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(So) Big Data and the transformation of the city

Andrienko, G. ORCID: 0000-0002-8574-6295, Andrienko, N. ORCID: 0000-0003-3313-1560, Boldrini, C., Caldarelli, G., Cintia, P., Cresci, S., Facchini, A., Giannotti, F., Gionis, A., Guidotti, R., Mathioudakis, M., Muntean, C. I., Pappalardo, L., Pedreschi, D., Pournaras, E., Pratesi, F., Tesconi, M. and Trasarti, R. (2020). (So) Big Data and the transformation of the city. International Journal of Data Science and Analytics, doi: 10.1007/s41060-020-00207-3

Abstract

The exponential increase in the availability of large-scale mobility data has fueled the vision of smart cities that will transform our lives. The truth is that we have just scratched the surface of the research challenges that should be tackled in order to make this vision a reality. Consequently, there is an increasing interest among different research communities (ranging from civil engineering to computer science) and industrial stakeholders in building knowledge discovery pipelines over such data sources. At the same time, this widespread data availability also raises privacy issues that must be considered by both industrial and academic stakeholders. In this paper, we provide a wide perspective on the role that big data have in reshaping cities. The paper covers the main aspects of urban data analytics, focusing on privacy issues, algorithms, applications and services, and georeferenced data from social media. In discussing these aspects, we leverage, as concrete examples and case studies of urban data science tools, the results obtained in the “City of Citizens” thematic area of the Horizon 2020 SoBigData initiative, which includes a virtual research environment with mobility datasets and urban analytics methods developed by several institutions around Europe. We conclude the paper outlining the main research challenges that urban data science has yet to address in order to help make the smart city vision a reality.

Publication Type: Article
Additional Information: Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Publisher Keywords: Big data, Urban data science, SoBigData, Mobility datasets
Subjects: H Social Sciences > HM Sociology
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments: School of Mathematics, Computer Science & Engineering > Computer Science > giCentre
Date Deposited: 31 Jul 2020 11:21
URI: https://openaccess.city.ac.uk/id/eprint/24627
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