Covid-19 pandemic and activity patterns in Milan. Wi-Fi sensors and location-based data

Keywords: Urban mobility, Traffic data, Wi-Fi sensors, GIS analysis, Covid-19 pandemic


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.



Download data is not yet available.

Author Biographies

Andrea Gorrini, Systematica Srl

He is an environmental psychologist with experience in human behavior in transport systems, pedestrian crowd dynamics and walkability. Since 2019, he collaborates with Systematica as Transport Research Consultant. He oversees research and development activities related to the identification, collection and exploitation of different mobility data sources through GIS, data analytics, video tracking tools and questionnaires.

Federico Messa, Systematica Srl

He joined Systematica in 2016. He is an Architect since 2021 and he has been active as a transport consultant on a diverse set of projects, ranging from territorial studies to masterplans and complex buildings mobility strategies. He is also involved in architecture and mobility research studies, mainly related to urban dynamics, mobility data analysis and visualization, project performance analysis and spatial analysis.

Giulia Ceccarelli, Systematica Srl

She joined Systematica in 2020, having a background in geomatics for the built environment and architecture. Her interests include deep learning methods for computer vision, location data analytics and spatial information modelling. In Systematica, she is involved in research projects focusing on emerging technologies.

Rawad Choubassi, Systematica Srl

He currently acts as a Director and Board member of Systematica where he leads planning and research projects on mobility in urban environments and complex buildings. Choubassi has gained previous experience, prior to joining Systematica in 2008, through intensive work on largescale projects during his work with Arata Isozaki (Tokyo), Kliment-Halsband Architects (New York) and others. He leads a team of multi-disciplinary consultants for expanding the limits of the science behind mobility engineering dynamics on every scale, Regional, National, Urban, etc.


Batty, M. (2010). The Pulse of the City. Environment and Planning B, 37(4): 575–577.

Batty, M. (2013). Big data, smart cities and city planning. Dialogues in Human Geography, 3(3), 274-279.

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.

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.

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.

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.

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.

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.

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.

European Platform on Sustainable Urban Mobility Plans (2020). COVID-19 SUMP Practitioner Briefing. CIVITAS SATELLITE CSA. Available online: (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.

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.

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.

Lee Rodgers, J., & Nicewander, W. A. (1988). Thirteen ways to look at the correlation coefficient. The American Statistician, 42(1), 59-66.

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.

Lin, M. and Hsu, W.J. (2014). Mining GPS data for mobility patterns: A survey. Pervasive and mobile computing, 12, 1-16.

National Centre for IoT and Privacy (2020). Physical audience measuring technologies and privacy concerns. White Paper v.01. Available online: (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.

Santos, A. & Moura, A.C. (2019). Mobility: Exploratory analysis for territorial preferences. Tema. Journal of Land Use, Mobility and Environment, 12(2), 147-156.

Sapiezynski, P., Stopczynski, A., Gatej, R. and Lehmann, S. (2015). Tracking human mobility using wifi signals. PloS one, 10(7), e0130824.

Schläpfer, M., Dong, L., O’Keeffe, K. et al. (2021). The universal visitation law of human mobility. Nature, 593, 522–527.

Song, Y., Merlin, L. and Rodriguez, D. (2013). Comparing measures of urban land use mix. Computers, Environment and Urban Systems, 42, 1-13.

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.

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: (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.

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.

How to Cite
GorriniA., MessaF., CeccarelliG., & ChoubassiR. (2021). Covid-19 pandemic and activity patterns in Milan. Wi-Fi sensors and location-based data. TeMA - Journal of Land Use, Mobility and Environment, 14(2), 211-226.