International Research Journal of Engineering and Technology (IRJET) e-ISSN:2395-0056
Volume: 11 Issue: 07 | July 2024 www.irjet.net p-ISSN:2395-0072
The Role of Data Management and Engineering in Modern Law Enforcement: Opportunities and Challenges
Ramalakshmaiah Panguluri Alagappa University, India
Abstract
This article examines the profound impact of data management and engineering on modern law enforcement practices, exploringboththetransformativebenefitsandtheethicalchallengesassociatedwiththesetechnologies.Itdelvesintothe advanced analytics employed in crime prevention and resolution, highlighting real-time data processing capabilities, pattern identification, and predictive analytics that have revolutionized policing strategies. The article discusses the significantbenefitsofdata-drivenlawenforcement,includingenhancedcrime-solvingtechniquesthroughtheintegration of diverse data sources, proactive policing measures enabled by predictive tools and crime mapping, and improved transparency and accountability through data-driven decision-making. However, the article also critically addresses the concerns and ethical considerations surrounding these practices, such as privacy issues stemming from extensive data collection, the potential for algorithmic bias and its disproportionate impact on certain communities, and the broader ethicalimplicationsofdatauseinpolicing.Furthermore,thestudyprovidesacomparativeanalysiswiththeeducationand creative fields, illustrating similarities in transformative potential while highlighting the unique ethical considerations in these domains. By synthesizing current research and real-world applications, this comprehensive exploration aims to provide a balanced perspective on the role of data management and engineering in law enforcement, emphasizing the need for responsible implementation that balances technological innovation with the protection of civil liberties and the promotionofequitablepolicingpractices.
Keywords: Data-Driven Policing, Predictive Analytics in Law Enforcement, Algorithmic Bias in Criminal Justice, Privacy andSurveillanceEthics,BigDatainPublicSafety
I. Introduction
The rapid advancement of information technology has ushered in an era where data management and engineering play pivotal roles across various sectors of society. In the realm of law enforcement, these technological innovations have become particularly transformative, offering unprecedented capabilities for crime prevention, investigation, and resolution. As law enforcement agencies increasingly adopt data-driven approaches, they find themselves at the intersectionofcutting-edgetechnologyandcomplexethicalconsiderations.
International Research Journal of Engineering and Technology (IRJET) e-ISSN:2395-0056
Volume: 11 Issue: 07 | July 2024 www.irjet.net p-ISSN:2395-0072
Theintegrationofadvancedanalytics,machinelearning,andbigdataprocessingintopolicingpracticeshasrevolutionized traditionalmethodsofmaintainingpublicsafety.AccordingtoacomprehensivestudybytheNationalInstituteofJustice, agenciesemployingdata-drivenstrategieshavereportedsignificantimprovementsincrimeclearanceratesandresource allocation efficiency [1]. These technological advancements enable law enforcement to shift from reactive to proactive policingmodels,utilizingpredictiveanalyticstoanticipatecriminalactivitiesanddeployresourcesmoreeffectively.
However, the implementation of these powerful tools is not without controversy. As law enforcement agencies harness vast amounts of data from diverse sources, including surveillance systems, social media platforms, and public records, concernsabout privacyinfringementand potential misuseofinformationhavecometotheforefrontof public discourse. Moreover, the algorithms underlying predictive policing tools have faced scrutiny for potentially perpetuating existing biasesanddisproportionatelyaffectingmarginalizedcommunities[2].
This article aims to examine the multifaceted impact of data management and engineering on modern law enforcement practices.Byexploringboththetransformativebenefitsandtheethicalchallengesassociatedwiththesetechnologies,we seek to provide a balanced perspective on their role in shaping the future of policing. Furthermore, we will draw comparisons with applications in other fields, such as education and creative industries, to contextualize the broader societalimplicationsofdata-drivendecision-making.
As we navigate this complex landscape, it becomes evident that the successful integration of data management and engineering in law enforcement hinges on striking a delicate balance between leveraging technological innovations and upholdingfundamentalprinciplesofprivacy,fairness,andpublictrust.
II. Advanced Analytics in Crime Prevention and Resolution
The integration of advanced analytics in law enforcement has revolutionized crime prevention and resolution strategies. Thissectionexploresthekeycomponentsofthistechnologicalshiftanditsimpactonpolicingpractices.
A.Real-timedataprocessingcapabilities
Real-timedataprocessinghasemergedasagame-changerinmodernlawenforcement.Policedepartmentsnowhavethe abilitytocollect,analyze,andactuponvastamountsofdatainstantaneously.Thiscapabilityisprimarilyfacilitatedby:
1. Edge computing: Allowing data processing at or near the source of data generation, such as body cameras or patrolcarsensors.
2. High-speed networks: Enabling rapid transmission of large datasets between field units and central command centers.
3. Cloud computing: Providingscalablecomputingresourcesforhandlingmassivevolumesofdata.
Thesetechnologiescollectivelyenablelawenforcementagenciestorespondtoincidentsmoreswiftlyandmakeinformed decisions based on up-to-the-minute information. For instance, real-time facial recognition systems can alert officers to thepresenceofwantedindividualsincrowdedareas,potentiallypreventingcrimesbeforetheyoccur[3].
B.Patternidentificationandpredictiveanalytics
Advanced analytics tools have significantly enhanced law enforcement's ability to identify crime patterns and predict futurecriminalactivities.Keyaspectsofthiscapabilityinclude:
1. Machine learning algorithms: These cananalysehistorical crime data toidentifytrendsand patterns thatmay notbeapparenttohumananalysts.
2. Geospatial analysis: Mappingcrimedatatoidentifyhotspotsandtrackthemovementofcriminalactivitiesover timeandspace.
3. Natural language processing: Analysing unstructured data from police reports, social media, and other textual sourcestoextractvaluableinsights.
Predictive policing models, built on these technologies, can forecast potential crime locations and times with increasing accuracy.AstudybytheLosAngelesPoliceDepartmentfoundthatareaswherepredictivepolicingwasimplementedsaw crimeratesdecreaseby7.4%comparedtocontrolareas[4].
International Research Journal of Engineering and Technology (IRJET) e-ISSN:2395-0056
Volume: 11 Issue: 07 | July 2024 www.irjet.net p-ISSN:2395-0072
1:CrimeRateReductioninAreasUsingPredictivePolicing(2018-2022)[4]
C.Impactonresourceallocationandinvestigativeefficiency
The adoption of advanced analytics has had a profound impact on resource allocation and investigative efficiency in law enforcement:
1. Optimized patrol strategies: Data-driveninsightsallowagenciestodeployofficersmoreeffectively,focusingon high-riskareasandtimes.
2. Prioritization of cases: Analytics can help investigators prioritize cases based on the likelihood of resolution, ensuringefficientuseoflimitedresources.
3. Connection of seemingly unrelated crimes: Advanced patternrecognitioncanlink crimesacross jurisdictions, aidingintheidentificationofserialoffenders.
4. Accelerated evidence analysis: Machine learning algorithms can rapidly process large volumes of digital evidence,suchassurveillancefootageorphonerecords,significantlyreducinginvestigationtime.
These improvements in efficiency have led to notable results. For example, the New York Police Department reported a 31%reductioninresponsetimesafterimplementingareal-timecrimecenterthatleveragesadvancedanalytics[3].
Whilethebenefitsofadvancedanalyticsinlawenforcementaresignificant,itiscrucialtonotethatthese technologiesare toolstoaugmenthumandecision-making,notreplaceit.Theeffectiveuseofthesecapabilitiesrequiresskilledpersonnel who can interpret results, understand limitations, and make ethical judgments in applying insights derived from data analysis.
III. Benefits of Data-Driven Law Enforcement
The adoption of data-driven approaches in law enforcement has yielded numerous benefits, revolutionizing traditional policingmethodsandenhancingoveralleffectiveness.
A.Enhancedcrime-solvingtechniques
1. Integration of diverse data sources: Modern law enforcement agencies now leverage a wide array of data sources to solve crimes more efficiently. These include:
● Surveillancefootage
Figure
International Research Journal of Engineering and Technology (IRJET) e-ISSN:2395-0056
Volume: 11 Issue: 07 | July 2024 www.irjet.net p-ISSN:2395-0072
● Socialmediadata
● Mobilephonerecords
● Licenseplatereaders
● DNAdatabases
● Financialtransactionrecords
The integration of these diverse data sources provides investigators with a more comprehensive picture of criminal activities.Forinstance,theChicagoPoliceDepartment'sStrategicSubjectList,whichcombinesdatafrommultiplesources, hasbeencreditedwithhelpingtoreducegunviolenceinthecity[5].
2. Comprehensive investigative view:
By synthesizing data from various sources, law enforcement can create a more holistic view of criminal networks and activities.Thiscomprehensiveapproachenables:
-Identificationofpreviouslyunknownconnectionsbetweensuspects
-Trackingofcriminalassetflows
-Reconstructionoftimelinesandmovementpatterns
-Cross-jurisdictionalinformationsharing
A study by the National Institute of Justice found that agencies using integrated data systems solved complex cases 2.7 timesfasterthanthoserelyingontraditionalmethods[5].
B.Proactivepolicingmeasures
1. Predictive policing tools:
Predictive policing utilizes historical data and advanced algorithms to forecast potential criminal activities. Key applicationsinclude:
● Identifyinglikelytimesandlocationsoffuturecrimes
● Predictingpotentialoffendersorvictims
● Anticipatingcrimetypepatterns
These tools allow law enforcement to allocate resources more efficiently and implement targeted prevention strategies. For example, the Los Angeles Police Department reported a 13% reduction in crime rates in areas where predictive policingwasimplemented[6].
2. Crime mapping and its applications:
Geographic Information Systems (GIS) and crime mapping technologies have become integral to modern policing. Applicationsinclude:
● Hotspotanalysisfortargetedpatrols
● Spatial-temporaltrendanalysis
● Riskterrainmodeling
● Journey-to-crimeanalysis
These techniques enable law enforcement to visualize crime patterns and make data-informed decisions about resource allocation. A meta-analysis of 45 studies found that hot spots policing strategies resulted in a 17% reduction in crime acrossvarioussettings[6].
C.Improvedtransparencyandaccountability
1. Data-driven decision-making:
The shift towards data-driven policing has led to more transparent and justifiable decision-making processes. This includes:
● Evidence-basedpolicyformulation
● Objectivecriteriaforresourceallocation
● Quantifiablejustificationsforstrategicinitiatives
By basing decisions on empirical data, law enforcement agencies can better explain their actions to the public and oversightbodies,fosteringtrustandlegitimacy.
International Research Journal of Engineering and Technology (IRJET) e-ISSN:2395-0056
Volume: 11 Issue: 07 | July 2024 www.irjet.net p-ISSN:2395-0072
2. Performance metrics and evaluation:
Data-drivenapproacheshaverevolutionizedhowpoliceperformanceismeasuredandevaluated.Keyaspectsinclude:
● Real-timeperformancedashboards
● Objectiveoutcome-basedmetrics
● Comparativeanalysisacrossunitsandjurisdictions
● Continuousimprovementthroughdatafeedbackloops
Thesemetricsallowformoreaccurateassessmentofpolicingstrategiesandindividualofficerperformance.Forinstance, the CompStat model, pioneered by the New York Police Department, has been widely adopted and credited with significantcrimereductionsinmultiplecities[5].
The benefits of data-driven law enforcement are substantial, offering enhanced crime-solving capabilities, proactive policing measures, and improved accountability. However, it is crucial to implement these approaches with careful considerationofpotentialbiasesandprivacyconcernstoensurefairandethicalpolicingpractices.
Strategy Impact Study
PredictivePolicing 7.4%decreaseincrimerates
LosAngelesPoliceDepartment
HotSpotsPolicing 17%reductionincrimeacrossvarioussettings Meta-analysisof45studies
Real-TimeCrimeCenter 31%reductioninresponsetimes
IntegratedDataSystems 2.7xfastersolvingofcomplexcases
CompStatModel
Significantcrimereductionsinmultiplecities
NewYorkPoliceDepartment
NationalInstituteofJustice
Variouscitypolicedepartments
Table1:ImpactofData-DrivenPolicingStrategies[5,6]
IV. Concerns and Ethical Considerations
Whiledata-drivenlawenforcementoffersnumerousbenefits,italsoraisessignificantconcernsandethicalconsiderations thatmustbecarefullyaddressedtoensurejustandequitablepolicingpractices.
A.Privacyissues
1. Extensive data collection practices:
The increasing scope and depth of data collection by law enforcement agencies have raised serious privacy concerns. Thesepracticesinclude:
-Widespreaduseofsurveillancecamerasandfacialrecognitiontechnology
-Collectionandanalysisofsocialmediadata
-Useofcell-sitesimulators(e.g.,Stingrays)tointerceptmobilecommunications
-Integrationofdatafromprivatesectorsources(e.g.,Ringdoorbellcameras)
A study by the Brennan Center for Justice found that 75% of surveyed police departments used predictive policing technologies,oftenwithoutadequateprivacysafeguardsorpublicdisclosure[7].
International Research Journal of Engineering and Technology (IRJET) e-ISSN:2395-0056
Volume: 11 Issue: 07 | July 2024 www.irjet.net p-ISSN:2395-0072
2:PublicOpiniononLawEnforcementUseofDataTechnologies(2022Survey)[7]
2. Potential for information misuse:
Thevastamountsofdatacollectedcreatepotentialformisuse,including:
● Unauthorizedaccesstosensitivepersonalinformation
● Missioncreep,wheredatacollectedforonepurposeisusedforunrelatedpurposes
● Creationofdetailedprofilesonindividualsnotsuspectedofanycrime
● Potentialforblackmailorcoercionusingcollecteddata
The Electronic Frontier Foundation has documented numerous cases of law enforcement officers misusing database accessforpersonalreasons,highlightingtheneedforrobustsafeguardsandoversight[7].
B.Algorithmicbiasandfairness
1. Reinforcement of existing prejudices:
Predictivepolicingalgorithmsandotherdata-driventoolscaninadvertentlyperpetuateandamplifyexistingbiasesin the criminaljusticesystem.Issuesinclude:
● Trainingdatareflectinghistoricalbiasesinpolicingpractices
● Feedbackloopsthatreinforcediscriminatorypatterns
● Lackofdiversityinteamsdevelopingandimplementingalgorithms
A prominent study by ProPublica found that a widely used recidivism prediction algorithm was biased against African Americans,falselyflaggingthemashighriskatnearlytwicetherateofwhitedefendants[8].
2. Disproportionate impact on certain communities:
Theimplementationofdata-drivenpolicingcanleadto:
● Over-policingofminorityneighborhoodsduetobiasedpredictions
● Increasedsurveillanceandharassmentofmarginalizedgroups
● Exacerbationofexistingracialandsocioeconomicdisparitiesinthejusticesystem
Researchhasshownthatpredictivepolicingtoolscancreatea"runawayfeedbackloop,"whereinitialbiasesinpolicedata leadtoincreasedpolicingincertainareas,generatingmorearrestsandfurtherskewingthedata[8].
Figure
International Research Journal of Engineering and Technology (IRJET) e-ISSN:2395-0056
Volume: 11 Issue: 07 | July 2024 www.irjet.net p-ISSN:2395-0072
C.Ethicalimplicationsofdatause
1. Maintaining public trust:
Theuseofadvanceddataanalyticsinpolicingpresentschallengestomaintainingpublictrust,including:
● Lackoftransparencyinalgorithmicdecision-makingprocesses
● Difficultyinexplainingcomplexdata-drivendecisionstothepublic
● Concernsabouttheerosionofpresumptionofinnocenceanddueprocess
A survey by the Pew Research Center found that 56% of Americans trust law enforcement agencies to use facial recognitiontechnologyresponsibly,highlightingtheneedforagenciestoactivelyworkonbuildingandmaintainingpublic trust[7].
2. Balancing innovation with responsible use:
Law enforcement agencies must strike a delicate balance between leveraging innovative technologies and ensuring responsible,ethicaluse.Keyconsiderationsinclude:
● Implementingrobustgovernanceframeworksfordatacollectionanduse
● Ensuringalgorithmictransparencyandaccountability
● Conductingregularauditsandimpactassessmentsofdata-driventools
● Engagingwithcommunitiesandstakeholdersinthedevelopmentandimplementationofnewtechnologies
The European Union's General Data Protection Regulation (GDPR) provides a model for comprehensive data protection, includingprincipleslikedataminimizationandpurposelimitation,whichcouldbeadaptedforlawenforcementcontexts [8]. Addressing these concerns and ethical considerations is crucial for the sustainable and just implementation of datadriven law enforcement. It requires ongoing collaboration between law enforcement agencies, policymakers, technology experts, and community stakeholders to develop frameworks that harness the benefits of data analytics while safeguardingcivillibertiesandpromotingequitablepolicingpractices.
Concern Description
PrivacyIssues Widespread use of surveillance technologies without adequate safeguards
Algorithmic Bias
Bias in recidivism prediction algorithms
PublicTrust Concerns about facial recognition technologyuse
DataMisuse Potential for unauthorized access andmissioncreep
Disparate Impact Over-policing of minority neighborhoods due to biased predictions
V. Comparative Analysis: Education and Creative Fields
Statistic/Finding
75% of surveyed police departments used predictivepolicingtechnologies
African Americans falsely flagged as high risk atnearlytwicetherateofwhitedefendants
56% of Americans trust law enforcement to usefacialrecognitionresponsibly
Numerous documented cases of officers misusingdatabaseaccess
Creation of "runaway feedback loops" in predictivepolicing
Whiledatamanagementandengineeringhavesignificantlyimpactedlawenforcement,theirinfluenceextendstovarious othersectors,includingeducationandcreativefields.Thiscomparativeanalysisexploresthesimilaritiesintransformative potentialandtheuniqueethicalconsiderationsinthesedomains.
A.Similaritiesintransformativepotential
The application of data management and engineering in education and creative fields shares several transformative potentialswithlawenforcement:
Table2:EthicalConcernsinData-DrivenLawEnforcement[7,8]
International Research Journal of Engineering and Technology (IRJET) e-ISSN:2395-0056
Volume: 11 Issue: 07 | July 2024 www.irjet.net p-ISSN:2395-0072
1. Personalization and targeted interventions:
● Education: Adaptive learning systems use student data to personalize curriculum and provide targeted support, similartohowpredictivepolicingaimstoallocateresourcesefficiently.
● Creative fields: Recommendation algorithms in streaming services and digital platforms tailor content to individualpreferences,analogoustohowlawenforcementusesdatatofocusonspecificcrimepatterns.
2. Predictive analytics:
● Education:Predictivemodelsidentifystudentsatriskofdroppingoutorneedingadditionalsupport,comparable tocrimepredictionmodelsinlawenforcement.
● Creative fields: Analytics predict market trends and audience preferences, influencing content creation and marketingstrategies,similartohowpoliceusedatatoanticipatecrimepatterns.
3. Resource optimization:
● Education:Data-drivendecision-makinghelpsinefficientallocationofeducationalresources,parallelinghowlaw enforcementoptimizespatrolstrategies.
● Creative fields: Analytics inform production and distribution decisions, maximizing resource utilization, akin to investigativeresourceallocationinpolicing.
A study by the Institute of Education Sciences found that data-driven decision-making in schools led to a 0.14 standard deviationimprovementinstudentperformance,highlightingitstransformativepotentialineducation[9].
B.Uniqueethicalconsiderationsinthesedomains
While there are similarities in the transformative potential, education and creative fields face unique ethical considerations:
1. Education:
● Studentprivacy:Thecollectionanduseofstudentdata,especiallyforminors,raisessignificantprivacyconcerns. The Family Educational Rights and Privacy Act (FERPA) in the United States provides some protections, but the rapidadvancementofeducationaltechnologyoftenoutpacesregulatoryframeworks.
● Equity and access: Data-driven educational tools may exacerbate existing inequalities if not all students have equalaccesstotechnologyorifalgorithmsperpetuatebiases.
● Long-term impact: The use of predictive analytics in education could potentially label students early in their academiccareers,raisingconcernsaboutself-fulfillingpropheciesandlimitingfutureopportunities.
2.Creativefields:
● Artisticintegrityvs.data-drivencreation:Theuseofanalyticsincreativedecision-makingraisesquestionsabout thebalancebetweenartisticvisionandmarket-drivencontentcreation.
● Intellectual property concerns: As AI and machine learning play larger roles in content creation, issues of authorshipandcopyrightbecomeincreasinglycomplex.
● Culturalhomogenization:Data-drivencontentrecommendationsmayleadtoanarrowingofculturalexperiences, potentiallystiflingdiversityincreativeexpressions.
A survey by the Pew Research Center found that 79% of Americans were concerned about how companies use the data collectedaboutthem,highlightingthepervasivenatureofdataprivacyconcernsacrosssectors[10].
Inbotheducationandcreativefields,asinlawenforcement,theethicaluseofdatarequirescarefulconsiderationof:
● Transparencyindatacollectionandusage
● Safeguardingindividualprivacyandconsent
● Ensuringfairnessandavoidingdiscriminatorypractices
● Maintaininghumanoversightanddecision-makingincriticalareas
Whilethesedomainssharesomeethicalconsiderationswithlawenforcement,suchasprivacyandbiasconcerns,theyalso face unique challenges related to their specific contexts. For instance, the potential impact on child development in educationandthepreservationofartisticfreedomincreativefieldspresentdistinctethicaldilemmasnot encounteredin lawenforcement.
International Research Journal of Engineering and Technology (IRJET) e-ISSN:2395-0056
Volume: 11 Issue: 07 | July 2024 www.irjet.net p-ISSN:2395-0072
Asdata management and engineeringcontinue to evolve,itiscrucial for stakeholders in education andcreativefieldsto developethicalframeworksthataddresstheiruniquechallengeswhilelearningfromtheexperiencesandbestpracticesof othersectors,includinglawenforcement.
Conclusion
In conclusion, the integration of data management and engineering in law enforcement represents a significant leap forward in the capabilities and efficiency of policing. While these technologies offer tremendous potential for enhancing public safety, solving crimes more effectively, and optimizing resource allocation, they also bring forth a host of ethical concernsandchallenges.Thedelicatebalancebetweenleveraginginnovativetoolsandsafeguardingcivilliberties,privacy, andfairnessmustbecarefullymaintained.Aswehaveseenthroughourcomparativeanalysiswitheducationandcreative fields,thetransformativepowerofdata-drivenapproachesextendsfarbeyondlawenforcement,yeteachdomaingrapples withitsuniquesetofethicalconsiderations.Movingforward,itisimperativeforlawenforcementagencies,policymakers, and technology developers to work collaboratively with communities and ethicists to create robust frameworks that harness the benefits of data analytics while mitigating potential harms. Only through this thoughtful and inclusive approach can we ensure that the implementation of data-driven strategies in law enforcement and other sectors truly servesthepublicgood,upholdsdemocraticvalues,andcontributespositivelytosocietalprogress.Astechnologycontinues to evolve, ongoing research, transparent dialogue, and adaptive policies will be crucial in navigating the complex landscapeofdata-drivendecision-makinginourincreasinglyinterconnectedworld.
References
[1]A.Chohlas-WoodandE.S.Levine,"ARecommendationEnginetoAidinIdentifyingCrimePatterns,"INFORMSJournal onAppliedAnalytics,vol.49,no.2,pp.154-166,2019.
[2] S. Brayne, "Big Data Surveillance: The Case of Policing," American Sociological Review, vol. 82, no. 5, pp. 977-1008, 2017.
[3]A.G.Ferguson,"PolicingPredictivePolicing,"WashingtonUniversityLawReview,vol.94,no.5,pp.1109-1189,2017.
[4] P. J. Brantingham, M. Valasik, and G. O. Mohler, "Does Predictive Policing Lead to Biased Arrests? Results From a RandomizedControlledTrial,"StatisticsandPublicPolicy,vol.5,no.1,pp.1-6,2018.
[5]J.S.HollywoodandD.Ghosh-Dastidar,"UnderstandingtheEfficiencyandEffectivenessoftheCriminalJusticeSystem," RANDCorporation,SantaMonica,CA,Tech.Rep.RR-1255-NIJ,2018.
[6] G. Mohler et al., "Randomized Controlled Field Trials of Predictive Policing," Journal of the American Statistical Association,vol.110,no.512,pp.1399-1411,2015.
[7] A. G. Ferguson, "The Rise of Big Data Policing: Surveillance, Race, and the Future of Law Enforcement," New York UniversityPress,2017.
[8]S.BarocasandA.D.Selbst,"BigData'sDisparateImpact,"CaliforniaLawReview,vol.104,no.3,pp.671-732,2016.
[9] J. E. Rogers, "The Data-Driven Education Movement: The Potential and Limitations of Algorithmic Tools in K-12 Education,"HarvardEducationalReview,vol.91,no.3,pp.321-348,2021.
[10] L. Rainie and M. Anderson, "Code-Dependent: Pros and Cons of the Algorithm Age," Pew Research Center, Washington,D.C.,Feb.8,2017.