Spatial Decision Support System

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The Spatial Decision Support Systems (sDSS) developed in parallel with the concept of Decision Support Systems (DSS). A DSS is part of the information system field and it is a computer program application that analyzes business data and presents it so that users can make business decisions more easily. [1] Therefore, 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.[2] 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, can make deciding difficult. To assist with these decisions, a possible solution is to incorporate a system where models are created 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.

  1. A database management system - This system holds and handles the geographical data. A standalone system for this is called a Geographical Information System, (GIS).
  2. A library of potential models that can be used to forecast the possible outcomes of decisions.
  3. An interface to aid the users interaction with the computer system and to assist in analysis of outcomes.

This concept fits dialog, data and modeling concepts outlined by Sprague and Watson as the DDM paradigm.[3]

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[4] and Agent Based Models (ABM).[5]

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.

Relationship of GIS to sDSS

Historically, GIS has developed differently than other information systems, such as DSS. However, because of the nature of DSS and the different problems it solves, geographic applications and the use of a GIS have been important to this type of business practice. This significance has lead to the creation of sDSS modeling because most GIS software on its own is incomplete as it does not possess the same problem-processing capability or the full model to implement decision support that a DSS can. By using GIS as the base and integrating additional modeling practices, there is flexibility in the sDSS that could be built that uses both spatial and non-spatial techniques. Some techniques that can be used to extend the range of a GIS include, dynamic data exchange (DDE), object linking and embedding (OLE), and open database connectivity (ODBC). Use of these techniques will allow facilities not found in the GIS to pass on data from the GIS to the model. As technology continues to develop, more applications will be available to combine the GIS and DSS interface in the form of an sDSS. [6]

Examples where an sDSS has been used


CommunityViz is a land-use planning sDSS that works as an extension to the ArcGIS suite of software produced by ESRI. [7] 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.

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[8] 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.


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.[9] 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)


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.[10] 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. This map shows models now covering 23 European cities.


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.


Zer0-M aims at concepts and technologies to achieve optimised close-loop usage of all water flows in small municipalities or settlements (e.g. tourism facilities) not connected to a central wastewater treatment - the Zero Outflow Municipality (Zer0-M).

Bike Parking Public Interface

The Chicago Bike Parking Program within the Chicago Department of Transportation uses two datasets to plan yearly bike rack distribution. The two datasets are available publicly. They are: number of bike rack installations and number of requests for new bike racks. These two sets are divided into geographic and political boundaries called Wards. On the Bike Parking website, users can run their own queries to determine the bike parking level of service in geographic and political boundaries (including ZIP Code, and Community Area). A map will display showing color coded points indicating bike rack requests and survey status.


  1. "What is a DSS?". Tech Target. Web. 2010.
  2. Sprague, R. H., and E. D. Carlson (1982) Building effective Decision Support Systems. Englewood Cliffs, N.J.:Prentice-Hall, Inc.
  3. Sprague, R. H. and H. J. Watson (1996) Decision support for management. Upper Saddle River, N.J.: Prentice Hall
  4. 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.
  5. 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.
  6. Bidgoli, Hossein. Geographic Information Systems and Decision Support Systems. "Encyclopedia of Information Systems Volume 2". 2003. pg 430.
  7. Community Viz. Orton Family Foundation. Web. 2013.
  8. Environment Explorer. LUMOS. Web. 2010.
  9. LUMOCAP. Research Institute for Knowledge. Web. 2009.
  10. Background. MOLAND: Monitoring Land Use Cover Dynamics. Web. 2012.

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