Crowdsensing for Characterizing Mobility and Its Impact on the Subjective Wellbeing in an Underdeveloped 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 subj...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-09-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/10/19/6686 |
| _version_ | 1797552669853745152 |
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| author | A. G. Ramos J. Antonio Garcia-Macias Monica Tentori |
| author_facet | A. G. Ramos J. Antonio Garcia-Macias Monica Tentori |
| author_sort | A. G. Ramos |
| collection | DOAJ |
| description | 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. <i>A posteriori</i> 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. |
| first_indexed | 2024-03-10T16:04:21Z |
| format | Article |
| id | doaj.art-14d08387cba0464f860d3aa76c3f9a3e |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T16:04:21Z |
| publishDate | 2020-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-14d08387cba0464f860d3aa76c3f9a3e2023-11-20T14:57:58ZengMDPI AGApplied Sciences2076-34172020-09-011019668610.3390/app10196686Crowdsensing for Characterizing Mobility and Its Impact on the Subjective Wellbeing in an Underdeveloped RegionA. G. Ramos0J. Antonio Garcia-Macias1Monica Tentori2CICESE Research Center, Ensenada 22860, B.C., MexicoCICESE Research Center, Ensenada 22860, B.C., MexicoCICESE Research Center, Ensenada 22860, B.C., MexicoLiving 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. <i>A posteriori</i> 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.https://www.mdpi.com/2076-3417/10/19/6686crowdsensinghuman mobilitysocial computing |
| spellingShingle | A. G. Ramos J. Antonio Garcia-Macias Monica Tentori Crowdsensing for Characterizing Mobility and Its Impact on the Subjective Wellbeing in an Underdeveloped Region Applied Sciences crowdsensing human mobility social computing |
| title | Crowdsensing for Characterizing Mobility and Its Impact on the Subjective Wellbeing in an Underdeveloped Region |
| title_full | Crowdsensing for Characterizing Mobility and Its Impact on the Subjective Wellbeing in an Underdeveloped Region |
| title_fullStr | Crowdsensing for Characterizing Mobility and Its Impact on the Subjective Wellbeing in an Underdeveloped Region |
| title_full_unstemmed | Crowdsensing for Characterizing Mobility and Its Impact on the Subjective Wellbeing in an Underdeveloped Region |
| title_short | Crowdsensing for Characterizing Mobility and Its Impact on the Subjective Wellbeing in an Underdeveloped Region |
| title_sort | crowdsensing for characterizing mobility and its impact on the subjective wellbeing in an underdeveloped region |
| topic | crowdsensing human mobility social computing |
| url | https://www.mdpi.com/2076-3417/10/19/6686 |
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