Estimating the Residential Population from Mobile Phone Data, an Initial Exploration

Sakarovitch, Benjamin; Bellefon, Marie-Pierre de; Givord, Pauline; Vanhoof, Maarten

doi:10.24187/ecostat.2018.505d.1968

Cited by 12 publications

(16 citation statements)

References 28 publications

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“…Literature does exist on the use of mobile phone data to estimate population numbers, see e.g. (Sakarovitch et al, 2018). Doing so is not within the scope of the research presented here but would be of value in testing mobile phone representability.…”

Section: Mobile Network Datamentioning

confidence: 99%

Modelling Representative Population Mobility for COVID-19 Spatial Transmission in South Africa

et al. 2021

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The COVID-19 pandemic starting in the first half of 2020 has changed the lives of everyone across the world. Reduced mobility was essential due to it being the largest impact possible against the spread of the little understood SARS-CoV-2 virus. To understand the spread, a comprehension of human mobility patterns is needed. The use of mobility data in modelling is thus essential to capture the intrinsic spread through the population. It is necessary to determine to what extent mobility data sources convey the same message of mobility within a region. This paper compares different mobility data sources by constructing spatial weight matrices at a variety of spatial resolutions and further compares the results through hierarchical clustering. We consider four methods for constructing spatial weight matrices representing mobility between spatial units, taking into account distance between spatial units as well as spatial covariates. This provides insight for the user into which data provides what type of information and in what situations a particular data source is most useful.

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Section: Mobile Network Datamentioning

confidence: 99%

Modelling Representative Population Mobility for COVID-19 Spatial Transmission in South Africa

et al. 2021

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“…Tessellations may be seen as a further special case of the particular on/off coverage case above: as each point in space is covered by exactly one and only one cell (k j = 1) the emission probabilities reduce to binary values p ij ∈ {0, 1}. In other words, when cells are mutually non-overlapping, the data generating process becomes deterministic: the binary model matrix P after consolidation reduces to the identity matrix, and the estimation problem becomes trivial as far as no external information is taken into account (such as, e.g., prior information in Bayesian settings as considered in [27]). In this contribution, we focus on the more general (and non trivial) case of overlapping cells, with the understanding that the proposed solution will be applicable also to the special case of tessellations.…”

Section: G Linking Cell Data To Emission Probabilitiesmentioning

confidence: 99%

On the Estimation of Spatial Density From Mobile Network Operator Data

Ricciato

Coluccia

2023

IEEE Trans. on Mobile Comput.

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We tackle the problem of estimating the spatial distribution of mobile phones from Mobile Network Operator (MNO) data, namely Call Detail Record (CDR) or signalling data. The process of transforming MNO data to a density map requires geolocating radio cells to determine their spatial footprint. Traditional geolocation solutions rely on Voronoi tessellations and approximate cell footprints by mutually disjoint regions. Recently, some pioneering work started to consider more elaborate geolocation methods with partially overlapping (nondisjoint) cell footprints coupled with a probabilistic model for phone-to-cell association. Estimating the spatial density in such a probabilistic setup is currently an open research problem and is the focus of the present work. We start by reviewing three different estimation methods proposed in literature and provide novel analytical insights that unveil some key aspects of their mutual relationships and properties. Furthermore, we develop a novel estimation approach for which a closed-form solution can be given. Numerical results based on semi-synthetic data are presented to assess the relative accuracy of each method. Our results indicate that the estimators based on overlapping cells have the potential to improve spatial accuracy over traditional approaches based on Voronoi tessellations.

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“…Various literature exists on the use of mobile phone data to estimate population numbers (see e.g. [21]).…”

Section: Mobile Network Datamentioning

confidence: 99%

Modelling Representative Population Mobility for COVID-19 Spatial Transmission in South Africa

Potgieter¹,

Fabris‐Rotelli²,

Kimmie³

et al. 2021

Preprint

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The COVID-19 pandemic starting in the first half of 2020 has changed the lives of everyone across the world. Reduced mobility was essential due to it being the largest impact possible against the spread of the little understood SARS-CoV-2 virus. To understand the spread, a comprehension of human mobility patterns is needed. The use of mobility data in modelling is thus essential to capture the intrinsic spread through the population. It is necessary to determine to what extent mobility data convey the same message of mobility within a region. This paper compares different mobility data sources by constructing spatial weight matrices and further compares the results through hierarchical clustering. This provides insight for the user into which data provides what type of information and in what situations a particular source is most useful.

show abstract

Estimating the Residential Population from Mobile Phone Data, an Initial Exploration

Cited by 12 publications

References 28 publications

Modelling Representative Population Mobility for COVID-19 Spatial Transmission in South Africa

Modelling Representative Population Mobility for COVID-19 Spatial Transmission in South Africa

On the Estimation of Spatial Density From Mobile Network Operator Data

Modelling Representative Population Mobility for COVID-19 Spatial Transmission in South Africa

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