Private Sources of Mobility Data Under COVID-19

Raquel Perez Arnal1, David Conesa2, Sergio Alvarez-Napagao1, Toyotaro Suzumura1,Martı Catala2,3, Enric Alvarez2, and Dario Garcia-Gasulla1

1Barcelona Supercomputing Center (BSC)2Department of Physics. Universitat Politecnica de Catalunya (UPC·BarcelonaTech), C.

Jordi Girona, 1-3, 08034 Barcelona, Spain3 Comparative Medicine and Bioimage Centre of Catalonia (CMCiB), Fundacio Institutd’Investigacio en Ciencies de la Salut Germans Trias i Pujol, Badalona, Catalonia, Spain{raquel.perez,sergio.alvarez,dario.garcia}@bsc.es, {david.conesa,enric.alvarez}@upc.edu,

mcatala@igtp.cat, suzumura@acm.org

July 2020

Abstract

The COVID-19 pandemic is changing the worldin unprecedented and unpredictable ways. Humanmobility is at the epicenter of that change, as thegreatest facilitator for the spread of the virus. Tostudy the change in mobility, to evaluate the ef-ficiency of mobility restriction policies, and to fa-cilitate a better response to possible future crisis,we need to properly understand all mobility datasources at our disposal. Our work is dedicatedto the study of private mobility sources, gatheredand released by large technological companies. Thisdata is of special interest because, unlike most pub-lic sources, it is focused on people, not transporta-tion means. i.e., its unit of measurement is the clos-est thing to a person in a western society: a phone.Furthermore, the sample of society they cover islarge and representative. On the other hand, thissort of data is not directly accessible for anonymityreasons. Thus, properly interpreting its patternsdemands caution. Aware of that, we set forth toexplore the behavior and inter-relations of privatesources of mobility data in the context of Spain.This country represents a good experimental settingbecause of its large and fast pandemic peak, andfor its implementation of a sustained, generalizedlockdown. We find private mobility sources to beboth correlated and complementary. Using them,we evaluate the efficiency of implemented policies,and provide a insights into what new normal meansin Spain.

1 Introduction

COVID-19 has produced an unprecedented changein mobility [1, 2]. Governments have implementedrestrictive measures to contain the pandemic, fo-cused on reducing social contacts [3]. This isa direct effort towards controlling the COVID-19spread, at a stage where the test and trace strat-egy is impractical [4]. Mobility restriction mea-sures range from the closure of schools and largegatherings, to the complete lockdown of the popula-tion and economic hibernation. Different countrieshave implemented different measures, varying theirseverity and duration [5]. Lifting these restrictionswill lead societies to a new normality, which is stillunclear how much will resemble the old normality.At this point it seems likely that societies will tran-sition to a new basal mobility structure [6]. Onewe need to analyze and understand the sooner thebetter.

There are many public sources of mobility datawhich can be used to measure the physical distanc-ing of population during the COVID-19 crisis [7–9].Unfortunately, most of these sources provide limitedinsights, focusing on a movement modality (e.g.,public transport occupancy, public bike system us-age, road densities). An alternative to public datais the data provided by private technological en-tities, which have access to a high volume of in-formation through applications installed in mobilephones [10]. Even though the segment of populationthat has these applications installed is not a perfect

1

arX

iv:2

007.

0709

5v1

[ph

ysic

s.so

c-ph

] 1

4 Ju

l 202

0

reflection of society (e.g., young segments of soci-ety are over-represented while elders and kids areunder-represented) it is large enough to provide re-liable estimations. At the moment, data providedby private entities represents the most reliable pub-lic source of information to explore the big picturefrom the perspective of people [11]. Such sources ofdata include the ones provided by Google, Facebookand Apple, as described in Section 2.

The main issue when working with private datasources is the imperfect knowledge regarding its na-ture. For privacy preserving reasons, raw data isnever provided. Instead, data goes through heavypre-processing and anonymization procedures [12].Additionally, the conditions under which data is col-lected are not fully transparent either, as baselinesand contextual information are often missing. Topartially overcome these issues, in this work we in-vestigate the relation between the different privatedata sources, and how can they be used complemen-tary to provide a better understanding of mobility.

For our analysis, we focus on the case of Spain.The COVID-19 pandemics in Spain, and the po-litical measures taken to control its spread in thecountry, provide an appropriate experimental setup.Spain implemented a complete and sudden lock-down on March 15, 2020, while the first mild re-strictions were put in place less than a week be-fore (progressive closure of schools and banning oflarge gatherings) [5]. After the pandemic peaked inApril, Spain gradually recovered mobility and ser-vices over the span of two months (May and June).We will study Spain’s demographics and considerthe relation between restriction policies, social be-havior and pandemic evolution. This could be help-ful for reacting to future mobility crisis. We willalso study its progression towards a new normal-ity in comparison with the old one. This could behelpful for the adaptation of mobility policies to thenew social setting.

The rest of the paper is structured as follows.First, we describe the data used in this work in§2. Then we review the social context of the studyin §3, including both the timeline of implementedpolicies, and an overview of Spain demographics.Most of our analysis takes place in §4. This in-cludes a general study of mobility trends for all re-gions and data sources (§4.1), a discussion on theanomalies observed (§4.2), an analysis on the dailytrends (§4.3) and some insights on the new normal-ity (§4.4). Finally, we review our conclusions in §5.

2 Data Sources

For this work we have considered the data publishedby the Facebook Data for Good program (FDG)[13], by the Google mobility assessments [14], andby Apple [15]. At the time of our analysis Ap-ple only provided one single mobility metric, thenumber of direction requests made from the AppleMaps application. In practice, this measure wastoo different from the other sources as to be di-rectly compared. Also, while it was possible to as-sess the anonymization effect on the data for FDGand Google, we have not been able to do the samefor Apple. For these reasons, we disregard Appledata in this analysis.

Private mobility indices are typically provided ata certain level of aggregated granularity. With ad-ministrative level 1 being country, level 2 in Spaincorresponds to region (17 Autonomous Communi-ties or CC.AA.), and level 3 to province (50 of themin Spain). As we will see next, the Google data isprovided at level 2 (regions), while Facebook data isprovided at level 3 (province), a smaller geograph-ical granularity. This will be aggregated to level 2on our work.

2.1 Facebook Data for Good

Facebook Data for Good presents multiple data setsof mobility, all of them with a general descriptionof how they are obtained. There are two maindata sets of particular relevance for the purpose ofthis work: a remain-in-tile index and movement-between-tiles index [16]. Both are based on GPSdata from a sample of users that have activatedthe tracking system on their mobile phones, divid-ing the space in level 16 tiles (squares of roughly500 × 500 m). The first one, remain-in-tile, pro-vides the percentage of people that remains in thesame tile, computed as the total ratio of mobilesproviding signal which do not change of tile duringa whole day. The second one, movement betweentiles, estimates mobility by computing how manydifferent tiles are visited by the sample of people,compared with the same number during the sameday of the week previous to the pandemics (Febru-ary 2020) [12].

For the rest of this work, we will use the remain-in-tile index, as it provides a more pure measure-ment. Notice the remain-in-tile is an absolute mea-sure (e.g., it does not depend on a baseline). Thismakes it straightforward to interpret, and a goodcandidate for counting people who are following

2

confinement, as long as the population who has ac-cepted to be pinned represents a good sample ofthe population. The anonymization of data pre-vents us from evaluating the fit between the datasample and the overall population distribution. Acertain amount of bias is to be expected, as thepenetration of smartphone and Facebook varies sig-nificantly among cohorts. As with Google, the el-der population and the younger population could beslightly underrepresented, which may entail a cer-tain bias. Nonetheless, the size of the sample leadus to believe that the data can provide a reliablepicture.

We assess Facebook’s sample size in two ways.First, by considering Facebook market penetrationin the smartphone market, which is around 50-60%[17] with smartphones being available for 70% ofthe population. The subsample with an activatedtracking system only needs to be around 10% tohave a sample of 4% of the total population. Even1% of the population would be a very large samplein any kind of poll. Second, data of active Facebookusers is also available from Facebook geoinsightsmaps and indicates that 1% is indeed the typicalorder of magnitude of the sample. The fact thatFacebook requires a minimum of 300 active usersto provide data (for anonymity reasons) allows usto validate the size of the sample. For example, theprovince of Spain with the smallest population isSoria, with roughly 90,000 people. This provinceand has never pined less than 300 people for thewhole period under study (i.e., there is data for So-ria for all days). This means that the sample inSoria is always above 0.5% of the population. Wehave no reason to suspect a lower coverage on theother provinces of Spain.

Facebook data is provided at level 3 (province)granularity. In order to compare with Google data(which is only available at level 2), we need to ag-gregate it. Any level 2 region is a sum of one ormore level 3 provinces. To compute the level 2 datafrom level 3 values we use the average of provincesweighted by their population. By doing so we ob-tain the ratio of people that remain in tile at level2 aggregation for Facebook.

2.2 Google

On April 2, 2020, Google released its COVID-19Community Mobility Reports [14]. Since then,Google periodically releases anonymyzed mobility

data 1, organized in a set of categories (e.g., re-tail, recreation, groceries, pharmacies, parks, tran-sit stations). This data is always provided at ad-ministrative level 2 granularity (region). In addi-tion to those, Google also releases a Residential anda Workplace measure, estimating how much time isspent at those places. In the case of Residential, itis based on the average number of hours spent atthe place of residence for each user within a geo-graphic location. This data is collected from thoseusers opting in to Location History, and is processedusing the same algorithms used for the detection ofuser location in Google Places, offering an accuracyof around 100m in urban areas [18]. Google Mobil-ity Reports releases relative information for everyday of the week. That is, mobility with respect toa baseline: the first five weeks of the year, fromJanuary 3 to February 6.

Given the large number of users of Google Mapsthere is no doubt that the sample size of Google isalso large compared with any standard polling sam-ple. However, the fact that Google does not providean absolute value in the way Facebook does presentsresearchers with serious challenges. The most im-portant is that the baseline can be indeed corruptedby local festivities or changes in the normal baselinedue to large-scale celebrations. As we will see, lackof access to the baseline prevents the direct compar-isons of regions, and even days, and can complicateits interpretaion.

Google’s residential index, which indicates the es-timated time spent at the residence, should stronglycorrelate with Facebook’s remain-in-tile. i.e., alarge fraction of people remaining at home shouldincrease both indices. Their direct comparisonshould provide context for their interpretation, andinsights on their applicability.

3 Social Context

The first cases COVID-19 in Spain were reported onJanuary 31st, in the Canary Islands. For roughly amonth, all detected cases were imported from othercountries. The first cases of community transmis-sion (i.e., source of contagion unknown) were di-agnosed on the February 26. In March is wherethe scope of our analysis begins. The number casesreached 999 on the 9th. At this point a few sev-eral regional governments took the first generalizedmeasures, with the closure of schools. This esca-lated until March 14, when the Spanish govern-

1http://google.com/covid19/mobility

3

Region Pop. density Population >50k inhabitants Attack rate / 105 inh

Andalucıa 96 8,446,561 50.82 200Aragon 28 1,324,397 55.19 533Asturias 96 1,019,993 60.75 238Canarias 298 2,220,270 54.26 114Cantabria 109 581,949 38.55 405Castilla y Leon 26 2,402,877 44.03 1,110Castilla-La Mancha 26 2,038,440 27.40 1,102Catalunya 237 7,609,499 53.90 762Comunitat Valenciana 215 4,998,711 45.50 300Euskadi 302 2,181,919 46.53 672Extremadura 26 1,062,797 28.66 535Galicia 91 2,698,764 36.71 402Illes Balears 240 1,198,576 40.60 199La Rioja 62 314,487 47.71 1,274Murcia 132 1,494,442 55.85 168Madrid 833 6,685,471 85.73 1,086Navarra 63 652,526 30.82 1,218

Table 1: Population density, population, percentage of population living in cities with more than 50,000inhabitants and attack rate (total cases) of COVID-19 for the 17 Spanish regions on the first of July

ment declared the state of alarm. This directly im-posed movement restrictions on the whole popula-tion, allowing only essential activities and work re-lated journeys. These restrictions where reinforcedon March 29 with the implementation of a hard-lockdown, which suspended all mobility not relatedto essential services. This lasted until the April 12,when work trips were approved again. The first gen-eralized de-confinement measures were adopted twoweeks later, on April 26, when kids under 14 (and atutor) were allowed outdoors for an hour. On May2nd, small businesses were allowed to open throughprevious appointment. This de-confinement processcontinued progressively until June 21, when the lastmobility restrictions in Spain were lifted. Our anal-ysis ends on June 27.

At administrative level 2, Spain is composed by17 regions and two autonomous cities (Ceuta andMelilla). The latter are not included in the analysisbecause their distinct nature would require a sepa-rate study. Even so, the 17 regions of Spain differsignificantly in socio-economic factors, as shown inTable 1. Two regions (Catalonia and Madrid) in-clude the largest metropolitan areas, and had themost COVID-19 cases in absolute terms. The restof regions can be categorized as being dense orsparse (as determined by the population density),and as having their population centered on urbanareas or not (as determined by the percentage of

people living in cities with 50K or more inhabi-tants). Cantabria and Aragon are examples of themost uncommon cases, the first being fairly dense,with population centered on small towns, and thesecond being sparse, with population centered oncities. Extremadura is a prototypical sparse andrural region, while Madrid is extremely dense andurban.

4 Analysis

In our analysis, we focus on Facebook’s remain intile index, and on Google’s residential index. Theformer is scaled (multiplied by 50) to approximateGoogle’s scale and facilitate the interpretation offigures. We have no means to assess the relationbetween the scale of both indices, which is why weavoid interpreting the relative volume between in-dices. Nonetheless, as we will see in Figure 1, ourscale provides a good approximation.

4.1 General Region Trend

Our first analysis is focused on the general mobil-ity trend of Spanish regions during the peak of thepandemic. For this, we focus on the three monthsaround the peak of the pandemic in Spain, as shownin Figure 1. These are March, April and May. Inthis period, mobility in Spain went through at least

4

Figure 1: Evolution of mobility according to Facebook (blue) and Google (purple) indices, from Marchto May. The vertical bands (green, orange, red, orange, green) correspond to pre-confinement, state ofalarm declaration and lockdown, hard-lockdown, lockdown and de-confinement stages. Grey vertical linesare aligned with every Sunday.

5

5 clear stages (the green, orange, red, orange andyellow bands in Figure 1):

1. March 1 - March 13 (2 weeks). A period previ-ous to the declaration of state of alarm, whenmobility is expected to be normal. That wouldmean around zero on the Google index, sincethis is a measure relative to normality.

2. March 14 - March 27 (2 weeks). A period ofmobility containment after the declaration ofthe state of alarm and the establishment of ageneral lockdown.

3. March 28 - April 12 (2 weeks). A period ofmaximum mobility containment, what we callhard lockdown, after the government reinforcedrestrictions forbidding all movements not re-lated with essential services.

4. April 13 - May 1 (3 weeks). General lockdownremains in place, but reinforced policies arelifted (mobility to the workplace is allowed).

5. May 2 - June 20 (7 weeks). A period ofconvergence towards new normality, as de-confinement measures are deployed: Kids areallowed outside for limited time, some storesare allowed to open under certain conditions,etc. This process is progressive, with new liftedrestrictions every 2 weeks. It goes on until June21, when the state of alarm ended and new nor-mality began.

First of all, let us remark the lack of significantdifferences among regions with regards to the gen-eral trend. All show the same overall behaviorthrough time. Mild differences exist in the degree ofconfinement, and on the recovery speed during de-confinement. Madrid for example reaches a higherlevel of confinement and recovers mobility muchslower than Extremadura. The main distinctive fac-tor of regions comes from the occurrence of periodicpeaks in the data (sometimes upwards, sometimesdownwards). We discuss those in detail on the fol-lowing section.

According to both mobility indicators, the Span-ish society assumed and implemented the generallockdown in a matter of 48 hours (from March 12to March 14). This level of mobility restrain wassustained for seven weeks. For reference, Wuhan,the source of the pandemic, held its lockdown forapproximately eight weeks [19]. Spain mobility wasminimized from March 15, with the declaration ofthe state of alarm, to May 2, with the approval of

de-confinement measures (consecutive orange, redand orange bands in Figure 1). After May 2, restric-tions were gradually lifted, causing a progressive re-covery of mobility that officially ended on June 21.

Within the seven lockdown weeks, two were underhard-lockdown. Since restrictions were enforced bypolice, mobility in this two week is a good estimateof the maximum mobility restriction that can beheld in Spain while keeping essential services run-ning.

To further understand the role of the hard-lockdown, we compute an estimate of its impact onmobility. We are interested in its effect when com-pared to mobility under regular lockdown. The reg-ular lockdown includes five weeks of lockdown data(the last two of March and the last three of April)during which traveling to industry and constructionworkplaces was allowed. This sort of lockdown is as-sumed to have a less damaging effect on the econ-omy, but it enables infection among co-workers. Tocompare mobility between both periods we measurethe corresponding area under the curve. The higherthe area, the higher the constraint. For each period,we normalize the area by the number of weeks. Theresults for all 17 regions are shown in Figure 2 as aseparate distribution for the Google and Facebookindices. In this case, Facebook is the most inter-esting source, since it is an absolute measure andallows us to measure volume of people. This dataindicates an increase between 10% and 15% in thepeople who stayed put. The relevance of that num-ber for the containment of the pandemic is unknownto us. i.e., we do not know which would have beenthe evolution of the pandemic if hard-lockdown hadnot been implemented. One may argue that many

Figure 2: Percentual increase in mobility reductionduring the hard-lockdown, with respect to mobilityduring regular lockdown. Distribution includes thevalues from all 17 Spanish regions.

6

Figure 3: Mobility containment in Madrid according to Facebook’s remain in tile and Google’s residen-tial index, together with the number of reported cases daily in Madrid [20] shifted two weeks early toapproximate contagion date.

more people would have been infected, since regularlockdown enables transmissions on the workplace.However, the number of detected cases did not showany change after hard-lockdown ended, it contin-ued to decrease at a similar rate. One may alsoargue that the hard-lockdown had a psychologicaleffect on society, boosting resiliency to confinement.Looking at how mobility recovers right after hard-lockdown is lifted, this seems to be a valid hypoth-esis. If that were the case, a state of alarm withouthard-lockdown may have lost adherence faster.

To observe the effect and timing of the policiesimplemented, we can compare it with the status ofthe pandemic. For that purpose we use the numberof daily reported cases, plotting it against the mo-bility curves. To approximate the date of the con-tagion from the date of report, we shift this datatwo weeks early. This is motivated by current es-timates [21], which assert that the vast majority ofCOVID-19 patients develop symptoms (if any) be-fore day 14 after contagion. Figure 3 shows thiscomparison, for the case of Madrid [20], the regionwith the most cases and the strongest lockdown ad-herence.

In Figure 3 we observe the beginning of the lock-

down overlapping with the initial containment ofthe pandemic. That is, the number of contagionshalts its exponential trend around the date of thestate of alarm declaration. Although we do notknow the exact role of the state of alarm, it is shownto be a clearly correlated factor. The seven weeksof general lockdown coincide with the seven weeksof strongest pandemic rate reduction. That is thetime it took the pandemic to reach a basal situationin the region, with less than 100 reported cases aday. According to this estimate, this situation mayhave been reached around the starting date of thede-confinement process. If all this was the case, theduration of the Spanish lockdown (7 weeks) was avery good fit to the evolution of the pandemic. Ashorter lockdown may have induced a significantlyhigher risk of relapse, and a longer one seems unnec-essary in sight of the contagion numbers estimatedto be taking place in early May. Let us remark thatduring the crisis policy makers could only use cur-rent daily cases for their decision making. That is,without the 2 week shift we performed in Figure 3.The de-confinement process was therefore a bolder(and riskier) initiative than Figure 3 illustrates.

7

4.2 Peaks

The general trend described in the previous sectionis explained by the different stages of confinementenforced by the Spanish government. On top ofthis trend, we can see the occurrence of a num-ber of peak values, happening periodically and onall regions. These peaks occur mostly on Sundays(marked with grey vertical lines in Figure 1), andto a lesser degree on Fridays and Saturdays. Letus discuss Sundays and Fridays in detail, since Sat-urdays seem to be a middle ground, transitioningbetween both.

Sunday peaks are anticorrelated in terms of rela-tive mobility (Google goes down) and absolute mo-bility (Facebook goes up). This means the numberof people moving is small when compared to near-bydays of confinement. It also means that the num-ber of people moving is not so different from whatit used to be, when compared to the same day ofthe week on normality. In other words, people werenot moving much on Sundays before the COVID-19 crisis, and during it they were moving less. Ac-cordingly, even though the decrease on mobility onSundays is not as big as on other days of the week,it still accounts for the day of the week with lessabsolute mobility. That would make Sundays thebest candidates for the mobility of risk population.

In contrast, Friday peaks exhibit a rather differ-ent behavior. In this case, the relative mobility de-creases sharply (Google goes up), while the absolutemobility remains stable or decreases mildly (Face-book goes flat or slightly up). This indicates Fridaysare the days with the most different mobility pat-terns with respect to the previous normality (rela-tive change), which speaks of the high mobility tak-ing place on a normal Friday. On these days is whensociety is showing its biggest change, leveling mo-bility to the rest of the working week. That wouldmake Fridays the best candidates for communica-tion and support (e.g., mental health assessment).Let us remind the reader that these insights maybe reinforced by the bias in the data, which favoursthe presence of the young segments of society.

Let us now conduct an experiment to validatethe hypothesis that peaks are related to the rela-tive or absolute nature of measures. We transformthe Facebook measure from an absolute one to arelative one, using as a baseline analogous remainin tile data, from February 24 to March 8, beforethe first regional restriction measures. This baselineis computed weekday-wise, like Google’s. The re-sult is shown in Figure 4, together with the original

Facebook data, and the Google measure. The firstobvious change are Sundays, which now peak down-wards like Google. In fact, our relative Facebookmeasure perfectly aligns with Google around week-ends (Friday to Monday) during the whole lock-down. This may be caused by differences in thedata (both data sources have different resolutions tomeasure movement), or by differences in the base-lines given the daily consistency.

Understanding the nature of these peaks is im-portant because of the effect these may have oncertain metrics. As shown in Figure 5, Pearsoncorrelation between Facebook’s remain in tile andGoogle’s residential index varies significantly frommonth to month. On March, mobility exhibiteda very clear trend as a result of the establishmentof confinement measures. In this setting, the cor-relation between both indices is clear (around 0.9Pearson correlation on average), and the peaks arenot disruptive enough as to alter it. On the otherhand, mobility during April was stable, as the wholemonth was under lockdown. This entails an overallflat behavior of the indicators. A context in whichthe inverted peaks have a dramatic effect, destroy-ing all correlation between indices. Finally, Mayappears as a middle ground. There is a generalizedmobility trend, which reduces the upsetting effect

Figure 4: Evolution of mobility according to Face-book absolute change (blue), Facebook relativechange (green) and Google (purple). Facebook rel-ative change has been divided by 100 to matchthe scale of the other two indices. The verti-cal bands (green, orange, red, orange, green) cor-respond to pre-confinement, state of alarm dec-laration and lockdown, hard-lockdown, lockdownand de-confinement stages. Grey vertical lines arealigned with every Sunday.

8

Figure 5: Pearson correlation between Facebook’s remain in tile and Google’s residential index for differentregions and months. Facebook plotted on the horizontal axis, and Google on the vertical one.

of the peaks, but the trend is not strong enough asto completely overpower the noise.

4.3 Daily Trend

Week days have an important role in the characteri-zation of mobility. Let us now study the same data,but this time from the perspective of days. To do sowe plot the Facebook and Google mobility indices astwo different axis. Figure 6 provides two visualiza-tions for the first three months of the pandemic inSpain. On the top row, the color gradient shows thechange through time, week by week. On the bot-tom row, week days are color coded to illustrate thedifferences between days. In these plots, horizontalaxis show absolute change (the more to the right,the bigger number of people stay at home) whilethe vertical axis shows relative change (the moreup, the more percentage of people stay at homewith respect with normal instances of that day).

The first visible thing in Figure 6 is the corre-lation between both values, as all data is mostlygathered around the diagonal. The top row showsthe evolution of mobility, starting from the axisorigin (bottom left) and suddenly jumping to thetop right quarter of the plot as lockdown is imple-mented. The last Friday and Saturday before thelockdown (second week of March) are the only daysin the middle of that jump. During confinement(April) data is rather stable in that area, until thede-confinement measures (May) bring it down andleft again, but this time in a slow manner.

The visualization using both Google and Face-book as axis shows the clear correlation be-

tween them. In general, as relative mobility in-creases/decreases, so does absolute mobility. How-ever, this relation seems to be somewhat dependanton the day of the week. As shown on the bottomrow of Figure 6, Sundays have a rather different be-havior in terms of relative mobility (it shows lessaffection in this metric) while Friday represents theopposite (it shows more affection in relative mobil-ity). This is a different visualization of the samephenomenon observed in the peaks of Figure 1 anddiscussed in §4.2.

4.4 The New Normality

On the second half of May, Spain started to lift theconfinement measures that had been in place in thecountry for two months [5]. The process was asym-metrical, with regions with better pandemic indica-tors (i.e., number of daily cases, number of avail-able hospital beds, etc.) de-confining faster thanothers. Detailed maps of the differential treatmentof regions can be found in governmental sources [22]This process ended on June 21, when the state ofalarm (and all mobility restrictions) was lifted. Onthat date, the whole country officially entered thenew normality.

Figure 7 includes the last four weeks of state ofalarm (but without a generalized lockdown), andthe first one of new normality. To facilitate visual-ization we change zoom in and the axis scale withrespect to Figure 6. Nonetheless, to enable com-parison with the rest of the period under study, inFigure 8 we plot the same data using the scale usedin Figure 6

9

Figure 6: Evolution of mobility for Facebook (horizontal axis) and Google (vertical axis) data. Each dotrepresents a single day in a single region. First column holds data for March, second for April and thirdfor May. First row shows the data with a color gradient indicating the week of the month. Second rowshows the same data colored based on day of the week.

The progression of mobility towards the axes ori-gin is still visible and this smaller scale as weeksgo by (in color gradient), for both working days(Monday to Friday) and weekend days (Saturdayand Sunday). To compare the new normality withthe old one, we must focus on the Google axis, sincethis is relative to a baseline (January 3 to February6). On the weekends mobility is already at Googlebaseline levels, with all values between -5 and 5 onthe last week (the new normal one). In contrast,working days are showing a higher difference withrespect to the baseline, with several values in thelast week between 5 and 15 in the Google axis. Thisindicates that the change implemented by the Span-ish society during the new normality is focused onworking days, while weekends are back to how theywere.

The recovery of old normality is not homogeneousamount regions either. Catalonia and Madrid, theregions with the biggest metropolitan areas, clearlylag behind. Asturias, Navarre, La Rioja, Region ofMurcia, Extremadura and Galicia are way ahead.Of those, only Murcia has a population densityabove 100 (132 people/km2), hinting a potential re-lation with this indicator. The lag of Catalonia andMadrid during the first 4 weeks is likely related withthe fact that these regions were slightly behind inthe removal of restriction measures. However, dur-ing the 5th and last week of data all regions were un-der the same conditions, and Catalonia and Madridstill exhibit higher levels of mobility reduction. Thismay be related with the role of large metropolitanareas, were it is harder to keep a safe distance, andwith the fact that both regions reported the high-

10

Figure 7: Mobility values from Monday to Friday (left plot) and from Saturday to Sunday (right plot).Color gradient indicates the week number (from March 25 to June 27), and the shape of the markerindicates the region set (Crosses: Asturias, Navarre, La Rioja, Region of Murcia, Extremadura andGalicia. Squares: Catalonia and Madrid). Grey diagonal line is the same on both plots, used for thepurpose of visual reference.

Figure 8: Mobility from March 25 to June 27, usingthe same scale of Figure 6. The data of this figureis zoomed in and split between working days andweekend days in Figure 7.

est absolute volume of infections during the pan-demic. Both these factors are strong psychologicalenablers of self-responsibility, which may have aneffect of adherence to mobility reduction during thenew normality.

5 Conclusions

In this work we consider the use of private datasources (Google and Facebook) for assessing the lev-els of mobility in a country like Spain. By doing so,we draw conclusions on two fronts. First, on the be-havior and particularities of private data sources.And second, on how mobility changed during theCOVID-19 pandemic in Spain.

Regarding private data sources, we have shownthe differences between using an absolute mea-sure (like Facebook) and a relative measure (likeGoogle). Both of them have limitations when usedin isolation. The former lacks a contextualization of

11

its values, while the latter depends entirely on thebaseline used. When used together, they providea visualizing of mobility where consistent patternscan be easily identified (as presented later in thissection). For specific purposes, using a single datasource may suffice, as long as it fits the goal:

• An absolute measure like Facebook’s can bevery useful for epidemiologic purposes, as itprovides an pure measurement of mobility.That includes estimating number of contactsin a society, modeling the spread of the virus,and measuring the impact of policies on abso-lute mobility.

• A relative measure like Google’s can be veryuseful for socio-economic purposes, as it pro-vides a contextualized measurement of mobil-ity. That includes understanding the changecaused by the new normality, and the economicimpact of mobility restriction policies.

On the second topic of this paper, the analysis ofSpanish mobility during the COVID-19 pandemic,we extract several conclusions. On one hand, datashows a huge mobility containment, sustained for amonth and a half (March 15 to May 1st, approxi-mately), very close to its theoretical limit (as repre-sented by mobility during the hard-lockdown). Thisduration was sufficient to contain the spread of thevirus and bring infection numbers down to trace-able scale. In hindsight, the policies implementedin Spain seem appropriate and proportional to theseverity of the situation. That being said, the role,timing and convenience of the hard-lockdown re-mains to be further discussed. Our work showsa relatively modest contribution of this policy tomobility reduction. On the other hand, the hard-lockdown may have had an effect on prolonging ad-herence.

Our work identifies mild differences between re-gions during the three months of restricted move-ment. Certain regions had a stronger adherence toconfinement than others, mostly in relative terms.This may be caused by regional differences in pre-pandemic mobility, which is used as baseline for therelative measurement. A similar artifact are the in-verted peaks of weekends, where a relative measurespikes down and an absolute measure spikes up. Asdemonstrated, this the result of combining a mea-sure relative to the weekday with an absolute mea-sure.

We also saw significant differences among days.Weekends exhibit the highest volume of mobility

reduction in absolute terms, even during the hard-lockdown, when traveling to work was forbiddenfor all except essential services. At the same time,weekends have the smallest mobility reduction inrelative terms, indicating that the effort society hadto make in this regard with respect to its previouspatterns was smaller. Fridays and Sundays are par-ticularly relevant days, the first because it repre-sents the biggest change from normal behavior, thesecond because it represents the biggest absolutedecrease in mobility. These particularities could beexploited for the general good.

Finally, we analyzed the new normality by look-ing at the weeks of de-confinement, up until June27, a week after the state of alarm was lifted on thewhole of Spain. In this period, we found Saturdaysand Sundays to be already at pre-pandemic levelsof mobility. In contrast, working days (Monday toFriday) still show significant differences. The newnormality also shows differences between regions,particularly for working days. Regions with largemetropolitan areas exhibit a reduction in mobilitybetween 4% and 14% after restrictions were lifted.Indeed, the new normality is most new on urbanworking days.

Acknowledgements

We would like to thank Facebook and Google for re-leasing the data that made this work possible. Wealso appreciate the insights and support of AmacHerdagdelen, Alex Pompe and Alex Dow on the in-terpretation of peaks. Part of this work was doneunder the Global Data Science Project for COVID-19. We would also like to thank Daniel Lopez-Codina, Sergio Alonso, and Clara Prats for fruitfuldiscussions. Finally, part of this research has re-ceived funding from the European Union’s Horizon2020 Programme under the SoBigData++ Project,grant agreement num. 871042.

References

[1] G. Bonaccorsi, F. Pierri, M. Cinelli, A. Flori,A. Galeazzi, F. Porcelli, A. L. Schmidt, C. M.Valensise, A. Scala, W. Quattrociocchi, andF. Pammolli, “Economic and social conse-quences of human mobility restrictions un-der COVID-19,” Proceedings of the NationalAcademy of Sciences of the United States ofAmerica, 6 2020.

12

[2] M. Gatto, E. Bertuzzo, L. Mari, S. Miccoli,L. Carraro, R. Casagrandi, and A. Rinaldo,“Spread and dynamics of the COVID-19 epi-demic in italy: Effects of emergency contain-ment measures,” Proceedings of the NationalAcademy of Sciences, vol. 117, pp. 10484—-10491, 5 2020.

[3] P. Beria and V. Lunkar, “Presence and mobil-ity of the population during covid-19 outbreakand lockdown in italy,” tech. rep., UniversityLibrary of Munich, Germany, 6 2020.

[4] M. U. Kraemer, C.-H. Yang, B. Gutierrez, C.-H. Wu, B. Klein, D. M. Pigott, L. Du Plessis,N. R. Faria, R. Li, W. P. Hanage, and oth-ers, “The effect of human mobility and controlmeasures on the COVID-19 epidemic in china,”Science, vol. 368, pp. 493—-497, 5 2020.

[5] A. Hegedus, A. Annunziato, A. Gerhardinger,A. Wania, B. Delipetrev, C. Gasparro, C. Fo-nio, C. Proietti, D. Turk, F. Sabo, F. Rios,G. Eklund, I. Joubert-Boitat, J. Monster,J. Swen, J. Kamberaj, K. Poljansek, K. Br-zostowska, M. Mastronunzio, M. Santini,M. Halkia, M. Kalas, M. M. Ferrer, N. Mc-Cormick, P. Probst, P. Rufolo, P. Barbosa,P. Vojnovic, P. Spruyt, S. G. Boskovic,T. Kemper, T. Antofie, T. Harmatha, T. Dur-rant, and V. Salvitti, “ECML covid dash-board.” European Commission Joint ResearchCentre - ISPRA - Space, Security and Migra-tion Directorate (JRC), 3 2020.

[6] S. Funk, E. Gilad, C. Watkins, and V. A.Jansen, “The spread of awareness and its im-pact on epidemic outbreaks,” Proceedings ofthe National Academy of Sciences, vol. 106,pp. 6872–6877, 3 2009.

[7] “Evolucion del nivel de movilidad del conjuntode provincias.”.

[8] “Openflights rolls out new maps, moves togithub.”.

[9] N. Oliver, B. Lepri, H. Sterly, R. Lambiotte,S. Deletaille, M. De Nadai, E. Letouze, A. A.Salah, R. Benjamins, C. Cattuto, and others,“Mobile phone data for informing public healthactions across the COVID-19 pandemic life cy-cle,” Science Advances, vol. 6, 6 2020.

[10] S. Spyratos, M. Vespe, F. Natale, I. We-ber, E. Zagheni, and M. Rango, “Quantify-ing international human mobility patterns us-ing facebook network data,” PloS one, vol. 14,p. e0224134, 10 2019.

[11] C. O. Buckee, S. Balsari, J. Chan, M. Crosas,F. Dominici, U. Gasser, Y. H. Grad, B. Gren-fell, M. E. Halloran, M. U. Kraemer, andothers, “Aggregated mobility data could helpfight COVID-19,” Science (New York, NY),vol. 368, p. 145, 1 2020.

[12] A. Herdagdelen, A. Dow, B. State, P. Mohas-sel, and A. Pompe, “Protecting privacy in face-book mobility data during the COVID-19 re-sponse.” Facebook Research, 6 2020.

[13] Facebook, “Facebook data for good publicdatasets,” 2020.

[14] S. Bavadekar, G. Cossoul, J. Davis, D. Des-fontaines, A. Fabrikant, E. Gabrilovich,K. Gadepalli, B. Gipson, M. Guevara, C. Ka-math, M. Kansal, A. Lange, C. Man-dayam, A. Oplinger, C. Pluntke, T. Roessler,A. Schlosberg, T. Shekel, S. Vispute, M. Vu,G. Wellenius, B. Williams, and R. J. Wil-son, “Google COVID-19 community mo-bility reports: Anonymization process de-scription (version 1.0),” arXiv preprint,vol. arXiv:2004.04145, 4 2020.

[15] Apple, “Mobility trends reports.” 2020.

[16] P. Maas, S. Iyer, A. Gros, W. Park, L. Mc-Gorman, C. Nayak, and P. A. Dow, “Facebookdisaster maps: Aggregate insights for crisis re-sponse & recovery,” in KDD ’19: Proceedingsof the 25th ACM SIGKDD International Con-ference on Knowledge Discovery & Data Min-ing, p. 3173, Association for Computing Ma-chinery, New York, NY, United States, 7 2019.

[17] IAB.spain and Presidencia del Gobierno deEspana, “Estudio anual mobile en espana 2019y tendencias.” 7 2019.

[18] A. M. Rodriguez, C. Tiberius, R. van Bree, andZ. Geradts, “Google timeline accuracy assess-ment and error prediction,” Forensic SciencesResearch, vol. 3, pp. 240–255, 8 2018.

[19] Reuters, “China scrambles to curb rise in im-ported coronavirus cases, wuhan eases lock-down.” 3 2020.

13

[20] Centro Nacional de Epidemiologıa and Insti-tuto de Salud Carlos III, “Situacion y evolucionde la pandemia de COVID-19 en espana.”2020.

[21] S. A. Lauer, K. H. Grantz, Q. Bi, F. K. Jones,Q. Zheng, H. R. Meredith, A. S. Azman, N. G.Reich, and J. Lessler, “The incubation periodof coronavirus disease 2019 (COVID-19) frompublicly reported confirmed cases: estimationand application,” Annals of internal medicine,vol. 172, pp. 577—-582, 1 2020.

[22] Presidencia del Gobierno de Espana, “Planpara la transicion hacia una nueva normali-dad.” 4 2020.

14