March 10, 2016


Bias-Based Policing Administrative Review

Introduction

It is the policy of the Toledo Police Department that “Biased-based racial, ethnic, and gender profiling is an unacceptable behavior and shall not be tolerated.  The Toledo Police Department shall utilize various management tools to ensure that racial/ethnic and/or gender profiling does not occur.”  Bias-based profiling is defined as the stopping, questioning, detention, arrest, or other disparate treatment of any person based solely on their race, ethnicity, gender, gender identity or sexual orientation. 

There are several procedures in place to ensure that racial/ethnic characteristics are not being used by officers as a basis for traffic stops/suspect stops and to attempt to identify potential training and policy issues related to bias-based policing.  The first of these procedures is training department personnel on bias-based policing issues in the academy and during annual in-service training.  The bias-based training includes topics that ensure all citizens receive fair and equal treatment and that officers are making traffic stops, field contacts, or any other formal actions on the basis of probable cause or reasonable suspicion.  Additionally, officers who have had bias-based or discrimination complaints sustained against them are subject to remedial training and the disciplinary process.  Finally, an annual review of the department’s bias-based profiling policy is conducted, as is a review of the department’s practices concerning bias-based profiling.

Police Department Training

The Toledo Police Academy conducts bias-based profiling training to all trainees during the “Stops and Approaches” portion of academy training. Additionally, the Toledo Police Department Manual requires that all department personnel receive training annual on bias-based policing.  This training was conducted, as part of a fall in-service training, for all sworn officers from January 12, 2015 to March 19, 2015.


Police Department Policy

Directive 103.10 was written to be in compliance with CALEA standard 1.2.9.  The directive covers all aspects of bias-based profiling:  definitions, prohibitions against bias-based profiling, bias-based profiling training, corrective measures, and an annual administrative review.  The directive is in the Department Manual that is issued to all sworn officers and is also available to officers through the Toledo Police Intranet.

 

Bias-Based Citizen Complaints

All allegations of bias-based profiling by citizens are thoroughly investigated by the Toledo Police Department. In 2013, the Internal Affairs Section began to specifically track all bias-based complaints in the Administrative Investigative Management system.  Additionally, the department uses video recording systems in marked police vehicles to assist in the investigation of alleged bias-based profiling by officers.  The Toledo Police Internal Affairs Section reports that there were no citizen complaints that alleged bias-based actions in 2015. 

 

Analysis of Traffic Stop and Field Interview Data


The Toledo Police Department collects data from traffic stops by recording the disposition codes given by officers at the conclusion of the interaction.  These disposition codes denote the perceived race and gender of the driver of the involved vehicle, once contact is made with the vehicle’s operator, as well as, the actual disposition of the traffic stop (arrest, citation, or warning).  In the past, the yearly totals for traffic stop data would be obtained and compared to the Census figures for the City of Toledo.  However, aggregate percentages do not reflect racial or ethnic population density for geographical areas.  Many neighborhoods are predominantly made up of one race or ethnicity.  Consequently, the numbers of traffic stops conducted in these neighborhoods appear skewed when compared with the aggregate census data.[1]  Additionally, police departments distribute manpower based on population density, calls for service, and the amount of crime that has occurred in an area.  If a higher percentage of police officers are assigned to an area where the residents and drivers are predominantly one race or ethnicity, then there will be a higher rate of traffic stops for persons of that race or ethnicity.  Therefore, additional data has been compiled for this analysis, in an effort to complete a more thorough evaluation of the traffic stop/suspect stop data for the City of Toledo.

In this analysis, UCR crime rates, calls for service, distribution of manpower, and demographic data will be collected and divided by police beat.  This data will then be used to determine which beats (or sectors) are likely to have the highest rates of proactive enforcement.  Once these areas of proactive enforcement are identified, the census data will be used to determine the demographic groups residing in the beats, and therefore, most likely to be stopped.  This data will then be compared with the actual traffic stop and field interview (by sector/beat) data in order to determine if those findings are similar to what could reasonably be expected, given the information provided.  

Crime Rates

Toledo Police

Department 2015 UCR

Part 1-Violent Crimes



Beat

Aggravated

Assault

Arson

Murder

Rape

Robbery



Beat Total



Sector Total

Sector 1

110

33

3

0

6

26

110



  120

54

13

4

8

57

150

204

Sector 2

210

80

34

0

16

61

177



  220

80

16

2

14

43

235

346

Sector 3

310

97

27

1

16

63

220





320

46

7

0

10

61

153

328

Sector 4

410

42

19

5

15

40

174





420

78

27

2

14

55

222

297

Sector 5

510

47

4

0

9

51

99





520

29

8

0

8

59

124

215

Sector 6

610

49

15

1

15

54

133





620

95

26

2

5

68

215

330

Sector 7

710

79

46

2

13

68

225





720

65

26

1

17

64

198

381

Sector 8

820

50

6

3

11

35

154





830

58

10

1

10

53

163

237



The above data was collected by the Criminal Intelligence Section.  This table displays the 2015 U.C.R. part 1 violent crimes for the City of Toledo.  The greatest percentage of violent crime occurred in Beats 710 with 8.8%, 310 with 8.7%, and Beat 620 with 8.3%.  Conversely, the least number of incidents were found in Beat 110, which accounted for only 2.9% of the total number of incidents of violent crime, followed by Beat 520 with 4.4% and Beat 820 with 4.5%.  Though not included in the table above, it should be noted that, while the number of crimes decreased by 414 incidents from 2014 to 2015. The number of reported crimes increased in Beats 210 and 510, but decreased in every other beat.  The number of murders, in the city, increased from 23 in 2014, to 24 in 2015, with the largest number of murders occurring in beats 410 and 120.

Based on this information, the department would be expected to conduct proactive police activities in the areas with the highest rates of violent crime.  Therefore, the number traffic stops and suspect stops would be expected to be higher in beats 710, 310, 620, 210, and 420.  The department would also likely deploy a greater number of officers to these areas to carry out the proactive police activities.

Calls for Service

Total Calls for Service



Beat      Calls             Total by Sector

110        7,016
120        12,757                

19,773

210        8,320
220        11,217                

19,537

310        10,810
320        12,042                                                                        

22,852

410        10,491
420        12,095                                                                        

22,702

510        9,935
520        10,951                                                                        

20,886

610        12,526
620        9,344                                                                          

21,870

710        10,000
720        10,170                                                                        

20,170

820        12,349
830        13,885                  

26,234

 

“Calls for Service” data was collected from the Communications Bureau.  The data was recorded by “Reporting Areas” and then analyzed to determine what “Beat” that Reporting District is located in.  8 Sector had the most calls for service in 2015 and 1 Sector had the least.  3 Sector, 4 Sector, and 6 Sector were the next three busiest districts for calls for service. 

One factor used to determine manpower allocation is the number of “calls for service” by sector, received by the department from citizens. Based on the information in this table, it would be expected that more officers would be assigned to beats 830, 420, 820, 120, 220, and 610.  However, because the department’s first priority is to respond to and reduce the rate of violent crime, the UCR rate is likely a more significant factor in the number of officers assigned to a beat. 


Distribution of Manpower

The following graph shows the distribution of manpower for the Toledo Police Department in 2015, over a 24 hour time period.  Dates were randomly selected and the number of officers assigned for those days was collected for shifts 4, 5, 6, and 8.  A count was taken of each officer by beat and that number was then divided to get the average number of officers present for an entire 24 hour work period.   

On the dates that were selected, Beat 210 averaged the most officers present with 13.  The next highest averages were found in beats 410 and 710 which averaged 11.5 and 10 officers respectively.  The lowest average number of officers was found in Beat 120 with 7 officers per day and Beats 520 and 830 with 8. 

In addition to these officers, the department expanded its use of “intelligence led policing” in 2015.  This practice uses intelligence developed by the Special Operations Bureau-Criminal Intelligence Section to identify “hot spots” within the city where recent criminal activity is used to predict future incidents of criminal activity.  Departmental resources (Operations, Gang Task Force, and the Special Investigations Section) are then deployed to those areas in an effort to disrupt the criminal activity. As part of their patrol techniques, these units utilize traffic stops/suspect stops, as a means of preventing crimes from occurring and increasing the visible police presence in these high crime areas.  In addition to an increased number of officers, this approach has also led to an increased number of traffic stops, citations, and arrests, particularly in the areas of the city with the highest predicted rates of violent crime. The majority of this activity has focused on Beats 620, 710, 210, and 310 areas. This is in response to the high number of shooting incidents in these beats (as illustrated in the graph below).  

Therefore, a high percentage of traffic stops and field interviews would be expected in these beats in 2015.

Demographic Data

The data in the table below was collected using the United States Census data from 2010.  This data was divided along the police “beats” used by the Toledo Police Department. 

Census Data  by Beat

% White

% Black

% Hispanic

% Other

 
110

92.25

.39

3.67

3.69

 
120

78.8

12.57

4.37

4.26

 
210

39.52

42.43

9.25

8.8

 
220

72.24

15.26

6.68

5.82

 
310

33.29

54.53

5.86

6.32

 
320

33.22

52.12

7.67

6.99



410

75.45

7.81

9.43

7.31



420

71.33

9.44

10.71

8.52



510

90.56

3.59

2.47

3.38



520

87.51

6.17

2.37

3.95



610

64.72

27.82

2.06

5.40



620

17.54

76.86

1.72

3.88



710

45.08

41.08

6.95

6.89



720

66.43

13.04

11.15

9.38



820

67.23

23.94

2.92

5.91



830

82.21

9.51

3.19

5.09



 

In the table above, the majority of white residents were located in beats 110, 510, and 520.  The beats with the largest percentages of black residents were 620, 310, and 320. The majority of Hispanic residents were found in beats 720 and 420.

There were two issues that were discovered when the data for this table was being analyzed.  First, the computer program that was used to produce the data below was not able to completely separate one police beat from another.  The program used census data compiled by zip code.  When a designated area (police beat) was selected for analysis, it included all of the demographic data for any zip code which fell within the selected area.  This resulted in several areas of the city being counted more than once, as police beat and zip codes overlapped.  As a consequence, the program calculated that the total population for the City of Toledo was 587,737, when the total population of each beat was combined.  The actual population for the City of Toledo in 2010 was 287,208, according to the United States Census Bureau.  However, the resulting demographic percentages are believed to closely represent the actual demographic percentages of the police beats.  This is based on the experience of officers, who have worked in those areas, and because there is a lack of viable alternative data, the table was still used for the purposes of this comparison.

The second issue discovered was the effectiveness of using census data, as a benchmark or baseline.  Census data provides the actual number of residents, in an area, and not the demographics of the actual drivers.  Also, according to a report produced by the National Organization of Black Law Enforcement Executives entitled Racial Profiling ‘What Does the Data Mean’?  “The census is also known to have high “miss” rates in the minority community, and like all statistical studies, the census also has an error rate.”  So, the possibility exists that actual demographic data in the areas most affected by this analysis, may be underreported. Finally, this census data is now four years old and has likely changed since this table was completed.

These demographic maps were located on City-Data.com and represent the percentage of African-American residents within beats 620 (shown above), 310, and 710 (shown below).  The more intense the color green, the higher percentage of African-American individuals residing in that area. 

With the higher percentage of proactive police activities conducted in theses beats in response to the U.C.R. data along with “hot spots” developed by the Criminal Intelligence Section, it would be expected that a large percentage of residents in these areas would represented in the city’s traffic stop data.

This chart displays the number of traffic stops that have occurred in each Toledo Police Beat in 2015.  The largest number of traffic stops occurred in 620’s beat (4,476), followed by beats 310 (4,211) and 220 (2,428).  The fewest number of traffic stops occurred in beats 510 (1,110) and 110 (1586).

As expected, beats 620, 310, and 710 each had a large percentage of the city’s traffic stops.  This is likely due to the additional proactive police activity that occurred in these beat. 

The above chart displays a comparison of the percentages of the total UCR rates, Calls for Service, and Traffic Stops in each sector. For example, Sector 1 had 9.4% of the UCR crime, 10.4% of the Calls for Service, 10.0% of the traffic stops conducted, and 9.2% of the officers assigned in the City of Toledo for 2015.

Traditionally, it would be expected that the percentages displayed in the chart would be proportional, and the percentage of Calls for Service, Crime Rates, Number of Traffic Stops, and Officers Assigned would be similar by sector.  In 2015, most of the percentages appear to be proportional.  The percentage of traffic stops conducted in 6 Sector is higher than in the other sectors, but this percentage is lower than in previous analysis.

The graph above displays the demographics of traffic stops that have taken place in each sector.  For example, of the 4,907 traffic stops that occurred in 1 Sector 1,726 were of white males, 1,105 were of white females, and 1,236 were of black males.  Predictably, the racial makeup of the drivers subjected to traffic stops in each sector coincided with the demographic makeup of that sector. 

Another factor related to Bias-Based Policing is the detention or arrest of individuals based solely on that individual race, gender, or ethnicity once the stop has been initiated. 

Result of Traffic

 Stop

Stops Resulting in Warnings

Stops Resulting in Citations

Stops Resulting in Arrest

White Male

50%

36%

14%

White Female

50%

38%

12%

Black Male

47%

32%

21%

Black Female

50%

36%

15%

Hispanic Male

55%

31%

14%

Hispanic Female

54%

32%

15%

Other

54%

42%

4%



The table above displays the dispositions of traffic stops divided by race and gender.  For example, the first row shows: of all white males subjected to traffic stops in 2015, 50% received a warning, 36% received a citation, and 14% were arrested.   

In 2015, it appears the majority of disposition percentages are similar when compared with the other categories, with the exception of those stops resulting in arrest.  Black males were arrested at higher rate than other groups in this category.  It is important to note that “arrest” in this category does not necessary indicate an individual was placed into custody and transported to Lucas County Corrections Center.  This usually occurs for arrest warrants involving some degree of violence and most on-view arrests.  Instead, the majority of individuals arrested on outstanding warrants during a traffic stop receives a Recognizance Summons from officers and are released at the scene.  These percentages are consistent with those found in previous analysis.

It should also be noted that an officer’s discretion is removed in instances where the driver has a valid arrest warrant, which removes the opportunity for bias to occur.  Currently, the department does not have the ability differentiate if an arrest stemming from a traffic stop occurred for an arrest warrant or for an on-view charge.  The department did attempt to address this issue by ordering officers to give a disposition code following each arrest that would designate if the arrest was for an “on-view” or “warrant.”  However, the order was not issued until several months had elapsed in 2015 and it appeared to take several months to gain compliance from officers.  Therefore, the information collected did not appear reliable enough to be used for this analysis.
2015 Field Interviews

By Race/Gender







110







120







210







220







310







320







410







420







510







520







610







620







710







720







820







830







Total

White Male

47

57

38

84

9

31

67

81

51

32

24

9

19

40

17

31

637

White Female

13

8

7

17

6

3

17

16

4

3

5

4

1

6

0

8

118

Black Male

20

47

91

109

60

74

67

46

31

25

77

74

66

44

40

29

900

Black Female

1

4

6

4

6

7

4

1

1

3

3

11

3

2

6

2

64

Hispanic Male

0

0

2

0

0

0

0

4

0

2

1

0

2

3

0

0

14

Hispanic Female

0

0

0

0

1

0

0

0

0

0

0

0

0

0

0

0

1

Other

0

1

0

0

0

1

0

0

0

0

0

0

1

0

0

0

3

Beat Total

82

117

144

214

82

116

155

148

87

65

110

98

91

95

63

70



Sector Total



199



358



198



303



152



208



186



133

1737


The table above displays the data for Field Interviews conducted by Toledo Police Officers, in 2015.  The data was compiled from Field Interview Reports completed by officers, after they were either, dispatched to a location with individuals involved in suspicious activity, or observed suspicious activity, while on routine patrol.  Though not represented in the above table, there were 267 fewer Field Interview Reports completed by officers in 2015 than in 2014.  The most Field Interview Reports were generated in 220’s Beat (214), 410’s Beat (155) and 420’s Beat (148).  The fewest number of reports were generated in Beats 520, 820, and 830.  Black males were the group that was recorded the most often on the reports totaling 900 (52%), followed by white males with 637 (32%).  Both of these figures are consistent with data that has been analyzed in previous years.  The suspect’s activity most often listed by officers, as the reason for the stop/report, was Suspicious/Gang activity.  Suspected Burglar/Prowler/Theft activity was cited as the second most frequent reason for the stop/report.  At this time, the department does not collect data related specifically to suspect stops and their dispositions. 

After analyzing the data above, race and gender do not appear to be factors in which individuals are stopped for Field Interviews by Toledo Police Officers.  Generally, the sectors where the most Field Interviews were conducted mirror the areas of the city with the highest rates of violent crime and calls for service.  The department generally assigns more officers to patrol those areas with higher rates of crime and calls for service, therefore more field interviews are expected to be conducted in those areas. 

Conclusion

The Toledo Police Department is proactively combating bias-based policing issues through the use of department policy, training of officers, thorough investigation of complaints, analysis of traffic stop data, and the annual review of all topics relating to bias-based policing.  The Toledo Police Department Manual clearly states, that “bias-based policing will not be tolerated by officers.”  The consequences, for officers found to be in violation of this policy, are remedial training and/or disciplinary action.  These issues are reviewed, on an annual basis.  All Toledo Police Officers receive training on bias-based policing, prior to graduation from the Toledo Police Academy, and receive additional training, on an annual basis.  All allegations of bias-based policing, by officers, are investigated by the Internal Affairs Section.  Finally, analysis of traffic stop/suspect stop data is compared with the demographics, crime rates, calls for service, and any other pertinent data to ensure that any bias-based policing issues are identified and addressed, immediately. 

In the 2015 bias-based policing analysis, traffic stop data was compiled and analyzed.  It is important to note that, the traffic stop statistics gathered by the Toledo Police Department are compiled by recording the officer’s perception of the vehicle driver’s race/gender after a traffic stop has been completed and not what the officer perceived the race/gender of the driver was, prior to initiating the traffic stop.  Therefore, it is difficult to ascertain if officers are conducting traffic stops based on a driver’s race/gender, when it is not known if the officer could determine the driver’s race/gender, prior to conducting a traffic stop. 

In 2015, black male drivers were stopped 7% more often than any other group.  This percent decreased 5 percent from the 2014 analysis.  Once, stopped for a violation, drivers in the “other” (Middle Eastern, Asian, and other applicable groups) were most likely to receive a citation, and black males were most likely to be arrested; although, many of these citations and arrests may have been the result of following department policies relating to license violations and warrant arrests, instead of officer discretion.  This data is consistent with analysis from previous years, with the exception of “others” being the most likely to receive a citation.

Toledo Police Officers initiated over 1,600 fewer traffic stops in 2015, than in 2014.  The number of Field Interviews conducted by officers also decreased significantly from the previous year.  Although, there was a decrease in the number of traffic stops conducted by officers, the total remained high when compared to previous years. It appears that some of the traffic stop data from 2014 may have been skewed from the volume of traffic stop conducted in 6 Sector.  There were 3,000 fewer traffic stops conducted in 6 Sector in 2015 than were conducted in 2014.  This reduction appears to be due to the shift of focused proactive police enforcement from 620’s beat to other areas of the city in response to increases in violent crime or gang activity in those areas.  This led to increases in the number of traffic stops conducted in beats 310 and 710.

During the course of this analysis it was discovered that the number of dispositions from traffic stops did not match the number of traffic stops by beat.  This was due to duplicate calls for service, cancelled calls, general broadcast calls, and units giving disposition codes on calls that did not involve traffic stops being counted in the totals for traffic stop dispositions.  A meeting was held to address this issue and these results will be filtered to reflect only traffic stop dispositions in the 2016 analysis.

As in previous years, the demographic percentages found in both the Traffic Stop data and Field Interview data remained consistent.  Again, black male drivers were stopped at a greater rate than other demographic groups. However, when this data is compared to the demographics of Toledo Police “beats,” and the distribution of officers to those “beats” no patterns of police conduct were detected to indicate that the Toledo Police Department, or any of its police officers, are inappropriately using racial, ethnic, or gender characteristics, while conducting traffic stops.

Recommendations

The Toledo Police Department should continue to take proactive measures to ensure that its officers enforce laws and investigate criminal activity on the basis of probable cause or reasonable suspicion and not based on the race, ethnicity, or gender of the citizens they encounter.  The department should continue to train officers that all citizens receive fair and equal treatment, to thoroughly investigate all bias-based related complaints, and take immediate corrective action when required. 

The significant drop on proactive police activity in beat 620 had a substantial impact on the traffic stop data in 2015.  It appears that the most likely cause of this reduction is department’s increased use of the intelligence led policing model.  By following this model, the department is now using the “real time” analysis of information to determine how to best use the resources of the department.  It is recommended that all future bias-based analysis should include all relevant information regarding the deployment of proactive police activities in response to the data obtained while using the intelligence led policing model. 

Finally, the department began collecting data demographic data on drivers involved in traffic accidents within city limits.  This was done in an effort to determine the “driving population” in different areas of the city.  While some demographic information was obtained, not enough data was collected to provide reliable data.  The department should continue to gather this demographic information to determine if it would be possible to obtain reliable demographic data.


[1] Racial Profiling:  “What does the data mean?” Practitioner’s Guide to Data Collection & Analysis

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