# An Online Multicriteria--Spatial Decision Support System for Public Services Planning
## 3 Requirements and Methodology
### Criteria, Data, and Weights  
The scope of our planning tool is to enable the planner to assess the available infrastructure of general interest (supply side) in relation to the requirements of the population (demand side). On the supply side, this mainly includes  
* Supermarkets, discounters, and other grocery shops (bakers, butchers, etc.);
* The health sector with the locations of pharmacies, clinics, and all primary care specialists (from general practitioners to outpatient surgeons);
* The education sector, consisting of day-care centres, primary schools, and secondary schools.  
On the demand side, this includes the residential locations of the population. Here, we used small-scale data with a resolution of 100 \(\times\) 100 m edge length per grid cell for the total population and for the different age groups.  
Table 1 provides an overview of the integrated data. For the federal state of Lower Saxony, a total of 112,326 data points (i.e., infrastructure locations) are provided in the categories of local supply, health, and education.  
According to the user requirements (problem definition), there are different dimensions of weighting. The first dimension is the relative importance of one type of infrastructure to another. The second dimension is the distance decay of the supply over an increasing distance (road network) between the supply location and the demand location.  
The latter is of particular importance because in many countries, including Germany, minimum standards for the supply of an infrastructure are legally set: undersupply is stated if a certain distance (or travelling time) from the place of residence to an infrastructure is exceeded. Countermeasures must be taken for residential locations in undersupplied areas. For example, the distance to child day-care facilities in cities may be a maximum of five kilometres. Thus, distance is not only a spatial weighting factor (the further away, the less important it is for the decision problem), but also a constraint if it exceeds a certain distance. For German planners, we set these weighting factors and constraints for all  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** \({}^{*}\) & **Dataset** \\ \hline \multirow{4}{*}{Local supply} & \multicolumn{2}{c}{Supermarkets} \\  & Discounter \\  & Other grocery stores \\ \hline \multirow{4}{*}{Health} & Pharmacies & Gynaecologists \\  & Hospitals & Dermatologists \\  & General Physicians & Otolaryngologists \\  & Paediatrics & Neurologists \\  & Ophthalmologists & Psychotherapists \\  & Surgeons and orthopaedists & Urologists \\ \hline \multirow{4}{*}{Education} & Nurgeries \\  & Primary schools \\ \cline{1-1}  & Secondary schools level 1 \\ \cline{1-1}  & Secondary schools level 2 \\ \hline Accessibility & OpenStreetMap Data (Network dataset) \\ \hline Population & Population grid (100 \(\times\) 100 m) \\ \hline \hline \end{tabular} \({}^{*}\)With the exception of accessibility data, all datasets were handed over by the Lower Saxony Ministry of Federal and European Affairs and Regional Development.  
\end{table}
Table 1: Overview of the provided datasets.  
existing infrastructures in the application on the basis of case law and legislation (where available) [36]. Within the application, however, we ensured that the planner or decision maker can adjust these default settings via the interface. The default pre-assignment was therefore only a suggestion based on the legal situation for the infrastructure in question.  
Figure 1 schematically illustrates the application goal of a gradual distance decay for one criterion. The supply situation becomes unfavourable as the distance increases. However, the distance boundaries (or respective travelling time limits) are predefined and can be manually changed by the planer.