# Predicting Socio-Economic Well-being Using Mobile Apps Data: A Case Study of
France

Rahul Goel, Angelo Furno, and Rajesh Sharma R. Goel and R. Sharma are with the
Institute of Computer Science
University of Tartu, Estonia, e-mail<EMAIL_ADDRESS>Furno is with LICIT-
ECO7 UMR_T9401, University of Lyon, ENTPE, University Gustave Eiffel, Lyon,
France, e-mail<EMAIL_ADDRESS>
###### Abstract

Socio-economic indicators provide context for assessing a country’s overall
condition. These indicators contain information about education, gender,
poverty, employment, and other factors. Therefore, reliable and accurate
information is critical for social research and government policing. Most data
sources available today, such as censuses, have sparse population coverage or
are updated infrequently. Nonetheless, alternative data sources, such as call
data records (CDR) and mobile app usage, can serve as cost-effective and up-
to-date sources for identifying socio-economic indicators. in

This work investigates mobile app data to predict socio-economic features. We
present a large-scale study using data that captures the traffic of thousands
of mobile applications by approximately 30 million users distributed over
550,000 km2 and served by over 25,000 base stations. The dataset covers the
whole France territory and spans more than 2.5 months, starting from 16th
March 2019 to 6th June 2019. Using the app usage patterns, our best model can
estimate socio-economic indicators (attaining an R-squared score upto 0.66).
Furthermore, using models’ explainability, we discover that mobile app usage
patterns have the potential to reveal socio-economic disparities in
IRIS111IRIS is a region used to divide the country into units of similar
population size.. Insights of this study provide several avenues for future
interventions, including user temporal network analysis to understand evolving
network patterns and exploration of alternative data sources.

###### Index Terms:

mobile applications, digital data, typical week signature (TWS), revealed
comparative advantage (RCA), standardized cumulative utilization (SCU)

##  I Introduction

One of the fundamental pillars for the growth of economy is the fast-growing
metropolitan areas. However, the prevalence of societal disparity is a major
concern that restricts the development of a nation. For example, the increased
clustering of individuals based on gender, religion, language, or color in
metropolitan cities comes at a tremendous cost in terms of segregation [1, 2,
3]. In a nutshell, segregation is defined as the degree to which two or more
demographic groups are separated, and it has drawn much attention for many
decades [4, 5]. Segregation has far-reaching consequences that go beyond
economics. In various parts of the same city, individuals may experience
linguistic isolation, widely divergent levels of education, health care,
employment, and other resources [6, 7, 8]. The development and successful
implementation of policies to address these issues need fine-grained, often
updated data on income, education, and inequality across metropolitan regions.
However, most data sources used today, such as demographic censuses or
surveys, have sparse population coverage or are updated infrequently due to
their high costs. As a result, they do not fit the swift evolution that urban
societies are experiencing nowadays.

Researchers have used alternate digital traces, such as Mobile call data
records and mobile application usage, to understand societal well-being [9,
11, 12]. According to [13], there were 5.31 billion unique mobile phone users
worldwide in January 2022 – equivalent to 67.1 percent of the world’s total
population. Of these mobile users, 4.95 billion people use the Internet –
equal to 62.5 percent of the world’s total population. What is more, fewer
than 3 billion people worldwide do not use the Internet, marking another
important milestone in the universal accessibility of mobile phones. These
figures show mobile reach and the significance of call and digital data in
studying people worldwide.

Call and Digital data has recently been recommended as an alternate source for
determining socio-economic status [9, 14, 15, 16]. Researchers have created
socio-economic prediction models with unprecedented temporal and geographical
resolutions owing to the increasing usage of mobile devices, social media, and
the expanding availability of ubiquitous satellite data [17, 18, 19, 20].
Human mobility and social contacts were shown to be linked to higher income in
data from mobile phones and social media [21, 22, 23, 24]. Although these
methodologies are pretty effective in forecasting socio-economic features in
underdeveloped countries, they are only somewhat accurate in developed
countries with more nuanced variations in mobile phone adoption [25, 26, 27].

Research question and highlights: Considering the literature on Call Data
Records (CDR) [1, 2, 3] and past research to predict socio-economic features
using mobile service consumption data [9], we show that mobile application
usage patterns would be able to predict socio-economic features in a nation.
The primary goal of using mobile digital data to anticipate socio-economic
features is to eliminate or replace costly and time-consuming censuses. This
leads to our research question, that is how accurately we can predict socio-
economic conditions using mobile applications usage patterns. The same
research question is answered in [9] using mobile application data and
population structure variables from census data. However, the focus of our
research is solely on the use of mobile applications to predict socioeconomic
features. The reason for not utilizing population data is that this study
attempts to provide an alternative to expensive and time-consuming census
chores to collect socioeconomic information. The highlights of this study are
as follows:

  1. 1.

We conduct a large-scale investigation using nationwide and multi-source
datasets. We utilized three different datasets: (i) Mobile applications usage
data of size 2TB (terabyte) spanning more than 2.5 months by approximately 30
million subscribers distributed over more than 550,000 km2 and served by over
25,000 base stations in a major European country with one of the world’s
largest economy, France [28].; (ii) Socio-economic features covering economic
status, population, and education information for around 49,000 IRIS1; and (3)
Geographical information provided by the French Institut national de
l’information géographique et forestière (IGN) for around 49,000 IRIS.

  2. 2.

Using Mobile apps data, we extracted three patterns for each IRIS, i.e., week
signature on an hourly basis, Revealed Comparative Advantage (RCA), and
Standardized Cumulative Utilization (SCU), explained in Section IV in detail.
These three extracted patterns cover a different aspects of information from
the dataset. The week signature on an hourly basis aims at summarizing the
mobile traffic behavior into a meaningful profile capable of showing the whole
week’s activity pattern. The RCA is an econometrics index capable of capturing
the comparative advantage or disadvantage of the consumption of an application
in a given IRIS. Lastly, the SCU index captures the statistics aspect of the
dataset.

Previous studies of predicting socio-economic features used the econometric
index RCA for calculating the relative advantage or disadvantage of mobile app
usage in IRIS areas and population data from census [9]. In contrast, this
paper examines the typical weekly temporal patterns emerging in the use of
mobile apps, which we call week signature, on an hourly basis. We discover
that the week signature not only helps to predict socioeconomic features but
also has the capability of capturing the entire week behavior (weekdays,
weekends, morning, afternoon, evening, and night activity). Details are
provided in Section VI.

  3. 3.

We experiment with various machine learning based regression models trained on
patterns (week signature, RCA, and SCU) extracted from the usage of the mobile
applications. However, we report the best regression model in our case,
CatBoost [29]. The proposed approach achieves an R-squared score up to 0.66
using the CatBoost regression model to estimate economic, educational, and
demographic features (Section VI). The results support the research question
of this study that mobile app data can be used to predict socio-economic
features. It is to be noted that the results of this study are not directly
comparable with [9] since the authors employed census population data along
with RCA as independent variables to predict socio-economic features.

Furthermore, for CatBoost model explainability, we use the SHAP (Shapley
additive explanations) value [38]. The SHAP is a cooperative game theory-based
strategy for increasing the transparency and interpretability of machine
learning models. Using SHAP we display the contribution or relevance of top-20
features for the CatBoost model’s prediction.

  4. 4.

Finally, we compared the two CatBoost model results, in which one of them is
trained on the mobile applications usage patterns (see Section VI-C) and the
other is trained on the actual population information or census of France (see
Section VI-D) for predicting Income, Gini index (Income inequality), and
people with higher education in IRIS. We find that mobile applications’ usage
patterns achieve scores that are at least as good as those obtained with
census data.

Paper organisation.  Next, we explain three datasets utilized in this study:
mobile app traffic, geographical, and socio-economic features in Section II.
Then, areal weighted interpolation of IRIS in Section III and extracted the
mobile apps patterns in Section IV. Section V explores the socio-economic
disparity from the extracted pattern. We cover the result and explainability
in Section VI. Finally, conclusions and limitations are outlined in Section
VII.

##  II Dataset Description

This section describes various datasets which are used in this study. First,
we discuss the mobile apps (or applications) data (in Section II-A), and next,
in Section II-B we explain in detail the geographical information and socio-
economic features dataset.

###  II-A Mobile Applications Traffic Data

This study uses mobile applications dataset which records the uplink (data
transferred by the user’s mobile) and downlink (data flowing to the user’s
mobile) byte counts per mobile app at a temporal granularity of 1 minute. The
dataset is aggregated for antennas located at base stations (BS). Orange
France collected the data which spans for more than 2.5 months, from 16th
March 2019 to 6th June 2019. The dataset contains the utilization of multiple
mobile apps and covers approximately 30 million subscribers distributed over
more than 550,000 km2 and by 25,000 base stations. The data is timestamped at
the 1-minute granularity and contains BS latitude and longitude information
records for various mobile apps, such as WhatsApp, Twitter, and YouTube-
including apps from both Apple Store for ios devices and Google Play for
android devices.

Mobile applications categorization. We manually categorized the top 99
percentile mobile applications in terms of traffic load into 19 classes. We
chose the top 99 percentile mobile apps since only a small number of apps
generate a significant volume of data according to the power law [14, 30]. The
idea of categorizing mobile apps is also explored in past research [31]. The
mobile apps that are selected include heterogeneous apps and encompass
Advertising (e.g., Web Advertising), Android download (e.g., Google Play
Store), Apple cloud (e.g., iCloud storage), etc. Table S1 in the supplementary
document contains the detailed information about application categorization.

Ethical concern. Our research follows strong ethical standards. Orange, France
provided the data and was responsible for data collection, processing, and
storage. They follow the rules of the European Commission’s General Data
Protection Regulation (GDPR). All these privacy-related activities were also
monitored by the Orange Data Privacy Officer and the CNIL, the French
governmental body responsible for safeguarding privacy in the use of personal
data. In this study, we used aggregated data among thousands of people at the
antenna level, which does not pose a privacy threat and does not qualify as
personal data.

###  II-B Geographical data and socio-economic features

This section describes the geographical and socio-economic features dataset
used in this paper. Please note that the mobile application data used in this
study is from 2019, whereas the geographical and socio-economic features
datasets are from 2018. The geographical and socio-economic features datasets
from 2018 are chosen for two reasons. First, keeping the geographic
information and socio-economic feature consistent is vital. Second, these
publicly available datasets are the ones that are temporally closest to our
mobile app traffic data.

Geographical data. We used publicly available geographical information
provided by the French Institut national de l’information géographique et
forestière (IGN). For the geographical description, we used IRIS-level
information. We downloaded the Contours IRIS édition 2018
dataset222https://www.data.gouv.fr/en/datasets/contours-iris-insee-ign/, which
contains coverage area for each IRIS zone in France, and also the IRIS code,
name, and type. According to the Institut national de la statistique et des
études économiques or National Institute of Statistics and Economic Studies
(INSEE) in France, IRIS divides the country into units of equal size in terms
of population, also known as IRIS2000. Here, IRIS refers to _aggregated units
for statistical information_ , and 2000 refers to the target size of 2000
residents per basic unit.

Socio-Economic features. For understanding the population structure and socio-
economic features in France, we leveraged the following datasets from INSEE:

  1. 1.

Economic status: For economic status, we downloaded the Revenus, pauvreté et
niveau de vie en 2018 (Iris)333https://www.insee.fr/fr/statistiques/5055909
dataset [32], which contains income information for residential IRIS zones.
Due to privacy reasons, IGN does not share features for areas with less than
1000 people. After filtering out IRIS zones with missing values, we have the
economic status for 9,145 IRIS zones (out of 48,931) of France as shown in
Fig. S1(a) in the supplementary document.

We work on disposable income, meaning people declare their total incomes once
a year (salary, allowances, rent, financial products, etc.) and pay taxes on
their total incomes. The disposable income is what they keep after
redistribution. Next, we select three features: Poverty, Median income, and
Gini Index from the economic statuses, which are vital for understanding the
economic situation of the IRIS. The selected features and their definition are
shown in Table I.

  2. 2.

Population information: For the population information, we utilized the
Population en 2018444https://www.insee.fr/fr/statistiques/5650720 dataset,
which contains the population information for various age groups, gender,
immigrants, and professional category. The selected features from the
population information based on correlation are shown in Table I. For
population information, we have data for 45,508 IRIS zones (out of 48,931) as
shown in Fig. S1(b) in the supplementary document.

  3. 3.

Education information: For education statistics, we used the Diplômes -
Formation en 2018555https://www.insee.fr/fr/statistiques/5650712 dataset,
which contains the academic level information (BEPC, BAC, SUP, CAPBEP, etc.).
The selected features from the education information are shown in Table I.
Similar to population, we have data for 45,508 IRIS zones (out of 48,931) for
education information.

Socio-Economic Indicator | Definition
---|---
Economic conditions |
Poverty | Poverty rate at the threshold of 60% of disposable income per metropolitian median per person (%)
Median Income | Median income per person (in euros)
Gini Index | The Gini Index summarizes the dispersion of income across the entire income distribution i.e, it measure income inequality
Population information |
Total population | Total number of people
Pop 0-14 | Number of people aged between 0 to 14 years
Pop 15-29 | Number of people aged between 15 to 29 years
Pop 30-44 | Number of people aged between 30 to 44 years
Pop 45-59 | Number of people aged between 45 to 59 years
Pop 60-74 | Number of people aged between 60 to 74 years
Pop 75+ | Number of people aged 75 years or more
Immigrants | Number of people who are immigrants
CS1 | Number of people aged 15 or more who are Farmer operators
CS2 | Number of people aged 15 or more who are Craftsmen, Traders, Company managers
CS3 | Number of people aged 15 or more who are Managers and higher intellectual professions
CS4 | Number of people aged 15 or more who are Intermediate professions
CS5 | Number of people aged 15 or more who are Employees
CS6 | Number of people aged 15 or more who are Worker
CS7 | Number of people aged 15 or more who are Retired
CS8 | Number of people aged 15 or more who fall in Others w/o professional activity
Male | Number of people who are male
Female | Number of people who are female
Education information |
No diploma | Number of out-of-school people aged 15 or over with no diploma or atleast a CEP
BEPC or CAPBEP | Number of out-of-school people aged 15 or over holding a BEPC, college certificate, DNB, CAP, BEP or equivalent;
BAC | Number of out-of-school people aged 15 or over with a Baccalaureate, professional certificate or equivalent
SUP | Number of out-of-school people aged 15 or over with a higher education diploma at Bac +2 level or more
TABLE I: Selected socio-economic features (per IRIS area) with their
definitions from INSEE.

Next, we performed the Pearson’s correlation test to study the correlation
between all 25 socio-economic features as shown in Figure 1. All weak
correlation values (between -0.5 and 0.5) are set to null for ease of
interpretation. From Figure 1, we observe that poverty is negatively
correlated with median income, which is obvious. The median income is
positively correlated with high intellectual profession and education,
implying that high professionals and highly educated individuals earn more.
Similarly, we can observe that individuals with age above 60 years are retired
(correlation value as 0.91).

Figure 1: Correlation plot for socio-economic features.

As mentioned earlier, the Geographical data and socio-economic features
mentioned in this section are at the IRIS level. However, the mobile app data
is at the antenna level. Therefore, we convert the mobile app data to IRIS
level using the areal weighted interpolation method.

##  III Areal weighted interpolation of IRIS

In this section, we identified the areal weighted interpolation of IRIS using
base station location and IRIS geographic information. As mentioned earlier,
our mobile app data is aggregated by antennas. These antennas are located at
base stations, which means a base station (BS) can correspond to single or
multiple antennas. Therefore, as a first step, we mapped all the antennas to
their respective BS based on their latitude (LAT) and longitude (LON)
information. Next, we followed the technique mentioned in [10, 9] for the
areal weighted interpolation of IRIS. We begin by modeling the coverage area
of each BS using the Voronoi polygon.

(a) Voronoi Polygon

(b) IRIS Zone

(c) Voronoi and IRIS zone mapping

Figure 2: Areal weighted interpolation for mapping base station (BS) to IRIS.
Figure 2a shows the Voronoi polygon generated using BS in a bounded plane. The
IRIS zone area coverage is shown in Figure 2b. In Figure 2c, the areal
weighted interpolation of mobile app usage into the IRIS zone is shown. The
highlighted box at the bottom-right of Figure 2c shows an IRIS, comprising the
weighted data from four BS. (best seen in color)

Let us suppose we have finite number of BS, $N$, in the two-dimensional
Euclidean plane, and assume that 2 $\leq$ $N$ $<\infty$. The $N$ number of BS
are labeled by $bs_{1}$, $bs_{2}$,…, $bs_{N}$ with the Cartesian coordinates
($x_{1}$, $y_{1}$), ($x_{2}$, $y_{2}$), …, ($x_{N}$, $y_{N}$) or location
vectors $X_{1}$, $X_{2}$, …, $X_{N}$. The $N$ points are distinct in the sense
that $X_{i}\neq X_{j}$ for $i\neq j$, where $i$, $j$ $\in I_{N}$ = {1, · · · ,
$N$}, where $I_{N}$ represents a set of $N$ natural numbers. Let $bs$ be an
arbitrary BS in the Euclidean plane with coordinates ($x$, $y$) or location
vector $X$. Then the Euclidean distance from $bs$ to $bs_{i}$ is given by

$d(bs,bs_{i})=||X-X_{i}||=\sqrt{(x-x_{i})^{2}+(y-y_{i})^{2}}$ (1)

If $bs_{i}$ is the nearest point from $bs$, we have the relation
$||X-X_{i}||\leq||X-X_{j}||$ for $j\neq i$, where $j\in I_{N}$. Let B =
{$bs_{1}$, $bs_{2}$,…, $bs_{N}$}, then the Voronoi polygon region associated
with $bs_{i}$ is given by

$V(bs_{i})=\\{X\ s.t.\ ||X-X_{i}||\leq||X-X_{j}||\ for\ j\neq i,j\in I_{N}\\}$
(2)

and the Voronoi diagram generated by $B$ is given by the set

$V=\\{V(bs_{1}),V(bs_{2}),...,V(bs_{N})\\}$ (3)

Figure 2a provides an example of the Voronoi polygon generated using BS in a
bounded plane. In equation 3, we have defined a Voronoi diagram in an
unbounded plane. However, in most cases we deal with a bounded region. Now,
the bounded Voronoi diagram generated by $B$ for a bounded region $R$ is given
by the set

$V_{bnd}=V\cap R=\\{V(bs_{1})\cap R,...,V(bs_{N})\cap R\\}$ (4)

Let suppose we have finite number of IRIS zones, $Z$, which are labelled as
$I_{1},I_{2},...,I_{Z}$. Then, the areal weight for IRIS zone $i$ with respect
to all the BS bounded Voronoi polygon is given by

$W(I_{i})=\left\\{\frac{A(V_{bnd}(bs_{1})\cap
I_{i})}{A(V_{bnd}(bs_{1}))},...,\frac{A(V_{bnd}(bs_{N})\cap
I_{i})}{A(V_{bnd}(bs_{N}))}\right\\}$ (5)

where, $V_{bnd}(bs_{i})$ represents the bounded Voronoi polygon for $bs_{1}$,
$(V_{bnd}(bs_{1})\cap I_{i})$ represents the overlapping area of Voronoi
polygon for $bs_{1}$ and IRIS zone $i$. A($\cdot$) represents the area of the
region or polygon within the parenthesis. Hence, $\frac{A(V_{bnd}(bs_{1})\cap
I_{i})}{A(V_{bnd}(bs_{1}))}$ is the areal weight for IRIS zone $i$ with
respect to the $bs_{1}$ bounded Voronoi polygon. This areal weight is further
multiplied by the total traffic recorded for the respective BS to calculate
the traffic consumption.

Next, the identified aerial weights are multiplied by the total traffic
recorded for an app category on the respective BS of an IRIS to calculate the
traffic consumption by an app category in the IRIS zone. For example, let us
consider the generic IRIS zone $Z$, containing three BS with aerial weights
$W_{1},W_{2}$ and $W_{3}$ and traffic $T_{1},T_{2}$ and $T_{3}$ for app
category $A$ at any given time. The total traffic consumption for app category
$A$ in the considered IRIS $Z$ will amount to
$T_{1}*W_{1}+T_{2}*W_{2}+T_{3}*W_{3}$. After this, we have per-application
traffic data entries recorded as the uplink and downlink at a 1-minute
granularity and aggregated by IRIS. As a next step, we also sum up the uplink
and downlink byte counts, which results in the dataset with per-application
traffic data entries recorded as the total byte counts at a temporal
granularity of 1 minute and aggregated by IRIS. In the next section, we refer
to this dataset as $\mathcal{D}$.

##  IV Mobile Apps Usage Features for IRIS sectors

Predicting socio-economic features using mobile app data significantly depends
upon distinctive usage patterns in mobile phone application data. In this
section, we derived a set of metrics from mobile phone app data able to
capture useful information from the large-scale raw mobile phone app data to
be later used in the classification stage. In particular, we identified week
signature (Section IV-A), Revealed Comparative Advantage (RCA) [35]
utilization (Section IV-B), and standardized cumulative utilization (Section
IV-C), formally defined in the following subsections.

###  IV-A Typical Week Signature (TWS) for IRIS

Let us consider our mobile app consumption dataset $\mathcal{D}$, describing
the usage of a set of mobile apps _A_ for the Orange subscriber population
during a set of _d_ days. For the definition of the signature, let us define
the signature support $\Delta$ of one week, i.e., from Monday to Sunday,
$\Delta$ = {mon, tue, wed, thu, fri, sat, sun}. Let then us denote as _d
${}^{\delta}\subset$_ _d_ the set of days in the dataset _D_ that correspond
to the day of the week $\delta\in\Delta$, with
$\bigcup_{\delta\in\Delta}\emph{d}^{\delta}$ = _d_. Then, the generic element
in the signature of an IRIS _i_ for mobile app _a_ at time _t_ for day of the
week $\delta$ is:

$s_{i}(\delta,t,a)=M(\\{v_{i}(d,t,a)|d\in d^{\delta}\\}),\hskip
8.53581pt\forall i\in\emph{I},a\in\emph{A},$ (6)

where, $v_{i}(d,t,a)$ describes the total mobile app _a_ usage within the IRIS
_i_ at time slot (hour in our case) _t_ of day _d_ from dataset $\mathcal{D}$.
M($\cdot$) represents the median of the set within parenthesis. Also in this
case, $\delta$ is small with respect to the overall set of _d_ days, which
implies data compression. _I_ and _A_ represents set of IRIS and applications
respectively.

Signatures then undergo a standard normalization. To that end, each element
obtained in Eq. 6 is normalized with respect to the mean and standard
deviation of all elements referring to the same IRIS and mobile application.
Formally, for a generic element of IRIS _i_ and mobile application _a_ is

$\tilde{s}_{i}(\delta,t,a)=\frac{s_{i}(\delta,t,a)-\mu(s_{i}(a))}{\sigma(s_{i}(a))},\hskip
2.84526pt\forall\delta\in\delta,i\in\emph{I},a\in\emph{A},$ (7)

where $\mu(s_{i}(a))$ and $\sigma(s_{i}(a))$ denote the mean and standard
deviation of the set of elements concatenated in the signature _s_ i.

The techniques for the construction of a TWS for per-application traffic data,
at a temporal granularity of 1 hour and for each IRIS [33, 34] requires to
process the dataset $\mathcal{D}$ through three phases. These phases aim at
summarizing the mobile traffic activity in each unit areas into a meaningful
profile, i.e., the IRIS signature for the mobile application category. The
steps involved in generating the TWS are as follows:

  1. 1.

We converted the timestamp value to the corresponding “Hour within week”
value, which means that all the timestamp values represent the hour and day of
the week. For example, all the timestamp values on Monday between 00:00 to
00:59 hours, will become 1 irrespective of the date. Similarly, all the
timestamp values on Tuesday between 16:00 to 16:59 hours, will become 41
(24+17) irrespective of the date.

  2. 2.

Next, we aggregate the data on “Hour within week” value, and calculate the
median total byte count per-application for each IRIS. We considered the
median value as denoising component and extracts information deemed to be
representative of the typical mobile traffic activity in an IRIS, isolating it
from the inherent noise in the data.

  3. 3.

Lastly, we standardised the signature making it independent from the absolute
volume of mobile traffic recorded at an IRIS. This allows comparing the per-
application activity at IRIS level on the sole basis of the traffic data
variations. This we named as Typical Week Signature.

The TWS is a relevant feature from mobile phone app usage data due to its
capability to depict the relative dynamics of mobile app consumption over the
course of an entire week under the premise of cyclic (weekly) regularity at
the scale of an IRIS region.

###  IV-B IRIS Revealed Comparative Advantage (RCA) utilization

Depending on the nature of the data, different mobile apps produce a
heterogeneous traffic volume over the network. For example, videos generate a
significantly larger load per session than messaging. As a result, the traffic
volume across app categories might vary by several orders of magnitude.
Therefore, we also computed the revealed comparative advantage (RCA) [35], a
relative measure that can compare traffic across IRIS zones and app
categories. The RCA index for IRIS zone $z$ and app category $a$ is calculated
as follows:

$RCA_{za}=\frac{T_{za}/\sum_{a^{\prime}\in
A}T_{za^{\prime}}}{\sum_{z^{\prime}\in Z}T_{z^{\prime}a}/\sum_{z^{\prime}\in
Z,a^{\prime}\in A}T_{z^{\prime}a^{\prime}}}$ (8)

where $T_{za}$ is the median hourly traffic per person in IRIS zone $z$ for
apps category $a$; $\sum_{a^{\prime}\in A}T_{za^{\prime}}$ is the median
hourly traffic per person in zone $z$ combinedly generated by all considered
apps; $\sum_{z^{\prime}\in Z}T_{z^{\prime}a}$ is the median hourly traffic per
person generated by app $a$ in all zones; $\sum_{z^{\prime}\in Z,a^{\prime}\in
A}T_{z^{\prime}a^{\prime}}$ is the median hourly traffic per person,
aggregated over all zones and apps. A value of $RCA_{za}$ $>$ 1 indicates more
than average usage of app $a$ in zone $z$; on the other hand, $RCA_{za}$ $<$ 1
indicates lower than average usage of app $a$ in zone $z$.

###  IV-C IRIS standardized cumulative utilization

Next, we extracted the cumulative utilization of apps in different IRIS. This
is defined as the total data transmitted in byte counts for each app
aggregated for the IRIS. This is instead a simple metric compared to the week
signature and RCA. The motivation behind calculating this information is to
check the effectiveness of straightforward metrics to predict socio-economic
features. We take the total data transmitted bytes during the 2.5-month
observation period for each IRIS zone and app category. Finally, we calculate
the standardized cumulative utilization (SCU) as follows:

$SCU_{za}=\frac{C_{za}-mean(\forall_{Z}C_{Za})}{sd(\forall_{Z}C_{Za})}$ (9)

where $C_{za}$ is the cumulative traffic in IRIS zone $z$ for app category
$a$; $mean(\forall_{Z}C_{za})$ is the mean total traffic in all zones $Z$
generated by app $a$; $sd(\forall_{Z}C_{za})$ is the standard deviation of
total traffic in all zones $Z$ generated by app $a$. Therefore, the SCU value
quantifies how far off the mean traffic generated by a mobile app in a given
IRIS zone is in terms of standard deviations.

##  V Social Disparity From Apps Usage Pattern

In this section, we explore the potential of all three previously extracted
mobile app usage features (i.e., weekly signature, RCA index, and SCU values,
introduced in Section IV) as predictors of the socio-economic characteristics
of IRIS sectors. In particular, we look for a significant disparity in mobile
phone app usage within groups of IRIS sectors with similar socio-economic
features (discussed in Sec. II-B). This analysis represents a preliminary
exploratory step toward predicting socio-economic sector features using mobile
phone app consumption. To do so, we considered the median income as a use
case. For better visualization, we used the quantile method to classify IRIS
based on median income into three classes: Low, Medium, and High, with an
equal number of IRIS in each class. Three classifications are created in order
to demonstrate socio-economic features’ distinct differences if any exist.

###  V-A Week Signature

In Section IV-A, we discussed the procedure to extract TWS with one-hour
granularity for each application category. Here, we show that the TWS helps
predict socio-economic features. As we know, the TWS for an application in an
IRIS is a vector with 168 values (7 days multiplied by 24 hours, i.e.,
$24\times 7=168$). As a first step, we generate TWS for each income class
(Low, Medium, and High). To do so, we compute the mean TWS of all IRIS in a
class.

Figure 3a shows the mean TWS for each income class. Here, we included the
outcomes of four application categories: Social media video, Apple Cloud,
Travel, and Productivity. These app categories are included due to their
distinct usage pattern for income class. The x-axis depicts the hour within a
week, and the y-axis represents the mean TWS of all IRIS in an income class.
The blue, orange and green lines reflect the Low, Medium, and High-income
IRIS, respectively. It is to be noted that the TWS are standardized, therefore
any choices we make moving forward in this section should be interpreted as
relevant to the usage as a whole (which is subtracted from the signal). In
other words, when we use the word “more”, it does not mean “more” in the
volume sense but rather ”more” in a relative sense, i.e., in relation to the
average standardized weekly usage for a particular income category.

(a) Week signature. Here, x-axis represents hour within week and y-axis shows
the mean TWS value of the income class.

(b) RCA index. Here, x-axis represents application category and y-axis shows
the mean RCA index of the income class.

(c) SCU index. Here, x-axis represents application category and y-axis shows
the mean SCU index of the income class.

Figure 3: IRIS income class (Low, Medium, and High) disparity from
applications usage pattern. Here, Figure 3a shows the week signature patterns
for Social media Video, Apple Cloud, Travel, and Productivity apps. Figure 3b,
and 3c shows RCA and SCU index respectively with the 95% confidence interval.
Furthermore, the line or bar with blue, orange, and green color represents the
Low, Medium, and High income IRIS. (best seen in color)

In the social media video, we can observe that the usage pattern for all three
classes is consistent, regardless of whether it is a weekend or weekday.
However, there is a difference in the use pattern of High-income IRIS compared
to Low and Medium income IRIS during the weekday late morning, where Low and
Medium income IRIS use social media video more than High-income IRIS. This is
most likely because the high-income IRIS includes company managers and highly
intellectual professionals, as shown in Figure 1, so they spend their morning
schedule working instead of watching social media videos. The social media
video usage is even more intriguing because the patterns of app utilization do
not alter much between weekdays and weekends. However, the apparent difference
in weekend early morning social media video usage among different income
classes demonstrates the significance of TWS as a good predictor.

Concerning the Apple Cloud app feature, we observe that all three income
classes have identical consumption patterns during the weekdays. However, the
usage is relatively more distinct on weekends than on weekdays. Furthermore,
the High-income group generates the most data on average on the Apple Cloud
app, followed by the Medium, and then the Low-income groups. When Apple
devices are plugged in, locked, and connected to Wi-Fi, Apple cloud (iCloud)
performs a daily backup automatically. When most people get the weekend off
from work, these conditions are more likely to be met. As a result, the high-
income group’s weekend usage of the Apple cloud suggests that they own more
Apple devices than individuals of the low and medium-income groups.

In Travel, usage is more on Friday. This suggests that irrespective of income
group, most individuals use traveling-related mobile applications on Friday.
This usage is probably because more people travel on Friday after work to
spend the weekend with their family. On weekdays, the usage patterns of the
Low and Middle-income classes are comparable, while the usage of the High-
income class is less. However, on weekends, this usage pattern is reversed.
This suggests that Low- and medium-income individuals travel less on weekends
than high-income groups. In the Productivity, usage order is
Low$>$Medium$>$High on weekdays. However, high-income group usage is more in
the late evening. On the weekend, this order is reversed, which suggests that
the high-income group, which includes company managers, and high intellectual
professions use productivity applications more than low and medium-income
groups on weekends. Another intriguing finding we can infer is that Tuesday is
the busiest day of the week when productivity app consumption is at its peak
for all the income groups. This further support our hypothesis that some
application categories play a more vital role in identifying socio-economic
features than others. From these observations, we can conclude that the TWS
helps predict socio-economic features.

###  V-B RCA Index

In Section IV-B, we discussed in detail the extraction of RCA index. This
index indicates that if $RCA_{za}>1$, the IRIS $z$ utilizes app category $a$
more than the other IRIS. If RCA is less than unity, the IRIS is said to have
less app consumption than other IRIS. Therefore, it reveals the comparative
advantage or disadvantage of the consumption of an application in an IRIS.

Figure 3b depicts the mean RCA index value for the three income classes across
all application categories. The x-axis represents the various app categories,
while the y-axis indicates the mean RCA index. The low, medium, and high-
income IRIS are represented by the blue, orange, and green bars, respectively.
The 95% confidence interval for each income class and app category is also
included in the bars. According to the figure, high-income IRIS utilizes more
the app categories like Apple Download, Music, Apple Cloud, Apple Music,
Cloud, News, Email, Travel, and Productivity. Furthermore, middle-income IRIS
prefers Android and Windows downloads. Finally, low-income IRIS are more
likely to use web browsing, video, and social media. These findings align with
the previous research, e.g., low-income families are more likely to utilize
online social media than higher-income families [36, 37].

###  V-C SCU Values

In Section IV-C, we discussed in detail the extraction of SCU values. The
value of the $SCU_{za}$ tells us how many standard deviations the value is
away from the mean consumption among all the IRIS of the application category
$a$. If an $SCU_{za}$ is equal to 0, it is on the mean. A positive $SCU_{za}$
value indicates the value is higher than the mean consumption among all the
IRIS for application category $a$. For example, if an $SCU$ value equals +1,
it is one standard deviation above the mean consumption.

Figure 3c shows the mean value of the SCU index of the three income classes
for all application categories. The x-axis represents the various app
category, and the y-axis represents the mean SCU index. The blue, orange, and
green bars show the Low, Medium, and High-income IRIS respectively. The bars
also have a 95% confidence interval for each income class and app category.
The figure shows that app categories such as Social media Video, Apple
Download, Music, Apple Cloud, Apple Music, Cloud, News, Email, Travel, and
Productivity are utilized more by high-income IRIS. The plausible explanation
of the observed differences in app usage in terms of socio-economic disparity
can be two-fold. First, wealthier individuals who work and travel more often
than low- and middle-income groups on weekends use Apple services more
frequently. Second, those with high incomes consume a significant amount of
music and videos. This is somewhat similar to what we observed with the RCA
index. However, these two index captures very different aspect of data
utilization. Then, there is Web Browsing, Video, Social media, Android
Download, and Window Download, which low-income IRIS uses more. This implies
that low- and middle-income groups have a higher prevalence of Android and
Windows devices.

Takeaways. Before proceeding to the modeling part, we inspected all three
generated patterns (TWS, RCA and SCU) for diversification of information. In
terms of information, we find that TWS is the richest of the three patterns,
followed by RCA, which is then followed by SCU (see Section 4 in the
supplementary document). In the next section, we investigate our findings in
light of the premise that the TWS collects more diverse information by
predicting socio-economic features.

##  VI Predicting socio-economic indicators

Here, we build machine learning models using the patterns (week signature, RCA
index, and SCU index) we explored in the previous sections to predict the
socio-economic features of IRIS in France.

###  VI-A Features Used For Learning

To illustrate the predictive power of the different feature sets reported
earlier in this paper, we define a series of models, each with a distinct
feature set corresponding to mobile app usage patterns. We focus on three
types of features:

1) IRIS weekly signature: In Section V-A, we observed that the week signature
of the apps category could distinguish the IRIS based on their socio-economic
features.

2) IRIS Revealed Comparative Advantage (RCA) utilization: In Section V-B, we
found that the RCA index patterns of various app categories are more or less
utilized based on socio-economic features. Multiple apps’ distinctive nature
can help predict an IRIS’s socio-economic features.

3) IRIS standardized cumulative utilization: In Section V-C, we observed SCU
index for app categories could be used to predict socio-economic features.

###  VI-B Experimental Setup

We experiment with several regression models, including Linear Regression,
Ridge Regression, and CatBoost. We find that CatBoost models are the most
effective for our task, and we thus report results from only those models.
Because of the regression task, we take care of the trade-off between bias and
variance of the model. During experimentation, we split our dataset into 80:20
train and test sets, ensuring that our CatBoost models are not overfitting our
training data. To achieve this, we used a 5-fold cross-validation on training
data. Finally, we report the R-squared value on test data.

###  VI-C Results with Explainability

We subdivide our results into three parts based on economic status, population
information, and education information (please see Section II-B for detail).

1) Economic status: Table II shows the R-squared value for the prediction of
our models. We used four variations of predictive features to predict Poverty,
Median income, and the Gini index. The variations of predictive features
include (1) Cumulative: that represents SCU values, (2) RCA: that includes RCA
index values, (3) TWS: that has typical week signature on hourly basis, and
(4) All: which contains all the three mentioned features combined, i.e., SCU
values, RCA index, and TWS.

Socio-Economic Features | Cumulative | RCA | TWS | All
---|---|---|---|---
Economic status |  |  |  |
Poverty | 0.306 | 0.366 | 0.458 | 0.482
Median Income | 0.551 | 0.592 | 0.631 | 0.659
Gini Index | 0.569 | 0.592 | 0.623 | 0.642
Education information |  |  |  |
No diploma | 0.380 | 0.392 | 0.450 | 0.456
Baccalauréat | 0.442 | 0.454 | 0.508 | 0.516
BEPC + CAPBEP | 0.381 | 0.398 | 0.457 | 0.464
College | 0.538 | 0.548 | 0.596 | 0.604
Population information |  |  |  |
Total population | 0.457 | 0.470 | 0.520 | 0.527
Population 0 to 14 yrs | 0.464 | 0.480 | 0.538 | 0.543
Population 15 to 29 yrs | 0.529 | 0.541 | 0.591 | 0.596
Population 30 to 44 yrs | 0.483 | 0.496 | 0.553 | 0.560
Population 45 to 59 yrs | 0.449 | 0.461 | 0.516 | 0.522
Population 60 to 74 yrs | 0.393 | 0.406 | 0.462 | 0.469
Population 75+ yrs | 0.368 | 0.378 | 0.428 | 0.435
Immigrants | 0.596 | 0.605 | 0.654 | 0.664
Farmer | 0.246 | 0.256 | 0.322 | 0.328
Craftsman, Trader | 0.363 | 0.372 | 0.407 | 0.417
Manager, high profession | 0.544 | 0.555 | 0.593 | 0.602
Intermediate profession | 0.473 | 0.484 | 0.537 | 0.543
Employees | 0.437 | 0.449 | 0.498 | 0.504
Worker | 0.354 | 0.369 | 0.432 | 0.437
Retired | 0.359 | 0.371 | 0.425 | 0.433
Other w/o profession | 0.485 | 0.494 | 0.536 | 0.540
Male population | 0.459 | 0.472 | 0.525 | 0.532
Female population | 0.467 | 0.478 | 0.529 | 0.535
TABLE II: R-squared scores using Cumulative, RCA, TWS, and All predictive
features for the socio-economic features. Best score is highlighted using bold
text.

(a) Poverty

(b) Median Income

(c) Gini Index

Figure 4: SHAP plot to determine feature importance for Economic status. The
SHAP values and feature names are represented by the x- and y-axes,
respectively. Each data point represents a single instance. The red color
represents a higher value for the feature than its average value, while the
blue color denotes a lower value. Red values on the right side of the x-axis
indicate a positive impact on the prediction and vice versa. The features are
listed in order of decreasing importance (best seen in color).

We find the most predictive features to be the week signature. Strikingly,
mobile app usage at different times of day is significantly more predictive
than the RCA index and cumulative usage patterns. With a model trained on all
available features, we achieve a score of 0.48, 0.66, and 0.64 for Poverty,
Median income, and Gini index, respectively. The results indicate that the
mobile app usage patterns, particularly the TWS, have the capability to
predict the reported socio-economic features.

Furthermore, to understand the features’ importance for the best model with
All predictive features, we use SHAP (Shapley additive explanations) values
[38]. A summary plot of SHAP values for top-20 features is shown in Figure 4.
Each point represents an IRIS, thus for each feature, the figure shows the
distribution of SHAP values across the dataset. The wider distribution
indicates a larger absolute impact of the feature in the prediction, while the
color of each point encodes the feature value (low or high). A feature with
high values corresponding to negative SHAP values (to the left) is negatively
correlated with economic status. For example, RCA index for News app category
or News (RCA) is negatively correlated with Poverty (see Figure 4b and 4c).
Conversely, features with high values corresponding to positive SHAP values
(to the right), are positively correlate with economic status. For example,
News (RCA) is positively correlated with Median income and Gini index (see
Figure 4b and 4c).

The most important feature for predicting economic status are News, Web
Browsing, Social media, Email, Social media video, etc. We observe that News
(RCA) consumption is more in IRIS with high median income and a high Gini
index. On the other hand, News (RCA) consumption is less in IRIS with high
Poverty. That means News is read more by high-income individuals.
Additionally, RCA index for Social media app category or Social media (RCA)
usage pattern is completely opposite compared to News (RCA). That indicates
that high-income people do not frequently use social media. Similarly, we find
that Web Browsing on Saturday from 10 to 12 is positively correlated with
Median income and negatively correlated with Poverty. Conversely, we find that
Web Browsing on Monday from 3 to 4 is negatively correlated with Median income
and positively correlated with Poverty. This implies that high-income
individuals avoid browsing the internet on Monday late afternoon. Based on the
conclusions above, it is worth to highlight this helpful property of TWS that
allows identifying relationships over time between mobile app consumption and
socio-economic features.

Takeaways. Social media applications are utilized less by the high-income
group than by the low-income group. On the other hand, the high-income group
frequently uses News and Productivity apps.

2) Education: Table II reports the R-squared values for the CatBoost models
for four education classes: No diploma, Compulsory Schooling (Baccalauréat),
High School (BEPC and CAPBEP), and College. Again, we find the most predictive
features to be the weekly signature. That means mobile app usage at different
times of the day is significant. With a model trained on all available
features, we achieve a score of 0.46, 0.52, 0.46, 0.60 for No diploma,
Compulsory Schooling (Baccalauréat), High School (BEPC and CAPBEP), and
College, respectively.

Takeaways. Due to space limitation, the SHAP plots for education classes, are
in the supplementary document (refer Figure S14). From the plots, we find that
the consumption of social media videos, social media, music, and travel
applications play a vital role in predicting the education status in the IRIS.
We also observe that people without a diploma, compulsory education, and high
school have more distinguished consumption of music and social media than
those with a college education.

3) Population: Table II report the R-squared values for the CatBoost models
for all population features. We achieve scores between 0.44 (age 75+) to 0.66
(Immigrants). Our model predicted the immigrant population and people ages 15
to 29 years more accurately. Similarly, we achieve scores for profession
information between 0.33 (Farmers) to 0.60 (Managers, high profession). The
model performed better in predicting high profession, Intermediate profession,
and Others with high scores.

Takeaways. The SHAP plots for population information are in the supplementary
document (refer Figure S15). We observe that most of the features among the
top 20 are from TWS. This further supports the relevance of TWS. We find that
social media video is highly consumed by people aged between 15 to 45 years.
The immigrant population, primarily young and working in positions with
unprofessional activities, are not frequent targets for the advertisement.
Additionally, high profession individuals, such as, Managers like to hear
music after working hours.

###  VI-D Relevance of the Findings

To show that the extracted patterns (TWS, RCA, and SCU) from mobile
applications are better/comparable than census information for predicting
socio-economic features, we further experimented using census data (Total pop,
age 0-14, 15-29, 30-44, 45-59, 60-74, and 75+), and All (Signature, RCA, and
Cumulative) to predict three socio-economic features: Median income, Gini
index, and College education. Table III reports the R-squared values for the
CatBoost models. We find that the model using All predictive features achieves
scores that are at least as good as those obtained with census data.

Socio-Economic Features | Census | All
---|---|---
Median income | 0.50 | 0.66
Gini Index | 0.38 | 0.64
College | 0.61 | 0.61
TABLE III: R-squared scores for CatBoost model using census, and All
(Cumulative, RCA, and TWS patterns) data for predicting Median income, Gini
index, and College education. Best score is highlighted using bold text.

Please note that, for the sake of completeness, we performed a comparability
analysis between the mobile applications usage patterns and census information
for predicting three socio-economic features: Median income, Gini index, and
College education. However, the use of population information for predicting
socio-economic features is not aligned with the purpose of this study. That
is, this study tries to provide an alternative to expensive, and time-
consuming census tasks to collect socio-economic information by mobile usage
to predict socio-economic features.

##  VII Conclusion

This work proposes a large-scale, quantitative, and predictive study of the
relationship between mobile app usage and socio-economic features. We analyzed
nationwide data from the leading mobile operators in France and extracted
three patterns: TWS, RCA, and SCU. We find that TWS has richer information
diversification than RCA and SCU. The best model using all three patterns
achieved an R-squared score up to 0.66, thus concluding our study goal that
mobile application usage can predict a nation’s socio-economic conditions.

Impact: Most countries conduct a census once every ten years and utilize the
same data to drive policies prior to the next census [39, 40]. To overcome the
limitation of census, this study makes a case for an inexpensive, privacy-
preserving, real-time and scalable method to understand the latest socio-
economic conditions and, by extension, poverty, unemployment, literacy, or
economic progress in our societies through mobile phone data (application
usage).

Limitations and Future scope: Obviously, the outcomes of this study are
confined to France and its specific geographical granularity, IRIS. Studies of
other countries’ call data records or mobile application usage will need to
define the geographical granularity required for this study. Another
limitation of this study when duplicating it in other countries is getting
socio-economic datasets for the desired geographical granularity. This study’s
findings suggest numerous possibilities for future directions, including user
temporal network analysis to understand emerging and evolving network
patterns. Another future direction is to investigate alternate sources of
information that can be used in conjunction with or independent of mobile data
to examine socio-economic conditions.

## Acknowledgment

This research is supported by EU H2020 program under the SoBigData++ project
(grant agreement No. 871042), the French ANR research projects DISCRET (grant
number ANR-19-FLJO-0002-01) and PROMENADE (grant number ANR-18-CE22-0008).

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|  Rahul Goel is a PhD scholar at the University of Tartu, Estonia. He has
BTech in IT and MTech from NIT Delhi, India in Computer Science and
Engineering (Analytics). He also worked as a data scientist and ASE in Impact
Big Data Analytics and Centre for Railway Information System respectively. His
research interests include social media study, network analysis, and natural
language processing using data science, machine learning, and deep learning.
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|  Angelo Furno is a researcher at the ENTPE, University of Lyon, and the
University Gustave Eiffel, France, where he has been a member of LICIT-ECO7
laboratory since 2016. From 2014 to 2016, he was a postdoctoral researcher
with INRIA at the CITI laboratory of INSA-Lyon, France. He obtained his Ph.D.
in computer science from the University of Sannio, Italy. His research
interests include urban computing, smart mobility, big data, machine learning,
mobile phone data analytics, and distributed computing. He is the author of
more than 50 contributions in leading international journals and conferences
in related fields.
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|  Rajesh Sharma is presently working as associate professor and leads the
computational social science group at the Institute of Computer Science at the
University of Tartu, Estonia, since January 2021. Rajesh joined the University
of Tartu in August 2017 and worked as a senior researcher (equivalent to
Associate Professor) till December 2020. From Jan 2014 to July 2017, he has
held Research Fellow and Postdoc positions at the University of Bristol,
Queen’s University, Belfast, UK and the University of Bologna, Italy. Prior to
that, he completed his PhD from Nanyang Technological University, Singapore,
in December 2013. He has also worked in the IT industry for about 2.5 years
after completing his Master’s from the Indian Institute of Technology (IIT),
Roorkee, India. Rajesh’s research interests lie in understanding users’
behavior, especially using social media data. His group often applies
techniques from AI, NLP, and most importantly, network science/social network
analysis.
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