Countrywide arrhythmia: emergency event detection using mobile phone data PDF
Preview Countrywide arrhythmia: emergency event detection using mobile phone data
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’(DD)[,]andcontaincallsbetweenmillioncustomers. 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.Sectiondiscussesrelatedworkonus- 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. AnumberofworksusedtheDDIvoryCoastdatasettovalidateeventdetectionap- 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.usedtheDDdatasetformodelingthemovementsofflocksofpeople[].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‘DDChal- 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,(referredtoasSetintheDDChal- lenge).Specifically,thedatasetcontainsthetotalvolume(numberofcallsandSMSs)and durationofcallsbetweeneachpairofcelltowersovertheentireperiod.Thetotalnumber ofcelltowersis,;however,inthepre-processingphase,wehaveeliminatedthecell towers,whicharenotpresentduringthewholeperiod,endingupwithcelltowersto 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).Tableprovidessum- 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 missingdatafromndofJanuarytothofJanuary.Topreservetheweeklyperiodicity withoutbeingaffectedbydatacollectionissues,weanalyzethedatafromDecemberto December,andfromJanuarytoMarch(daysintotal). Toprovidesomegeographicalcontext,Figureshowstheroughlocationsofcelltowers 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)andnon-emergencyevents(e.g.,nationalandregionalholidays,AfricanFoot- ball Cup games). The complete list of events (emergency and social non-emergency events)isshowninTableandTable. 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,fromtheannotatedemer- 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.proposedamethodologyinforeventidentificationbasedonmodeling individuals’mobilityasflocksthroughthecity,andtheytestedthismethodologyonthe DDdataset[].Theyreportedaprecisionvalueof.andarecallvalueof.. Inordertoprovideafaircomparison,wetestedtheMMPPapproachonthesameexperi- mentalsettingproposedintheirpaper,withdaysofdataandgroundtruthevents. TheresultsshowthatMMPPoutperformsthisapproachinallthemeasures(seeTable). WealsocompareourapproachwiththeoneproposedbyYoungetal.intoclas- 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 detectsocialevents.Wenowdescribethefindingsofourmethodindetail. 6 Discussionandlimitations Inthissection,wematchsomeoftheanomaliesidentifiedalongwithkeyeventsthatoc- curredinIvoryCoastandwediscusswhatwehavelearnedfrommatchedandunmatched events.Then,wediscusssomelimitationsofourstudy. Inand,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 therd,theproposedsystemdidnotdetectanysignificantchangesincallvolumenear thelocationoftheevent,closetoBouaké.Instead,weseeaclearanomalyinthisareaon Februarytheth(seeFigure). Similarly,noanomaliesweredetectedonFebruarytheth,whileonMarchthethwe detectananomalyclosetoBouaké(celltower)aroundam.Interestingly,thisfinding isnotdetectedbythebaselinemodel. Violenceagainstcivilians-FebruaryandFebruary, InArrah,itwasreported thattheconfrontationoftwogroupsendedwiththreepeoplekilledandatleastinjured. Thevisualizationofourmodel’soutputisshowninFigureforcelltower.Inthetop plot,theredlinedenotesthehourlyanddailyaveragesandthedarklinedenotestheob- servedcallingvolume.Theplotinthemiddleshowsthepredictionsofaneventoccurring, withtheannotatedeventsmarkedbelow. Protestsagainstthegovernment-February, MMPPdetectscallingvolumein- creasesfromasinglecelltoweratpmandpmonFebruary,.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, Afterpm,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,wedetectananomalyinAbidjanafterpm.
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