# An Online Multicriteria--Spatial Decision Support System for Public Services Planning
## 3 Requirements and Methodology
### Concept and Methods
Additionally, the _accessibility weighting parameters_ have been predefined but can also be adjusted by the user. In _step 6_, the relative weights for the selected infrastructure types are defined by the user. The relative value between the types is determined by the weighting on a scale of 0-100. With reference to [PERSON] [17], the decision was made to use a ranking method based on the ratio estimation procedure--whereby, in this case, equivalent rankings are permitted. As [PERSON] [17] also mentions, the method can be criticised for its lack of formal and theoretical background. However, even if the accuracy of the estimation of the criteria importance is affected, this method is suitable here as it is very quick and easy for a user to understand. In this case, we can provide planners and decision makers with intuitive access to MCDA methods and comprehensive map results. As described in more detail in the following two steps, the individual values and the overall result can be traced logically and intuitively in the final maps. The map result is much more comprehensible for the user. Once the user has stored the desired weights in the interface, the simple additive weighting methods (SAW), also referred to as weighted linear combination (WLC) or scoring methods, are implemented. The WLC process consists of the following six steps:  
1. Defining each criterion in the form of a map layer;
2. Standardising each criterion layer;
3. Defining a weighting for each criterion;
4. Creating the weighted standardised criteria layers;
5. Generating an overall assessment by totalling the selected standardised criteria layers;
6. Categorising the alternatives according to the overall performance rating. After a summary of the input parameters is given in _step 7_, the user can finally view the analysis results in _step 8_. Here, the user has various options: It is possible to view the general supply situation (corresponding to the parameters set). This allows for the identification of good and deficient supply situations. The optional inclusion of population weighting makes it easier to identify deficient supply situations which affect large sections of the population. However, the user can also analyse individual results and assess the supply situation (and values) of each individual infrastructure type. In the context of this web-based implementation, we refrained from an in-depth realisation of a technical-methodical sensitivity analysis, mainly because of usability and performance reasons. Such an analysis examines the relationship between the result achieved and the input parameters. The aim is to show the extent to which the assumptions made and the preferences of the decision maker have an influence on the result. The sensitivity analysis can therefore be used to deal with uncertainties in the weighting or the design of alternatives and criteria values. On the other side, the parametrization and the resulting representation of such an analysis is complex and would overburden the user interface of our web application. Further, practical tests have showed that the calculation times of a sensibility analysis would not be acceptable in the context of such a web application. However, if the influence of a specific input parameter is to be analysed, a manual procedure allows for the user to switch back to step 3, influence criteria, change weights and thresholds, and explore the effects in an iterative process. After we work out the details of the analysis process, we will discuss the implemented user interface and the analysis results in the following section. Figure 3: Workflow of the analysis process in interaction with the user interface.