Slow Mobility and Big Data: Exploring the Nexus Between Urban Mobility and Heritage Through GeoAI

Michela Lazzeroni; Antonello Romano; Paola Zamperlin

doi:10.36253/bsgi-7516

Authors

Michela Lazzeroni Department of Civilisations and Forms of Knowledge, University of Pisa, Italy
Antonello Romano Department of Civilisations and Forms of Knowledge, University of Pisa, Italy
Paola Zamperlin Department of Civilisations and Forms of Knowledge, University of Pisa, Italy

DOI:

https://doi.org/10.36253/bsgi-7516

Keywords:

slow mobility, heritage, big data, GeoAI, Strava

Abstract

Starting from the new mobility paradigm (Sheller, Urry, 2006), this work explores the use of spatial big data in the field of urban mobility and their connection with cultural and natural heritage at the local scale. The focus is on the nexus between slow mobility and heritage in Pisa (Italy) at the intra-urban scale, employing GeoAI and big data analysis techniques. First, through the analysis of 151,110 GPS tracks of pedestrian trips recorded by the Strava Metro platform over a period of one year (2022) in the province of Pisa, we describe the most popular locations for slow mobility, and we predict the areas where people tend to linger. Second, we study in depth the case of the municipality of Pisa to critically discuss: a) the influence of heritage on mobility spatial trends, b) the link between slow mobility and urban heritage fruition; c) the contribution of big data and GeoAI models to analyze this nexus. The observation of these patterns is helpful in terms of policies: i) for the identification of the most used corridors and alternative solutions for wider access to urban heritage, which also includes “minor” sites; ii) for the development of spatial models to highlight walking routes in near real-time and encourage slow mobility practices.

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References

Annunziata, A., Garau, C. (2020). A Literature Review on Walkability and its Theoretical Framework. Emerging Perspectives for Research Developments. In Gervasi, O. et al. (Eds.). Proceedings of the International Conference on Computational Science and Its Applications – ICCSA 2020. ICCSA 2020. Lecture Notes in Computer Science, vol 12255. Cham, Springer, 422-437. https://doi.org/10.1007/978-3-030-58820-5_32 DOI: https://doi.org/10.1007/978-3-030-58820-5_32

Balletto, G., Milesi, A., Battino, S., Borruso, G., Mundula, L. (2019). Slow Tourism and Smart Community. The Case of Sulcis - Iglesiente (Sardinia -Italy). In Misra, S. et al. (Eds.). Proceedings of the International Conference on Computational Science and Its Applications – ICCSA 2019. Lecture Notes in Computer Science, vol 11624. Cham, Springer. https://doi.org/10.1007/978-3-030-24311-1_13 DOI: https://doi.org/10.1007/978-3-030-24311-1_13

Bissell, D. (2020). Mobilities and place. In Edensor, T., Kalandides, A., & Kothari, U. (Eds.). The Routledge Handbook of Place. London and New York, Routledge. DOI: https://doi.org/10.4324/9780429453267-8

Chapin, F.S. Jr. (1974). Human Activity Patterns and the City: Things People Do in Time and in Space. New York, Wiley.

Chapin, F.S. Jr. (1978). Human Time Allocation in the City. In Carlstein, T., Parkes, D.N., Thrift, N.J. (Eds.). Human Activity and Time Geography. London, Edward Arnolds, 13-26.

Cisani, M., Lo Presti, L., Pearce, L., Peterle, G., Rabbiosi, C. (2022). Introduction. Unruly Landscapes: Mobility, Transience, and Transformation. Transfers, 12(1), 3-8. DOI: https://doi.org/10.3167/TRANS.2022.120102 DOI: https://doi.org/10.3167/TRANS.2022.120102

Cresswell, T. (2006). On the move: Mobility in the modern western world. New York, Taylor & Francis.

Daniels, P.W., Warnes, A.M. (1980). Movement in Cities: Spatial Perspectives on Urban Transport and Travel. London, Methuen.

Fan, W., Lin, Z. (2019). Evaluating the potential use of crowdsourced bicycle data in North Carolina. Charlotte (NC), The University of North Carolina at Charlotte, Center for Advanced Multimodal Mobility Solutions and Education.

Genuer, R., Poggi, J. M., Tuleau-Malot, C., Villa-Vialaneix, N. (2017). Random forests for big data. Big Data Research, 9, 28-46. DOI: https://doi.org/10.48550/arXiv.1511.08327 DOI: https://doi.org/10.1016/j.bdr.2017.07.003

Giaccardi, E. (Eds.). Heritage and social media: Understanding heritage in a participatory culture. London, Routledge.

Greene, M., Rau, H. (2018). Moving across the life course: A biographic approach to researching dynamics of everyday mobility practices. Journal of Consumer Culture, 18(1), 60-82. https://doi.org/10.1177/1469540516634417 DOI: https://doi.org/10.1177/1469540516634417

Jiang, Z., & Shekhar, S. (2017). Spatial big data science. Schweiz, Springer International Publishing AG. DOI: https://doi.org/10.1007/978-3-319-60195-3

Harvey, D.C. (2015). Landscape and heritage: trajectories and consequences. Landscape Research, 40(8), 911-924. DOI: https://doi.org/10.1080/01426397.2014.967668 DOI: https://doi.org/10.1080/01426397.2014.967668

Harvey D.C. (2024). Traces of mobility in the landscape: creative possibilities of everyday heritage practice. Landscape Research, 50(2), 351-367. DOI: https://doi.org/10.1080/01426397.2024.2377207 DOI: https://doi.org/10.1080/01426397.2024.2377207

Heesch, K. C., James, B., Washington, T. L., Zuniga, K., Burke, M. (2016). Evaluation of the Veloway 1: A natural experiment of new bicycle infrastructure in Brisbane. Australia. Journal of Transport and Health, 3(3), 366-376. DOI: https://doi.org/10.1016/j.jth.2016.06.006 DOI: https://doi.org/10.1016/j.jth.2016.06.006

Hochmair, H. H., Bardin, E., Ahmouda, A. (2019). Estimating bicycle trip volume for Miami-Dade County from Strava tracking data. Journal of transport geography, 75, 58-69. DOI: https://doi.org/10.1016/j.jtrangeo.2019.01.013 DOI: https://doi.org/10.1016/j.jtrangeo.2019.01.013

Huber, S., Lißner, S. (2019). Disaggregation of aggregate GPS-based cycling data — How to enrich commercial cycling data sets for detailed cycling behavior analysis. Transportation Research Interdisciplinary Perspectives, 2, 100041. DOI: https://doi.org/10.1016/j.trip.2019.100041 DOI: https://doi.org/10.1016/j.trip.2019.100041

Hui, A. (2013). Moving with practices: The discontinuous, rhythmic and material mobilities of leisure. Social & Cultural Geography, 14(8), 888-908. DOI: https://doi.org/10.1080/14649365.2013.827736 DOI: https://doi.org/10.1080/14649365.2013.827736

Kitchin, R. (2014). Big Data, new epistemologies and paradigm shifts. Big data & society, 1(1). DOI: https://doi.org/10.1177/2053951714528481 DOI: https://doi.org/10.1177/2053951714528481

Knapskoga M., Hagena O. H., Tennøya A., Rynning M. K. (2019). Exploring ways of measuring walkability. Transportation Research Procedia, 41, 264-282. DOI: https://doi.org/10.1016/j.trpro.2019.09.047 DOI: https://doi.org/10.1016/j.trpro.2019.09.047

Lee, K., Sener, I. N. (2021). Strava Metro data for bicycle monitoring: a literature review. Transport reviews, 41(1), 27-47. DOI: https://doi.org/10.1080/01441647.2020.1798558 DOI: https://doi.org/10.1080/01441647.2020.1798558

Lee, R. J., Sener, I. N., Mullins, J. A. (2016). An evaluation of emerging data collection technologies for travel demand modeling: From research to practice. Transportation Letters, 8(4), 181-193. DOI: 10.1080/19427867.2015.1106787 DOI: https://doi.org/10.1080/19427867.2015.1106787

Leslie E., Coffee N., Frank L., Owen N., Bauman A., Graeme H. (2007). Walkability of local communities: Using geographic information systems to objectively assess relevant environmental attributes. Health & Place, 13, 111-122. DOI: https://doi.org/10.1016/j.healthplace.2005.11.001 DOI: https://doi.org/10.1016/j.healthplace.2005.11.001

Leszczynski, A., Crampton, J. W. (2016). Introduction: The Spatial Big Data Debate, Big Data & Society, 3(2), 1-12. DOI: https://doi.org/10.1177/2053951716661366 DOI: https://doi.org/10.1177/2053951716661366

Liu, P., Biljecki, F. (2022). A review of spatially-explicit GeoAI applications in Urban Geography. International Journal of Applied Earth Observation and Geoinformation, 112, 102936. DOI: https://doi.org/10.1016/j.jag.2022.102936 DOI: https://doi.org/10.1016/j.jag.2022.102936

Maltese I., Mariotti I., Oppio A., Boscacci F. (2017), Assessing the benefits of slow mobility connecting a cultural heritage. Journal of Cultural Heritage, 26, 153-159. DOI: https://doi.org/10.1016/j.culher.2017.01.006 DOI: https://doi.org/10.1016/j.culher.2017.01.006

Martinotti, G. (1993). Metropoli. La nuova morfologia sociale della città. Bologna il Mulino.

Martinotti, G. (1999). A City for Whom? Transients and Public Life in the Second-Generation Metropolis. In Beauregard, R.A., Body-Gendrot, S. (Eds.). The Urban Moment. Cosmopolitan Assays in the Late-20th Century City. London, Sage, 155-183.

Morri, R. (2003). I city users. In Cristaldi F. (a cura di). Le mille popolazioni metropolitane. Un’analisi geografica dell’area romana. Roma, Università degli Studi di Roma La Sapienza, 77-116.

Mortaheb, R., Jankowski, P. (2023). Smart city re-imagined: City planning and GeoAI in the age of big data. Journal of Urban Management, 12(1), 4-15. https://doi.org/10.1016/j.jum.2022.08.001 DOI: https://doi.org/10.1016/j.jum.2022.08.001

Nikielska-Sekuła, K., Banaszkiewicz, M. (2025). Heritage and Mobility from a Multisensory Perspective. In Banaszkiewicz, M., Nikielska-Sekuła, K. (Eds.). Heritage and Mobility from a Multisensory Perspective. London / New York, Routledge, 1-15. DOI: https://doi.org/10.4324/9781032713786-1

Nuvolati, G. (2007). Mobilità quotidiana e complessità urbana. Firenze, Firenze University Press. DOI: https://doi.org/10.36253/978-88-8453-630-3

Rolando, A., Scandiffio, A. (2022). Multimodal Access to Minor Places in Heritage-Rich Landscapes: GIS Mapping to Define Slow-Tourism Routes from the Stations in the Railway Networks in-between Turin and Milan. Sustainability, 14, 15723. DOI; https://doi.org/10.3390/su142315723 DOI: https://doi.org/10.3390/su142315723

Romano, A. (2022). La geografia delle piattaforme digitali. Mappe, spazi e dati dell’intermediazione digitale. Firenze, Firenze University Press. DOI: https://doi.org/10.36253/978-88-5518-602-5 DOI: https://doi.org/10.36253/978-88-5518-602-5

Romano, A., Capineri, C. (2020). Piattaforme e Big data per una nuova geografia digitale. In Lazzeroni M., Morazzoni M. (Eds.). Interpretare la quarta rivoluzione industriale. La geografia in dialogo con le altre discipline. Roma, Carocci editore, 227-240.

Roy, A., Nelson, T. A., Fotheringham, A. S., Winters, M. (2019). Correcting bias in crowdsourced data to map bicycle ridership of all bicyclists. Urban Science, 3(62), 1-20. DOI: https://doi.org/10.3390/urbansci3020062 DOI: https://doi.org/10.3390/urbansci3020062

Sheller, M., Urry, J. (2006). The new mobilities paradigm. Environment and Planning A, 38(2), 207-226. DOI: 10.1068/a37268 DOI: https://doi.org/10.1068/a37268

Shields, R., Gomes da Silva, E. J., Lima e Lima, T., Osorio, N. (2021). Walkability: a review of trends. Journal of Urbanism: International Research on Placemaking and Urban Sustainability, 16(1), 19-41. DOI: https://doi.org/10.1080/17549175.2021.1936601 DOI: https://doi.org/10.1080/17549175.2021.1936601

Singh, R. (2016). Factors affecting walkability of neighborhoods. Urban Planning and Architecture Design for Sustainable Development, 216, 643-654. DOI: https://doi.org/10.1016/j.sbspro.2015.12.048 DOI: https://doi.org/10.1016/j.sbspro.2015.12.048

Sun, Y., Mobasheri, A. (2017). Utilizing crowdsourced data for studies of cycling and air pollution exposure: A case study using Strava data. International journal of environmental research and public health, 14(3), 274. DOI: https://doi.org/10.3390/ijerph14030274 DOI: https://doi.org/10.3390/ijerph14030274

Solnit, R. (2001). Wanderlust. A history of walking. New York, Penguin.

Van Der Zee, E. L., Bertocchi, D., Janusz, K. (2017). Using Big Data to discover how the maturity of a heritage destination influences the use and attractiveness of urban cultural landscape. A case study of Antwerp, Bolzano and Kraków. In Proceedings of TLC2016 Conference–Tourism and cultural landscape: Towards a sustainable approach. Foundation for Information Society (INFOTA), 614-628.

Venter, Z. S., Gundersen, V., Scott, S. L., Barton, D. N. (2023). Bias and precision of crowdsourced recreational activity data from Strava. Landscape and Urban Planning, 232, 104686. DOI: https://doi.org/10.1016/j.landurbplan.2023.104686

Visentin, C. (2021). Slowness to Discover the Ordinary Italian Landscape. In Pileri, P., Moscarelli, R. (Eds.). Cycling & Walking for Regional Development, Research for Development. Cham, Springer, 63-81. DOI: https://doi.org/10.1007/978-3-030-44003-9_6 DOI: https://doi.org/10.1007/978-3-030-44003-9_6