# Statistical Model for Mobile User Positioning Based on Social Information
## 4 Model Assessment
### Validation Scenario  
The scenario used to develop the proposed model corresponds to a city of 800,000 inhabitants and its suburbs, covering a geographical area of approximately 125 km\({}^{2}\). In this area, 400 LTE cells are located, grouped in 175 trisectorized sites, with a carrier frequency of 2325 MHz and a system bandwidth of 15 MHz. In these cells, traces are collected for 2 h, obtaining 166,561 connections. To present the indoor context of these connections, Figure 2 shows the Cumulative Distribution Function (CDF) of the probabilities of each connection to have occurred indoors.  
The land use map and the geolocated publication distribution map are defined as a grid, with 10 \(\times\) 10 m\({}^{2}\) tiles for sufficient resolution. Table 2 shows a breakdown of the land use types, their description, indoor/outdoor classification and percentage of occupancy in the scenario.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Land Use** & **Description** & **Indoor/Outdoor** & **Share** \\ \hline \multirow{2}{*}{Services} & Areas for institutional, cultural, & \multirow{2}{*}{Indoors} & \multirow{2}{*}{10\%} \\  & medical or academic use. & & \\ \hline \multirow{2}{*}{Offices} & Management, information or work & \multirow{2}{*}{Indoors} & \multirow{2}{*}{3\%} \\  & offices. & & \\ \hline \multirow{2}{*}{Mixed use} & Areas with no defined primary land & \multirow{2}{*}{Indoors} & \multirow{2}{*}{11\%} \\  & use. & & \\ \hline \multirow{2}{*}{Residential Entertainment} & Homes, hotels and lodging. & \multirow{2}{*}{Indoors} & \multirow{2}{*}{15\%} \\  & Retail and entertainment. & & \\ \hline \multirow{2}{*}{Industrial} & Industrial or manufacturing work & \multirow{2}{*}{Indoors} & \multirow{2}{*}{3\%} \\  & areas. & & \\ \hline \multirow{2}{*}{Open space} & Fields, parks and green areas. & Outdoors & 22\% \\ \multirow{2}{*}{Roads} & Roads, highways and footpaths. & Outdoors & 30\% \\ \multirow{2}{*}{Rivers/Lakes} & Water areas on the ground. & Outdoors & 2\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Land use types in the scenario.  
Figure 2: CDF of indoor connections probabilities, \(P_{indoor}(k)\), in the scenario.  
The geotagged social media posts in the area are collected in real-time for 16 months for Twitter and 12 months for Flickr, resulting in 785,515 and 33,519 posts, respectively. Figure 3 shows the CDF of the number of posts in each tile of the scenario, \(M_{p}(x,y)\), broken down by application (dashed line for Twitter, and dotted line for Flickr). It can be seen that most of the messages come from Twitter posts (coinciding in their CDF with the distribution of total posts), so it will be the main source of information, leaving Flickr posts as an auxiliary source, to reinforce certain points in the scenario.  
To observe the resolution of the spatial distribution of publications, \(M^{\prime}_{p}(x,y)\), Figure 4 presents two sections of \(M^{\prime}_{p}(x,y)\) in the scenario, one with high population density per square meter (i.e., city center, Figure 3(a)) and one that is relatively unpopulated (i.e., the suburbs of the city, Figure 3(b)).  
Figure 4: Sections of \(M^{\prime}_{p}(x,y)\) in the scenario.  
Figure 3: CDF of geotagged posts in the scenario.