## 5 Cellular Network Analysis
### Spatial Analyses  
As an immediate byproduct, the developed database allows us to instantly obtain a visual representation of network coverage across the entire globe. For instance, as shown in Figure 14, we can directly display the derived coverage area of each PLMN. Obviously, the depicted polygons are the convex hulls built from all the validated observations belonging to each specific PLMN. For this reason, geographical areas not covered by any polygons do not necessarily correspond to zones with no radio coverage at all, but are probably characterized by a number of points too small to build a polygon. Figure 14: A general overview of PLMN coverage. Figure 12: Number of PLMNs per country. At this point, thanks to the structuring of our model, other information can be immediately retrieved at each level of the administrative organization of the cellular network and with respect to different technologies. For instance, let us consider Germany, which is the country with the maximum number of observations (21,713,580) and the second according to their density (after Singapore) in our database; it is the second country by number of cells (560,803, after US) and the twelfth for their density. In the following, we show how different levels of the network architecture hierarchy provide different kinds of information regarding the chosen area. _subPLMNs._ This is the case where one wants to inspect the coverage for a given area with respect to a single PLMN and/or a specific technology. Analyses like these may, e.g., point out which PLMN has the best coverage with respect to a specific cellular technology. While comparing different technologies and working at the level of subPLMN, it is immediately clear that around 50% of PLMNs (combination of mcc and mnc) were detected to have all three considered technologies (457 out of 996). Note that 302 had two technologies, and only 237 had only one. As an example, Figure 15 (top part) shows the coverages associated with the three different technologies considered in this work for a German PLMN (mcc = 262, mnc = 1). Although their areas may look very similar, if we calculate the bounding boxes at the cell level (still grouping them by subPLMN), we obtain a very different picture, as shown in Figure 15 (bottom part). This remarks on the usefulness of structuring the information at different granularity levels, modeled within a flexible hierarchy. _Administrative areas._ Proceeding down the hierarchy of the network, we find the logical grouping of cells represented by administrative areas. These play a major role in localization at a coarser granularity, as described in [37; 38; 39]. Some works [37; 40; 41] point out that the density of the cells within an area is likely useful to distinguish between urban and rural environments. In such a context, administrative areas are a simple way to sample the territory for computing the density and check whether it is a rural or an urban area: our model makes it easy to compute the density of cells following the administrative partitioning. As an example, considering the GSM network with \(mcc=262\) and \(mnc=3\), Figure 16 shows some administrative areas close to the city of Berlin, each one being characterized by  
Figure 15: Coverage of different technologies by the same PLMN: mcc=262 (Germany) and mnc=1. at least 9000 related observations. The violet and pink polygons correspond, respectively, to administrative areas identified by _lac_ 21493 and 20473 covering two urban areas, and the green polygon on the left is the administrative area with _lac_ 25503--in essence a rural area. It can be easily seen that the pink and violet polygons are characterized by a density of cells far higher than the green one. Cells.As described in [40] cells can be split into two categories: macrocells and microcells. Macrocells are usually related to a higher transmitting power, leading to a larger coverage area, whereas microcells are smaller and low-powered.