Prediction limits of mobile phone activity modelling

Kondor, Dániel; Grauwin, Sébastian; Kallus, Zsófia; Gódor, István; Sobolevsky, Stanislav; Ratti, Carlo

doi:10.1098/rsos.160900

Cited by 11 publications

(14 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, handling such sensitive data requires appropriate protocols to address concerns around data privacy. While anonymizing data is necessary, Kondor et al (2015) show that it is theoretically possible to identify users based on their mobility patterns alone (Kondor et al, 2015). It is, therefore, best practice to restrict access to individual observations and use aggregated indicators for the purpose of analysis.…”

Section: Context and Literature Reviewmentioning

confidence: 99%

The hidden potential of call detail records in The Gambia

et al. 2021

View full text Add to dashboard Cite

Aggregated data from mobile network operators (MNOs) can provide snapshots of population mobility patterns in real time, generating valuable insights when other more traditional data sources are unavailable or out-of-date. The COVID-19 pandemic has highlighted the value of remotely-collected, high-frequency, localized data in inferring the economic impact of shocks to inform decision-making. However, proper protocols must be put in place to ensure end-to-end user-confidentiality and compliance with international best practice. We demonstrate how to build such a data pipeline, channeling data from MNOs through the national regulator to the analytical users, who in turn produce policy-relevant insights. The aggregated indicators analyzed offer a detailed snapshot of the decrease in mobility and increased out-migration from urban to rural areas during the COVID-19 lockdown. Recommendations based on lessons learned from this process can inform engagements with other regulators in creating data pipelines to inform policy-making.

show abstract

Section: Context and Literature Reviewmentioning

confidence: 99%

The hidden potential of call detail records in The Gambia

et al. 2021

View full text Add to dashboard Cite

show abstract

“…For omnidirectional cells, propagation of the signal strength S(g, a) is modelled as S(g, a) := S 0 − S dist (r g,a ), (18) where S 0 is the signal strength at r 0 = 1 meter distance from the cell in dBm and r g,a is the distance between the middle point of grid tile g and cell a in meters. The value of S 0 can be different for every cell and is assumed to be a known property.…”

Section: Omnidirectional Cellsmentioning

confidence: 99%

“…Mobile network operator (MNO) data have shown to be a rich potential source for official statistics, in particular on present population [2,6,7,18,35,43], mobility [3,8,9,13,15,21,30,41,44], migration [23,24,42], and tourism [7]. Such statistics can be used to support a wide range of policy issues, for instance regarding mobility restrictions in order to delay and contain the COVID-19 pandemic [4,12,28].…”

Section: Introductionmentioning

confidence: 99%

A Bayesian approach to location estimation of mobile devices from mobile network operator data

Tennekes¹,

Gootzen²

2021

Preprint

View full text Add to dashboard Cite

Mobile network operator (MNO) data are a rich data source for official statistics, such as present population, mobility, migration, and tourism. Estimating the geographic location of mobile devices is an essential step for statistical inference. Most studies use the Voronoi tessellation for this, which is based on the assumption that mobile devices are always connected to the nearest radio cell.This paper uses a modular Bayesian approach, allowing for different modules of prior knowledge about where devices are expected to be, and different modules for the likelihood of connection given a geographic location. We discuss and compare the use of several prior modules, including one that is based on land use. We show that the Voronoi tessellation can be used as a likelihood module. Alternatively, we propose a signal strength model using radio cell properties such as antenna height, propagation direction, and power. Using Bayes' rule, we derive a posterior probability distribution that is an estimate for the geographic location, which can be used for further statistical inference.We describe the method and provide illustrations of a fictional example that resembles a real-world situation. The method has been implemented in the R packages mobloc and mobvis, which are briefly described.

show abstract

“…Cell data has been extensively used to analyze transportation infrastructure and commuting patterns [10,11], human mobility [14,16,[21][22][23][24], transportation mode inference [25], and tourism dynamics and human behavior during special events [26][27][28]. Only a few studies have used cell data to understand human interaction with natural resources.…”

Section: Introductionmentioning

confidence: 99%

Evaluating water quality impacts on visitation to coastal recreation areas using data derived from cell phone locations

et al. 2022

View full text Add to dashboard Cite

Linking human behavior to environmental quality is critical for effective natural resource management. While it is commonly assumed that environmental conditions partially explain variation in visitation to coastal recreation areas across space and time, scarce and inconsistent visitation observations challenge our ability to reveal these connections. With the ubiquity of mobile phone usage, novel sources of digitally derived data are increasingly available at a massive scale. Applications of mobile phone locational data have been effective in research on urban-centric human mobility and transportation, but little work has been conducted on understanding behavioral patterns surrounding dynamic natural resources. We present an application of cell phone locational data to estimate the effects of beach closures, based on measured bacteria threshold exceedances, on visitation to coastal access points. Our results indicate that beach closures on Cape Cod, MA, USA have a significant negative effect on visitation at those beaches with closures, while closures at a sample of coastal access points elsewhere in New England have no detected impact on visitation. Our findings represent geographic mobility patterns for over 7 million unique coastal visits and suggest that closures resulted in approximately 1,800 (0.026%) displaced visits for Cape Cod during the summer season of 2017. We demonstrate the potential for human-mobility data derived from mobile phones to reveal the scale of use and behavior in response to changes in dynamic natural resources. Potential future applications of passively collected geocoded data to human-environmental systems are vast.

show abstract

Prediction limits of mobile phone activity modelling

Cited by 11 publications

References 40 publications

The hidden potential of call detail records in The Gambia

The hidden potential of call detail records in The Gambia

A Bayesian approach to location estimation of mobile devices from mobile network operator data

Evaluating water quality impacts on visitation to coastal recreation areas using data derived from cell phone locations

Contact Info

Product

Resources

About