Spatial Decision Support System
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Spatial Decision Support Systems (sDSS) developed in parallel with the concept of Decision Support Systems (DSS).
An sDSS is an interactive, computer-based system designed to support a user or group of users in achieving a higher effectiveness of decision making while solving a semi-structured spatial problem.[1] It is designed to assist the spatial planner with guidance in making land use decisions. For example, when deciding where to build a new airport many contrasting criteria, such as noise pollution vs. employment prospects or the knock on effect on transportation links, which make the decision difficult. A system which models decisions could be used to help identify the most effective decision path.
An sDSS is sometimes referred to as a Policy Support System
A spatial decision support system typically consists of the following components.
- A database management system - This system holds and handles the geographical data. A standalone system for this is called a Geographical Information System, (GIS).
- A library of potential models that can be used to forecast the possible outcomes of decisions.
- An interface to aid the users interaction with the computer system and to assist in analysis of outcomes.
This concept fits dialog, data and modelling concepts outlined by Sprague and Watson as the DDM paradigm.[2]
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[edit] How does an sDSS work?
An sDSS usually exists in the form of a computer model or collection of interlinked computer models, including a land use model. Although various techniques are available to simulate land use dynamics, two types are particularly suitable for sDSS. These are Cellular Automata (CA) based models[3] and Agent Based Models (ABM).[4]
An sDSS typically uses a variety of spatial and nonspatial information, like data on land use, transportation, water management, demographics, agriculture, climate or employment. By using two (or, better, more) known points in history the models can be calibrated and then projections into the future can be made to analyze different spatial policy options. Using these techniques spatial planners can investigate the effects of different scenarios, and provide information to make informed decisions. To allow the user to easily adapt the system to deal with possible intervention possibilities an interface allows for simple modification to be made.
[edit] Examples where an sDSS has been used
[edit] CommunityViz
CommunityViz is a land-use planning sDSS that works as an extension to ArcGIS Geographic Information System software produced by ESRI. It uses a scenario planning approach and calculates economic, environmental, social and visual impacts and indicators dynamically as users explore alternatives. Interactive 3D models and various tools for public participation and collaboration are also included. It has been commercially available since 2001.
CommunityViz web site
[edit] Environment Explorer
The Environment Explorer (LOV) is a spatial, dynamic model, in which land use and the effects on social, economic and ecological indicators are modeled in an integrated way. Its primary goal is to explore future developments, combining autonomous developments with alternative policy options, in relation to the quality of the environment in which inhabitants of the Netherlands live, work and recreate. Various policy options from governmental departments are translated into a spatial, dynamic image of the Netherlands future with respect to issues such as: economic activity, employment, social well-being, transportation and accessibility, and the natural environment. The model covers the whole of The Netherlands.
Environment Explorer web site
[edit] LUMOCAP
LUMOCAP aims at delivering an operational tool for assessing land use changes and their impact on the rural landscape according to a Common Agricultural Policy (CAP) orientation. It focuses on the relations between the CAP and landscape changes and emphasizes the spatio-temporal dimension of the former. The core of the tool is a dynamic Cellular Automata based land use model. Current usage areas - Poland (2 areas), Germany / The Netherlands (1 cross border area)
LUMOCAP web site
[edit] MOLAND
The aim of MOLAND is to provide a spatial planning tool that can be used for assessing, monitoring and modeling the development of urban and regional environments. The project was initiated in 1998 (under the name of MURBANDY – Monitoring Urban Dynamics) with the objective to monitor the developments of urban areas and identify trends at the European scale. The work includes the computation of indicators and the assessment of the impact of anthropogenic stress factors (with a focus on expanding settlements, transport and tourism) in and around urban areas, and along development corridors. Models now covering 23 European cities (map)
MOLAND web site
[edit] MURBANDY
The overall objective of MURBANDY is to provide datasets to study past and current land uses, to develop an Earth Observation based procedure to monitor the dynamics of European cities; to develop a number of "urban" and "environmental" indicators that allow to understand these dynamics and the impact these cities have on the environment, and finally to elaborate scenarios of urban growth. Initially this project covered 5 European cities, but the project has expanded into the MOLAND project.
[edit] References
- ^ Sprague, R. H., and E. D. Carlson (1982) Building effective Decision Support Systems. Englewood Cliffs, N.J.:Prentice-Hall, Inc.
- ^ Sprague, R. H. and H. J. Watson (1996) Decision support for management. Upper Saddle River, N.J.: Prentice Hall
- ^ White, R., and G. Engelen (2000) High-resolution integrated modeling of spatial dynamics of urban and regional systems. Computers, Environment, and Urban Systems 24: 383–400.
- ^ Parker, D. C., Manson, S. M., Janssen, M. A., Hoffmann, M., Deadman, P., June (2003) Multi-agent systems for the simulation of land-use and land-cover change: A review. Annals of the Association of American Geographers 93 (2): 314-337.
