Big (Geo)Data en Ciencias Sociales: Retos y Oportunidades

  1. Javier Gutiérrez-Puebla
  2. María Henar Salas-Olmedo
  3. Juan Carlos García Palomares
Revista:
Revista de estudios andaluces

ISSN: 0212-8594 2340-2776

Año de publicación: 2016

Número: 33

Páginas: 1-23

Tipo: Artículo

DOI: 10.12795/REA.2016.I33.01 DIALNET GOOGLE SCHOLAR lock_openAcceso abierto editor

Otras publicaciones en: Revista de estudios andaluces

Resumen

Actualmente asistimos a una verdadera revolución en la producción y el tratamiento de datos masivos (Big Data). Aunque los principales usuarios de este tipo de datos son las empresas, el mundo de la investigación ha encontrado también interesantes posibilidades en el análisis de Big Data, con abordajes nuevos a viejos problemas o incluso con el planteamiento de cuestiones que no podían ser abordadas con datos tradicionales. El presente artículo constituye una revisión de trabajos de investigación que utilizan datos masivos geolocalizados, Big (Geo)Data, y muestra ejemplos de aplicación en la investigación, ordenando los trabajos revisados según fuentes de datos: registros de llamadas de teléfonos móviles, redes sociales, comunidades de fotografías geolocalizadas, registros de transacciones con tarjetas de crédito, tarjetas inteligentes de transporte, navegadores, etc. El trabajo concluye con unas reflexiones sobre las ventajas que ofrece el Big (Geo)Data para el investigador, como la alta resolución espacial y temporal de los datos y, en muchos casos, su cobertura global y su carácter gratuito, pero también resalta algunos de los principales inconvenientes que plantea su uso, como el sesgo y la dificultad de su proceso y, en muchos casos, de acceso a los mismos.

Referencias bibliográficas

  • Bagchi, M., & White, P. R. (2005): “The potential of public transport smart card data”, Transport Policy, 12(5), 464-474. http://dx.doi.org/10.1016/j.tranpol.2005.06.008
  • Bar-Gera, H. (2007): “Evaluation of a Cellular Phone-based System for Measurements of Traffic Speeds and Travel Times: A Case Study from Israel”, Transportation Research Part C: Emerging Technologies, 15(6): 380-391. http://dx.doi.org/10.1016/j.trc.2007.06.003
  • 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
  • Cáceres, N. (2012): “Traffic Flow Estimation Models Using Cellular Phone Data”, IEEE Transactions on Intelligent Transportation Systems, pages 1–12. http://dx.doi.org/10.1109/TITS.2012.2189006
  • Cáceres, N., Wideberg, J.P y Benítez, F.G. (2007): “Deriving origin–destination data from a mobile phone network”, IET Intelligent Transport Systems, 1, 15–26. http://dx.doi.org/10.1049/iet-its:20060020
  • Chershire, J. and Uberti, O. (2014): London: The Information Capital. 100 Maps and Graphics that Will Change How You View the City. Penguin Group. London.
  • Ciuccarelli, P., Lupi, G., & Simeone, L. (2014): Visualizing the Data City. (pp. 17-22). Springer International Publishing. http://dx.doi.org/10.1007/978-3-319-02195-9_3
  • Conover, M. D., Davis, C., Ferrara, E., McKelvey, K., Menczer, F. y Flammini, A. (2013): “The geospatial characteristics of a social movement communication network”, PloS One, 8(3), e55957. http://dx.doi.org/10.1371/journal.pone.0055957
  • De Choudhury, M., Feldman, M., Amer-Yahia, S., Golbandi, N., Lempel, R. y Yu, C. (2010): Automatic construction of travel itineraries using social breadcrumbs [Electronic Version], http://research.microsoft.com/enus/um/people/munmund/pubs/ht_10_long.pdf
  • De Domenico, M., Lima, A. y Musolesi, M. (2013): “Interdependence and predictability of human mobility and social interactions”, Pervasive and Mobile Computing, 9(6), 798- 807. http://dx.doi.org/10.1016/j.pmcj.2013.07.008
  • Eagle, N., Pentland, A. y Lazer, D. (2009): Inferring friendship network structure by using mobile phone data”, PNAS, 9 (36): 15274-15278. http://dx.doi.org/10.1073/pnas.0900282106
  • Ferreras Rodríguez, E. M. (2011): “El movimiento 15 m y su evolución en Twitter”, Telos, Cuadernos de Comunicación e Innovación, nº 89.
  • Frías-Martínez, V., Soto, V., Hohwald, H., y Frías-Martínez, E. (2012): “Characterizing urban landscapes using geolocated tweets”, in Privacy, Security, Risk and Trust (PASSAT), 2012 International Conference on and 2012 International Conference on Social Computing (SocialCom) (pp. 239-248). IEEE. http://dx.doi.org/10.1109/SocialCom-PASSAT.2012.19
  • García-Palomares, J.C., Gutiérrez, J. y Mínguez, C. (2015): “Identification of tourist hot spots based on social networks: a comparative analysis of European metropolises using photo-sharing services and GIS”, Applied Geography, 63, 408–417. http://dx.doi.org/10.1016/j.apgeog.2015.08.002
  • Gavric, K. D., Culibrk, D. R., Lugonja, P. I., Mirkovic, M. R. y Crnojevic, V. S. (2011): “Detecting attractive locations and tourists' dynamics using geo-referenced images”, in 2011 10th International Conference on Telecommunication in Modern Satellite Cable and Broadcasting Services (TELSIKS) (pp. 208–211), Belgrade, Oct 5–8. http://dx.doi.org/10.1109/TELSKS.2011.6112035
  • 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. http://dx.doi.org/10.1177/2043820613513121
  • Guttentag, D. (2013): “Airbnb: disruptive innovation and the rise of an informal tourism accommodation sector”, Current Issues in Tourism, 18, 1192-1217. http://dx.doi.org/10.1080/13683500.2013.827159
  • Hadas, Y. (2013): “Assessing public transport systems connectivity based on Google Transit data”, Journal of Transport Geography, 33, 105–116. http://dx.doi.org/10.1016/j.jtrangeo.2013.09.015
  • Heerschap, N., Ortega, S., Priem, A. y Offermans, M. (2014, May): “Innovation of tourism statistics through the use of new big data sources”, in 12th Global Forum on Tourism Statistics, Prague, CZ. http://tsf2014prague.cz/assets/downloads/Paper%201.2_Nicolaes%20Heerschap_NL. pdf
  • Kachkaev, A. & Wood, J. (2013): “Investigating Spatial Patterns in User-Generated Photographic Datasets by Means of Interactive Visual Analytics”, Paper presented at the GeoViz Hamburg: Interactive Maps that Help People Think, 6 8 Mar 2013, HafenCity University, Hamburg, Germany http://openaccess.city.ac.uk/2829/1/kachkaev_geovizhamburg_2013-final.pdf
  • Kisilevich, S., Krstajic, M., Keim, D., Andrienko, N., & Andrienko, G. (2010): “Eventbased analysis of people's activities and behavior using Flickr and Panoramio geotagged photo collections”, in 2010 14th International Conference Information Visualisation (pp. 289-296). IEEE. http://dx.doi.org/10.1109/iv.2010.94
  • Kitchin, R. (2013): “Big Data and human geography Opportunities, challenges and risks2, Dialogues in Human Geography, 3(3), 262-267. http://dx.doi.org/10.1177/2043820613513388
  • Koerbitz, W., Önder, I., y Hubmann-Haidvogel, A. C. (2013): Identifying Tourist Dispersion in Austria by Digital Footprints (pp. 495-506). Springer Berlin Heidelberg.
  • Krumme, C., Llorente, A., Cebrian, M., y Moro, E. (2013): “The predictability of consumer visitation patterns”, Scientific Reports, 3. http://dx.doi.org/10.1038/srep01645
  • Kurashima, T., Iwata, T., Irie, G. y Fujimura, K. (2013): “Travel route recommendation using geotagged photos”, Knowledge and Information Systems, 37(1), 37–60. http://link.springer.com/article/10.1007/s10115-012-0580-z http://dx.doi.org/10.1007/s10115-012-0580-z
  • Lenormand, M.; Picornell, M.; Cantú-Ros, O.; Tugores, A.; Louail, T.; Herranz, R.; Barthelemy, M.; Frías-Martínez, E. y Ramasco, JJ. (2014): “Cross-checking different sources of mobility information”, PLoS ONE, 9, e105184. http://dx.doi.org/10.1371/journal.pone.0105184
  • Lenormand, M., Louail, T., Cantú-Ros, O. G., Picornell, M., Herranz, R., Arias, J. M. y Ramasco, J. J. (2015): “Influence of sociodemographics on human mobility”, Scientific Reports, 5. http://dx.doi.org/10.1038/srep10075
  • Li, X. (2013): “Multi-day and multi-stay travel planning using geo-tagged photos”, in Proceedings of the Second ACM SIGSPATIAL International Workshop on Crowdsourced and Volunteered Geographic Information (pp. 1-8). ACM. http://dx.doi.org/10.1145/2534732.2534733
  • Llorente, A., García-Herranz, M., Cebrián, M., & Moro, E. (2015): “Social media fingerprints of unemployment”, PloS One, 10(5), e0128692. http://dx.doi.org/10.1371/journal.pone.0128692
  • Louail, T.; Lenormand, M.; García Cantú, O.; Picornell, M.; Herranz, R.; Frías-Martínez, E.; Ramasco, JJ. y Barthélemy, M. (2014): “From mobile phone data to the spatial structure of cities”, Scientific Reports 4, 5276. http://dx.doi.org/10.1038/srep05276
  • Lu, X., Wang, C., Yang, J. M., Pang, Y. y Zhang, L. (2010): “Photo2trip: generating travel routes from geo-tagged photos for trip planning”, in Proceedings of the international conference on Multimedia (pp. 143–152). ACM. http://dx.doi.org/10.1145/1873951.1873972
  • Mocanu, D., Baronchelli, A., Perra, N., Gonçalves, B., Zhang, Q. y Vespignani, A. (2013): “The twitter of babel: Mapping world languages through microblogging platforms”, PloS One, 8(4), e61981. http://dx.doi.org/10.1371/journal.pone.0061981
  • Moya-Gómez, B. and García-Palomares, J.C. (2015): “Working with the daily variation in infrastructure performance. The cases of Madrid and Barcelona”, European Transport Research Review 7(2), 20, 1-13.
  • Munizaga, M., Palma, C. y Mora, P. 2010: “Public transport OD matrix estimation from smart card payment system data”, in Proceedings from 12th World Conference on Transport Research, Lisbon, Paper (No. 2988).
  • Murthy, D. (2013): Twitter: Social communication in the Twitter age. John Wiley & Sons.
  • Netto, V. M., Pinheiro, M., Meirelles, J. V. y Leite, H. (2015): Digital footprints in the cityscape. International Conference on Social Networks, Athens, USA.
  • Neutens, T., Schwanen, T., Witlox, F. y De Maeyer, P. (2010): “Evaluating the temporal organization of public service provision using space-time accessibility analysis”, Urban Geography, 31(8), 1039-1064. http://dx.doi.org/10.2747/0272-3638.31.8.1039
  • Nolasco-Cirugeda, A. y García Mayor, C. (2014): Aplicación de los indicadores de complejidad urbana a través de las redes sociales y TIG: El caso de los paseos marítimos de Levante y Poniente en Benidorm. Alicante, XVI Congreso Nacional de Tecnologías de la Información Geográfica, 25, 26 y 27 de Junio de 2014.
  • O'Connor, P. (2008). “User-generated content and travel: A case study on Tripadvisor. Com”, Information and Communication Technologies in Tourism, 47-58.
  • O'Connor, P. (2010): “Managing a hotel's image on TripAdvisor”, Journal of Hospitality Marketing & Management, 19(7), 754-772. http://dx.doi.org/10.1080/19368623.2010.508007
  • Pastor-Escuredo, D., Morales-Guzmán, A., Torres-Fernández, Y., Bauer, J. M., Wadhwa, A., Castro-Correa, C., y Luengo-Oroz, M. (2014): “Flooding through the lens of mobile phone activity”, in Global Humanitarian Technology Conference (GHTC), 2014 IEEE (pp. 279-286). IEEE. http://dx.doi.org/10.1109/GHTC.2014.6970293
  • Pelletier, M. P., Trépanier, M. y Morency, C. (2011). Smart card data use in public transit: A literature review. Transportation Research Part C: Emerging Technologies, 19(4), 557-568. http://dx.doi.org/10.1016/j.trc.2010.12.003
  • Popescu, A., Grefenstette, G. y Moellic, P.-A. (2009): Mining Tourist Information from User-Supplied Collections. Paper presented at the Conference on Information and Knowledge Management. http://comupedia.org/adrian/articles/sp0668-popescu.pdf
  • Ratti, C., D. Frenchman, R.M. Pulselli y S. Williams (2006): "Mobile Landscapes: using location data from cell phones for urban analysis", Environment and Planning B: Planning and Design, 33: 727-748. http://dx.doi.org/10.1068/b32047
  • Reades, J., F. Calabrese y C. Ratti, Eigenplaces (2009): “Analyzing Cities Using the Space-time Structure of the Mobile Phone Network”, Environment & Planning B, 36, 824-836. http://dx.doi.org/10.1068/b34133t
  • Sakaki, T., Okazaki, M. y Matsuo, Y. (2010): “Earthquake shakes Twitter users: real-time event detection by social sensors”, in Proceedings of the 19th international conference on World Wide Web (pp. 851-860). ACM. http://dx.doi.org/10.1145/1772690.1772777
  • Segerberg, A. y Bennett, W. L. (2011): “Social media and the organization of collective action: Using Twitter to explore the ecologies of two climate change protests”, The Communication Review, 14(3), 197215. http://dx.doi.org/10.1080/10714421.2011.597250
  • Shelton, T., Poorthuis, A., & Zook, M. (2015): “Social media and the city: Rethinking urban socio-spatial inequality using user-generated geographic information”, Landscape and Urban Planning, 142, 198-211. http://dx.doi.org/10.1016/j.landurbplan.2015.02.020
  • Serrano-Estrada, L., Serrano Salazara, S. y Álvarez Álvarez. F.J. (2014): Las redes sociales y los SIG como herramientas para conocer las preferencias sociales en las ciudades turísticas: el caso de Benidorm. Alicante, XVI Congreso Nacional de Tecnologías de la Información Geográfica, 25, 26 y 27 de Junio de 2014.
  • Sobolevsky S, Sitko I, Grauwin S, Des Combes RT, Hawelka B, et al. (2014a): “Mining Urban Performance: Scale-Independent Classification of Cities based on Individual Economic Transactions”, in: Big Data Science and Computing, 2014 ASE International Conference on, May 27–31, Stanford University. p. 10.
  • Sobolevsky S, Sitko I, Tachet des Combes R, Hawelka B, Murillo Arias J, et al. (2014b): “Money on the Move: Big Data of Bank Card Transactions as the New Proxy for Human Mobility Patterns and Regional Delineation. The Case of Residents and Foreign Visitors in Spain2, in: Big Data (BigData Congress), 2014 IEEE International Congress on, Jun 27- Jul 2, Anchorage, AK. pp.136–143. http://dx.doi.org/10.1109/BigData.Congress.2014.28
  • Sobolevsky S, Bojic I, Belyi A, Sitko I, Hawelka B, et al. (2015a): “Scaling of city attractiveness for foreign visitors through big data of human economical and social media activity”, arXiv:150406003.
  • Sobolevsky, S., Sitko, I., Combes, R. T. D., Hawelka, B., Arias, J. M., & Ratti, C. (2015b): “Cities through the Prism of People's Spending Behavior”, arXiv preprint arXiv:1505.03854.
  • Straumann, R. K., Çöltekin, A., & Andrienko, G. (2014): “Towards (Re) Constructing Narratives from Georeferenced Photographs through Visual Analytics”, The Cartographic Journal, 51(2), 152–165. http://dx.doi.org/10.1179/1743277414Y.0000000079
  • Sun, Y., & Fan, H. (2014): “Event Identification from Georeferenced Images”, in Connecting a Digital Europe through Location and Place. (pp. 73-88). Springer International Publishing. http://dx.doi.org/10.1007/978-3-319-03611-3_5
  • Takhteyev, Y., Gruzd, A., & Wellman, B. (2012): “Geography of Twitter networks”, Social Networks, 34(1), 73-81. http://dx.doi.org/10.1016/j.socnet.2011.05.006
  • Tao, S., Rohde, D., & Corcoran, J. (2014): “Examining the spatial–temporal dynamics of bus passenger travel behaviour using smart card data and the flow-comap”, Journal of Transport Geography, 41, 21-36. http://dx.doi.org/10.1016/j.jtrangeo.2014.08.006
  • Wu, L., Zhi, Y., Sui, Z., & Liu, Y. (2014): “Intra-urban human mobility and activity transition: evidence from social media check-in data”, PloS One, 9(5), e97010. http://dx.doi.org/10.1371/journal.pone.0097010
  • Zielstra, D., & Hochmair, H. H. (2011): “Comparative Study of Pedestrian Accessibility to Transit Stations Using Free and Proprietary Network Data”, Transportation Research Record: Journal of the Transportation Research Board, 2217(1), 145-152. http://dx.doi.org/10.3141/2217-18
  • Zhai, S., Xu, X., Yang, L., Zhou, M., Zhang, L. y Qiu, B. (2015): “Mapping the popularity of urban restaurants using social media data”, Applied Geography, 63, 113-120. http://dx.doi.org/10.1016/j.apgeog.2015.06.006