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A multi-site study on walkability, data sharing and privacy perception using mobile sensing data gathered from the mk-sense platform


Walking is a fundamental part of a physically active lifestyle, it is one of everyday activities that positively impacts health and wellbeing. In this paper we describe the challenges and experiences of conducting a sensing campaign in the wild. We make use of mk-sense; a software platform to facilitate the deployment of collaborative sensing campaigns. We elaborate on two cross-cultural studies conducted in four different countries (Mexico, Turkey, Spain, and Switzerland) with a total of 77 participants. We present a detailed description of the data collected from one of the studies aimed at measuring walkability around three different university campuses. The analysis of the data shows that walkability can be assessed using information from the sensors in the smartphones and results from surveys answered by participants. In addition, we analyze issues about data sharing and privacy awareness.

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  1. Funf: Open sensing framework.


  3. Walkscore:




  • Abdelzaher T, Anokwa Y, Boda P et al (2007) Mobiscopes for human spaces. IEEE Pervasive Comput 6:20–29.

    Article  Google Scholar 

  • Bengtsson L, Lu X, Thorson A et al (2011) Improved response to disasters and outbreaks by tracking population movements with mobile phone network data: a post-earthquake geospatial study in haiti. PLoS Med.

    Google Scholar 

  • Berke EM, Choudhury T, Ali S, Rabbi M (2011) Objective measurement of sociability and activity: mobile sensing in the community. Ann Fam Med 9:344–350.

    Article  Google Scholar 

  • Berkeley J (2015) Planet of the phones. Economics.

    Google Scholar 

  • Brownstein JS, Freifeld CC, Madoff LC (2009) Digital disease detection-harnessing the Web for public health surveillance. N Engl J Med 360:2153–2157.

    Article  Google Scholar 

  • Campbell AT, Eisenman SB, Lane ND et al (2008) The rise of people-centric sensing. IEEE Internet Comput 12:12–21.

    Article  Google Scholar 

  • Chen PY, Cheng SM, Ting PS et al (2015) When crowdsourcing meets mobile sensing: a social network perspective. IEEE Commun Mag 53:157–163.

    Article  Google Scholar 

  • Christian HE, Bull FC, Middleton NJ et al (2011) How important is the land use mix measure in understanding walking behaviour? Results from the RESIDE study. Int J Behav Nutr Phys Act 8:55.

    Article  Google Scholar 

  • Cornelius C, Kapadia A, Kotz D et al (2008) Anonysense: privacy-aware people-centric sensing. In: Mobisys08-Proceeding of the 6th international conference on mobile systems, applications, and services.

  • Corno F, De Russis L, Montanaro T (2017) Estimate user meaningful places through low-energy mobile sensing. In: 2016 IEEE international conference on systems, man, and cybernetics, SMC 2016—conference proceedings, pp 3039–3044

  • Das T, Mohan P, Padmanabhan VN et al (2010) PRISM: platform for remote sensing using smartphones. In: Proceedings of 8th international conference on Mobile systems, applications, and services-MobiSys’, vol 10, pp 63–76.

  • Eagle N, Pentland A (2006) Reality mining: sensing complex social systems. Pers Ubiquitous Comput 10:255–268.

    Article  Google Scholar 

  • Froehlich J, Chen MY, Consolvo S et al (2007) MyExperience: a system for in situ tracing and capturing of user feedback on mobile phones. In: Proceedings of 5th international conference on Mobile systems, applications, and services San Juan, pp 57–70.

  • Harari GM, Lane ND, Wang R et al (2016) Using smartphones to collect behavioral data in psychological science: opportunities, practical considerations, and challenges. Perspect Psychol Sci 11:838–854.

    Article  Google Scholar 

  • Hernández N, Yavuz G, Eşrefoğlu R et al (2015) Thought and life logging: a pilot study. Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics). pp 26–36

  • Kanhere SS (2013) Participatory sensing: crowdsourcing data from mobile smartphones in urban spaces. Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics), pp 19–26

  • Kim S, Mankoff J, Paulos E (2013) Sensr: evaluating a flexible framework for authoring mobile data-collection tools for citizen science. In: Proceedings of the 2013 conference on, pp 1453–1462

  • Lu H, Lane ND, Eisenman SB, Campbell AT (2010) Bubble-sensing: binding sensing tasks to the physical world. Pervasive Mob Comput 6:58–71.

    Article  Google Scholar 

  • Macias E, Suarez A, Lloret J (2013) Mobile sensing systems. Sensors 13:17292–17321.

    Article  Google Scholar 

  • Moraes ALD, Fonseca F, Esteves MGP et al (2014) A meta-model for crowdsourcing platforms in data collection and participatory sensing. In: Proceedings of the 2014 IEEE 18th international conference on computer supported cooperative work in design, CSCWD 2014, pp 429–434

  • Nandugudi A, Maiti A, Ki T et al (2013) {PhoneLab}: a large programmable smartphone testbed. In: Proceedings of first international work on sensors big data mining, pp 1–6.

  • Pasupathi M, Carstensen LL (2003) Age and emotional experience during mutual reminiscing. Psychol Aging 18:430–442.

    Article  Google Scholar 

  • Perdue WC, Stone LA, Gostin LO (2003) The built environment and its relationship to the public’s health: the legal framework. Am J Public Health 93:1390–1394

    Article  Google Scholar 

  • Quercia D, Aiello LM, Schifanella R, Davies A (2015) The digital life of walkable streets.

  • Satyanarayanan M (2001) Pervasive computing: vision and challenges. IEEE Pers Commun 8:10–17

    Article  Google Scholar 

  • Silva TH, Vaz de Melo POS, Almeida JM et al (2014) Revealing the city that we cannot see. ACM Trans Internet Technol 14:1–23.

    Article  Google Scholar 

  • Suddendorf T, Corballis MC (2007) The evolution of foresight: what is mental time travel, and is it unique to humans?. Behav Brain Sci.

    Google Scholar 

  • Tentori M, Favela J, González V (2006) Quality of privacy (QoP) for the design of ubiquitous healthcare applications. J Univ Comput Sci 12:252–269

    Google Scholar 

  • Villanueva K, Pereira G, Knuiman M et al (2013) The impact of the built environment on health across the life course: design of a cross-sectional data linkage study. BMJ Open 3:e002482.

    Article  Google Scholar 

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We thank the participants of the two sensing campaigns described in the paper for contributing their time and effort in making them successful. This work was partially funded by (1) the Co-Funded Brain Circulation Scheme Project “Pervasive Healthcare: Towards Computational Networked Life Science” (TÜBITAK Co-Circ 2236, Grant agreement number: 112C005) supported by TÜBİTAK and EC FP7 Marie Curie Action COFUND and (2) the EC FP7 Marie Curie Action “UBIHEALTH-Exchange of Excellence in Ubiquitous Computing Technologies to Address Healthcare Challenges” (Project number 316337).

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Correspondence to N. Hernández.

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Hernández, N., Arnrich, B., Favela, J. et al. A multi-site study on walkability, data sharing and privacy perception using mobile sensing data gathered from the mk-sense platform. J Ambient Intell Human Comput 10, 2199–2211 (2019).

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  • Smartphone
  • Sensing campaign
  • Data sharing
  • Privacy concern
  • Completeness data