## 2 Mobility data
### Testing Scenarios
#### 5.3.2 Significance of the Performance Tests  
Queries discussed in the previous section are designed to be generic enough to demonstrate the versatility of our EMDI architecture. Those queries are non-trivial and computationally expensive enough to render the testing scenarios realistic, especially with heterogeneous datasets that are relatively big.  
By performing those tests, we aimed at showcasing a range of capabilities that are novel in the field of joint mobility, pollution, and meteorological data analytics. With the EMDI system, we introduce a tool to integrate pollution, meteorological and mobility data, to generate a unified view for combined analytics. The aim of the two tests is precise and well-defined to test the skills of our EMDI system in real settings.  
Specifically, for the geospatial geo-statistical 'average' test, we calculate average PM\({}_{10}\) values by districts where the pollutant concentration levels are higher than a specific threshold. This kind of queries is very relevant for urban planning and decision making, and we have designed EMDI in such a way that those single geo-statistical queries can be executed by varying the level of granularity so that we can calculate at coarser level of provinces and regions, thus varying the granularity level of aggregation between granular and coarser. Consequently, this can reveal the way the concentrations of PM\({}_{10}\) pollutants influence human mobility on a granular or coarser scale, thus highlighting micro and macro patterns of human urban mobility, respectively.  
For Top-N queries, we tested the possibility to rank municipalities which have the highest levels of human mobility and high levels of PM\({}_{10}\) simultaneously. This kind of queries, which requires ranking cities (or districts within a city at a granular level of details) and discarding municipalities with pollutants concentrations that are below a certain threshold, wouldn't be possible without integrating pollution and mobility data within the same data view. This is so because in this kind of queries, we are counting mobility data in each district with a predicate that is imposed on pollution data, thus contextualizing mobility data with pollution data tags.  
Such an application scenario is interesting for city planners and decision makers in metropolitan cities because they may need to check relationships between PM\({}_{10}\) concentrations and mobility of city dwellers and vehicles circulating within their cities. This is beneficial, for example, to enforce mobility policies that help them to adhere to national or international regulations regarding allowed pollution levels in major metropolitan cities.  
In the same vein, other types of more complicated queries are thus constructable from those baselines queries. For instance, trajectory queries such as \"finding out whether people walk less in certain areas of a city or a street because of high PM\({}_{10}\) concentration\", which could be unintentional without prior knowledge, but simply feeling unwell by commuting regularly in those areas with high pollution levels. Also, queries such as \"is there a correlation between people using their cars rather than walking in a certain area and high concentration of polluting substances\". Those kinds of queries can be answered by our system by simply gathering granular data instead of designing a new domain-specific system.  
By technologically abstracting optimization layers such as spatial join inner logistics handling to decision makers and front-end developers and enabling a seamless querying of integrated pollution and mobility data through a simplified interface, we provide an added novel value that is offered by our system.