Covid-19 pandemic and activity patterns in Milan. Wi-Fi sensors and location-based data
Abstract
The recent development of location detection systems allows to monitor, understand and predict the activity patterns of the city users. In this framework, the research focuses on the analysis of a sample of aggregated traffic data, based on the number of mobile devices detected through a network of 55 Wi-Fi Access Points in Milan. Data was collected over 7 months (January to July 2020), allowing for a study on the impact of the Covid-19 pandemic on activity patterns. Data analysis was based on merging: (i) time series analysis of trends, peak hours and mobility profiles; (ii) GIS-based spatial analysis of land data and Public Transport data. Results showed the effectiveness of Wi-Fi location data to monitor and characterize long-term trends about activity patterns in large scale urban scenarios. Results also showed a significant correlation between Wi-Fi data and the density distribution of residential buildings, service and transportation facilities, entertainment, financial amenities, department stores and bike-sharing docking stations. In this context, a Suitability Analysis Index is proposed, aiming at identifying the areas of Milan which could be exploited for more extensive data collection campaigns by means of the installation of additional Wi-Fi sensors. Future work is based on the development of Wi-Fi sensing applications for monitoring mobility data in real time.
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References
Batty, M. (2010). The Pulse of the City. Environment and Planning B, 37(4): 575–577. https://doi.org/10.1068/b3704ed
Batty, M. (2013). Big data, smart cities and city planning. Dialogues in Human Geography, 3(3), 274-279. https://doi.org/10.1177%2F2043820613513390
Becker, R., C´aceres, R., Hanson, K., Isaacman, S., Loh, J.M., Martonosi, M., Rowland, J., Urbanek, S., Varshavsky, A. and Volinsky, C. (2013). Human mobility characterization from cellular network data. Communications of the ACM, 56(1), 74-82. https://doi.org/10.1145/2398356.2398375
Bellini, P., Cenni, D., Nesi, P. and Paoli, I. (2017). Wi-Fi based city users behaviour analysis for smart city. Journal of Visual Languages and Computing, 42, 31-45. https://doi.org/10.1016/j.jvlc.2017.08.005
Bernas, P., Korski, L., Smya, J., and Szymaa, P. (2018). A survey and comparison of low-cost sensing technologies for road traffic monitoring. Sensors, 18(10), 32-43. https://doi.org/10.3390/s18103243
Buch, N., Velastin, S.A. and Orwell, J. (2011). A review of computer vision techniques for the analysis of urban traffic. IEEE Transactions on Intelligent Transportation Systems, 12(3), 920–939. https://doi.org/10.1109/TITS.2011.2119372
Buhrmann, S., Wefering, F., Rupprecht, S. (2019). Guidelines for Developing and implementing a sustainable urban mobility plan 2nd edition. Rupprecht Consult-Forschung und Beratung GmbH.
Clifford, P., Richardson, S., & Hémon, D. (1989). Assessing the significance of the correlation between two spatial processes. Biometrics, 123-134. https://doi.org/10.2307/2532039
Coppola, P., & De Fabiis, F. (2020). Evolution of mobility sector during and beyond Covid-19 emergency: a viewpoint of industry consultancies and public transport companies. TeMA-Journal of Land Use, Mobility and Environment, 81-90. https://doi.org/10.6092/1970-9870/6900
Crist, P., Greer, E., Ratti, C., Humanes, P., Konzett, G., Tijink, J., Figuero, D. and Lax, R. (2015). Big Data and Transport: Understanding and assessing options. International Transport Forum Data Base.
Deponte, D., Fossa, G., & Gorrini, A. (2020). Shaping space for ever-changing mobility. Covid-19 lesson learned from Milan and its region. TeMA-Journal of Land Use, Mobility and Environment, 133-149. https://doi.org/10.6092/1970-9870/6857
European Platform on Sustainable Urban Mobility Plans (2020). COVID-19 SUMP Practitioner Briefing. CIVITAS SATELLITE CSA. Available online: https://www.eltis.org/sites/default/files/covid-19_sumppractitionersbriefing_final.pdf (accessed on 1 March 2021)
Foth, M., Choi, J.H. and Satchell, C. (2011). Urban informatics. In: Proceedings of the ACM 2011 conference on Computer supported cooperative work, 1–8. https://doi.org/10.1145/1958824.1958826
Jacobs, J. (1961). The Death and Birth of Great American Cities. London: Penguin.
Kontokosta, C.E. and Johnson, N. (2017). Urban phenology: Toward a real-time census of the city using wi-fi data. Computers. Environment and Urban Systems, 64, 144-153. https://doi.org/10.1016/j.compenvurbsys.2017.01.011
Kostakos, V., Ojala, T. and Juntunen, T. (2013). Traffic in the smart city: Exploring city-wide sensing for traffic control center augmentation. IEEE Internet Computing, 17(6), 22-29. https://doi.ieeecomputersociety.org/10.1109/MIC.2013.83
Lee Rodgers, J., & Nicewander, W. A. (1988). Thirteen ways to look at the correlation coefficient. The American Statistician, 42(1), 59-66. https://doi.org/10.1080/00031305.1988.10475524
Li, W., Batty, M. and Goodchild, M.F. (2020). Real-time gis for smart cities. International Journal of Geographical Information Science, 34(2), 311-324. https://doi.org/10.1080/13658816.2019.1673397
Lin, M. and Hsu, W.J. (2014). Mining GPS data for mobility patterns: A survey. Pervasive and mobile computing, 12, 1-16. http://dx.doi.org/10.1016/j.pmcj.2013.06.005
National Centre for IoT and Privacy (2020). Physical audience measuring technologies and privacy concerns. White Paper v.01. Available online: https://iotprivacy.it/wp-content/uploads/2020/06/Whitepaper-Physical-Audience-Measuring-Technologies-and-Privacy-Concerns-ENG.pdf (accessed on 1 March 2021)
Odat, E., Shamma, J.S. and Claudel, C. (2017). Vehicle classification and speed estimation using combined passive infrared/ultrasonic sensors. IEEE transactions on intelligent transportation systems, 19(5), 1593-1606. https://doi.org/10.1109/TITS.2017.2727224
Santos, A. & Moura, A.C. (2019). Mobility: Exploratory analysis for territorial preferences. Tema. Journal of Land Use, Mobility and Environment, 12(2), 147-156. http://dx.doi.org/10.6092/1970-9870/6126
Sapiezynski, P., Stopczynski, A., Gatej, R. and Lehmann, S. (2015). Tracking human mobility using wifi signals. PloS one, 10(7), e0130824. https://doi.org/10.1371/journal.pone.0130824
Schläpfer, M., Dong, L., O’Keeffe, K. et al. (2021). The universal visitation law of human mobility. Nature, 593, 522–527. https://doi.org/10.1038/s41586-021-03480-9
Song, Y., Merlin, L. and Rodriguez, D. (2013). Comparing measures of urban land use mix. Computers, Environment and Urban Systems, 42, 1-13. https://doi.org/10.1016/j.compenvurbsys.2013.08.001
Soundararaj, B., Cheshire, J. and Longley, P. (2020). Estimating real-time high-street footfall from wi-fi probe requests. Int. Journal of Geographical Information Science, 34(2), 325-343. https://doi.org/10.1080/13658816.2019.1587616
Speck, J. (2013). Walkable city: How downtown can save America, one step at a time. macmillan.
Transport for London (2019). Review of the TfL WiFi pilot. Our findings. Mayor of London. Available online: http://content.tfl.gov.uk/review-tfl-wifi-pilot.pdf (accessed on 1 March 2021)
Xhafa, F., Leu, F.Y. and Hung, L.L. (2017). Smart sensors networks: Communication technologies and intelligent applications. Academic Press.
Zecca, C., Gaglione F., Laing, R., Gargiulo C., (2020). Pedestrian routes and accessibility to urban services. Rhythmic analysis on people's behaviour before and during the Covid-19. Tema. Journal of Land Use, Mobility and Environment, 13 (2), 241-256. http://dx.doi.org/10.6092/1970-9870/7051
Zhao, Z.Q., Zheng. P., Xu, S.T. and Wu, X. (2019). Object detection with deep learning: A review. IEEE transactions on neural networks and learning systems, 30(11), 3212-3232. https://doi.org/10.1109/tnnls.2018.2876865
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