## 7 Challenges and Future Research Perspectives  
A challenge that is typically encountered is the availability of data. Also, the data being it meteorological, pollution or mobility data have some issues with either spatial or temporal coverage. Other challenges include the fact that mobility and pollution data come in different formats. Mobility data comes in tabular (e.g., CSV) and georeferenced meteorological and pollution data in NetCDF or GRIB formats. Selecting the right data from a constellation of heterogeneous sources adds to the challenge, also GPS data is not 100% accurate, and there is a loss of accuracy during data collection, hence GPS coordinates can be inaccurate when the handset is moving quickly, such as in a car or airplane. In addition, meteorological and pollution data may have been collected with differing sets of spatial granularities (granular & coarser). In this case, interoperability is key, where spatial interoperability means that data matches up in the spatial dimension. On the other hand, temporal interoperability means that meteorological, pollution and mobility data match up in temporal space. Other questions may arise, including the following scenario, imagine the earth flattened and gridded, what is the size of each grid cell for which meteorological or pollution data is aggregated? What distributed data management methods can be used to store and process such georeferenced multidomain data, at scale? Also, talking about spatial resolution, \"what is the smallest unit of area measured?\", We obtain a lower resolution by aggregating the data over a greater area, which makes it more difficult to reason about the data on a smaller scale. Also, answering whether there had been an increasing median PM\({}_{10}\) caused by vehicles density on a granular level. It would then be hard to establish the pattern, considering that mobility resolution is substantially lower than meteorological and pollution data resolution. Changes of median vehicle density in other parts of the coarser resolution might obscure or falsely enhance what is happening on granular level. Moreover, considering temporal resolution, the frequency at which spatial data has been collected, and how often the measurements are taken, as we may collect mobility data daily, whereas meteorological and pollution data is taken every few hours for the same geography. It is not easy to discover the correlation between mobility, pollution, and meteorological data on an hourly basis, based on different temporal resolutions. As a future research perspective, we consider adding support for new spatial queries. We currently support single geo-statistical queries that return single values, such as 'count' and'mean'. In addition, we support aggregation spatial queries, including Top-N. Moreover, we support geo-visualization in the form of region-based aggregate geo-maps such  
Figure 17: Mobility density in correlation with PM\({}_{10}\) (specifically where PM\({}_{10}\geq\) 24.5) and mobility greater than 2000.  
as heatmaps and choropleth maps. All those queries are supported on predefined set of geometries with extents, i.e., administrative regions division of a city in the form of irregularly-sized polygons served to the system in the form of GeoJson or shapefiles, together with the other data sources. Proximity queries are potential candidates. Those require defining customized regions of search within the study area. For example, \"what are the nearest 5 Points-of-Interest (POIs) to my current location with a radius less than 10 km and a PM\({}_{10}\) average value less than 11\". Also, trajectory analytics support can be added to the EMDI system by plugging functionalities provided by other systems such as scikit-mobility [50]. It would then be possible to develop an interactive map for daily dwellers in metropolitan highly-polluted cities, such as to show the 'green paths' where those dwellers and cyclers can practice sport without being exposed to high levels of detrimental substances such as PM\({}_{10}\).