Mobile Landscapes: Using Location Data from Cell Phones for Urban Analysis

By Carlo Ratti, Dennis Frenchman, R. Pulselli, Sarah Williams. 2006

918 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, Geography, Planning and Development, Transportation

View Paper: https://www.semanticscholar.org/paper/ee52d52b700880e725af592224fc8127a4087a59

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In Electrical and Electronic Engineering

In Geography, Planning and Development

In Transportation

Citations in the corpus

From mobile phone data to the spatial structure of cities (2014, neutral): “(eds)] [1–25](ISTE-Wiley, 2011).”
The Geography of Taste: Analyzing Cell-Phone Mobility and Social Events (2010, background): “GPS, digital cameras, Bluetooth, WiFi) allow for unforeseen amounts of data from urban areas through which to study both groups [9–11] , individuals [12] or both [3].”
Mapping Urban Land Use by Using Landsat Images and Open Social Data (2016, methodology): “Efforts have been made to use social data sets, such as mobile phone records [26–28], taxi data [29,30], and smart card data [31], to reveal characteristics of urban land uses.”
Deriving origin destination data from a mobile phone network (2007, methodology): “The most recent investigation was carried out by Ratti at the MIT SENSEable City Laboratory [ 10 , 11].”
Dynamics of Person-to-Person Interactions from Distributed RFID Sensor Networks (2008, background): “Monitoring human mobility and social relationships using mobile phone traces [7,13,14,15,16,17] scales up to millions of individuals but provides no direct information on face-to-face interactions unless”
A new insight into land use classification based on aggregated mobile phone data (2013, background): “…approaches include the description of urban landscapes (i.e., the space–time structure of residents’ activities in a city) (Pulselli et al. 2006, Ratti et al. 2006, Sevtsuk and Ratti 2010, Sun et al. 2011, Jacobs-Crisioni and Koomen 2012, Loibl and Peters-Anders 2012), population estimates…”
Understanding intra-urban trip patterns from taxi trajectory data (2012, methodology): “The motion of mobile users leads to varying traffic intensities of corresponding base stations, which can be measured using Erlang values.1 Ratti et al. (2006) mapped the dynamics of urban activities in the metropolitan area of Milan, Italy, using the Erlang values of cell phone stations.”
Prediction of urban human mobility using large-scale taxi traces and its applications (2012, background): “From these digital footprints, it is feasible for researchers to extract social and community intelligence [1], ranging from urban environment dynamics [2,3] to social events [4,5].”
Human mobility: Models and applications (2017, background): “[320] was one of the first papers to explicitly discuss the possibilities offered by individual digital footprints for urban analysis.”
The statistical physics of cities (2019, background): “However, more recently, cell phone networks, which capture large amounts of human behavioural data [11], have provided information about the structure of cities [38] and their dynamical properties.”
Wearable Environmental Sensors and Infrastructure for Mobile Large-Scale Urban Deployment (2016, background): “THE problem of reliable large-scale mobile urban data collection is often approached by either gaining access to data sets from cellular providers [1], or by deploying custom applications written for popular mobile platforms [2].”