Mapping Urban Practices Through Mobile Phone Data

Pucci, Paola; Manfredini, Fabio; Tagliolato, Paolo

doi:10.1007/978-3-319-14833-5

Cited by 28 publications

(19 citation statements)

References 15 publications

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“…Maia et al [40] proposed a methodology for characterizing and identifying user behaviors in online social networks and introduced a clustering algorithm to group users that share the similar behavioral pattern in the social network. Pucci et al [41] presented an analysis of the large-scale event and aimed to observe inconsistency of urban spaces and to formulate policies in keeping with the molecular daily practices, and emerging demands can be made by diverse populations using the city and its services at varying rhythms and intensities. While Hong [42] explored the user participation in order to provide insight into Seoul city and analyzed social media data from Foursquare.…”

Section: Related Workmentioning

confidence: 99%

Big Data Analysis to Observe Check-in Behavior Using Location-Based Social Media Data

Muhammad

Wan

2018

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With rapid advancement in location-based services (LBS), their acquisition has become a powerful tool to link people with similar interests across long distances, as well as connecting family and friends. To observe human behavior towards using social media, it is essential to understand and measure the check-in behavior towards a location-based social network (LBSN). This check-in phenomenon of sharing location, activities, and time by users has encouraged this research on the frequency of using an LBSN. In this paper, we investigate the check-in behavior of several million individuals, for whom we observe the gender and their frequency of using Chinese microblog Sina Weibo (referred as “Weibo”) over a period in Shanghai, China. To produce a smooth density surface of check-ins, we analyze the overall spatial patterns by using the kernel density estimation (KDE) by using ArcGIS. Furthermore, our results reveal that female users are more inclined towards using social media, and a difference in check-in behavior during weekday and weekend is also observed. From the results, LBSN data seems to be a complement to traditional methods (i.e., survey, census) and is used to study gender-based check-in behavior.

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Section: Related Workmentioning

confidence: 99%

Big Data Analysis to Observe Check-in Behavior Using Location-Based Social Media Data

Muhammad

Wan

2018

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“…More recent studies (Pasqui, 2008) have proposed an analysis of populations inhabiting the city precisely for specific practices happening there, in order to address more effectively urban policies and planning. Analysis and description of these populations still represent an open question, mostly developed in reference to urban mobility, through the innovative use of phone data (Ratti et al, 2006;Pulselli and Romano, 2009;Pucci, Manfredini and Tagliolato, 2015). In urbanism, following a different path, Anglo-American approaches favour phenomenological descriptions of what happens in the public space, with an interest in spontaneous and informal uses as a key passage to realize an "everyday urbanism" (Leighton Chase, Crawford and Kaliski, 1999).…”

Section: Different Conjugations and Approachesmentioning

confidence: 99%

Vol. 2 n. 1 | 2017 | FULL ISSUE

Bravo¹

2017

JPS

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“…The large penetration of mobile phones, and the ability to locate users by analyzing telecommunication traffic, opens up the possibility of using this technology to study the population mobility at high spatial and temporal resolution. The potential of population data derived from mobile phone traffic, with respect to conventional data, opened up new implications for a better understanding of urban usages, in time and space [1]. Some studies [2,3] first introduced the use of mobile phone data for urban population analysis.…”

Section: Introductionmentioning

confidence: 99%

“…In particular, Traag et al [34] and Furletti et al [29] used such data to detect social events and to develop a framework for deciding who attended an event. Pucci et al [1] analyzed mobile phone data to study important business and sport events occurring in the city of Milan, Italy.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Analysis of Urban Mobility Using Aggregate Mobile Phone Derived Presence and Demographic Data: A Case Study in the City of Rome, Italy

Gariazzo

Pelliccioni

Bogliolo

2019

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Urban mobility is known to have a relevant impact on work related car accidents especially during commuting. It is characterized by highly dynamic spatial–temporal variability. There are open questions about the size of this phenomenon; its spatial, temporal, and demographic characteristics; and driving mechanisms. A case study is here presented for the city of Rome, Italy. High-resolution population presence and demographic data, derived from mobile phone traffic, were used. Hourly profiles of a defined mobility factor (NPM) were calculated for a gridded domain during working days and cluster analyzed to obtain mean diurnal NPM mobility patterns. Age distributions of the population were calculated from demographic data to get insight in the type of population involved in mobility, and spatially linked with the mobility patterns. Census data about production units and their employees were related with the classified NPM mobility patterns. Seven different NPM mobility patterns were identified and mapped over the study area. The mobility slightly deviates from the census-based demography (0.15 on average, in a range of 0 to 1). The number of employees per 100 inhabitants was found to be the main driving mechanism of mobility. Finally, contributions of people employed in different economic macrocategories were assigned to each mobility time-pattern. Results provide a deeper knowledge of urban dynamics and their driving mechanisms in Rome.

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Mapping Urban Practices Through Mobile Phone Data

Cited by 28 publications

References 15 publications

Big Data Analysis to Observe Check-in Behavior Using Location-Based Social Media Data

Big Data Analysis to Observe Check-in Behavior Using Location-Based Social Media Data

Vol. 2 n. 1 | 2017 | FULL ISSUE

Spatiotemporal Analysis of Urban Mobility Using Aggregate Mobile Phone Derived Presence and Demographic Data: A Case Study in the City of Rome, Italy

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