**4. Conclusion and further work**

*3.5.2. Data classification method*

242 Decision Support Systems

method is recommended;

following assumptions are considered:

or Jenks methods fit the data better.

tile methods are then suggested to be used;

**Figure 12.** Example of generated theme table and associated statistics

*3.5.3. Feedback on map quality*

For numerical data only, the expert system measures statistics for the theme data: standard deviation, variance, mean, deviance from mean (Figure 12). These are all taken in order to measure kurtosis (Dent [23]), which is a metric to measure the flatness of a distribution. The

**•** When kurtosis is between 2.5 and 3.5, the distribution is normal. Equal interval and quin‐

**•** When kurtosis is below 2.5, the distribution is considered flat and a standard deviation

**•** When kurtosis is above 3.5, the distribution is full of peaks. In this case, maximum breaks

This is simply asking the specialist: "How would you classify the quality of the representa‐ tion generated?" Answers can vary from 0 to 10, and only higher grades should be assumed An expert system development should consider the subject particularities. In the case of maps, use and users are mandatory issues to be taken into account when designing data storage and analysis and also the way to interact with them. The presented ES can manage not only the LOAS data, users and framework, but is now designed to cover an unpredicta‐ ble amount of uses, since users can upload and analyze their own data. The system's inter‐ face was also carefully discussed in order to present to users the most practical and simple way to interact with complex map design decisions. Usability pre-tests have been carried out, and current feedback is positive.

After testing this first version of the online Atlas, it is intended to develop additional func‐ tionalities in order to improve the expert system concept that has been started with this re‐ search, as well as the interface use experience. The main objective is to make this system a reference, not only for LOAS technicians but also for ordinary internet users who need to get their data symbolized according to map design expertise.

Currently, users have the option to upload their own data, both geometry and attributes. There is work in progress for the system to recognize if the mapping method is suitable for the data characteristics. One aspect that can be discussed, regarding the analysis of numeric data types, is a mapping technique chosen against relative or absolute data. Here, absolute data are considered those not related to any other data, e.g. people counting; relative data are related to any other data and can be related to area units, e.g. population density. Be‐ sides the importance of this classification, there is no formal way to discover if data are ab‐ solute or relative. A possible solution for this problem could be to ask the user a set of questions in order to verify this information. After this questionnaire, the system would sug‐ gest the most suitable method for data classification and, consequently, the choice of map‐ ping technique.

Another issue that has to be considered is the number of classes. This first version allows the user (with no distinction between common users and specialists) to choose this parameter without restriction. However, this is an important decision that can affect map understand‐ ing and legibility, since it can mask the whole distribution, given the number of elements per class and the relationship among the theme elements. To prevent incorrect choices, it is necessary also that system suggests to users a suitable number of classes. This parameter is then decided considering the number of elements in the raw data, i.e., the number of ele‐ ments in the sample and their metrics, as median or standard deviation.

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