## 3 Prioritizing Geo-Spatial Data Collection
### Iterative Multi-Stage Surveys  
Multi-staged surveys can be used when a survey cannot be conducted in a single stage. This can, for example, happen when critical information that could optimize/prioritize the _in situ_ survey is not available _a priori_ or the survey to be conducted is too complex and lengthy to be carried out in one stage. Multi-stage sampling can therefore be particularly interesting in the case of a post-disaster survey, where neither the actual distribution of damages, nor the condition of the roads, is well known prior to an initial survey. In this case, a multi-stage survey would be used to iteratively constrain the subsequent surveys based on the data collected directly in the field. Also, in a pre-disaster case, when, for example, an entire town or a sizable district needs to be surveyed, multi-stage sampling can be a useful approach to split the survey into different, subsequent stages--especially considering that a mobile mapping system usually works only in daylight, hence acquiring data for 6-8 h. The splitting can be either systematic (for instance, by dividing the originally planned survey into different parts, to be conducted separately) or iterative. In the latter case, each survey is generated by a different focus map which takes into account the extent (and possibly also the quality) of the data collected in the previous stages. This approach can be useful, for instance, when the actual route deviates from the optimized routing (for unexpected road blockages or traffic jams).  
In order to implement iterative, multi-stage surveys, different focus maps are computed by considering as additional prioritization criteria the amount, spatial extent, and quality of the information already collected. The operational workflow would be the same as is used for single-stage surveys (Figure 3). An initial focus map is computed to generate an initial survey. A second focus map is then computed, which also takes into account the expected locations covered by the first survey. Based on the latter, a new sampling set and according routing can be generated, and thus a second survey. This allows a survey to be split into smaller stages which can be iteratively designed. The use of different focus maps allows the original prioritization to be retained while at the same time avoiding as much as possible superposition and redundancy between the surveys.  
In the case of such an iterative multi-stage sampling that involves several iterations of data capturing, the routing engine allows for a further optimization of the _in situ_ data capturing in that penalties on the observation redundancy can also be considered during the routing in addition to the simple costs (e.g., travel distance). Assigning higher costs to travel through road segments that have already been visited in previous surveys would give preference to roads that have not yet been visited while still keeping the overall cost minimal.  
Since all surveys take place in the same road network, a degree of overlap across iterative surveys cannot be avoided, especially since all surveys will tend to privilege areas with higher sampling probability (_i.e._, where the focus maps have higher values). Nevertheless, the extent of overlap will decrease for bigger target areas (or in areas with more complex road network), where more alternative paths are available for routing.