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There are currently many applications to assist in navigating urban spaces. However, in underserved regions where even the most essential urban infrastructure is lacking, these applications are not appropriate either because the zone has not been properly mapped, or because important information such as informal routes or semantic knowledge about the region is not considered. Based on a contextual study, we implemented a system to run crowdsensing campaigns with the neighbors and document their mobility decisions; the system also included functionalities to map and analyze the collected geolocated information. An important finding was that the actual routes taken during their daily mobility differ from the routes suggested by typical navigation applications. This also helped to inform the design of a system to provide navigation aids considering the specific context of the region.

Living in an underdeveloped region implies a higher cost of living: access to services, such as school, work, medical care, and groceries, becomes more costly than those who live in regions with better infrastructure. We are interested in studying how mobility affects the cost of living and the subjective wellbeing of residents in underdeveloped regions. We conducted a four-weeks sensing campaign with 14 users in Camino Verde (an underserved region in Tijuana, Mexico). All of the participants used a mobile system that we developed to track their daily mobility. The participants were indicated not to change their daily routine for the study as they carried the tracking device. We analyzed 537 individual routes from different city points and calculated their mobility divergences, while comparing the actual route chosen against the route that was suggested by Google Maps and using this not as the optimal route, but as the baseline. Our results allowed for us to quantify and observe how Camino Verde residents are affected in their mobility in four crucial aspects: geography, time, economy, and safety. A posteriori qualitative analysis, using semi-structured interviews, complemented the quantitative observations and provided insights into the mobility decisions that those people living in underserved regions have to take.

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