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Lately I have been doing some work as a volunteer in the aftermath of the victorian bushfires. We have been using GIS a lot but there was always something missing during the emergency mapping and spatial analysis. This was a Spatial Decision Support System.

So what is a SDSS and how is it different from a GIS?

In order to understand SDSS and know the difference between GIS and SDSS it is important to understand what a GIS is. GIS is a piece of software that can perform generic spatial analysis and geoprocessing methods on geographic data. It requires an GIS Analyst or an expert to operate it. In contrast a Spatial Decision Support System is a domain or an industry specific software. It doesn’t require a GIS expert to operate but rather a domain expert. As the name suggests the software provides decision support but to do so makes use of spatial analysis, geo-statistics, geo-processing or other tools from spatial information sciences. To begin with a SDSS must be designed to answer some domain specific questions that have strong elements of geography.

This is best illustrated through an example: say, in coordinating the containment of a bush fire a sector coordinator needs to decide on where to deploy bulldozers to create a containment line or a barrier to the advancing fire-front. The job of the software is to provide the coordinator with a number of alternative answers that they can choose from based on their experience. In this instance the SDSS will take into consideration a number of information sources, perform a combination of spatial analysis and use sophisticated fire modeling to determine answers.

From the above example it is clear that a Spatial DSS must have access to relevant up-to-date spatial data, contain algorithms from spatial information science and domain-specifc models to answer domain-specific questions and a method for visualizing the answers. So for the sector coordinator the relevant data required would be topography, vegetation fuel maps, weather forecast, real-time weather measurements, verbal information from lookout towers and/or air surveillance, satellite reconnaissance, information from thermal cameras. All this would need to be geo-referenced and lie with in the spatial extent of her sector.

Next component is the algorithms that will convert raw data into useful knowledge. It is important to note that data and algorithms are closely linked since some of the data sources may be derivative products. For example vegetation fuel map may have been derived by applying a Normalized Difference Vegetation Index (NDVI) algorithm to multispectral remotely sensed satellite imagery.

Another example may have an algorithm to convert topography into slope. This slope may be used to rule out areas that are too steep for bulldozers to operate. This nicely leads us to the related component of domains-specific models. In this case the information from the slope, weather conditions (such as wind direction and humidity) as well as the vegetation fuel data may form inputs into a fire-model that predicts the future course of the fire. This information may then be combine with areas where bulldozers can operate to give deployment alternatives to the coordinator.

In a SDSS the above process would form a seamless chain of inputs and give an output. While in a GIS the above would be done by an expert spatial analyst who must be aware of all the pitfalls of combining different spatial data and deal with spatial coordinate systems. But above all a GIS will lack the modeling capability to predict the future course of the fire let alone understand what a bulldozer is. This brings us to another important difference between a GIS and SDSS. SDSS must deal with semantic information. In a SDSS spatial data cannot exist in isolation from its meaning.