Salta al menu principale di navigazione Salta al contenuto principale Salta al piè di pagina del sito
FrancoAngeli Journals

Articoli

N. 4 (2021)

Pandemia e (im)mobilità: gli effetti spaziali del lockdown attraverso i Big Data delle piattaforme digitali

  • Antonello Romano
DOI
https://doi.org/10.3280/rgioa4-2021oa12956
Inviata
19 novembre 2021
Pubblicato
03-12-2021

Abstract

In assenza di vaccini e in una situazione di emergenza generata dalla rapida diffusione della pandemia, la strategia adottata per contrastare la diffusione del COVID-19 è stata quella del distanziamento sociale e del lockdown che hanno fortemente influenzato la mobilità degli individui. In tale contesto il presente studio si pone l’obiettivo di misurare gli effetti spaziali dei provvedimenti restrittivi alla mobilità individuale in due momenti – durante e nel post-lockdown italiano – e per funzioni (residenza, luoghi di lavoro, svago, trasporti) e a scale differenti. A tal fine, il contributo analizza i dati resi ineditamente
disponibili dalle piattaforme digitali Google e Facebook attraverso i programmi Google Mobility Report e Facebook Data for Good. I risultati da un lato mostrano le aree attrattive e repulsive di popolazione insistente in (near) real-time per macro e micro area, dall’altro permettono di effettuare una riflessione sul ruolo dei ‘dati delle piattaforme’ in uno scenario di crescente infrastrutturazione delle piattaforme digitali nella società.

Riferimenti bibliografici

  1. Barns S. (2019). Platform Urbanism. Negotiating Platform Ecosystems in Connected Cities. Londra: Palgrave Macmillan.
  2. Bengtsson L., Lu X., Thorson A., Garfield R., von Schreeb J. (2011). Improved response to disasters and outbreaks by tracking population movements with mobile phone network data: a post-earthquake geospatial study in Haiti. PLoS Medicine, 8(8): e1001083.
  3. Bucher T. (2016). The Algorithmic Imaginary: Exploring the Ordinary Affects of Facebook Algorithms. Information, Communication & Society, 20(1): 30-44. DOI: 10.1080/1369118X.2016.1154086
  4. Buckee C. (2020). Improving epidemic surveillance and response: Big data is dead, long live big data. The Lancet Digital Health, 2(5): e218-e220. DOI: 10.1016/S2589-7500(20)30059-5
  5. Buckee C.O., Balsari S., Chan J., Crosas M., Dominici F., Gasser U., Grad Y.H., Grenfell B., Halloran M.E., Kraemer M.U.G., Lipsitch M., Metcalf C.J.E., Meyers L.A., Perkins T.A., Santillana M., Scarpino S.V., Viboud C., Wesolowski A., Schroeder A. (2020). Aggregated mobility data could help fight COVID-19. Science, 368(6487): 145-146. DOI: 10.1126/science.abb8021
  6. Campos-Vazquez R.M., Esquivel G. (2021). Consumption and geographic mobility in pandemic times. Evidence from Mexico. Review of Economics of the Household: 1-19. DOI: 10.1007/s11150-020-09539-2
  7. Celata F. (2018). Il capitalismo delle piattaforme e le nuove logiche di mercificazione dei luoghi. Territorio, 86: 48-56. DOI: 10.3280/TR2018-086006
  8. Id., Capineri C., Romano A. (2020). A room with a (re)view. Short-term rentals, digital reputation and the uneven spatiality of platform-mediated tourism. Geoforum, 112: 129-138. DOI: 10.1016/j.geoforum.2020.04.007
  9. Dolnicar S., Zare S. (2020). COVID-19 and Airbnb – Disrupting the disruptor. Annals of tourism research, 83: 102961. DOI: 10.1016/j.annals.2020.102961
  10. Fields D., Bissell D., Macrorie R. (2020). Platform methods: studying platform urbanism outside the black box. Urban Geography, 41(2): 1-7. DOI: 10.1080/02723638.2020.1730642
  11. Gillespie T. (2010). The politics of ‘platforms’. New media and Society, 12(3): 347-364. DOI: 10.1177/1461444809342738
  12. Hao Q., Chen L., Xu F., Li Y. (2020, August). Understanding the Urban Pandemic Spreading of COVID-19 with Real World Mobility Data. In: Aa.Vv., Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 3485-3492). DOI: https://doi.org/10.1145/3394486.3412860
  13. Huang J., Wang H., Fan M., Zhuo A., Sun Y., Li Y. (2020, August). Understanding the impact of the COVID-19 pandemic on transportation-related behaviors with human mobility data. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 3443-3450). DOI: https://doi.org/10.1145/3394486.3412856
  14. Kenney M., Zysman J. (2016). The rise of the platform economy. Issues in science and technology, 32(3), 61.
  15. Kitchin R. (2014). Big Data, new epistemologies and paradigm shifts. Big data & society, 1(1): 2053951714528481. DOI: 10.1177/2053951714528481
  16. Kuchler T., Russel D., Stroebel J. (2020). The geographic spread of Covid-19 correlates with the structure of social networks as measured by Facebook. Cambridge MA: National Bureau of Economic Research. www.nber.org/papers/w26990
  17. Layer R.M., Fosdick B., Larremore D.B., Bradshaw M., Doherty P. (2020). Case Study: Using Facebook Data to Monitor Adherence to Stay-at-home Orders in Colorado and Utah. MedRxiv. DOI: 10.1101/2020.06.04.20122093
  18. Leszczynski A. (2019). Glitchy vignettes of platform urbanism. Environment and Planning D: Society and Space, 38(2): 189-208. DOI: 10.1177/0263775819878721
  19. Libert B., Wind Y., Fenley M. (2014). What Airbnb, Uber, and Alibaba Have in Common. Harvard Business Review. https://hbsp.harvard.edu/product/H01PPE-PDF-ENG.
  20. Micheli D., Muratore G., Vannelli A., Sola G. (2020). Un modello dinamico su un approccio big-data alla mobilità per lo studio della diffusione del Covid-19 nel Nord Italia. Gruppo TIM: Notiziario tecnico, n. 1 (www.gruppotim.it/tit/it/notiziariotecnico/edizioni-2020/n-1-2020/Modello-dinamico-approccio-Big-Data.html).
  21. Nouvellet P., Bhatia S., Cori A., Ainslie K.E., Baguelin M., Bhatt S., Donnelly C.A. (2021). Reduction in mobility and COVID-19 transmission. Nature communications, 12(1): 1-9. DOI: 10.1038/s41467-021-21358-2
  22. Oliver N., Lepri B., Sterly H. et al. (2020). Mobile phone data for informing public health actions across the COVID-19 pandemic life cycle. Science Advances, 6(23): eabc0764. DOI: 10.1126/sciadv.abc0764
  23. Plantin J.-C., Lagoze C., Edwards P. (2018). Re-integrating scholarly infrastructure: the ambiguous role of data sharing platforms. Big data And Society, 1: 1-14. DOI: 10.1177/2053951718756683
  24. Poom A., Järv O., Zook M., Toivonen T. (2020). COVID-19 is spatial: Ensuring that mobile Big Data is used for social good. Big Data & Society, 7(2): 2053951720952088. DOI: 10.1177/2053951720952088
  25. Sadowski J. (2020). Too smart: how digital capitalism is extracting data, controlling our lives, and taking over the world. Cambridge (MA): MIT Press.
  26. Srnicek N. (2017). Platform capitalism. Cambridge: Polity Press.
  27. Thatcher J., O’Sullivan D., Mahmoudi D. (2016). Data colonialism through accumulation by dispossession: New metaphors for daily data. Environment and Planning D: Society and Space, 34(6): 990-1006. DOI: 10.1177/0263775816633195
  28. Van Dijck J., Poell T., de Waal M. (2018). The platform society. Public values in a connective world. Oxford: Oxford University Press.
  29. Wellenius G.A., Vispute S., Espinosa V., Fabrikant A., Tsai T.C., Hennessy J., Gabrilovich E. (2020). Impacts of state-level policies on social distancing in the United States using aggregated mobility data during the COVID-19 pandemic. ArXiv preprint:2004.10172.
  30. Wesolowski A., Buckee C.O., Bengtsson L., Wetter E., Lu X., Tatem A.J. (2014). Commentary: containing the Ebola outbreak-the potential and challenge of mobile network data. PLoS currents, 6. DOI: 10.1371/currents.outbreaks.0177e7fcf52217b8b634376e2f3efc5e
  31. Zachreson C. et al. (2020). Risk mapping for COVID-19 outbreaks in Australia using mobility data. https://arxiv.org/abs/2008.06193
  32. Zuboff S. (2019). The age of surveillance capitalism: The fight for a human future at the new frontier of power. London: Profile Books.

Metriche

Caricamento metriche ...