## 3 From Understanding GNSS Data to Problem Definition
### Case Study of GPS-Based Localization Performance  
During our extensive survey in a city, we have collected the following data for various location scenarios: (i) raw GNSS measurements, (ii) Android GPS location indicators, (iii) ground truth location indicators (manually labelled), and (iv) Google Maps snapshots. We briefly report a few typical results in Figure 1, where we choose to put the overlay of (ii) and (iii) onto (iv) in the first row, along with the corresponding (photographic)  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Fields Name** & **Description** \\ \hline \hline \multicolumn{3}{c}{**Binary Fields**} \\ \hline \hline STATE\_SYMBOL\_SYNC & is tracking synchronized at the symbol level? \\ \hline STATE\_TOW\_DECODED & is the time of week known? \\ \hline STATE\_BIT\_SYNC & is tracking synchronized at the bit level? \\ \hline MultipathIndicator & has a multipath been detected? \\ \hline \hline \(\cdots\) & \(\cdots\) \\ \hline \hline \multicolumn{3}{c}{**Scalar Fields**} \\ \hline \hline ReceivedSvTimeNanos & the received GNSS satellite time in ns \\ \hline BiasUncertaintyNanos & the clock’s bias uncertainty in ns \\ \hline PseudorangeRateUncertainty... & rate uncertainty (1-sigma) in m/s. \\ \hline Cn0 DbHz & the carrier-to-noise density in dB-Hz. \\ \hline \(\cdots\) & \(\cdots\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Raw GNSS data structure.  
views in the second row, then (i) alone in the third row, and finally the errors distribution derived from (ii) and (iii) in the last row. As it is impossible to present all GNSS data in one figure, we show the derived location patterns for visible satellites, as inspired by constellation-based astronomical localization [32]. The following are a few key observations on these results.  
* **Satellite pattern matters**: a wide street, shown in Figure 0(a), enjoys a balanced satellite pattern and thus an almost perfect (for commercial GPS) average localization error of 5-10 m, but semi-exposed indoor areas Figure 0(b) (glass-covered building or void deck) cause a biased pattern and larger variations in GPS errors. A balanced pattern obtained for a sheltered street, shown in Figure 0(c), again leads to consistent GPS performance, though the location scenario is quite close to what would be considered indoor.
* **Correlations between GNSS data and locations exist**: besides the discussions in the previous bullet, a high-rise-surrounded square or narrow street (scenarios often termed \"building well\" or \"urban canyon\"), as shown in Figure 0(d), leads to a more skewed pattern, as well as a more severe multipath effect (another GNSS field not shown in the figure). Supposing we may properly differentiate Figure 0(a) from Figure 0(d) via contextual information inferred by GNSS data, adequate corrections by other complementary signal sources can be put into action. Note that such a differentiation is achieved without deriving GPS location indicators that could indicate locations erroneously.
* **Other GNSS features may help further**: differentiating Figure 0(a) from Figure 0(d) may sometimes require more features than provided by the satellite pattern itself. In addition, even when the location remains roughly the same, Figure 0(e),\(\ell\) exhibit very different GPS tracking performance during two distinct time periods, indicating a change of contextual information beyond pure location. Nevertheless, attributing the context change solely to satellite pattern may not be sufficient; a holistic mining of several GNSS features is necessary.  
Essentially, our preliminary studies have demonstrated the necessity and benefit of profiling _location context_ that mainly involves a time-independent location scenario and time-related GNSS features. Moreover, the importance of satellite pattern (and its independence on satellite IDs) may have suggested an effective way to re-organize the GNSS data so as to enable effective mining.