ARTICLE
Exploring the use of mobile phone data for national
migration statistics
Shengjie Lai 1,2,3, Elisabeth zu Erbach-Schoenberg1,2, Carla Pezzulo1, Nick W. Ruktanonchai1,2,
Alessandro Sorichetta1,2, Jessica Steele1, Tracey Li2, Claire A. Dooley1,2 & Andrew J. Tatem1,2
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. Analyzing 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 modeled 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 with censuses. Moreover, we
show how gravity-type spatial interaction models built using CDRs can accurately capture
migration flows. The 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.
https://doi.org/10.1057/s41599-019-0242-9 OPEN
1 WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton SO17 1BJ, UK. 2 Flowminder Foundation, SE-113 55
Stockholm, Sweden. 3 School of Public Health, Key Laboratory of Public Health Safety of the Ministry of Education, Fudan University, 130 Dongan Road,
200032 Shanghai, China. These authors contributed equally: Shengjie Lai, Elisabeth zu Erbach-Schoenberg Correspondence and requests for materials
should be addressed to S.L. (email: laishengjie@foxmail.com) or to A.J.T. (email: Andy.tatem@gmail.com)
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Introduction
H
uman populations are highly mobile in the modern world,
and migration is one of the main factors that determines
changes in population size, distribution, and structure
(Abel and Sander, 2014; Agliari et al. 2018). As migration impacts
the demographic and socio-economic aspects of a country, it has
become one of the most challenging issues confronting policymakers
for nations around the world (International Organization
for Migration, 2017a, c). Understanding internal migration,
which is normally substantially larger than international migration
rates, and their changes over time is critical for keeping
subnational population numbers up-to-date (Frayne, 2005; Pendleton
et al. 2014; Wardrop et al. 2018). Contemporary data on
internal migration flows are valuable for urban planning, resource
allocation, infrastructure development, public service provision,
and impact assessments. For instance, identifying where people
migrate internally is often vital in development work, as migrants
might be marginalized and at higher risk due to a lack of
resources to meet demands (Lu et al. 2012; Lu et al. 2016; Ruktanonchai
et al. 2016a). However, our knowledge of contemporary
internal migration patterns remains poor for many
countries (Garcia et al. 2015; Sorichetta et al. 2016; International
Organization for Migration, 2017b), and is difficult to update
between data collections for the majority of countries around the
world.
Data collected from traditional sources, such as national
population and housing censuses and household surveys, are the
primary source for migration statistics (International Organization
for Migration, 2018). Within population and housing censuses,
migration is typically measured through a change in
residence over a 1-year or 5-year period prior to the census. The
increasing use of global positioning systems (GPS) has supported
the collection of more spatially precise data, but each census only
provides a single snapshot of migration flows, commonly once
every decade, and migration patterns typically change over time
between censuses or surveys (Namibia Statistics Agency, 2013;
Wesolowski et al. 2013). Moreover, surveys only sample a small
proportion of population, and the logistical challenge of censuses
makes them an infrequent and expensive source of demographic
data (Wardrop et al. 2018).
Moreover, as migration is anticipated to continue to rise, both
in terms of volume and reach, the need for timely updates to
demographic statistics and inform migration policy development
increases–a need that traditional sources are typically not wellequipped
to meet (International Organization for Migration,
2018). To predict contemporary migration for many countries, a
growing interest in the modeling of migration flows emerged,
leading to the advanced development of modeling methodologies
to estimate migration rates (Courgeau, 1995; Henry et al. 2003;
Cohen et al. 2008; Abel, 2013; Abel and Sander, 2014; Garcia et al.
2015; Sorichetta et al. 2016; Vobruba et al. 2016). However,
regardless of how sophisticated these methods are, these estimates
remain largely constrained by the lack of contemporary input
data and often their coarse spatiotemporal resolution (Garcia
et al. 2015; Sorichetta et al. 2016).
Call detail records (CDRs) routinely collected by mobile phone
operators for billing purposes are particularly promising for
analyzing migration-related phenomena and a potential solution
to existing data gaps (International Organization for Migration,
2018). CDRs contain an entry for each call or text (or other
billable event) made or received by any anonymous user, together
with the date and time of each communication and an identifier
for the tower that the communication was routed through within
the operator’s network (Ruktanonchai et al. 2016b; Zu ErbachSchoenberg
et al. 2016). Then the tower-level location of each
communication can be identified, and from this, spatially and
temporarily explicit estimates of human mobility, which can be
derived from anonymised CDRs from the movement of individual
mobile user between different communications. These data
have been increasingly used for quantifying short-term human
mobility, mapping dynamically changing population densities,
estimating infectious disease spread risk, and measuring population
displacements due to disasters and conflicts (Lu et al. 2012;
Wesolowski et al. 2012; Deville et al. 2014; Tatem et al. 2014;
Wesolowski et al. 2014a; Wesolowski et al. 2015a; Wesolowski
et al. 2015c; Lu et al. 2016; Ruktanonchai et al. 2016b; Zu ErbachSchoenberg
et al. 2016; Wesolowski et al. 2017). Moreover, previous
work on defining overall and seasonal patterns of population
movement using CDRs suggested they could also be used to
model internal migration (Blumenstock, 2012; Wesolowski et al.
2013; Ruktanonchai et al. 2016a; Wesolowski et al. 2017).
In previous studies, however, CDRs frequently spanned much
shorter periods than one year, or multi-year mobility analysis
using CDRs have been presented, but no studies have compared
individual places of usual residence across different years to
estimate migration flows by matching the definition of migration
used in censuses (Blumenstock, 2012; Zu Erbach-Schoenberg
et al. 2016; Wesolowski et al. 2017). Based on a multiannual CDR
dataset in Namibia, for the first time, we assess how CDRs as a
novel data source might be used efficiently and accurately to
replicate the internal migration statistics produced in a census,
and examine how CDRs could improve the estimates made using
classical gravity models. This study also reveals otherwise
unmeasurable year-by-year migration patterns to assess the
potential of CDRs for updating internal migration statistics.
Datasets
Census migration statistics. The most recent census in Namibia
was conducted in 2011, and we obtained the internal migration
statistics between regions from a census-based migration report
published by the Namibia Statistics Agency in 2015 (Namibia
Statistics Agency, 2015). To derive the 1-year internal migration
statistics, the census (with a reference night of 28 August 2011)
asked about each individual’s place of usual residence (where does
the person usually live?) and the place of previous residence
(where did the person usually live since September 2010?). The
place of residence refers to the location where a person usually
lives for the majority part of any year (at least six months). An
individual was considered as an internal migrant if the regions of
usual residence and previous residence did not match in the 2011
census.
CDR-derived flow data. To assess whether mobile network data
could produce comparable migration statistics, we obtained a
large dataset of anonymized 72 billion CDRs between October
2010 and April 2014 from Mobile Telecommunications Limited
(MTC) (Mobile Telecommunications, 2018) (Mobile Telecommunications,
2018) (Mobile Telecommunications, 2018).
MTC is the leading network operator in Namibia with a 76%
market share and providing network spatial coverage 95%
population (Mobile Telecommunications, 2018). The CDR dataset
obtained from MTC included the time and routing tower for
each call and text and a random uniquely hashed number for each
user. The approximate location of a user was defined by the
location of the routing mobile phone tower for each communication.
The data were spatially aggregated to regional level to
match the census migration data and to further reduce sensitivities
of using individual level data. We estimated a user's place of
residence for a given period as the region where the user was
observed most frequently during the period of interest. As the
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data on very infrequent mobile phone users or seasonal movement
(e.g., short-term travels in holidays), might introduce noise
in defining residential places, we only included any user who was
active for more than 30 days each year (12 months) defined as
below.
To match as closely as possible the time frame used in census
and to be comparable between the 2011 and 2012 periods, we
defined the residence of each user for each year: Year 1 (October
2010–September 2011), Year 2 (October 2011–September 2012),
and Year 3 (October 2012–September 2013), respectively. We
derived migration flows of mobile phone users for periods 2011
and 2012 by comparing residences between Years 1 and 2, and
between Years 2 and 3, respectively. If mobile users changed
residence between the two years, they were identified as migrants,
otherwise as non-migrants. In addition, we also assessed the
potential impact of data filtering and different time lengths on
defining residences (Supplementary information [SI] text).
Model covariates. For estimating migration by models for the
2011 period, we also collated potential migration-related demographic,
socioeconomic, geographic, and environmental variables,
as described in previous studies (Garcia et al. 2015; Sorichetta
et al. 2016), including population by region in 2010 and 2011
(Namibia Statistics Agency, 2013); the proportions of population
living in urban areas, male population, population aged 15–59,
educated population, labor force participation, and marital status
in population at aged 15 years and above; administrative unit
boundaries to define the distance and contiguity between regions
and their area (Zhao et al. 2012); and the average annual precipitation
by region. The collation of covariates is detailed in the
SI Text.
Models and analysis
We fit three types of models to census data to explore whether
CDR-derived migration data can accurately replicate traditional
census-derived migration statistics. Three types of models were
included (Table S1): (1) CDR-based linear models (CDRLMs),
simply using CDR-derived migrating user data alone or combined
with covariates used in gravity models; (2) gravity-type
spatial interaction models (GTSIMs), which have been applied
extensively to estimate migration flows based on a range of
migration-related push-pull factors including populations and
distance between origin and destination (Zipf, 1946; Hua and
Porell, 1979; Garcia et al. 2015; Wesolowski et al. 2015b; Ruktanonchai
et al. 2016a; Sorichetta et al. 2016; Vobruba et al. 2016);
and (3) GTSIMs extended using CDR data (thereafter called
CGTSIMs).
CDR-based linear models. Initially, we used Pearson correlation
coefficients to assess the relationship between CDR and census
data. To investigate how well the CDRs can replicate the census
migration numbers, we built four sub-models of CDRLMs using
independent variables of CDR-derived migrating user numbers or
integrating with other covariates:
MIGi;j ¼ β0 þ β1CDRi;j þ β
!
X½ Š ð1Þ
where the dependent variable MIGi,j is comprised of the observed
migration flows between regions in Namibia from the census.
CDRi,j is the number of CDR-derived migrations from origin i to
destination j, with the coefficient β1 and the constant β0. The suite
of models was built by successively adding same covariates that
were used in GTSIMs and represented by the matrix X and its
vector of coefficients β
!
.
Gravity-type spatial interaction models. In the simplest form of
gravity models (Zipf, 1946), the flow of migration between
regions is proportional to their total populations and inversely
proportional to the distance between them:
MIGi;j ¼
POP
β1
i POP
β2
j
DIST
β3
i;j
ð2Þ
where POPi and POPj refer to populations at an origin i and a
destination j in 2010, respectively; DISTi,j represents the distance
between i and j; The exponents, β1, β2, and β3, are used to indicate
the magnitude of the effect for each variable.
As a range of potential push-pull factors, e.g., urbanization and
natural disaster, could affect human migration, the models can be
further extended to reach more accurate estimates as described in
previous studies (Garcia et al. 2015; Sorichetta et al. 2016).
However, given that the number of regions in Namibia is small
(13 regions) and to prevent overfitting, we only tested models by
replacing the total population variables with the percentage of
population living in urban areas (URBANi and URBANj) and the
precipitation (RAINi and RAINj) in origin and destination,
respectively (SI text). Although both logistic and Poisson
regressions have been widely used in gravity models to predict
migration flows, the outputs from logistic regression should be
identical to estimates of Poisson regression by adding an offset
variable of non-migrating populations (Garcia et al. 2015;
Ruktanonchai et al. 2016a; Sorichetta et al. 2016). Therefore, we
only fit GTSIMs using the logistic regression function here:
MIGi;j
TOTi
¼
eβ0þβ1Piþβ2PjÀβ3DISTi;j
1 þ eβ0þβ1Piþβ2PjÀβ3DISTi;j
ð3Þ
where TOTi represents the total population residing in an origin i
in 2010, and where Pi and Pj refer to the push factor at origin and
pull factor at destination, respectively (Table S1). Moreover, the
CGTSIMs with additional CDRs variables were tested to assess
how well the CDR-derived migration data could improve the
performance of gravity models.
Model comparisons. By fitting to census statistics for each model,
we used a leave-one-out-cross-validation approach (Hastie et al.
2009) to split the dataset to calculate the goodness-of-fit indicators,
including root-mean-square error (RMSE), R-squared (R2)
and Akaike Information Criterion (AIC). The model with the
lowest RMSE was determined as the best model of each model
family. The estimates of migration between regions were then
calculated using the optimal model, and the inflow, outflow and
netflow for each region in Namibia were also aggregated.
As our models used non-spatial regression approaches, and
spatial autocorrelation may exist in migration data (Tobler, 1970;
Getis, 2008; Sorichetta et al. 2016), a shuffle test was used to assess
whether any spatial dependencies significantly affected the
performance of our models. First, we randomly permuted the
census-derived migration data across all regions. Then each
model was fitted to calculate RMSE by using each shuffled
dependent variable, and the distribution of RMSE could be
produced through 1000 iterations. If the “real” RMSE of each
model that was fitted with the “ground truth” migration data was
less than all 1000 simulated values of RMSE using the shuffled
data, we assumed that the spatial dependencies were not
significant in our models. All analyses were done within the R
statistical environment (version 3.5.2), and fitting procedures of
models were conducted using caret Package (Kuhn, 2008; R Core
Team, 2018).
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Estimating migration for the 2012 period. Due to the lack of
migration statistics in 2012 for fitting models in Period 2012, the
CDRLM using only CDR data and its coefficients fitted for Period
2011 were used to predict the migration for Period 2012 and
compare the pattern of migration across periods. Moreover, to
account for increasing numbers of mobile phone users from 2011,
the CDR-derived data for migrating users for Period 2012 were
inversely weighted by the increasing rate of mobile phone users
for each region to offset the potential bias introduced by
increasing mobile ownership across periods.
Mobile phone ownership analysis. As mobile phone users only
represent a proportion of the whole population, we utilized data
from the 2013 Namibia DHS (The Namibia Ministry of Health
et al. 2014) to assess the extent to which there is a possible
exclusion of certain groups at a household level within the CDRs
in the context of Namibia (SI text). To account for potential
mobile phone ownership biases across regions, the models
mentioned above were also tested by using CDR data adjusted by
two approaches respectively: (1) using the proportion of mobile
phone ownership to inversely weight CDR-derived migration
data by region; and (2) adding the proportion of ownership as an
additional variable into models.
Results
Correlations between census-derived and CDR-derived
migrations. According to the 2011 Namibia population and
housing census (Namibia Statistics Agency, 2015), a total of
40,867 Namibian (2.0% of 2,013,671 people) migrated by changing
their places of residence between regions in Namibia over
the one-year period prior to the census in August 2011, with the
highest migration into Khomas, the capital region of Namibia,
and the highest migration out from the Zambezi region in the
northeast of Namibia (see Fig. 1 and S1). Based on the anonymized
CDRs in Namibia between October 2010 and April 2014,
we estimated the number of migrating mobile users by comparing
their residences between two years of October 2010–September
2011 and October 2011–September 2012 in Period 2011 (SI text;
Figs S2 and S3). A high correlation (Pearson's coefficient,
r = 0.91) was found between the numbers of census-derived
population and mobile phone users included in Period 2011 (Fig.
2a). Furthermore, the migration flows were also highly correlated
(r = 0.84) between census data and CDR-derived 117,173
migrating mobile users (11.2% of 1,049,379 users) (Figs S4 and
S5).
Substantial differences in the Zambezi region were observed
when comparing the census and CDR data, with more censusderived
migrants than from the CDRs (Figs S5 and S6). The
Zambezi region lost a significant proportion of its population
(5.5%), which was attributed to displacement due to floods in the
period of April-June 2010, out of the time frame of the census and
CDRs (International Federation of Red Cross and Red Crescent
Societies, 2011; Namibia Statistics Agency, 2015). According to
definitions used in census (SI text) (Namibia Statistics Agency,
2015), if people moved to the places of displacement before
September 2010 and still lived in the same places by the time of
census, they should be considered as non-migrants. Therefore,
the displaced populations from Zambezi before September 2010
may well have been misclassified as migrants in the census.
Moreover, based on the data of CDR-derived monthly residence,
the inflow and outflow of Zambezi seem to be seasonal without
aberrational high movements from October 2010 to April 2014
(Fig. S7). After removing the data from Zambezi, the relationships
between census-derived and CDR-derived migration data
significantly improved, with the r value increasing from 0.84 to
0.96. Therefore, we present the following results without the
Zambezi region, and relevant comparable analyses for all regions
are provided in the SI.
Comparing migration prediction models. In general, the
goodness-of-fit indicators, including RMSE, R2, and AIC, show
Fig. 1 Census-derived internal migration in Namibia, September 2010–August 2011. a Net migration by region. The number of net migrants by region is
presented under the name of each region. b Circular plot of migrant flows between regions. The origins and destinations of migrants are each assigned a
color and represented by the circle’s segments. The direction of the flow is encoded by both the origin region’s color and a gap between the flow and the
destination region’s segment. The volume of movement is indicated by the width of the flow at the beginning and end points. Tick marks on the circle
segments show the number of migrants (inflows and outflows)
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that CDRLMs using only CDR data could precisely and accurately
replicate census-derived statistics, with a better predictability
than GTSIMs (Figs S8–S10). Moreover, the performance of
GTSIMs could be substantially improved by using CDRs. Comparing
the “real” RMSE with the distributions of RMSEs generated
by the shuffled census data, it was evident that spatial
autocorrelation was not significant in our models (Fig. S11).
According to the optimized model with the lowest RMSE, all
three families of models could capture the patterns of migration
flows between regions (Fig. S12), but CDRLMs had a higher
accuracy in predictions compared with GTSIMs and CGTSIMs
(Fig. 3). Additionally, in terms of outflow, inflow, and net
Fig. 2 Logarithmic relation between census-derived populations in 2011 and the number of CDR-derived mobile phone users in Periods 2011 (a) and 2012
(b) at regional level. The green solid lines represent linear regression fit, with p and R2 values provided
Fig. 3 Precision and accuracy assessments of models for replicating 2011 census-derived migration statistics. The indicators of a root-mean-square error
(RMSE), b R2, and c Akaike Information Criterion (AIC), were computed to compare three types of models: CDR-based linear model (CDRLM), gravity-type
spatial interaction model (GTSIM), and CDR-based GTSIM (CGTSIM). The scatterplots of census data versus estimates using models are presented in d–f,
respectively. The Zambezi region as an outliner is excluded, and unadjusted CDR data are used. For GTSIM and CGTSIM, only models with the lowest
RMSE are showed here. The formula and results of all models are presented in Table S1 and Figs S8–S10. *The model #1 of CDRLMs. **The model #3 of
CDRLMs
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migration aggregated by region, the estimates from CDRLM were
highly correlated (R2 = 0.97, 0.97, and 0.94 respectively) with the
census-derived data (Fig. 4 and S13).
Mobile phone ownership bias and model adjustment. As
mobile users only represent a proportion of the population, to
understand the potential phone ownership bias, we utilized data
from the 2013 Namibia Demographic and Health Survey (DHS)
(The Namibia Ministry of Health et al. 2014) to assess to the
extent to which there is a possible exclusion of certain groups
with specific characteristics from CDRs in Namibia. The 2013
DHS reported that although the large majority (88.5%) of
households interviewed owned at least one mobile phone (The
Namibia Ministry of Health et al. 2014), the lower-income and
rural households with older and uneducated heads were less likely
to be able to afford a cell phone, and there was a significant
ownership differential between regions in Namibia (SI text;
Tables S2 and S3). To account for the potential mobile ownership
bias between regions, two approaches were used to adjust CDRs,
respectively. However, the performance of both CDRLMs and
CGTSIMs were not significantly improved by these adjustments
(Figs S8–S10).
Predicting migration in 2012. The multiannual time series of
CDRs in Namibia allows us to assess their potential to be used to
update intercensal national statistics and understand the changing
patterns of internal migrations across years. By comparing
the places of residence between the two years of October
2011–September 2012 and October 2012–September 2013
(hereafter called Period 2012), we captured 144,064 migrants in
1,238,124 mobile users, with a similar proportion of 11.6% as
Period 2011. The increasing numbers of migrations between
Fig. 4 Comparing regional outflow, inflow and net migration between census data and models’ estimates for Period 2011: CDRLM in a–c, GTSIM in d–f, and
CGTSIM in g–i. For each type of model, the results of model with the lowest RMSE are presented. The Zambezi region as an outliner is excluded, and
unadjusted CDR data are used
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periods was likely due to the increasing penetration rate of mobile
phones across years (Figs S4 and S14). To compare migration
patterns between two periods, we adjusted the number of CDRderived
migrating users in Period 2012 by region to offset the
increasing mobile phone ownership across periods. Then, the
simplest CDRLM using only CDR data and its coefficients estimated
for Period 2011 were used to predict migration for Period
2012 using the corresponding adjusted CDR data (Fig. S14). We
observed highly consistent patterns of migration flows between
Periods 2011 and 2012 as well as the outflows, inflows and net
migration aggregated by region (Figs S14–S16). However, the
relative differences across periods show greater variations in
outflow than in inflow between regions, with more people moving
out from the West-South regions and into the northern regions in
Namibia (see Fig. 5).
Discussion
Migration is difficult to measure frequently, particularly at local
scales, and data from censuses are typically collected just once every
decade, pushing a need for innovation in the production of
migration statistics (International Organization for Migration,
2018). The penetration rate of mobile phones is now high across the
globe, and analyzing the changing spatiotemporal distribution of
mobile phone users through anonymized CDRs offers the possibility
to measure migration at multiple temporal and spatial scales.
Global mobile phone network subscriber numbers passed the five
billion mark in 2017 with a global penetration rate of 66%, and the
number is forecasted to continue to grow, moving upto 71% by
2025, with rapid recent increases in ownership in low-income
countries (The GSM Association, 2018). The data collected every
second by mobile network operators have the potential to contribute
to the “big data revolution” in complementing more
traditional statistics through updating internal migration statistics
in a timely, accurate and low-cost way.
This study demonstrates how the analysis of CDRs can replicate
national internal migration statistics to complement outputs
from censuses. The multiannual time series of CDRs with high
spatiotemporal resolution facilitates the derivation of residence
measures, matching closely the definitions used in censuses. We
found that not only can the estimates of migration produced
through CDRs be as accurate as census data-derived measures,
but these data offer additional benefits in terms of updating
intercensal migration numbers and understanding changing
patterns of annual internal migration. Additionally, the methodologies
presented are designed to be easy to implement while
considering the impact of heterogeneous phone ownership across
regions and years, and the simple linear model built using CDRs
results in estimates with high precision and accuracy.
Results here suggest that CDRs can also improve the performance
of gravity models. The GTSIMs explicitly state the spatial
interaction relationship between migration and the push-pull factors
that represent the benefits and costs of migration (Zipf, 1946;
Hua and Porell, 1979). The estimates made using gravity models
contribute to a better understanding of migration patterns, with
known boundaries to their accuracy in the absence of censuses or
surveys. However, due to the lack of high spatiotemporal resolution
input data on contemporary population movements, such models
used in previous studies resulted in high uncertainties in estimates
(Garcia et al. 2015; Sorichetta et al. 2016; Vobruba et al. 2016).
Though biases exist, as CDR-derived migration data directly relate
to populations who moved across the country over years, a combination
of CDRs and other migration-related covariates could
facilitate a significant improvement in the precision and accuracy
of outputs from gravity models.
Fig. 5 Relative difference of regional outflow a and inflow b between Period 2011 and Period 2012. The migrations were estimated by the CDRLM using only
CDRs, and the adjusted CDR data of Period 2012 were used to offset the impact of the increasing mobile phone ownership across periods. The numbers of
migrants by region in Period 2011 and Period 2012 are presented under the name of each region, respectively, and the Zambezi region as a significant
outliner is excluded
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Internal migration is common in Namibia, and we estimated a
larger number of migrating mobile phone users compared with
those migrating within the census data. One reason is that CDRs
do not suffer from recall bias (Wesolowski et al. 2014b) and
capture missing data from people who moved, but did not register
their previous residence in the census. Moreover, different time
windows for data capture may also have contributed, with the
CDR-based home definition window used here being wider than
the census collection date. As elsewhere, the largest proportion of
migration in Namibia is rural-to-urban migration, a phenomenon
that relates partly to rapid urbanization (Garcia et al. 2015;
Namibia Statistics Agency, 2015; International Organization for
Migration, 2016). However, to accurately derive these migration
flows and patterns using CDRs, any impacts from seasonal
temporary movement should be minimized, such as holidayrelated
travel in December, patterns that are highly repetitive in
Namibia (Zu Erbach-Schoenberg et al. 2016; Wesolowski et al.
2017). Using a 12-month time frame to define residence of mobile
users may prevent bias of residence towards the temporary
locations of seasonal travel (SI text). Further, the high temporal
resolution of longitudinal CDRs enables the derivation and
update of different statistical indicators of migration using varying
periods, e.g., 2-year or 3-year migrations.
Some limitations must be acknowledged. First, to prevent
overfitting and multicollinearity, our models did not test a large
number of demographic, socioeconomic, geographic, and environmental
factors and their combinations that might potentially
affect migration as described before (Henry et al. 2003; Henry
et al. 2004; Garcia et al. 2015; Wesolowski et al. 2015b; Ruktanonchai
et al. 2016a; Sorichetta et al. 2016; Vobruba et al. 2016).
Another methodological shortcoming is the lack of correction for
spatial autocorrelation in the modeling by using a spatial
regression model. However, a shuffle approach showed that any
spatial dependencies likely did not significantly affect the performance
of our models.
Mobile users only cover a proportion of the population,
therefore, CDRs may provide an incomplete picture, not
accounting for those who do not own and use a phone, mobile
phone sharing, network coverage, or alternative networks. The
spatiotemporal and demographic variations in the behavior of
phone users can also bias population distribution and migration
estimates (Lu et al. 2012; Deville et al. 2014). Mobile phone
ownership typically biases toward more educated, urban males (SI
text), and mobile network coverage may be substantially lower in
remote rural locations (Wesolowski et al. 2017). However, a high
proportion of the population in Namibia were SIM card owners
that appeared in the CDRs (Stork, 2011), and a high share of
ownership at household level was also found in the 2013 DHS
data (The Namibia Ministry of Health et al. 2014). With continuously
increasing mobile coverage and declining costs for
handsets and network usage, the proportion of people owning
and using mobile phones has been steadily increasing (The GSM
Association, 2018), which will also decrease the influence of the
problem of phone sharing, which is common in areas with low
cell phone penetration.
In addition, to account for the impact of increasing user
numbers across years on migration estimates, we adjusted the
CDR-derived data for comparing interannual migration patterns,
but these only represent an initial step for adjusting for mobile
phone usage changes. Future studies on estimating migration
could use other appropriate data, such as travel history and
mobile phone use surveys to infer possible correlation in mobile
use and migration in demographic-specific subgroups. In addition,
due to the availability of data, we only investigated here
internal migration over the course of a year. Long-term internal
migration (>5 years) could be estimated by analyzing CDRs over
a longer period and these could be integrated with additional data
sources, such as Google Location History data (Ruktanonchai
et al. 2018), to address relevant underlying research questions and
technical issues in the future.
The results here show that estimates of migration flows made
using CDRs is a promising avenue for complementing more
traditional national statistics and obtaining more timely and local
data. The metrics and approaches can inform distinctly different
policy-relevant needs that require migration statistics and the
implementation of policies geared towards providing relevant
public services. Partnerships between governments and phone
companies supported by appropriate incentives could enable
accurate and rapid production of national migration statistics to
complement census and survey-based data collection.
Data availability
The internal migration statistics between regions in Namibia in
2011 are available in the migration report published by the
Namibia Statistics Agency in 2015 (https://cms.my.na/assets/
documents/Migration_Report.pdf). The data of demographic and
socioeconomic covariates used in this study were obtained from
the main report of the Namibia 2011 Population and Housing
Census (
https://cms.my.na/assets/documents/
p19dmn58guram30ttun89rdrp1.pdf). The administrative unit
boundary at regional level matching the year of the census in
Namibia is available at the Global Administrative Areas Database
(https://gadm.org/maps/NAM_1.html), and the precipitation
data can be obtained from the WorldClim version 2 (http://
worldclim.org/version2). The call detail records datasets analyzed
during the current study are not publicly available since that
would compromise the agreement with the mobile phone
operator that made the data available for research, but information
about the process of requesting access to the mobile phone
data that support the findings of this study are available from the
corresponding author on reasonable request.
Received: 13 November 2018 Accepted: 1 March 2019
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Acknowledgements
The authors would like to thank MTC for providing access to the mobile phone data. S.L. is
supported by the grants from the National Natural Science Fund (No. 81773498), the
Ministry of Science and Technology of China (2016ZX10004222-009), and the Program of
Shanghai Academic/Technology Research Leader (No. 18XD1400300). A.J.T. is supported
by funding from the Bill and Melinda Gates Foundation (OPP1106427, 1032350,
OPP1134076, OPP1094793), the Clinton Health Access Initiative, the UK Department for
International Development (DFID) and the Wellcome Trust (106866/Z/15/Z, 204613/Z/16/
Z). C.P. is supported by funding from the Bill and Melinda Gates Foundation
(OPP1134076). J.S. is supported by funding from the Belgian Federal Science Policy Office
(BELSPO). This work forms part of the outputs of WorldPop (www.worldpop.org) and
Flowminder (www.flowminder.org). The funders had no role in the study design, data
collection, and analysis, decision to publish, or preparation of the manuscript.
Author contributions
S.L., E.z.E.-S., and A.J.T. conceived and designed the manuscript; S.L., E.z.E.-S., and C.P.
performed the analysis; S.L., E.z.E.-S., C.P., N.W.R., A.S., J.S., T.L., C.A.D., and A.J.T.
wrote the paper.
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