Exploring the use of mobile phone data for national migration statistics

Abstract

Statistics on internal migration are important for keeping estimates of subnational population numbers up-to-date as well as urban planning, infrastructure development and impact assessment, among other applications. However, migration flow statistics typically remain constrained by the logistics of infrequent censuses or surveys. The penetration rate of mobile phones is now high across the globe with rapid recent increases in ownership in low-income countries. Analysing the changing spatiotemporal distribution of mobile phone users through anonymized call detail records (CDRs) offers the possibility to measure migration at multiple temporal and spatial scales. Based on a dataset of 72 billion anonymized CDRs in Namibia from October 2010 to April 2014, we explore how internal migration estimates can be derived and modelled from CDRs at subnational and annual scales, and how precision and accuracy of these estimates compare to census-derived migration statistics. We also demonstrate the use of CDRs to assess how migration patterns change over time, with a finer temporal resolution compared to censuses. Moreover, we show how gravity-type spatial interaction models built using CDRs can accurately capture migration flows. Results highlight that estimates of migration flows made using mobile phone data is a promising avenue for complementing more traditional national migration statistics and obtaining more timely and local data.

Measuring mobility, disease connectivity and individual risk: a review of using mobile phone data and mHealth for travel medicine

“…a subscriber with 30 days or less worth of data for each year, could be filtered out to obtain more accurate estimates of population movements. 58 Furthermore, these passive positioning data derived from CDRs can also be used to measure seasonal changes in subnational population numbers and produce density maps of human distribution changes over multiple timescales, providing more precise denominators for health metrics than static measures from censuses. 20 However, CDRs cannot measure spatial movements finer than tower-level spatial resolution, and estimates are limited to domestic movements, as it is more difficult to obtain CDRs from operators in different countries to get estimates of international traveller flows.…”

Mapping monthly population distribution and variation at 1-km resolution across China

“…However, these static data lack sufficient information on short-term (e.g., seasonal or monthly) fluctuations in population distribution. With the availability of emerging data from mobile phones (Deville et al 2014, Ma 2018, Lai et al 2019 and social media platforms (Longley andAdnan 2016, Dong et al 2019), an increasing number of studies have applied such dynamic big data to overcome the limitations associated with static models. Such dynamic data reveal people's movement at the individual level, improve population estimates (Patel et al 2017), and also enable dynamic population mapping.…”

A method to estimate population densities and electricity consumption from mobile phone data in developing countries

“…al. which shares the same aim, but uses direct regressions from average values only [19]. Note that contrary to previous methods, the clustering relies solely on mobile phone data, without requiring a population training dataset.…”