Mobile phone data: challenges for spatial research

Keywords: Mobile phone data, Digital data, Spatial analysis

Abstract

The paper investigates if and how mobile phone data can help to describe the complexity of urban phenomena, highlighting the challenges faced by researchers integrating mobile phone data into their activities. Two perspectives are offered: the first is a reflection on the features of these data collected anonymously by mobile phone users, as a condition for understanding its potentialities and limits for the analysis, visualization, and interpretation of people’s presence and movements in urban spaces (research on mobile phone data). The second perspective focuses on the uses of mobile phone data in spatial research, starting from the outcomes described in this special issue and highlighting the potentialities and limits of these data in facing several research questions in urban studies (research with mobile phone data).

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Author Biography

Paola Pucci, Politecnico di Milano, Dipartimento Architettura e Studi Urbani
professore associato di urbanistica presso il Politecnico di Milano. Coordinatore del dottorato in Urban Planning, Design and Policy del Dipartimento di Architettura e Studi Urbani.

References

Ahas, R., Mark, U. (2005). Location based services: new challenges for planning and public administrations? Futures 37:547–561

Ahas, R., Aasa, A., Silm, S., Tiru, M. (2010). Daily rhythms of suburban commuters’ movements in the Tallinn metropolitan area: Case study with mobile positioning data. Transportation Research Part C: Emerging Technologies 18 (1), 45–54. http://dx.doi.org/10.1016/J.TRC.2009.04.011

Bachir, D., Khodabandelou, G., Gauthier, V., El Yacoubi, M., & Puchinger, J. (2019). Inferring dynamic origin-destination flows by transport mode using mobile phone data. Transportation Research Part C: Emerging Technologies, 101, 254-275.

Batini C. (2018) Datacy, perché una scienza per studiare l’impatto dei dati sulla società. Agenda Digitale. Available at https://agendadigitale.eu/cittadinanza-digitale/datacy-percheuna-scienza-per-studiare-limpatto-dei-dati-sulla-societa/a.

Batty, M. (2013) Big data, smart cities and city planning. Dialogues in Human Geography 3(3) 274–279 http://dx.doi.org/10.1177/2043820613513390

Becker, R., Caceres, R., Hanson, K., Isaacman, S., Loh, J.M., Martonosi, M., Rowland, J., Urbanek, S., Varshavsky, A. and Volinsky, C. (2013). Human mobility characterization from cellular network data. Communications of the ACM 56 (1), 74-82. https://doi.org/10.1145/2398356.2398375

Bibri, S.E. (2018). Smart sustainable cities of the future: the untapped potential of big data analytics and context-aware computing for advancing sustainability. Cham: Springer

Bayir, M. A., Demirbas, M., Eagle, N. (2010). Mobility profiler: A framework for discovering mobility profiles of cell phone users. Pervasive and Mobile Computing, 6(4), 435–454. http://dx.doi.org/10.1016/J.PMCJ.2010.01.003

Candela, L., Castelli, D., & Thanos, C. (2010). Making Digital Library Content Interoperable. Digital Libraries - 6th Italian Research Conference, IRCDL 2010, Padua, 13-25

Concilio G., Pucci P. (2021). The Data Shake: An Opportunity for Experiment-Driven Policy Making. In: Concilio G., Pucci P., Raes L., Mareels G. (eds) The Data Shake. SpringerBriefs in Applied Sciences and Technology. Springer, Cham. https://doi.org/10.1007/978-3-030-63693-7_1

Einav, L., Levin, J. D. (2013). The Data Revolution and Economic Analysis (NBER Working Paper Series No. 19035). Cambridge.

European Commission (2004). European Interoperability Framework for Pan-European eGovernment Services. Luxembourg: European Commission.

Fontaine, M., Smith, B. (2005). Part 1: freeway operations: probe-based traffic monitoring systems with wireless location technology: an investigation of the relationship between system design and effectiveness. Transp Res Rec: J Transp Res Board, 1925 (1), 2–11

Geraci, A. (1991) IEEE Standard Computer Dictionary: Compilation of IEEE Standard Computer Glossaries. IEEE Press.

Gillespie, T., Boczkowski, P. J., Foot, K. A. (2014). Media technologies: es-says on communication, materiality, and society. Cambridge - London: MIT Press.

Graham, M., & Shelton, T. (2013). Geography and the future of big data, big data and the future of geography. Dialogues in Human geography 3 (3), 255-261.

Greenfield, A. (2017). Radical technologies: the design of everyday life. Brooklyn: Verso.

Halevy, A., Rajaraman, A., & Ordille, J. (2006) Data Integration: The Teenage Years. Proceedings of the 32nd International Conference on Very Large Data Bases (VLDB '06), 9-16

Jarv, O., Ahas, R., Witlox, F. (2014). Understanding monthly variability in human activity spaces: a twelve-month study using mobile phone call detail records. Transportation Research Part C 38, 122-135, http://dx.doi.org/10.1016/j.trc.2013.11.003

Kitchin, R. (2014). The real-time city? Big data and smart urbanism. GeoJournal 79, 1-14, http://dx.doi.org/10.1007/s10708-013-9516-8

Kitchin, R. (2021). The Data Revolution: A critical analysis of big data, open data and data infrastructures. Sage Publications Ltd, London.

Kitchin, R., & Lauriault, T. P. (2018). Toward critical data studies. In: Eckert, J., Shears, A. and Thatcher, J. (eds) Geoweb and Big Data. University of Nebraska Press.

Kwan, M. P. (2016). Algorithmic geographies: Big data, algorithmic uncertainty, and the production of geographic knowledge. Annals of the American Association of Geographers 106 (2), 274-282.

Kwan, M.P., Dijst, M., Schwanen, T. (2007). The interaction between ICT and human activity-travel behaviour. Transportation Research Part A, 41 (2), 121–124. http://linkinghub.elsevier.com/retrieve/pii/S0965856406000255.

Mattern, S. (2017). Mapping’s Intelligent Agents. Places Journal. http://dx.doi.org/10.22269/170926

Noulas, A., Scellato, S., Lambiotte, R., Pontil, M., Mascolo, C. (2012). A Tale of Many Cities: Universal Patterns in Human Urban Mobility. PLoS ONE, 7(5), e37027. http://dx.doi.org/10.1371/journal.pone.0037027

Poorthuis, A., & Zook, M. (2017). Making big data small: strategies to expand urban and geographical research using social media. Journal of Urban Technology 24 (4), 115-135.

Pagano, P., Candela, L., Castelli D., (2013). Data interoperability. Data Science Journal, 12, 23 July.

Pucci, P., Manfredini, F., Tagliolato, P. (2015). Mapping urban practices through mobile phone data. Berlin: Springer.

Pucci,P. (2017). Revealing the temporal profile of the city through mobile phone data, in Besecke, A., Meier, J., Patzold, R., Thomaier S., (eds.). Perspectives on Urban Economics. A General Merchandise Store, Universitätsverlag der TU Berlin, Berlin, ISBN 978-3-7938-2918-8 (print); ISBN 978-3-7983-2919-5 (online), 146-149.

Rabari, C., and Storper, M. (2015). The digital skin of cities: urban theory and research in the age of the sensored and metered city, ubiquitous computing and big data. Cambridge Journal of Regions, Economy and Society, 8(1), 27–42. http://dx.doi.org/10.1093/cjres/rsu021

Ratti, C., Frenchman, D., Pulselli, R. M., Williams, S. (2006). Mobile Land-scapes: Using Location Data from Cell Phones for Urban Analysis. Environment and Planning B: Planning and Design, 33(5), 727–748. http://dx.doi.org/10.1068/b32047

Reades, J., Calabrese, F., Sevtsuk, A., Ratti, C. (2007). Cellular Census: Explo-rations in Urban Data Collection. IEEE Pervasive Computing, 6(3), 30–38. http://dx.doi.org/10.1109/MPRV.2007.53

Schwanen, T. (2015). Beyond instrument: Smartphone app and sustainable mobility. European Journal of Transport and Infrastructure Research, 15(4), 675–690.

Schwanen, T. (2017). Geographies of transport II: Reconciling the general and the particular. Progress in Human Geography, 41(3), 355–364. https://doi.org/10.1177/0309132516628259

Semanjski, I., Bellens, R., Gautama, S., & Witlox, F. (2016). Integrating big data into a sustainable mobility policy 2.0 planning support system. Sustainability 8 (11), 1142.

Srinivasan, K. K., & Raghavender, P. N. (2006). Impact of mobile phones on travel: Empirical analysis of activity chaining, ridesharing, and virtual shopping. Transportation Research Record, 1977(1), 258-267.

Sevtsuk, A., Ratti, C. (2010). Does Urban Mobility Have a Daily Routine? Learning from the Aggregate Data of Mobile Networks. Journal of Urban Technology, 17(1), 41–60. http://dx.doi.org/10.1080/10630731003597322

Soto, V., Frías-Martínez, E. (2011). Automated land use identification using cell-phone records. In Proceedings of the 3rd ACM international workshop on MobiArch.

Steenbruggen, J., Borzacchiello, M. T., Nijkamp, P., & Scholten, H. (2013). Mobile phone data from GSM networks for traffic parameter and urban spatial pattern assessment: a review of applications and opportunities. GeoJournal, 78(2), 223-243.Vecchio, G. (2019). Big data: hidden challenges for a fair mobility planning in: Pucci, P., Vecchio G., Enabling mobilities. PoliMi Springer Brief: Springer, 57-73

Yue, Y., Lan, T., Yeh, A. G., & Li, Q. Q. (2014). Zooming into individuals to understand the collective: A review of trajectory-based travel behaviour studies. Travel Behaviour and Society, 1(2), 69-78.

Wang, Y., Li, J., Zhao, X., Feng, G., & Luo, X. R. (2020). Using mobile phone data for emergency management: a systematic literature review. Information Systems Frontiers, 22 (6), 1539-1559.

Published
2022-11-30
How to Cite
PucciP. (2022). Mobile phone data: challenges for spatial research. TeMA - Journal of Land Use, Mobility and Environment, 91-98. https://doi.org/10.6093/1970-9870/8918
Section
Mobile phone data for exploring spatio-temporal transformations in contemporary