<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Mobile network data records are promising for measuring temporal changes in present populations. This promise has been boosted since high-frequency passively-collected signaling data became available. Its temporal event rate is considerably higher than that of Call Detail Records – on which most of the previous literature is based. Yet, we show it remains a challenge to produce statistics consistent over time, robust to changes in the “measuring instruments” and conveying spatial uncertainty to the end user. In this article, we propose a methodology to estimate – consistently over several months – hourly population presence over France based on signaling data spatially merged with fine-grained official population counts. We draw particular attention to consistency at several spatial scales and over time and to spatial mapping reflecting spatial accuracy. We compare the results with external references and discuss the challenges which remain. We argue data fusion approaches between fine-grained official statistics data sets and mobile network data, spatially merged to preserve privacy, are promising for future methodologies.</p>
</abstract>

Mobile network data records are promising for measuring temporal changes in present populations. This promise has been boosted since high-frequency passively-collected signaling data became available. Its temporal event rate is considerably higher than that of Call Detail Records – on which most of the previous literature is based. Yet, we show it remains a challenge to produce statistics consistent over time, robust to changes in the “measuring instruments” and conveying spatial uncertainty to the end user. In this article, we propose a methodology to estimate – consistently over several months – hourly population presence over France based on signaling data spatially merged with fine-grained official population counts. We draw particular attention to consistency at several spatial scales and over time and to spatial mapping reflecting spatial accuracy. We compare the results with external references and discuss the challenges which remain. We argue data fusion approaches between fine-grained official statistics data sets and mobile network data, spatially merged to preserve privacy, are promising for future methodologies.

Temporally Consistent Present Population from Mobile Network Signaling Data for Official Statistics

Journal of Official Statistics

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Mobile network data records are promising for measuring temporal changes in present populations. This promise has been boosted since high-frequency...

Temporally Consistent Present Population from Mobile Network Signaling Data for Official Statistics

Online veröffentlicht: 10. Dez. 2023

Seitenbereich: 535 - 570

Eingereicht: 01. März 2022

Akzeptiert: 01. März 2023

DOI: https://doi.org/10.2478/jos-2023-0025

Schlüsselwörter
Big data, High frequency statistics, dynamic population mapping, spatial accuracy

© 2023 Milena Suarez Castillo et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Temporally Consistent Present Population from Mobile Network Signaling Data for Official Statistics

Online veröffentlicht: 10. Dez. 2023

Seitenbereich: 535 - 570

Eingereicht: 01. März 2022

Akzeptiert: 01. März 2023

DOI: https://doi.org/10.2478/jos-2023-0025

SchlüsselwörterBig data, High frequency statistics, dynamic population mapping, spatial accuracy

© 2023 Milena Suarez Castillo et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
Big data, High frequency statistics, dynamic population mapping, spatial accuracy