By Paola Pucci, Fabio Manfredini, Paolo Tagliolato
This e-book explains the aptitude price of utilizing cell phone information to watch city practices and determine rhythms of use in today’s towns. Drawing upon examine carried out within the Italian quarter of Lombardy, the authors show how maps according to cell phone facts, that are larger adapted to the dynamic techniques at paintings in towns, can rfile city practices, supply new insights into spatial and temporal styles of mobility, and help in spotting diverse groups of perform. The defined method allows distinct visualization of the spatial distribution of mobility flows and provides a extra huge and sophisticated description of the distribution of city job than is supplied via conventional shuttle surveys. The ebook additionally info how maps derived through processing cellphone information may also help within the definition of city rules that might convey providers that fit towns’ wishes, facilitate the administration of enormous occasions (inflow, outflow, and monitoring), and mirror time-dependent phenomena now not incorporated in conventional analyses.
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Additional resources for Mapping Urban Practices Through Mobile Phone Data
The second typology of data consists in localized and aggregated tracks of anonymized mobile phone users. It is an origin-destination datum derived from the Call Detail Record database. The third type of data refers to the mobile switching centre (MSC), which is the primary service delivery node for GSM, responsible for routing voice calls and text messages. With the maps based on the processing of the three types of mobile phone data, it was possible to offer information on temporary populations and city usage patterns (daily/nightly practices, non-systematic mobility).
8 highlights the main residential districts of the Milan urban region along the second circular ring of the city, where the density of the inhabitants reaches the highest value of Milan, as well as some municipalities with large scale social housing and residential neighborhoods in the South, South-West and in the North (Corsico, Rozzano, Sesto San Giovanni). The city centre of Milan appears as a void, due to the gradual replacement of the dwellings with ofﬁces, services and retail spaces over the last few decades.
J Transp Geogr 15(3):172–183 Wolman A (1965) The metabolism of cities. Sci Am 213(3):178–193 Yuan J, Zheng Y, Xie X (2012) Discovering regions of different functions in a city using human mobility and pois. In: Proceedings of the 18th ACM SIGKDD international conference on knowledge discovery and data mining, KDD’, vol 12. ACM, New York, pp 186–194 Chapter 3 Daily Mobility Practices Through Mobile Phone Data: An Application in Lombardy Region Abstract Beginning with the results of a research carried out in the Italian region of Lombardy utilising mobile phone data provided by Telecom Italia, this chapter will demonstrate how new maps, based on mobile phone data and better tailored to the dynamic processes taking place, can represent spatialized urban practices and origin-destination flows of daily movements.