GIS and to indicate how they can be enhanced with analytical modeling capabilities for the needs of the considered community.

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Vyas, Ranjana, Lokesh Kumar Sharma, and U. S. Tiwary. "Exploring Spatial ARM (Spatial Association Rule Mining) for Geo-Decision Support System." Journal of Computer Science 3, no. 11 (November 1, 2007): 882–86. http://dx.doi.org/10.3844/jcssp.2007.882.886.

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Sutrisno, Dewayany, Suzan Novtalia Gill, and Suseno Suseno. "The development of spatial decision support system tool for marine spatial planning." International Journal of Digital Earth 11, no. 9 (August 17, 2017): 863–79. http://dx.doi.org/10.1080/17538947.2017.1363825.

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Coutinho-Rodrigues, João, John Current, João Climaco, and Samuel Ratick. "Interactive Spatial Decision-Support System for Multiobjective Hazardous Materials Location-Routing Problems." Transportation Research Record: Journal of the Transportation Research Board 1602, no. 1 (January 1997): 101–9. http://dx.doi.org/10.3141/1602-15.

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Hazardous materials (hazmat) logistics management has received increased attention in the past two decades. Important decisions in such management include the selection of sites for hazmat processing and storage, the selection of transportation routes from sources to processing facilities, and the determination of quantities of hazmat shipped over these routes. These decisions are frequently based on multiple criteria (e.g., cost, risk, equity). A personal computer–based, interactive spatial decision-support system was designed to assist decision makers with such problems. Although presented within the framework of a hazmat problem, the system’s components can be modified to analyse any multiobjective location, routing, or location-routing problem.

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Zhang, Min Li, and Guo Chao Han. "Application of Spatial Decision Support System in Real Estate Appraisal." Applied Mechanics and Materials 174-177 (May 2012): 3286–90. http://dx.doi.org/10.4028/www.scientific.net/amm.174-177.3286.

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After the economic crisis in 2009, the real estate market has gone through a temporary prosperity, but now it is undergoing the country’s another round of macro control. In order to comply with the national’s implementation of "one-room, one- price" policy and make second-hand housing market standardized. A comprehensive analysis of our country’s real estate prices and the latest development trends is made in this paper, besides; the analysis is based on the real estate evaluation theory. This paper presents a novel real estate estimation method based on spatial decision support system, theoretical analysis and the application of computer technology, this approach is an integration of decision support systems (DSS) and Geographic Information Systems (GIS). Meanwhile, it can make our country’s real estate evaluation more regular and scientifically.

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PIZZIRANI, STEFANIA, and STEPHEN BATHGATE. "INTEGRATION OF FORESTRY DECISION SUPPORT SYSTEMS IN GIS." Journal of Environmental Assessment Policy and Management 14, no. 02 (June 2012): 1250014. http://dx.doi.org/10.1142/s1464333212500147.

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Landscape characteristics underpin the ability of the forestry industry to deliver in an increasingly complex operational environment. However, the range of site types within the British public forest estate includes many with soil or exposure constraints. Until recently it was not possible to effectively assess the scale of constraints and spatially allocate land appropriately to the objectives suggested by policy makers. Stand level forestry decision support systems have been developed to address these issues but it is difficult to incorporate their outputs into the spatial forest plans, limiting their operational use. The MOTIVE project (EU 7th Framework Programme) provided an opportunity to integrate stand level tools with spatial inventory data. The output is a comprehensive GIS layer containing silvicultural, geophysical and climate data, and site-specific decision support system outputs. This paper describes the climate and inventory data, models, GIS methods, the visualisation of outputs, and the impact on stakeholders.

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Puletti, N., A. Floris, G. Scrinzi, F. Chianucci, G. Colle, T. Michelini, N. Pedot, A. Penasa, S. Scalercio, and P. Corona. "CFOR: a spatial decision support system dedicated to forest management in Calabria." Forest@ - Rivista di Selvicoltura ed Ecologia Forestale 14, no. 2 (April 30, 2017): 135–40. http://dx.doi.org/10.3832/efor2363-014.

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Lakshmanan, Valliappa, Travis Smith, Gregory Stumpf, and Kurt Hondl. "The Warning Decision Support System–Integrated Information." Weather and Forecasting 22, no. 3 (June 1, 2007): 596–612. http://dx.doi.org/10.1175/waf1009.1.

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Abstract The Warning Decision Support System–Integrated Information (WDSS-II) is the second generation of a system of tools for the analysis, diagnosis, and visualization of remotely sensed weather data. WDSS-II provides a number of automated algorithms that operate on data from multiple radars to provide information with a greater temporal resolution and better spatial coverage than their currently operational counterparts. The individual automated algorithms that have been developed using the WDSS-II infrastructure together yield a forecasting and analysis system providing real-time products useful in severe weather nowcasting. The purposes of the individual algorithms and their relationships to each other are described, as is the method of dissemination of the created products.

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Cerreta, M., and P. De Toro. "Urbanization suitability maps: a dynamic spatial decision support system for sustainable land use." Earth System Dynamics 3, no. 2 (November 20, 2012): 157–71. http://dx.doi.org/10.5194/esd-3-157-2012.

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Abstract. Recent developments in land consumption assessment identify the need to implement integrated evaluation approaches, with particular attention to the development of multidimensional tools for guiding and managing sustainable land use. Land use policy decisions are implemented mostly through spatial planning and its related zoning. This involves trade-offs between many sectorial interests and conflicting challenges seeking win-win solutions. In order to identify a decision-making process for land use allocation, this paper proposes a methodological approach for developing a Dynamic Spatial Decision Support System (DSDSS), denominated Integrated Spatial Assessment (ISA), supported by Geographical Information Systems (GIS) combined with the Analytic Hierarchy Process (AHP). Through empirical investigation in an operative case study, an integrated evaluation approach implemented in a DSDSS helps produce "urbanization suitability maps" in which spatial analysis combined with multi-criteria evaluation methods proved to be useful for both facing the main issues relating to land consumption as well as minimizing environmental impacts of spatial planning.

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Cerreta, M., and P. De Toro. "Urbanization susceptibility maps: a dynamic spatial decision support system for sustainable land use." Earth System Dynamics Discussions 3, no. 2 (October 4, 2012): 1159–90. http://dx.doi.org/10.5194/esdd-3-1159-2012.

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Abstract. Recent developments in land consumption assessment identify the need to implement integrated evaluative approaches, with particular attention to the identification of multidimensional tools for guiding and managing sustainable land use. Policy decisions defining land use are mostly implemented through spatial planning and related zoning, and this involves trade-offs between many sectoral interests and conflicting challenges aimed at win-win solutions. In order to identify a decision-making process for land use allocation, the paper proposes a methodological approach for a Dynamic Spatial Decision Support System (DSDSS), named Integrated Spatial Assessment (ISA), supported by Geographical Information Systems (GIS) combined with Analytic Hierarchy Process (AHP). Through the empirical investigation in an operative case study, an integrated evaluative approach implemented in a DSDSS helps to elaborate "urbanization susceptibility maps", where spatial analysis combined with a multi-criteria method proved to be useful for facing the main issues related to land consumption and minimizing environmental impacts of spatial planning.

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Zhang, Zhe, Lei Zou, Wenwen Li, Lynn Usery, Jochen Albrecht, and Marc Armstrong. "Cyberinfrastructure and intelligent spatial decision support systems." Transactions in GIS 25, no. 4 (August 2021): 1651–53. http://dx.doi.org/10.1111/tgis.12835.

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Keenan, Peter B. "Spatial decision support systems for vehicle routing." Decision Support Systems 22, no. 1 (January 1998): 65–71. http://dx.doi.org/10.1016/s0167-9236(97)00054-7.

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Keenan, Peter Bernard, and Piotr Jankowski. "Spatial Decision Support Systems: Three decades on." Decision Support Systems 116 (January 2019): 64–76. http://dx.doi.org/10.1016/j.dss.2018.10.010.

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Idrees, Amira M., Mohamed H. Ibrahim, and Ahmed I. El Seddawy. "Applying spatial intelligence for decision support systems." Future Computing and Informatics Journal 3, no. 2 (December 2018): 384–90. http://dx.doi.org/10.1016/j.fcij.2018.11.001.

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Arezki, Sekhri A., Hamdadou B. Djamila, and Beldjilali C. Bouziane. "AQUAZONE: A Spatial Decision Support System for Aquatic Zone Management." International Journal of Information Technology and Computer Science 7, no. 4 (March 8, 2015): 1–13. http://dx.doi.org/10.5815/ijitcs.2015.04.01.

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Mileu, Nelson, and Margarida Queirós. "Integrating Risk Assessment into Spatial Planning: RiskOTe Decision Support System." ISPRS International Journal of Geo-Information 7, no. 5 (May 11, 2018): 184. http://dx.doi.org/10.3390/ijgi7050184.

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Oyedepo, John A., E. O. Oyedepo, A. A. Adeola, and A. M. Omotayo. "SPATIAL DECISION SUPPORT SYSTEM FOR LIVELIHOOD PROGRAMMES IN NORTHERN NIGERIA." FUDMA JOURNAL OF SCIENCES 5, no. 1 (June 25, 2021): 174–85. http://dx.doi.org/10.33003/fjs-2021-0501-551.

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The paper examines the relevance of geospatial maps and technics in execution of livelihood support projects among rural poor. Previous attempts towards driving the poor along prosperity pathways in Nigeria have failed because of incorrect deployment of interventions in time and space. Global positioning systems and Geographical information systems were employed in this study to provide insight to the challenges faced by previous livelihood support projects in the country while also providing a robust spatial decision support system for geographical targeting of interventions to vulnerable households. The study selected 1,459 households from the 42,000 households adopted for the Feed the Future Project in 3 northern States namely; Sokoto, Kebbi States and the Federal Capital Territory. Acquired survey data were converted into GIS maps so as to expose hidden trends in the characteristics of target population and to suggest the best options for interventions. The data were subjected to spatial analysis such as simple distance analysis. The outcomes revealed 86% of sampled households to be largely below the poverty line. This is mainly due to poor access to facilities like health care, markets, good roads, agricultural inputs, agricultural information and advisory services. 10 % of children’s absolute score of Mid Upper Arm Circumference (MUAC) revealed acute malnutrition. 42 % of the household however now have stronger safety nets as a result of the interventions. The study found Spatial Information technology highly useful in social intervention project as the one by the Feed the Future project

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Pelizaro, Claudia, and David McDonald. "Spatial (GIS-based) decision support system for the Westernport region." Applied GIS 2, no. 3 (January 2006): 17.1–21.1. http://dx.doi.org/10.2104/ag060017.

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Makropoulos, C. K., and D. Butler. "A neurofuzzy spatial decision support system for pipe replacement prioritisation." Urban Water Journal 2, no. 3 (September 2005): 141–50. http://dx.doi.org/10.1080/15730620500236674.

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Medeiros, Sergio Palma J., Julia Celia M. Strauch, Jano Moreira de Souza, and Gustavo da Rocha B. Pinto. "SPeCS - a spatial decision support collaborative system for environment design." International Journal of Computer Applications in Technology 14, no. 4/5/6 (2001): 158. http://dx.doi.org/10.1504/ijcat.2001.000270.

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Sailaja, B., S. R. Voleti, D. Subrahmanyam, P. Raghuveer Rao, S. Gayatri, R. Nagarjuna Kumar, and Shaik N. Meera. "Spatial Rice Decision Support System for Effective Rice Crop Management." Current Science 116, no. 3 (February 10, 2019): 412. http://dx.doi.org/10.18520/cs/v116/i3/412-421.

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Shim, Kyu-Cheoul, Darrell G. Fontane, and John W. Labadie. "Spatial Decision Support System for Integrated River Basin Flood Control." Journal of Water Resources Planning and Management 128, no. 3 (May 2002): 190–201. http://dx.doi.org/10.1061/(asce)0733-9496(2002)128:3(190).

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Makropoulos, C. K., D. Butler, and C. Maksimovic. "Fuzzy Logic Spatial Decision Support System for Urban Water Management." Journal of Water Resources Planning and Management 129, no. 1 (January 2003): 69–77. http://dx.doi.org/10.1061/(asce)0733-9496(2003)129:1(69).

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Dymond, R. L., B. Regmi, V. K. Lohani, and R. Dietz. "Interdisciplinary Web-Enabled Spatial Decision Support System for Watershed Management." Journal of Water Resources Planning and Management 130, no. 4 (July 2004): 290–300. http://dx.doi.org/10.1061/(asce)0733-9496(2004)130:4(290).

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WARD, DOUG, ROBERT STIMSON, and ALAN MURRAY. "A SPATIAL DECISION SUPPORT SYSTEM TO MODEL FUTURE URBAN GROWTH." Australian Planner 38, no. 2 (January 2001): 80–89. http://dx.doi.org/10.1080/07293682.2001.9657943.

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Ruiz, M. C., and I. Fernández. "Environmental assessment in construction using a Spatial Decision Support System." Automation in Construction 18, no. 8 (December 2009): 1135–43. http://dx.doi.org/10.1016/j.autcon.2009.07.005.

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Kumbhar, Vidya, Akhil Maru, and Sneha Kumari. "Spatial Data Mining- A tool for Spatial Decision Support System in Agriculture Management." Journal of Engineering Science and Technology Review 15, no. 1 (2022): 128–33. http://dx.doi.org/10.25103/jestr.151.16.

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Prah, Klemen, Andrej Lisec, and Anka Lisec. "Digital spatial data as support for river basin management: The case of Sotla river basin." Spatium, no. 29 (2013): 59–67. http://dx.doi.org/10.2298/spat1329059p.

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Abstract:

Many real-world spatially related problems, including river-basin planning and management, give rise to geographical information system based decision making, since the performance of spatial policy alternatives were traditionally and are still often represented by thematic maps. Advanced technologies and approaches, such as geographical information systems (GIS), offer a unique opportunity to tackle spatial problems traditionally associated with more efficient and effective data collection, analysis, and alternative evaluation. This paper discusses the advantages and challenges of the use of digital spatial data and geographical information systems in river basis management. Spatial data on social, environmental and other spatial conditions for the study area of 451.77 km2, the Slovenian part of the Sotla river basin, are used to study the GIS capabilities of supporting spatial decisions in the framework of river basin management.

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Rozos, D., P. Tsangaratos, C. Loupasakis, I. Koumantakis, and K. Markantonis. "Assessing areas of slope instability through a spatial decision support system." Bulletin of the Geological Society of Greece 47, no. 4 (September 5, 2013): 1844. http://dx.doi.org/10.12681/bgsg.11064.

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The purpose of this study was to highlight the analytic power of a Spatial Decision Support System (SDSS) in slope stability problems and to present the process followed during the systematic study of the landslide phenomena manifested in the Chalki village, Korinthos Prefecture, Greece. The mass movements affected the residential area of Chalki village making urgent the need of immediate mitigation measures. The two main objectives of the developed Spatial Decision Support System (SDSS) were to evaluate the landslide susceptibility of the research area and to locate the most suitable areas for addressing investigation schemes and installing landslide monitoring systems.

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