Lo cal Polici ng Data and Be st Practices · No current reports, but the Community Policing Act...

Report Number 2020-9 July 21, 2020

Lo cal Polici

ng Data andBe st Practices

Elaine Bonner-Tompkins Natalia Carrizosa

Office of Legislative Oversight Montgomery County, Maryland

LocalPolicingDataandBestPracticesExecutiveSummaryofOLOReportNumber2020-9July21,2020Summary: This report describes the Montgomery County Police Department’s practices forcompiling data on police interactionswith the public, and their alignmentwith best practices toadvanceconstitutionalandcommunitypolicing.Overall,OLOfindsthat:

• MCPDtracksanumberofpolicingmetricsthatalignwithbestpracticesandwillreportmoredatapubliclytocomplywiththeCommunityPolicingLaw(Bill33-19)inFebruary2021.

• MCPDdoesnot trackdataon street stops (e.g. stopand frisks)anddoesnot consistently

recorddatabyethnicity,whichmayundercountMCPD’sinteractionswithLatinxresidents.

• Available data demonstrates wide disparities in police-public interactions by race andethnicityintheCounty,especiallyfortrafficstopsandviolations,arrests,anduseofforce.

ThesefindingssuggestthatimprovedcollectionandmonitoringofMCPDpolicingdataiswarrantedtoevaluateandmonitorforconstitutionalandcommunitypolicing.Basedonthese,OLOofferssixrecommendationsforimprovingthealignmentoflocalpolicingdatapracticestobestpractices.

BestPracticesforPolicingData

MCPD, likemostother lawenforcementagencies,prioritizesthecollectingandreportingofcrimestatisticsasperformancemeasuresofeffectiveness.Toensurethatagenciesdonotunderminethelawtoenforcethelaw,researchersrecommendthatagenciesalsotrackandmonitorpolicingdatathatdescribestheir interactionswiththepublictoassesshowwelltheyconducttheirwork. Twosetsofpolicingdatabestpracticesemergefromtheresearch:

• Collectandmonitordataonpoliceinteractionswiththepublicbyraceandethnicity.

• Collectandmonitordataonfoursetsofpoliceinteractionswiththepublic:

o Detentions(includingallstops,searches,citations,anduseofforceincidents),o Police-andresident-initiatedcontacts,o Civilianandinternalcomplaintsagainstthepolice,ando Surveysofpolice-communityrelationsfromresidentsandlawenforcement.

MCPDPolicingDataPracticesandAlignmenttoBestPractices

MCPDcollectsavarietyofcrimeandpolicingdatainelectronicandpaperfilesasnotedinChart1.1.Ingeneral,MCPD’sinternaldatasetsoffermoreinformationthanthesubsetsofdataexcerptedonDataMontgomery or described inMCPD annual reports. Additionally, severalMCPD datasets, atleastpartially,alignwithpolicingdatabestpractices.Theseincludetrackingdataon:

• Detentionsbyraceandethnicityfortrafficstops,violations,searchesandarreststrackedviaE-Tix,arrestdatatrackedinCRIMS,anduseofforcedatacompiledfromMCPForm37.

• Police-publicinteractionsdistinguishingbetweenpolice-andresident-initiatedcontactstrackedbyMCPD’sComputerAidedDispatchsystem;and

LocalPolicingDataandBestPractices

OLOReport2020-9July21,20203

• PolicecomplaintstrackedbytheInternalAffairsDivision.Yet,MCPD’spolicingdatapracticesdonotcompletelyalignwithbestpractices.Forexample:

• MCPD’sdetentiondatasetsdonottrackstreetstops(i.e.stopandfrisks)betweenofficersandresidentsthatdonotresultinanarrest,citationorsummons;

• MCPDdoesnotmaintainanelectronicdatabaseofcriminalandcivilcitations(includingtrespassingtickets)thatwouldenablethemtomonitorfordisparities;

• MCPD’sexistingformsandsystemsdonotconsistentlyrecorddataonethnicity.RaceandethnicitydataarealsonotcollectedasfieldsintheComputerAssistedDispatch;

• MCPD’sinternalaffairspolicecomplaintsdatabasedoesnotcollectraceandethnicitydataforeverycomplainant,despitepromptsfordoingsoincludedinFormMCP580;and

• MCPDneithersurveysnorreportsresidents’/staff’sperceptionsofpolice-communityrelations.

Chart1.1:MontgomeryCountyPoliceDepartmentCrimeandPolicingDatasets

Category Database Datasets/FormsElectronicDataSets

CrimeData

E-Justice CrimeIncidents*ΔBiasIncidents*Δ

PolicingData

ComputerAssistedDispatch Police-InitiatedIncidentsΔResident-InitiatedIncidentsΔ

CRIMS(DOCR) Arrests*InternalAffairsDivision IADAllegations(PoliceComplaints)*ΔCommunityEngagementDivision CommunityEngagementEvents*ΔVehiclePursuits MCP610Forms*UseofForce MCP37Forms*DeltaPlus(MarylandStatePolice) E-Tix(TrafficViolations)Δ

AutomatedCrashReportingSystemΔFieldInterviewReports

DepartmentofJuvenileServices DataResourceGuide(JuvenileCitations)

PaperDataSets

PolicingData

CriminalCitations(e.g.Trespassing)

UniformCitationForm(DC/CR45)

CivilCitations AlcoholBeverageViolationPossessionofMarijuana(<10grams)SmokingMarijuanainPublicPlaceOtherinfractions(Municipal,DNR)

ΔMCPDdatapostedinDataMontgomeryhttps://data.montgomerycountymd.gov/Public-Safety/Crime/icn6-v9z3*MCPDpublishesannualreportsusingthesedatasetshttps://montgomerycountymd.gov/pol/crime-data.html



DisparitiesinLocalPolice-PublicInteractions

Availabledatadisplayswidedisparitiesinpoliceinteractionsbyraceandethnicity.Forexample,comparedtorepresenting18percentoftheCounty’spopulation,AfricanAmericansaccountedfor:

• 32%ofMCPDtrafficstopsin2018;

• 44%ofMCPDarrestsin2017;and

• 55%ofMCPDuseofforcecasescomparedin2018.Further,ananalysisof2019trafficstopandviolationdatasuggeststhat:

• 27%ofBlackadultsexperiencedatrafficstopcomparedto14-17%ofWhiteandLatinxadults,and7%ofAsianadults;

• BlackmenwerethreetimesaslikelyasWhitementoreceiveanytrafficviolation(46%v.17%),Latinomenwerenearlytwiceaslikely(32%v.17%)andOthermenweremorethantwiceaslikely(42%v.17%).

Theseracialandethnicdisparitiesinpoliceinteractionswiththepublicsuggestthatdisparitiesmaycharacterizeothermeasuresofpolice-communityinteractions.Inturn,pervasivedisparitiesinpolice-communityinteractionsmaysignalbiasedpolicing.Whiledisparitiesdonotprovebiasedpolicing,theysignalthatunconstitutionalpolicingcouldbeaproblemthatmeritsinvestigation.OLORecommendations

Based on these findings, OLO offers six recommendations for improving the alignment ofMCPDpolicingdatapracticestobestpractices.

1. CountyCouncildefinetheterm“detention”intheCounty’sCommunityPolicingLaw(Bill

33-19)toincludeallstops,searches,citations,arrests,anduseofforce.

2. MCPDtrackandreporttodataonstreetstops(i.e.stopandfrisks)andfieldinterviews.

3. MCPDregularlysurveyresidentsandstaffonpolice-communityrelationsandcontact.

4. MCPDbuildcapacitytousepolicingdatatoadvancebestpracticesinconstitutionalandcommunitypolicing.

5. MCPDcollectandreportraceandethnicitydataforeverypolicingdataset.

6. MCPDpostadditionalpolicingdataonDataMontgomerythatalignswiththeirinternal

datasets,includingdataoncriminalandcivilcitations.

ForacompletecopyofOLO-Report2020-9,goto:http://www.montgomerycountymd.gov/OLO/Reports/CurrentOLOReports.html



OfficeofLegislativeOversightReport2020-9

TableofContentsExecutiveSummary...............................................................................................................2

1. Authority,Scope,andOrganization...........................................................................10

2. ConstitutionalandCommunityPolicing......................................................................13

3. RecommendedPolicingDataandLocalPractices........................................................19

4. DatasetsCollectedbyMCPD......................................................................................32

5. AvenuesforFutureDataAnalysisandReporting........................................................47

6. FindingsandRecommendations.................................................................................60

7. AgencyComments......................................................................................................68



ListofCharts,TablesandExhibits

Numbers Charts Pages

3.1 PubliclyReportedDataonStops 21

3.2 PubliclyReportedDataonSearches 22

3.3 PubliclyReportedDataonCitations 23

3.4 PubliclyReportedDataonArrests 24

3.5 PubliclyReportedDataonUseofForce 25

3.6 PubliclyReportedDataonFieldInterviewReports 26

3.7 PubliclyReportedDataonIncidentsandTrafficAccidents 28

3.8 PubliclyReportedDataonPoliceComplaints 29

3.9 PubliclyReportedSurveyDataonPolice-CommunityRelations 30

3.10 MCPDDatasetsthatAlignwithPolicingDataBestPractices 31

4.1 DataPointsintheComputerAidedDispatchSystem(CAD) 33

4.2 DataPointsCapturedinE*Justice 35



Numbers Charts Pages

4.3DataPointsinCorrectionandRehabilitationInformation

ManagementSystem 37

4.4 DataPointsCapturedinMCPD’sUseofForceReports 38

4.5 DataPointsCapturedinMCPD’sVehicularPursuitsReports 40

4.6 DataPointsCapturedinE-TIX 43

4.7 MontgomeryCountyPoliceDepartmentDisciplinaryProcess 44

4.8 DataPointsCapturedintheInternalAffairsDivisionDatabase 45

5.1 MarijuanaandTrespassingOffenses,FY2017-CY2019 50

5.2 ViolationsRelatedtoPedestrians'RightsandRules,CY2012-CY2019 51

5.3 PercentagesofViolationsThatResultedinCitations,WarningsandSEROs,CY2019 54

6.1 MCPDDataSets 62

6.2 MCPDDatasetsthatAlignwithPolicingDataBestPractices 63



Numbers Tables Pages

3.1

PercentofU.S.Residentsage16orolderwithAnyPoliceContact,2015

27

5.1 DataonMCPDAvailableonDataMontgomery 47

5.2TenMostFrequentIncidentTypesinthePoliceDispatched

IncidentsDataset,CY2019 48

5.3 TrafficStopsbyRace,Ethnicity,andGender,CY2019 52

5.4 NumberofViolationsPerTrafficStopbyRaceandEthnicity 53

5.5 PercentagesofViolationsThatResultedinCitations,WarningsandSEROs,CY2019 54

5.6 CY2019TrafficViolationsWithSearchesConducted 55

5.7 ViolationsforTenMostFrequentlyCitedStatutes,CY2019 56

5.8 TrafficStopsByGeographicalLocation,CY2019 57

5.9 TrafficStopsByGeographicalLocation,Race,andEthnicity,CY2019 58

5.10 PoliceCommunityEventsbyType,2017-2019 59



Numbers Exhibits Pages

5.1 AverageSecondsFromDispatchtoPoliceArrivalByElectionDistrict,CY2019 49



Chapter1: AuthorityandScope

OLOFY20WorkProgram,Resolution19-173,AdoptedJuly23,2019TheMontgomeryCountyPoliceDepartment(MCPD)collectsavarietyofcriminaljusticedatathatatthebroadestlevelscanbecategorizedintwoways:

• Crimestatistics/datathatdescribecriminalactivitybytype,severityandlocation.

• Policingdatathatdescribepoliceinteractionswiththepublic,includingarrests,citationsandvideofromvehicledashboardandbodycameras.

Whereascrimestatisticscanserveasmetricsofalawenforcementagency’seffectivenessatpreventingandreducingcrime,policingdatacanserveasmetricsofhowanagencyconductstheirwork.Recognizingthatsharingdataonpolicingpracticesandoutcomescanenhancetrust,transparency,andaccountabilitywithcommunities,MCPDparticipatesinthePoliceDataInitiativebypostingseveraldatasetsonline.1Theintentofopendataistoenableindividualstoreviewinformationforthemselvesratherthantorelyonother’sexplanations.ThepolicingdatapostedonDataMontgomery,however,usuallyrepresentsonlyasubsetoftheinformationthatMCPDcollectswithinitsinternaldatasets.MCPDalsoannuallyreleasesasuiteofreportsthatdescribeandanalyzedatapointsonpolicingpractices.But,likeDataMontgomery,thedatapresentedinMCPD’sannualreportsrepresentasubsetoftheinformationthatMCPDcollectsandtracks.ToimprovetheCouncil’sunderstandingofthedatapointsthatMCPDcollects,thisOLOprojectdescribespolicingdatapointscurrentlycollectedbyMCPD.ThisprojectalsoincludesdescriptionsofpolicingdatacollectedbyMCPDbutmanagedbyotheragencies,suchastheMarylandStatePolice.Thisreport’soverviewofMCPDdatapointsisintendedtohelpinformtheCountyCouncil’soversightandspecificityofdatarequests.Further,thefocusofthisreportistodescribeMCPD’scollectionofpolicingdatathatdescribesitsinteractionswiththepublic,ratherthantodescribecrimedataroutinelyreportedtothepublic,thestate,andtheFederalBureauofInvestigation.GiventheCouncil’sincreasingfocusonracialequity,socialjusticeandcommunitypolicing,thisOLOreportalsofocusesontheavailabilityofMCPDpolicingdatabyraceandethnicity.ThisOLOreportispresentedinsixchapters:Chapter2,ConstitutionalandCommunityPolicing,setsthecontextforwhypolicingdatamatters.This

chapterdescribeshowconstitutionalandcommunitypolicinganddatametricsreflectingtheseperformancegoalscanenhancelawenforcementeffectiveness.

Chapter3,RecommendedPolicingDataandLocalPractices,comparesrecommendedpracticesfor

trackingdataonpolice-communityinteractionswithdatapointstrackedinMontgomeryCounty.Chapter4,DatasetsCollectedbyMCPD,describeslocalpolicingdataindetailbydescribingthedata

pointscollectedwithineachMCPDdatasetanddatalimitations.

1https://www.policedatainitiative.org/



Chapter5,AvenuesforFutureDataAnalysisandReporting,offersasampleoftheanalysesthatcanbeconductedwithavailableMCPDpolicingdata.

Chapter6,FindingsandRecommendations,summarizeskeyprojectfindingsandoffers

recommendationsforCountyCouncilandMCPDaction.OLOSeniorLegislativeAnalystElaineBonner-TompkinsandOLOLegislativeAnalystNataliaCarrizosaauthoredthisreport.Literaturereviewsonpolicingdata,communitypolicing,andbestpracticesforusingdatatopromotetransparencyinformedthedevelopmentofthisreport,aswellasinterviewswithMCPDpersonnelandreviewsofMCPDdocumentsthatincludedepartmentalpolicies,regulations,reportsandforms.MCPDdataavailableonDataMontgomeryandCountyCouncilworksessionsandpublichearingsoncommunitypolicingalsoinformedthedevelopmentofthisreport.Severalkeyfindingsemergefromtheinformationanddatareviewed:

• Bestpracticesrecommendsthatpolicedepartmentscollectdataontheirinteractionswiththepublicdisaggregatedbyrace,ethnicity,gender,andlocation.

• MCPDcollectsandreportsdataonavarietyofmetrics,someofwhichalignwithbestpracticesfortrackingandreportingpolicingdatadisaggregatedbyrace,ethnicityandgender.

• MCPDdatasetsavailableinDataMontgomeryoftenrepresentasubsetoftheactualdatathatMCPDcollectsandtracks.

• AnalysesofMCPDdatasetsandannualreportswithavailabledatademonstratesizabledisparitiesinpoliceinteractionswiththepublicbyrace,ethnicityandgender.

Basedonthesefindings,OLOoffersthefollowingrecommendationsforCountyCouncilaction:

• ClarifyMCPDreportingrequirementsundertheCommunityPolicingAct(CouncilBill33-19)toincludereportingdataonallstops,searches,andcriminalandcivilcitations.

• RequireMCPDtoannuallysurveyresidentsanddepartmentalemployeesonthequalityofpoliceinteractionswiththepublicandresidentsontheirinteractionswiththepolice.

• RequestMCPDtocollectandreportallpolicingdatabyraceandethnicity.• EncourageMCPDtodevelopitscapacitytocompileandanalyzepolicingdatatohelpinformits

constitutionalandcommunitypolicingefforts.• EncourageMCPDtomakeavailabledatasetsonDataMontgomerythatmirrortheirinternal

datasetsandthedatapointscollectedinthemaspermissiblebylaw.



Acknowledgements.OLOreceivedahighlevelofcooperationandassistancefortheOfficeoftheChiefAdministrativeOfficerandtheMontgomeryCountyPoliceDepartmentwiththisreport.OCAOandMCPDleadershipandstaffinterviewedandconsultedonthisreportinclude:

• FaribaKassiri,DeputyChiefAdministrativeOfficer• MarcusJones,ChiefofPolice• DinishPatil,AssistantChief• AdamKisthardt,Director,InformationManagementandTechnologyDivision• MaryDavison,Director,MontgomeryCountyPoliceRecords• SoniaPruitt,Captain,CommunityEngagementDivision• WilliamMontgomery,Captain,InternalAffairsDivision• EdwardPallas,Captain,PolicyandPlanningDivision• NickPicerno,Lieutenant,PolicyandPlanningDivision• MichelleIezzi,Manager• TiLor,CrimeAnalystSr.Lead• PeterMargelis,CrimeAnalystSr.Lead• AnthonyScafide,CrimeAnalystSr.Lead• BrandiBarber,RecordsManagementUnit• GeorgiaFrazier,PolicyandPlanningDivision• AngelaComer,InternalAffairsDivision• MichaelPierre-Lewis,PatrolOfficer



Chapter2: ConstitutionalandCommunityPolicingPolicedepartmentsarepartofalargercriminaljusticesystemthatincludesprosecutors,courts,juvenilejusticesystems,prisons,andprobationandparoledepartments.2Policedepartmentsdonotwritelaws;theyaretaskedwiththeresponsibilityofenforcinglawsthatareenactedbyelectedofficialsandinterpretedbythecourts.Enforcinglawsisjustoneofmanydifferentrolesofthepolice.Otherimportantrolesincludeworkingwithcommunitiestopreventcrimesandsolvevarious“qualityoflife”problems,maintainingorder,andconductinginvestigations.Whilelawenforcementagenciescareaboutanumberofpriorities,whatoftengetsprioritizedforperformancemanagementiscrimeprevention.Inresponsetothequestionof“WhatmetricsdoesMCPDtrack?”themostoftencitedansweramongvariousMCPDrespondentswascrimestatistics.Onseveraloccasions,thisresponseledtoanextensivediscussiononthedistinctionbetweenNIEBRRs,andUCRcrimeandincidentreportingrequirementstothefederalgovernment.JessicaSandersoftheRANDCorporation,however,warnsthatto“focusexclusivelyononegoalattheexpenseoftheothersistoinvitepoorperformanceonalternativegoals.”3Shewarnsthatinadditiontostatisticsonpropertyandviolentcrimes,policedepartmentsneed“performancemetricstoincentivizeanddemonstrateconstitutionalpolicingthatisbiasfree”andthat“placingallemphasisoncrimelevelscreatesadangeroustensionbecauseitoverlookspoliceofficersotherrolesandfunctionsthatshouldincludepolice-communityrelations.”4Thischapterdescribesconstitutionalandcommunitypolicing,anddatametricsthatlawenforcementcanusetomonitorprogressacrosstheseperformancegoals.Subjectmatterexpertsfindthateffectivelawenforcementagenciescombineconstitutionalandcommunitypolicingmethods–theygohand-in-hand,buttheyarenotthesame.Theyfindthatconstitutional,bias-freepolicinglaystheframeworkforimplementingcommunitypolicingapproachesthatbuildtrustandfosterlegitimacyforlocallawenforcementamongimpactedcommunities.Adescriptionofthesetwoconceptsandhowoversightbodiescanuseperformancemeasurestoadvanceconstitutionalandcommunitypolicingfollows.1. ConstitutionalPolicingConstitutionalPolicing(whichcanbedescribedaslegalpolicing,unbiasedpolicing,proceduraljusticeorfairandimpartialpolicing)referstopolicingconductedinaccordancewiththeparameterssetbytheU.S.Constitution,stateconstitutions,andthemanycourtdecisionsthathavedefinedwhatthetextoftheConstitutionmeansrelativetopolicingpractices.5Constitutionalpolicingrecognizesindividual’scivilrightsandtreatscitizen’sequallyregardlessofrace,ethnicity,genderidentity,age,religion,sexualorientation,orotherqualifiers.Inshort,constitutionalpolicingensuresthatlawenforcementofficerstreateveryonefairlyandimpartially.

2SeeU.S.JusticeDepartment’sPolicing101(https://www.justice.gov/crs/file/836401/download)3JessicaSanders,TheRANDCorporation,PerformanceMetricstoImprovePolice-CommunityRelations,beforetheCommitteesonPublicSafety,CaliforniaStateAssemblyandSenate,February10,20154Ibid5Policing101



Inpolicing,biasescanleadtoracialprofiling,anunconstitutionalpractice.AccordingtotheNationalInstituteforJustice,racialprofilingbylawenforcementiscommonlydefinedasapracticethattargetspeopleforsuspicionofcrimebasedontheirrace,ethnicity,religion,ornationalorigin.6Whencommunitiesbelievethatthepoliceengageinbiasedpolicingbehaviors,theirtrustinlawenforcementisdamaged.ThePoliceExecutiveResearchForumfurthernotesthatconstitutionalpolicingismorethanjustpoliciesthatholdupincourt.7Itsayspolicedepartmentsshouldcontinuallyexaminepracticestomakesurethey“advancethebroadconstitutionalgoalofprotectingeveryone’scivillibertiesandprovidingequalprotectionunderthelaw.”8Moreover,PERFfindsthatafoundationofconstitutionalpolicingshouldinformeverythingpolicedo.However,therearecertainareaswherelawenforcementleadersshouldbeespeciallycarefultopromoteconstitutionalpolicing.Theseincludepolice:

• Useofforce,• Stopandfrisks,• Issuesofracialbias,and• Interactionswithpeoplewhohaveamentalillness.

PERFadvisesthatineveryinteraction,policemustwalkthelineofenforcingthelawtokeeppeopleandcommunitiessafe,whilealsorespectingtherightsofeveryindividualtheyinteractwith.ThePresident’sTaskForceon21stCenturyPolicingalsoadvisesthatpoliceagenciesmustalsopromotetransparencyandaccountabilitytodemonstratetothecommunitythatofficersactfairlyandimpartially,andthattherearesystemsinplacetodetectmistakesorabusesofauthority.9Theyfurthernotethatpublictrustandcooperationarekeyelementsofeffectivepolicing,andarelostwhenpoliceofficersandemployeesengageinunconstitutionalorunprofessionalconduct.Totrackwhetherlawenforcementagenciesengageinconstitutionalpolicing,thePresident’sTaskForceadvisesthatlawenforcementagenciesshouldtrackandanalyzetheleveloftrustcommunitieshaveinthepolice,justastheymeasurechangesincrime.10Thiscanbeaccomplishedthroughannualcommunitysurveys.Further,theyrecommendagenciespartnerwithlocaluniversitiestoconductsurveysbyzipcode,forexample,tomeasuretheeffectivenessofspecificpolicingstrategies,assessanynegativeimpacttheyhaveonacommunity’sviewofpolice,andgainthecommunity’sinput.2. CommunityPolicingExpertsadvisethatoncealawenforcementagencyhasestablishedabaseofconstitutionalpolicing,theycanapplyandadaptthoseconceptstoadvancecommunitypolicing.11Communitypolicing,orcommunity-orientedpolicing,referstoastrategyofpolicingthatfocusesonbuildingtiesandworkingcloselywithmembersofcommunitiestobuildmutualunderstandingandtrust.Howstakeholdersapproachcommunitypolicing,however,candependontheirvantage.

6Ibid7https://cops.usdoj.gov/RIC/Publications/cops-p324-pub.pdf8Ibid9https://cops.usdoj.gov/pdf/taskforce/taskforce_finalreport.pdf10https://cops.usdoj.gov/RIC/Publications/cops-p324-pub.pdf11https://www.powerdms.com/blog/constitutional-policing-vs-community-policing-looking-at-complementary-strategies/



Forsomepolicedepartments,changingcommunitybehaviortoreducecriminalityservesasthefocusofcommunityengagementandpolicing.Towardsthisend,policedepartmentsfocusondevelopingrelationshipswithcommunitymembersand,inparticular,youthaimedatimprovingpublicrelationswithcommunitiesimpactedbycrime.Thiscanincludehostingcommunityevents,mentoringyouthandengaginginothereffortsthatfosterfavorableimpressionsofthepolice.Theimpliedtheoryofactionisthatifcommunitiesdevelopstrongeraffinitiesforlawenforcement,theirratesofcriminalitywilldecreaseand/ortheircooperationincriminalinvestigationswillincrease.Formanycommunity-basedstakeholders,however,changingpolicingbehaviorratherthancommunitybehaviorservesastheprimaryfocusofcommunitypolicing.Thereisrecognitionthatbiasedpolicinghasunderminedthelegitimacyoflawenforcementamongcommunitymembers,poisoningpolice-communitypartnershipsessentialtoreducingcrime.Toreversethispattern,communitystakeholderspartnerwithlawenforcementtoplan,problemsolveandimplementactivitiesaimedatbuildingtrustandmutualaccountabilitybetweenlawenforcementandcommunities.Theyalsousethispartnershipasabridgetodevelopingandimplementingcrimereductioneffortsthataresupportedbyimpactedcommunities.Thetheoryofactionisthataspolicedepartmentsadvanceunbiasedpolicingandpartnershipswithimpactedcommunities,theywillincreasetheirlegitimacywithinthosecommunitiesandtheeffectivenessoftheircrimereductionefforts.Bestpracticesforcommunitypolicinggenerallyendorsethecommunity-basedvantage.TheU.S.DepartmentofJusticefindsthatpositivepolice-communityrelationshipsareessentialtomaintainingpublicsafety.12Theynotethattheserelationshipshelptoreducefearandbiasesandbuildmutualunderstandingandtrustbetweenthepoliceandthecommunity.Towardsthisend,theDepartmentofJustice’sOfficeofCommunityOrientedPolicingServicesdescribesthreeessentialcomponentstocommunitypolicingthatfocusonlawenforcementchangeratherthancommunitychange:

• CommunityPartnershipsbetweenthelawenforcementagencyandtheindividualsandorganizationstheyservetodevelopsolutionstoproblemsandincreasetrustinpolice;

• OrganizationalTransformationthatalignsorganizationalmanagement,structure,personnel,

andinformationsystemstosupportcommunitypartnershipsandproactiveproblemsolving;

• Problem-SolvingProcessesthatengageintheproactiveandsystemicexaminationofidentifiedproblemswiththecommunitytodevelopandevaluateeffectiveresponses.

Assuch,communitypolicingismorethanaprogramfocusedonenhancingthepublic’sperceptionsofthepolice:itisanorganizationalphilosophythatrecognizesthatthecommunity’ssupportisacriticalfactorintheabilityofthepolicetoeffectivelyaddresscrime.Therelationshipbetweenthepoliceandthecommunitiestheyservedetermineswhetherornotpolicewillhavecommunitysupport,andtheserelationshipsarestrengthenedorweakenedbyeverypolice-communityinteraction.

12U.S.DepartmentofJustice–CommunityRelationsServicesToolkitforPolicing101



AsnotedbythePoliceExecutiveResearchForum,13positivepolice-communityrelationshipscontributetoincreasedcommunityperceptionsofthelegitimacyofthepolicetoenforcethelaw.Perceptionsofpolicelegitimacyimpactthewillingnessofcommunitymemberstosupportpolicingstrategiesandcooperatewithpolicedirectives.Inshort,policeneedthecommunity’shelpinmaintainingorderjustasthecommunityneedsfair,just,andeffectivelawenforcement.Thiscollaborationandcooperationimprovepublicsafetyandofficersafety.Andperhapsmostimportantly,acommunity-policingphilosophyemphasizespolicerelationshipswithinthecommunity.Ratherthanjustsendingofficersintoanareatorespondtocalls,manydepartmentsarerequiringofficerstopatrolonfoot.Theyencourageofficerstogetoutoftheirsquadcarsandregularlyinteractwithcivilians.Totracklawenforcementagenciesperformancewithcommunitypolicing,thePresident’sTaskForcerecommendsthatagenciescollaboratewithcommunitiestodevelopcomprehensivepoliciesontheiruseofforce,massdemonstrations,consentbeforesearches,genderidentification,andracialprofiling.Further,theyrecommendthateachofthesepoliciesincludeprovisionsforcollectingdemographicdataforallpartiesinvolved.Theyalsoencouragelawenforcementagenciestocollect,maintain,andanalyzedemographicdataonalldetentions(stops,frisks,searches,summons,andarrests).Lastyear,theCountyCouncilenactedBill33-19requiringMCPDtoimplementspecificcommunitypolicingpracticesthatincludeensuringculturalcompetencythroughoutthedepartment,increasingcommunityoutreachactivities,andprovidingadequatetraininginde-escalationtactics.TheCommunityPolicingActalsorequiresMCPDtoreportdataon:

• Useofforceanddetention• Civiliancomplaintsregardinguseofforce,discriminationandharassment• Officerssuspendedwithandwithoutpay• Youthreferredtointerventionprograms• Servicecallsreceivedforsubstanceabuseandmentalhealthissues

Latein2019,theCountyCouncilalsoenactedBill14-19establishingthePoliceAdvisoryCommissiontoadvisetheCouncilonpolicingmatters,provideinformationonbestpractices,recommendpolicies,programs,legislationand/orregulation,andtoconductatleastonepublicforumannuallyseekingcommunityinputonpolicingmatters.3. PerformanceMetricsforConstitutionalandCommunityPolicingMuchoftheresearchonbestpracticesforadvancingconstitutionalandcommunitypolicingemergesfromjurisdictionsthathavebeenforcedtoreformwhileunderfederalconsentdecrees.14Forexample,inresponsetoaconsentdecreerequiringthemtobecomeaneffectiveandconstitutionalpoliceforce,theLosAngelesPoliceDepartment(LAPD)adoptedasetofperformancemetricsforconstitutionalandcommunitypolicingthattransformedtheirdepartment.15

13https://cops.usdoj.gov/RIC/Publications/cops-p324-pub.pdf14In2015,JessicaSandersoftheRANDCorporationintestimonytotheCaliforniaStateAssemblyandSenatenotedthatabouttwentypolicedepartmentshadenteredintoagreementstobemonitoredusuallyunderthethreatofcivilrightslawsuits.15SeeStone,et.al.–PolicingLosAngelesUnderaConsentDecree



BasedonLAPD’sexperienceandotherjurisdictions,JessicaSandersoftheRANDCorporationrecommendsthatlegislaturesrequirelawenforcementagenciestoreportperformancemetricsthatincludeconstitutionalpolicingpractices(bias-freepolicinganduseofforce)andpolice-communityrelations(policesatisfaction,trustinpolice,andpolicelegitimacy)to“demonstratethattheagenciesaremeetingtheserequirementsforallofthecommunitiestheyserve.”16Sherecommendsthatnewdatacollectioneffortsincludecommunitysurveystogaugepublicsatisfactionanddatalookingfortheabsenceofbiasindetentionsanduseofforce.Sandersoffersthreeadditionalfindings,relativetopolicedepartmentsusingperformancemetrics,toimprovepolice-communityrelations:

• Placingalltheemphasisoncrimelevelscreatesadangeroustensionbecauseitoverlookspoliceofficers’otherrolesandfunctionsthatshouldincludepolice-communityrelations.Tofocusexclusivelyononegoal(e.g.crimereduction)attheexpenseoftheothersistoinvitepoorperformanceonalternativegoals(e.g.constitutionalandcommunitypolicing).

• Collectingdata,inandofitself,changesbehaviorbecauseperformancemetricsareoneofthe

policyleverstoinfluenceactions.Measuringpolice-communityrelationsandincorporatingthesemeasuresintothewaypoliceofficersanddepartmentsarejudgedwillchangebehavior.Thereshouldalsobeperformancemetricsthatincentivizeanddemonstrateconstitutionalpolicing,meaningpolicingthatisbias-freeandthatusesforceonlywhennecessary.

• Transparencyiskeytobuildingcommunitytrust.Thevacuuminperformancedatatracking

publicsatisfactionwiththepolice,useofforce,biasedpolicing,complaintsagainstthepoliceandholdingofficersaccountableformisconductmakesthepublicdependentonopinions,newsstoriesandtheirownanecdotalexperiencewithlawenforcementforinformation.Inturn,lawenforcementadoptingpolice-communityperformancemetricsonthesemeasurescouldimprovecommunitymembers’understandingandsupportforlawenforcementefforts.

Sandersconcludesherremarksbyencouraginggovernmentsto:

• Assessthepoliceonmorethancrimestatistics;and

• Partnerwithexternalresearch/oversightbodiestocollectandaccessnewdimensionsofperformancethatincludepublicsatisfactionandconstitutionalpractices.

Ratherthanrelyingonexternalpartnershipstoenhanceoversight,theCenterforPolicingEquityrecommendsthatlawenforcementagenciesdevelopPlanningandAnalysisUnitsspecificallychargedwithtrackingandanalyzingdataonstops,useofforce,andpatternsofdiscriminatorybehavior.17ThisissimilartotheLosAngelesPolicingCommission’srecommendationsforLAPDtodevelop“systemsandmechanismsfortheanalysisofstopandsearchdatatoidentifypotentialevidenceofdisparatetreatment,implicitorexplicitbias,differentialenforcement,and4thamendmentconcerns.”18

16https://www.rand.org/content/dam/rand/pubs/testimonies/CT400/CT423/RAND_CT423.pdf17CenterforPolicingEquityPolicyFramework,p.7918LosAngelesPoliceCommissionandOfficeofInspectorGeneral,ReviewofNationalBestPractices,May2,2017



CPE’sCompstatforJusticeProject19furtherrecommendsthatlawenforcementagenciescreatea“publicinterface”(i.e.aone-stopshop)toreportdataoncommunity-policeinteractionsthatenablemutualaccountability“tothevaluesoffairness”thatlawenforcementandthepublicshare.Baltimore’s2017consentdecreewiththeU.S.DepartmentofJustice20embodiesbestpracticesutilizedinotherjurisdictionsunderconsentdecrees,andalignswithbothSander’sandtheCenterforPolicingEquity’srecommendationsforusingperformancetoadvanceconstitutionalandcommunitypolicing.SpecificfeaturesoftheBaltimorePoliceDepartmentconsentdecreeinclude:

• Assessingcommunityengagementeffortsatleastonanannualbasisbysurveyingresidents’andpoliceofficers’perceptionsofpolicingandpublicsafetyinEnglishandSpanish;

• Collectingallstopandsearchdatawhetherornottheyresultinanarrestorissuanceofasummonsorcitationandanalyzingthisinformationatleastannually;

• Collectingdataregardingcallsforservicethatinvolvepossiblebehavioralhealthdisabilitiesandpeopleincrisisandanalyzingthisdata;

• Creatingandmaintainingareliableandaccurateelectronicsystemtotrackuseofforcedataandallegationsofuseofforcemisconduct;

• Maintainingacentralizedelectronicnumberingandtrackingsystemforallallegationsofmisconductandsharinginformationwithcomplainantsandthepublicaspermissiblebylaw;

• AssessingwhetherBPDdeliverspoliceservices,“withoutanunnecessarydisproportionate

impactonindividualsbasedondemographiccategory”,byanalyzingdataonstops,frisks,searches,andarrestsbyrace,ethnicity,andgender;and

• StaffingaComplianceUnitthatwillcoordinateBPD’scomplianceandimplementation

activities;facilitatetheprovisionofdata,documents,andaccess;andensurethatalldata,documents,andrecordsrequiredbytheconsentdecreearemaintainedinausableformat.

19https://policingequity.org/what-we-do/compstat-for-justice20https://www.justice.gov/opa/file/925056/download



Chapter3: RecommendedPolicingDataandLocalPractices

Thischapterdescribesrecommendedpracticesfortrackingdataonpolice-communityinteractionsandcomparesthemwithdatapointstrackedbytheMontgomeryCountyPoliceDepartment.Thischapter’slistingofrecommendedpolicingdatapointsprimarilyemergefromthreesources:

• TheCenterforPolicingEquitythatadvocatesforpolicedepartmentstousedatatoholdthemselvesaccountableforunbiasedpolicinginthesamewaystheyusetheCompstatprocesstoreducecrime.Towardthisend,CPEencourageslawenforcementtotrackdataonpolicestops,useofforce,andperceptionsofpolice-communityinteractions.

• TheLosAngelesPoliceDepartmentthattrackspolicingdataaimedatpromotingconstitutionalpolicingasaresultoftheirfederalconsentdecree.LAPD’spolicingdatacollectionpracticesincludesurveyingresidentsandofficersontheirperceptionsofpolice-communityinteractionsandreportingdataoncomplaints,investigations,adjudications,disciplinaryactions,andmediations.

• BureauofJusticeStatistics’Police-PublicContactSurveythatdescribesthepolice’sinteractionswiththepeopleusinganationallyrepresentativesample.Itscategorizationofpolice-publicinteractionsisessentialtounderstandingwhatpolicingdatashoulddescribe:police-initiatedcontacts,resident-initiatedcontacts,andtrafficaccidents.

Basedonthetypesofdatacollectedfromthesesources,lawenforcementagenciesareencouragedtocollectandmonitordataacrossfourcategoriesofpolicingdatadescribedbelow.Tomonitorforconstitutionalandcommunitypolicing,eachofthesedatasetsshouldprovidedisaggregatedinformationbyrace,ethnicity,andlocation.

1. DetentionDatathatdescribesstops,searches,citations,arrests,anduseofforcefordefendants(driversandpedestrians)andforofficers;

2. DataonPolice-andResident-InitiatedContactsandTrafficAccidentsthatbroadlydescribethewaysthatthepublicinteractswiththepolice;

3. PoliceComplaintDatathatdescribescivilianandinternalcomplaintsagainstpoliceemployeesbyreasonanddisposition;and

4. SurveyDataonPolice-CommunityRelationsfromresidentsandlawenforcementemployeestoassessperceptionsofpolice-communityinteractionsandtrust.

TheremainderofthischapterdescribeseachoftheserecommendedpolicingdatasetsandtheiravailabilityinMontgomeryCounty.ThechapterconcludeswithafifthsectionthatsummarizesthealignmentbetweenMCPD’spolicingdatasetsandbestpractices.ThenextchapterdescribestheseandrelatedMCPDpolicingdatasetsingreaterdetail.



1. DetentionData

TheCenterforPolicingEquity(CPE)recommendslawenforcementagenciescollectandanalyzedetentiondatabyrace,ethnicity,andlocation,tomonitortheirconstitutionalpolicingpractices.Thisincludesdataonstops,arrests,andusesofforce.TheLosAngelesPolicingCommission’sreviewofnationalbestpracticesalsorecognizescollectingdetentiondatadisaggregatedbyraceandethnicityasabestpractice.Theyalsorecommendthatlawenforcementagenciesregularlypostpolicingdata,includingstops,summonses,arrests,reportedcrimes,andotheractivitiesandagenciesmaintainandanalyzedemographicdataonalldetentions.Thissectiondescribesrecommendedpracticesfortrackingdetentiondataforlawenforcementagencies.Datapracticesaredescribedacrossfivetypesofpolice-initiatedcontacts:

• Stops• Searches• Citations• Arrests• Useofforce

ThissectiondescribeshowMCPDdatapracticesalignwithrecommendedpracticesacrossthesefivetypesofpolice-contact,anddescribesasixthcategoryofcontact:Fieldinterviewreports.Overall,OLOfindsthatMCPDreliesonavarietyofsourcesandreportingpracticestodescribeitsdetentiondata.Somedetentiondatapointsarerequiredbythestateandtrackedintheirdatasystems(e.g.E-Tix),someofthesearealsoreportedonDataMontgomery(e.g.,TrafficViolationsDataset),andsomearethesubjectofMCPDannualreports(e.g.,UseofForceAnnualReport).Generally,thereismoredataavailabletothepublicontrafficstopsthanpedestrianstops,andthereisinconsistentdatareportedondetentiondatapointsbyraceandethnicity(e.g.arrests).Assuch,detentiondataiscurrentlyreportedinavarietyofwaysinMontgomeryCounty.TheimplementationoftheCounty’sCommunityPolicingAct,however,shouldaddgreatercoherencetoMCPD’sreportingofdetentiondataandalignmentwithrecommendedpolicingdatapractices.

A.StopDataBestpracticesforconstitutionalpolicingrecommendsthecollectionandanalysisof“stopandfrisk”datafordrivers,passengersandpedestrians.BothNewYorkCityandLosAngelesutilizethisbestpractice.21InMaryland,stopdatafordriversandpassengersarereportedinE-Tixasrequiredbythestate.ThestaterequiresMCPDtoreporttraffic-relatedstops,searches,andarrestsbyrace,ethnicity,age,stopreason,andoutcome.TheGovernor’sOfficeofCrimeControlandPreventionmaintainsa“Race-BasedTrafficStopDataDashboard”thatdescribesdriverstopdatabyjurisdiction;DataMontgomery’sTrafficViolationsDatasetalsoincludesthisinformation.

21AsnotedbyAndrewFergusoninTheRiseofBigDataPolicing,inNewYorkCity,policefilloutaUF-250cardmemorializingtheexactlocationofeverypolice-citizeninteractionandtheLosAngelesPoliceDepartmentutilizesfieldinterviewcardsthatareuploadedtoadatabasethatcanbeusedtotrackpatternsofpolicecontacts.



Thereis,however,nostaterequirementtotrackpedestrianstopsorthedemographicsofciviliansorofficersinvolvedinstreetstops.22InterviewswithMCPDofficersclarifythatonlyasubsetofstreetstopsisroutinelydocumented:pedestrianstopsinresponsetoresident-initiated(911)calls.Whenofficersmakethesestops,theycallthemintoMCPD’sdispatchsystemandthestopisdocumented.However,officersdonothavetocallthedispatchforpolice-initiatedstopsofpedestriansunlessthestopresultsinanarrest.Assuch,“stopandfrisk”dataonallpedestrianstopsarenottrackedbyMCPD.Chart3.1describeslocalandstatesourcesofstopdataforMontgomeryCountydrivers,passengersandpedestrians.Ananalysisofthe2018Race-BasedTrafficStopDataDashboardforMCPDandpopulationdatafromtheAmericanCommunitySurveyshowsthatBlackdriversexperiencedadisproportionatelyhighershareoftrafficstopsinMontgomeryCounty.Morespecifically:

• Blackpeopleaccountedfor18percentofallresidentsv.32percentofMCPDtrafficstops• Whitepeopleaccountedfor44percentofallresidentsv.35percentofMCPDtrafficstops• Latinxpeopleaccountedfor19percentofallresidentsv.20percentofMCPDtrafficstops• Asianpeopleaccountedfor15percentofallresidentsv.7percentofMCPDtrafficstops

Chart3.1:PubliclyReportedDataonStops

DriversandPassengers Pedestrians

DataMontgomery

TrafficViolationsDatasethttps://data.montgomerycountymd.gov/Public-Safety/Traffic-Violations/4mse-ku6q

Notreported

MCPDAnnualReports

Nocurrentreports,buttheCommunityPolicingActrequiresannualreportingofpersonsdetainedbyMCPDbyrace,ethnicity,andgender.Ifpolicestopsareconsidereddetentions,thenthisinformationwillbereportedannuallybyFebruary1st

StateAnnualReports

Race-BasedTrafficStopDataDashboardhttp://goccp.maryland.gov/reports-publications/data-dashboards/traffic-stop-data-dashboard/

Notreported

B. SearchData

Examiningsearchdataand“search-hit”ratesthatidentifycontrabandisanotherpolicingdatabestpractice.Disparitiesinsearchratesbyraceandethnicity,andinhitrates,maysignalbiasesinpolicetreatmentbyraceandethnicitythatshouldbeinvestigatedandaddressedifwarranted.ForMontgomeryCounty,searchdatafordriversandpassengersfortrafficstopsarealsoreportedinE-Tixasrequiredbythestate.DataMontgomery’sTrafficViolationsDatasetincludesthisinformation.However,therearenoreportingrequirementsforsearchesofpedestriansduringstreetstops.Norisdataonsearchwarrantsreported.Assuch,nolocaldataisavailabletodiscernwhethertherearedisparitiesinMCPDsearchpracticesamongpedestriansbyrace,ethnicityorlocation.

22Aspartoftheirfederalconsentdecree,theBaltimorePoliceDepartmenttracksallstops,pedestrianandvehicle,includingthosethatdonotresultinacitation,warning,searchorarrest.



Chart3.2describeslocalandstatesourcesofsearchdataforMontgomeryCountydrivers,passengers,andpedestrians.Ananalysisofthe2018Race-BasedTrafficStopDataDashboardshowsthatMCPDsearchedBlackdriversduringtrafficstopsatahigherratethanotherdrivers.Morespecifically,4.4percentofBlackdriversweresearchedcomparedto3.3percentofLatinodrivers,2.0percentofWhitedrivers,and1.3percentofAsiandrivers.

Chart3.2:PubliclyReportedDataonSearches


DataMontgomery


Notreported

MCPDAnnualReports

Nocurrentreports,buttheCommunityPolicingActrequiresannualreportingofpersonsdetainedbyMCPDbyrace,ethnicity,andgender.Ifpolicesearchesareconsidereddetentions,thenthisinformationwillbereportedannuallybyFebruary1st

StateAnnualReports

Race-BasedTrafficStopDataDashboardhttp://goccp.maryland.gov/reports-publications/data-dashboards/traffic-stop-data-dashboard/

Notreported

TheCommunityPolicingActrequiresMCPDtoannuallyreportdemographicinformation“regardingindividualsdetainedbytheDepartment”byFebruary1st.Thetermsdetainedanddetention,however,arenotdefinedinthelaw.Assuch,itremainsunclearwhetherthelawrequiresMCPDtodescribethedemographicsofresidentssearchedbythepoliceoutsideoftrafficstopsasrequiredbythestate.

C. CitationDataDisparitiesbyraceandethnicityincitationratesmaysignalunconstitutionalpolicingpracticesthatshouldbeuncoveredandaddressed.MCPDissuesfourtypesofcitations:

• Trafficviolations(i.e.tickets)todrivers,passengersandpedestrians

• Civilcitationsforadultdefendantstypicallychargedwithpettyfirsttimealcoholormarijuanaoffenses(e.g.possessinglessthan10mg)ordistributingnicotinedevicestominorsorother

• Criminalcitationsforadultdefendantschargedwithmisdemeanorsthatdonotcarrypenaltyofimprisonmentorthemaximumpenaltyis90daysorless

• JuvenilecitationsthatprimarilyrepresentpolicedepartmentsreferralsforchildrentotheDepartmentofJuvenileServicesforstatusand/orcriminaloffenses



Eachcitationtypehasdifferentdatacollectionandreportingrequirements.DataontrafficviolationsarereportedtothestateviaE-Tix,whiledataoncivilcitationsarehousedatdistrictpolicestationsandsharedwithDistrictCourts.Alternatively,between2014and2018,thestaterequiredMCPDtosubmitdataoncriminalcitationsinclusiveofrace,ethnicity,gender,age,andchargestotheMarylandStatisticalAnalysisCenter(viaDeltaplus),whiletheDepartmentofJuvenileServicescompilesjuvenilecitationdata.Thestate’scriminalcitationreport,23however,wasnotasusefulasDJS’sDataResourceGuidebecauseitdidnotdisaggregatedatabyraceandethnicitybyjurisdiction.Chart3.3describeslocalandstatesourcesofcitationdataforMontgomeryCountybycitationtype.Ananalysisof2019datashowsthatBlackchildrenbetweentheagesof11and17weremorelikelyreceivejuvenilecitationsandbereferredtoDJSthatothergroupsofyouth.Whereas,

• Blackchildrenaccountedfor20percentofyouth,theywere54percentofDJSreferrals• Whitechildrenaccountedfor37percentofyouth,theywere20percentofDJSreferrals• Latinx/Otherchildrenaccountedfor43percentofyouth,theywere33percentofDJSreferrals

Chart3.3:PubliclyReportedDataonCitations

Drivers,Passengers,andPedestrians

DataMontgomery

TrafficViolationsDatasethttps://data.montgomerycountymd.gov/Public-Safety/Traffic-Violations/4mse-ku6qNolocaldatasetsoncivilcitations,criminalcitationsorjuvenilecitationsposted

MCPDAnnualReports

NocurrentMCPDannualreportsontrafficviolations,civilcitations,criminalcitationsorjuvenilecitations;unclearifMCPDCommunityPolicingReportsrequiredundertheCommunityPolicingActwillrequireMCPDtopubliclyreportdataoncitations

StateAnnualReports

CriminalCitationsReport(available2014–2018)https://goccp.maryland.gov/wp-content/uploads/criminal-citations-report-2018.pdfDataResourceGuide(DepartmentofJuvenileServices)https://djs.maryland.gov/Documents/DRG/Data_Resource_Guide_FY2019_.pdfNoStateannualreportsissuedontrafficviolationsorcivilcitations

TheCommunityPolicingActrequiresMCPDtoannuallyreportthenumberofyouthundertheageof18referredtointerventionprogramsbyofficers.TheActalsorequiresMCPDtoreportdemographicinformation“regardingindividualsdetainedbytheDepartment”annuallybyFebruary1st.Detainedanddetention,however,arenotdefinedtermsinthelegislation.Assuch,itremainsunclearwhetherthelawrequiresMCPDtodescribethedemographicsofresidentswhoreceivecitationsandsummons.

23WiththesunsetofSB422,Marylandnolongerrequirespolicedepartmentstosubmitcriminalcitationdata.



D. ArrestDataExaminingarrestdatabyrace,ethnicity,andotherfactorsisanotherpolicingdatabestpractice.Disparitiesinarrestratesmaysignalbiasesinpolicingthatshouldbeinvestigatedandaddressed.MontgomeryCountyarrestdata,resultingfromtrafficstops,arereportedtothestateviaE-Tix.Alllocalarrests(trafficandnon-traffic)arealsotrackedintheCRIMSdatabasemaintainedbytheDepartmentofCorrectionsandRehabilitation.DataMontgomeryreportsdailyarrestdatabyname,age,addressandoffensebutnotbyraceorethnicityfordefendantsorarrestingofficers.Assuch,nolocaldataispubliclyreportedtodiscernwhethertherearedisparitiesinoverallarrestratesbyraceorethnicity.AccordingtoMCPD,thedailyarrestdatacompiledinCRIMSdiffersfromtheFBIarreststatisticsforMontgomeryCountythatthestatereferencesitsUniformCrimeReports(UCR).TheDOCR/CRIMSarrestdatareferstotheactualnumberofarrestswhiletheFBIarreststatisticstrackarrestdataamongclosedcases.Assuch,theFBIarrestdatacompiledbytheStatetracksasmalleruniversethantheCRIMSarrestdata(incidentsv.crimes).LocallawenforcementagencydataonarrestsratesareincludedintheMarylandUCRreportbyoffensetypeandamongadultsandjuveniles,butarenotpubliclyreportedbyrace,ethnicityorgender.OLO’sRacialEquityProfile,however,reportsthatBlackandLatinopersonsaccountedfor44%and26%ofMCPDarrestsin2017comparedtoaccountingfor20%and19%ofCountyresidents.24Chart3.4describeslocalandstatesourcesofarrestdataforMontgomeryCountydrivers,passengersandpedestrians.Ananalysisofthe2018datashowshigherMCPDarrestratesBlackandLatinodriversduringtrafficstops:2.2–2.3percentofLatinxandBlackdriverswerearrestedcomparedto1.3percentofWhitedriversandlessthanonepercent(0.9%)percentofAsiandrivers.

Chart3.4:PubliclyReportedDataonArrests


DataMontgomery


NotreportedonDataMontgomery,butavailableviaCRIMS.

DailyArrestsDatasethttps://data.montgomerycountymd.gov/Public-Safety/Daily-Arrests/xhwt-7h2h

MCPDAnnualReport

Nocurrentreports,buttheCommunityPolicingActrequiresannualreportingofpersonsdetainedbyMCPDbyrace,ethnicity,andgenderbyFebruary1st

StateAnnualReports

Race-BasedTrafficStopDataDashboardhttp://goccp.maryland.gov/reports-publications/data-dashboards/traffic-stop-data-dashboard/2018UniformCrimeReportlistsarrestsbyjurisdictionhttps://mdsp.maryland.gov/Document%20Downloads/Crime%20in%20Maryland%202018%20Uniform%20Crime%20Report.pdf

Nostateleveldatareportedonpedestrianarrests

24https://www.montgomerycountymd.gov/OLO/Resources/Files/2019%20Reports/RevisedOLO2019-7.pdf



E. UseofForceDataUseofforcereferstowheneverforceisusedtocounteractaphysicalstruggle,orwhenafirearmisdischarged.Itisoneofthemostcommonmetricsforconsideringdisparitiesinpolicingpractices.Marylandrequireslawenforcementagenciestoreporttheuseoftasers(electroniccontroldevices)andofficer-relateddeaths.Fortasers,thestaterequiresdatareportedbydefendantrace,ethnicity,age,time,date,zipcode,precipitatingevent,reasonfordischarge,location,andinjury/deathresultingfromtasers.Thestatealsorequiresreportingonraceandethnicityofofficersinthedeathofacivilian.Additionally,MCPDproducesanAnnualUseofForceReportdescribingthetypesofforcemostoftenusedandthedemographicsofciviliansandofficersinuseofforceincidents.25MCPDFunctionCode131requirestheUseofForceandWeaponsReviewCommitteetoreviewtheUseofForceannualreport,andafterreviewingit,reportitsanalysesandanyrecommendationstotheChiefofPolice.Chart3.5describeslocalandstatesourcesofuseofforcedataforMontgomeryCounty.AnanalysisofMCPD’s2018useofforcedata,andpopulationdatafortheCountyfromtheAmericanCommunitySurvey,showsthatMCPDdisproportionatelyusedforceamongAfricanAmericans.Morespecifically,inMontgomeryCounty:

• Blackpeopleaccountedfor18percentofallresidentsv.55percentofuseofforceincidents• Whitepeopleaccountedfor44percentofallresidentsv.26percentofuseofforceincidents• Latinxpeopleaccountedfor19percentofallresidentsv.18percentofuseofforceincidents• Asianpeopleaccountedfor15percentofallresidentsv.1percentofuseofforceincidents

Chart3.5:PubliclyReportedDataonUseofForce

DataMontgomery NolocaldatasetsonUseofForceDataMCPDAnnualReports

MCPDAnnualUseofForceReportshttps://www.montgomerycountymd.gov/pol/data/use-of-force-report.html

StateAnnualReports ElectronicControlDeviceDataReports(2013–2016)http://goccp.maryland.gov/reports-publications/law-enforcement-reports/electronic-control-device/DeathsInvolvingaLawEnforcementOfficerReportshttp://goccp.maryland.gov/reports-publications/law-enforcement-reports/deaths-involving-law-enforcement/

F.FieldInterviewReports

TheintentofFieldInterviewReportsistodocumentpotentialsubjectsofinterestforcurrentandfutureinvestigations.Inotherjurisdictions,FieldInterviewReportscanbeusedtodocumentwarningsandsuspectsfortrespassing.26Disparitiesbyrace,ethnicityandotherfactorsmaysignalunconstitutionalpolicingpracticesthatwarrantedfurtherinvestigation.

25TheCommissiononAccreditationforLawEnforcementAgencies(CALDEA)requiresaccreditedagenciestoreporttheiruseofforcedataannually.26SeeforexampleTakomaParkPoliceDepartment,GeneralOrdersNo.656



InMontgomeryCounty,policeofficerscanphotographpersonstheyconsidersuspiciousandentertheirphotosandcontactinformationintoaFieldInterviewReport.AsnotedinMCPDFunctionCode625,“fieldinterviewinformationisintendedforuseinconjunctionwithothertypesofinformationforthepurposeofdevelopingleadsoncrimepatterns,criminalactivity,orhomelandsecurityspecialactivity.”TherearenopublicreportdataonFieldInterviewReports;theFIRdatacollectedbyofficersisenteredintothestate’sDeltaPlusdatabasethatincludesE-TixandACRSdata.

Chart3.6:PubliclyReportedDataonFieldInterviewReports


DataMontgomery NolocaldatasetsonFieldInterviewReportMCPDAnnualReports

None;unclearifCommunityPolicingActwillrequireMCPDtopubliclyreportdataonFieldInterviewReports

StateAnnualReports NostatereportsonFieldInterviewReports

2. Police-andResident-InitiatedContactsandTrafficAccidentsInadditiontohavinganunderstandingofdisparitiesindetentionratesbyrace,ethnicity,andlocation,itisalsoabestpracticeforlawenforcementtohaveabroaderunderstandingofdisparitiesinpoliceinteractionswiththepublic.Monitoringdataonthreetypesofinteractionscanassisttowardthisend:

• Resident-initiatedcontactswithpolicethatincludingreportingacrime,disturbance,orsuspiciousactivity;reportinganon-crimeemergency,suchasamedicalemergencyorparticipatinginananti-crimeprogram;orapproachingthepoliceforanotherreason.

• Police-initiatedcontactswhenpoliceapproachorstopindividuals.Theseincludebeingstoppedwhileinapublicplaceoraparkedcar(i.e.streetstop),beingstoppedwhiledrivingamotorvehicle(i.e.trafficstop)orridingasapassengerinacarthatwasstopped,beingarrested,orbeingstoppedorapproachedbythepoliceforsomeotherreason.Police-initiatedcontactsarebroadlydefinedasdetentions,becausepolicedetainindividualsintheseencounters.

• Trafficaccidentsthatresultedinpolicecontacts.

Monitoringandcomparingtrendsamongthesethreemetricscanbeusefulforconsideringwhetherdisparitiesincontactsreflectdifferencesinpolicingorotherfactors.Forexample,differencesintrafficaccidentratesamongpopulationslikelyreflectobjectivedifferencesindrivingpatterns,whereasdifferencesinresident-andpolice-initiatedcontactsmayreflectamixofdifferencesintheactualoccurrenceofcrimesaswellassomebiasinperceptionsofwhatconstitutessuspiciousactivity.Assuch,trafficaccidentdatacanbeusedasacounterfactualtoresident-andpolice-initiatedcontactdatatoconsiderwhetherdisparities,ifevident,reflectobjectivedifferencesintheoccurrenceofcrimeorpotentialbiasesinpolicingorresidentreportingbyrace,ethnicity,orlocation.TheBureauofJusticeStatisticsperiodicallyconductsthePolice-PublicContactSurveyasasupplementtotheNationalCrimeVictimizationSurveytodescribetheexperiencesofindividualsage16orolderwiththepolice.Table3.1describestheresultsofthemostrecentPPCsurveyadministeredin2015.27

27ElizabethDavis,AnthonyWhyle,andLynnLangston-ContactBetweenPoliceandthePublic,2015–SpecialReport,U.S.DepartmentofJustice,October2018



Table3.1:PercentofU.S.Residentsage16orolderwithAnyPoliceContact,2015

Demographics AnyContact

Police-Initiated

Resident-Initiated

TrafficAccident

Total 21.1% 10.8% 10.7% 3.1%

Male 22.0% 12.5% 10.2% 3.2%

Female 20.2% 9.2% 11.1% 3.0%

White 22.7% 11.2% 11.9% 3.2%

Black 19.8% 11.3% 8.7% 3.4%

Latino 16.8% 9.0% 8.0% 2.6%

Other28 18.4% 10.6% 8.3% 3.1%

ThePPC2015datashowscommonaccidentratesbyraceandgender(rangingfrom3.0–3.2%),butdisparitiesinresident-initiatedcontacts,withWhiteresidentsbeingmorelikelytocontactthepolicethanBlack,Latino,orOtherresidents(11.9%v.8.0–8.7%).Disparitiesbygroup,inratesofresident-initiatedcontacts,arewiderthandisparitiesbygroupinratesofpolice-initiatedcontacts.However,thedisparitiesbetweenresident-andpolice-initiatedcontactswithingroupsbyraceandethnicityisstriking:whereassimilarratesofWhiteandLatinoresidentshadcontactwiththepolicebasedoneitherresident-andpolice-initiatedcontacts,Blackresidentswerefarmorelikelytohavecontactwiththepolicebasedonpolice-initiatedcontactsthanresident-initiatedcontacts.InMontgomeryCounty,theabilitytocompilelocaldataonresident-andpolice-initiatedcontactscouldpotentiallyrelyonananalysisofMCPD’sComputerAssistedDispatch(CAD)data.AnalogoustothePPCsurvey,MCPD’sCADsystem,recordstwodifferenttypesofcalls:

• Officer-initiatedcalls.ThecallsourcefortheseintheCADaremarked“FIELD”• Resident-initiatedcalls.ThecallsourcefortheseintheCADaremarked“911”

MCPDdispatcherdataisalsomarkedbypolicedistrictandGPSlocation,permittingananalysisofofficer-andresident-initiatedcontactsbylocation.Yet,whiletheCADsystemcanbeusedtocollectrace,ethnicity,andgenderdataofsuspects,itdoesnottracktheraceorethnicityofresidentswhoinitiatecalls.Nordoesthetrafficaccidentdatacompiledinthestate’sACRSsystembydriver,non-motorist,andincidenttracktherace,ethnicityorgenderofpersonsinvolvedintrafficaccidents.AlocalsurveyofCountyresidentsanalogoustothenationalPolice-PublicContactSurveydescribedabove,however,couldimproveMCPD’s,theCouncil’sandthepublic’sunderstandingofhowresidentcontactswithlawenforcementmayvarybygender,race,andethnicitylocally.

28IncludesAsians,NativeAmericans,OtherPacificIslanders,AmericanIndiansandAlaskaNatives,andpersonsoftwoofmoreraces.



Insum,existingsourcesofdispatcherandtrafficaccidentdatacannotbeusedtotrackorconsiderthesourceofdisparitiesinpolice-andresident-initiatedcontactsinMontgomeryCounty.Asurveyofresidentsregardingtheirinteractionswithlawenforcementmaybenecessarytocompilethisinformation.Ofnote,theCommunityPolicingActrequiresMCPDtoannuallyreportthenumberofcallsforserviceforsubstanceabuseandmentalhealthcrisesbyFebruary1stbeginningin2021.

Chart3.7:PubliclyReportedDataonIncidentsandTrafficAccidents

IncidentsandAccidents

DataMontgomery

PoliceDispatchedIncidents:https://data.montgomerycountymd.gov/Public-Safety/Police-Dispatched-Incidents/98cc-bc7dCrashReporting–Driver’sDatahttps://data.montgomerycountymd.gov/Public-Safety/Crash-Reporting-Drivers-Data/mmzv-x632CrashReporting–Non-MotoristDatahttps://data.montgomerycountymd.gov/Public-Safety/Crash-Reporting-Non-Motorists-Data/n7fk-dce5CrashReporting–IncidentData:https://data.montgomerycountymd.gov/Public-Safety/Crash-Reporting-Incidents-Data/bhju-22kf

MCPDAnnualReports

Nocurrentreports,buttheCommunityPolicingActrequiresannualreportingofnumberofyouthreferredtointerventionprogramsbyofficers,numberofcallsforserviceforsubstanceabuse,andnumberofcallsforserviceformentalhealthbyFebruary1st

3. PoliceComplaintDataThecollectionandanalysisofdataoncivilianandinternalcomplaintsagainstthepoliceisanotherrecommendedpolicingdatapractice.LAPDpubliclyreportsinternaldisciplinaryprogramdataonpersonnelcomplaintsinitiated,theresultsofinvestigations,andanyassociateddisciplineaspartofitsconstitutionalpolicingoversight.LAPDalsoissuesanannualreportthatprovidesdetailedinformationaboutthecharacteristicsandoutcomesofcomplaintsofbiasedpolicing.AnumberofMCPDdepartmentrulesguidetheprocessingofpolicecomplaintsfromresidents.ResidentsareencouragedtocompleteFormMCP580todescribetheircomplaint.Theformsolicitsracialdataforthecomplainant,butitisnotrequired.Theformalsosolicitsanopenedendedresponseto“whathappened?”thatInternalAffairsDivisionclassifiesasallegationsofanofficerbreakingaspecificdepartmentalrulelistedinFunctionCode300.Afterthecomplaintissubmitted,IADstaffinputdataintotheIADloganddecidewhetherthecomplaintwillbedeclinedorinvestigatedasaminorallegationofmisconductthroughtheemployee’ssupervisororasamajorallegationofmisconductthroughemployee’schainofcommandorIADinvestigatorsduetoallegationsofbreakingthelaw.Forcomplaintsallegingbrutality,complainantsmustbeswornpriortoanyinvestigationandthecomplaintsmustbemadewithina90-daytimelimitinmostcircumstances.



ExcerptsofthedataMCPDIADcollectsonpolicecomplaintsareavailabletothepublicthroughtwosources.TheInternalAffairsAllegationsdatasetpostedonDataMontgomerydescribesthedateofthecomplaint,itssource(externalorinternal),thedepartmentrulesthatwereallegedlyviolated,thestatusoftheinvestigation,andthedisposition.TheInternalAffairsDivisionalsopublishesanannualreportthatdescribesthenumberofallegations,allegationsinvestigatedasintakes(minorincidentsofmisconduct)v.formalcomplaints(moreseriousallegations),dispositions,anddemographicsofthedepartmentandofficersaccusedofmisconduct.Ofnote,theIADdatasetpostedonDataMontgomerydoesnotdescribetheraceorethnicityofthecomplainantsortheemployeesaccusedofmisconduct.NordoestheIADdatasetorIADannualreportdescribethelocations/policedistrictswhereallegationsarise.AlthoughtheIADannualreportdescribesthedemographicsofMCPDpersonneloverallaccusedofmisconduct,itdoesnotdescribethedemographicsofcomplainants.Finally,neithertheIADdatasetnorannualreportdescribestheconsequencesemployeesfaceifallegationsagainstthemaresustained.TheCommunityPolicingAct,however,requiresMCPDtoannuallyreportonthenumberofofficerssuspendedwithorwithoutpay,andthenumberofciviliancomplaintsagainstIADregardingallegationsofexcessiveuseofforce,discrimination,andharassment.

Chart3.8:PubliclyReportedDataonPoliceComplaints

ExternalandInternalComplaints

DataMontgomery InternalAffairsAllegationshttps://data.montgomerycountymd.gov/Public-Safety/Internal-Affairs-Allegations/usip-62e2

MCPDAnnualReport InternalAffairsDivisionReports,2017and2018https://www.montgomerycountymd.gov/pol/data/iad-reports.htmlAdditionally,theCommunityPolicingActrequiresMCPDtodescribebyFebruary1stofeachyear:• Numberofciviliancomplaintsabouttheuseofforcebyanofficer• Numberofciviliancomplaintsregardingdiscriminationandharassment• Numberofofficerswhoweresuspendedwithoutpay• Numberofofficerswhoweresuspendedwithoutpay

4. SurveyDataonPolice-CommunityRelationsSurveysofpolice-communityrelationsarecriticaltounderstandingwhetherpolicedepartmentsaremakingprogressontheircommunitypolicinggoalsofbuildingtrustwithcommunitymembers.TheCenterforPolicingEquityrecommendstheuseofsurveydatatotrackperceptionsofpolice-communityrelationsamongresidentsandofficersasabestpractice.LAPDregularlysurveystheiremployeesabouttheirperceptionsofpolice-communityinteractions.LAPDalsosurveysarepresentativesampleofresidentsregardingtheirperceptionsofpolice-communityrelations,anddisaggregatesfindingsbyrace,ethnicity,andlocation.Noregularassessmentsofpolice-communityrelationshipsoccuramongciviliansorofficersinMontgomeryCounty.However,thereweretwocommunitysurveysin2019thataskedresidentswhattheprioritiesofMCPDshouldbeandabouttheirperceptionsofsafetyintheircommunities.



• PoliceChiefRecruitmentCommunityInputSurveywasadministeredonlineandelicited1,123responses.Thesamplewasnotrandomizedsoitsresultsarenotgeneralizable.Nevertheless,surveyparticipationwasethnicallydiverse,althoughbiasedbygender(58%ofrespondentswomen),age(80%ofrespondentsage45orolder),andincome(68%ofrespondentshadannualhouseholdincomesof$100,000ormore).Ofnote,responsestothequestionof“whatshouldthechieffocuson”variedbyraceandethnicitywith“crimeandsafety”emergingasthetopresponseforWhiteandAsianresidentswhile“communityoutreach/engagement”wasthetopresponseforLatinxandAfricanAmericanresidents.

• NationalCommunitySurvey,alsoadministeredin2019,includesseveralpromptsaboutpublicsafetyandresident’sperceptionsofMCPD.TheCountymailedthesurveyto5,000residentsinrandomlyselectedhouseholdsandreceivedfeedbackfrom954respondents.Theresults,publishedinNCSCommunityLivabilityReportforMontgomeryCounty,aregeneralizabletotheCountyoverallandforWhite,Non-HispaniccomparedtoHispanicand/ornon-Whiteresidents(i.e.PeopleofColor).MontgomeryCountyalsoadministeredtheNCSin2017,sotrenddataonchangingperceptionsofpublicsafetyareavailable.

Chart3.9:PubliclyReportedSurveyDataonPolice-CommunityRelations

ResidentSurveys

CountyResults

PoliceChiefRecruitmentCommunityInputSurvey,2019https://montgomerycountymd.gov/OPI/Resources/Files/2019/PoliceChiefSurveyResults-6-2019.pdfCrimeandPublicSafetyPromptsfrom2019NationalCommunitySurveyhttps://www.montgomerycountymd.gov/OPI/survey2019.html

5. SummaryofMCPDPolicingDatasetsAlignmentwithBestPracticesMCPD’sdatasetsthatalign,atleastpartially,withbestpracticesformonitoringpolicingdatainclude:

• Detentiondatapointstrackedbyraceandethnicityon

o Trafficstops,trafficviolations,searches,andarrestsamongdriversandpassengersinE-Tix,o ArrestdatatrackedinCRIMS,ando UseofforcedatacompiledfromMCPForm37.

• Police-publicinteractionsdistinguishingbetweenpolice-andresident-initiatedcontactstracked

byMCPD’sComputerAidedDispatchsystem;and

• PolicecomplaintstrackedbytheInternalAffairsDivision.Thedatapointsincludedinthesedatasets,however,areatbestincomplete.Morespecifically:

• Thedetentiondatasetsdonottrackstreetstopsbetweenofficersandresidentsthatdonotresultinanarrest,citationorsummons;



• MCPDdoesnotmaintainanelectronicdatabaseofthecriminalandcivilcitationsthatwouldenablethemtomonitorfordisparities;

• RaceandethnicitydataarenotcollectedasfieldsintheCAD;

• Thepolicecomplaintsdatabasedoesnotcollectraceandethnicitydataforeverycomplainant;

• AMCPDdatasetofsurveyresponsesregardingpoliceandcommunityrelationshipsdoesnotexistbecauseMCPDdoesnotsurveyitspersonnelorresidents.Assuch,therearenodatasetsthattracktheeffectivenessofMCPD’scommunityengagementactivities.

Chart3.10describesthelocaldatasetsthatalign,atleastinpart,withpolicingdatabestpractices.

Chart3.10:MCPDDatasetsthatAlignwithPolicingDataBestPractices

Database Datasets/Forms DataLimits

DataonDetentions

DeltaPlus(MarylandStatePolice)

E-Tix(TrafficViolations) Nodataonstreetstops

CRIMS(DOCR) Arrests


Other=Latinx/Asian

CriminalCitations UniformCitationForm(DC/CR45) DataatMCPDDistrictStationsandDistrictCourt

CivilCitations AlcoholBeverageViolation

PossessionofMarijuana(<10gram)

SmokingMarijuanainaPublicPlace

UseofForce MCP37Forms

DataonPolice-PublicInteractions

ComputerAssistedDispatch Police-InitiatedIncidentsResident-InitiatedIncidents

Norace,ethnicitydataNodataonreferrals

DeltaPlus ACRS(Collisions) Nodataonrace,ethnicity

DataonPoliceComplaints

InternalAffairs IADAllegations Incompleteinformation



Chapter4. DataCollectedbyMCPDThischapterdescribesindetailthedatathatMCPDcollectselectronicallyacrossitsdivisionsandcomparesitwiththedatathataremadeavailablethroughDataMontgomery,theCountyGovernment’sopendataportal.Thechapterisorganizedbythedatasysteminwhichthedataarecollectedandisorganizedasfollows:

• SectionAdescribesdatacollectedintheCounty’sComputerAidedDispatchsystem(CAD);

• SectionBexaminesdatacollectedinE*Justice,anelectronictoolforwritingPolicereports;

• SectionCdescribesarrestdatacompiledintheCorrectionandRehabilitationInformation

ManagementSystem(CRIMS)

• SectionDexaminesdataavailablefromFieldInterviewReports;

• SectionEdescribesdataonuseofforcebypoliceofficers;

• SectionFsummarizesdataonvehicularpursuits;

• SectionGexaminesdataavailableintheAutomatedCrashReportingSystem;

• SectionHdescribesdataavailableinE-Tix;

• SectionIdescribesdatafromtheInternalAffairsDivisiononinternalandexternalcomplaints

aboutPoliceofficers;and

• SectionJdescribestheCommunityEngagementDivision’sdatabaseofevents.

A. ComputerAidedDispatchSystem

TheCounty’sComputerAidedDispatchSystem(CAD)isthesystemusedbytheEmergencyCommunicationsCenter(ECC)todispatchMontgomeryCountypublicsafetyservices,includingPoliceandFireandRescue,andtracktheiractivitiesduringtheresponse.In2017,theCountyacquiredanewCADsystem.TheCADcapturesalldispatchedcallsforserviceandpoliceself-dispatchestoanincident.Italsocapturesotherincidentsreportedtopolice:ifaresidentwalksintoastationandreportsanincident,thenaCADeventiscreated.However,theCADrecordsonlybasicinformationwhiletheofficer(s)respond(s)totheincidentanddoesnotincludeupdatedinformationinresponsetoinvestigations.ThetableonthefollowingpagesummarizesdatapointscapturedintheCAD.TheCADalsocapturesfurtherdetailsaboutincidentsascomments.Theseunstructuredentriescanincludebasicdescriptionsofpersonsinvolved(e.g.drivers,suspectsorvictims)andtheirstatus,aswellasupdatesontheresponseprovidedbyofficers.



Chart4.1:DataPointsintheComputerAidedDispatchSystem(CAD)

Source(e.g.911or“Field”whichindicatesself-dispatch)Caller

• Name(maybefirstnameonly)• Phonenumber

Dateandtime• Incidentstart• Firstunitdispatched• Firstunitenroute• Firstunitarrived

Eachunitandofficerdispatched• Callsign• Vehicleidentificationnumber• Officername• Officeridentificationnumber• Timedispatched,enroute,onscene,andcleared

Locationtowhichunitsweredispatched• Intersection• Longitudeandlatitude• City• Policedistrict,beatandpolicereportingarea(PRA)

Incident• Initialincidenttype• Incidenttypeatendofcall• Calldispositionatthetimelastunitcleared• Prioritylevelofthedispatch• LinktoincidentreportinE*Justiceand/orcrashreportinACRSwhereavailable

Vehiclesinvolvedinincident• Role• Makeandcolor• VIN• Licenseplateandstate• Iftowed,towreason,dateandstoringcompany

Datalimitations.Asnotedabove,theCADcapturesonlybasicdatapointsaboutanincident.TheinformationintheCADisnotasdetailedasanincidentreport(seesectiononE*Justicebelow),andisnotupdatedwhennewinformationbecomesavailable.Forexample,Policepersonnelmaybedispatchedtoaparticularaddress,whichwouldberecordedintheCAD,butthenlearnthattheincidentoccurredatadifferentaddress.TheCADwouldonlyincludetheaddresstowhichtheofficer(s)isdispatched.Similarly,thecalldispositionenteredintotheCADprovidesinformationintothebasicnatureoftheincidentasdescribedtothedispatcher,butdoesnotreflectinformationthatmaylaterberevealedduringthecourseofaninvestigationorevenduringtheresponse.



Afurtherlimitationresultsfromthefactthatdemographicdetailsofpersonsinvolvedinincidents,suchasage,sex,race,andethnicity,whenprovided,arenotverifiedandarecapturedasunstructuredcommentsratherthaninindividualfields.Asaresult,itmaybetime-consumingtoincorporatetheseelementsintoadataanalysis.TheCADsoftwareincludesfieldstorecordnumerouscharacteristicsofthecallerandpersonsinvolvedinanincident,includinghaircolor,eyecolor,gender,age,andrace,butmostofthesefieldsarenotcurrentlybeingused.29Finally,theCADdoesnotcaptureallpoliceinteractionswiththepublic.Forexample,officersthatarepatrollinganareaonfootarenotrequiredtoreportintotheCADstopsofpedestriansorothersthatdonotresultinanarrestorcitation.Additionally,bycollectivebargainingagreement,policyandlaw,officersarenotrequiredtoreporttrafficstopstotheCAD.CADDataAvailableonDataMontgomery.DataMontgomeryincludesadatasetonpolice-dispatchedincidentssinceAprilof2017,basedondatafromtheCAD.ThisdatasetincludesmostofthedatapointslistedinSection1above,includingatimelinefortheoverallresponse.However,itdoesnotincludethefollowingdatapoints:

• Sourceofthedispatch(e.g.911callorself-dispatch)

• Unitsthatweredispatched(includingvehicleidentificationnumbersandofficernamesandidentificationnumbers)oratimelineofeachspecificunit’sresponse;

• Detailsofanyvehiclesinvolvedintheincident(suchasmake,licenseplate,VINnumber);or

• Incidentdetails,suchasdescriptionsofpersonsinvolved(e.g.drivers,suspectsorvictims)ortheirstatus.

Additionally,manyoftheentriesinthe“DispositionDescription”columnareabbreviated,andtheirmeaningisnotapparentinallcases.Nodocumentationisavailablethatmightclarifythemeaningoftheseentries.

B. E*Justice(IncidentReports)

Policeofficersarerequiredtowriteanincidentreportforincidentsofcrimeandotherevents,suchassuicideattemptsandmissingpersons,thatareverifiedandreportablebasedonavarietyofFederal,StateandCountyrequirements.NoteveryincidentcapturedintheCADresultsinanincidentreport.Forexample,trafficcollisionsarereportedinaseparatesystemandwouldnotresultinanincidentreportunlessanincarcerabletrafficviolationoccurred.Ontheotherhand,everyincidentreportmusthaveacorrespondingrecordintheCAD.E*JusticeisMCPD’selectronicincidentreport-writingtoolandrecordsmanagementsystemandisalegacysystem.Atthetimeofwritingofthisreport,MCPDwasintheprocessofprocuringanewelectronicrecordsmanagementsystem.ThetableonthefollowingpagesummarizesdatapointsavailableinE*Justice.

29“PremierOne:ReportingDataWarehouse(RDW)DataDictionaryVersion4.4CU3”,MotorolaSolutionsInc.,2019



Chart4.2:DataPointsCapturedinE*Justice

Locationwheretheincidentoccurred• Intersection• Longitudeandlatitude

Detailsofeachspecificoffenseassociatedwiththeincident• Anoffensecategoryandcodeforthespecificoffense• Whethertheoffensewasattemptedorcompleted• Thetypeoflocationwheretheoffenseoccurred• Whetherevidenceofahatecrimeorbiasincidentwasfound• Offensestatus(openorclosed,andhowitwasclosed,forexamplethrougharrest)• Weaponsinvolved• Suspecteduseofalcohol,drugs,orcomputerequipment• Otherdetailsspecifictooffensetypes(e.g.methodofentryforburglariesorautothefts)

Officersthatrespondedtoincidentand/orapprovedthereport• Name• Identificationnumber

Detailsofvictims,witnesses,arrestees,andsuspectsincluding:• Name(required)• Dateofbirth(required)• Sex(required)• Race(required)• Role(required)• Residentornon-resident(required)• Ethnicity• SocialSecurityNumber• Address• Phonenumber(s)• Physicalcharacteristicssuchasheight,weight,build,andhaircolor• Forarrestees,arrestdateandtype(e.g.on-viewarrestversussummons/citation)

NamesandaddressesofbusinessesinvolvedintheincidentVehiclesinvolvedintheincident

• Make,YearandColor• Vehicletype• Licenseplate• VINnumber

Lost,stolenorseizedproperty• Typeofproperty• Make,modelandcolor• Dollarvalue(exceptfordrugsornarcoticsseizedinconnectionwithadrug-relatedoffense)• Status(lost,stolen,seized)• Ownerdetails

OtherassociatedincidentsIncidentreportsalsoincludeanincidentnarrative,whichisachronologicalwrittenaccountoftheinvestigation.Subsequenttothefilingoftheinitialreport,supplementalreportsmustbesubmittedwhennewinformationisobtained,ortodocumentnewdevelopmentsinthecase.



DataLimitations.Officersarerequiredtoenterina“Race”foreachvictim,witness,arresteeandsuspect.Aseparatefieldfor“Ethnicity”existsinE*Justice,butitisnotarequiredfield.Becausethe“Race”fielddoesnothavetheoptiontoindicatethatapersonisLatinx,datafromE*JusticelikelyunderreportsnumbersofLatinxvictims,witnesses,arrestees,andsuspects.E*JusticeDataAvailableonDataMontgomery.DataMontgomeryincludestwodatasetsderivedfromE*Justice.Thefirstdataset,“Crime”,includesdatapointsfromE*JusticereportsbetweenJuly1,2016andthepresent.Theseconddataset,“MCPDBiasIncidents”,isspecifictoincidentswhereevidenceofahatecrimeorbiasincidentwasfound.The“Crime”datasetisthemostcomprehensive,anditincludesbasicdatapointsabouteachincident,includingthespecificoffense,thelocation(longitudeandlatitude)andthedateandtimetheincidentoccurred.However,itdoesnotinclude:

• Demographicdetailsorotherinformationonthepersonsorbusinessesinvolved;• Informationonanyarrestsmadeorthetypeofarrest;• Informationontheofficersthatresponded;• Informationonanypropertythatwasstolen,lostorseized;• Informationonvehiclesinvolvedintheincident;• Thestatusofthecase

The“MCPDBiasIncidents”datasetprovidessomeadditionalbasicdatapointsforeachincidentwhereevidenceofahatecrimeorbiasincidentwasfound,includingthetargetedgroup(e.g.anti-Jewish,anti-Hispanic),thenatureofthecrime(e.g.vandalism),thestatusofthecase,andthenumberofsuspectsbyagegroup.Neitherdatasetincludesinformationfromtheincidentreportnarrative.

C. CorrectionandRehabilitationInformationManagementSystem(CRIMS)TheCorrectionandRehabilitationInformationManagementSystem(CRIMS)istheDepartmentofCorrectionandRehabilitation’s(DOCR)jailmanagementsystem.ThissystemrecordsallarrestsintheCounty,asopposedtoincidentswhicharecapturedinE*Justice.ThetableonthenextpageliststhedatapointsthatarecapturedinCRIMS.DataLimitations.MCPDstaffreportthatitisnotcurrentlypossibletoautomaticallylinkarresteesintheCRIMSdatabasetosuspectsandotherpersonsenteredintoE*Justice.Staffarecurrentlyworkingtodevelopthiscapability.CRIMSdataavailableinDataMontgomery.TheDailyArrestsdatasetinDataMontgomeryprovidesthenames,ages,addresses,arrestdatesandallegedoffensesforallpersonsarrestedduringtheprior30days.Thisdatasetdoesnotincluderaceorgenderinformation,andarrestsareremovedfromthedatasetafter30days.



Chart4.3:DataPointsinCorrectionandRehabilitationInformationManagementSystem(CRIMS)

Arrestee• Name• Dateofbirth• Homeaddress• Place,stateandcountryofbirth• CountryofBirth• StateofBirth• Race• Gender

Arrest• Dateandtime• Arrestingofficer• Officersinvolvedintransport,search,andcollectionofpossessions• Arrestingagency• Typeofarrest(criminal,traffic,civil)• Typeofbooking(statementofchargesorwarrant)• Typeofwarrant• Warrantnumber• Policearrestrecordnumber

Courtinformation• Courtcasenumber• Court(DistrictCourtRockville,DistrictCourtSilverSpring,CircuitCourt)• Statefilingnumber

Charges• Offensecode• Statutecode• Dateofcharge• Locationofcharge• Statementofcharges

D. FieldInterviewReports

MCPDusesfieldinterviewreportstorecorddataoncertaininteractionsbetweenpoliceofficersandmembersofthepublic.Anofficerwhoobservesbehaviordeemedsuspiciousorconcerningtypicallyinitiatestheseinteractions.Theinteractionsrecordedinfieldinterviewreportsdonotresultinarrestsorcitations,butmayberelevantatafuturedate.DataMontgomerydoesnotincludeanydatafromMCPDfieldinterviewreports.FieldinterviewreportsarestoredinasystemcalledDeltaPlus,whichismaintainedbytheStateofMaryland.Fieldinterviewreportsincludethelocationoftheinterview(addressandlongitude/latitude),andthefollowingdataonthepersonthatwasinterviewed:

• Name• Age• Race/ethnicity• Alias• Identificationinformation(e.g.driver’slicense)• Variousdescriptors(skintone,haircolor,facialhair,build,eyecolor,eyewear,height,weight)• Scars,marks,tattoos,andother“identifiers”



• ClothingThereportalsoincludesanarrativeoftheinterviewandaphotograph.

E. UseofForceTheCommissiononAccreditationforLawEnforcementAgencieshasaccreditedMCPDsince1993.CALEArequireslawenforcementagenciestoreportannuallyonuseofforcebyofficers.MCPDpolicy30requiresofficerstocompleteaUseofForceReport(MCP37)forthefollowingtypesofincidents:

• Anytimeforceisusedtocounteractaphysicalstruggle.• Followingtheuseofanyforcethatresultsinaninjurytoanindividual.• Whenanindividualclaimstohavebeeninjuredasaresultofuseofforce.• Wheneverforceisappliedusingaprotectiveinstrument.• Wheneverafirearmisdischargedotherthanauthorizedtargetpractice.• Wheneveradepartmentcanineinflictsinjuryonanysubjectorsuspect.• Anytimeanofficerisassaultedorambushed.

Chart4.4:DataPointsCapturedinMCPD’sUseofForceReports

Suspect• Firstandlastname• Race• Sex• Age• Height&weight• Useofalcoholordrugs• Whethermentalillnessissuspected• Typeofinjuryorinjuriessustainedandtreatmentreceived

Officer(s)involved• Identificationnumber• Race,sex,&age• Height&weight• TenureatMCPD(years)• District/Unitofassignment• Whethertheofficerwasassaulted• Whethertheofficerwasinjured,andtypeofinjuryorinjuriessustained• Whethertheofficerwasambushed• Typeofforceusedbyofficer• Ifelectroniccontroldevicewasused,typeofdeploymentandpointofimpact• Treatmentreceived

Incidentcategory• ReasontypeforcompletingUseofForceReport(e.g.injury,accidentaldischarge)• Activitycode(e.g.arrest,trafficstop)

30“UseofForce,”FCNo.131,9/21/2016,MontgomeryCountyPoliceDepartment



MCPDhasanonlinesystemforcollectingandmaintainingUseofForcereportssubmittedbyofficers.ThefollowingtablesummarizesthedatapointscollectedinMCPD’sUseofForceReport(MCP37).NopublicdatasetsonUseofForceReportsareavailableonDataMontgomery.DataLimitations.Thefieldsfortheraceofthesuspectandofficersinvolvedareopen-ended,andnofieldexistsforethnicity.Assuch,dataontheraceorethnicityofsuspectsandofficersmaybebasedoninconsistentterminologyfromreports.Inaddition,thedatamayundercountLatinxsuspectsorofficers,iftheofficerwritingthereportdoesnotconsiderLatinxtobearace.

F. VehicularPursuitsMCPDpolicyrequiresthatanytimeanMCPDofficerengagesinavehicularpursuit,asupervisorfromtheofficer’sdistrictmustcompleteaMotorVehiclePursuitReport(MCP610)andforwarditthroughthechainofcommandtotherespectiveassistantchief.Avehicularpursuitis“Anactiveattemptbyanofficerinavehicletoapprehendanoccupantofamovingmotorvehiclewhoexhibitsaclearintentiontoavoidapprehension.”31MCPDusesaMicrosoftAccessdatabasetostoredatacollectedfromMotorVehiclePursuitReports(MCP610).ThetableonthefollowingpagesummarizesthefieldsontheMotorVehiclePursuitReport.MotorVehiclePursuitReportsalsoincludeasupplementarynarrativeaswrittenbythesupervisor.NopublicdatasetsonVehicularPursuitsareavailableonDataMontgomery.DataLimitations.Thefieldsforthesuspect’sraceareopen-ended,andnofieldexistsforethnicity.Assuch,dataontheraceorethnicityofsuspectsmaybebasedoninconsistentterminologyfromreports.Inaddition,thedatamayundercountLatinxsuspectsifthesupervisorwritingthereportdoesnotconsiderLatinxtobearace.

31“VehicularPursuits,”FCNo.135,5/22/2009,MontgomeryCountyPoliceDepartment



Chart4.5:DataPointsCapturedinMCPD’sVehicularPursuitsReports

Suspect• Race• Sex• Age

Primarypursuingofficer• Name• Identificationnumber

Dateandtime• Date• Timestarted• Timeended

Location• Districtwherepursuitwasinitiated• Address/GPSlocationorcrossstreetstarted• Address/GPSlocationorcrossstreetended• WhetherpursuitextendedoutsideCountyboundaries• Categoryofarea(s)traveledthrough(commercial,residential,school/recreation,opencountry,

other)• Roadcondition(wet,dry,snow,ice,orother)• Trafficdensity(light,medium,heavy,other)

Policevehiclesandotherresourcesinvolvedinpursuit• Primaryvehiclestocknumber• Primaryvehicletype(markedorunmarked)• Whetherprimaryvehicleusedsiren• Whetherprimaryvehicleusedemergencylightsandwhichtype(e.g.dashlights,4-cornerstrobes)• Totalunmarkedandmarkedpolicevehicles• Additionalresourcesused(none,aircraft,otherdepartment,PMARS,stopstick,other

Notifications• Whethersupervisorwasnotified,timeofnotificationandsupervisornameandidentification

number• WhethertheDutyCommanderwasnotified,timeofnotificationandDutyCommander’snameand

identificationnumberReasonandresults

• Reasonpursuitinitiated(felony,DUI,assistinganotheragency,other)• Ifsuspectwasapprehended,how(e.g.voluntarilystopped,collision,roadblock)• Ifsuspectescaped,how(e.g.outranpolice,policevehicleincollision,pursuitorderedterminated)• Suspectcharged(felony,DUIorother)• Whetheracollisionoccurred• Ifacollisionoccurred,whetheritresultedininjuriesandifso,theirseverity• Whethernon-vehicularpropertydamageoccurred

Reviewofpursuit• Supervisor’srank,nameandidentificationnumber• Supervisor’sanswerto“Didthepursuitcomplywithdepartmentpolicy?”(YesorNo)• UnitCommander’srank,nameandidentificationnumber• UnitCommander’sanswerto“Didthepursuitcomplywithdepartmentpolicy?”(YesorNo)



G. MarylandStatePoliceAutomatedCrashReportingSystem

TheMarylandStatePoliceAutomatedCrashReportingSystem(ACRS)isthesystemusedtocollectdataonmotorvehiclecollisionsacrosstheState.ACRSreplacedthestate’spreviousmotorvehiclecollisionreportingsystem,theMarylandAutomatedAccidentReportingSystem(MAARS)in2015.MCPDpolicyrequirespoliceofficerstoconductcollisioninvestigationsandreportdatatotheStateforallseriousmotorvehiclecollisionsincluding:

• Fatalcollisions;• Collisionsthatresultedininjuries;• Collisionsassociatedwithanincarcerableoffensesuchashit-and-run;• Collisionsinvolvinggovernmentownedvehicles;• Collisionsafterwhichavehiclecannotbesafelydrivenfromthescene;and• Collisionsinvolvinghazardousmaterials.32

ACRSisahighlystructureddatabasewith165separatefieldsusedtodocumentdataoneverycollision,includingeachvehicleandeachpersoninvolvedorwhowitnessedthecollision.TheACRSFieldReportingGuideprovidesdetailsonthedataineachfield.Thefollowingprovidesahigh-levelsummaryofthedatapointsavailableinACRS:

• Crashelements.44fieldscaptureinformationonthecollisionandthecircumstancessurroundingit,includingthelocation,thetypeofcollision(e.g.head-on),androadandweatherconditions.

• Vehicleelements.41fieldscollectdetailsoneachvehicleinvolvedinthecollision,thedamagesustainedtoitandtheroleofthevehicleinthecollision.

• Driverelements.28fieldscaptureinformationoneachdriver,includingthedriver’saddressandphonenumber,whetherthedriverwasatfault,theirinjuriesandcondition,aswellastheresultsofanyalcoholordrugtests.Demographicdetailsarelimitedtodateofbirthandsex.

• Passengerelements.18fieldsdescribethepassenger,theiraddressandphonenumber,positioninthevehicleatthetimeofthecollisionandtheseverityofthepassenger’sinjuries.Demographicdetailsarelimitedtodateofbirthandsex.

• Non-motoristelements.28fieldscaptureinformationoneachpersonotherthantheoccupantofamotorvehicleintransport,suchaspedestrians,bicyclists,andoccupantsofstationaryvehicles.Thesefieldsincludetheperson’sdateofbirthandsex,addressandphonenumber,theirpositionandactionsatthetimeofthecollision,whetherthenon-motoristwasatfault,theseverityofanyinjuriesandresultsofanydrugoralcoholtests.

• Witnesselements.6fieldscaptureeachwitness’sname,address,andphonenumber.DataenteredintoACRSproducesaStateofMarylandMotorVehicleCrashReport,whichalsoincludesashortnarrativeandaccidentdiagram.Datalimitations.Demographicdetailsforpersonsinvolvedinmotorvehiclecollisionsarelimitedtodateofbirthandsex.ACRSdoesnothavefieldstoenterraceorethnicity.

32F.C.No.1021



ACRSDataAvailableonDataMontgomery.DataMontgomeryoffersthreedatasetsthatcontaindatafromACRS:“IncidentsData,”“DriversData,”and“Non-MotoristsData.”Eachofthesedatasetscanbelinkedwiththeotherdatasetsviaareportnumber.ThesedatasetscontainnumerousdatapointsfromACRSandthereforeprovideextensivedetailsonmotorvehiclecollisionsinMontgomeryCounty,includingtherolesofeachdriverandnon-motorist(e.g.pedestriansorcyclists).Thesedatasetsdonotincludeanyidentifyingordemographicinformation(suchasageorsex)fordrivers,non-motorists,oranyotherpersonsinvolvedincollisions.Thedatasetsalsodonotprovideanyinformationonpassengersorwitnesses.

H. ElectronicTrafficInformationExchange(E-TIX)StatelawrequiresthatlawenforcementofficersinMarylandreportinformationforeachtrafficstoptheyconduct,meaningwhenanofficerstopsadriverornon-motoristforaviolationoftheMarylandVehicleLaw.Thelawrequiresofficerstoreportspecificdatapointsincludingthegender,dateofbirthandraceorethnicityofthedriver.33LawenforcementagenciesmustreportaggregatedataontrafficstopstotheState.Ofnote,thefollowingtypesofstopsareexcludedfromthisreportingrequirement:

• Acheckpointorroadblockstop;• Astopofmultiplevehiclesduetoatrafficaccidentoremergencysituation;• Astopbasedontheuseofradar,laser,orvascartechnology;or• Astopbasedontheuseoflicenseplatereadertechnology,suchasaspeedcameraorred-light

camera.TheElectronicTrafficInformationExchange(E-TIX)istheelectronicsystemforissuingtrafficcitationsandtrackingdataontrafficstopsinMaryland,andismanagedbytheMarylandStatePolice.ThetableonthefollowingpagesummarizesthedatapointscapturedinE-TIX.E-TIXdatamayincludesomestopsthatareexcludedfromthereportingrequirement,suchasstopsbasedontheuseofradarorlaser,iftheofficerusedE-TIXtoissuethecitation.DataLimitations.MCPDstaffreportthattheCountydoesnothavefullaccesstoE-TIXreportingtools,becauseE-TIXisaStatesystem.Asaresult,MCPDislimitedinthenatureofthedataanalysisitcanconductwithE-TIX.Citationsbasedonspeedcamerasandred-lightcameras,whichcitethevehicle,notthedrive,arenotreportedinE-TIXanddonothavedemographicdataassociatedwiththem.E-TIXDataAvailableonDataMontgomery.TheDataMontgomeryTrafficViolationsDatasetincludesdataontrafficstopsfrom2012tothepresentthatresultedinacitation,warning,orsafetyequipmentrepairorder.ThedatasetincludesseveralfieldsfromE-TIX.Theseincludesomedemographicinformationonthedriver,includingrace/ethnicity,genderandthecitywherethedriverresides,informationonthestopitself,thesearchifapplicableandtheviolation.Thedatasetdoesnotinclude:

• Identifyinginformationonthedriverortheofficerthatconductedthestop;• Thedurationofthestop;or• Whetheranarrestwasmade.

Thedatasetalsodoesnotincludeanystopsthatdidnotresultinacitation,warningorsafetyequipmentrepairorder.

33MDCode,Transportation,§25-113



Chart4.6:DataPointsCapturedinE-TIX

Driver• Driver’sLicensenumber,classandstateofissue• Name• Address• Raceorethnicity(Asian,Black,Latino,White,orOther)basedonofficer’sobservation• Gender• DateofBirth• Height&Weight• Vehicleregistrationnumberandstate• Vehiclemake,modelandcolor

Trafficstop• Date• Time• Location• Duration• Whetherawarning,safetyequipmentrepairorder,orcitationwasissuedasaresultofthestop

andifso,thebasisforit• Whetheranarrestwasmadeandifso,thecrimecharged

Searchconducted(ifapplicable)• Whetherasearchwasconductedasaresultofthestop• Thereasonforthesearch• Whetherthesearchwasconsensualornonconsensual• Whetherapersonand/oraperson’spropertywassearched• Whetheranycontrabandorotherpropertywasseizedinthecourseofthesearch

Informationrelatedtotheviolation• Whethertheviolationcontributedtoanaccident• Whetherseatbeltswereused• Whetherapersonwasinjured• Whetherpropertydamageoccurred• Whetherafatalityoccurred• Whethertheviolationinvolvedhazardousmaterials• Whethertheviolationoccurredinaworkzone

Units/OfficersConductingtheStop

I. InternalAffairsDivisionDataTheInternalAffairsDivision(IAD)istheentityresponsibleforinvestigatinginternalandexternalcomplaintsofemployeemisconduct,andforimplementingandcoordinatingdisciplinaryactionsandproceduresinstitutedbytheOfficeoftheChief.TheLawEnforcementOfficers’BillofRights,aStatelaw,governssignificantaspectsofthecomplaintanddisciplinaryprocess.Theprocessmapbelowsummarizestheprocess.IADusesadatabasetocollectandtrackcomplaintsofemployeemisconduct.ThetableonthefollowingpagesummarizesthedatapointsinIAD’sdatabaseoncomplaintsofMCPDemployeemisconduct.



Chart4.7:MontgomeryCountyPoliceDepartmentDisciplinaryProcess

IADreceivescomplaintofmisconduct

IADforwardscomplainttoemployee'schainofcommand(minormisconduct)

Correctiveaction

Nocorrectiveaction

IADinitiatesformalinvestigation(serious

misconduct)

Complaintsustained

InternalInvestigationReviewPanelrecommendsfindingsanddisciplinaryactiontoChiefofPolice

Employeeacceptsfindingsanddiscipinaryaction

Employeeappealsfindingsordisciplinaryaction

Hearingboardhearscaseandissuesdecision

FOPemployeeelectsalternativehearingboard,whichhearscaseand

issuesdecision

Complaintnotsustained

IADdeclinestoinvestigatecomplaintbecauseitdeemsithasnomerit



Chart4.8:DataPointsCapturedintheInternalAffairsDivisionDatabase

Complainant(optionaltoprovide)• Name• Dateofbirth• Sex• Race• Phonenumber• E-mail• Streetaddress• Internalorexternalcomplainant

Allegation• Dateandtimecomplaintwasreceived• Dateandtimeofallegedincident• Addressorlocationofallegedincident• Witnessname(s)andcontactdetails• Therulethattheemployeeallegedlybroke

Employee• Name• Demographicdetailsfrompersonneldatabase

Disciplinaryprocess• InitialdeterminationbyIADastowhetherallegationisminor,seriousorhasnomerit• Finding(s)followinginvestigation

Thedatabasealsoincludesanarrativeofthecomplaintasreportedbythecomplainant.Datalimitations.IAD’sdatabasehasseverallimitations,aslistedbelow:

• Noinformationonthesourceofthecomplaint(e.g.phone,mail,in-person)isavailable;

• Staffreportthatmostcomplainantsdonotreporttheirrace.Althoughtheformusedtodocumentcomplaints,MCP580,hasaspacetolistthecomplainant’srace,IADstaffreportthattheyprefertonotrequestthatcomplainantsstatetheirracewhentheymakeacomplaintbyphone.

• InconformancewithrequirementsinStatelaw,informationonswornofficersassociatedwithminorallegationsareexpungedafteroneyear,andinformationonswornofficerswithseriousallegationsthatwerenotsustainedareexpungedafterthreeyears.Complainantinformationdoesnotgetexpunged.

• IAD’sdatabasedoesnotcontaininformationonthedisciplinaryactiontakenoranyappealsfiledbyemployeesthatarethesubjectsofcomplaints.

DataavailableonDataMontgomery.TheDataMontgomeryInternalAffairsAllegationsdatasetprovidesbasicdatapointsregardingcomplaintsreceivedbyIADsinceAugustof2013.Thisdatasetincludesthedateofeachallegation,whetheritwasaninternalorexternalcomplaint,thenatureoftheallegation,thestatus(activeorcompleted),andafindingforcompletedinvestigations.Thefollowingarelimitationsofthisdataset:



• Nodemographicdataoncomplainantsoremployeesareprovided;

• Nolocationdata,suchasthelocationoftheallegedincidentorthecomplainant’saddress(e.g.ZIPcode)areavailable;

• Theentriesfortherulethattheemployeeallegedlybrokedonotappeartobestandardized–forexample,24allegationsarecategorizedas“Discrim/Race/Sex”and46arecategorizedas“Discrimination/Harassment”,yetbothofthesecategoriesrefertothesamerule.

• ThedatasetdoesnotspecifywhethertheallegationwasdeemedminororseriousbyIAD,andauserwouldneedtobefamiliarwithIAD’sprocessestodeducethisfromthe“Findings”column;and

• Manyentriesareincomplete–forexample,148oftheallegationslistedas“completed”donothaveanassociatedfinding.

J. CommunityEngagementDivisionEventData

TheMCPDCommunityEngagementDivision(CED)trackseventsinthecommunitythatMCPDhosts,facilitates,presentsat,orattends.ThisdatasetishostedonDataMontgomery,andapersonmustbeaCountyemployeetoaccessthefulldataset.CEDtracksthefollowingdatapoints:

• EventName • Facilitynameand/oraddress • StartDateandtime • EndDateandtime• PoliceDistrict(s) • EventType • MCPDlevelofparticipation(hosted,facilitated/presentedat,orattended) • Targetaudience • Contactname • Contacte-mail

Datalimitations.Thisdatasethastwolimitations.First,theeventsinthedatasetcannoteasilybemappedgeographicallybecausethelocationinformationisnotpresentedinastandardformat.Second,sometypesofeventsmaybelistedinsomeyearsbutnotinothers.Forexample,thecorrespondingpublicDataMontgomerydatasetlists78“recruitment”eventsin2019,butno“recruitment”eventsintheprevioustwoyears.DataavailableonDataMontgomery.DataMontgomeryalsohostsacorrespondingpublicdatasetwithmanyofthesamedatapoints.ThepubliclyavailableDataMontgomeryPoliceCommunityEventdatasetdoesnotincludethefollowingdatapoints:

• MCPDlevelofparticipation • Targetaudience • Contactname • Contacte-mail



Chapter5. AvenuesforFutureDataAnalysisandReportingAsshowninChapter4,MCPDcollectsandstoresawiderangeofdatapointsinseveraldatasystems.Theseincludedataondispatchesofpoliceofficers,crime,useofforcebypoliceofficers,vehicularpursuits,trafficcollisions,trafficstops,andinternalandexternalcomplaintsaboutMCPDemployees.TheCouncilrequestedthatOLOdescribehowavailabledatacouldinformtheiroversightandongoingpolicymaking,withanemphasisonMCPDinteractionswiththepublicbytrackingrace,ethnicity,andotherdemographicfactors.ThischapterprovidesexamplesofanalysesthatcanbeperformedwithfourdatasetscurrentlyavailableonDataMontgomery.Itisorganizedasfollows.

• SectionAprovidesanoverviewofthedatasetsavailableonDataMontgomery;

• SectionBprovidesanexampleofanalysisfromthePoliceDispatchedIncidentsdataset;

• SectionCdescribesanexampleofanalysisthatcanbeconductedwiththeCrimesdataset;

• SectionDprovidesexamplesofanalysisoftheTrafficViolationsdataset;and

• SectionEsummarizesdataoncommunityeventsorganizedorattendedbyMCPDofficers.

A. OverviewofDataAvailableonDataMontgomeryDataMontgomerycontainstendatasetsrelatedtoMCPD,listedinthetablebelow.Thesedatasetsarederivedfromsevendifferentdatabases–theCAD,E*Justice,ACRS,E-TIXandtheIAD’sdatabaseoninternalandexternalcomplaints(seechapter4forinformationonthesedatabases).Asshownonthetablebelow,threeofthedatasetscontainsomedemographicdata.Nodemographicdataonpoliceofficersareincludedinthesedatasets.

Table5.1:DataonMCPDAvailableonDataMontgomery

Dataset Database DataFrom Updated DemographicData CY2019DataPoliceDispatchedIncidents

CAD April2017 4XDaily None 210,118incidents

Crime E*Justice July2016 Daily None 51,051incidentsofcrime

MCPDBiasIncidents E*Justice January2016 Monthly Suspectsbyage112hatecrimesorbiasincidents

CrashReporting-IncidentsData ACRS January2015 Weekly None 11,658crashes

CrashReporting-DriversData ACRS January2015 Weekly None

20,931driversinvolvedincrashes

CrashReporting-Non-MotoristsData

ACRS January2015 Weekly None 657non-motoristsinvolvedincrashes

TrafficViolations E-TIX January2012 Daily Driver’srace,ethnicity&gender

188,555violations

InternalAffairsAllegations IAD August,2013 Weekly None 521allegations

PoliceCommunityEvents

CommunityEngagement July,2016 Weekly None 2001events

DailyArrests CRIMS Past30days Daily Defendants’ageNone(dataremovedafter30days)

Source:OLOreviewofDataMontgomery



ThesedatasetscontainawealthofdataontheincidentstowhichMCPDofficersrespond.MCPDstaffcurrentlyconductsextensiveanalysisofcrimeandtrafficcollisions.Giventhedatacurrentlyincludedinthesedatasets,OLOfoundthatfourdatasets–PoliceDispatchedIncidents,Crime,TrafficViolations,andCommunityEvents–containthemostusefulinformationthatmightinformtheCouncil’soversightofMCPD.

B. ExampleAnalysisofPoliceDispatchedIncidentsAsdescribedinChapter4,theCounty’sComputerAidedDispatchSystem(CAD)isthesystemusedbytheEmergencyCommunicationsCenter(ECC)todispatchMontgomeryCountypublicsafetyservices,includingPolice,andFireandRescue,andtracktheiractivitiesduringtheresponse.TheDataMontgomeryPoliceDispatchedIncidentsdatasetcontainsdatafromtheCADonincidentstowhichpoliceofficersweredispatched.Asnotedabove,forcalendaryear2019thedatasetincludes210,118incidents.Toprovideadditionalcontextonthisdataset,thetablebelowsummarizesthetenmostcommontypesofincidentstowhichpolicerespondedin2019,ascategorizedattheendofthedispatch.

Table5.2:TenMostFrequentIncidentTypesinthePoliceDispatchedIncidentsDataset,CY2019

Incidenttype CY2019Incidents

Traffic/transportationincident 17,831Suspiciouscircumstance,personsorvehicle 15,161Disturbance/nuisance 11,149Alarm-residentialburglary/intrusion 11,032Trafficviolation 10,817Domesticdisturbance/violence 9,857Checkwelfare 9,521Noise 6,724Alarm-commercialburglary/intrusion 6,245Trespassing/unwanted 5,943

Source:OLOanalysisofDataMontgomeryPoliceDispatchedIncidentsdatasetThePolice-DispatchedIncidentsdatasetincludesinformationonthetimelineofthepoliceresponse,including:

• Secondsfromcallpickuptofirstunitdispatched• Secondsfromfirstunitdispatchedtofirstunitarrivedon-scene• Secondsfromfirstunitarrivedon-scenetolastunitcleared.

Theexhibitonthefollowingpagemapstheaveragenumbersecondsfromfirstunitdispatched,tofirstunitarrivedonsceneforeachoftheCounty’sadministrativeelectiondistricts.34

34ElectiondistrictsarerelativelylargesubdivisionsoftheCountyinwhichpollingplacesarelocatedandtowhichregisteredvotersareassigned(votersareassignedtoadistrictandaprecinct).In2020,MontgomeryCountyhas13electiondistricts(foradetailedmap,seetheMontgomeryCountyBoardofElectionswebsite:https://www.montgomerycountymd.gov/Elections/Resources/Files/pdfs/maps/UpdateYear/PrecinctswElectionDistricts2018.pdf).



Exhibit5.1:AverageSecondsFromDispatchtoPoliceArrivalByElectionDistrict,CY2019

ThesedatashowthataveragePoliceresponsetimesin2019rangedfrom618seconds,orabout10minutes,to1,220seconds,orabout20minutes.ThemapshowsthataverageresponsetimeswereshortestintheI-495andI-270corridors.Forfutureanalyses,thesedatacouldbefilteredbycalltypeand/ortheprioritylevelofthecall,mappedontosmallergeographicareas,and/oranalyzedforchangesovertime.Ofnote,thesedataincludeself-dispatchedincidents.However,theDataMontgomerydatasetdoesnotspecifywhetheradispatchresultedfromacallto911orifapoliceofficerself-dispatchedtoanincident.



A. ExampleAnalysisofCrimeDatasetAsnotedinChapter4,policeofficersarerequiredtowriteanincidentreportforincidentsofcrimeandotherevents,suchassuicideattemptsandmissingpersons.E*JusticeisMCPD’selectronicincidentreport-writingtoolandrecordsmanagementsystem.TheDataMontgomeryCrimesdatasetprovidesaccesstobasicdatapointsfromE*Justice,includingthelocationandnatureofcrimesthatMCPDpoliceofficersinvestigated.Foreachincident,theCrimesdatasetliststhespecificcrimethatwascommitted.Forexample,thechartbelowprovidesdataonincidentsofmarijuanaandtrespassingoffensesfromcalendaryears2017to2019.ItshowsthatMCPDinvestigationsofmarijuanaoffensesdecreasedfrom2018to2019,apartfromasharpspikeinearly2019.Thischartalsoshowsfairlyconsistentincidencesoftrespassingovertime.Whilethesedatadonotprovideinformationonthereasonsforanyincreases,decreasesorstagnations,theyprovideastartingpointforbetterunderstandingMCPD’senforcementefforts.

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Chart5.1.MarijuanaandTrespassingOffenses,FY2017-CY2019

DRUGS-MARIJUANA-POSSESS TRESPASSING



B. ExampleAnalysesofTrafficViolationDataAsnotedinChapter4,theElectronicTrafficInformationExchange(E-TIX)isMaryland’selectronicsystemforissuingtrafficcitationsandtrackingdataontrafficstopsandismanagedbytheMarylandStatePolice.Statelawrequiresofficerstoreportspecificdatapointsontrafficstopsincludingthegender,dateofbirth,andraceorethnicityofthedriver.TheDataMontgomerytrafficviolationsdatasetincludesdataonindividualtrafficviolationsforwhichdriversreceivedacitation,awarning,orasafetyequipmentrepairorder(SERO).Thisdatasetincludesthedriver’sraceorethnicityandthedriver’sgender.Thissectionprovidesexamplesofanalysesthatcouldbeconductedwiththesedata.Inthefuture,thedatapointspresentedbelowcouldbetrackedovertimeand/ormappedgeographically.

1. EnforcementTrendsForeachviolation,theTrafficViolationsdatasetspecifiesthestatuteviolated.SimilartotheCrimesdataset,theTrafficViolationsdatasetthereforeoffersinformationontrendsinMCPD’senforcementofspecificareasofthelaw.Forexample,thechartbelowdisplaysthenumberofviolationsrelatedtopedestrians’rightsandrulesfrom2012to2019.Itshowsthatoverall,MCPD’senforcementactionsregardingrulesrelatedtopedestrians(suchasyieldingtoapedestrianinacrosswalk)increasedsteadilybetween2012and2017andthendecreasedsharplyin2018.Between2012and2018,issuanceofcitationsdecreasedwhileissuanceofwarningsincreased.Whilethesedatadonotprovideinformationonthereasonsforanyincreasesordecreases,theyprovideastartingpointforbetterunderstandingMCPD’senforcementefforts.

Source:OLOanalysisofDataMontgomeryTrafficViolationsdataset,filteredforthosewithchargesrelatedtoMDTransportationTitle21,Subtitle5

2012 2013 2014 2015 2016 2017 2018 2019Citation 681 1,016 1,119 1,034 1,006 809 415 299

Warning 212 336 490 653 818 1,052 882 1,060

Total 893 1,352 1,609 1,687 1,824 1,861 1,297 1,359

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Chart5.2:ViolationsRelatedtoPedestrians'RightsandRules,CY2012-CY2019



Asimilartrendanalysiscouldbeconductedforotherrulesoftheroad,suchasrulesrelatedtospeedingorstoplights.

2. ViolationsbyRace,Ethnicity,andGenderTheTrafficViolationsdatasetoffersseveralwaystoanalyzeMCPD’sinteractionswiththepublicbyrace,ethnicity,andgender.Thissectionprovidessixexamplesofanalyses:

• Trafficstopsbyrace,ethnicity,andgender• Trafficviolationsbyrace,ethnicity,andgender• Numberoftrafficviolationsbyraceandethnicity• Percentageofviolationsthatresultedincitation,warningorsafetyequipmentrepairorderby

race,ethnicity,andgender• Percentagesofstopsthatresultedinasearchbyrace,ethnicity,andgender• Violationsbyrace,ethnicity,andstatute• Violationsbygeographicallocation,raceandethnicity.

TrafficStops.ThetablebelowcomparestheCounty’sadultpopulationtothenumberof2019trafficstopsbyrace,ethnicity,andgendertocalculatetrafficstopratesbysubgroup.ItshowsthatOtherandBlackmenhadthehighesttrafficstoprates(38–42%)followedbyLatinomen(25%)whileAsianwomenhadthelowestrates(6%).

Table5.3:TrafficStopsbyRace,Ethnicity,andGender,CY2019

DriverCharacteristics

AdultPopulation(18-64)

NumberofTrafficStops

%AdultStopped

Black 116,432 31,866 27.4%Female 62,045 11,285 18.2%Male 54,275 20,575 37.9%

White 282,509 38,151 13.5%Female 145,243 15,419 10.6%Male 137,235 22,730 16.6%

Latino 122,879 21,091 17.2%Female 60,722 5,908 9.7%Male 62,031 15,178 24.5%

Other 24,628 8,162 33.1%Female 12,579 2,689 21.4%Male 12,070 5,117 42.4%

Asian 93,360 6,706 7.2%Female 49,375 2,784 5.6%Male 44,005 3,920 8.9%

NativeAmerican 856 99 11.6%Female 427 36 8.4%Male 429 63 14.7%



TrafficViolations.Thetablebelowdisplaysthenumbersofviolationsandviolationsper1,000personsbyrace,ethnicity,andgenderduringcalendaryear2019.ThisanalysisissimilartothatpresentedbyMarkPastorduringapublichearingin2019regardingBill14-19.35ThesedatashowthatBlackdriversreceivedviolationsatthehighestrates(321violationsper1,000population),followedcloselybydriverswhoseraceorethnicitywasidentifiedas“Other”(319violationsper1,000population).

Table5.4:TrafficViolationsbyRace,Ethnicity,andGender,CY2019

DriverCharacteristicsViolations ViolationsPer1,000Population

All Citations Warnings SEROs All Citations Warnings SEROs

Black 60,970 23,222 35,563 2,185 321 122 187 12

Female 20,142 6,681 12,708 753 199 66 126 7

Male 40,817 16,537 22,848 1,432 461 187 258 16

White 60,834 19,664 38,994 2,176 132 43 84 5

Female 23,220 6,633 15,813 774 98 28 66 3

Male 37,611 13,028 23,181 1,402 168 58 103 6

Latino 43,098 19,098 21,915 2,085 215 95 109 10

Female 10,401 3,647 6,306 448 105 37 64 5

Male 32,685 15,440 15,608 1,637 323 152 154 16

Other 12,816 3,546 8,798 472 319 88 219 12

Female 4,104 1,044 2,909 151 200 51 142 7

Male 8,270 2,460 5,489 321 420 125 279 16

Asian 10,661 3,007 7,262 392 70 20 48 3

Female 4,269 1,054 3,074 141 53 13 38 2

Male 6,389 1,953 4,185 251 89 27 58 3

NativeAmerican 176 36 127 13 126 26 91 9

Female 55 6 46 3 79 9 66 4

Male 121 30 81 10 173 43 116 14Source:OLOanalysisofDataMontgomeryTrafficViolationsDatasetBasedonPopulationDatafromtheAmericanCommunitySurvey,20185-YearEstimates

35Mihill,A.,Memorandum:Bill14-19-Police,PolicingAdvisoryCommission–Established,November27,2019,MontgomeryCountyCouncil,©24-30.



NumberofViolations.Thetablebelowdescribesthenumberofviolationsissuedpertrafficstopbyraceandethnicityfor2019.ItshowsthatLatinxandBlackdriversweremorelikelytoearnsixormoreviolationsduringasingletrafficstopthananyotherracialandethnicgroup.

Table5.5:NumberofViolationsPerTrafficStopbyRaceandEthnicity

RaceandEthnicity 1 2to3 4to5 6ormore

Asian 41% 40% 12% 7%

Black 30% 37% 16% 17%

Latino 27% 35% 15% 22%

NativeAmerican 35% 36% 20% 9%

Other 42% 40% 11% 6%

White 43% 36% 12% 10%ViolationsResultinginCitations,Warnings,andSEROs.DataMontgomery’sTrafficViolationsdatasetcanalsoprovideinsightintothesharesofviolationsthatresultedincitations,warnings,andSEROs.Asshownonthechartbelow,Hispanicdrivers,especiallymales,receivedcitationsratherthanwarningsathigherrates,asashareoftotalviolations,thanotherpopulationgroups.

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

AllNativeAmericanNativeAmericanFemaleNativeAmericanMale

AllAsianAsianFemaleAsianMale

AllOtherOtherFemaleOtherMale

AllHispanicHispanicFemaleHispanicMale

AllWhiteWhiteFemaleWhiteMale

AllBlackBlackFemaleBlackMale

Chart5.3:PercentagesofViolationsThatResultedinCitations,WarningsandSEROs,CY2019

Citations Warnings SEROs



TrafficViolationswithSearches.TheTrafficViolationsdatasetalsoincludesdataonwhethertheofficer(s)conductedasearch.DataonsearchesareavailableforapproximatelytwothirdsofstopsinCY2019.Thetablebelowdisplaysthepercentageofstopsforeachgroupofdriversthatresultedinasearch.ThedatashowsthatBlackandLatinodrivers,especiallymales,aresubjectedtosearchesathigherratesthanothergroups.Thedataalsoshowthatoverhalf(54%)ofsearchesconductedduringstopsofBlackdriverswerebasedonprobablecause,whereasforWhiteandLatinxdriverstheshareofsearchesbasedonprobablecausewasunder40%.

Table5.6:CY2019TrafficViolationsWithSearchesConducted

Drivers’Race,Ethnicityand

Gender

%ofStopsWithSearch

ShareofSearchesbyReason

ProbableCause

IncidenttoArrest Consensual K-9

(Canine)

Alldrivers 2.6% 45% 37% 12% 4%

Black 3.8% 54% 26% 14% 4%Female 1.9% 53% 31% 8% 5%Male 4.9% 55% 26% 15% 4%

Latino 3.4% 37% 51% 9% 1%Female 1.5% 52% 40% 5% 0%Male 4.1% 35% 53% 10% 1%

White 1.6% 37% 41% 13% 6%Female 0.9% 38% 48% 6% 8%Male 2.1% 37% 40% 15% 6%

Other 1.4% 45% 41% 10% 3%Female 0.6% 40% 60% 0% 0%Male 1.9% 46% 38% 11% 3%

Asian 1.0% 34% 43% 18% 5%Female 0.4% 50% 38% 0% 13%Male 1.4% 31% 44% 22% 3%

NativeAmerican 0.0% 0% 0% 0% 0%Female 0.0% 0% 0% 0% 0%Male 0.0% 0% 0% 0% 0%

Source:OLOanalysisofDataMontgomeryTrafficViolations

ViolationsByStatute.TheDataMontgomeryTrafficViolationsdatasetliststhestatuteassociatedwitheachviolation.Thetableonthefollowingpagedisplaysnumbersofviolationsforthetoptenmostfrequentstatutesviolated,aswellaspercentagesofviolationsforeachstatutebyrace/ethnicity.Thedatashowthatthedistributionofviolationsbyraceandethnicityvariessignificantlydependingonthenatureoftheviolation.Forexample,Whitedriversaccountedfor42%ofspeedingviolationsbutonly18%ofviolationsrelatedtodrivingwithoutalicense.



Table5.7:ViolationsforTenMostFrequentlyCitedStatutes,CY2019

Descriptionofviolation Total White Black Hispanic Asian NativeAmerican

Other

Populationdata36 1,040,133 44% 18% 19% 15% <1% 4%

Violationsrelatedtoactionswhiledriving

Exceedingthespeedlimit37 22,772 42% 24% 18% 7% <1% 9%

Driverfailuretoobeytrafficcontrolsign,signal,markingordevice38

17,984 39% 28% 19% 7% <1% 8%

Failuretostopatstopsignorlineoryieldsignorline39

6,527 44% 23% 17% 9% <1% 8%

Driverusinghandstousetelephonewhilevehicleisinmotion40

5,005 40% 25% 20% 7% <1% 8%

Drivingvehicleinexcessofreasonableandprudentspeed41

3,764 38% 29% 20% 6% <1% 7%

Violationsrelatedtolicense,registration,orregistrationplates

Failuretodisplayregistrationcardupondemandbypoliceofficer42

8,036 32% 34% 20% 7% <1% 7%

Displayingexpiredregistrationplates43

5,277 39% 35% 14% 6% <1% 6%

Failuretodisplaylicensetouniformedpoliceondemand44

4,634 23% 36% 32% 4% <1% 5%

Drivingvehiclewithsuspendedregistration45

4,400 27% 44% 21% 3% <1% 5%

DrivingvehiclewithoutrequiredLicenseandauthorization46

4,226 18% 38% 39% 2% <1% 4%

Source:OLOanalysisofDataMontgomeryTrafficViolationsdataset

36AmericanCommunitySurvey2014-20185-YearEstimates;percentagesfor“White”and“Other”arefornon-HispanicWhiteandnon-Hispanic“Someotherrace”and“Twoormoreraces”,respectively.37Statutecited:MDCodeAnn.TransportationArt.§21-801.138Statutecited:MDCodeAnn.TransportationArt.§21-201(a1)39Statutecited:MDCodeAnn.TransportationArt.§21-707(a)40Statutecited:MDCodeAnn.TransportationArt.§21-1124.2(d2)41Statutecited:MDCodeAnn.TransportationArt.§21-801(a)42Statutecited:MDCodeAnn.TransportationArt.§13-409(b)43Statutecited:MDCodeAnn.TransportationArt.§13-411(f)44Statutecited:MDCodeAnn.TransportationArt.§16-112(c)45Statutecited:MDCodeAnn.TransportationArt.§13-401(h)46Statutecited:MDCodeAnn.TransportationArt.§16-101(a1)



StopsByGeographicalLocation.TheDataMontgomeryTrafficViolationsdatasetincludesgeographicdataforeachviolation/stop.OLOusedGISsoftwaretomaptrafficstopsbytheCounty’sadministrativeelectiondistricts.47Tables5.8and5.9showthenumberofstopsbydistrict,asstopsper100populationbydistrict,percentagesofstopsbytheraceandethnicityofthedriver,alongwithpopulationdataforeachdistrict.ThedatashowthatDistrict13(SilverSpring&Wheaton-Glenmont),theCounty’smostpopulousdistrict,hadthelargestnumberoftrafficstops.However,District11(Barnesville)andDistrict7(Bethesda,GlenEcho&Somerset)hadthemoststopsper100population.Ofnote,whilepolicestoppedBlackdriversatdisproportionateratesacrosstheCounty,policestoppedBlackdriversatparticularlydisproportionateratesinDistricts7(Bethesda,GlenEcho&Somerset),4(Rockville),and13(SilverSpring&Wheaton-Glenmont).

Table5.8:TrafficStopsByGeographicalLocation,CY2019

District Place(s) Population StopsStopsPer

100Population

13 SilverSpring&Wheaton-Glenmont 268,180 28,876 11

9 Gaith.,Mont.Vill.&SouthGermtwn 183,988 18,661 10

7 Bethesda,GlenEcho&Somerset 99,768 13,725 14

4 Rockville 128,906 13,592 11

5 BurtonsvilleandWhiteOak 112,658 8,036 7

2 Clarksburg&northGermantown 58,836 6,193 11

8 Olney&Brookeville 49,193 3,075 6

6 Darnestown&NorthPotomac 51,377 2,723 5

10 Potomac 37,196 1,886 5

1 Laytonsville 21,580 1,261 6

11 Barnesville 2,075 1,113 54

12 Damascus 19,696 945 5

3 Poolesville 6,680 454 7Sources:DataMontgomeryTrafficViolationsDatasetBasedonPopulationDatafromtheAmericanCommunitySurvey,20185-YearEstimates 47ElectiondistrictsarerelativelylargesubdivisionsoftheCountyinwhichpollingplacesarelocatedandtowhichregisteredvotersareassigned(votersareassignedtoadistrictandaprecinct).In2020,MontgomeryCountyhas13electiondistricts(foradetailedmap,seetheMontgomeryCountyBoardofElectionswebsite:https://www.montgomerycountymd.gov/Elections/Resources/Files/pdfs/maps/UpdateYear/PrecinctswElectionDistricts2018.pdf).



Table5.9:TrafficStopsByGeographicalLocation,Race,andEthnicity,CY2019

District Place(s) Percentageof Asian Black Latinx Native

American Other White

13 SilverSpring&Wheaton-GlenmontStops 4% 36% 27% 0.1% 8% 25%Population 9% 22% 27% 0.2% 4% 38%

9 Gaith.,Mont.Vill.&SouthGermtwnStops 7% 31% 24% 0.2% 7% 30%Population 16% 19% 29% 0.1% 4% 31%

7 Bethesda,GlenEcho&SomersetStops 6% 20% 12% 0.1% 8% 54%Population 9% 4% 8% 0.1% 3% 76%

4 RockvilleStops 9% 25% 17% 0.1% 9% 40%Population 20% 9% 15% 0.1% 5% 52%

5 BurtonsvilleandWhiteOakStops 5% 49% 19% 0.1% 6% 20%Population 15% 40% 18% 0.1% 3% 25%

2 Clarksburg&northGermantownStops 8% 30% 14% 0.2% 8% 40%Population 23% 21% 15% 0.3% 3% 37%

8 Olney&BrookevilleStops 6% 21% 13% 0.0% 12% 48%Population 12% 10% 10% 0.2% 4% 64%

6 Darnestown&NorthPotomacStops 13% 17% 12% 0.1% 7% 52%Population 30% 9% 10% 0.0% 4% 47%

10 PotomacStops 10% 13% 8% 0.2% 10% 58%Population 21% 6% 7% 0.0% 3% 62%

1 LaytonsvilleStops 6% 23% 16% 0.2% 11% 44%Population 13% 17% 13% 0.0% 4% 52%

11 BarnesvilleStops 3% 7% 2% 0.0% 4% 84%Population 5% 5% 4% 0.0% 1% 85%

12 DamascusStops 3% 18% 14% 0.0% 4% 62%Population 7% 8% 12% 0.1% 4% 69%

3 PoolesvilleStops 6% 10% 11% 0.0% 3% 70%Population 3% 7% 9% 0.7% 2% 79%

Sources:DataMontgomeryTrafficViolationsDatasetBasedonPopulationDatafromtheAmericanCommunitySurvey,20185-YearEstimates



C. PoliceCommunityEventDataThePoliceCommunityEventdatasetlistseventsinthecommunitythatMCPDhosted,facilitated,presentedat,orattended.ThisdatasetprovidesinsightintoMCPD’scommunityengagementefforts.Table5.10listseventsbyyearandthecategorylistedinthedataset.Thedatashow2,001eventsfor2019,significantlymorethanthoselistedfor2017and2018.Theincreaseinthenumberofeventslistedmayreflecttheinclusionofcertainevents(e.g.recruitment)thatwerenotincludedinthedatasetinpreviousyears.Whilethecurrentdatasetdoesnotallowforgeographicalmapping,eventscanbecategorizedbythePolicedistrictwheretheywereheld.

Table5.10:PoliceCommunityEventsbyType,2017-2019

EventCategory 2017 2018 2019Engagement 416 353 470SchoolEvent 303 301 462Prevention 189 236 350Training/Education 105 139 345CrimeUpdates/Trends/Awareness 154 139 150Chief/CommanderAdvisoryMeeting 42 36 51Recruitment 78Faith/InterfaithMeeting 13 26 26Award/Recognition 14 13 21TownHall 10 12 9Planning 30CountyCouncil/PSCMeeting 2NoCategoryListed 7Total 1,246 1,255 2,001

Source:OLOAnalysisofDataMontgomeryPoliceCommunityEventData



Chapter6: FindingsandRecommendationsThisreportrespondstotheCountyCouncil’srequestfortheOfficeofLegislativeOversighttoreviewanddescribeMontgomeryCountyPoliceDepartment’sdatasetsanddatapractices.ThisreportisintendedtoimproveCouncil’sunderstandingandoversightofMCPDoperationsbyhelpingtoinformtheCouncil’srequestsforMCPDdatawithanunderstandingofthemetricsittracks.GiventhisCouncil’sfocusoncommunitypolicing,racialequity,andsocialjustice,thisreporthighlightsMCPD’spolicingdatasetsthatdescribeMCPD’sinteractionswiththepublic.Severalsourcesofinformationwerecompiledandanalyzedforthisreport.Theseincludereviewsof:

• Researchliteratureonpolicingdatabestpractices,• Annualreportsofpolicingdatafromstateandlocalsources,• CodebooksforexistingMCPDdatasets,and• InterviewswithMCPDleadershipandstaff.

ThischapterispresentedintwopartstodescribefivekeyprojectfindingsandsixrecommendationsforCountyCouncilandMCPDaction.

FindingsFinding1: Bestpracticesrecommendlawenforcementcollectandmonitorpolicingdatathat

trackstheirpolice-communityinteractionsbyrace,ethnicity,andlocation.Whilelawenforcementagenciescareaboutanumberofpriorities,whatoftengetsprioritizedforperformancemanagementiscrimeprevention.Inresponsetothequestionof“WhatmetricsdoesMCPDtrack?”themostoftencitedansweramongvariousMCPDrespondentswascrimestatistics.JessicaSandersoftheRANDCorporation,however,warnsthatto“focusexclusivelyononegoalattheexpenseoftheothersistoinvitepoorperformanceonalternativegoals.”48Shewarnsthatinadditiontostatisticsonpropertyandviolentcrimes,policedepartmentsneed“performancemetricstoincentivizeanddemonstrateconstitutionalpolicingthatisbiasfree”andthat“placingallemphasisoncrimelevelscreatesadangeroustensionbecauseitoverlookspoliceofficersotherrolesandfunctionsthatshouldincludepolice-communityrelations.”49ResearcherssuchasSandersandothersfindthatbestpracticesfortrackingpolicingdatahaveemergedfromlessonslearnedamongjurisdictionsthathavebeenunderconsentdecreestoaddressbiasedpolicing.Inparticular,bestpracticesforcompilingandmonitoringpolicingdatahaveemergedfromtheexperiencesofNewYorkCityandLosAngeles’spolicedepartmentswhileunderfederalmonitoring.Thesejurisdictionscommittotwopolicingdatapriorities:

48JessicaSanders,TheRANDCorporation,PerformanceMetricstoImprovePolice-CommunityRelations,beforetheCommitteesonPublicSafety,CaliforniaStateAssemblyandSenate,February10,2015https://www.rand.org/content/dam/rand/pubs/testimonies/CT400/CT423/RAND_CT423.pdf49Ibid



• Compilingandmonitoringdataonpoliceinteractionswiththepublicbyrace,ethnicityandlocationforresidentsandpersonneltouncoverandtrackdisparitiesinpoliceinteractionswiththepublicthatmayresultfrombiasedpolicing.

• Collectingdataacrossfoursetsofpolice-interactionswiththepublic–

o Detentionsthatincludestops,searches,citations,arrests,anduseofforceincidents.Inparticular,dataaretrackedforallstopsandsearches,notjustthosethatresultinlawenforcement(e.g.,citation,summons,orarrest).

o Police-andResident-InitiatedContactsandTrafficAccidentstounderstandwhether

disparitiesamongtheseinteractionswithlawenforcementaccountfordisparitiesindetentionsifevidentbyrace,ethnicityandlocation.

o PoliceComplaintsthatdescribescivilianandinternalcomplaintsagainstpoliceemployees

byreason,disposition,andconsequence.

o Police-CommunityRelationsSurveysofresidentsandlawenforcementemployeesthatassessandmonitorperceptionsofpolice-communityinteractionsandtrust.

Finding2: MCPDcurrentlytracksseveralpolicingdatapointsandwilltrackmoreasrequiredundertheCommunityPolicingAct

Assummarizedinthechartonthenextpage,MCPDcurrentlycollectsbothcrimeandpolicingdataacrossseveraldatasetsthataremaintainedelectronicallyandbypaper.Ofnote,theDepartmentofCorrectionsandRehabilitationservesasthesourceofMCPD’sarrestdata,andphysicalrecordsofcivilandcriminalcitationsissuedbyMCPDaremaintainedattheirdistrictstationsandbytheDistrictCourt.ExcerptsofthecrimeandpolicingdatasetsthatMCPDcompilesandutilizesareavailableasopendatainDataMontgomeryandmarkedbydelta(Δ)onChart6.1.Theseincludedataon:

• Crimeincidents• Biasincidents• Police-initiatedevents(CAD)• Resident-initiatedevents(CAD)• Arrests

• Internalaffairs• Communityengagement• E-Tix(TrafficViolations)• AutomatedCrashReportingSystem

MCPDalsoreleasesannualreportsutilizingseveralofitsdatasetsasmarkedbyanasterisk(*)onChart6.1.Theseincludeannualreportson:

• Crimeincidents• Biasincidents• Internalaffairs

• Communityengagement• Vehiclepursuits• Useofforce



Chart6.1:MCPDDataSets

Category Database Datasets/Forms

ElectronicDataSets

CrimeData

E-Justice CrimeIncidents*ΔBiasIncidents*Δ

PolicingData

ComputerAssistedDispatch Police-InitiatedIncidentsΔResident-InitiatedIncidentsΔ

CRIMS(DOCR) Arrests*InternalAffairsDivision IADAllegations(PoliceComplaints)*ΔCommunityEngagementDivision CommunityEngagementEvents*ΔVehiclePursuits MCP610Forms*UseofForce MCP37Forms*DeltaPlus(StatePolice) E-Tix(TrafficViolations)Δ

AutomatedCrashReportingSystemΔFieldInterviewReports

DepartmentofJuvenileServices DataResourceGuide(JuvenileCitations)PaperDataSets

PolicingData

CriminalCitations(e.g.Trespassing) UniformCitationForm(DC/CR45)CivilCitations AlcoholBeverageViolation

PossessionofMarijuana(<10grams)SmokingMarijuanainPublicPlaceOtherinfractions(Municipal,DNR)

*MCPDpublishesannualreportsusingthesedatasetshttps://montgomerycountymd.gov/pol/crime-data.htmlΔMCPDdatapostedinDataMontgomeryhttps://data.montgomerycountymd.gov/Public-Safety/Crime/icn6-v9z3In2019,theCouncilenactedtheCommunityPolicingLaw(Bill33-19)requiringMCPDtoreportdataon:

• Useofforceanddetentionbyrace,ethnicity,andgender• Civiliancomplaintsagainstthepoliceregardingtheuseofforce,discriminationandharassment• Officerssuspendedwithandwithoutpay• Youthreferredtointerventionprograms• Servicecallsreceivedforsubstanceabuseandmentalhealthissues

MCPDmustsubmitdataontheseandothermetricsannuallytotheCouncilbyFebruary1stFinding3: SeveralMCPDpolicingdatasetsandpracticesalignwithbestpracticesMCPDcollectsandcompilesseveralpolicingdatapointsthatalign,atleastpartially,withbestpracticesformonitoringpolicingdata.Theseincludetracking:

• Detentiondatapointsbyraceandethnicityfor

o Trafficstops,trafficviolations,searches,andarrestsamongdriversandpassengersinE-Tix,o ArrestdatatrackedinCRIMS,ando UseofforcedatacompiledfromMCPForm37.



• Police-publicinteractionsdistinguishingbetweenpolice-andresident-initiatedcontactstrackedbyMCPD’sComputerAidedDispatchsystem;and

• PolicecomplaintstrackedbytheInternalAffairsDivision.Chart6.2summarizesthelocaldatasetsthatalign,atleastinpart,withpolicingdatabestpractices.Thedatapointsincludedinthesedatasets,however,areincomplete.Morespecifically:

• MCPD’sdetentiondatasetsdonottrackstreetstopsbetweenofficersandresidentsthatdonotresultinanarrest,citationorsummons;

• MCPDdoesnotmaintainanelectronicdatabaseofthecriminalandcivilcitationsthatitissuesthatwouldenablethemtomonitorfordisparitiesamongtheselawenforcementactions;

• ExistingformsandsystemsdonotconsistentlyrecorddataonethnicityandthereforelikelyundercountinteractionswithLatinxindividuals;

• RaceandethnicitydataarenotcollectedasfieldsintheComputerAssistedDispatch;

• Theinternalaffairsdatabasedoesnotcollectraceandethnicitydataforeverycomplainant;

• AMCPDdatasetofsurveyresponsesregardingpoliceandcommunityrelationshipsdoesnotexistbecauseMCPDdoesnotsurveyitspersonnelorresidents.

Chart6.2:MCPDDatasetsthatAlignwithPolicingDataBestPractices

Database Datasets/Forms DataLimits

DetentionMetricsDeltaPlus(MarylandStatePolice) E-Tix(TrafficViolations) Nodataonstreetstops

CRIMS(DOCR) Arrests


Other=Latinx/Asian

CriminalCitations UniformCitationForm(DC/CR45) DataatMCPDDistrictStationsandDistrictCourt

CivilCitations AlcoholBeverageViolationPossessionofMarijuana(<10gm)

SmokingMarijuanainaPublicPlace

UseofForce MCP37Forms

Police-PublicInteractionsComputerAssistedDispatch Police-InitiatedIncidents

Resident-InitiatedIncidentsNorace,ethnicitydataNodataonreferrals

DeltaPlus(MarylandStatePolice) ACRS(Collisions) Nodataonrace,ethnicity

PoliceComplaintsInternalAffairs IADAllegations Incompleteinformation



Finding4: MCPD’sinternaldatabasesoffermorecomprehensiveinformationthattheirannualreportsorDataMontgomerydatasets.

AsmentionedinFinding2,MCPDreliesonitsinternaldatasetstoproduceseveralannualreports,andtoprovideopendatatothepublicviaDataMontgomery.MCPD’sannualreportsandopendatasets,however,tendtoincludeonlyasubsetoftheinformationincludedintheirinternaldatabases.ThisisthecaseforarrestdatapostedonDataMontgomerythatonlyprovidesamonth’sworthofdataandexcludesdefendant’sraceandethnicity.ItisalsothecasewiththepolicecomplaintdatapostedonDataMontgomerythatitexcludescomplainants’raceandethnicityandalsofailstodescribetheconsequencesofcasedispositions.TheCommunityPolicingActrequiresthatMCPDprovidemoresubstantiveinformationondetentiontrendsbyrace,ethnicity,andgenderthatwillincludearrestdata.TheActalsorequiresthatMCPDprovideadditionaldataonthepolicecomplaintprocessthatincludesthenumberof:

• Civiliancomplaintsabouttheuseofforcebyofficers• Civiliancomplaintsregardingdiscriminationandharassment• Officerssuspendedwithpay• Officerssuspendedwithoutpay

AstheCouncilconsidersotherquestionsofMCPDinitsoversightrole,itshouldcontinuetoposequestionsdirectlytothedepartmentratherthantorelyontheirannualreports,orDataMontgomerydatasets,becausetheirinternaldatabasesoftenprovidemoreextensiveinformation.Finding5: Availabledataontrafficstops,trafficviolations,anduseofforceevidenceswide

disparitiesbyraceandethnicityinpolice-publicinteractionsTheStateofMarylandrequireseachlawenforcementagencytosubmitdataintoitsE-Tixdatabasedescribingpolice-interactionswiththepublictopopulatetheRace-BasedTrafficStopDashboardforeachjurisdiction.ThisstaterequirementmakesMCPD’strafficviolationsdatasetoneofitsmostcomprehensivepolicingdatasetsandinstructiveforanalyzingdisparitiesinpoliceinteractionswiththepublicbyraceandethnicity.TrafficStops:Ananalysisof2018trafficstopdataforMCPDandpopulationdatafortheCountybasedonestimatesfromtheAmericanCommunitySurveyshowsthatBlackdriversexperiencedasignificantlyhighershareoftrafficstopsinMontgomeryCounty.Morespecifically:

• Blackpeopleaccountedfor18percentofallresidentsv.32percentofMCPDtrafficstops;• Whitepeopleaccountedfor44percentofallresidentsv.35percentofMCPDtrafficstops;• Latinxpeopleaccountedfor19percentofallresidentsv.20percentofMCPDtrafficstops;• Asianpeopleaccountedfor15percentofallresidentsv.7percentofMCPDtrafficstops.

Ananalysisof2019trafficstopdatafurtherestimatesthat27percentofBlackadultsintheCountyexperiencedatrafficstopcomparedto17percentofLatinxadults,14percentofWhiteadults,and7percentofAsianadults.



SearchesDuringTrafficStops:Ananalysisofthe2018Race-BasedTrafficStopDataDashboardalsoshowsthatMCPDsearchedBlackdriversmoreoftenduringtrafficstopsthanotherracialandethnicgroups.Morespecifically,4.4percentofBlackdriversweresearchedin2018comparedto3.3percentofLatinodrivers,2.0percentofWhitedrivers,and1.3percentofAsiandrivers.Further,ananalysisof2019trafficstopdatashowsthatamongthosereceivingviolations,6-7percentofBlackandLatinomenweresearchedcomparedto2-3percentofAsian,WhiteandOthermen,and1percentofAsian,White,andOtherwomen.TrafficViolationEnforcement:MCPD’sTrafficViolationsdatasetpostedonDataMontgomeryenablesananalysisofMCPD’sinteractionswiththepublicresultingincitations,warnings,andrepairorders(SEROs)byrace,ethnicity,andgender.AnanalysisofthisdatashowsthatBlack,Latinx,andOthermenexperiencedthehighestviolationratesin2019.Morespecifically,

• BlackmenwerethreetimesaslikelyasWhitementoreceiveanyviolation(46%v.17%),Latinomenweretwiceaslikely(32%)andOthermenweremorethantwiceaslikely(42%).

• BlackmenwerealsothreetimesaslikelyasWhitementoreceiveacitation(19%v.6%),Latino

menweremorethantwiceaslikely(15%)andOthermenweretwiceaslikely(13%).

• OthermenwerenearlythreetimesaslikelyasWhitementoreceiveawarning(28%v.10%),Blackmenweremorethantwiceaslikely(26%)andLatinomenwere50%morelikely(15%).

• Black,Latino,andOthermenwerenearlythreetimesaslikelytoreceivearepairorderthanWhitemen(1.6%v.0.6%).

UseofForce:AnanalysisofMCPD’s2018useofforcedataandpopulationdatafortheCountyfromtheAmericanCommunitySurveyalsoshowsthatMCPDdisproportionatelyusedforceamongAfricanAmericans.Morespecifically:

• Blackpeopleaccountedfor18percentofallresidentsv.55percentofuseofforceincidents• Whitepeopleaccountedfor44percentofallresidentsv.26percentofuseofforceincidents• Latinxpeopleaccountedfor19percentofallresidentsv.18percentofuseofforceincidents• Asianpeopleaccountedfor15percentofallresidentsv.1percentofuseofforceincidents

ThepersistentdisparitiesbyraceandethnicitycapturedamongthefewMCPDpolicingdatasetswithcompletedemographicdatasuggestthatdisparitiesmaycharacterizeothermeasuresofpolice-communityinteractions.Inturn,pervasivedisparitiesbyraceandethnicityinpolice-communityinteractionsmaybesymptomaticofdifferentialpolicingthatisantitheticaltotheconstitutionandthegoalsofcommunitypolicing.Disparitiesinpolice-communityinteractionsdonotprovebiasedpolicing.However,theysignalthatunconstitutionalpolicingcouldbeaproblemthatneedstobeinvestigatedandaddressed.Collectingandanalyzingmorepolicingdatapointsbyraceandethnicityisnecessarytounderstandingthepotentialscopeoftheproblemofbiasedpolicingsothatitcanbeaddressedandresolved.



RecommendationsAsdemonstratedinthisreport,MCPDcollectsandtracksdataonseveralpolicingdatametricsthatalignwithbestpractices.Expertsrecommendthatpolicedepartmentsseekingtoadvanceconstitutionalandcommunitypolicingshouldtrackdataondetentions,police-andresident-initiatedcalls,complaintsofpolicemisconduct,andsurveysofpersonnelandthepublictoassesstheeffectivenessofpoliceefforts.Bestpracticesfurtherrecommendthatlawenforcementagenciestrackthisinformationbyrace,ethnicity,andlocationtoassesswhetherpolicedepartmentsareservingallresidentswell.MCPD’spolicingdatapracticesgenerallyalignwithrecommendedpractices,butthisreport’sanalysisidentifiesafewopportunitiesforimprovingalignment.TheyincludeMCPDcollectingandmonitoringdataonstreetstops(i.e.stopandfrisks)withpedestrians,surveyingpersonnelandthepublicregardingpolice-communityrelations,andmonitoringraceandethnicitydataforeverypolicingdatadataset.Toaddressthesegapsbetweenrecommendedandcurrentpractice,OLOofferssixrecommendationsforCountyCouncilandMCPDactionaimedatadvancingconstitutionalpolicing,communitypolicing,racialequity,andsocialjusticeinlawenforcement.Recommendation1. CountyCouncildefinetheterm“detention”intheCounty’sCommunity

PolicingLawtoincludeallstops,searches,citations,arrests,anduseofforce.TheCommunityPolicingActrequiresMCPDtoreportdemographicinformation“regardingindividualsdetainedbytheDepartment”annuallybyFebruary1st.Detainedanddetention,however,arenotdefinedinthelegislation.OLOrecommendstheCouncildefinedetentiontoincludeallstops(includingstopsandrisksthatdonotresultincitationsorarrests),searches,citations,arrestsanduseofforceincidentsfordatareportingpurposessothattheCouncilcanconsiderchangesacrossthesepolicingmetricsasitadministersoversightofMCPD’sconstitutionalandcommunitypolicinginvestments.Recommendation2. MCPDtrackandreportdataonstreetstops(stops&frisks)andfield

interviews.SomeMCPDinteractionswithnon-motoristsaredocumented;othersarenot.Topromotetransparencyandanimprovedunderstandingofpolice-interactionswiththepublic,OLOrecommendsthatMCPDtrackandreportallstopsandsearches,andprovideinformationandanalysisofthedataitcollectson“personsofinterest”aspartofitsFieldInterviewReports.Datareportedonstreetstopsandfieldinterviewsshouldincludedemographicdataonrace,ethnicity,gender,andlocation.Recommendation3. MCPDsurveyresidentsandstaffonpolice-communityrelationsandcontact.Buildingtrustandmutualaccountabilitybetweenlawenforcementandcommunitymembersisaprimarygoalofcommunitypolicing.Assessingprogressonthisgoalrequiresregularassessmentsofrepresentativegroupsofresidentsandlawenforcementpersonneltogaugewhethercommunityengagementeffortsareworkingasintended.Assuch,OLOrecommendsthatMCPDworkwithexternalpartnerstodevelopandimplementanannual/biannualassessmentofpoliceandresidentperceptionsofpolice-communityinteractionsandclimateandthattheysharethisinformationwiththepublic.Additionally,OLOadvisesthatMCPDadministerapolice-publiccontactsurveytoarepresentativesampleofCountyresidentstoimprovetheirs,theCouncil’sandthepublic’sunderstandingofhowresidentcontactswithlawenforcementmayvarybyrace,ethnicity,gender,andlocation.



Recommendation4. MCPDbuildcapacitytousepolicingdatatoadvancebestpracticesinconstitutionalandcommunitypolicing.

Tofocusoncrimeprevention,MCPDhasdevelopedaninfrastructurewherecrimeanalystssystematicallyexaminecrimedatatotargetMCPDeffortandresources.Tofocusonconstitutionalandcommunitypolicing,theCenterforPolicingEquityrecommendsthatpolicedepartmentsdevelopparallelinfrastructurestoanalyzeandactondataonpolice-communityinteractions.Theirrecommended“CompstatforJustice”approachparallelstheinvestmentpolicedepartmentshavemadeinusingcrimedatatotargettheircrimepreventionandreductionefforts.OLOrecommendsthatMCPDadopta“CompstatforJustice”approachbyassigningMCPDstafftocollectandanalyzepolicingdatatotargetMCPDeffortandresourcestoadvanceconstitutionalandcommunitypolicing.Recommendation5. MCPDcollectandreportraceandethnicitydataforeverypolicingdataset.MCPDcollectsraceandethnicitydataonmostmetricsofpolice-communityinteractions,butnotall.Forexample,accordingtoIADstaff,raceandethnicitydataforcomplainantsofpolicemisconductarenotroutinelycollectedorsolicited.Further,somepolicingdatasets,whiletrackingrace,failtotrackethnicityandinturnmayconflateoutcomesbetweenWhite,Non-HispanicandLatinxsubgroups.Analysesofdisparitiesbyraceandethnicitytotrackconstitutionalandcommunitypolicingcannotbeaccomplishedifdatasetsdonotcapturepolice-communityinteractionsbyraceandethnicity.OLOrecommendsthatMCPDcollectandreportraceandethnicitydataforeverydatasetitmaintainsinternallyandpostsonDataMontgomery.Recommendation6. MCPDpostadditionaldataandpolicingdatasetsonDataMontgomerythat

alignwithinternaldatasets,includingdataoncriminalandcivilcitations.TheinclusionofMCPDdatasetsintheDataMontgomeryopendataportalpromotestransparencyandtrustbetweenthepoliceandthepublic.Tofurtherthesetwocentraltenetsofcommunitypolicing–transparencyandtrust–OLOofferstworelatedrecommendationsforMCPDaction.• OLOrecommendsthatMCPDupdateitsarrestsandinternalaffairsdatasetspostedonData

Montgomerytoincluderaceandethnicitydata,morethanamonth’sworthofarrestdata,andinformationaboutallegationsandinvestigationoutcomesintheIADdatasetonDataMontgomery.

• OLOrecommendsthatMCPDcommittoaddingthefollowinginternaldatasetstoDataMontgomerytofurtherpromotetransparencyandtrustinpolice-communityrelations:

o Useofforceo Fieldinterviewreportso Juvenilecitationso Criminalcitations(includingtrespassingcitations)o Alcoholbeverageviolationso Possessionofmarijuanaviolations(lessthan10grams)o Smokingmarijuanainpublicplaces

MakingtheMCPDdatasetspostedonDataMontgomerymoreconsistentandinclusiveofthedatathatMCPDcompilesinternallywillenhancetheusefulnessofMCPDdatasetspostedtoDataMontgomerytotheCouncilandtothepublicatlarge.



Chapter7: AgencyCommentsOLOrecognizesandappreciatesthetechnicalcommentsofferedbyMontgomeryCountyDepartmentofPoliceChiefMarcusJonestodraftversionofthisreport.Thisfinalreportwasupdatedbasedonthisfeedback.TheChiefAdministrativeOfficer’scommentstoafinaldraftofthisreportareattached.

OFFICE OF THE COUNTY EXECUTIVE

Marc Elrich Andrew Kleine County Executive Chief Administrative Officer

Memorandum

July 17, 2020 To: Chris Cihlar, Director

Office of Legislative Oversight From: Andrew Kleine, Chief Administrative Officer Subject: OLO Draft Report 2020-9: Local Policing Data and Best Practices

Thank you for the opportunity to comment on the Office of Legislative Oversight’s (OLO) Draft Report 2020-9: Local Policing Data and Best Practices. We have reviewed the report, find it to be informative and insightful, and generally agree with the recommendations. The information from this report will be very useful in our Reimagining Public Safety initiative.

If you have questions or need additional information, please contact Caroline

Sturgis, Assistant Chief Administrative Officer, who will be coordinating all aspects of this report with our Reimagining Public Safety initiative.

I thank the Office of Legislative Oversight for its thorough and expert work on

this report.

cc: Fariba Kassiri, Deputy Chief Administrative Officer Caroline Sturgis, Assistant Chief Administrative Officer Dale Tibbitts, Special Assistant to the County Executive Debbie Spielberg, Special Assistant to the County Executive

Marcus Jones, Chief, Montgomery County Police Department Tiffany Ward, Chief Equity Officer Dinesh Patil, Assistant Chief, Montgomery County Police Department

Lo cal Polici ng Data and Be st Practices · No current reports, but the Community Policing Act...

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