# Crowdsourcing Based Performance Analysis of Mobile User Heterogeneous Services
## 5 Use Cases
### Use Case 2: Root Cause Analysis for Service Problems
#### 5.2.2 Geographical-Based Analysis  
The second mode to analyze the measurements dataset consists of the subdivision of the global geographic region in \(N\times N\) small zones according to the GPS coordinates. The goal is to find the number and size of small zones where the problem events (Figure 8) occur and try to map what we have found for the geographical placement of the base stations and also for the mobility mode (e.g., highway or dense urban sector). This may give us an idea on the handover moments, the existence of near or far stations, possible radio problems due to fast fading, etc.  
Initially, the measurements were collected over the Ile-de-France geographical region in the format of a rectangle 96 (km) wide (longitude) and 68 (km) long (height). To find the interesting geographic zone that helps us to understand the root cause of problem events, we divide the region into 1024 zones (\(32\times 32\)) as illustrated in Figure 8.  
Using the same methodology as the previous mode, based on Python capabilities, we achieve the statistics of service problem events in the context of geographical-based.  
Using the same methodology as the previous mode, based on the capabilities of Python, we perform statistics on the number of resulting problems compared to geographic areas. The results are given in Table 9 below.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Problem According To** & **Problem Label** & **\(\sharp\)Measures** \\ \hline \multirow{4}{*}{System view} & Radio provisioning & 4 parts. \\  & Web problem & 4 parts. \\  & Download problem & 8 parts. \\  & Servers problem & 2 parts. \\ \hline \multirow{4}{*}{User view} & Radio provisioning & 3 parts. \\  & Web problem & 2 parts. \\ \cline{1-1}  & Download problem & 13 parts. \\ \cline{1-1}  & Servers problem & 2 parts. \\ \hline \hline \end{tabular}
\end{table}
Table 9: Statistics of problem events that occurred compared to geographic parts.  
Figure 8: Geographical-based analysis results using 1024 small zones (\(32\times 32\)).  
As a result, we found that, in the existing 1024 (32 \(\times\) 32) geographical parts, 771 parts are empty, where we did not obtain any measure. From the rest of the geographical parts, we see in Table 9 the number of those where problems happen. Thus, we confirm the conclusions of the first mode, which indicate that the problems occur rarely with approximately 2% of all considered geographic area (Ile-de-France region) and the download service does not work well while the video service does, in 8 geographic parts and 13 geographic parts, respectively, according to system point view and user point view, respectively.  
Concerning the other problems, we can visualize in Figure 8 some locations in the map where these events have occurred. We see clearly that the Radio provisioning problems occur in the geographical parts where red color (bad quality) dominates. Furthermore, we observe that the web problems occur where green color dominates due to the good quality of video service in this place. Finally and concerning the Servers problems, we notice that the dominant color is orange and red because of the bad quality of downloadingn and web. This can be explained by the fact that, when moving from good to bad cellular cover, the video application can mitigate (overcomes) the temporary disconnection by its applicative buffer, whereas the web service can not leak of buffer.