Population Mobility Dynamics Estimated from Mobile Telephony Data

Doyle, John C.; Hung, Peter; Farrell, Alison; McLoone, Seán

doi:10.1080/10630732.2014.888904

Cited by 33 publications

(27 citation statements)

References 37 publications

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“…CDRs are routinely collected by MNOs to facilitate customer billing, problem diagnostics, and network planning, and they have a massive potential to illuminate the spatio-temporal dynamics of individuals and populations at a very high resolution in near real-time (Deville et al, 2014). Researchers are starting to tap into these data sources to understand population characteristics (Douglass et al, 2015), transportation and mobility (Doyle et al, 2014), socio-spatial behaviours and interactions (Gao et al, 2013;Järv et al, 2014), urban spatio-temporal dynamics (Ahas et al, 2015) and inferred aggregate economic activity (Scepanovic et al, 2015). CDRs are thus poised to significantly advance knowledge in these areas, especially in domains that have conventionally relied on out-dated, unrepresentative, or low-resolution data sources (e.g.…”

Section: Introductionmentioning

confidence: 99%

Evidence and future potential of mobile phone data for disease disaster management

2016

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Global health threats such as the recent Ebola and Zika virus outbreaks require rapid and robust responses to prevent, reduce and recover from disease dispersion. As part of broader big data and digital humanitarianism discourses, there is an emerging interest in data produced through mobile phone communications for enhancing the data environment in such circumstances. This paper assembles user perspectives and critically examines existing evidence and future potential of mobile phone data derived from call detail records (CDRs) and two-way short message service (SMS) platforms, for managing and responding to humanitarian disasters caused by communicable disease outbreaks. We undertake a scoping review of relevant literature and in-depth interviews with key informants to ascertain the: i) information that can be gathered from CDRs or SMS data; ii) phase(s) in the disease disaster management cycle when mobile data may be useful; iii) value added over conventional approaches to data collection and transfer; iv) barriers and enablers to use of mobile data in disaster contexts; and v) the social and ethical challenges. Based on this evidence we develop a typology of mobile phone data sources, types, and end-uses, and a decision-tree for mobile data use, designed to enable effective use of mobile data for disease disaster management. We show that mobile data holds great potential for improving the quality, quantity and timing of selected information required for disaster management, but that testing and evaluation of the benefits, constraints and limitations of mobile data use in a wider range of mobile-user and disaster contexts is needed to fully understand its utility, validity, and limitations.

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Section: Introductionmentioning

confidence: 99%

Evidence and future potential of mobile phone data for disease disaster management

2016

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“…The most extreme form of this is when the data is not even available on the individual level but only aggregated on the level of the cells (Louail et al 2014;Ahas et al 2015). Even if the information is available on an individual level, if a fine spatial granularity is not the primary interest, aggregating into broader geographical regions can help reduce the uncertainty and noise in the data as well as simplify inference (Tanahashi et al 2012;Doyle et al 2014). Depending on the analysis that is to be performed, it can be necessary to restrict the user base by dropping users that do not have enough CDR Zhao et al 2016;Ahas et al 2010).…”

Section: Related Workmentioning

confidence: 99%

“…Should mobile internet achieve similar penetration rates in the future as do mobile phones today or if handover data becomes more widely available, these methods can be applied to the full breadth of CDR data. While some work has been put into reconstructing movement from CDR requiring less data, it mostly still interprets the data similar to GPS trajectories (Doyle et al 2014;Schulz, Bothe, and Körner 2012;Calabrese et al 2013). However, we think that this should only be done if the CDR are temporally dense enough.…”

Section: Related Workmentioning

confidence: 99%

“…They can be used as an alternative to GPS to infer estimates of the temporally varying locations of significant fractions of the population of a given geographical area, such as a city, region or country (Ahas et al 2015;Trasarti et al 2015;Doyle et al 2014;Blondel, Decuyper, and Krings 2015). Similar to GPS-trajectories, CDR datasets are typically "data rich" but semantically very poor.…”

Section: Introduction and Aim Of This Workmentioning

confidence: 99%

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Extracting regular mobility patterns from sparse CDR data without a priori assumptions

Burkhard

Ahas

Saluveer

et al. 2017

Journal of Location Based Services

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In this work we present two methods that can extract habitual movement patterns and reconstruct the underlying movement of users from their call detail records (CDR) in a way that works for users with only moderate numbers of CDRs and that does not make any prior assumptions on the behaviour of the users. The methods allow for a more comprehensive user base in large-scale studies due to the fact that users that might otherwise have to be discarded can also be analysed. The first one is computationally not overly intense and is based on association mining. The second one, which we named DAMOCLES, is based on extracting idiosyncratic daily patterns from clustered daily activities. The methods are evaluated on real data of 140 users over an average of 200 days against benchmarks using assumptions commonly found in the literature such as a work week from Monday to Friday on GPS ground truth. Both methods clearly outperform the benchmarks and for many users retrieve similar regularities. Additionally a simulation study is performed that allows to evaluate the methods in a more controlled environment. In this work we present two methods that can extract habitual movement patterns and reconstruct the underlying movement of users from their call detail records (CDR) in a way that works for users with only moderate numbers of CDRs and that does not make any prior assumptions on the behaviour of the users. The methods allow for a more comprehensive user base in large scale studies due to the fact that users that might otherwise have to be discarded can also be analysed. The first one is computationally not overly intense and is based on association mining. The second one, which we named DAMOCLES is based on extracting idiosyncratic daily patterns from clustered daily activities. The methods are evaluated on real data of 140 users over an average of 200 days against benchmarks using assumptions commonly found in the literature such as a work week from Monday to Friday on GPS ground truth. Both methods clearly outperform the benchmarks and for many users retrieve similar regularities. Additionally a simulation study is performed that allows to evaluate the methods in a more controlled environment.

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“…These studies advanced not only in terms of data size but also in visualization and representation. In recent years, mobile data have been used to identify the commuting of residents, and further the functional zones of the city [23][24][25][26]. By comparing them to actual census data, the results of analysis have been proven relatively accurate with evidently improved data size and precision.…”

Section: Introductionmentioning

confidence: 99%

Evaluation and Planning of Urban Green Space Distribution Based on Mobile Phone Data and Two-Step Floating Catchment Area Method

Liu

et al. 2018

Sustainability

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Urban green space is closely related to the quality of life of residents. However, the traditional approach to its planning often fails to address its actual service capacity and users' demand. In this study, facilitated by mobile phone location data, more specific features of the spatial distribution of urban residents are identified. Further, population distribution in relation to traffic analysis zones is mapped. On this basis, the two-step floating catchment area method (2SFCA) is adopted in combination with urban green space planning to evaluate the per capita area of green space and its accessibility in practice. Subsequently, classification of per capita area and spatial distribution of green spaces within the study area are obtained; thus, urban districts currently with low accessibility to green areas are identified and can be deemed as key areas for the planning of green areas in the future. The study concludes that mobile phone data can be used to more accurately map the spatial distribution of residents; while, the 2SFCA offers a more comprehensive quantitative measuring of the supply and demand of green spaces. The two combined can be used as an important basis for decision-making in the planning of urban green spaces. Since urban green space can be regarded as a kind of public facility, the methodology of the present study is also believed to be applicable in studies of other types of urban facilities.

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Population Mobility Dynamics Estimated from Mobile Telephony Data

Cited by 33 publications

References 37 publications

Evidence and future potential of mobile phone data for disease disaster management

Evidence and future potential of mobile phone data for disease disaster management

Extracting regular mobility patterns from sparse CDR data without a priori assumptions

Evaluation and Planning of Urban Green Space Distribution Based on Mobile Phone Data and Two-Step Floating Catchment Area Method

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