Web-based Spatial Decision Support: Status and Research Directions

This paper summarizes research on Web-based spatial decision support systems (WebSDSS). The review distinguishes early server-side from more recent client-side applications. A third category of WebSDSS focusing on spatial decision support in public participation is typically implemented as a mixed client/server-based system. Conclusions drawn from previous work include the need for systematic user studies of WebSDSS, and the adoption of interoperable architectures for distributed spatial decision support. Furthermore, a conceptual framework is proposed to facilitate further studies of WebSDSS methods.

Web-based Spatial Decision Support: Status and Research Directions

This paper summarizes research on Web-based spatial decision support systems (WebSDSS). The review distinguishes early server-side from more recent client-side applications. A third category of WebSDSS focusing on spatial decision support in public participation is typically implemented as a mixed client/server-based system. Conclusions drawn from previous work include the need for systematic user studies of WebSDSS, and the adoption of interoperable architectures for distributed spatial decision support. Furthermore, a conceptual framework is proposed to facilitate further studies of WebSDSS methods.

A new combination rule for Spatial Decision Support Systems for epidemiology

Background Decision making in the health area usually involves several factors, options and data. In addition, it should take into account technological, social and spatial aspects, among others. Decision making methodologies need to address this set of information , and there is a small group of them with focus on epidemiological purposes, in particular Spatial Decision Support Systems (SDSS). Methods Makes uses a Multiple Criteria Decision Making (MCDM) method as a combining rule of results from a set of SDSS, where each one of them analyzes specific aspects of a complex problem. Specifically, each geo-object of the geographic region is processed, according to its own spatial information, by an SDSS using spatial and non-spatial data, inferential statistics and spatial and spatio-temporal analysis, which are then grouped together by a fuzzy rule-based system that will produce a georeferenced map. This means that, each SDSS provides an initial evaluation for each variable of the problem. The results are combined by the weighted linear combination (WLC) as a criterion in a MCDM problem, producing a final decision map about the priority levels for fight against a disease. In fact, the WLC works as a combining rule for those initial evaluations in a weighted manner, more than a MCDM, i.e., it combines those initial evaluations in order to build the final decision map. Results An example of using this new approach with real epidemiological data of tuberculosis in a Brazilian municipality is provided. As a result, the new approach provides a final map with four priority levels: “non-priority”, “non-priority tendency”, “priority tendency” and “priority”, for the fight against diseases. Conclusion The new approach may help public managers in the planning and direction of health actions, in the reorganization of public services, especially with regard to their levels of priorities.