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

Author

Listed:
  • Castillo Milena Suarez
  • Sémécurbe Francois
  • Tao Haixuan Xavier
  • Seimandi Tom

    (1 INSEE, SSP Lab, 88 avenue Verdier, Montrouge,Île-de-France, 92120, France .)

  • Ziemlicki Cezary

    (2 Orange Labs R&D Châtillon, Chatillon,Île-de-France, France .)

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.

Suggested Citation

  • Castillo Milena Suarez & Sémécurbe Francois & Tao Haixuan Xavier & Seimandi Tom & Ziemlicki Cezary, 2023. "Temporally Consistent Present Population from Mobile Network Signaling Data for Official Statistics," Journal of Official Statistics, Sciendo, vol. 39(4), pages 535-570, December.
    • Handle: RePEc:vrs:offsta:v:39:y:2023:i:4:p:535-570:n:8
    DOI: 10.2478/jos-2023-0025
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