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Privacy-Aware Data Gathering for Urban Analytics

  • Miguel Nunez-del-PradoEmail author
  • Bruno Esposito
  • Ana Luna
  • Juandiego Morzan
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 795)

Abstract

Nowadays, there are a mature set of tools and techniques for data analytics, which help Data Scientists to extract knowledge from raw heterogeneous data. Nonetheless, there is still a lack of spatiotemporal historical dataset allowing to study everyday life phenomena, such as vehicular congestion, press influence, the effect of politicians comments on stock exchange markets, the relation between food prices evolution and temperatures or rainfall, social structure resilience against extreme climate events, among others. Unfortunately, few datasets are combining from different sources of urban data to carry out studies of phenomena occurring in cities (i.e., Urban Analytics). To solve this problem, we have implemented a Web crawler platform for gathering a different kind of available public datasets.

Keywords

Privacy Data collection Urban analytics Open data 

References

  1. 1.
    Abbar, S., Zanouda, T., Borge-Holthoefer, J.: Robustness and resilience of cities around the world. arXiv preprint arXiv:1608.01709 (2016)
  2. 2.
    Barlacchi, G., De Nadai, M., Larcher, R., Casella, A., Chitic, C., Torrisi, G., Antonelli, F., Vespignani, A., Pentland, A., Lepri, B.: A multi-source dataset of urban life in the city of milan and the province of trentino. Sci. Data 2, 150055 (2015)CrossRefGoogle Scholar
  3. 3.
    Di Clemente, R., Luengo-Oroz, M., Travizano, M., Vaitla, B., Gonzalez, M.C.: Sequence of purchases in credit card data reveal life styles in urban populations. arXiv preprint arXiv:1703.00409 (2017)
  4. 4.
    Gambs, S., Killijian, M.O., del Prado Cortez, M.N.: De-anonymization attack on geolocated data. J. Comput. Syst. Sci. 80(8), 1597–1614 (2014)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Gray, S., O’Brien, O., Hügel, S.: Collecting and visualizing real-time urban data through city dashboards. Built Environ. 42(3), 498–509 (2016)CrossRefGoogle Scholar
  6. 6.
    Nunez-del Prado, M., Bravo, E., Sierra, M., Canchay, M., Hoyos, I.: Knowledge tier platform for graph mining in (smart) cities. In: Proceedings of Symposium on Information Management and Big Data (2016)Google Scholar
  7. 7.
    Panagiotou, N., et al.: Intelligent urban data monitoring for smart cities. In: Berendt, B., Bringmann, B., Fromont, É., Garriga, G., Miettinen, P., Tatti, N., Tresp, V. (eds.) ECML PKDD 2016. LNCS (LNAI), vol. 9853, pp. 177–192. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-46131-1_23CrossRefGoogle Scholar
  8. 8.
    Rathore, M.M., Ahmad, A., Paul, A., Rho, S.: Urban planning and building smart cities based on the internet of things using big data analytics. Comput. Netw. 101, 63–80 (2016)CrossRefGoogle Scholar
  9. 9.
    Santos, H., Furtado, V., Pinheiro, P., McGuinness, D.L.: Contextual data collection for smart cities. arXiv preprint arXiv:1704.01802 (2017)
  10. 10.
  11. 11.
    Srivastava, A.K.: Segregated data of urban poor for inclusive urban planning in India: needs and challenges. SAGE Open 7(1), 2158244016689377 (2017)CrossRefGoogle Scholar
  12. 12.
    Xu, Z., Liu, Y., Yen, N., Mei, L., Luo, X., Wei, X., Hu, C.: Crowdsourcing based description of urban emergency events using social media big data. IEEE Trans. Cloud Comput. 99(PP), 1–10 (2016)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Universidad del PacíficoLimaPeru

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