Understanding individual mobility patterns from urban sensing data: A mobile phone trace example

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Geo-located Twitter as proxy for global mobility patterns (2013, background): “Because cell phones are almost universally used, data about their use have led to important findings about movement in urban (Calabrese et al. 2010; Kang et al. 2012), regional (Calabrese et al. 2013; Sagl et al. 2012), and national scales (Krings et al. 2009; Simini et al. 2012).”
The promises of big data and small data for travel behavior (aka human mobility) analysis (2016, methodology): “The locations reported in CDR are cell phone tower locations and thus depend on the density of the cellular network, which varies from as little as a few hundred meters in metropolitan areas to a few kilometers in rural regions (e.g., Calabrese et al., 2013; Chen et al., 2014).”
Exploring Universal Patterns in Human Home-Work Commuting from Mobile Phone Data (2013, methodology): “t the cellphone could randomly switch amongst different cell towers even if the caller actually did not move, we also employed spatial filtering using the same method as described in Calabrese et al. [32]. Essentially, we treated all movements within 1-km radius of the original cell tower as “noise””