Countrywide arrhythmia: emergency event detection using mobile phone data

19 Pages·2016·2.43 MB·English

by  Didem Gundogdu

Gundogduetal.EPJDataScience (2016) 5:25
DOI10.1140/epjds/s13688-016-0086-0
REGULAR ARTICLE OpenAccess
Countrywide arrhythmia: emergency
event detection using mobile phone data
DidemGundogdu1,2*,OzlemDIncel3,AlbertASalah4andBrunoLepri1
*Correspondence:
[email protected] Abstract
1FondazioneBrunoKessler,via
Largescalesocialeventsthatinvolveviolencemayhavedramaticpolitical,economic
Sommarive,18,Trento,Italy
2DISI,UniversityofTrento, andsocialconsequences.Theseeventsmayresultinhighercrimerates,spreadingof
viaSommarive,9,Trento,Italy infectiousdiseases,economiccrises,andeveninmigrationphenomena(e.g.,
Fulllistofauthorinformationis
refugeesacrossbordersorinternallydisplacedpeople).Hence,researchershave
availableattheendofthearticle
startedusingmobilephonedatafordevelopingtoolstoidentifysuchemergency
eventsinrealtime.Inourpaper,weapplyastochasticmodel,namelyaMarkov
modulatedPoissonprocess,forspatio-temporaldetectionofhourlyanddaily
behavioralanomalies.Weusethecallvolumescollectedfromanentiregeographic
region.Ourworkisbasedontheassumptionthatpeopletendtomakecallswhen
extraordinaryeventstakeplace.Wevalidateourmethodologyusingadatasetof
mobilephonerecordsandevents(emergencyandnon-emergency)fromthe
RepublicofCôted’Ivoire.Ourresultsshowthatwecansuccessfullycapture
anomalouscallingpatternsassociatedwithviolentevents,riots,aswellassocial
non-emergencyeventssuchasholidays,sportsevents.Moreover,callvolume
changesalsoshowsignificanttemporalandspatialdifferencesdependingonthe
typeofanevent.Ourresultsprovideinsightsforthelong-termgoalofdevelopinga
real-timeeventdetectionsystembasedonmobilephonedata.
Keywords: behavioralanomalydetection;emergencyevents;mobilephonedata;
MarkovmodulatedPoissonprocess
1 Introduction
Largescalesocialeventscanhappenanytime,anywhere,andwithoutwarning.Examples
areclashesbetweenethniccommunities,violenceamongsupportersofpoliticalgroups
orsportsclubs,demonstrationsandcelebrations.Someoftheseeventscausemigration
phenomena(e.g.,refugeesandinternallydisplacedpeople)[,],highercrimeratesand
spreadingofinfectiousdiseases[,],andresultineconomiccrises[].Subsequently,re-
searchershaverecentlystartedtoautomaticallyidentifyemergencyeventsbyusingnew
sourcesofdata,suchasgeo-referencedsocialmediaandmobilephonedata[–].Inpar-
ticular,thealmostuniversaladoptionofmobilephonesisgeneratinganenormousamount
ofdataabouthumanbehaviorwithabreadthanddepththatwerepreviouslyinconceiv-
able.In,therewere . billionmobilephonesubscriber accountsworldwide,with
millionsofnewsubscriberseveryday,correspondingtoapenetrationof%inthede-
velopedworldand%indevelopingcountries[].
©2016Gundogduetal.ThisarticleisdistributedunderthetermsoftheCreativeCommonsAttribution4.0InternationalLicense
(http://creativecommons.org/licenses/by/4.0/),whichpermitsunrestricteduse,distribution,andreproductioninanymedium,pro-
videdyougiveappropriatecredittotheoriginalauthor(s)andthesource,providealinktotheCreativeCommonslicense,and
indicateifchangesweremade.
Gundogduetal.EPJDataScience (2016) 5:25 Page2of19
In our paper, we investigate the usage of mobile phone data to detect and character-
izeemergencyandnon-emergencyevents.Specifically,weadopttheMarkovmodulated
Poissonprocessframework[,](MMPP)forthespatio-temporaldetectionofhourly
anddailybehavioralanomaliesincallvolume,andwediscusstherelationshipbetween
these anomalies and the actual emergency and non-emergency events that might have
causedthem.Comparedtopreviousstudies[,–],wedonotstartwiththelocation
andthetimeofanalreadyknownevent,butweuseanunsupervisedapproachthatspatio-
temporallyidentifiesunusualcallingbehavior.Moreover,ourapproachdetectsnotonly
dailyanomalies,butalsohourlyanomalies.Hence,weareabletocapturebehavioralre-
sponsesoccurringwithinhoursofanevent.
Tovalidateourapproach,weusemobilephonerecordsfromIvoryCoast(officiallyRe-
publicofCôted’Ivoire)inAfrica.Thedata,collectedfromDecember,toApril,
 during the post-election crisis, were released for Orange’s ‘Data for Development
Challenge’(DD)[,]andcontaincallsbetweenmillioncustomers.
Ourresultsshowthatwecansuccessfullycaptureanomalouscallingpatternsassociated
withviolentevents,riots,aswellassocialnon-emergencyeventssuchasholidays,sports
eventsonanhourlyanddailybasis.Moreover,weillustratethatcallvolumechangesalso
showsignificanttemporalandspatialdifferencesdependingonthetypeofanevent.Unlike
previousworkonclassificationofsocialevents[],wefindthatthecoverageofthespatial
impactismoresignificantthantheduration.
Insummary,themaincontributionsofthisstudyare:
• Weconstructadetaileddatabaseofemergencyandnon-emergencyeventsinthe
IvoryCoastusingmultiplesourcesofdataandmergethemwiththegeographical
locationsofthecelltowers.Thisdatabaseisusedasgroundtruthinouranalysis;
• WeadoptaMarkovmodulatedPoissonprocess(MMPP)tospatio-temporallydetect
hourlyanddailybehavioralanomaliesincallvolume;
• WetestourmethodologyusingtheCallDetailRecords(CDRs),aggregatedtothecell
towerlevel,ofanentirecountry;
• Wediscussthecorrespondencebetweentheanomaliesfoundandtheactual
emergencyandnon-emergencyeventsthatmighthavecausedthem;
• Wehighlightanddiscussthedifferentspatialandtemporalsignaturesofthe
discoveredevents.
Therestofthepaperisstructuredasfollows.Sectiondiscussesrelatedworkonus-
ingmobilephonedataformeasuringhumanbehaviorandpreviousapproachestosimi-
lareventdetectionproblems.InSection,wepresenttheproposedMarkovmodulated
Poissonprocessforidentifyinganomalies.Wedescribethe‘IvoryCoastDataset’,theCall
DetailRecords(CDRs)andtheeventrecords,usedtovalidateourapproachinSection.
InSection,weevaluatethecorrespondencebetweenanomaliesfoundandactualemer-
gencyandnon-emergencyevents,reportingcomparativeexperimentalresultswithtwo
otherapproachesfromtheliterature.Beforeconcludingthepaper,weelaborateonwhat
wehavelearnedfrommatchedandunmatchedevents,andwediscusssomelimitations
ofourstudy(Section).
2 Relatedwork
InthissectionwereviewtheliteratureonunderstandinghumanbehaviorsfromCallDe-
tailRecords(CDRs),andeventdetectionmethodologies.
Gundogduetal.EPJDataScience (2016) 5:25 Page3of19
2.1 Understandinghumanbehaviorsfromcalldetailrecordsdata
Mobilephoneoperatorscananalyzethebehaviorofalargenumberofpeoplefromtheir
aggregatedmobilephoneusage[].TheCallDetailRecords(CDRs)storedbyoperators
(typicallyforbillingpurposes)canbeexploitedtoextractmobilitypatterns[,,],
to model social interactions [, ], to analyze the dynamics of a city [, ], to un-
derstandepidemics[,],toestimatepopulationdensities[],andtopredictenergy
consumptionpatterns[],andsocio-economicindicatorsandoutcomesofterritorialdis-
putes[–].
Inthelastfewyears,severalstudieshaveshownthatnaturalandman-madeemergency
events(e.g.earthquakes,floods,bombings,riots)canbereflectedbydramaticincreases
incallingandmobilitybehaviors[,–,,].
Theassumptionbehindtheseworksisthatsignificantchangesinbehavior,capturedby
mobilephonedata,willindicatetheoccurrenceofextremeevents.Indeed,peopletendto
shareandinformeachotheraboutanemergencyeventtypicallyrightafteritisrealized
[]. However, planned non-emergency events also occur and may provoke significant
changes in mobile phone behavior. Bagrow et al. found easily detected changes in call
frequency during festivals, concerts, sport events, and it is likely that also other events
suchasholidaysmayproducesimilarchanges[].
The call data can be analyzed at the individual level, to detect the changes from the
expectedcallandmobilitybehavior[,,].Thisapproachrequiressomerestrictions
fordataprivacy,andincurshighcomputationalcost.Theseproblemscanbeovercomeby
computingontheaggregatedcallvolumeofeachcelltower[].
Typically, the approaches that analyze CDR data start with the time and the location
ofanalreadyknowneventandthenlookforanomalouscallingbehavioratthattimeand
location.Themostcommonwayofanomalydetectionintimeseriesistodefineabaseline.
Withasupervisedapproach,verygoodresultscanbeachieved.Inthispaper,wetakean
unsupervisedapproachtobuildamodelwithouthavingpriorinformation.
Recently,Dobraetal.proposedanunsupervisedbehavioralanomalydetectionsystem
thatidentifiesdaysandlocationswithunusualcallingormobilitybehaviorwithoutknow-
ingtheevent[].TheauthorsusedmobilephonerecordsfromRwandainordertocon-
nect the identified anomalous days and locations with extensive records of violent and
politicalevents(e.g.,protests,violenceagainstcivilians)andnaturaldisasters(e.g.,earth-
quakes).Specifically,theycomputedtheaggregateddailymobilityandcallingpatternsof
eachsite,startingfromtheindividualbehaviorofallassignedsubscribersforthecorre-
spondingsite.Thismethodologysuffersfromhighcomputationalcostsandworksonly
on a dailybasis. Ourapproachinstead takes asinput the callingvolume of a celltower
withoutaccessingindividualdata.Hence,wearealsoabletodetecthourlyvariationsand
thebehavioralresponsesoccurringwithinhoursofanevent.
There are two main approaches for classification of emergency and non-emergency
eventsfrommobilephonedata.Thefirstapproachistheanalysisofmobilitypatternsto
modelindividualsandcrowds,whichhasbeenusedtoidentifyconcertsandsportevents
[,].Thesecondapproachistheanalysisoftemporalpatterns,whichcanbeusedto
differentiatebetweeneventtypesaccordingtothedurationofbehaviors[].Wepropose
athirdapproachbyobservingspatio-temporalcharacteristicsofanomalousbehaviors.
AnumberofworksusedtheDDIvoryCoastdatasettovalidateeventdetectionap-
proaches []. Paraskevopoulos et al. [] used numerical analysis to detect anomalies.
Gundogduetal.EPJDataScience (2016) 5:25 Page4of19
Theirmodelisbasedoncalldurationstatistics,whichiscalculatedbydividingthecumu-
lativecalldurationbythenumberofcalls,foreachdayandforeachcelltower.Duringan
emergency,peoplemayindeedtendtomakemorecalls,butthesecanbewithshorterdu-
rations.Hence,lookingatthetotaldurationmaynotbesufficientlydiscriminative.Dong
etal.usedtheDDdatasetformodelingthemovementsofflocksofpeople[].Their
assumption is that people move in groups when an extraordinary event happens. This
assumptioncan provide valuableinsights todetect andcharacterize protests.However,
thismobilitypatternisnotobservedduringattacksagainstciviliansthattakeplaceinur-
banareas.InSection.wewillprovidecomparativeresultswiththetworecentmethods
describedbrieflyhere.
2.2 Eventdetectionmethodologies
Intheliterature,anomalouseventdetection,whichcorrespondstothetaskofdetecting
anomaliesfromtimeseries,isstronglyrelatedtooutlierdetection[],andchangepoint
detection[,].
Inourapplicationscenario,thenormalbehaviorisobservedbythetemporal(hourly)
changesoftheaggregatedcallvolumes,collectedfromdifferentcelltowers.Similardis-
crete,count-basedanalysisproblemshavebeentackledinstatistics,econometrics,psy-
chology,andecology[–].
In particular, the change point methodology is a well-studied topic in statistics [].
Previously,Zhangetal.usedthisapproachtodetectanomaliesinmobilephonedata[].
However,theproblemofthismethodisthatitdoesnotpreservetheperiodicityofdata.
OtherresearchersproposedtheusageofHiddenMarkovModels(HMM)[,]for
theanomalydetectiontask.Ashighlightedby[],theMMPPapproachthatwepropose
touseinthispaperisaspecialcaseofHMMandMarkovChainMonteCarlo(MCMC)
approaches.MMPPhasalsosimilaritieswiththechangepointmethodology[]butit
hastheadvantageofpreservingperiodicity.
3 Methodology
Ourgoalistoinvestigatetheusageofmobilephonedatatodetectandcharacterizeanoma-
lousevents.Poissonprocessesarecommonlyusedtomodelcountdata(datainwhichthe
observationscantakeonlythenon-negativeintegervalues)[,]inmanydomains,such
asmodelingrareincidentsinpsychiatrichospitals[],andtrafficanalysis[,].
3.1 MarkovmodulatedPoissonprocess
Inoursetting,thecountdataarerepresentedbythenumberoftotalcallsin-hourtime
intervals,denotedasN(t),foreachcelltower,denotedask.
ThemodelisrepresentedasatwostateMarkovchain,withanormalcallbehaviorstate
z(t)= and an abnormal call behavior state z(t)= (see Equation ()). The transitions
betweenthestatesaredefinedwithatransitionprobabilitymatrixM inEquation()
z
⎧
⎨
, ifthereisaneventintimet,
z(t)= ()
⎩
, otherwise,
(cid:5) (cid:6)
–z z
M =   . ()
z
z –z
 
Gundogduetal.EPJDataScience (2016) 5:25 Page5of19
OurobservationsaredenotedasN(t),andhiddenvariablesaretheamountofcallsin
a normal call pattern N (t), and the amount of calls initiated because of an anomalous

eventN (t).ThedefinitionofNk(t)foracelltowerkisgivenbythesummationofhidden
E
variablesNk(t)andNk(t),asshowninEquation()
 E
Nk(t)=Nk(t)+Nk(t). ()
 E
Hence,weassumethatthenumberofcallsaregeneratedthroughaheterogeneousPois-
sondistributionPois(N,λ(t)),withtheratevaluethatisafunctionoftimeλ(t),asshown
inEquation()
(cid:7) (cid:8) (cid:7) (cid:8)
Pois N;λ(t) =e–λ(t) λ(t)N/N! . ()
Thenormalcallpatternofacelltowerkisdependentontheday(i)oftheweek(δk)and
i
thehour(j)ofthedayηk,andλk representstheaveragerateofcelltower(k)inoneweek.
j,i 
WecanformulatetheratefunctionofthePoissondistributionλk(t)astheproductofthe
initialrateλk,thedailyeffectδk,andthehourlyeffectsηk.Thedetailedderivationscan
 i j,i
befoundinAppendixA..
Toevaluateeventdetectionmodels,itisnecessarytoannotateexistingdatabycross-
referencing it with news sources. We provide a list of important events in Table  and
Table  (details explained in Section .). For each important event, we have grouped
thecelltowersclosetothelocationprovidedinthegroundtruth,andassociatedeventsto
celltowersprobabilistically.Eachcelltower’sprobabilitydistributionisnormalizedbefore
calculatingtheaverageprobabilityoftheregion.Iftheeventprobabilityishigherthana
definedthresholdτ (e.g..),weclassifiedthiseventasdetectedintheregion.
3.2 Baselinemodel
ToassesstheMMPPperformance,wehaveimplementedabaselinemodel,whichwede-
scribeinthissection.Foreachcelltower,thehourlyaveragecallvolumeiscalculatedas
showninEquation().Inournotation,eachcelltowerisdenotedwithk,daysareindexed
withi,hourswithj,N(t)istheobservationfortimet,andDisthetotalnumberofdays
forthedatacollectionperiod.Oncetheaveragesarecalculated,wesubtracttheaverage
value,(cid:6),fromtheobservedcallvolumefortimet.Iftheobtainedvalueishigherthana
definedthreshold(τ),theeventislabeledasanomalous.
(cid:9) (cid:9)
D  N(i,j)k
(cid:6)k = i=,j i,j= , ∀k,
i,j D ()
(cid:10)(cid:7) (cid:8)(cid:10)
(cid:10) N(i,j)k–(cid:6)k (cid:10)>τ.
i,j
4 TheIvoryCoastdataset
Inthissection,weintroducethedatasetsthathavebeenusedtoevaluateourproposed
methodology:(i)amobilephonerecordsdataset,obtainedbyOrangeforthe‘DDChal-
lenge,’a and (ii) an event recordsdataset obtained froma variety of public sources(e.g.,
ArmedConflictLocationandEventDataProject,UnitedNationsCouncilandInterna-
tionalCrisisGroupreports,localandinternationalnews).
Gundogduetal.EPJDataScience (2016) 5:25 Page6of19
Table1 SummarystatisticsoftheIvoryCoastdatasetandthecountry
Totalareaofthecountry(hectares) 32,246,300
Populationofthecountry around20million
Numberofsubscribers 5million
Typeofthedata Celltowertocelltower
Totalnumberofcalls 190,638,909
Timeperiod 7weeks/20weeks
4.1 Calldetailsrecords
The Orange dataset contains anonymized and aggregated calls between  million cus-
tomersfromDecember,toApril,(referredtoasSetintheDDChal-
lenge).Specifically,thedatasetcontainsthetotalvolume(numberofcallsandSMSs)and
durationofcallsbetweeneachpairofcelltowersovertheentireperiod.Thetotalnumber
ofcelltowersis,;however,inthepre-processingphase,wehaveeliminatedthecell
towers,whicharenotpresentduringthewholeperiod,endingupwithcelltowersto
analyze.TheexactlocationsofthetowersarenotprovidedbyOrangeTelecom,duetothe
company’soperationalconfidentiality.Finally,itisworthnoticingthatthedataconsistof
thetotaltrafficbetweencelltowers,andhencenoindividualdataareeveraccessed.
Mobile phone penetration is high enough (%) [] in Ivory Coast to make such
a dataset sufficiently representative of the population. Moreover, the network operator
holdsadominantpositioninIvoryCoast(%ofthemarketshare).Tableprovidessum-
marystatisticsforthecountryandthedataset.
Specifically,thecalldataconsistof(i)date,(ii)hour,(iii)initiatingcelltower,(iv)des-
tinationcelltower,(v)aggregatednumberofcalls,and(vi)aggregateddurationofcalls.
TheCDRdatawithanunassignedcelltoweraredeleted.FromDecember,toJan-
uary,,halfofthecountryhadashortageofenergy,confirmedbytheOrangeTele-
comauthorities.Thedatacollectedduringthisperiodaredeleted.Additionally,wehave
missingdatafromndofJanuarytothofJanuary.Topreservetheweeklyperiodicity
withoutbeingaffectedbydatacollectionissues,weanalyzethedatafromDecemberto
December,andfromJanuarytoMarch(daysintotal).
Toprovidesomegeographicalcontext,Figureshowstheroughlocationsofcelltowers
in relation to the regional boundaries of Ivory Coast ( subprefectures). It shows the
denseconcentrationofcelltowersinandaroundthelargestcitiesofeachregion.Abidjan,
theeconomiccapitalofIvoryCoast,hasasignificantlyhigherconcentrationofcelltowers
comparedtotherestofthecountry.
4.2 Eventrecords
Ourdataonviolentandpoliticalevents,majorholidaysandmajorevents(e.g.,elections,
theAfricanFootballCup)comefromavariousstructuredandunstructuredpublicdata
sourcessuchasArmedConflictLocationandEventDataProject(ACLED)[],United
Nations Council and International Crisis Group reports, local and international news.
ACLEDandUNSecurityreportsincludeextensivedataonconflict-relatedeventsinclud-
ingriots,protests,killings,andbattles.Theinformationisobtainedbylocalorinterna-
tionalnewspapers(e.g.NotreVoie,LePatriote,France,BBC)andradiosources,andit
includesdetailssuchasthedateandlocationofeachevent,thetypeofevent,thegroups
involved, and the fatalities. Ivory Coast has been suffering unstable political conditions
during the data collection period. According to the United Nations Human Rights De-
Gundogduetal.EPJDataScience (2016) 5:25 Page7of19
Figure1 CelltowerlocationswithinsubprefecturesinIvoryCoast.
partment,morethan,Ivoriansweredisplacedinthecountryandaround,
Ivoriansmigratedtoneighboringcountriesinordertobeinsecurelivingconditions[].
In total, we have gathered  emergencyevents (e.g., confrontationsbetween groups,
protests)andnon-emergencyevents(e.g.,nationalandregionalholidays,AfricanFoot-
ball Cup games). The complete list of events (emergency and social non-emergency
events)isshowninTableandTable.
5 Experimentalresults
Our approach identifies many hours and days with unusual increase in calling volume.
Asin[],theseanomaliesarefoundsometimesinaspecificsiteandsometimesacross
multiplesites.Specifically,ourapproachprovidestheprobabilityofhavingananomalous
eventinaspecificlocation.
Aswe mention in Section ,ourgoal is toidentify emergencyevents. However, non-
emergencyeventsmightalsoproducebehavioralchangesincallingvolume.Hence,itis
relevanttohighlightanddiscussthespecificbehavioralsignaturesofthedifferentevents
inordertodetecttheemergencieseffectively.Inthisregard,fromtheannotatedemer-
gencyevents,ourmethodautomaticallydetected,whileforthesocialnon-emergency
events it was able to identify  out of  events. Similar to [] and [], we find some
hourlyanddailyanomaliesthatwewerenotabletomatchtoanyoftherecordedevents
(i.e.possiblefalsepositives).
We compare our MMPP approach with a baseline model, which has been explained
in Section ., and show that our approachoutperformsthe baseline. Indeed, fromthe
annotatedemergencyevents,thebaselineapproachisabletoidentify,whileforthe
social non-emergency events, it is able to identify  out of . While the nature of the
problemdictatesaverylargedatasetwithverysparsetruepositives,theresultsarequite
promising.Beforedescribingourresultsindetail,wecompareourmethodwiththeevent
Gundogduetal.EPJDataScience (2016) 5:25 Page8of19
Table2 ComparativeresultsfortheMMPPmodelandforDongetal.’sapproach[38]
Precision Recall F-score
MMPP 0.31786 0.9219 0.4727
Dongetal. 0.0676 0.92 0.125
Figure2 Thedistributionofdetectedsocialeventswiththeapproachof[20].Selectingalonger
durationthresholdforunexpectedbehaviorreducesthenumberofdetectedevents.
identification approach by Dong et al. [], as well as with the event type classification
approachdescribedbyYoungetal.[]inthenextsubsection.
5.1 Comparisonwithrecentapproaches
Dongetal.proposedamethodologyinforeventidentificationbasedonmodeling
individuals’mobilityasflocksthroughthecity,andtheytestedthismethodologyonthe
DDdataset[].Theyreportedaprecisionvalueof.andarecallvalueof..
Inordertoprovideafaircomparison,wetestedtheMMPPapproachonthesameexperi-
mentalsettingproposedintheirpaper,withdaysofdataandgroundtruthevents.
TheresultsshowthatMMPPoutperformsthisapproachinallthemeasures(seeTable).
WealsocompareourapproachwiththeoneproposedbyYoungetal.intoclas-
sifysocialnon-emergencyevents[].Thisapproachpositsthatsocialeventshavelonger
temporaldurationthanemergencyevents.Sincethedurationofdeviationisamajorpa-
rameter,wecomparedthemethodsforobservationperiodsoftwo,three,andfourcon-
secutivehours.Settingthistotwohoursmeansthataneventisdetectediftheobserved
behaviordeviatesfromtheexpectedfortwohours.Foreachofthethreesettings,Figure
showsthenumberofsocialeventsdetectedbyagivennumberofcelltowers.Thebest
performanceisobtainedbyusingtwoconsecutivehours,wheretheaveragenumberof
detected social events is equalto .. Using MMPP, on the other hand, we are able to
detectsocialevents.Wenowdescribethefindingsofourmethodindetail.
6 Discussionandlimitations
Inthissection,wematchsomeoftheanomaliesidentifiedalongwithkeyeventsthatoc-
curredinIvoryCoastandwediscusswhatwehavelearnedfrommatchedandunmatched
events.Then,wediscusssomelimitationsofourstudy.
Inand,IvoryCoastsufferedfromapostelectioncrisis.Theelectionresultsini-
tiatedacivilwar,andeffectsofthewarwerestillpresentduringthedatacollectionperiod.
Therefore,ouremergencyeventsaremainlyduetotheseunstablepoliticalconditions.
Gundogduetal.EPJDataScience (2016) 5:25 Page9of19
Figure3 OntheleftsideweshowthecelltowerlocationsontheBouaké-Katiolaroad,whileonthe
rightside,weshowthecorrespondingprobabilitiesofhavinganeventonthe4thofFebruary.
Figure4 MMPPresultsforcelltower113(Arrah).Inthetopplot,theredlinedenotesthehourlyanddaily
averagesandthedarklinedenotestheobservedcallingvolume.Theplotinthemiddleshowsthe
predictionsofhavinganevent,withtheannotatedeventsmarkedbelow.
Violence against civilians - February , February , and March ,  On February
therd,theproposedsystemdidnotdetectanysignificantchangesincallvolumenear
thelocationoftheevent,closetoBouaké.Instead,weseeaclearanomalyinthisareaon
Februarytheth(seeFigure).
Similarly,noanomaliesweredetectedonFebruarytheth,whileonMarchthethwe
detectananomalyclosetoBouaké(celltower)aroundam.Interestingly,thisfinding
isnotdetectedbythebaselinemodel.
Violenceagainstcivilians-FebruaryandFebruary, InArrah,itwasreported
thattheconfrontationoftwogroupsendedwiththreepeoplekilledandatleastinjured.
Thevisualizationofourmodel’soutputisshowninFigureforcelltower.Inthetop
plot,theredlinedenotesthehourlyanddailyaveragesandthedarklinedenotestheob-
servedcallingvolume.Theplotinthemiddleshowsthepredictionsofaneventoccurring,
withtheannotatedeventsmarkedbelow.
Protestsagainstthegovernment-February, MMPPdetectscallingvolumein-
creasesfromasinglecelltoweratpmandpmonFebruary,.Thecelltoweris
Gundogduetal.EPJDataScience (2016) 5:25 Page10of19
Figure5 CelltowerlocationsofAbidjanwithhighcallvolumeanomaliesduringthe‘Congres
NationaldelaResistancePourlaDemocratie’meetingonthe18thofFebruary.
Figure6 OverallcountryanomalyprobabilitiesonFebruary26,duringrenewalofelectionsinBonon
andFacobly.
locatedinthecitycenterofAbidjan,theeconomiccapitaloftheIvoryCoast.Anon-violent
protestagainstthegovernmentwasreportedbytheUnitedNationsonthesamedayin
Abidjan,infrontofthe‘CongresNationaldelaResistancePourlaDemocratie’headquar-
terslocatedinthecitycenter.Weassumethoseproteststookplaceinthewesternpart
ofthemap,showninFigure.Eachorangedotrepresentscelltowerswithananomalous
callvolume.Unfortunately,thereportsofUnitedNationsdonotspecifythedurationof
theprotest.
ElectionsandviolentclashesinBononandFacobly-February, Afterpm,we
observeanomalouscallingpatternsintworegionsofthemid-west,BononandFacobly,
forthecelltowers,,,.Onthesameday,acoupleofeventswererecorded
forthesetworegions,politicalelectionsandconsequentviolentclashesbetweenpolitical
opponents,asshowninFigure.Theviolenteventsendedwiththedeathoffivepeople.
Interestingly, we observe that these violent events seem to have effects not only in the
regionsinvolved,butalsoinAbidjan.Indeed,wedetectananomalyinAbidjanafterpm.