HomeMy WebLinkAbout10-01-2012 Ad Hoc Diversity Committeer
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MEMORANDUM
Date: September 28, 2012
To: Ad Hoc Diversity Committee Members
From: Marian K. Karr, City Clerk
Re: Committee Packet for meeting on October 1, 2012
The following documents are for your review and comment at the next Committee meeting:
Agenda for 10/01/12 (page 1)
Minutes of the meeting on 09/24/12 (page 2 -8)
Materials re Police Department:
6 Email from City Manager (page 9)
• Five Year Arrest Summary (page 10)
• 2002 Police Traffic Stop Data Study (page 11 -55)
Census Data from Community Development Department
• Race /Ethnicity (page 56)
• Hispanic Population (page 57)
Material from Committee Member Roberts
• Press Citizen article "City Bus Route Changes: It's a win -win" (page 58)
Proposed list of recommendations (page 59)
THE PUBLIC IS INVITED TO ATTEND ALL MEETINGS
AD HOC DIVERSITY COMMITTEE
MEETING AGENDA
Monday, October 1, 2012
Helling Conference Room in City Hall
410 East Washington Street
4:00 PM
1. Approve September 24 minutes
2. Wrap up of Police discussion by Board
3. Transportation discussion among Board Members
• Identify issues and questions for presentation at next meeting
4. General Board discussion
5. Tentative Meeting Schedule
• October 8 — Transportation Dir. presentation
• October 15
• October 22
• October 29
6. Public Input
7. Adjournment
r/
Ad Hoc Diversity Committee, September 24, 2012
MINUTES DRAFT
CITY COUNCIL AD HOC DIVERSITY COMMITTEE
SEPTEMBER 24, 2012
HARVAT HALL /CITY HALL, 4:00 P.M.
Members Present: Bakhit Bakhit, Cindy Roberts, Joe Dan Coulter, Orville Townsend,
Sr., Kingsley Botchway II (4:15), Joan Vanden Berg, LaTasha
Massey
Staff Present: Eleanor Dilkes, Marian Karr, Tom Markus, Sam Hargadine, Rick
Wyss, Jim Steffen
Others Present: Charlie Eastham
WELCOME AND INTRODUCTIONS:
Karr noted that Chairperson Botchway would be late to the meeting this evening and that
Roberts should go ahead and begin the meeting. She then introduced the new
Committee Member, LaTasha Massey, who will be filling Donna Henry's unexpired term.
Roberts then asked that everyone present introduce themselves, and also welcomed
Massey to the Committee.
APPROVAL OF MEETING MINUTES:
Coulter moved to approve the minutes of the 9/17/12 meeting minutes as
submitted; seconded by Townsend. Motion carried 6 -0; Botchway absent.
PRESENTATION BY POLICE CHIEF:
Roberts welcomed the Police Chief and his staff back for a continuation of their
presentation and interaction with the Committee. Hargadine started off by saying he
would like to answer some of the questions posed last week. One question raised was
whether or not there had been a meeting between the Police Department and minority
taxi cab drivers. Hargadine stated that no such meeting has taken place. Another
question centered around what type of mechanism is in place for citizens to provide
input to the Police Department. Hargadine responded that the Department receives
input and comments from the public in several ways — email, phone, web page — and
that the Department is very open about communicating with the public. A third question
that Hargadine responded to asked if the Police Department ever holds public meetings
or forums, to which he gave several examples of the Department's involvement to
invitations offered.
Roberts then asked the Chief if the Department's complaint form and process are
reviewed as part of their accreditation process or if it is reviewed separately. Hargadine
responded that it would be the policy governing the whole complaint process that would
be reviewed. Coulter asked what other data is reviewed during the accreditation
process, specifically statistical demographic data. Hargadine stated that their recruiting
is scrutinized by doing a racial and male /female breakdown which is then compared to
the Iowa City workforce. The review process really looks at whether the Department is
doing what they say they are doing. Massey asked how often the accreditation is done
and whether any upcoming public hearings in the future could be at a better time, to
hopefully allow public participation. Chief Hargadine responded that the review process
r(�- r
Ad Hoe Diversity Committee, September 24, 2012
is on a three -year cycle and that this is typically always done in late December, when
unfortunately the weather can be questionable. He added that they advertise at least a
month in advance about these public hearings but that other things seem to hold more
interest for people. Members asked if this accreditation process could take place at a
different time of the year. The response was that the CALEA organization has always
set the time, but that the Department could ask for a change.
Hargadine then shared a handout with Members which showed a list of the various
community outreach programs they are involved with. He then noted that Coulter had
asked several times at the last meeting for data. He invited Coulter to come to his office
where they could sit down and look at the Excel spreadsheet together and decide just
what data Coulter is looking for. Coulter responded that he was under the impression
that the Department would already have some type of reporting done at least annually
where certain data would be made available to the Council, for example. Coulter
continued, stating that the way the Department is collecting data is not how the federal
government does it. He shared a handout with Members, further clarifying how this
demographic data is to be collected per federal requirements. The issue of requesting
race and ethnicity was discussed, with Coulter stating that he does not understand how
the City cannot be conforming to state and federal requirements for this type of data.
Dilkes responded that the Census information in the Committee's packet came from the
Community Development Department, not the Police Department. She suggested that
the Committee pose these types of questions to that department instead. Hargadine
responded further to Coulter's questions and concerns about not collecting race and
ethnicity data on their forms. Karr clarified that now that staff knows what data the
Members are looking for, Community Development staff can supply by the next meeting.
The discussion continued, with Bakhit asking the Chief to clarify what 'total stops' refers
to (handout distributed 9/17 by Police). Steffen explained that whenever an officer with
the ICPD initiates a traffic stop, they are required to fill out a form, including race and
ethnicity, registration, address, and the sex of the individual, as well as the reason for
the stop itself. In 2011, Steffen noted that there were 13,177 traffic stops made by the
ICPD. Call -based traffic stops, where someone would call in to report a speeding car in
their neighborhood, for example, are not part of this stop information. A question was
raised about multi - racial individuals, and Chief Hargadine stated that it would be up to
the officer's judgment on this. Townsend asked if there is a mechanism that would show
if a particular officer was making more stops of a certain race. Hargadine stated that
there is a monthly report prepared for both traffic and arrests, and that, as he stated
previously, the on -dash camera videos are also reviewed to help eliminate this type of
bias. Steffen then spoke about the Department's 'early warning system.' He added that
there are a number of things that are monitored regarding officer behavior, things that if
left unchecked over time could become a problem. Performance evaluations and activity
reports are reviewed regularly to look for increases or decreases in particular areas,
such as citizen complaints, absenteeism, and even excessive force instances.
Massey asked for further clarification on this, specifically how a situation would be
handled if indeed an officer was found to have race and ethnicity issues. The Chief
addressed this question, giving the Members an example of an actual incident in town
involving a car stopped numerous times and the officer knew the driver was not the
owner and the license suspended. He explained how he was able to delve into this
issue, only to discover that it was the car's owner who was in fact in the wrong. Massey
followed up this question with another one, asking how the Chief would handle such a
-3-
Ad Hoc Diversity Committee, September 24, 2012
situation if indeed the officer was using racial profiling. Besides an investigation of the
incident, she asked if there is a system set up for training or some type of education for
the officer. Hargadine stated that to date they have not had a founded complaint of
racial profiling. He added that the ICPD does have a policy prohibiting this. There is
also continuing legal education for officers on this topic, letting them know what they
could expect if found guilty of this. The topic of race and ethnicity reporting was brought
up by both Roberts and Coulter, with Coulter further clarifying why he believes this is
important to do in the same manner across the board. Roberts stated that due to there
not being consistent race and ethnicity reporting, she believes they don't really have a
good mechanism for determining whether racial profiling is actually occurring or not.
Hargadine reiterated that, again, this is a very subjective thing for the officer to
determine.
Botchway asked about the Department's database, if when a stop is made and a
person's name is put in the system, if it doesn't already note their race and ethnicity.
Hargadine responded that he knows there is on a warrant, but that he is unsure on traffic
stops. Botchway added that if this were already available it would eliminate that
subjectivity on the officer's part. Botchway then brought the discussion around to the
survey done on the ICPD and whether the CALEA review brought any further insight into
the results. Hargadine and his staff responded to this, with Wyss stating that
somewhere on there it stated that due to the low return rate the results were probably
not valid. He also offered to get a copy of this survey for the Members, if they would like
to see what is asked. Every 100th call for service receives this survey, according to
Wyss.
The discussion then turned to the requirements for continuing education that each officer
must meet. Hargadine noted that many of these requirements, such as CPR, are
mandated yearly for the ICPD. There is also a continuing ed class on legal issues, such
as race -based stops, according to Hargadine, that must be met. Townsend brought up
the issue of the individual who complained about constantly being stopped, due to
expired registration, and how perhaps this is a financial situation. He suggested that
officers talk to people in such situations to see what is happening in their life and that
perhaps the person could be given a deadline in which to complete the registration,
instead of stopping them daily over the same issue. Hargadine responded that this is a
good point. Steffen asked how this could be verified, that officers don't know the people
they stop well enough to know if it is true or not. Townsend noted that the long -term
benefit of having this attitude would go a long way toward better relationships between
police and the community.
Hargadine attempted to clarify the totality of such a situation and how officers typically
would react. Townsend followed up by saying the point he is attempting to make is
community involvement between the police and the citizens, and how this can be
improved. Steffen noted that each officer has their own idea of discretion. He added
that some officers may feel that if they stop you, they will ticket you, whereas others may
not feel that way. He added that they cannot dictate what each officer does at a stop,
but that being consistent is the key. Wyss stated that they do encourage officers to use
discretion, and that quite a few warnings are given to citizens when warranted.
Botchway then asked about the PCRB and how the Police feel about its effectiveness.
He added that at the last public forum there was a lot of discussion about the PCRB not
having much 'power.' The Chief explained how the investigative process gets started,
Ad Hoc Diversity Committee, September 24, 2012
with both Steffen and Wyss addressing the mediation issue as it relates to the Police
Union. As for the power that PCRB has, or lack of, Hargadine stated that the PCRB is a
board compiled by the City Council that reports directly to the City Council. The Chief
stated that he believes that this does give the PCRB power, that the ICPD would not
want an unfavorable report given to the Council. In other words, the Department works
to accommodate any requests the PCRB may have and works with them to investigate
complaints. Townsend noted that public opinion shows a lack of trust in this
arrangement, and he asked if there isn't a way to increase the public trust. Hargadine
responded to these comments, stating that the ICPD does police themselves and that it
is his job as Chief to make sure this is done.
He further clarified the investigative process, noting the role of the PCRB. Townsend
stated that perception is still at play here that the public believes things are being done
that should not be done. He would like to see this perception changed.
The conversation continued on the topic of perceptions versus facts, and how this gap
could be filled. Botchway stated that the public tends to have more trust in the PCRB
than they do the Department. Hargadine attempted to further explain how the PCRB
receives complaints, and how the Department handles internal complaints. Internal
complaints are summarized for PCRB informational purposes. No internal complaints go
to the PCRB unless filed by the complainant separately. Townsend stated that although
he himself would back the ICPD, he feels he needs to know what process is in place to
handle complaints or large events — something that he could share with the community
to prove the ICPD is doing what it should be doing in handling such situations. Attorney
Dilkes added that the PCRB does have the ability to do further investigating of a
situation, beyond what the ICPD has done. Roberts asked for an example of something
that could be changed that would actually improve the PCRB's process. Botchway gave
some examples, noting ways to educate the public on the complaint process. Karr
added that another suggestion mentioned was when ICPD is doing the investigative
interview with the complainant that a member of the PCRB be present. This would allow
the PCRB to substantiate that the ICPD is following through. Hargadine gave several
examples of how during his investigations the PCRB does request things like videos and
numbers of participants, so they can follow up on the ICPD's investigation.
Coulter then reiterated what the process involves — that the PCRB receives quarterly
reports from the ICPD on all complaints and how they have been resolved. The PCRB
has subpoena power if they should decide to further pursue one of these complaints, but
Dilkes added that they have not used this power to date. Karr shared that at the last
public forum of the PCRB, statistics showed that since 1997, there have been 80
complaints filed. Of the 177 allegations, only six were sustained. Dilkes reminded the
Committee of the memo received in their first information packet regarding some of the
legal obstacles that come into play when talking about changing the structure of the
PCRB. She briefly reviewed the issue and what it would take to do this. Dilkes
suggested the group continue to discuss whether or not they think the current system is
broken; and if they believe it is, she would be happy to discuss with them what can and
cannot be implemented.
Chief Hargadine noted that PCRB meetings are open to the public, except when they
convene to Executive Session to discuss specifics of a complaint. He asked if this is
one of the things the public does not like. Townsend responded that what he is hearing
is that by the time the complaint information gets to the PCRB, the Police Department
has already done the investigation. He stated that he is asking for more involvement in
—5—
Ad Hoc Diversity Committee, September 24, 2012
the investigation, done by the ICPD, by someone outside of the Department. The Chief
explained that all of these investigations are videotaped, and that the PCRB can see
them at any time. Dilkes added that the current ordinance provides for a complainant to
be accompanied to the investigative interview.
Coulter stated that in light of the conversation centering on the PCRB and perceptions
there, he wonders where their focus needs to be in this process. He added that he is not
hearing concrete information that could be parlayed into recommendations from this
Committee, and questions what is it exactly that is 'not working' in the system.
Botchway interjected, stating that he sees from the information reviewed that there is a
great distrust of the ICPD. He added that it appears the public needs to be educated on
what the PCRB can do. As for the PCRB, the issue becomes what they are willing to do
when investigating a complaint. Roberts followed up by asking if this is a communication
issue. The Chief stated that the best way to explain the entire process would be for the
Members to see an investigation, beginning to end.
Vanden Berg asked what their exact charge is, adding that it appears the Committee
wants to change perception, yet the ICPD works on facts. She stated that they could go
around and around for a long time and not get anywhere, until they know what it is they
are attempting to do. Massey then addressed the group, stating that educating the
public on the entire process of how they can register a complaint needs to be done, as
she herself does not even know how to start such a process. She suggested that being
able to go to a place such as the substation in the Broadway neighborhood to file a
complaint might be a good way to educate the public on the process. Coulter spoke
next, stating that this Committee needs to make a list of the things they believe need to
be done. Coulter made a motion that public education about the PCRB process be
developed, and that this be part of the list that he expects this Committee will develop.
BOARD DISCUSSION:
After the Chief and his officers left the meeting, the conversation turned to Coulter's
motion to develop a public education piece. Townsend asked if a motion was truly
necessary, and receiving no second, the motion died. Karr stated that she will start a list
for the Members, adding this public education component to it. Botchway stated that
they should probably share their thoughts on all that they have read and heard the last
two meetings, perhaps doing this at the beginning of the next meeting. They could then
move on to preparing for the Transportation presentation. Vanden Berg stated that she
believes they are at somewhat of an impasse that the ICPD doesn't seem to see
anything they need to work on. Townsend asked that they all look at this from the
ICPD's point of view for a minute. He reminded Members that their job is to make
recommendations to the City Council. Coulter stated that another item on the list, in his
opinion, is the data that he originally requested. Botchway noted that the Chief said this
information is available, but that it would need to be compiled. Dilkes suggested this
group decide exactly what data they want to see, and what form they want to see this
data presented in.
Dilkes asked for clarification on the information being requested — race, ethnicity, etc. —
and Coulter attempted to state what he is requesting. He added that he would like to
also see what CALEA requires, and the state and federal entities, as well. Members
continued to share their thoughts on the ICPD's presentation, giving their opinions on
information they have heard thus far and what they hope to hear in other presentations.
Ad Hoc Diversity Committee, September 24, 2012
Coulter again stated that he needs to see real data — that until then it is difficult to move
forward on these issues. Vanden Berg stated that she believes this needs to be a
collaborative effort and others agreed. Roberts shared with Members some history of
past programs used for community outreach and which ones seemed to work well.
Coulter stated that he feels the Committee is making progress and he thanked the
others for their input during this difficult process. Chairperson Botchway asked that
Members give some thought to questions for the next presentation — Transportation. He
stated that he would like to do this in a more structured manner this time around.
Townsend asked what transportation issues there are, other than the complaints about
the bus stop area by Old Capitol Mall. He asked if they could have a list of
transportation issues to prepare for the next meeting. Karr added that in addition to
what's already been received, she will check to see if anything new has arisen.
Townsend threw out the suggestion that they invite the City Manager to a meeting and
get his take on this issue. City Manager Markus stated he attends all meeting and is
available as needed.
TENTATIVE MEETING SCHEDULE:
Roberts stated that she may be late to the next meeting due to travel, but hopes to still
make the meeting. Members agreed to retain the 4:00 PM start time for the meeting
Monday, October 1, 20121.
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Markus stated that he thought it was unfortunate that the ICPD left the meeting when
they did, missing the discussion by the Committee. He added that he believes the
Committee should be candid with the ICPD when they are present, that they need to
hear what Members are saying. He stated that staff will definitely obtain the data that
Members have requested.
ADJOURNMENT:
Coulter moved to adjourn the meeting at 6:30 P.M.; seconded by Bakhit. Motion
carried 7 -0.
7
Ad Hoc Diversity Committee, September 24, 2012
Ad Hoc Diversity Committee
ATTENDANCE RECORD
2012
Key.,
X = Present
O = Absent
O/E = Absent/Excused
-- = Not a Member
I
TERM
o
0
0
0
-
NAME
EXP.
N
CD
(0
N
o
(0
O
V
p
0000
Donna
03/10/13
O/
X
--
Henry
E
Cindy
03/10/13
X
X
X
X
Roberts
Joan
03/10/13
X
—X
X
X
Vanden
Berg
Bakhit
03/10/13
X
X
X
X
Bakhit
Kingsley
03/10/13
X
X
X
X
Botchway
Orville
0340113
X
X
X
X
Townsend
Joe Dan
03/10/13
X
X
X
X
Coulter
LaTasha
X
Massey
Key.,
X = Present
O = Absent
O/E = Absent/Excused
-- = Not a Member
I
Marian Karr
From:
Tom Markus
Sent:
Tuesday, September 25, 2012 12:35 PM
To:
Sam Hargadine
Cc:
Marian Karr; Eleanor M. Dikes; Geoff Fruin
Subject:
Follow -up to Diversity Committee Meeting
Sam: Based on the conversations from the meeting last night I would ask that you provide the following information to
Marian for distribution to the Committee in time for next week's meeting packet distribution.
1. State or Federal summary forms /reports that report and include statistical /demographic summaries of arrest
and traffic stop data that you are required to file. This is data that you regularly file not something specifically
created in response to the activities of this committee.
2. If there are none of the above reports then provide a memo to the committee that summarizes the data that
you do collect so that the committee can determine what summary and format of the information they want the
information in. In other words you need to deliver them a product so that they can tell you yes that is what we
are looking for or no we want the material in another format.
3. With respect the your handout of 9 -17 -12 summarizing your traffic stop data you need to explain the presumed
disparity between Black /African American stops relative to the ratio of the makeup of the city at large.
4. 1 have asked Marian to copy the consultant review of ICPD traffic stop data from 2002 est. You need to prepare
a review of this report and the conclusions from that report and what the ICPD has undertaken since that report
regarding ongoing reporting and what those traffic stop stats look like compared to the ratio of the makeup of
the city at large.
Marian: Please place a copy of this email and Sam's response with his reports in the next packet.
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i
IOWA CITY POLICE DEPARTMENT
410 EAST WASHINGTON STREET, IOWA CITY, IA 52240
(319) 356 -5275 1 FAX # (319) 356 -5449
"An Accredited Police Department"
Date:
August 7, 2002
To:
City Council
From:
RJ Winkelhake
Ref:
Police Traffic Stop Data Study
rte.._. .
The University of Louisville will present the results of the study of the Traffic stop
Practices of the Iowa City Police Department at the work session of the City Council on
the 191" of August 2002.
A copy of the report is in the Council packet.
Research Team
Terry D. Edwards, J.D.
Elizabeth L. Grossi, Ph.D.
Gennaro F. Vito, Ph.D.
Angela D. West, Ph.D.
University of Louisville
Department of Justice Administration
Brigman Hall, 2 "d Floor.
Louisville, KY 40292
(502) 852 -6567
June 13, 2002
Department:
*This report is confidential and is intended for the Iowa City Police Department to use as it deems
necessary. It is not to be distributed, quoted, or cited without the express written consent of the
authors, of Chief R.J. Winkethake, or others that the ICPD may designate.
--102
Executive Summary
This report summarizes the findings of a study conducted using data collected by the Iowa City
Police Department between April 1, 2001 and December 31, 2001. These data resulted from 9,702
interactions between law enforcement officers and citizens during traffic-related contacts.
Information was collected about the driver, the officer, and the stop event. Driver demographics
included race, sex, age, residency, and vehicle registration. The only information collected about the
officer was officer badge number. Finally, data collected about the stop event include the date, time of
day, "reason for stop," "search," "property seized," "force," and "outcome of the stop."
Data analysis was conducted with the aid of SPSS -11.0 (Statistical Package for the Social
Sciences). Analyses were conducted on two levels. First, descriptive analysis, using percentages,
summarized stop patterns, stop characteristics, and driver demographics. This information is useful only
to describe the existing state of affairs ("what is'), but not to explain them ("why ") or to formulate
predictions about future events ("what if"). To address the complex relationships that exist among
different variables, a program called "chi- square automatic interaction detector" or CHAID was used to
evaluate the variables in terms of their relatiopships with one another (multivariate analysis).
The greatest percentage of stops was made in the month of April (15 %), with the fewest in June
(9 0/6). Interestingly, 41% of stops occurred between midnight and 3am, with the third shift (I 1pm -lam)
responsible for the greatest percentage (54 %).
Stopped drivers were mostly White (84 1/6), male (65 9/o), young (median age of 23), Iowa City
residents (62 %), with Iowa vehicle registrations (86.5 %). Drivers were mainly stopped for moving
violations (69 1/o), were not searched (95 %), and were released with a warning (58 %).
The descriptive analysis indicated some slight percentage differences among the races in certain
events (e.g., stopped for equipment/registration violations). These percentage differences, however,
cannot be used to infer correlation or causation ("racial profiling "), To make these types of inferences,
multivariate analyses using CHAID were conducted. CHAID segments the sample of traffic stops and
reveals the interrelationship between the potential predictors and the events involved in the stop. The
CHAID procedure generates a "decision tree" that identifies significant predictors of each decision in
question. In effect, the procedure "cross- references" each event with each potential predictor.
Results from CRAM analyses resulted in only three events (moving violation, being warned,
being cited) with significant predictors. Being stopped for a moving violation was significantly related to
the age of the driver; the youngest and oldest drivers were most likely to be stopped for this reason.
Warned drivers were those least likely to have been searched, and cited drivers were those least likely to
have been stopped for an equipment/registration violation. Race of the driver never appeared as an
independent predictor of any event.
These data provide no empirical evidence that the ICPD is systematically engaging in
discriminatory stop practices. Stops conducted by the Iowa City Police Department, as a whole, during
the study period, do not involve the race of the driver as a significant factor related to events and
outcomes. This does not mean, however, that no individual citizen ever experienced discrimination. It is
always possible that individual officers may engage in racially biased practices, both in determining
which drivers they will or will not stop and in determining what steps to take after the initial contact. To
detect discriminatory practices at this level, however, requires constant vigilance by the community, by
all the officers within the department, and by the departmental administration. Statistical analysis, while
valuable, cannot substitute for community involvement and effective management.
The full report notes some minor problems with the data entry process, provides a discussion of
the "baseline dilemma," makes recommendations for continued study to obtain a full year of "clean" data,
and suggests modifications of the data collection instrument to include more variables (e.g., warrant
check information).
Table Of Contents
Introduction........................................................................ ..............................1,3
Methods ....................................................................................... ..............................3 -7
DataCollection ................................................................ ..............................3
Variables............................................................................. ..............................4
Collection and Measurement Concerns ............................... ..............................5
Analysesand Results ........................................................... ..............................6
Descriptive Analyses and Results .............................................. ..............................7 —16
Driver Demographics .................................... ..............................7
StopEvent .................................................. .............................10
Summary of Descriptive Analyses .................................... .............................16
CHAIDAnalyses and Results ...................................................... .............................16-23
CHAIDResults. ............................................................................................ 20
Reasonfor Stop .................................... .............................20
Outcome............................................ .............................22
Summary of CHAID Analyses .............................................. .............................23
The"Baseline" Dilemma ................................................................ .............................23
Legal Issues Relating to B1as\Racial Profiling Data Collection and Analysis ........ 26 - 29
Overview.................................................... .............................26
CivilLiability ............................................... .............................26
Disclosure of Information / Records ...................... .............................28
Conclusion................................................... .............................29
Conclusion and Recommendations . ...............................
.30-33
Bibliography......................................................... .............................34
Appendices.......................................................... .............................35
Appendix A: Iowa City Police Department Policy on Racial Profiling
Appendix B: Iowa City Police Contact Sheet
, �—/Z
Introduction
Racial Profiling
Accusations of discriminatory traffic stop practices ( "racial profilino have emerged as a
critical issue facing law enforcement. According to a 1999 Gallup poll and research conducted
by the American Institute of Public Opinion (2000), many believe that racial profiling is
widespread and disapprove of the practice of stopping motorists simply because the driver fits a
particular profile (Newport, 1999). In response to this growing concern regarding traffic stops
and a more general distrust of law enforcement personnel, many police departments across the
U.S. have begun to more closely examine their traffic stop policies and procedures. Further,
some police departments have begun collecting traffic stop data. The collection of traffic stop
data initially may appear to be a rather straightforward process. In reality, however, the
collection and analysis of traffic stop data is far from simplistic. A number of concerns must be
addressed by any agency contemplating such an endeavor. These concerns range from defining
the issues, developing data collection instruments and procedures, training personnel to collect
data, and determining the most appropriate means to analyze the data.
Defining Racial Profiling
The precise definition of racial profiling is a matter of debate. While no universal
definition exists, racial profiling is generally regarded as any act by law enforcement, whether it
involves motorists or pedestrians, based solely on the race of the alleged violator (FAmirez,
McDevitt & Farrell, 2000). In expanding on this broad definition, the U.S. Department of Justice
considers racial profiling to be "any police action that relies upon the race, ethnicity or national
origin of an individual rather than behavior of that individual that leads the police to a particular
individual who has been engaged in or having been engaged in criminal activity" (Ramirez,
—/�- �
McDevitt & Farrell, 2000). Accordingly, police may use race and ethnicity to determine if an
individual matches a suspect description but police may not use stereotypes when deciding who
to stop, to search, or make subject to other stop — related actions.
Further, as Withrow (2002) notes, profiling by police can be further defined based on
specific factors used in profiling. MacDonald (2000) suggests that profiling can be considered
hard or soft. Hard profiling occurs when race is the one and only factor used in police decisions
to stop a particular motorist. Soft profiling occurs when race is one of several factors the police
use in determining whom they stop.
For this report, the Iowa City Police Department defines racial profiling as "the detention,
interdiction, exercise of discretion or use of authority against any person on the basis of their
racial or ethnic status or characteristics" (Racial Profiling, General Order 01 -01). A copy of this
policy is contained in Appendix A.
Collecting Data
Many departments have, independently or in collaboration with others, undertaken the
task of analyzing traffic stop data. These agencies vary in terms of their structure and function,
as well as in the type of data they collect. In addition, some data collection efforts involve
sophisticated data analyses where others simply compare basic percentages. These differences,
on the surface, are not all that dramatic. When making conclusions about the practices of a
department, however, these methodological considerations take on more importance. In fact,
methodological considerations are considered paramount by prevailing judicial opinions (see
following discussion on legal issues). It should be noted; however, just as there are no widely
accepted standards for defining racial profiling, the methods of collecting and analyzing traffic
stop data are not universal.
2
Police departments across the country collect a variety of data elements in their analysis
of racial profiling. Some agencies collect a minimal amount of data such as the race, age, and
gender of the driver, along with the reason for, and outcome of the traffic stop. Other agencies
collect data pertaining to all passengers of the vehicle, key events that may occur during a traffic
stop (e.g. warrant check, search), and police officer demographics. There appears to be no
consensus regarding the most appropriate data collection elements across departments. The
National Institute of Justice (NIJ), however, recommends certain data be collected on a "routine"
basis (Ramirez, McDevitt & Farrell, 2000). These data elements include: date, time, and
location of stop, license number and description of vehicle, length of stop, and name and
identification number of the officer initiating the stop. The NIJ also recommends that certain
"study specific" variables be considered. These include the race, date of birth and sex of the
driver, the reason for stop; the outcome of the stop, and whether or not a search was conducted.
Methods
Data Collection
Data were collected about each traffic stop (N = 9,702) made by officers of the Iowa City
Police Department over the nine-month period between April and December, 2001. Officers
were required to enter data into mobile data terminals (MDTs) after each traffic stop interaction.
A copy of this form is contained in Appendix B.
When an officer would initiate a traffic stop, he or she would call that stop into the
dispatcher, who would document the contact. After the stop, the officer would fill out a screen
on the MDT located in the vehicle. These data were centrally stored in a Microsoft Excel
spreadsheet. Each stop became a case for analysis. The Excel file was subsequently transferred
into SPSS for analysis.
-l'J_
V is Les
Information was collected about the driver, the officer, and the stop event. Driver
demographics included race, sex, age, residency, and vehicle registration. The only information
collected about the officer was officer badge number. More data about the officer, such as sex,
race, age, time in service, etc., can be entered at a later date. Several items of interest pertain to
the stop event, including the date and the time of day.
One broad category related to the stop event involved the `reason for the stop." These
were coded dichotomously (yes/no) and included the following: moving violation,
equipment/registration violation, criminal offense, other violation, call for service/suspect or
vehicle description, preexisting knowledge or information, special detail, and other.
Information regarding any "search" that might have been requested or conducted also
was collected and included the following dichotomous variables: consent search requested,
consent search of vehicle requested, consent search of person requested, consent search
conducted, officer safety search conducted, search incident to arrest conducted, and probable
cause search conducted.
Data pertaining to any "property seized" also was collected and included the following
dichotomous variables: property seized, alcohol seized, weapons seized, money seized, narcotics
seized, evidence seized, other seized.
The `outcome of the stop" also was measured, and included the following dichotomous
variables: no action, citation, arrest, warning, and field interview. Finally, information also was
collected about whether any "force" was used during the stop and whether the force was against
the driver or a passenger.
.lf
Originally, 38 variables were measured and entered for analysis. Some of these were
recoded for analysis. For example, driver race was collected in 7 categories (Caucasian, Black,
Asian, Spanish, Native American, Other, and Unknown). These were collapsed into 3 categories
for the analyses (White, Black, Other). In addition, some new variables were created to obtain a
clearer picture of the data. For example, it is logical to assume that a person stopped for multiple
reasons might be more likely than a person stopped for only one reason to get a citation or to be
arrested. The dichotomous variable "multiple reasons" was created by distinguishing between
cases with "only one reason" for the stop and cases with "more than one reason."
Collection and Measurement Conce rns
Glitches arise at the initial stages of any large data collection undertaking. This study
was no exception. Although the ICPD engaged in a series of training sessions to familiarize
officers with the data collection form, the use of the MDT, and the procedure, difficulties and
oversights still occurred. This most problematic of these became apparent when Excel entries
(from the MDTs) were cross - referenced with CAD entries. A discrepancy was noted between
the number of stops as indicated by the CAD system and the number of stops as indicated by the
Excel entries. In addition, stops were referenced by officer badge number. A routine check of
CAD and MDT entries appeared that some officers were calling stops into the CAD system and
not entering them into the MDT, or entering them into the MDT without calling them into the
CAD. After a series of inquiries and discussions with the officers, it was obvious that the
problem was related to training and/or to data entry. For example, in situations where 2 officers
were in the car during a traffic stop, one officer may call in the stop to the dispatcher, who enters
the stop under that officers badge number. The second officer in the car may take responsibility
for entering the data into the MDT, which then gets entered under his or her badge number, In
this way, it may appear as if an officer is either under- or over - reporting on the MDT system. In
addition, some difficulties were noted with officers forgetting to "save" the data into the MDT
after they had entered it, resulting in some lost information. These problems were quickly
caught and corrected, but it is recommended that any conclusions drawn from this data keep
these difficulties in mind. A full year of " glitch -free" data collection is recommended for use as
a baseline for this department. Moreover, any close inspection of stopping behavior by
individual officers should not be undertaken until a full year of corrected data collection is
completed.
Analyses & Results
Analyses were conducted on two levels. First, descriptive analysis takes a broad look at
stop patterns, stop characteristics, and driver demographics. This information is useful for
descriptive purposes only. That is, this type of analysis is useful in understanding the existing
state of affairs ( "what is" ). Descriptive analyses are neither predictive nor explanatory. They
cannot explain "why" things are as they are and they cannot predict how things might be in the
future. Comparisons using descriptive analyses also are problematic given that descriptive
statistics do not consider relationships among different variables involved in any given situation.
To address the complex relationships that exist among different variables, multivariate
analyses also were conducted. Specifically, a program called "chi- square automatic interaction
detector" or CIIAID, was used to evaluate the variables in terms of their relationships with one
another. For example, this type of analysis is able to determine whether the sex of a driver is
related to the reason for stop, given all the other variables that might interact, such as race or age.
A more detailed explanation of this process is contained below.
Descriptive Analyses & Results
Driver Demographics
The variables related to the driver involved in the stop were the following: race, sex, age,
residency, and vehicle registration. Drivers stopped were mostly White (84 %), male (65 %),
younger, Iowa City residents (62 %), and from Iowa (86.5 %).
Race. As indicated below, 84% of the drivers stopped were White, 9% were Black, and
7% were Other. The "Other" category includes Asian, Hispanic, Native American, and Other.
There were 13 cases in which the race of the driver was coded as "unknown" and these were
counted as "missing" in the analyses (See Table 1).
Table 1: Percentage of Stops by Race of Driver
White Black Other
7
. - _ .. _ - - .... .... IC
Sex: Sex b�Race, Most (65 %) slopped drivers were nmlo (See Table 2), A higher
percentage of Non -White males that White males were stopped, whereas a higher percentage of
White females than Non -White females were stopped (See Table 3),
Table 2: Percentage of Stops by Sex ofllrivcr
Female More
Table 3: Percentage ot'Stops by Sox and Race of Driver
Female Nido
VO) ,
R WhiteBlack other
Age: Agcy byl& - The median age of drivers stopped was 23, with most stopped drivers
behig 21. Lt fact, more thtut 7 in 10 drivers stopped were under the age of 30 (See'rable 4).
Higher perecntages of Non -Wbito drivers than White drivers between the agar of 25 and 44 were
stopped, ht genertd, younger (24 & under) nod older (45 and over) White drivers than Non -
White drivers were more likely to be slopped (,See fable 5).
Table 4• Age Ektcgories of Drivers Stopped
Under 18 18.20 21 -30 31.40 Over 40
Table 5• Percent4go of Il ivers Stopped by Race and Abe
Under 18 -20 21 -30 31 -40 Over40
18
Gl Whtte
Li Olac:k
n Other
9
Residency and Vehicle Registration. Of all drivers stopped, less than two- thirds (62 %)
were city residents (See Table 6). Another 11 % were Johnson County residents. An equal
percentage either was ftom other Iowa locations or from out of state (13.5% respectively). This
is probably characteristic of a city with commuters and a large college campus. Given this, city
census figures should NOT be used as a baseline for comparison to the overall stop data.
Table 6• Percentage of Drivers Stopped by Residency/Registration
Iowa City Johnson Other Iowa Non -Iowa
County
Stop Event
Temporal Distribution. The most active month for stops was April (15 %), followed by
May (13 1/o), and November (12 %). June (9 %) was the least active month (See Table 7). The
time distribution of stops was unusual, with 41 % of all stops occurring between midnight and
3:00 am. In fact, the most active time was between 1 -2am (17 %) (See Table 8). This is likely
due to the fact that Iowa City is a college town with a high concentration of bars and restaurants
that close around that general time. Drivers probably get stopped as they are leaving a bar or
restaurant after closing time. Given this time distribution, it is no surprise that the third shift is
responsible for the highest percentage of stops (54 %), followed by the second shift (25 %), and
the first (21 %) (See Table 9).
a-
Table 7: Percent of Stops by Month
do. �cm
ym 0 V"
Table 8: Percent of Stops by Hour of Day
pti� coo ��� ��(01 �ti��
11
Table 9: Percent of Stops by Shift
1st Shift 2nd Shift 3rd Shift
In general, drivers were stopped for moving violations (69 %) or equipment /registration
violations (26 %), were not searched (95 1/o), and were released with a warning (56 %). Only 10
cases involved use of force, so this variable was not used in any analyses. Likewise, only 147
(1.5 %) cases involved any type of property seizure (mainly narcotics) so this variable is not
considered further. Only 5% of the cases involved a search, and these were mostly (75 %)
incident to arrest.
Reason for Stop by Race. The three most -cited reasons for stops were 1) moving
violations (69 0/o); 2) equipment /registration violations (26 %); and 3) other violations (6 %). Stops
of `other" drivers (71 %) were more likely than stops of white (69 %) or black drivers (63 %) to involve a
moving violation. Twenty -six percent (26 %) of all stops were for equipment/registration violations; stops
of black drivers (31 %) were more likely than stops of white (25 %) or other drivers (24 1/o) to involve this
reason. Other violations (6 9/o) involved white drivers (6 %) more often than black (5 %) or other drivers
(5 %) (See Table 10).
12
-ae,
Table 10: Percentage of S1ops IaV Itcason and Race of Drlvet
80%
70%
60 %
50 %
40%
30%
20 %
10%
0%
F 0�
■ TOTAL
❑ white
1I Black
G Other
Searches by Race: Most searches (75 %) were conducted incident to arrest; "Other"
drivers (85 %) and Whkc drivers (77 %) were more likely [Iran Black drivers (64 %) to be
searched for this reason. Consent to search was given in 23% of cases, more so by Black drivers
(28 %) than by Wite (23 %) or "Other" drivers (8 9%) (See Tablo 11).
There were 359 searches incident to arrest ( <4 %of all slops) out of total cases in
wluoli a search was conducted (75 %). Of alt drivers stopped, 3% of Wldte drivers, 7% of Black
drivers; and 3% of "Other" drivers wcrc searched incident to arrest. Of all the drivers searched
for this reason, 79% were White, 15% were Black, and 6% were "Other" (Sco Table 12).
Out of till drivers stopped, there were 83 consent searches (less than 10% ofall cases):
2.4% of all Black drivers were involved in this event, compared to .7% of all White drivers, and
.4% of all "other" drivers. Of the consent searches (n - 83), white drivers comprised 72 %,
Blacks were 24%, and other drivers were 4% (See Table 13).
1.3
—�21'_
Table I I ; Pere c ntuge of Searches (n = 479) by Type and Mace of Driver
90%
80%
70%
60%
50%
40%
n
7_0 %n
1,M
0%
P�VyK'
Al
GO
e� TOTAL
LI White
17 Black
❑ Other
��„lile 1 ?;,_,Y,�+r,�e��a�c of Searches Incident io Arresl (u= 359) by Mace ofI7river
White Black Other
14
htble 13: Percentaac of Consent Searc)ti�s (n = 83) by Race of Urivcr
White Black Other
Outcome of Stop by Rae e. Other drivers (65 %) and black drivers (61 %) were more. likely
than white drivers (57 %) to be issued a Warning as a stop outcome (See Table 14). White drivers
(41 ON were more likely than black (37 %) or other drivers (34 %) to be issued citations. A much
biglier percentage of black drivers (13 %), however, bad arrest as the outcome ortheir stop. Only
7% of white drivers and 6% of other drivers were arrested. 'These pereemnbes do not include the
431 cases in which the outcome was "no action."
'1'hble 14: Perotintaea of 17civcrs Sior�tx c�Rv Outcome and Rnce
P
P,:;?, % —
� 7t7Tll L
White
C3131cick
C] tither
15
SuMMM of Descriptive Anal
At first glance, one might be tempted to conclude that race is a factor in some events. For
example, higher percentages of "Other" drivers were stopped for moving violations while higher
percentages of Black drivers were stopped for equipment/registration violations. Similarly, the
sex and age of the driver also appear to be factors given that higher percentages of White females
were stopped, as were higher percentages of Non -White males.
Descriptive statistics are very superficial and only give the broadest picture of the data,
This type of analysis lacks inferential ability. One cannot use it to predict events or to describe
the relationships among characteristics and events. Descriptive statistics only should be used to
describe the state of affairs. They will not help to: 1) understand why the percentages are the
way they are; 2) determine the relationships among the characteristics and events; 3) predict one
outcome or event over some other outcome or event.
Providing a description of the data should only be the first step in a thorough analysis.
More comprehensive multivariate analysis is required to understand the relationships between
and among variables, and to understand how these variables interact with one another to produce
a certain reality, as portrayed by the descriptive statistics, In this case, a procedure called chi -
square- automatic interaction detector (CHAID) was used to more fully explore the relationships
between and among the various variables.
CHAID Analysis & Results
This portion of the report examines the relationship between three demographic
predictors (age, race, sex), vehicle registration (Iowa /non -lowa) and several events related to the
traffic stop. These events involve the following questions:
16
1) Reason for the Stop? (moving violation, equipmentlregistration violation, pre - existing
knowledge, other violation, crime, special detail, other).
2) Search Conducted? (vehicle search or driver search).
3) Type of Search? (search incident to arrest or consent search).
4) Property Seized?
5) Outcome (warning, citation, or arrest).
Some of these decision points also were examined as predictors of subsequent events. For
example, whether property was seized might be related to whether a driver was warned, cited, or
arrested.
CRAID is based on an analytical technique called chi - square. Chi - square analysis
demonstrates whether a particular observed proportion within a sample is statistically different
from a particular expected proportion within that sample. The expected proportion is based on
the premise that there is no relationship (i.e., one has no impact on the other) between the two
variables in question within the population from which the sample under study was drawn. It is
calculated using information from the entire group.
For example, if we were interested in whether race (White, Other) and being arrested
(Yes, No) are related in a population, we would use chi - square analysis. The chi -square
procedure would determine that 25% of all the persons (regardless of race) were arrested and
75% were not. Then, the chi - square procedure would determine that, of all the "Other" drivers,
30% were arrested. Chi- square analysis would then conclude whether the 5% difference
between all persons arrested and "Other" persons arrested is attributable to chance, or whether it
is likely that there is a true difference in the population between White and Other drivers in being
arrested. If the chi - square value is "statistically significant," this 5% difference is not
17
_31-
attributable to chance and represents a true difference between White and Other drivers in being
arrested. By convention, statistical significance is reached when the probability of error in this
conclusion is less than .05 (i.e., only 5 times out of 100 would one reach this conclusion in
error).
Race, however, is just one factor that could be related to any event in a stop situation.
Other variables may be more important. They may mediate, or even eliminate the influence of
race. This is why we use a "measure of association" called the `phi coefficient" with chi - square
analysis. The phi coefficient ranges in value from 0 (no relationship) to 1 (perfect or very strong
relationship). If chi - square analysis indicates statistical significance (that the 2 variables are
related), it is then necessary to determine the strength of that relationship. In the previous
example, the 5% difference in the proportion of Black drivers arrested and the proportion of all
drivers arrested was statistically significant. The question now relates to how strong the
relationship is between race and arrest. The chi - square analysis determines the phi coefficient
for this relationship to be .03. This indicates an extremely weak, almost non - existent relationship
between race and attest because .03 is much closer to 0 than to 1. In fact, this means that very
little variation in arrest is explained by the race of the driver. Another variable or set of variables
is more influential in arrest than the race of the driver. This is where it becomes necessary to
conduct multivariate analysis.
CHAID is a multivariate technique that segments the sample of traffic stops and reveals
the interrelationship between the potential predictors and the events involved in the stop. The
CHAID procedure generates a "decision tree" that identifies significant predictors or each
decision in question. In effect, the procedure "cross- references" each event with each potential
predictor.
18
CHAID simultaneously considers the impact of several independent variables (age, race,
sex) upon a particular event in question (arrest, in the example above). The CHAID results
indicate the strongest predictor of the event, while taking the other variables into account. It may
be that no variable or set of variables is a predictor of the event when the other variables are
considered. This means that any original relationship (e.g., between race and arrest) is so weak
that when other independent variables are considered (age, sex), nothing predicts the event.
In the arrest example, the program examines all the cases in which individuals were
arrested, It then examines all the factors associated with each case and determines the ones that
keep occurring in conjunction with an arrest. Then, the program compares that state of affairs
with the cases in which drivers were NOT arrested. In this way, it is possible to determine
whether factors are really predictive of an event or whether observed differences between those
arrested and those not arrested occurred purely by chance.
For example, if descriptive analysis determines that 30% of the drivers arrested were
White and 70% were Black, one might be tempted to conclude that there was a racial bias in
arrests. However, the CHAID analysis would examine the cases and simultaneously consider all
the other potential factors involved in an arrest. The decision tree that it generates might indicate
that the most significant factor related to arrest is a stop for `pre- existing knowledge." The
analyses demonstrate which of the potential predictors (if any) had the strongest and most
important relationship to the events or outcomes. In this case, the potential predictors were used
to examine the five events listed above to determine if they were actually related or whether any
observed differences occurred purely by chance.
The advantage of multivariate analysis is that it reveals the strongest predictors of the
event in question. In other words, if race is a factor, it will emerge independently of the other
19
-�33 -
factors. If race is not a factor, then the one or more of the other predictors will emerge, or none
of the selected predictors will emerge as related to the evenWoutcomes. If no significant
predictor emerges, it either means that the analyses did not include the most relevant predictors
or that no measured factor is related to the event.
This attribute is particularly relevant for a traffic stop situation in which many things go
unmeasured. For example, one cannot measure the quality of the personal interactions between
an officer and the individuals stopped. One cannot measure the demeanor of the driver. In this
case, one cannot measure any information about the passengers in the vehicle. Finally,
extraneous factors such as the weather, the time of year, the social environment, and the location
are not measured in this study.
CRAID Results
Results from CHAID analyses using the 5 event categories and the potential predictors
outlined above resulted in only three events that had significant predictors. Within "Reason for
Stop," being stopped for a moving violation was significantly related to one or more of the
potential predictors. Having a search conducted (vehicle search or driver search), type of search
(search incident to arrest or consent search), and having property seized had no significant
predictors. Two outcomes (warning and citation), however, did have significant predictors.
Arrest, on the other hand, did not.
Reason for Stop
Reasons for stop included the following! 1) moving violation; 2) equipment/registration
violation; 3) other violation; 4) pre - existing knowledge; 5) criminal offense; 6) special detail;
and 7) other. A stop for a moving violation was the only variable that had significantly related
predictors.
20
Moving Violation. For the entire group, 68.6% (6656/9702) of the drivers were stopped
for a moving violation. This is the base rate for moving violations. The question is whether any
subgroups formed based on the potential predictors had moving violation rates significantly
different from (greater than or less than) that of the entire group.
The most significant predictor of a moving violation was the "age" of the driver. Second-
order predictors were "sex of driver" and "vehicle registration" Sex (being female) was a
predictor for the 10 -17 age group. Vehicle registration (non -Iowa) was a predictor for the 18 -20,
21 -30, and over 40 age groups. Finally, `race" and "sex" emerged as third -order predictors.
Race (being an "other person of color") was a relevant factor among the 18 -20 and 21 -30 year
old Iowa residents. Sex (being female) was relevant among the over 40 year old Iowa residents.
Apart from order of significance, the subgroups also can be described in terms of their
proportion, remembering that 68.6% is the base to which comparisons are made. The subgroups
receiving moving violations, in order of proportion are:
1. Persons over 40 with non -Iowa registration: 84.5%
2. Persons 10 -17 who are female: 81.8%
3. Persons over 40 with Iowa registrations who are female: 77.8%
4. Persons 10 -17: 75.0%
5. Persons over 40: 74.6%
Thus, age (being in the youngest group or being in the oldest group) was the strongest
predictor of a moving violation stop. Sex of the driver, registration, and race only emerge in
combination with the other variables, not as independent predictors. There is no evidence of
racial bias in drivers being stopped for a moving violation.
21
—2
Outcome
Warning. Nearly two - thirds (65.5 %, or 5383/9702) of the entire group was given a
warning. The most significant predictor of being given a warning was whether a search was
conducted. Those who were not searched were significantly more likely to receive a warning.
The second -order predictor was being stopped for an equipment /registration violation, and the
third -order predictor was having a non -Iowa registration.
Those most likely to receive warnings were:
1. Persons who were not searched, who were stopped for equipment/registration
violations, and who were non -Iowa registrants: 77.1 %.
2. Persons who were not searched, who were stopped for equipment/registration
violations: 70.2%
The strongest predictor of a warning was whether a search was conducted. These finding
are logical in that drivers who were searched would be more likely to have been stopped for a
more serious violation and would therefore not receive a warning. Also, drivers stopped for only
having an equipment/registration violation and/or to be from "out of town" might be less likely
to be issued citations. Overall, no bias was detected in the issuance of warnings.
Citation. With this event, the base rate for the entire group was 38.7% (3753/9702). The
most significant predictor of a citation was whether the driver was stopped for an
equipment /registration violation. Being stopped for something other than an
equipment/registration was the most significant predictor of receiving a citation. The second -
order predictor for this group was age (10 -17), and the third -order predictor was having an Iowa
vehicle registration (among those over 30 years old not stopped for E/R violations).
Describing the subgroups in terms of their proportion (compared to the 38.7% base), the
subgroups receiving citations are:
Pia
1. Persons not stopped for equipment/registration violations who were over 30 years old,
with Iowa registrations: 50.4%
2. Persons not stopped for equipment/registration violations who were between 10 -17
years old: 50.2%
Therefore, not having an equipment/registration violation was the strongest predictor of a
citation. This is logical given that the other biggest category of stops involved moving violations.
This is the type of situation in which an individual is more likely to be issued a citation. Age and
registration were related to receiving a citation in combination with equipment /registration
violation, not as independent predictors. There is no evidence of racial bias in drivers being
issued a citation.
SpwgIM Qf CHAID Analyses
Only three events involved significant relationships to tested predictors. Receiving a
moving violation (being in the youngest or the oldest age groups), receiving a citation (not being
stopped for an equipment/registration violation), and receiving a warning (not being searched)
were the only events that CHAID analysis determined to have significant relationships with
predictor variables. Sex of the driver and the vehicle registration also were related in
conjunction with the significant predictors in some situations, but not as independent predictors
of any given event. Race of the driver (being an "other person of color ") only appeared once, as
a third -order predictor among certain age groups of Iowa residents in being stopped for a moving
violation
The "Baseline" Dilemma
The most problematic part of any study of this nature is determining the baseline to
which collected data should be compared. We want to look at "what is" and compare that state
of affairs to "what should be." However, determining "what should be" is troublesome. In
23
137_
theory, the racial distribution of drivers stopped should represent the racial distribution of drivers
doing something that makes them eligible to be stopped. For example, if 20% of the drivers
doing something that makes them eligible to be stopped by the police are Black and 80% are
White, one would expect that 20% of the drivers stopped are Black and 80% are White. This
comparison has very little, if anything, to do with any racial distribution in the city or county
population. It has everything to do with the racial distribution of drivers on the roadways and the
driving behaviors or characteristics that they exhibit.
Making decisions as to whether a department is engaging in discriminatory stop practices
depends on the ability to identify the racial distribution of stops that would exist in the absence
of discriminatory stop practices. That is, one must know the true racial distribution of drivers
eligible to be stopped (i.e., doing anything that could get them warned, cited, or arrested —
anything that creates reasonable suspicion or probable cause). Stops in the absence of
discriminatory practices, then, would be the "right" proportions. One could then compare the
research findings to the "right" proportions to determine whether discrimination exists.
Unfortunately, we cannot measure this objective reality. Determining the "right" proportion of
stops is impossible because of the infinite variations in driving behaviors and police response
within various locations at various times on various days in various months during various years.
Also missing is a measure of the interactions between those stopped and the officers. Demeanor
is thought to significantly contribute to stop outcome as well as to other law enforcement
outcomes such as warning, citation, and arrest.
This reality, however, is extremely difficult, if not impossible, to measure. We cannot
know the racial distribution of drivers doing something that makes them eligible to be stopped.
Some research has attempted to measure this, but the methodology employed is often seriously
24
flawed. The most common method involves posting trained observers at strategic locations
armed with stopwatches to determine the racial distribution of speeders. Obviously, this method
is extremely limited, relying on split second judgment by observers as to the race of drivers. In
addition, this method rests on the assumption that speeding is the only thing for which drivers get
stopped. In the current study, moving violations were the most commonly cited reason for a
stop, but equipment/registration violations and other violations accounted for about 3 in 10 stops.
Given that comparison to population data is invalid, we suggest that the current data become the
baseline from which to evaluate future practices.
The initial analysis of a law enforcement agency's traffic stops does establish a
benchmark for that department. Once an initial study is completed, a department has an
empirical basis for comparison in the future. If an initial study indicates the possibility of bias
(race appears as a significant predictor of some event), future research will provide data for
comparison to help determine whether the relationship previously observed between race and
some outcome persists or whether it has disappeared. If an initial study shows no evidence of
bias (race does not appear as a significant predictor of any outcome), the department in question
should attempt to maintain this desirable result.
These data, collected from traffic stops made by the Iowa City Police Department
between April I and December 31, 2001, provide no evidence that the ICPD is systematically
engaging in discriminatory stop practices. Stops conducted by the Iowa City Police Department,
as a whole, during the study period, do not involve the race of the driver as a significant factor
related to events and outcomes (e.g., arrest, search, etc.). This does not mean, however, that no
individual citizen was ever discriminated against. There is always the possibility that individual
officers may be engaging in racially biased practices, both in determining which drivers they will
25
or will not stop and in determining what steps to take after the initial contact. This is a serious
possibility that is not likely to be revealed with statistical analysis. To detect discriminatory
practices at this level requires constant vigilance by the community, by all the officers within the
department, and by the departmental administration. Statistical analysis, while valuable, cannot
substitute for community involvement and effective management.
Legal Issues Relating to Dias/Racial Profiling Data Collection and Analysis
verview
The findings and conclusions of any study involving bias/mcial profiling are often used,
or interpreted, in a number of ways, for a variety of purposes, by many factions. These studies
often raise issues related to the management and administration of the agency, issues relating to
the recruiting, training and attitude of the officers, and issues related to the community, just to
name a few. This section focuses strictly on the legal issues involved with this, or any, study of
bias/racial profiling.
Civil Liability
Without a doubt, the central legal issue relating to any study of bias/racial profiling by a
law enforcement agency is the degree to which the agency, or the individual officers employed
by the agency, may be subject to civil liability for their actions. While the terms "bias profiling"
and `racial profiling" are of relatively recent origin, and neither are legal terms, the practice of
bias/racial profiling, if substantiated, allows victims to pursue civil claims against an offending
agency, or officer, under a variety of legal theories. Although each legal theory has its own
strengths and weaknesses, for a number of reasons, the theory employed by most plaintiffs, and
the one that is arguably the most difficult for plaintiffs to obtain evidence and prove, is that of a
Constitutional violation of the 10 Amendment's Equal Protection Clause. Generally speaking,
26
the standard required for a plaintiff to win in an Equal Protection claim is that the plaintiff must
prove that other similarly situated individuals, of a different race, were treated differently.
Likewise, proving, or disproving, disparity of treatment based on race should also be the focus of
any study of bias/racial profiling. Thus, the key importance of any study on bias/racial
profiling, from a legal perspective, is that the study's findings and conclusions can become the
evidentiary basis for supporting, or defending, such claims. In short, the data, and more
importantly the findings and conclusions of the evaluators, of bias/racial profiling studies serve
as the statistical evidence used by plaintiffs or defendants to support or defend the legal claims.
Several courts have addressed the issue of civil liability under the 10 Amendment based
on a claim of bias/racial profiling and the evidentiary requirements needed to support such a
claim. These courts repeatedly emphasize the need for both plaintiffs and defendants to
introduce valid and reliable statistical evidence establishing, or disproving, disparate treatment
based on race. Evidence taking the form of statistics based on anecdotal sources, or data
evaluated using unacceptable methodology, are universally rejected by the courts.
In Chavez v. Illinois State Police, 251 F.3d 612 (7s' Cir. 2001), a typical Equal Protection
lawsuit, the court went to great lengths to outline the validity and reliability standards required of
evidence relating to the collection and/or analysis of data regarding bias/racial profiling. The
court noted that statistical evidence may be used to establish that other similarly situated
individuals, of a different race, were treated differently; however, to be admissible and of any
relevance to the issues before the court, such statistical evidence must be collected and analyzed
in a universally scientifically acceptable manner. Further, the court noted that the statistical
evidence must be subject to rigorous methodological procedures and evaluated by persons with
the academic credentials and practical experience to qualify as experts. The court specifically
27
%Z//,
noted the inherent problems with statistical evidence relating to bias/racial profiling with regard
to the following: establishing base lines, determining the quantity and quality of the data being
collected, sample groups, and interpretation. Accordingly, if the statistical analysis and findings
and conclusions of this, or any, study of bias/racial profiling are to be of any value from a legal
perspective, the study should comply with the evidentiary requirements currently being imposed
by the courts.
This study seems to satisfy the admissibility requirements for evidence relating to
disparate treatment based on race, currently being imposed by courts in bias/racial profiling
cases. This study employed sound methodological techniques with regard to the collection and
analysis of data and was performed by individuals with nationally recognized expertise in
statistical analysis.
Disclosure of Information/Records
Although generally not rising-to the level of concern as civil liability, law enforcement
agencies engaged in the collection of information and analysis of data, whether related to bias
profiling or some other topic, must be familiar with the applicable statutes and/or ordinances
governing the release of public records. Typically referred to as "Open Records Acts ", virtually
all jurisdictions have enacted laws requiring certain records in the possession of police agencies
to be released to the public. These "Open Records Acts" vary tremendously from jurisdiction to
jurisdiction; however, in all jurisdictions, to some degree, the data collected as part of a bias
profiling project will be subject to disclosure to the public, and to the media. Ideally, agencies
will address this legal issue before initiating any data collection to ensure they knew, going into
the project, what records, if any, will be subject to disclosure, and under what circumstances.
0
The fundamental questions to be resolved relating to the release of data and information
collected as part of a bias profiling project are:
1) Who, exactly, is the custodian of the data and information relating to the project?
[This can become very complex in situations where agencies contract all, or part, of
the project out to a consultant.]
2) What records are, and are not, subject to disclosure?
3) Can any of the information collected be "masked" or otherwise shielded from
disclosure? Must any information be shielded from disclosure?
4) If large data sets are subject to disclosure, what format is required?
5) Where disclosure of large, bulky, data sets is required, what costs, if any, may be
recovered by the agency?
6) Is the analysis/interpretation of the data subject to disclosure also?
7) When must data/information be released? [This can pose difficulties in multi -year,
on going, projects.]
8) How long must the data/information be retained and who had responsibility for
archiving the materials?
Conclusion
It is imperative that agencies practice proactive risk management with regard to the
collection and analysis of data relating to bias/racial profiling. In addition to serving as the basis
for addressing a host of management, administration and personnel issues, bias/racial profiling
studies can also serve as useful tools for developing statistical evidence for defending against
lawsuits alleging civil rights violations. However, experts in statistical analysis must conduct
any study using scientifically acceptable methodology. The statistical analyses involved in this
study appear to satisfy the legal requirements currently being imposed by the courts and the
findings and recommendations should serve as valid evidence relating to allegations of
bias/racial profiling. Finally, a determination should be ascertained as to what degree the
information/records will be subject to disclosure under the applicable Open Records laws.
09
Conclusion and Recommendations
The Iowa City Police Department, as a whole, does not appear to be systematically
stopping drivers based on their "racial or ethnic status or characteristics" as defused by
departmental policy (Racial Profiling, General Order 01 -01). While the percentages of races
were not always equal in some categories, the discrepancies are most likely explained by factors
other than the driver's race. For example, the age and sex of the driver were important
explanatory factors in many events. This makes sense given that we know driving behavior to be
different among various ages and between the sexes; younger drivers drive differently than older
drivers and males drive differently than females.
This study used a fairly comprehensive set of data collected about a population of stops
over an 8 -month period. The data were collected in a consistent manner, with only minor
problems pertaining to entry and recording that were addressed as they were discovered. The
statistical analysis used to evaluate the data was rigorous, thorough, and conducted by
academicians with expertise in the collection, analysis, and interpretation of such data. Further,
this analysis was conducted on a contractual basis with researchers from the University of
Louisville in Louisville, Kentucky, providing a level of objectivity that is necessary to avoid any
conflicts of interest or appearances of impropriety. These factors have yielded valid data,
making valid conclusions highly likely. The only caveat is that one full year's worth of data
should be collected and analyzed to provide a baseline from which to evaluate future stop
practices.
Moreover, the legal considerations set forth by the courts have been met, making legal
actions against the Iowa City Police Department based on accusations of "racial profiling" very
unlikely. However, the Department must still recognize that this does not preclude the actions of
30
any one officer becoming suspect. Our findings do not conclude that such profiling might not be
occurring against individual citizens by one or more individual officers. This type of
discrimination on an individual level, however, is virtually impossible to detect or to prove given
the type and amount of discretion that officers must use in the completion of their duties. These
matters are more likely to be discovered through administrative and supervisory vigilance, and
through community awareness, rather than through the collection and analysis of traffic stop
data.
The Iowa City Police Department can enhance their collection of traffic stop data. The
recommendations offered here involve both process and content elements of the project. First, it
is suggested that a full year of data be collected and subsequently used as a baseline for
analyzing future department practices. The data in the study covers only 8 months of the year
2001 and may not fully reflect the traffic stops practices of the department on an annual basis.
Second, census population data should not be used as a baseline. As previously discussed,
census data does not provide for an appropriate point of comparison and should only be used
when nothing else is available. Clearly, with the adoption of the recommendation for a full year
of data collection, the use of census data can be avoided.
Third, data collected for the year 2001 (April- December) should be viewed carefully as
the department experienced considerable challenges in refining the data entry process.
Throughout the course of this project quality assurance checks were employed to ensure that the
data collected was valid although it is suggested that the validity of the data may continue to be
somewhat suspect. Continued monitoring of date entry and fine- tuning of the department's
quality assurance mechanisms, however, must be a priority. A fourth recommendation involves
the training of all officers in regard to departmental policy, data collection procedures, and the
31
results of the analysis. Officers collecting the data must have a thorough understanding of the
project in order to ensure more accurate and complete data collection and entry. In a similar
vein, supervisors must be proactive in ensuring line officers understand the policies and
procedures related to the project. Supervisors also should identify officers who require
additional training or closer supervision to ensure adequate understanding of the data entry
procedures as well as policy compliance.
Fifth, it is imperative that the department establish clear, written guidelines regarding the
entry of traffic stops into both the CAD and MDT database systems. These guidelines should be
made available to all personnel involved in the data entry process and should be incorporated
into departmental training as required. Further, dispatchers should receive guidelines and
training regarding recording calls when more than one officer in involved in a traffic stop. This
will allow for more timely and accurate quality assurance checks as well as enhance the validity
of the data.
In terms of the content of the data collection forms, several data elements could be added
to the form. First, in attempt to control for variations in traffic stop practices by location, the
quadrant in which the stop occurred could be added to the form allowing for traffic stop
identification. Also, the form should contain information about warrant checks. First, there
should be a question that asks whether a warrant check was performed during the stop.
Secondly, the form should contain a section addressing the outcome of the warrant check.
Currently, information about outstanding warrants is obtained through a plate and/or license
check. These types of checks, however, are not performed routinely. Finally, the form should
include an item that indicates whether the driver was asked to exit the vehicle. These additions
are consistent with data collection efforts throughout the country, require minor modifications to
32
the form, and would aid in the development of a more accurate understanding of the key events
that are likely to occur during traffic stops.
These recommendations are offered to improve the data collection process and to
enhance the quality of the data. Several of these recommendations were communicated to the
Department as the study progressed and have been addressed. Others are currently being
implemented. Overall, the departmental administration has been receptive to recommendations
for the improvement of their data collection and analysis, and seems genuinely concerned about
the accurate measurement of traffic stop practices. Again, the only major concern is that this
study is based on only 8 months of data with which some minor collection and entry problems
were noted. Therefore, it is necessary that a full year's worth of "clean" data be collected and
analyzed to provide the best baseline from which to evaluate the future stop practices of the
department. Although no evidence of departmental discriminatory stop practices may be
welcome news, the department now is faced with the responsibility of continual monitoring to
maintain these practices for the continued benefit of both the department and the community.
33
e1G7—
Biblioeranbv
MacDonald, H. (2000). The burden of bad ideas: How modern intellectuals misshape
our society. Chicago: Ivan R. Dee.
Newport, F. (1999). Racial profiling is seen as widespread, particularly among young
Black men. Gallup Poll December 1999, #411, 18 -23.
Ramirez, D., McDevitt, J. & Farrell, A. (2000). A resource guide on racial profiling data
collection systems: Promising practices and lessons learned.
Simms, J. (2000). The Maryland I -95 corridor study. University of Washington in
Missouri. ( http: / /www.adsci.wustl.edu/— focus205 /supreme /stats_i95.html).
Smith, M. & Petrocelli, P. (2000). Racial profiling: A multivariate analysis of police
traffic stop data.
Withrow, B.L. (2002). Race based policing: An initial analysis of the Wichita Stop
Study. Paper presented at the meeting of the Academy of Criminal Justice Sciences, Anaheim,
CA,
Zingraff, M. Warren, P., Tomaskovic - Devey, D. Smith, W., McMurray, H., Mason, M &
Fenlon, C. (2001). Evaluating North Carolina State Highway Patrol Data: Citations, warnings
and searches in 1998. North Carolina Department of Crime Control and Public Safety. (On-
line). Available: www.nccrimecontrol.or shp/ncsfirceport.htm
34
G`-awL X34"
Iowa City Police Department Policy on Racial Profiling
General Order # 01 -01
Section Code OPS -17
35
Zza
OPS -17.1
RACIAL
PROFILING
Date of Issue General Order Number
January 10, 2001 01.01
Effective Date Section Code
February _ 1, 2001 OPS47
Reevaluation Date Amends /Cancels
December 2001 1 New
C.A.L. E.A. Reference
1.2.4,1.2.9,41.3.8,61.1.2.9
INDEX AS:
Racial Profiling Search and Seizure
Complaints Traffic Stops
Supervisor Responsibilities Arrests
Warrants Discipline
I. PURPOSE
The purpose of this order is to unequivocally state that racial and ethnic profiling by members of this
department in the discharge of their duties is totally unacceptable, to provide guidelines for officers to
prevent such occurrences, and to protect officers from unfounded accusations when they act within the
parameters of the law and departmental policy.
11. POLICY
It is the policy of the Iowa City Police Department to patrol in a proactive manner, to investigate
suspicious persons and circumstances, and to actively enforce the laws, while insisting that citizens will
only be detained when there exists reasonable suspicion (i.e. articulable objective facts) to believe they
have committed, are committing, or are about to commit an infraction of the law. Additionally, the
seizure and request for forfeiture of property shall be based solely on the facts of the case and without
regard to race, ethnicity or sex.
111. DEFINITIONS
Racial profiling - The detention, interdiction, exercise of discretion or use of authority against any person
on the basis of their racial or ethnic status or characteristics.
Reasonable suspicion - Suspicion that is more than a "mere hunch" or curiosity, but is based on a set of
articulable facts and circumstances that would warrant a person of reasonable caution to believe that an
infraction of the law has been committed, is about to be committed or is in the process of being
committed, by the person or persons under suspicion. ( "Specific and articulable cause to reasonably
believe criminal activity is afoot.")
OPS -17.2
IV. PROCEDURES
The department's enforcement efforts will be directed toward assigning officers to those areas where
there is the highest likelihood that vehicle crashes will be reduced, complaints effectively responded to,
and/or crimes prevented through proactive patrol.
A. In the absence of a specific, credible report containing a physical description, a person's race,
ethnicity, or gender, or any combination of these shall not be a factor in determining probable cause
for an arrest or reasonable suspicion for a stop.
B. Motorists and pedestrians shall only be subjected to investigatory stops or brief detentions upon
reasonable suspicion.
C. Traffic enforcement shall be accompanied by consistent, ongoing supervisory oversight to ensure
that officers do not go beyond the parameters of reasonableness in conducting such activities.
1. Officers shall cause accurate statistical information to be recorded in accordance with
departmental guidelines.
2. The deliberate recording of any Inaccurate information regarding a person stopped for
investigative or enforcement purposes is prohibited and a cause for disciplinary action, up to and
including dismissal.
D. Motorists and pedestrians shall only be subjected to investigatory stops or brief detentions upon
reasonable suspicion that they have committed, are committing, or are about to commit an infraction
of the law. Each time a motorist is stopped or detained, the officer shall radio to the dispatcher the
location of the stop, the description of the person detained, and the reason for the stop, and this
information shall be recorded.
E. If the police vehicle is equipped with a video camera, the video and sound shall be activated prior to
the stop to record the circumstances surrounding the stop, and shall remain activated until the
person Is released.
F. No motorist, once cited or warned, shall be detained beyond the point where there exists no
reasonable suspicion of further criminal activity.
G. No person or vehicle shall be searched in the absence of a warrant, a legally recognized
exception to the warrant requirement as identified in General Order 00.01, Search and Seizure,
or the person's voluntary consent.
1. In each case where a search is conducted, information shall be recorded, including the legal
basis for the search, and the results thereof.
2. A cursory "sniff' of the exterior of a vehicle stopped for a traffic violation by a police canine
may be recorded on the department's canine action report form.
TRAINING
Officers shall receive initial and ongoing training in proactive enforcement tactics, including
training in officer safety, courtesy, cultural diversity, the laws governing search and seizure,
and interpersonal communications skills.
1. Training programs will emphasize the need to respect the rights of all citizens to be free
from unreasonable government Intrusion or police action.
COMPLAINTS OF RACIAUETHNIC PROFILING
Any person may file a complaint with the department if they feel they have been stopped or
searched based on racial, ethnic, or gender -based profiling. No person shall be discouraged
—51-
OPS -17.3
or Intimidated from filing such a complaint, or discriminated against because they have filed
such a complaint.
1. Any member of the department contacted by a person, who wishes to file such a
complaint shall refer the complainant to a Watch Supervisor who shall provide them with
a departmental or PCRB complaint form. The supervisor shall provide information on
how to complete the departmental complaint form and shall record the complainants
name, address and telephone number.
2. Any supervisor receiving a departmental complaint form regarding racial /ethnic profiling,
shall forward it to the Commanding Officer Field Operations and all such complaints
shall be reviewed and the complaint acknowledged in writing. The complainant shall be
informed of the results of the department's review within a reasonable period of time.
The report and the reviewer's conclusion shall be filed with the Chief of Police, and shall
contain findings and any recommendations for disciplinary action or changes in policy,
training, or tactics.
3. Supervisors shall review profiling complaints, as well as periodically review a sample of
in-car videotapes of stops of officers under their command. Additionally, supervisors
shall review reports relating to stops by officers under their command, and respond at
random to back officers on vehicle stops.
4. Supervisors shall take appropriate action whenever it appears that this policy is being
violated.
REVIEW
1. On an annual basis or as requested by the Chief of Police, the Commanding Officer
Administrative Services, shall provide reports to the Chief of Police with a summary of
the sex, race, and/or ethnicity of persons stopped.
2, if it reasonably appears that the number of self - initiated traffic contacts by officers has
unduly resulted in disproportionate contacts with members of a racial or ethnic minority,
a determination shall be made as to whether such disproportionality appears department
wide, or is related to a specific unit, section, or individual. The commander of the
affected unit, section, or officer shall provide written notice to the Chief of Police of any
reasons or grounds for the disproportionate rate of contacts.
3. Upon review of the written notice, the Chief of Police may direct additional training
towards the affected units/sections or to individual officers.
4. On an annual basis, the department may make public a statistical summary of the race,
ethnicity, and sex of persons stopped for traffic violations.
5. On an annual basis, the department may make public a statistical summary of all
profiling complaints for the year, including the findings as to whether they were
sustained, not sustained, or exonerated.
B. If evidence supports a finding of a continued ongoing pattern of racial or ethnic profiling,
the Chief of Police may Institute disciplinary action up to and including termination of
employment of any involved individual offioer(s) and /or their supervisors.
WARNING
R. J. Winkelhake, Chief of Police
t5a -
OPS47.4
This directive is for departmental use only and does not apply in any criminal or civil proceeding. The
department policy should not be construed as a creation of a higher legal standard of safety or care in an
evidentiary sense with respect to third -party claims. Violations of this directive will only form the basis
for departmental administrative sanctions.
_52-
APPENDIX B
Iowa City Police Contact Sheet
01
5%
IOWA CITY POLICE CONTACT SHEET
Moving Violation
Use of Force
Equipment or Registation Violation
None
Criminal Offense
i-f
Other Violation
Passenger
0 0
1 1
2 2
3 3
44
5 5
7T
8 8
9 9
0 0
1 1
2 2
3 3
44
5 5
6 6
7-7
8 8
8 9
Male
Female
Unknown
Iowa City
Johnson County
Other County
Out of State
Other
Iowa
BNon-Iowa
month day
year
minute
0
0
0-0-0-0
0
0
Consent Search
Requested?
®Yes ®Vehicle
No Person
1
1
1
1
1
1
1
1
2
2
2
2
2
2
2
3
3
3
3
3
3
3
3
3
RaiCaucasian
Caucasian
Black/Negro/African American
AsiarVPacific Islander
Spanish/LatinotHispanio
Native American Indian
Other
444444
444
5
5
5
5
5
5
5
5
5
Type
of Search
Consent
Officer Safety
Incident to Arrest
Probable Cause
6
6
6
6
6
6
767
7
8
9
7
8
9
777777
8
818
8
8
8
9
9
9
9
8
9
Moving Violation
Use of Force
Equipment or Registation Violation
None
Criminal Offense
Driver
Other Violation
Passenger
Call for Service-Suspect Deso.Nehicle Dew.
Pre-existing knowledge or information
Special Detail
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UUM 7 7 7 7 7 7 SpanisFJLatiro/Hispanic Officer Safety
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Iowa City Press Citizen 09/10/2012, Page AO1
Submitted by Committee Member Roberts
City bus route changes: `It's a win -win'
Routes altered to help out students
Officials, residents work to solve issues brought up last spring
By Lee Hermiston
Iowa City Press- Citizen
City and school officials say changes made to the eastside bus loop is not only beneficial to students who take
city buses home from school, but also an example of the city responding to the concerns of its citizens.
Up until this semester, students at City High and South East Junior High who relied on the PM East Side Bus
Loop to get home from school each afternoon faced long waits for the bus and lengthy bus rides home, said
Chris O'Brien, director of transportation services for the city.
"There used to be a really long wait at City High and South East," O'Brien said. "They weren't able to board at
City High until 3:48 p.m. What it used to do was pick up at South East and then pick up at City High. If you
lived south of Highway 6, it was a long bus ride."
A side effect of that problem was some students opted to take a different bus to downtown Iowa City and
transfer to another bus at the Sycamore Mall stop. Business owners at the Old Capitol Town Center downtown
and citizens who passed by the stop raised concerns about rowdiness occurring among the students waiting for
the bus.
"They would end up downtown for bus transfers and that's where the problems were happening," Iowa City
Police Sgt. Denise Brotherton said.
The problems at the stop led police to increase their presence around the mall and the city to put supervisory
staff on the bus to quell disruptions. Ultimately, the various issues with the bus route caught the attention of
Valerie Kemp at the Broadway Neighborhood Center. Marcia Bollinger, neighborhood services coordinator for
the city, said Kemp worked with Americorps students to survey bus riders.
Once they got a sense of what the issues were, they brought them to the city, O'Brien said. Based on those
suggestions, the city made a number of changes to the AM and PM routes. The AM route now services areas
south of Highway 6 and west of Sycamore Street.
However, it's the PM route that has undergone the biggest changes. "We completely reversed the route,"
O'Brien said, noting bus arrival times coincide more closely with release times.. Neighborhoods south of
Highway 6 and west of Sycamore Street serviced by the AM loop also are served by the PM loop. The bus now
picks up at the Regina Catholic Education Center at 3:20 p.m., City High at 3:25 p.m. and South East Junior
High at 3:31 p.m. For students who live east of Scott Boulevard, the city created an Eastside Express route that
picks up on Court Street and goes out to Arlington Drive. "I think it's an improvement," City High Principal
John Bacon said. "The times are just more conducive, they just work better. I'm pleased they slid up the
departure time. It's just timed nicely." South East Junior High principal Deb Wretman said the changes are an
excellent example of students "stepping forward" and voicing their concerns and the city responding to those
needs.
"It's a win- win," she said. "Thatdoesn'thappenoften." With the weather still warm and relatively dry and fall
sports in fill swing, the city and schools don't have a great sense of how the new routes are working out. But
they are confident the changes will be positive.
"I think a few weeks out, we'll have more information," O'Brien said. "I think anytime you make a route
change, there's going to be an impact. You hope it's more positive than negative. Time will tell. On paper, we
feel it's a positive change if you have less waiting time, less time on the bus and cover more area."
Reach Lee Hermiston at 887 -5413 or lermiston @press- eitizen.com.
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