SDSSs

(redirected from Spatial Decision Support Systems)
AcronymDefinition
SDSSsSpatial Decision Support Systems (operations research)
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References in periodicals archive ?
Based on the type, the smart agriculture market can be segmented into management information system, remote sensing systems/ sensors, spatial decision support systems, geographic information system, and yield mapping.
On the other hand, spatial decision support systems as the one we presented combined computer-based tools that used geospatial data sets and were integrated into a structured framework.
Spatial decision support systems; principles and practices.
The main role of multiple criteria spatial decision support systems (thereafter MC--SDSS) is to deal with the difficulties that human decision-makers have encountered in handling large pieces of complex information in a consistent way (Yoon and Hwang 1995).
Multi-criteria spatial decision support systems (MC-SDSS) integrate GIS-based data processing and analysis techniques and multi-criteria decision analysis.
Spatial Decision Support Systems (SDSS) have become a necessary tool for retailers when planning new sites and running store forecasts.
Cornett) could have been examined more critically in light of the literature on spatial decision support systems by experts such as Densham and Rushton.
JEFFREY YOUNG, ASLA, AICP is director of the Department of Planning & Community Development of the City of Concord, North Carolina, and a landscape architect WEI-NING XIANG, Ph.D., is with the Department of Geography and Earth Sciences, University of North Carolina at Charlotte, where he teaches GIS, land use and environmental planning, spatial analysis and reasoning, and spatial decision support systems. OWEN J.
Spatial decision support systems (SDSS) is an interactive computer-based system (Malczewski 1977), designed for the purpose of solving problems/generating solutions in relation to spatial decision making process (Jankowski, 1995).
Their topics include the concentration and dynamics of Brazilian agriculture, mining climate and remote sensing time series to improve the monitoring of sugar cane fields, using self-organizing maps for rural territorial typology, zoning based on climate and soil for planting eucalyptus in the southern region of Brazil's Rio Grande du Sul State, mathematical modeling simulation to help assess the environmental impact of agriculture, a computational agent model of flood management strategies, descriptive methods and compromise programming for promoting the agricultural reuse of treated wastewater, and spatial decision support systems for animal traceability.
It is against this backdrop that this article sets out a basic case for investment in spatial decision support systems in retail location planning.
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