Abstract
Urbanization represents a huge opportunity for computer applications enabling cities to be managed more efficiently while, at the same time, improving the life quality of their citizens. One of the potential applications of this kind of systems is a bottom-up evaluation of the level of walkability of the city (namely, the level of usefulness, comfort, safety and attractiveness of an urban area for walking). This is based on the usage of data from social media for the computation of structured indicators describing the actual usage of areas by pedestrians. This paper will present an experimentation of analysis of data about the city of Milano (Italy) acquired from Flickr and Foursquare. Over 500 thousand points, which represent the photos and the POIs collected from the above-mentioned social media, were clustered through an iterative approach based on the DBSCAN algorithm, in order to achieve homogeneous areas defined by the actual activity of inhabitants and tourists rather than by a top-down administrative procedure and to supply useful indications on the level of walkability of the city of Milan.
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References
Abley, S.: Walkability scoping paper. Charted Traffic Transp. Eng. 4, 2011 (2005)
Agampatian, R.: Using GIS to Measure Walkability: A Case study in New York City (April), pp. 1–65. Royal Institute of Technology (KTH), Stockholm (2014)
Brindley, P., Goulding, J., Wilson, M.L.: Generating vague neighbourhoods through data mining of passive web data. Int. J. Geogr. Inf. Sci. 1–26 (2017)
Day, K., Boarnet, M., Alfonzo, M., Forsyth, A.: Irvine Minnesota Inventory, pp. 1–6. (2005)
Duncan, D.T.: Whats your walk score®? Am. J. Prev. Med. 45(2), 244–245 (2013)
Ester, M., Kriegel, H.P., Sander, J., Xu, X., et al.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: KDD, vol. 96, pp. 226–231. AAAI Press, Portland (1996)
Ewing, R., Handy, S.: Measuring the unmeasurable: urban design qualities related to walkability. J. Urban Des. 14(1), 65–84 (2009)
Foth, M., Choi, J.H.J., Satchell, C.: Urban informatics. In: Proceedings of the ACM 2011 Conference on Computer Supported Cooperative Work, pp. 1–8. ACM, New York (2011)
Han, J., Pei, J., Kamber, M.: Data mining: concepts and techniques. Elsevier, Amsterdam (2011)
Hu, Y., Gao, S., Janowicz, K., Yu, B., Li, W., Prasad, S.: Extracting and understanding urban areas of interest using geotagged photos. Comput. Environ. Urban. Syst. 54(Supplement C), 240–254 (2015)
Quercia, D., Aiello, L.M., Schifanella, R., Davies, A.: The digital life of walkable streets. In: Proceedings of the 24th International Conference on World Wide Web, WWW ’15, pp. 875–884. International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, Geneva (2015)
Quercia, D., Schifanella, R., Aiello, L.M.: The shortest path to happiness: recommending beautiful, quiet, and happy routes in the city. In: Proceedings of the 25th ACM Conference on Hypertext and Social Media, HT ’14, pp. 116–125. ACM, New York (2014)
Salton, G., Wong, A., Yang, C.S.: A vector space model for automatic indexing. Commun. ACM 18(11), 613–620 (1975)
Speck, J.: Walkable city: how downtown can save America, one step at a time. Farrar, Straus and Giroux (2012)
United Nations: World Urbanization Prospects: The 2014 Revision. United Nations, Department of Economic and Social Affairs, Population Division (2014)
Walkable America: Walkability checklist: How walkable is your community? (2013). https://www.nhtsa.gov/sites/nhtsa.dot.gov/files/walkingchecklist.pdf
Wefering, F., Rupprecht, S., Bührmann, S., Böhler-Baedeker, S.: Guidelines. Developing and implementing a sustainable urban mobility plan. In: Workshop, March, p. 117 (2013)
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Berzi, C., Gorrini, A., Vizzari, G. (2019). Mining the Social Media Data for a Bottom-Up Evaluation of Walkability. In: Hamdar, S. (eds) Traffic and Granular Flow '17. TGF 2017. Springer, Cham. https://doi.org/10.1007/978-3-030-11440-4_20
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DOI: https://doi.org/10.1007/978-3-030-11440-4_20
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