The European Journal of Development Research

, Volume 27, Issue 4, pp 505–522 | Cite as

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)

  • Karin PfefferEmail author
  • Hebe Verrest
  • Ate Poorthuis
Original Article


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.


big data Caribbean inclusive development mapping social media urban 


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.


  1. ABS (2013) 8ste volks- en woningtelling in Suriname (vol 1): Demografische en sociale karakteristieken en migratie. Paramaribo, Suriname: ABS.Google Scholar
  2. Arribas-Bel, D. (2014) Accidental, open and everywhere: Emerging data sources for the understanding of cities. Applied Geography 49: 45–53.CrossRefGoogle Scholar
  3. Batty, M. (2013) Big data, smart cities and city planning. Dialogues in Human Geography 3 (3): 274–279.CrossRefGoogle Scholar
  4. Batty, M. et al. (2012) Smart cities of the future. The European Physical Journal Special Topics 214 (1): 481–518.CrossRefGoogle Scholar
  5. 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.Google Scholar
  6. Baud, I. et al. (2013) The development of Kalyan Dombivili: Fringe city in a metropolitan region. City report. Bonn: EADI – chance2sustain.Google Scholar
  7. 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.CrossRefGoogle Scholar
  8. Boyd, D. and Crawford, K. (2012) Critical questions for big data. Information, Communication & Society 15 (5): 662–679.CrossRefGoogle Scholar
  9. Bruns, A. and Burgess, J. (2012) Researching news discussion on twitter. Journalism Studies 13 (5–6): 801–814.CrossRefGoogle Scholar
  10. Bruns, A. and Liang, Y. (2012) Tools and methods for capturing twitter data during natural disasters. First Monday 17 (4), 2 April.Google Scholar
  11. 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.CrossRefGoogle Scholar
  12. Census Office (2004) Districtsresultaten 1: Paramaribo. Paramaribo, Algemeen Bureau voor de Statistiek. Paramaribo, Algemeen Bureau voor de Statistiek.Google Scholar
  13. Cohen, B. (2006) Urbanization in developing countries: Current trends, future projections, and key challenges for sustainability. Technology in Society 28 (1): 63–80.CrossRefGoogle Scholar
  14. 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.CrossRefGoogle Scholar
  15. 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
  16. Crutcher, M. and Zook, M. (2009) Placemarks and waterlines: Racialized cyberscapes in post-Katrina google earth. Geoforum 40 (4): 523–534.CrossRefGoogle Scholar
  17. CSO (2012) Trinidad and Tobago 2011 Population and Housing Census: Demographic Report. Port of Spain: CSO.Google Scholar
  18. Delgado, R. (2014) Lifting up: How big data can help eliminate poverty. Blog posted on 23 May,, accessed 11 June 2014.
  19. Eagle, N. and Greene, K. (2014) Reality Mining. Cambridge, MA; London: MIT Press.Google Scholar
  20. Floating.Sheep (n.d.) DOLLY,, accessed 24 October 2014.
  21. Graham, M. (2011) Time machines and virtual portals: The spatialities of the digital divide. Progress in Development Studies 11 (3): 211–227.CrossRefGoogle Scholar
  22. 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.CrossRefGoogle Scholar
  23. Haklay, M. (2013) Neogeography and the delusion of democratisation. Environment and Planning A 45 (1): 55–69.CrossRefGoogle Scholar
  24. Harris, R., Sleight, P. and Webber, R. (2005) Geodemographics, GIS and Neighbourhood Targeting. Chichester: Wiley.Google Scholar
  25. Heinzelman, J. and Waters, C. (2010) Crowdsourcing Crisis Information in Disaster-Affected Haiti. Washington DC: U.S. Institute of Peace.Google Scholar
  26. 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.CrossRefGoogle Scholar
  27. IBM (2012) How to transform a city: Lessons from the IBM smarter cities challenge. IBM Smarter Cities White Paper,, accessed 24 October 2014.
  28. Internet World Stats (2013) Internet usage, facebook subscribers and population statistics for all the Americas world region countries 31 December,, accessed 20 June 2014.
  29. Kitchin, R. (2011) The programmable city. Environment and Planning B: Planning and Design 38 (6): 945–951.CrossRefGoogle Scholar
  30. Kitchin, R. (2013) Big data and human geography: Opportunities, challenges and risks. Dialogues in Human Geography 3 (3): 262–267.CrossRefGoogle Scholar
  31. 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.CrossRefGoogle Scholar
  32. Laney, D. (2001) 3D Data Management: Controlling Data Volume, Velocity and Variety. Stamford, CT: Meta Group.Google Scholar
  33. 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.CrossRefGoogle Scholar
  34. 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.CrossRefGoogle Scholar
  35. 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
  36. Martine, G. and Marshall, A. (2007) State of World Population 2007: Unleashing the Potential of Urban Growth, UNFPA.Google Scholar
  37. 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.Google Scholar
  38. 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.CrossRefGoogle Scholar
  39. 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
  40. 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.Google Scholar
  41. 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
  42. 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.CrossRefGoogle Scholar
  43. 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.CrossRefGoogle Scholar
  44. Piotrowski, J. (2014) Big obstacles ahead for big data for development. Posted online 15 April,, accessed 12 June 2014.
  45. Press, G. (2013) A very short history of big data. Posted on 13 September,, accessed 24 October 2014.
  46. Rai, S. (2014) India’s push for 100 smart cities has tech firms scrambling for contracts. Posted 31 July,, accessed 22 October 2014.
  47. Richter, C. (2014) Digital transformations in Indian cities: Between paper list and GIS map. PhD Thesis. Enschede, The Netherlands: University of Twente.Google Scholar
  48. 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.CrossRefGoogle Scholar
  49. 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:
  50. 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.Google Scholar
  51. 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.CrossRefGoogle Scholar
  52. 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.CrossRefGoogle Scholar
  53. 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.Google Scholar
  54. Takhteyev, Y., Gruzd, A. and Wellman, B. (2012) Geography of twitter networks. Social Networks 34 (1): 73–81.CrossRefGoogle Scholar
  55. 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
  56. Taylor, L. (2014) Sustainable data science for sustainable cities: Big data and the challenge of urban development. Opinion paper. Bonn: EADI – chance2sustain.Google Scholar
  57. 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:
  58. Taylor, L. and Schröder, R. (2014) Is bigger better? The emergence of big data as a tool for international development policy. GeoJournal 1–16,
  59. 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.CrossRefGoogle Scholar
  60. Thatcher, J. and Shears, A. (2014) AAG Annual Meeting, Tampa, Florida.Google Scholar
  61. Torfing, J., Peters, G., Pierre, J. and Sørensen, E. (2012) Interactive Governance: Advancing the Paradigm. Oxford, New York: Oxford University Press.CrossRefGoogle Scholar
  62. 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
  63. TT Crime (2014) 1994 to present, crime statistics,, accessed 22 October 2014.
  64. Tufekci, Z. (2014) Engineering the public: Big data, surveillance and computational politics. First Monday 19 (7).Google Scholar
  65. UN Data (2014) Mobile-cellular telephone subscriptions per 100 inhabitants,, accessed 23 June 2014.
  66. UN Global Pulse (2012) Big data for development: Opportunities & challenges. White paper, Global pulse, New York, May,
  67. UN Global Pulse (2014) Understanding the post-2015 global conversation through big data,, accessed 20 June 2014.
  68. UN-Habitat (2013) State of the World’s Cities 2012/2013: Prosperity of Cities. New York: Routledge.Google Scholar
  69. Verrest, H. (2013) Rethinking microentrepreneurship and business development programs: Vulnerability and ambition in low-income urban Caribbean households. World Development 47: 58–70.CrossRefGoogle Scholar
  70. Wesolowski, A. et al. (2012) Quantifying the impact of human mobility on malaria. Science 338 (6104): 267–270.CrossRefGoogle Scholar
  71. 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.Google Scholar

Copyright information

© European Association of Development Research and Training Institutes 2015

Authors and Affiliations

  1. 1.University of AmsterdamAmsterdamNetherlands
  2. 2.University of KentuckyLexington

Personalised recommendations