Abstract
Big data provides unprecedented opportunities for understanding and planning urban mobility. Big data makes huge amounts of information available with a high level of detail, potentially precisely depicting overall macrotrends while also detecting micropractices that elude traditional investigative approaches. Despite this unprecedented insight on mobility issues, big data is not the ultimate solution for dealing with urban mobility, especially when considering social dimensions. The chapter intends to discuss at what conditions big data may contribute to mobility planning and policy approaches that may effectively enable individuals and their opportunities. After introducing the concept of big data and its increasing relevance, the significance for urban policy of such a knowledge source is briefly presented. The discussion then moves to four critical issues that question the contribution of big data to enabling mobilities. These include the representativeness of the information provided by big data; the interpretative issues associated with understanding mobilities through manifold digital technologies; the differentiated individual ability to produce information through portable devices and emerging actors who operate through big data and influence urban mobility dynamics in unforeseen ways. These dimensions highlight three forms of partiality that affect the completeness, the neutrality and the usability of big data in relation to mobility issues, leading to a call for a critical usage of big data. This approach can contribute to enabling mobilities thanks to the enriched information it provides, without delegating to it the responsibility of defining urban problems and solutions.
Data are both social and material, and they do not merely represent the world but actively produce it.
(Kitchin 2014b, p. 226)
This chapter was authored by Giovanni Vecchio.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
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. Transp Res Part C Emerg Technol 18(1):45–54. https://doi.org/10.1016/j.trc.2009.04.011
Amer M, Daim TU, Jetter A (2013) A review of scenario planning. Futures 46:23–40. https://doi.org/10.1016/j.futures.2012.10.003
Anderson C (2008) The end of theory: the data deluge makes the scientific method obsolete. Wired
Banister D, Hickman R (2013) Transport futures: thinking the unthinkable. Transp Policy 29:283–293. https://doi.org/10.1016/J.tranpol.2012.07.005
Bayir MA, Demirbas M, Eagle N (2010) Mobility profiler: a framework for discovering mobility profiles of cell phone users. Pervasive Mob Comput 6(4):435–454. https://doi.org/10.1016/j.pmcj.2010.01.003
Boyd D, Crawford K (2012) Critical questions for big data. Inf Commun Soc 15(5):662–679. https://doi.org/10.1080/1369118x.2012.678878
Brabham DC (2009) Crowdsourcing the public participation process for planning projects. Plann Theory 8(3):242–262. https://doi.org/10.1177/1473095209104824
Chen Z, Schintler LA (2015) Sensitivity of location-sharing services data: evidence from American travel pattern. Transportation 42(4):669–682. https://doi.org/10.1007/s11116-015-9596-z
Citymapper (2017) Say hello to the Citymapper smartbus. Retrieved from https://citymapper.com/smartbus
Cohen N (2018) Algorithms can be a tool for justice—if used the right way. Wired. Retrieved 14 Dec 2018, from https://www.wired.com/story/algorithms-netflix-tool-for-justice/
Crusoe D (2016) Data literacy defined pro populo: to read this article, please provide a little information. J Community Inform 12(3)
Data-Pop Alliance (2015) Beyond data literacy: reinventing community engagement and empowerment in the age of data. Data-Pop Alliance white paper series
Docherty I, Marsden G, Anable J (2017) The governance of smart mobility. Transp Res Part A Policy Pract. https://doi.org/10.1016/j.tra.2017.09.012
Elliott A, Urry J (2010) Mobile lives. Routledge, London
Floridi L (2012) Big data and their epistemological challenge. Philos Technol 25(4):435–437. https://doi.org/10.1007/s13347-012-0093-4
Gandomi A, Haider M (2015) Beyond the hype: big data concepts, methods, and analytics. Int J Inf Manage 35(2):137–144. https://doi.org/10.1016/j.ijinfomgt.2014.10.007
Gartner (2013) Gartner IT glossary—big data. Retrieved 14 Dec 2018, from https://www.gartner.com/it-glossary/big-data/
Ge Y, Knittel CR, MacKenzie D, Zoepf S (2016) Racial and gender discrimination in transportation network companies. National Bureau of Economic Research working paper, 22776. https://doi.org/10.3386/w22776
Gillespie T, Boczkowski PJ, Foot KA (2014) Media technologies: essays on communication, materiality, and society. MIT Press, Cambridge, London
Goodchild MF (2007) Citizens as sensors: the world of volunteered geography. GeoJournal 69(4):211–221. https://doi.org/10.1007/s10708-007-9111-y
Graham M (2011) Time machines and virtual portals: the spatialities of the digital divide. Prog Dev Stud 11(3):211–227. https://doi.org/10.1177/146499341001100303
Greenfield A (2017) Radical technologies: the design of everyday life. Verso, Brooklyn
Haklay M (2013) Citizen science and volunteered geographic information: overview and typology of participation. In: Sui D, Elwood S, Goodchild M (eds) Crowdsourcing geographic knowledge: volunteered geographic information (VGI) in theory and practice. Springer Netherlands, Dordrecht, pp 105–122. https://doi.org/10.1007/978-94-007-4587-2_7
Kitchin R (2014a) Big data, new epistemologies and paradigm shifts. Big Data Soc 1(1). https://doi.org/10.1177/2053951714528481
Kitchin R (2014b) The data revolution. Big data, open data, data infrastructures and their consequences. Sage, London
Kitchin R (2014c) The real-time city? Big data and smart urbanism. GeoJournal 79(1):1–14. https://doi.org/10.1007/s10708-013-9516-8
Kitchin R, Dodge M (2011) Code/space. Software and everyday life. MIT Press, Cambridge, London
Kloeckl K, Senn O, Ratti C (2012) Enabling the real-time city: LIVE Singapore! J Urban Technol 19(10):89–112. https://doi.org/10.1080/10630732.2012.698068
Kwan M-P (2016) Algorithmic geographies: big data, algorithmic uncertainty, and the production of geographic knowledge. Ann Am Assoc Geogr 106(2):274–282. https://doi.org/10.1080/00045608.2015.1117937
Liu Y, Sui Z, Kang C, Gao Y (2014) Uncovering patterns of inter-urban trip and spatial interaction from social media check-in data. PLoS ONE 9(1):e86026. https://doi.org/10.1371/journal.pone.0086026
Lovelace R, Birkin M, Cross P, Clarke M (2016) From big noise to big data: toward the verification of large data sets for understanding regional retail flows. Geogr Anal 48(1):59–81. https://doi.org/10.1111/gean.12081
Lu H, Sun Z, Qu W (2015) Big data-driven based real-time traffic flow state identification and prediction. Discrete Dyn Nat Soc 2015(284906):1–11. https://doi.org/10.1155/2015/284906
Lv Y, Duan Y, Kang W, Li Z, Wang F-Y (2014) Traffic flow prediction with big data: a deep learning approach. IEEE Trans Intell Transp Syst 1–9. https://doi.org/10.1109/tits.2014.2345663
Marsden G, Reardon L (2017) Questions of governance: rethinking the study of transportation policy. Transp Res Part A Policy Pract 101:238–251. https://doi.org/10.1016/j.tra.2017.05.008
Martens K (2006) Basing transport planning on principles of social justice. Berkeley Plann J 19:1–17
Marz N, Warren J (2015) Big data: principles and best practices of scalable real-time data systems. Manning, Greenwich
Mattern S (2017) Mapping’s intelligent agents. Places J. https://doi.org/10.22269/170926
Mayer-Schönberger V, Cukier K (2013) Big data: a revolution that will transform how we live, work, and think. Houghton Mifflin Harcourt, Boston, New York
Milne D, Watling D (2019) Big data and understanding change in the context of planning transport systems. J Transp Geogr 76:235–244. https://doi.org/10.1016/j.jtrangeo.2017.11.004
Morozov E (2013) To save everything, click here: the folly of technological solutionism. Public Affairs, New York
Neis P, Zielstra D (2014) Recent developments and future trends in volunteered geographic information research: the case of OpenStreetMap. Future Internet 6(1):76–106. https://doi.org/10.3390/fi6010076
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. https://doi.org/10.1371/journal.pone.0037027
Pang B, Lee L (2008) Opinion mining and sentiment analysis. Found Trends Inf Retrieval 2(1–2):1–135. https://doi.org/10.1561/1500000011
Pelletier M-P, Trépanier M, Morency C (2011) Smart card data use in public transit: a literature review. Transp Res Part C Emerg Technol 19(4):557–568. https://doi.org/10.1016/j.trc.2010.12.003
Persily N (2017) The 2016 U.S. election: can democracy survive the internet? J Democr 28(2):63–76. https://doi.org/10.1353/jod.2017.0025
Pew Research Centre (2016) Shared, collaborative and on demand: the new digital economy. Pew Research Centre, Washington
Plyushteva A, Schwanen T (2018) Care-related journeys over the life course: thinking mobility biographies with gender, care and the household. Geoforum 97:131–141. https://doi.org/10.1016/j.geoforum.2018.10.025
Poorthuis A, Zook M (2017) Making big data small: strategies to expand urban and geographical research using social media. J Urban Technol 24(4):115–135. https://doi.org/10.1080/10630732.2017.1335153
Pucci P, Manfredini F, Tagliolato P (2015) Mapping urban practices through mobile phone data. Springer, Berlin
Pucci P, Vecchio G, Concilio G (forthcoming) Big data and urban mobility: a policy making perspective. Transp Res Procedia
Rabari C, 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 J Reg Econ Soc 8(1):27–42. https://doi.org/10.1093/cjres/rsu021
Ratti C, Frenchman D, Pulselli RM, Williams S (2006) Mobile landscapes: using location data from cell phones for urban analysis. Environ Plann B Plann Des 33(5):727–748. https://doi.org/10.1068/b32047
Reades J, Calabrese F, Sevtsuk A, Ratti C (2007) Cellular census: explorations in urban data collection. IEEE Pervasive Comput 6(3):30–38. https://doi.org/10.1109/mprv.2007.53
Sager T (2006) Freedom as mobility: implications of the distinction between actual and potential travelling. Mobilities 1(3):465–488. https://doi.org/10.1080/17450100600902420
Schwanen T (2015) Beyond instrument: smartphone app and sustainable mobility. Eur J Transp Infrastruct Res 15(4):675–690
Sevtsuk A, Ratti C (2010) Does urban mobility have a daily routine? Learning from the aggregate data of mobile networks. J Urban Technol 17(1):41–60. https://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
Taylor L (2016) No place to hide? The ethics and analytics of tracking mobility using mobile phone data. Environ Plann D Soc Space 34(2):319–336. https://doi.org/10.1177/0263775815608851
Townsend AM (2013) Smart cities: big data, civic hackers, and the quest for a new utopia. W.W. Norton and Company, New York, London
Uber (2014) Uber economic study: Uber serves underserved neighborhoods in Chicago as well as the Loop. Does taxi? Retrieved 17 Dec 2018, from https://www.uber.com/blog/chicago/uber-economic-study-uber-serves-underserved-neighborhoods-in-chicago-as-well-as-the-loop-does-taxi/
Vecchio G, Tricarico L (2019) “May the force move you”: roles and actors of information sharing devices in urban mobility. Cities 88:261–268. https://doi.org/10.1016/j.cities.2018.11.007
Villanueva FJ, Aguirre C, Rubio A, Villa D, Santofimia MJ, López JC (2016) Data stream visualization framework for smart cities. Soft Comput 20(5):1671–1681. https://doi.org/10.1007/s00500-015-1829-8
Zikopoulos P, Eaton C (2011) Understanding big data: analytics for enterprise class hadoop and streaming data. McGraw-Hill, New York
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2019 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Pucci, P., Vecchio, G. (2019). Big Data: Hidden Challenges for a Fair Mobility Planning. In: Enabling Mobilities. SpringerBriefs in Applied Sciences and Technology(). Springer, Cham. https://doi.org/10.1007/978-3-030-19581-6_4
Download citation
DOI: https://doi.org/10.1007/978-3-030-19581-6_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-19580-9
Online ISBN: 978-3-030-19581-6
eBook Packages: EngineeringEngineering (R0)