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Big Data for Better Urban Life? – An Exploratory Study of Critical Urban Issues in Two Caribbean Cities: Paramaribo (Suriname) and Port of Spain (Trinidad and Tobago)

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Abstract

Big Data is increasingly seen as important in studying the city. This pertains to both its methodological capacity and the societal implications it may have. In this article we draw on contemporary literature to discuss the potentials and challenges of Big Data for addressing pressing urban issues. In addition, we examine the potential of Big Data as a methodological tool for two Caribbean cities, Paramaribo and Port of Spain, for developing new knowledge on urban issues that matter in such cities, specifically water-related risks and security. We do so by interrogating Twitter data to uncover relevant geographical and social patterns of tweets pertaining to water-related risks (Paramaribo) and security/crime issues (Port of Spain) and confronting these with qualitative knowledge about these places. We argue that Big Data are a powerful resource for discovering interesting patterns, but one needs to be critical of the methodological caveats and consider the social-cultural specificities of ICT use.

Abstract

Les mégadonnées ou ‘Big Data’ sont considérées comme de plus en plus importantes dans l’étude d’une ville, du fait à la fois de leur capacité méthodologique, mais aussi des implications sociétales qu’elles peuvent avoir. Dans cet article, nous nous appuyons sur la littérature contemporaine pour discuter du potentiel et des défis des mégadonnées pour régler les enjeux urbains pressants. En outre, nous examinons le potentiel de Big Data comme un outil méthodologique pour deux villes des Caraïbes, Paramaribo et Port-d’Espagne, pour développer de nouvelles connaissances sur des questions urbaines primordiales dans ces villes, en particulier sur les risques liés à l’eau et la sécurité. Nous utilisons les données de Twitter pour découvrir des schémas géographiques et sociaux pertinents de Tweets relatifs aux risques liés à l’eau (Paramaribo) et aux questions de la sécurité / criminalité (Port-d’Espagne). Nous comparons ces connaissances avec la connaissance qualitative de ces lieux. Nous soutenons que Big Data est une ressource puissante pour découvrir des schémas intéressants, mais il faut être critique des mises en garde méthodologiques et tenir compte des spécificités socioculturelles de l’utilisation des technologies de l’information et de la communication.

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References

  • ABS (2013) 8ste volks- en woningtelling in Suriname (vol 1): Demografische en sociale karakteristieken en migratie. Paramaribo, Suriname: ABS.

  • Arribas-Bel, D. (2014) Accidental, open and everywhere: Emerging data sources for the understanding of cities. Applied Geography 49: 45–53.

    Article  Google Scholar 

  • Batty, M. (2013) Big data, smart cities and city planning. Dialogues in Human Geography 3 (3): 274–279.

    Article  Google Scholar 

  • Batty, M. et al. (2012) Smart cities of the future. The European Physical Journal Special Topics 214 (1): 481–518.

    Article  Google Scholar 

  • Baud, I., Verrest, H., Eleftheriadou, E., Muiderman, K. and Van der Staak, K. (under review) Building adaptive capacity to climate change and reducing urban injustices in the Southern Caribbean: Risk perceptions, policies, and implementation in fragmented governance arrangements. Geoforum, submitted.

  • Baud, I. et al. (2013) The development of Kalyan Dombivili: Fringe city in a metropolitan region. City report. Bonn: EADI – chance2sustain.

  • Baud, I., Pfeffer, K., Sridharan, N. and Nainan, N. (2009) Matching deprivation mapping to urban governance in three Indian mega-cities. Habitat International 33 (4): 365–377.

    Article  Google Scholar 

  • Boyd, D. and Crawford, K. (2012) Critical questions for big data. Information, Communication & Society 15 (5): 662–679.

    Article  Google Scholar 

  • Bruns, A. and Burgess, J. (2012) Researching news discussion on twitter. Journalism Studies 13 (5–6): 801–814.

    Article  Google Scholar 

  • Bruns, A. and Liang, Y. (2012) Tools and methods for capturing twitter data during natural disasters. First Monday 17 (4), 2 April.

  • Bruns, A. and Stieglitz, S. (2012) Quantitative approaches to comparing communication patterns on twitter. Journal of Technology in Human Services 30 (3–4): 160–185.

    Article  Google Scholar 

  • Census Office (2004) Districtsresultaten 1: Paramaribo. Paramaribo, Algemeen Bureau voor de Statistiek. Paramaribo, Algemeen Bureau voor de Statistiek.

  • Cohen, B. (2006) Urbanization in developing countries: Current trends, future projections, and key challenges for sustainability. Technology in Society 28 (1): 63–80.

    Article  Google Scholar 

  • Crampton, J.W. et al. (2013) Beyond the geotag: Situating big data and leveraging the potential of the geoweb. Cartography and Geographic Information Science 40 (2): 130–139.

    Article  Google Scholar 

  • Cranshaw, J., Schwartz, R., Hong, J.I. and Sadeh, N.M. (2012) The Livehoods Project: Utilizing Social Media to Understand the Dynamics of a City.. Palo Alto, California: ICWSM, The AAAI Press.

    Google Scholar 

  • Crutcher, M. and Zook, M. (2009) Placemarks and waterlines: Racialized cyberscapes in post-Katrina google earth. Geoforum 40 (4): 523–534.

    Article  Google Scholar 

  • CSO (2012) Trinidad and Tobago 2011 Population and Housing Census: Demographic Report. Port of Spain: CSO.

  • Delgado, R. (2014) Lifting up: How big data can help eliminate poverty. Blog posted on 23 May, https://smartdatacollective.com/rick-delgado/200566/lifting-how-big-data-can-help-eliminate-poverty, accessed 11 June 2014.

  • Eagle, N. and Greene, K. (2014) Reality Mining. Cambridge, MA; London: MIT Press.

    Google Scholar 

  • Floating.Sheep (n.d.) DOLLY, http://www.floatingsheep.org/p/dolly.html, accessed 24 October 2014.

  • Graham, M. (2011) Time machines and virtual portals: The spatialities of the digital divide. Progress in Development Studies 11 (3): 211–227.

    Article  Google Scholar 

  • Graham, M. and 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.

    Article  Google Scholar 

  • Haklay, M. (2013) Neogeography and the delusion of democratisation. Environment and Planning A 45 (1): 55–69.

    Article  Google Scholar 

  • Harris, R., Sleight, P. and Webber, R. (2005) Geodemographics, GIS and Neighbourhood Targeting. Chichester: Wiley.

    Google Scholar 

  • Heinzelman, J. and Waters, C. (2010) Crowdsourcing Crisis Information in Disaster-Affected Haiti. Washington DC: U.S. Institute of Peace.

    Google Scholar 

  • Hordijk, M. and Baud, I. (2006) The role of research and knowledge generation in collective action and urban governance: How can researchers act as catalysts? Habitat International 30 (3): 668–689 doi: 10.1016/j.habitatint.2005.04.002.

    Article  Google Scholar 

  • IBM (2012) How to transform a city: Lessons from the IBM smarter cities challenge. IBM Smarter Cities White Paper, http://asmarterplanet.com/files/2012/11/Smarter-Cities-WhitePaper_031412b.pdf, accessed 24 October 2014.

  • Internet World Stats (2013) Internet usage, facebook subscribers and population statistics for all the Americas world region countries 31 December, http://www.internetworldstats.com/stats2.htm#americas, accessed 20 June 2014.

  • Kitchin, R. (2011) The programmable city. Environment and Planning B: Planning and Design 38 (6): 945–951.

    Article  Google Scholar 

  • Kitchin, R. (2013) Big data and human geography: Opportunities, challenges and risks. Dialogues in Human Geography 3 (3): 262–267.

    Article  Google Scholar 

  • Kitchin, R. (2014) The real-time city? Big data and smart urbanism. Geojournal 79 (1): 1–14, Knowledge management tools. Information, Communication & Society, 16(2): 258–285.

    Article  Google Scholar 

  • Laney, D. (2001) 3D Data Management: Controlling Data Volume, Velocity and Variety. Stamford, CT: Meta Group.

    Google Scholar 

  • Lazer, D., Kennedy, R., King, G. and Vespignani, A. (2014) The parable of google flu: Traps in big data analysis. Science 343 (6176): 1203–1205.

    Article  Google Scholar 

  • Lewis, S.C., Zamith, R. and Hermida, A. (2013) Content analysis in an era of big data: A hybrid approach to computational and manual methods. Journal of Broadcasting & Electronic Media 57 (1): 34–52.

    Article  Google Scholar 

  • Linnekamp, F., Koedam, A. and Baud, I.S.A. (2013) Content analysis in an era of big data: A hybrid approach to computational and manual methods. Journal of Broadcasting & Electronic Media 35 (3): 447–456.

    Google Scholar 

  • Martine, G. and Marshall, A. (2007) State of World Population 2007: Unleashing the Potential of Urban Growth, UNFPA.

    Google Scholar 

  • Martinez, J.A. (2005) Monitoring intra-urban inequalities with GIS-based indicators – With a case study in Rosario, Argentina. PhD thesis. Utrecht, The Netherlands: Utrecht University/ITC.

  • McGranahan, G., Balk, D. and Anderson, B. (2007) The rising tide: Assessing the risks of climate change and human settlements in low elevation coastal zones. Environment and Urbanization 19 (1): 17–37.

    Article  Google Scholar 

  • Moe, H. (2012) Who participates and how? Twitter as an arena for public debate about the data retention directive in Norway. International Journal of Communication 6: 1222–1244.

    Google Scholar 

  • Nanni, M. et al. (2014) Transportation planning based on GSM traces: A case study on Ivory Coast. In: J. Nin and D. Villatoro (eds.), Springer International Publishing, Cham: CH, pp. 15–25.

  • Noor, A.M., Alegana, V.A., Gething, P.W., Tatem, A.J. and Snow, R.W. (2008) Using remotely sensed night-time light as a proxy for poverty in Africa. Population Health Metrics 6 (5): 1–15.

    Google Scholar 

  • Pfeffer, K., Deurloo, M.C. and Veldhuizen, E.M. (2012) Visualising postcode data for urban analysis and planning: The Amsterdam city monitor. Area 44 (3): 326–335.

    Article  Google Scholar 

  • Pfeffer, K., Baud, I., Denis, E., Scott, D. and Sydenstricker-Neto, J. (2013) Participatory spatial knowledge management tools: Empowerment and upscaling or exclusion? Information. Information, Communication & Society 16 (2): 258–285.

    Article  Google Scholar 

  • Piotrowski, J. (2014) Big obstacles ahead for big data for development. Posted online 15 April, http://www.scidev.net/global/data/feature/obstacles-big-data-development.html, accessed 12 June 2014.

  • Press, G. (2013) A very short history of big data. Posted on 13 September, http://www.forbes.com/sites/gilpress/2013/05/09/a-very-short-history-of-big-data/, accessed 24 October 2014.

  • Rai, S. (2014) India’s push for 100 smart cities has tech firms scrambling for contracts. Posted 31 July, http://www.techrepublic.com/article/indias-push-for-100-smart-cities-has-tech-firms-scrambling-for-contracts, accessed 22 October 2014.

  • Richter, C. (2014) Digital transformations in Indian cities: Between paper list and GIS map. PhD Thesis. Enschede, The Netherlands: University of Twente.

  • Sevtsuk, A. and 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.

    Article  Google Scholar 

  • Shelton, T., Zook, M. and Wiig, A. (2015) The ‘actually existing smart city’. Cambridge Journal of Regions, Economy and Society, 8(10): 13–25, available at SSRN:http://ssrn.com/abstract=2477482.

  • Smith-Clarke, C., Mashhadi, A. and Capra, L. (2014) Poverty on the cheap: Estimating poverty maps using aggregated mobile communication networks. In: Proceedings of the 32nd annual ACM conference on Human factors in computing systems. (pp. 511–520). April 2014, Toronto, Canada: ACM.

  • Steenbruggen, J., Borzacchiello, M.T., Nijkamp, P. and Scholten, H. (2013a) Data from telecommunication networks for incident management: An exploratory review on transport safety and security. Transport Policy 28 (0): 86–102.

    Article  Google Scholar 

  • Steenbruggen, J., Borzacchiello, M., Nijkamp, P. and Scholten, H. (2013b) 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.

    Article  Google Scholar 

  • Sui, D. (2014) Discussant in the panel: alt.conference on Big Data: Lightning Talk Discussion, organized by Jim Thatcher and Andrew Shears, AAG Annual Meeting 2014, Tampa, Florida.

  • Takhteyev, Y., Gruzd, A. and Wellman, B. (2012) Geography of twitter networks. Social Networks 34 (1): 73–81.

    Article  Google Scholar 

  • Tatem, A.J. et al. (2014) Integrating rapid risk mapping and mobile phone call record data for strategic malaria elimination planning. Malaria Journal 13 (52): 1–15.

    Google Scholar 

  • Taylor, L. (2014) Sustainable data science for sustainable cities: Big data and the challenge of urban development. Opinion paper. Bonn: EADI – chance2sustain.

  • Taylor, L. (under review) No place to hide? The ethics and analytics of tracking mobility using mobile phone data. Environment and Planning D, submitted, at: https://www.academia.edu/7502204/No_place_to_hide_The_ethics_and_analytics_of_tracking_mobility_using_mobile_phone_data.

  • Taylor, L. and Schröder, R. (2014) Is bigger better? The emergence of big data as a tool for international development policy. GeoJournal 1–16, http://link.springer.com/article/10.1007/s10708-014-9603-5.

  • Taylor, L., Cowls, J., Schroeder, R. and Meyer, E.T. (2014) Big data and positive change in the developing world. Policy & Internet 6 (4): 418–444.

    Article  Google Scholar 

  • Thatcher, J. and Shears, A. (2014) AAG Annual Meeting, Tampa, Florida.

  • Torfing, J., Peters, G., Pierre, J. and Sørensen, E. (2012) Interactive Governance: Advancing the Paradigm. Oxford, New York: Oxford University Press.

    Book  Google Scholar 

  • Townsend, Anthony M. (2013) Smart Cities: Big Data, Civic Hackers, and the Quest for a New Utopia. Cambridge, MA: W. W. Norton & Company, p. 400.

    Google Scholar 

  • TT Crime (2014) 1994 to present, crime statistics, http://www.ttcrime.com/stats.php, accessed 22 October 2014.

  • Tufekci, Z. (2014) Engineering the public: Big data, surveillance and computational politics. First Monday 19 (7).

  • UN Data (2014) Mobile-cellular telephone subscriptions per 100 inhabitants, http://data.un.org/Data.aspx?q=mobile&d=ITU&f=ind1Code%3aI911, accessed 23 June 2014.

  • UN Global Pulse (2012) Big data for development: Opportunities & challenges. White paper, Global pulse, New York, May, http://www.unglobalpulse.org/sites/default/files/BigDataforDevelopment-GlobalPulseMay2012.pdf.

  • UN Global Pulse (2014) Understanding the post-2015 global conversation through big data, http://www.unglobalpulse.org/projects/Post2015, accessed 20 June 2014.

  • UN-Habitat (2013) State of the World’s Cities 2012/2013: Prosperity of Cities. New York: Routledge.

  • Verrest, H. (2013) Rethinking microentrepreneurship and business development programs: Vulnerability and ambition in low-income urban Caribbean households. World Development 47: 58–70.

    Article  Google Scholar 

  • Wesolowski, A. et al. (2012) Quantifying the impact of human mobility on malaria. Science 338 (6104): 267–270.

    Article  Google Scholar 

  • Zimmermann, R., Lawes, C. and Svenson, N. (2012) Caribbean Human Development Report 2–12: Human Development and the shift to better citizen security. New York: UNDP.

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Authors and Affiliations

  1. University of Amsterdam, Amsterdam, Netherlands

    Karin Pfeffer & Hebe Verrest

  2. University of Kentucky, Lexington

    Ate Poorthuis

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  1. Karin Pfeffer
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  2. Hebe Verrest
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  3. Ate Poorthuis
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Correspondence to Karin Pfeffer.

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Pfeffer, K., Verrest, H. & Poorthuis, A. Big Data for Better Urban Life? – An Exploratory Study of Critical Urban Issues in Two Caribbean Cities: Paramaribo (Suriname) and Port of Spain (Trinidad and Tobago). Eur J Dev Res 27, 505–522 (2015). https://doi.org/10.1057/ejdr.2015.48

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