Decision Support Systems Frameworks

Beyond the basic definition of the salient characteristics of DSS summarized in the previous section, there is a need to define the elementary structure and components of a DSS, formalised in a development framework, without which the system would not be able to interact and support the decision maker. Sprague & Watson (1993) offer a fundamental structure of elements or components needed for the development of a relevant and useful DSS, including (1) data representing the state of the real world; (2) procedures, the steps needed to solve the problem; (3) goals (evaluation criteria) and constraints, the desired results and limitations of the decision space; and (4) strategies that indicate which procedures to apply to achieve goals.

From a structured systems approach, the implementation of these elements then necessitates the development of the following components (Sprague 1980):

 Specific DSS: The hardware/software that actually accomplishes the work, e.g. the specific application with which the users interact and form their decision-making process.

 DSS Generator: A package of tools and software programs specifically adapted to enable users to generate from them a set of specific DSS implementations.

 DSS Tools: The foundational elements of hardware and software supporting the development of DSS. These include general-purpose programming languages and input-output devices used in a DSS.

In conclusion, according to Densham (1991), a Decision Support System framework needs to satisfy the following characteristics:

 an explicit design to solve ill-structured problems;

 powerful and easy-to-use user interfaces;

 ability to combine analytical models flexibly with data;

 ability to explore the solution space by building alternatives;

 capability of supporting a variety of decision-making styles; and

 allowing interactive and recursive problem solving.

These characteristics allow the definition of the most important functionality and capabilities of DSS, in the context of this research thesis. The following section takes into account the supplemental characteristics that Spatial Decision Support Systems have beyond DSS.

4.1.1.1 Spatial Decision Support Systems

In addition to the six generic characteristics of a DSS (Densham 1991) highlighted in the previous section, a Spatial Decision Support System needs to support further functionality, resulting from the inclusion of an explicit geographic component. In contrast to conventional DSS, SDSS not only require information on the criterion values, but also information on the geographical locations of alternatives. Analytical results thus not only depend on value judgements attached to the decision-making process, but also on the geographic locations of attributes and alternatives (Ascough et al. 2002). The special nature of spatial data and attached spatial processes necessitates the following supplemental conditions, which Densham expressed in his framework conditions:

 Input of spatial data into the system;

 Representation of spatial relations and structures;

 Spatial and geographical analysis techniques; and

 Output in a variety of formats including maps and graphs.

Together, these characteristics define the functionality required by any SDSS, and describe salient characteristics that differentiate such as system from, for example, a Geographical

Information System (GIS). A GIS is often defined as offering necessary functionality for the capture, storage, manipulation, analysis, and display of geographical data, and the idea that a GIS is suitable to support spatial decision-making is implicit (Densham 1991). However, Densham specifically identifies the lack of analytical modelling capabilities in GIS designed to support ill-structured problems, with most functionality geared towards the production of cartographic products. The main communication metaphor between the user and GIS are maps, along with tabular database reports, whereas a decision maker will likely need a richer and more flexible interaction with a SDSS, involving communication means such as reports, graphics, and charts that are specific to the expert domain. Finally, GIS are not designed to support diverse decision-making strategies adopted by different users. Individual judgments on variable weights and relationships, as well as the selective use of information to reach decisions, are not explicitly supported by GIS. Such judgements and analysis of potentially conflicting priorities is formalised in a structured approach for developing weights associated with different objectives or criteria, in terms of subjective importance to decision makers, with the overall score of one alternative outcome being the overall performance of the different criteria. This process has been formalised as Multi-Criteria Analysis methodologies (MCDM).

The assumptions behind MCDM analysis then match and further specify the definition of DSS (Malczewski, 1999).

“The Multi-Criteria Decision Making Process assists stakeholders and decision makers in analysing the decision problem at hand, specifically for decisions which present trade-offs between different objectives, with no obvious optimal solution”

(Reyck et al. 2005).

From a structured system development perspective, the implementation of a SDSS then necessitates a set of fundamental components or building blocks that satisfy the following conditions: (1) a relevant model (base) enabling the capture and processing of decision makers’ preferences into discrete decision alternatives, such as Multi Criteria Decision Making Methodologies; (2) a spatial database of location variables (Carver 1991; Jankowski 1995); (3) a graphical user (base) interface supporting decision makers through all the steps of the decision process and enabling a dynamic and interactive session (Malczewski 1999; Jankowski 1995); and (4) a computation base enabling the proper acquisition, storage, retrieval,

manipulation, and analysis capability (Ascough et al. 2002). Therefore, a significant contribution of a SDSS is the integration of previously separate tool sets (data and model)

through improved computation and user interfaces into a unified whole (see Figure 15) more valuable than the sum of its parts (Malczewski, 1999).

Figure 15: Conceptual model of principal Spatial Decision Support System components