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7 11 17 Work Session - St. Ambrose University / Dubuque Police Department Traffic Study AnalysisCopyrighted July 11, 2017 City of Dubuque Work Session - Bottom # 1. IT EM T IT LE:St. Ambrose University - Dubuque Police Department Traffic Stop Analysis SUM MARY:Representatives f rom St. Ambrose University and City staff will conduct a work session on the Dubuque Police Department Traffic Stop Analysis. SUGGEST ED DISPOSIT ION: ATTACHMENTS: Description Type St Ambrose University Traffic Stop Study Presentation- MVM Memo City Manager Memo Staff Memo Staff Memo TO: The Honorable Mayor and City Council Members FROM: Michael C. Van Milligen, City Manager SUBJECT: Work Session – July 11, 2017 St. Ambrose University Traffic Stop Study Presentation DATE: July 6, 2017 Chief of Police Mark Dalsing is transmitting information for the St. Ambrose University Traffic Stop Study presentation. _____________________________________ Michael C. Van Milligen MCVM:jh Attachment cc: Crenna Brumwell, City Attorney Teri Goodmann, Assistant City Manager Mark Dalsing, Chief of Police MEMORANDUM July 6, 2017 TO: Michael C. Van Milligen City Manager FR: Mark M. Dalsing Chief of Police RE: St. Ambrose University Traffic Stop Study Presentation INTRODUCTION This memo will briefly describe the Traffic Stop Study project conducted by St. Ambrose university. BACKGROUND As part of the FY17 budget process, the Dubuque Police Department submitted an improvement package for a traffic stop study to be completed by an independent researcher. This was approved as part of the budget. Traffic stops are an area of policing that often receives public scrutiny across the country. More than many other aspects of policing, officer discretion plays a primary role on whether to initiate action, and therefore traffic stops are more open to concerns about personal bias impacting an officer’s decisions. Officer training and internal analysis are tools we use locally to ensure proper police behavior, however, an independent external analysis will provide a transparent, objective review of our traffic stops and provide us information to use as we work to use best practices, as well as meet some of the recommendations identified in President’s Task Force on 21st Century Policing, which the Council has adopted as a priority. Dr. Chris Barnum and St. Ambrose University (SAU) have conducted similar studies in Iowa. Their methodology includes using multiple sources of data to establish baselines on population. Included in this data is the use of point-in-time traffic counts. SAU uses trained observers to gather the driving demographics at various times during the day in locations where traffic stops generally occur. This data is used in conjunction with census and other measured demographic data and is compared with department traffic stop data. A memorandum of understanding (MOU)/contract between the City and SAU was completed in September 2016. Dubuque Police Department traffic stop data was given to SAU in November 2016 and SAU observers monitored Dubuque traffic through the first several months of 2017. SAU researchers then analyzed all the data and have prepared a presentation on their findings. ACTION REQUESTED City Council review of the SAU presentation materials on the Dubuque Police Department traffic stops. Dubuque Police Traffic Study Executive Summary Prepared by: Chris Barnum Jackson Boland Austin Mack Marc McClory St. Ambrose University July, 2017 1 Dubuque Police Traffic Study In 2016, the City of Dubuque partnered with St. Ambrose University to develop and implement an analysis of the Dubuque Police Department’s traffic stop activity. The investigation focused on evaluating stops made by the DPD between January 1st, 2015 and December 31st 2015 and centered on two broad categories of discretionary police conduct: (i) racial disparity in vehicle stops—instantiated as racial differences in the likelihood of being stopped by the DPD and (ii) dissimilarities across racial demographics in the outcome or disposition of a stop. To evaluate the racial demographics of stops, our research team utilized driver-population benchmarks fashioned from roadside observations and census data. A benchmark should be thought of as the proportion of minority drivers on the roads in a given location. In plain terms, the benchmark is a standard that can be used to judge the percentage of minority drivers that should be stopped by the police when no bias is occurring. In Dubuque, the population characteristics of individual neighborhoods vary across the city. Consequently, it was necessary to form separate benchmarks for these distinct areas. The first step in the process involved strategically placing traffic observers in these neighborhoods or ‘observation zones.’ To facilitate this, we first isolated two key features of the city: (i) locations where the police tended to make a lot of stops and (ii) areas characterized by comparatively high concentrations minority member residents. Interestingly, in Dubuque these areas also happen to roughly parallel 2010 US Census Tracts. This was fortuitous because it enabled us to use the boundaries of US Census tracts as observation zones borders. The two figures below illustrate this by showing the locations and frequency-density of traffic stops made by the DPD and then the US Census Tract locations. Figure 1 2 Figure 2 Once the boundaries of the observation zones were determined, roadside surveyors were deployed to monitor traffic at several locales within selected zones. The observers watched traffic at various times of the day ranging from 7:00 am until 2:00 am. They surveyed vehicles seven days a week from mid-January, 2017 through mid-May, 2017. The observers logged more than 55,000 observations from fifty-three locations across the city. Analyses show that the traffic observers saw similar percentages of minority drivers on the roads both day and night.1 Additionally, results show a high degree of consistency between observers across all zones.2 These findings suggest that the benchmarks used for the analyses were a valid and representative sampling of the drivers on the roadways in Dubuque. The process of comparing police data to benchmarks is straight forward. It centers on identifying differences between the demographic percentages from DPD traffic stop data and benchmark information. Any positive difference between benchmark values and police data signifies disproportionality—or an over representation of minority drivers in the data. Although, 1 Roadside observers’ night and day observations values did not significantly differ within zones: t = 0.057; p = 0.95; 12 df. 2 Correlation r for inter-surveyor observations within each location equals 0.63 3 disproportionality can indicate bias or discrimination, it does not necessarily indicate bias. It is possible for disproportionality to occur for a number of legitimate reasons.3 The central feature of this methodology is a disparity index or weighted average. For each observation zone, the disparity index equals the difference between the percentage of minority drivers stopped and the benchmark value, weighted by the number of traffic stops made in the zone. When summed across all observation zones, this index gives a summary measure of disproportionality. The methodology makes it possible to track disproportionality by area, by time of day, by duty assignment and by individual officer. While the disparity index serves as a useful tool in assessing disproportionality, please keep in mind that the index value is only an estimate of disproportionality in police activity, not a cast-iron fact. This is because the index is predicated on benchmarks which are formed from samples of the drivers on the roads in a given area and time. And consequently like any sample, a benchmark is associated with a degree of sampling error. Stop Analyses Results: The disparity index value for the DPD indicates that on average, the 2015 level of disproportionality in traffic stops for the entire department was low at about 2% points above benchmark values. 4 Table 1 below gives the information used to calculate the disparity index.5 Table 1 information for calculating disparity index Department zone min % bench stops 1.1 0.13 0.11 312 1.2 0.11 0.11 320 1.3 0.18 0.15 633 4 0.03 0.05 266 5 0.24 0.2 680 6 0.14 0.12 174 7.1 0.25 0.12 103 7.2 0.12 0.1 143 9 0.12 0.08 346 11 0.05 0.05 523 99 0.08 0.05 1684 The differences in table 1 between the percentages of minority drivers stopped by the DPD (column 2) and corresponding benchmark values (column 3) are consistently small. Only in 3 Including differences between racial groups in driving behavior, vehicle condition, drivers’ license status and so forth. 4 T-tests show that census and final benchmark values did not significantly differ t = 0.99; p = 0.33; df = 18. However, in comparison to other areas, zone 1 census values tended to run substantially higher than roadside observations. Consequently, we broke census tract 1 into three smaller census block group areas and then modified the benchmark. 5 Zone 99 represents areas of Dubuque that were not included in our observation zones. These areas are generally well outside the city-center. Additionally, these areas are not included in the high density traffic stop areas of Dubuque (represented by blue in figure 1.). Analysis of US Census data indicates that the mean aggregate minority population in these areas is roughly 5%. This value was used as the benchmark in zone 99 areas. 4 zone 7.1 is there a substantial difference. However, comparatively few stops were made in this zone, so this difference did not substantively impact of the overall level of disproportionality. Comparisons of mean-differences show that the overall level of disproportionality is not significantly different from zero.6 In plain terms, the average level of disproportionality of 2% points above benchmarks falls within the margin of error and is likely not meaningful.7 Table 2 information for calculating disparity index based on time of day. Days Nights zone min % bench stops zone min % bench stops 1.1 0.12 0.11 170 1.1 0.15 0.11 142 1.2 0.08 0.11 219 1.2 0.17 0.11 101 1.3 0.17 0.15 393 1.3 0.20 0.15 240 4 0.02 0.05 214 4 0.06 0.05 52 5 0.24 0.2 330 5 0.23 0.2 350 6 0.11 0.12 101 6 0.19 0.12 73 7.1 0.23 0.12 64 7.1 0.28 0.12 39 7.2 0.11 0.1 106 7.2 0.14 0.1 37 9 0.10 0.08 176 9 0.15 0.08 170 11 0.03 0.05 349 11 0.07 0.05 174 99 0.07 0.05 1024 99 0.09 0.05 660 Table 2 above, breaks out information by time-of-day. The findings indicate that roughly 60% of all DPD stops were made during daylight hours. Additionally, calculations for the disparity index show virtually no disproportionality for stops made during the day. Here the disparity index value is less than 1% point above benchmarks and is well within the margin of error. The index value for stops made during nighttime hours is higher at roughly 4% points above benchmarks. Although this finding is significant in a statistical sense, substantively this value is generally lower than comparative disparity index values for other police departments in Eastern Iowa. Stop Outcomes Results: We used an examination of stop outcomes to assess disproportionality in citations, arrests and consent searches. As the name implies, a stop outcome gives information about the consequence of a stop. An example would be whether or not a driver received a ticket as a result of the stop. In what follows we measure disproportionality using an estimator called an odds ratio. This estimator is a measure of effect size and association. It is useful when comparing two distinct groups and summarizes the odds of something happening to one group to the odds of it happening to another group. Univariate odds ratio analyses shown in table 3 indicate that Dubuque officers were slightly more likely to ticket minority ticket minority drivers than others but were also significantly more likely to arrest minority drivers and to ask for consent to search their vehicles. 6 Unweighted t-test (t = 1.11, p = 0.27, df =20). 7 Although there are a number of ways to calculate margin of error, the one used for this analysis is simple and conservative—an unweighted t-test of comparison between means. 5 Table 3 department outcomes and odds ratios Outcome Odds-ratio citations 1.21 arrests (raw) 3.49 arrests (adjusted) 2.66 search requests 6.08 Care should be exercised when evaluating findings for search requests. Although the odds ratio of 6.08 is large, Dubuque officers asked drivers for permission to search very infrequently. In fact, this occurred only twenty times during the entire study period (or roughly once every 260 stops). This small sample size makes it virtually impossible to draw meaningful conclusions and should be taken into consideration when interpreting results. In addition, we were not able to exclude several potentially important alternative explanations when analyzing arrest data using univariate odds-ratios.8 For instance, raw arrest information from table 3 indicates that DPD officers were roughly three-and-one-half times more likely to arrest minority members as others on traffic stops (OR = 3.49). However, the limited nature of the data make it impossible to evaluate whether this level of disproportionality was a product of bias, other factors or a combination of both. We did however have access to one piece of information that may hint at an answer. When we excluded from the analyses stops that were made for warrant checks, we found that the odds ratio for arrests meaningfully decreased. This finding suggests multiple factors may account for arrest disproportionality. Finally, we also conducted multivariate logistic regression analysis to control for several important contextual variables—including time-of-day, age and gender of driver as well as stop location. Results of these analyses were substantively similar to those from univariate findings. This congruence suggests the univariate findings are reasonable. Findings from multivariate analyses also suggest that DPD officers stopped minority and non-minority drivers in similar proportions for moving violations and equipment violations. 9 Conclusions This study examined the traffic stop behavior of the Dubuque Police Department using data from 2015—more than 5000 stops. The investigation focused on two broad categories of discretionary police conduct: (i) racial disparity in vehicle stops and (ii) disparity in the outcome or disposition of a stop. Findings from the examination of disproportionality in vehicle stops show little or no evidence of disparity. Given that the term racial profiling is generally applied to contexts where the police use race as a factor in deciding whether to stop a vehicle, and that racial profiling routinely shows up as disparity in traffic stops, the results from this portion of the analyses provide no evidence that the Dubuque Police Department systematically engaged in racial profiling in 2015. However, in addition to profiling, racially biased policing can take other forms, including disparity in stop outcomes. Notably, the results of the analyses for stop outcomes do indicate some racial disproportionality in certain outcomes—specifically, moderate amounts in arrests and lesser amounts in citations. These findings may signify bias, however 8 These factors include things like whether the arrest was discretionary, potential differences in driving status or offending rates, and subject demeanor, etc. These factors could serve as competing explanations for the observed results. 9 The complete list of control variables include: time of day, gender of driver, age of driver, location of stop and type of violation (moving or equipment violation). Results of logistic regression are available upon request. 6 further work is needed to know for sure. Any future analyses should focus on assessing the likelihood of potential alternative explanations for the findings. Consequently, we suggest that the City of Dubuque conduct one additional round of analyses. The proposed work could utilize DPD traffic stop data from 2016 and 2017, with no updates to observational zone benchmarks needed. The additional analyses should take a two-prong approach by: (i) looking for trends or changes in disproportionality 10 and (ii) by developing and evaluating supplementary measures of arrests and counts of outcomes.11 The new measures should focus on identifying whether arrests were the result of discretionary police decisions and include more nuanced measures for counts of outcomes. 10 A good way to gain confidence in the truthfulness of a statistical finding (including the disparity index) is to look for changes in values across time rather than concentrating on single year values. 11 The current data set indicates whether a given outcome (such as a citation or arrest) occurred on a traffic stop but does not count the number of such events. Q,^ s O v +, �, U 4- Q 4_ a) CI Day verses Night Observations zone night day 1 0. 11 0.09 Night and day benchmark values are not significantly different 4 0.04 0.08 from one another (mean difference = 0. 001; t = 0. 057; p = 5 0.20 0. 17 0. 95) 7. 1 0. 10 0. 12 7. 2 0.07 0.08 9 0.07 0. 07 11. 1 0.06 0. 05 Correspondence between bench Ft census zon: 4 day-nite aveArAMMEIZI MIL % 0.10 0.11 0.26 4 0.06 0.08 0.04 5 0.19 0.20 0.2 Census and Max benchmark 7.1 0.11 0.12 0.12 values are not significantly different from one another 7.2 0.08 0.08 0.1 (mean difference=0.01;t= 0.47;p=0.64) 9 0.07 0.07 0.08 11.1 0.06 0.06 0.04 African American Population Densiti4I .' City o f Dubuque `', ; 1111% 14P -L..) __A.\\---\--it1/4 ......_ ac___. .. . . 1, . .... KENNEDY 1 \ - arc # 1 — ---,—.. . .... te , t tam L2, .„ 4) - '• 4.r 4y A if --- _. . 32t49 ril 0% "I ,d t At " 11111t4 ,',7 i`{-1 m - • C%'.1- ‘1111\II IIIair, VRY RD V >,./ �," SaaAA 9 �.;: N. . -ASB .r v Erin vrf�1� �` .i_R3�F 111 SII lig — �1t� z��/ ""'_: :,�, 1 . w, f it'll t ! \�� ' "s++ Pio 1 �►�o• PENNSYLVANIA AVE ' dfr�, AVE'�1`1 AA c' op MIDDLE RD r \ Isint.+y,���ilj1, 71.14 \ f N -/-i...-' as Cjilittir al ii 4:‘ 0 ji--.... , 7:724,..ti. 0 Pily , .. acillerilis- 00 . Ni.0.aier. • .3:eia II a �` ` / S Zone 1 adjustments •Break into block groups zone census obs 1.1 0.27 0.11 1.2 0.17 0.11 1.3 0.3 0.11 Final Benchmark Values in Observation Zones zone tench 2 1.1 0.27 0.11 1.2 0.17 0.11 1.3 0.3 0.15 4 0.04 0.05 5 0.2 0.2census and Final benchmark values are not 2 d signhicancly different from one another(mean 7.1 0.12 0.12� difference=0.032;t=0.995;p=0.33) 7.2 0.1 0.1 9 0.08 0.08 11.1 0.04 0.05 Naeuapuaq an al paiedwo sdols ada Stops compared to benchmark zone total stops 'white stops min % bench 1.1 312 270 0.13 0.11 1.2 320 286 0.11 0.11 1.3 633 518 0.18 0.15 4 266 259 0.03 0.05 5 680 519 0.24 0.2 6 174 149 0.14 0.12 7.1 103 77 0.25 ` 1 .12 7.2 143 126 0.12 1 r 0.1 9 346 303 0.12 1 0.08 11 523 499 0.05 1 0.05 99 1684 1554 0.08 1 0.05 Weighted average across all zones for department as a whole • Weighted Average = 0. 02 • A Low Level of Disproportionality • Estimated margin of error is greater than +- 0 . 02 . Days vs Nights Days Nights zone min % bench stops zone min % bench stops 1.1 0.12 0.11 170 1.1 0.15 0.11 142 1.2 0.08 0.11 219 1.2 0.17 0.11 101 1.3 0.17 0.15 393 1.3 0.20 0.15 240 4 0.02 0.05 214 4 0.06 0.05 52 5 0.24 0.2 330 5 0.23 0.2 350 6 0.11 0.12 101 6 0.19 0.12 73 7.1 0.23 0.12 64 7.1 0.28 0.12 39 7.2 0.11 0.1 106 7.2 0.14 0.1 37 9 0.10 0.08 176 9 0.15 0.08 170 11 0.03 0.05 349 11 0.07 0.05 174 99 0.07 0.05 1024 99 0.09 0.05 660 Disparity Index Summary • Days Nights Dept. Disparity 0.009 0.045 0.024 Stops 3146 2038 5184 Goat of the Study • Look for Disproportionality in : • ( 1 ) The decision to make a stop, and • (2 ) The outcome of the stop Discussion • ( 1 ) The mean level of disproportionality for the department as a whole is low at roughly 2 percentage points above benchmark values . Within margin of error • (2 ) The mean level of disproportionality for stops occurring during daylight hours is less than 1 percentage point above benchmark values . Within margin of error • (3 ) The mean level of disproportionality for stops occurring during nighttime hours equals roughly 4 . 5 percentage points above benchmark values . Just outside margin of error Stop Decision Conclusions • Analyses found no significant levels of disproportionality for the department as whole. Specifically, • Virtually no disproportionality in traffic stops during daylight hours ( 60% of all stops occur in daylight) . • Low levels of disproportionality during nighttime hours. Outcomes Citations, Arrests, and Search Requests Citations tickets no yes min 174 451 Odds Ratio = 1.21 white 1462 3133 - Minority drivers are slightly more likely to be ticketed on a traffic stop • M IIE o 17) •^ ra N L QJ 0- -0 -0 o N >IN O VI L U O 4-- i L • a) m 0 rl M Cral te L. a � a) L. - . C 4-+ L. a) •— •- a i 3 cti • • N ni II E o O , •^ m N N QQ) L QJ a -c _a O N i-, N L •U O � y_ 2 L cn C a 3 d• ril M k N1/40 1/4111 (I) LI) O M •. C to LI IC 14. rCS In C) cin C .� •_ •— L. a � 3 Search Requests search no yes min 616 9 Odds Ratio = 6 . 08 white 4589 11 Note: The number of search requests is very small. Therefore, the odds ratio may not be a valid indicator of disproportionality. The data show that the DPD request to search very infrequently. On average this occurs once every 260 stops. Reason for the Stop Moving or Equipment Violation ass „oat • N O . _0o o 4-J ra II � o la) on —• � c fie cu o V'1 cu a) a 1 '1 N V) 01 N >4 M C O 4-0 O = N ri 0 • on a) o 3 VI Q 11. • a.) r c a) v • V) a) �o -o Ocu >, a � •r- a Q •V C O O � n > .r� C O •III cn C 4—)i� N kip e-1 •O CD cj O 111 C N 4) E Q Q .^ .� w � E Definitions • Disproportionality • A difference between police data and an expected value • Stop Disproportionality • A difference between racial demographics of police stops Et a benchmark • Outcome • A difference in the racial demographics percentages of stop outcomes ^� W C a) The control variables used • Time of day • Gender • Type of violation • Age of driver • Location of the stop Logistic Regression Results outcome or The results of logistic regression show citation 1.28 * that controlling for important variables does not substantively change the results arrest 2.63 * for citations, arrests and search requests. However, the results also show search req 4. 11 * that racial differences in stops for moving a equipment violations become my 0 .98 no longer significant equip 1 .07 Conclusions • Very low levels of disproportionality in traffic stops. Minority member drivers are not over represented in stops. No evidence of racial profiling • Low levels of disproportionality in citations. White drivers and minority members drivers are nearly equally likely to receive a citation as the result of a stop • Higher levels of disproportionality in arrests. The odds that minority member drivers are arrested on a traffic stop are about two-and-a- half times higher than for white drivers. Decision to stop • We analyzed traffic stops made by the DPD between January 1st, 2015 and December 31st 2015 . • Goal: to compare the percentage of minority drivers stopped to a valid benchmark N0_ ID 0 rzs N 3 asi a) (L) a) illab NJ J `' '1144;;0, Traffic Stops -alk Z . City of DuI uqu - ii gJ ate. sigli o � •* J 32—a \ T r��, 'f_ Al I i' „,.....r...__(/ . \ I OF 1 ry 94?j: i1J147$P .��' •;;;aa;; F t E `p W fllu „ � � 14 -: 17 Iitd on Ilin ,w. �izm r q`y " c 000ce sr r<LL!� . . .e ► •�• • ; r yo a -7-Tri:_i_y____7- --I- -_ . -S a :-. , ,,1\., Y lea _Y - ; -.. ori:. / . >------') IlkLower Density Higher Density i NO H Dubuque City Limits DLIBbtE en City of Duuqu 2000 Censu racts U .c„ r. 49T9 t - S t Q' N. .4 / CI YMPF..: .P 'S k 11.117 © cA Qn` f eit: . t.C� 36oRTT > f): . Po .2"Lt1'R. �fi `i S 11.01 Wvc \ CTI _. .z qp-r p GE lu �t., '- v.VA" tqr _ R 2 .2,0,0ND I:Vtk nD rNc silR O P SPG • RR( . .n r3'ER so .47 �,. 1 ( C. a LOV.Tv.Ly- I l 01 C Il RK•jai L • 1 \ teat`\ DRow " tS t�p '\. , Q' \ v\ t w� • .If R 41\6i u+ Ii.0.9:�s` ;4�2i.Cr<' rhp � _ 14.70.% \101\ D)?f,Yli\\ it 54 C, . L 6-77, N • �,5T ` . \ , \ \` In C< ' V • .N:,\ \.tg a 49' �� 4GRACC T \ _� \ tyti4bsr N[t/NETT" _ \ r \b1 ,rs9 .. IT . f Ion / \ s rCI a- nlo�1 " ^ r_ . _ . nv : d 7.01: oGs}� R•xa. . o`\nttx• a000 ' U La-) N Q) on > : L •� N C 0 L.L -0 4- c (5 N I- 0 O 4--) 05 O Observation Locations and Numbers Zone Locations Observation 1 12 16722 Correlation r for inter-surveyor 4 4 1415 observations within each location equals 0. 63 5 12 16553 7.1 5 3525 7.2 5 4595 9 8 4261 11.1 7 8694 TOTAL 53 55765