While the overall strategy often takes the form of the generic workflow, as illustrated in Figure 2, the data analysis and sampling design **depend strongly on the situation, the available data and the specific goals and constraints**. We however attempt to bring some structure to the typical workflow here, by dividing the process in a pre-processing step, the exploratory data analysis, the actual data analysis, potentially a post-processing step and the sampling design. Checking if the objective is achieved, and whether the constraints are violated, is of course also necessary at some point. The **pre-processing** step is still relatively straightforward, and compasses checking for errors, outliers, making possible corrections and removing parts of the data irrelevant to the problem at hand. The **exploratory data analysis** can also still be structured in a way that it is applicable to most problems in D&D for constrained environments, by looking into four aspects of the data:

- Is this a
**univariate or multivariate**problem? - Is this a problem involving
**spatial trends**? - Is this a problem involving
**spatial structure**? - Is this data requiring
**robust methods**?

We use the outcome of the exploratory data analysis, i.e. the answers to the above questions, to bring some structure in the range of methods for the actual **data analysis**, possibly applicable to the problem at hand. As the range of situations and methods is vast, we cannot discuss every possible road to selecting a specific approach. This only provides some guidance on the type of methods to use, and the expertise of the user of this strategy comes in at this point to make a final decision, based on the individual method descriptions, suggestions and remarks contained in this document. A **post-processing** step can be required to translate the obtained results into the required information for checking if the **objective** is achieved. If it is not, a **sampling design** approach has to be selected. Similar to the data analysis, we can also only provide general recommendations here, and use a classification in terms of:

- Is this sampling design
**probabilistic**or not? - Are the
**selection probabilities equal**or not?

We do try to relate the different approaches to the four, above defined, aspects of the data as well, in the list of approaches in Section 5. When a sampling design is proposed, a final check on violation of the defined **constraints** should be made, before moving to the corresponding characterization campaign. The different steps outlined here are further discussed in detail one by one below.