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Crowdsensing for Characterizing Mobility and Its Impact on the Subjective Wellbeing in an Underdeveloped Region

CICESE Research Center, Ensenada 22860, B.C., Mexico
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Current address: Km 107, 3918 Zona Playitas, Ensenada 22860, C.P., Baja California, Mexico.
Appl. Sci. 2020, 10(19), 6686; https://doi.org/10.3390/app10196686
Received: 14 August 2020 / Revised: 20 September 2020 / Accepted: 21 September 2020 / Published: 24 September 2020
(This article belongs to the Section Computing and Artificial Intelligence)
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. View Full-Text
Keywords: crowdsensing; human mobility; social computing crowdsensing; human mobility; social computing
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MDPI and ACS Style

Ramos, A.G.; Garcia-Macias, J.A.; Tentori, M. Crowdsensing for Characterizing Mobility and Its Impact on the Subjective Wellbeing in an Underdeveloped Region. Appl. Sci. 2020, 10, 6686. https://doi.org/10.3390/app10196686

AMA Style

Ramos AG, Garcia-Macias JA, Tentori M. Crowdsensing for Characterizing Mobility and Its Impact on the Subjective Wellbeing in an Underdeveloped Region. Applied Sciences. 2020; 10(19):6686. https://doi.org/10.3390/app10196686

Chicago/Turabian Style

Ramos, A. G., J. A. Garcia-Macias, and Monica Tentori. 2020. "Crowdsensing for Characterizing Mobility and Its Impact on the Subjective Wellbeing in an Underdeveloped Region" Applied Sciences 10, no. 19: 6686. https://doi.org/10.3390/app10196686

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