## 4 Field Study
### Data Analysis  
We logged the daily route from each user in order o analyze the quantitative data. We created our database from the GeoJSON files uploaded automatically by the users' devices. Each GeoJSON file was processed while using the iCluster algorithm [24] to create a list of points in the route, which we used to obtain the users' daily routes that were traveled by each participant. Each route is a vector of the user's location at instance \(i\) and it is defined by the following equation:  
\[RealRoute=[Point_{i},P_{i+1},\ldots,P_{n}]\]  
where each point (\(P_{i}\)) in the real route is composed of a latitude, a longitude, an altitude and a time at an instance \(i\), such as that  
\[P_{i}=[latitude_{i},longitude_{i},altitude_{i},time_{i}]\]  
From each real route, we were able to obtain the suggested Google Maps route while using the Maps API, with \(P_{i}\) and \(P_{n}\) as arguments. This, in with some pre-processing in turn, gave us the suggested route that is given by the following equation. \[SuggestedRoute=[S_{i},S_{i+1},...,S_{m}]\]  
where \(P_{0}=S_{0}\) and \(P_{n}=S_{n}\), thus both routes have the same starting and finishing points by latitude and longitude. From these routes, we extracted the distance, as seen in Table 2; although the participants did not drive their own vehicle, we added to the same table how the fuel consumption and cost features would be extracted, in case some comparison should be made. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Study ID** & **Gender** & **Age** & **Occupation** \\ \hline
33 & M & 19 & Handyman \\
35 & M & 19 & Student \\
36 & F & 33 & Homemaker \\
37 & M & 16 & Student \\
38 & M & 37 & Student \\
39 & M & 39 & Construction \\
40 & F & 18 & Stay at home mom \\
41 & F & 36 & Homemaker \\
42 & F & 30 & Collaborator at La Granja \\
43 & F & 19 & Student \\
44 & M & 34 & Construction \\
45 & M & 15 & Student \\
48 & M & 17 & Student \\
49 & F & 17 & Student \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Participants in the study. We used our routes database to calculate the divergences between the participant's route from point A to point B and the route that was suggested by Google Maps for the same two points. It should be noted that we used routes in both pedestrian and vehicular (public transport) modes, as compared with their respective suggestions by Google Maps, i.e., we did not compare vehicular versus pedestrian routes. We calculated the areas that formed inside these divergences, naming these areas geodesics (from GEOgraphic DifferenceES) and manually tagged 537 of them; Figure 4 shows two of such geodesics. For each geode, we calculated its area, centroid, and the actual route distance (Table 3). We stipulated that if two geodesics had a similar area and a similar central point (composed of a latitude and a longitude), they would probably be the same geode. Using the area and the center of the geode of these tagged divergences, we determined whether the divergence had previously been found. This way, we created a data set that contained the total amount of times that any of the participants took the same divergence. The Geodes allow for both visualizing and quantifying the differences between the routes that Camino Verde residents take, according to their knowledge and possibilities, and the routes that a non-resident without knowledge of the region would take by consulting a route suggestion app. In the case of vehicular mobility, it is essential to remember that Camino Verde residents only used public transportation; therefore, the comparison was made under the assumption that non-residents would drive their own vehicle. There were two main reasons to make the comparisons in this way: first, it would not make any sense to compare with other people while using the same bus routes, and second, none of the route suggestion apps that we tried (including Google Maps) had the possibility of suggesting public transportation routes.