Cellular Census: Explorations in Urban Data Collection

By Jonathan Reades, Francesco Calabrese, Andres Sevtsuk, Carlo Ratti. IEEE pervasive computing, 2007

452 citations recorded by the source corpus.

Much of our understanding of urban systems comes from traditional data collection methods such as surveys by person or phone. These approaches can provide detailed information about urban behaviors, but they're hard to update and might limit results to "snapshots in time." In the past few years, some innovative approaches have sought to use mobile devices to collect spatiotemporal data. But little research has been done to develop and analyze the much larger samples of existing data generated daily by mobile networks. The most common explanation for this is that the challenge of data-sharing with the telecommunications industry has hampered data access. However, in early 2006, a collaboration between Telecom Italia, which serves 40 percent of the Roman market, and MIT's SKNSEable City Laboratory (http://senseable.mit.edu) allowed unprecedented access to aggregate mobile phone data from Rome. Here, we explore how researchers might be able to use data for an entire metropolitan region to analyze urban dynamics.

Subfields: Global and Planetary Change, Marketing, Transportation

View Paper: https://www.semanticscholar.org/paper/12dab7e37946b2e35e595f414e35513833225ddf

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Global and Planetary Change

In Marketing

In Transportation

Citations in the corpus

Urban Sensing Using Mobile Phone Network Data: A Survey of Research (2014, methodology): “Classical time series analysis is initially performed (for example, the Principal Component Analysis technique has been used in (Reades et al., 2007) or the Dynamic Time Warping technique in (Yuan and Raubal, 2012)) and clustering of time series can classify places based on usage (like the Fuzzy…”
Hyrax: Cloud Computing on Mobile Devices using MapReduce (2009, methodology): “This could be used, for instance, to estimate levels of danger in a crisis situation in terms of temperatures, light, and noise levels and how they have been changing over time, or to visualize mobile usage distributions in a city as in Reades et al. [2007].”
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].”
Understanding individual mobility patterns from urban sensing data: A mobile phone trace example (2013, background): “…of pervasive systems like cellular networks, GPS devices, and WiFi hotspots that allow us to capture massive 2 https://www.iea.org/impagr/cip/archived_bulletins/issue_no23.htm amounts of real-time data related to people and cities (Reades et al. 2007, Gonzalez et al. 2008, Wang et al. 2009).”
Eigenplaces: Analysing Cities Using the Space–Time Structure of the Mobile Phone Network (2009, methodology): “More information on the tradeoffs involved in the use of Erlang for urban research can be found in Reades et al (2007).”
Using Mobile Positioning Data to Model Locations Meaningful to Users of Mobile Phones (2010, methodology): “Other sources than call activity are also used for passive positioning, such as Erlang of antennae (Reades et al. , 2007).”
Human mobility: Models and applications (2017, background): “Following this, a series of papers highlighted possibilities of real-time visualization and monitoring of displacements in cities [321, 322, 94].”
C-SPARQL: a Continuous Query Language for RDF Data Streams (2010, background): “Several interesting cases of people acting as sensors are already documented, for instance in Wikimapia and OpenStreetMap [19] people voluntarily provide geographic information, in [32] the presence and the movements of people is estimated using the density of communication and the number of cellula”
Real-Time Urban Monitoring Using Cell Phones: A Case Study in Rome (2011, background): “Different analysis is being done on the acquired data (see, for instance, [15] and [16]) to quantitatively evaluate the usability of the data for urban-planning purposes and tourist management, with the principal aim being to show how modern digital technologies and their applications could give cit”