Preliminary Review of City of Palo Alto Traffic Stop Demographic Data

(2009/4th Quarter) And Other Traffic-Related Issues

Table Of Contents

Section Topic   

1.0 Summary   

2.0 Foreword   

3.0 Structure of This Report   

4.0 Initial Discussion4.1 Traffic Stops On A National Level4.2 Traffic Stops In Palo Alto4.3 Racial Profiling Defined4.4 No Yearly Palo Alto Police Performance Report4.5 Crime And Race In Palo Alto

4.6Broken Windows vs. Community Policing: The Context of Racial Profiling Studies

4.7 Proactive Policing--“Good” vs “Bad” (“Pretext) Traffic Stops4.8 Proving “Racial Profiling” Using Traffic Stop Data4.9 Linkages Between Traffic Stops And Traffic Accidents4.10 Alcohol-related Accident Locations And Arrest Stop Locations4.11 Costs vs Benefits of “Traffic Services” 4.12 Yearly Contacts--A Metric Of “Service Levels”4.13 Costs of Traffic Stops For County/State/Federal Governments4.14 Issues Suggesting Regionalization Of Public Safety Agencies

   5.0 Presentation of US Census Data5.1 SF.BayArea 2010 Census Data5.2 Regional/State/US Census Data   

6.0 Presentation of SWITRS Traffic Accident Data (By Race)6.1 Traffic Accidents In Palo Alto (2009) (By Race)   

7.0 Presentation of Palo Alto Traffic Stop Data7.1 Basic Traffic Stops Data7.2 City-of-Residence For Majority of Traffic Stops (~80%)

7.3Comparison of Accidents Locations vs Traffic Stops Locations (By Streets)

7.4 Stops On Major Streets, By Race7.5 Stops--By City/By Race7.6 Stops---By Month: All Cities, All Races7.7 Traffic Stops—All Cities/By Race7.8 Stops—By Time-of-Day7.9 Stops Resulting In Searches7.10 Stops Resulting In Arrests7.14 East Palo Alto Stops—A Closer Look.7.15 Traffic Stop Productivity—Citations vs. No-Citation Stops

   8.0 “Data Not Available”   

9.0Comparison Of Palo Alto Stop Data With Other Communities’ Stop Data

   10.0 List Of “Red Flags”

   11.0 General Discussion11.1 Monitoring Police Performance Requires Collecting Traffic Stop Data.11.2 Future Traffic Stop Data Review Need Only Focus On Local Cities.11.3 Surprises—Good and Bad11.4 Comparison Of Stops Data With Other Jurisdictions.11.5 Evidence of Traffic Stop “Quotas” In Palo Alto

11.7Probability of Being Stopped While Driving In Palo Alto, On Yearly Basis

11.8 Racial/Cultural Differences In Driver Behavior

11.9Racial Components Of Traffic Stops--A Reflection of Immigration Trends?

11.10 Productivity of Traffic Stop Searches11.11 Value of “No Action/Warning” Stops11.12 Issues With Current Review11.13 Areas For Future Inquiry11.14 Problems With Independent Police Auditor’s Review

   

12.0 Conclusion12.1 Data Review

12.2 Process Review   

13.0 Recommendations   

14.0 Final Thoughts/Comments14.1 Point-of-View Of This Review.14.2 Problems With Data Availability.14.3 Evidence For Terminating/Reducing “Street Teams”/”Traffic Services”14.4 Complexity Of Modeling Traffic Stops14.5 Police Not Supportive14.6 Costs Associated With This Review14.7 Report Generation Tools Needed.14.8 Concern Over “Red Flags”

1.0 Summary

The issue of “racial profiling” ( or “bias”) by police, arising in the execution of traffic stops has been raised at a national level for some time now. This concern has resulted in numerous studies by various governmental, and non-governmental, agencies/organizations around the country for well over a decade. These studies, as it turns out, have not produced a consistent statistical/data collection methodology which can be applied by any local law enforcement agency to produce “hard” data that will establish, prove, or disprove, that “bias” exists in the execution of traffic stops. The difficulty of this problem in “statistical analysis” is discussed in great detail in the papers generated by every organization involved in this work. (Links provided to several studies in Appendix.G. Persons unfamiliar with this topic are encouraged to review these papers. Also, a glossary of terms is provided in Appendix.A to provide additional information about terms and concepts pertinent to this topic.)

Here in Palo Alto, there have been more than a few complaints about “racial profiling” directed against the local police, where traffic stops are concerned, over the years. These complaints gained sufficient attention, at some point, so that the City Council directed the Palo Alto Police to keep records on individual traffic stops--recording the race the driver. Responding to the Council’s concerns about racial bias in policing, the Palo Alto Police Department in July, 2000, became the fourth city police department in the state to record the race of every person stopped by its officers. This data was then analyzed, and reports provided to the City Council (see links below) periodically.

As it turned out, the Police Department design of the data collection exercise, and analysis of the collected data, never revealed any “smoking guns” clearly proving bias, or “racial profiling”, in the execution of traffic stops by the traffic police. Reviewing the data collected by the Palo Alto Police, compared to that collected by other agencies, it becomes clear that not enough data was collected here in Palo Alto, so that a robust analysis of the traffic stop data could be achieved. Moreover, it also becomes clear that traffic stop data collected in other jurisdictions may well not be relevant, other than from a “big picture” point-of-view, to use as valid “baselines” for determining “bias”.

Over time, there were a number of complaints raised about the process, and the data collected, which the police attempted to address. Eventually, the outside Police Auditor, when asked to review the traffic stop data, more-or-less declared that “bias could be not demonstrated”. The Council eventually agreed that the demographic data collection should be terminated, which it promptly was.

Using raw data released by the Police Department, and relational database technology, a review of the traffic stop data has been undertaken that produces a somewhat different view of traffic stops in Palo Alto than that generated by the police. Additionally, this review links the traffic accident history in Palo Alto (available via CHP/SWITRS) with the traffic stop data, and also uses 2010 Census data to adjust the race data to reflect the racial makeup of San Francisco BayArea, as well as the city of residence of each driver, so that a better “apples-to-apples” comparison of the race data can be achieved. Data from other police agencies is also introduced, where meaningful.

1.1 Highlights Of This Review:

The number of vehicle trips in Palo Alto is somewhere between 500,000 and 600,000 per day (data previously published by the Palo Alto Traffic Engineering Department).

On average, there were about 44 traffic stops per day in Palo Alto during 2009/Q4--with a low of fourteen (14) stops on Christmas day, and a high of ninety-three (93) stops on the December 19th.

The likelihood of being stopped while driving in Palo Alto, is very low—about .01% per vehicle trip (44 stops/500,000 vehicle trips daily).

80% of all stops involved residents of Palo Alto, eleven neighboring cities, and Stanford.

The remaining 20% of all stops involved residents of 180 California cities, and beyond.

40% of traffic stops involved Palo Alto residents (about 1,600 stops). 8% of all traffic stops involved East Palo Alto residents, with Mountain View

residents stopped about 6% of the time, and Menlo Park Residents only 4% of the time.

Men are stopped about two (2) times more frequently than women, which tends to be true on a national basis, also.

Based on Census data, “Blacks” and Hispanics (residents of Palo Alto, and East Palo Alto), are stopped, and searched, more often than White residents of Palo Alto and East Palo Alto, based on their representation in the Census data.

Asians are stopped less frequently than their representation in Census data. On average, three (3) arrests per day (about 7.3% of all stops) resulted from traffic

stops in Palo Alto during this time frame. Data from the Police section of Palo Alto budget (2010) suggests there are

upwards of 50 DUI arrests per quarter. (Information indicating alcohol-related arrests resulting from traffic stops is not found in the traffic stop data.)

The race of the “at-fault” parties causing traffic accidents in Palo Alto (obtained from SWITRS data) generally track the regional racial components found in the US Census. This SWITRS data thus serves as a “base line” for detecting “bias” in Palo Alto traffic stops.

42.2% of all traffic stops were because of “EQUIPMENT/REG” violations. 48.1% of all traffic stops were for “MOVING/HAZARD” violations. 37.2% of all traffic stops resulted in a “Citation”. 55% of all traffic stops resulted in “No Action” or “Warning”. 9.3% of all traffic stops resulted in a vehicle search. 7.3% of all traffic stops resulted in an arrest (263). Whites comprised about 80% of all arrests by Palo Alto Police (all reasons). “Blacks” comprised about 20% of all arrests by Palo Alto Police (all reasons). Of the 127 DUIs Reported to the Bureau of Justice Statistics for 2009:

o Whites accounted for 96.6% (123) of DUI arrests.o “Blacks” accounted for 3.4% (4) of DUI arrests.

38.4% of those arrested during traffic stops resided in Palo Alto. 12.2% of those arrested during traffic stops resided in East Palo Alto. 75% of traffic stops involving residents of East Palo Alto resulted in “No

Action/Warning”. 37.3% of those stopped were less than 30 years of age. 62.7% of those stopped were older than 30 years of age. 6.9% of those stopped were 60 years of age, or older. 5.9% of those stopped, and searched, were less than 30 years old. About 15% of all traffic stops occur between 00:00 (am) and 06:00 (am). About 55% of all traffic stops occur between 08:00 (am) and 19:00 (pm). On streets/roads where traffic accidents occur most frequently, the traffic stop

rates closely track the accident occurrence rates. No linkage between traffic stops, and the occurrence of traffic accidents, can be

established from this data. Based on a yearly $2+M expenditure, and a $500K revenue stream, “traffic

services”, on average, could cost the taxpayers upwards of $200/stop. High variability in yearly traffic stops and citations written, over the past decade

—providing clear evidence of traffic stops being effectively “quota” driven.

Relative to the basic issue of “fairness”, and/or “racial profiling” on the part of the police, this review of the traffic stop data reveals that “Blacks” residing in Palo Alto are stopped in numbers equal to more than half of the “Black” population old enough to have a

driver’s license (and presumably driving). Statistically, this means that every eighteen to twenty-four months each “Black” person in Palo Alto will be stopped while driving. For East Palo Alto residents, every “Black” person who drives in Palo Alto can expect to be stopped at least once every four years. For whites, in Palo Alto, the expectation of a traffic stop is once every eight to ten years. Also, it appears that 55% of all traffic stops result in “No Action/Warning”, which begs the question: “Why were these stops initiated?” This statistic alone goes a long way towards answering the question as to the extent “pretext” traffic stops are being utilized by the Palo Alto Police.

The results of this study are necessarily limited by the data available to the public. However, there are sufficient “red flags” raised by this review that Palo Alto residents –and the City Council--should be concerned about these results, and make inquires of the Police Department to further explain the disproportionate number of stops of “Blacks” and Hispanics in Palo Alto.

From a financial point-of-view, given the costs of the so-called “police street teams” (and the administrative overhead needed to support these employees), it seems clear that robust cost/benefit reviews of this police activity should be conducted periodically to ensure that the public expense of providing this “service” is justified in terms of increased public safety (reduced traffic accidents, etc.), as well as insuring that all motorists are treated fairly when stopped by the police. This study poses some questions about the cost/benefit of “traffic services”.

Statistical analysis, based on relatively small data samples, may not easily prove “bias”. Such exercises can, however, provide a strong basis for making the case that “bias” exists--which does seems to be the case for Hispanic and “Black” motorists who are stopped by the Palo Alto Police. Certainly, more study is strongly suggested from the results of this review of this one ninety-day reporting period to more fully characterize the practices of the Palo Alto in their execution of traffic monitoring and traffic stops.

As with all analyses/reviews involving “data”, the results of this inquiry are necessarily data intensive, and will doubtless require a number of readings to fully appreciate the data, and implications, of the review. Findings of this report that are considered as “troubling” have been tagged in terms of “red flags”, and should be given the reader’s full attention.

2.0 Foreword

As a long time resident of Palo Alto, when this issue of “racial profiling” first became a matter of public concern, I dismissed the claims as “without merit”. I had been stopped once or twice every ten years, for what seemed to be justifiable reasons. The officers involved seemed professional in our contact, and I never considered the possibility that they (the police) would treat other motorists any differently than myself. In short, I just couldn’t believe that “racial profiling” was going on in Palo Alto.

But at the same time, I was keenly aware that the Palo Alto Police did not produce a yearly performance report, and revealed little information to the public about its activities, in general. Even though the City Auditor commenced publication of a yearly Services and Accomplishment’s Report around 2002, the details about each of the City’s departments could only be considered as a “key-hole” view of the activities of that department, including the police. So, having the police collect, and publish, this data seemed like a good idea—if only to prove that “racial profiling” did not exist.

As the quarterly reports were released, they did not seem to “prove” much. There were a number of complaints from various corners, including myself, that suggested that without detailed Census data, the data collection would not provide enough information to provide any kind of meaningful result. As memory serves, the Police Department, under former Chief Lynn Johnson, did make adjustments to the data collection template, although the raw data was not generally made available to the public during that time frame. However, it seemed clear that the Palo Alto Police were Police not fully “On Board” with this exercise. As it turned out, few people in Palo Alto seemed interested in the results of the demographic data analysis. Given the professional nature of most of the residents of Palo Alto, requiring fifty-plus hour work weeks, finding time to conduct significant research on such topics is virtually impossible for those individuals who do possess the skill set needed to model an organization’s performance via data-based methods. This lack of time, and inclination was true of myself, also, until recently. Moreover, since the Police did not make raw data available until the program was near termination, results from any such research would be necessarily limited in its scope, and conclusions.

The last decade has seen the Internet grow so that it now available on “smart phones” that most people carry around in their pockets. The fusion of “compute power” and data availability has radically altered the toolkits of everyone in the world. Information/data that once was “locked up” now is open to worldwide distribution, and use.

Unfortunately, we have not seen the Palo Alto Police jump onto this “wave” of technological revolution during this decade, as much as we have seen it championing the need for an expensive new building that is modeled on the service delivery model of the decades ago. Concepts like “mesh networks”, vehicular telemetrics, and traffic monitoring automation have not seen the light of day, at least in any procedural way—such as producing a 5-Year/10-Year Technology Plan. Nor has the Palo Alto Police

demonstrated any understanding of regionalizing its redundant functionalities, such as records management and information sharing via a common computer system. Even if there has been some thinking on the part of the Palo Alto Police along these lines, there have been no “white papers” published in order to share this thinking with the public, and other law enforcement agencies. This preliminary report is expected to require several revisions, as traffic stop data from other communities becomes available for comparison with Palo Alto’s data. Continued thinking about this topic makes it clear that this traffic stop data is very important to understanding the activities, and need for, a police presence in the community. This issue of the continuation of traditional “traffic services” becomes increasingly important if the costs of these services were to continue to double every twelve to fifteen (12-15) years, as they have done in the past.

On a personal note, this review of traffic stops, as part of a more extensive review of traffic accidents in Palo Alto, is intended to provide examples to the Palo Alto City Council, and the public, of the possibilities that exist for meaningful reporting of police activities using readily available computer hardware and software.

3.0 Structure of This Report

This report has been structured to facilitate the logical flow/presentation of the data needed to support the methodology utilized to make meaningful interpretations, and comparisons, of the traffic stop data, and to support the conclusions of this review:

Sections Summary Initial Discussion Presentation of Baseline/Census Data Presentation of SWITRS Accident Data Presentation of PA Traffic Stop Data List of Review’s “Red Flags” General Discussion Conclusions Recommendations Final Comments Appendices

The summary provides a thumbnail overview of the results of the review of the traffic stop data for 2009/Q4. The initial discussion presents a high-level view of the issues involved with attempting to analyze the Palo Alto traffic stop demographic data. The next two sections present the most recent Census data for the San Francisco Bay Area, and some SWITRS data that will be used to help establish baselines for interpreting some of the results. The detailed review of the published traffic stop data under study is presented next. To help readers understand the key issues identified in the detailed

review of the traffic stop data, which is necessarily “data intensive”, a section listing only the “red flags” is included. This section is followed by a general discussion of both the data, the “red flags”, and issues that range from problems with the data collection template to regionalization of the traffic stop monitoring process, in order to reduce costs, and provide a regional platform to monitor police activities in general. A section on recommendations provides some specific suggestions that follow from the issues identified in the general discussion section. The section on final comments adds some opinion that falls outside the scope of pure “analysis”. To enhance readability, data considered too detailed to be included in the body of the paper, or generally more supportive in nature than directly contributory to the narrative, can be found in the Appendices.

It might prove less arduous for some readers to read the narrative sections in the beginning and end of the report, and then review the data sections after the summarized information has been presented in the more “readable” sections of narrative.

4.0 Initial Discussion

The issue of “racial profiling” by law enforcement agencies emerged on the national stage in the late 1990s, calling into question the promise of equality of all before he law—resulting in many state, and local, agencies initiating traffic stop data collection that included the race/ethnicity of those stopped. These disparate efforts involved little, or no, collaboration between agencies, which produced results that were often incompatible and often were not very productive in proving “racial profiling”, although all of these efforts did seem to demonstrate “disparities” between the races when it came to searching, during the traffic stop.

4.1 Traffic Stops On A National Level

The US Bureau of Justice Statistics report on “Police Contacts” (Appendix.H), offers the following overview of traffic stops, where the race of the driver is concerned:

An estimated 19% of U.S. residents age 16 or older had a face-to-face contact with a police officer in 2005, a decrease from 21% of residents who had contact with police in 2002. Contact between police and the public was more common among males, whites, and younger residents. Overall, about 9 out of 10 persons who had contact with police in 2005 felt the police acted properly.

Of the 43.5 million persons who had face-to-face contact with police in 2005, 29% had more than one contact. The most common reason for contact with police in both 2002 and 2005 involved a driver in a traffic stop. Other frequent reasons for contact included reporting a crime to police or being involved in a traffic accident.

Nearly 18 million persons — or 41% of all contacts in 2005 — indicated that their most recent contact with police was as a driver in a traffic stop. This represented about 8.8% of drivers in the United States, a percentage unchanged from 2002. Stopped drivers reported speeding as the most common reason for being pulled over in 2005. Approximately 86% of stopped drivers felt they were pulled over for a legitimate reason.

In both 2002 and 2005, white, black, and Hispanic drivers were stopped by police at similar rates, while blacks and Hispanics were more likely than whites to be searched by police. About 5% of all stopped drivers were searched by police during a traffic stop. Police found evidence of criminal wrong-doing (such as drugs, illegal weapons, or other evidence of a possible crime) in 11.6% of searches in 2005.

Table.4.1—Results of National Survey of Stopped Drivers

White, black, and Hispanic drivers were stopped by Police at similar rates; blacks and Hispanics were

searched by police at higher rates than whites         

Race/Hispanic

Percent of drivers stopped by police

Percent of stopped drivers searched by police

origin of resident 2002 2005 2002 2005Total 8.80% 8.80% 5.00% 4.80%White 8.8 8.9 3.5 3.6Black/African American 9.2 8.1 10.2 9.5Hispanic/Latino 8.6 8.9 11.4 8.0

Source:    US Bureau of Justice Statistics

While national traffic stop data may not necessarily have any direct relationship to any specific community, these percentages can become a useful backstop for evaluating local traffic stop data.

4.2 Traffic Stops In Palo Alto

In time, claims of “racial profiling” on the part of the Palo Alto Police Department entered the public discussion of local government about the same time. The people alleging “racial profiling” by the Palo Alto Police provided no hard evidence to substantiate their fears, but were nonetheless adamant about their claims. The Council responded to this situation by directing the police was to collect data that would hopefully prove, or disprove, these assertions. Unfortunately, the quarterly reports that resulted from these data collections were not particularly informative, and generally proved little.

In addition to investigating the possibility of racial “bias” on the part of the Palo Alto Police, the cost of the police function is increasing on a year-by-year basis, showing a roughly thirty-five percent cost increase in the last five years alone, is considered in this study. At these rates, Palo Alto taxpayers will see a possible doubling of the cost of “police services” every fifteen years. Therefore, residents, business owners, and City Officials all need to be scrutinizing every possibility of reducing the cost of local government—including the reorganization, and possibly reduction, of police services.

Moreover, the persistent demand for a new police station continues to percolate in the mix of Palo Alto infrastructure backlog. This collection of public works projects probably is in the area of $600M-$1B ($1.2B-$2B with bond financing considered). Should this station be built, it would add (nominally) about $30,000/officer/year in cost to provide police “services” (over a thirty year period), to the already staggering cost of over $185,000+/officer now being borne by Alto taxpayers. This review is intended to throw some light on the activities of the “traffic services” unit, with an eye towards questioning its costs vs its benefits, in light of the well-established cost increases of the near past, and the future.

This inquiry was initiated to provide insight into the relationship between traffic stops and traffic accidents, with no preconceived notions about wrong doing on the part of the Palo Alto Police. Additionally, the expectation that relational database technology would provide additional insights into this data that less capable tools might not added the possibility that a “fresh eyes” approach might produce different results than those produced by the Palo Alto Police Department.

While this review was originally intended to only focus on traffic stops in Palo Alto, given the complexity of the issue of “racial profiling” and the establishment of “bias” on the part of the police, a number of “framework” issues need to be discussed prior to commencing the presentation, and analysis, of the Palo Alto traffic stop data.

4.3 Racial Profiling Defined

The following broad definition of “racial profiling” is taken directly from the June, 2000 Minnesota House of Representative Information Brief on Racial Profiling (link in Appendix.G.38):

Under the broader definition, racial profiling occurs when a law enforcement officer uses race or ethnicity as one of several factors in deciding to stop, question, arrest, and/or search someone. An example of racial profiling under this broader definition would be a police stop based on the confluence of the following factors:

• age (young); • dress (hooded sweatshirt, baggy pants, etc.);• time of day (late evening);• geography (in the “wrong” neighborhood); and• race or ethnicity (black or Hispanic).

Under this broader definition, then, racial profiling occurs whenever police routinely use race as a factor that, along with an accumulation of other factors, causes an officer to react with suspicion and take action

Hopefully, this definition is sufficiently clear that it can be introduced, and used, with no further discussion.

4.4 No Yearly Palo Alto Police Performance Report

The Palo Alto Police seems to report various aspects of its yearly performance to a number of Federal/State and local agencies:

US Department of Justice Federal Bureau of Investigation California State Attorney General California Highway Patrol/SWIRTS Palo Alto City Auditor The Palo Alto City Manager Public Records Requests (per CA State Law)

However, the Palo Alto Police does not issue a comprehensive yearly performance report that offers all of this information in a unified presentation to the Palo Alto public. This lack of transparency is one of the most disappointing aspects of the operation of the Palo Alto Police Department. Given the power of on-the-shelf hardware, and software, and ubiquitous Internet access, there is no reason such a report can not be generated on any desk-top computer.

Moreover, with the high cost of providing public safety “services”, this lack of transparency leaves every resident without any clear understanding of the operations of these departments, and no basis for making decisions about the need, or value, of continuing these departments into the future.

In terms of charges of “racial profiling”, residents generally have little (if any) knowledge about the operation of these departments to make informed conclusions about claims such as “racial profiling”. When people claiming to be “victims” of illegal police actions do appear, from time-to-time, the public is too often likely to respond with “knee jerk” reactions to these claims, as well as easily being manipulated by special interest groups pushing their individual agendas.

The level of effort for parties outside the Police Department to draw all of this information together is non-trivial. Local media outlets can not be expected to exert the level of effort found in this study, nor can they be expected to possess the necessary expertise in government organizations, or analytical tools--such as statistics and data modeling--to draw the information found in this review together in a way that would be comprehensible to the public at large.

It also should be noted that performance reports from every police department in California are needed, and that the content and format might be best defined by an outside agency, such as the Office of the State Auditor, or even a national entity. It is important that all of the details of a police agency’s activities be documented, which might prove difficult for local agencies to fully understand, or even be interested in giving the public access to such information.

4.5 Crime And Race In Palo Alto

A review of traffic stop data also needs to consider the overall crime situation in a community, with particular attention to property crimes. (Palo Alto’s Part I crimes for 2005-2010 can be found in Appendix.F.) Given the relatively high property-crime rate in Palo Alto, and the relatively low “closure rate” for these crimes (15%-20%), residents no doubt expect the police to be on the look out for suspicious activities by individuals, and vehicles, that are moving about the community, and especially at night. Traffic stops that prove productive in terms of interdicting crime (often called a high “hit rate”) would seem both appropriate and prudent for local police; however, insuring that these stops are, in fact, necessary (and productive) is also appropriate, and prudent, on the part of the police.

Table.4.2 (below) provides a five-year history of the racial makeup of those arrested in Palo Alto, while Appendix.J provides the detailed arrest information reported by the Palo Alto Police for 2009:

Table.4.2—Five-Year History of All Arrests In Palo Alto, By Race.

Year Total White Black AIAN API % White %Black2009 1,528 1,224 286 3 15 80.1% 18.7%2008 1,947 1,537 393 3 14 78.9% 20.2%2007 2,058 1,630 401 9 18 79.2% 19.5%2006 1,608 1,257 333 5 13 78.2% 20.7%2005 1,553 1,209 326 6 12 77.8% 21.0%

WhereAIAN = American Indians and Native AlaskansAPI = Asians and Pacific Islanders

Source: US BJS (Bureau of Justice Statistics)

With “Blacks” comprising less than two percent (2%) of the population of Palo Alto, the percentage of “Blacks” being arrested is roughly ten (10) times the Census-based representation. Even though the city-of-residence of those arrested is not listed in the Federal data, it would be easy to speculate that most “Blacks” arrested are not Palo Alto residents; as such, it would be another easy speculation to assert that those non-resident “Blacks” arrested in Palo Alto used a motor vehicle to travel to/from their city-of-residence to Palo Alto where they were arrested for whatever reason.

The use of “red” in Table.4.2 is not intended to be a “red flag” situation, but simply to highlight this striking statistical fact.

4.6 Broken Windows vs. Community Policing: The Context of Racial Profiling Studies

To better appreciate the issue of traffic stops as a part of an “integrated” police response to “crime” in a given community, the following is taken from Minnesota Information Report on Racial Profiling (Appendix G.38):

The recent concern over racial profiling in police stops can be viewed as part of a larger law enforcement debate, pitting the broken windows theory against the community policing approach to law enforcement.

Some experts maintain that the aggressive use of high-discretion police stops can deter crime, as when the stop results in detection of contraband like guns and drugs, or when it intercepts and frightens off a would-be criminal on the way to commit a burglary, rape, robbery, or other serious crime. Indeed, aggressive police stops of this type are a basic element of “zero-tolerance” tactics, a component of the “broken windows theory” policing strategy that was pioneered by New York City, and which has been adopted by numerous other jurisdictions throughout the country. Though this theory of policing has been widely credited with much of the crime reduction see throughout the nation during the 1990s, it is

increasingly being questioned as being overly harsh and subject to excessive use of force.

The broken windows policing strategy has been criticized, for example, for its possible role in some recent mistaken shooting deaths and other allegedly harsh behaviors by a few NYPD officers; incidents that have been widely publicized and, which some say, may be unfairly targeting members of minority groups. Some critics have suggested that such excesses of police force are an inevitable development under zero-tolerance policing tactics. Such critics claim that racial profiling in both traffic and pedestrian stops is yet another consequence of this policing approach

4.7 Proactive Policing--“Good” vs “Bad” (“Pretext) Traffic Stops

Because motor vehicles are often involved in felony crimes that far exceed the scope of speeding, or other vehicle code violations, police are doubtless keenly aware that stopping “suspicious” vehicles quite often leads to arrests for transporting stolen property, contraband or weapons. Proactive policing might result in a fairly high number of traffic stops for a given community, particularly when the productivity of such stops results in a sufficient number of arrests (and convictions) to justify the practice.

There are always “gray” areas associated with traffic stops, which can call into question the fairness of the police towards some stopped motorists. The lack of openness on the part of most police agencies relative to the execution of their duties contributes to doubts that society harbors over the quality of police services in their communities. Relative to traffic stops, motorists often are left with the question: “was there good reason to stop me, or was this stop unnecessary?” Collection, and publication of detailed traffic stop data, helps to illuminate police activities, and possibly highlight practices that might demonstrate “bias” on the part of individual officers, or the department as a whole.

One of the terms that emerges from the many studies of traffic stop data collections across the US is that of “pretext traffic stops”. “Pretext stops” are vehicle stops initiated by the police which are intended to investigate some other matter, such as a search for weapons, or illegal drugs, rather than the stated reason for the stop (such as an equipment violation). While most police departments make no public admission of their use of “pretext” stops, a review of the traffic stop data from those communities would likely provide “red flags” as to the use, or abuse, of this police power.

The Minnesota Information Brief (Appendix G.38) included this defense of Racial Profiling:

A related rationale for racial profiling is that it may help to deter some crimes. For example, one police officer noted in conversation that the tactic of assertively stopping, questioning, and identifying young males seen driving during evening and nighttime hours explicitly identifies those drivers at a given time and place, thus depriving any potential perpetrator among them of the anonymity that is necessary to avoid arrest for certain crimes like burglary, robbery, or rape. Thus, he noted, racial profiling might well deter some would-be criminals from following through with some planned crimes.

Traffic stops resulting in citations that are warranted (speeding, improper turns, etc.), as well as stops that result in arrests for other crimes in progress, would no doubt be seen as “good” traffic stops by most people. But when the traffic stop results in “NO ACTION” or a “WARNING”, then it becomes less clear why it was initiated, and what the ultimate purpose of such a stop might be. It might follow that such stops might be considered “bad”, unless the police provided enough information to the public to justify their actions. Increased use of “pretext stops” would no doubt result in complaints from the public about police harassment, and possibly “racial profiling”, in some communities.

To make matters worse for local law enforcement, trafficking in illegal drugs (contraband), illegal weapons, and even humans, has now become a problem of national proportions. The 9-11 attack has seen the emergence of the Department of Homeland Security (DHS), which has, in many ways, attempted to “deputize” all of the nation’s local law enforcement officers, to become America’s first line of defense against domestic, terrorism. The requirements on local law enforcement have become markedly more demanding in the last twenty-thirty years, particularly in this country where personal freedom is considered to be a cherished right by most people.

So, one of the goals for reviewing the data provided by the Palo Alto Police would be to recognize patterns that might suggest that the Palo Alto Police have been engaging in “bad” traffic stops. Certainly DUI stops must be seen as a “benefit”, but these “good stops” would be offset if it turned out that the police were also engaging in stops that were initiated to harass people “driving while black” on the city streets. Without periodic release to the public of traffic stop data, there is no obvious way to know whether the police are engaging in “bad” stops, or not.

The police have been entrusted with extensive powers of detention, and arrest, which society entrusts to individual police officers, and the police management teams, to properly exercise. Trust, however, must be earned, and that process necessarily involves periodic releases of performance data that is complete and accurate. Currently, the Palo Alto Police does little to engender such trust by failing to producing performance data that documents its activities.

Ultimately, the success of any review/analysis of the collected traffic stop data hinges on the robustness of the design of the data collection template, and the professionalism of the officers making the traffic stops in recording the needed data. Without a good, top-down design of the collection template, and meticulous recording of all data from each stop, it would be very easy for the analysis of the data to render either incorrect results, or indeterminate results.

4.8 Proving “Racial Profiling” Using Traffic Stop Data

“Racial profiling”, while easily claimed by some motorists who are dissatisfied with having been stopped/cited by the police, has proven to be a most elusive thing to substantiate from the data collected by the nation’s police, in their attempt to respond to these concerns. The literature abounds with reasons why these studies have been less than successful in proving “bias” on the part of the police. One clear reason emerges from reviewing these studies, however. It would appear that the data needed to prove this sort of activity in an analytic fashion does not seem to have been collected by the police agencies, in general. Whether this lack of appropriate data collection has been by design, or a result of a lack of statistical expertise in the design of the data collection templates when these data collection activities were initiated, is an open question.

Traffic stops are executed in two distinct phases: 1) the decision to initiate a vehicle stop by the traffic officer, and 2) the decisions as to how to dispose of the stop by the officer. While many of the verbal complaints about “racial profiling” focus on the traffic police being able to visually indentify drivers by race, few of those commenting on the matter seem to have considered the possibility of “bias” during the processing of the stop, where the decision to cite, or only warn, the driver occurs. Therefore, the design of the data collection “template” becomes crucial to having sufficient information available (after the fact) to make inferences about possible “bias” on the part of individual police officers, as well as a given police agency.

It is clear that too many police agencies started collecting data without first consulting statistical analysts/experts about what kinds of data would be needed to actually prove “racial profiling” from their data collections. As a result, far too many studies have resulted in inconclusive results. The same complaint can be lodged against the Palo Alto data collection effort. However, the Palo Alto Police did collect the “city-of-residence” of those stopped, which offers a subsequent analysis a link to Census data that is more granular than that of the regional San Francisco Bay Area. Review of web-based traffic stop data collections from law enforcement agencies across the country, as well as the studies of “interested parties” studying possible “racial profiling” in traffic stops, reveals that proving police “bias” in the execution of traffic stops is difficult. Moreover, data collection templates, and statistical methodologies for processing collected data, and have not been generally been adopted at the national level. In fact, most of the studies/reports investigating this matter conclude by admitting that “smoking guns” are almost impossible to locate in the data available to researchers during the study periods, although disparities between racial components of the

populations being studies were generally demonstrated. (Details of the many problems encountered in this sort of inquiry can be found in the studies/reports identified by the links in Appendix.G.)

Moreover, given the highly mobile nature of the California lifestyle, the assumptions needed to make valid statistical calculations about a given population’s racial makeup do not play out as true, on an hour-by-hour, and day-by-day, basis.

However, the US Census provides accurate, and credible, racial components of the population of each city, county, and state, so Census data will be used as a primary “base line” to demonstrate variations in the racial components of the drivers involved in Palo Alto traffic stops. Additionally, CHP/SWITRS data (reported to the CHP by the Palo Alto Police Department) exists which also includes race data. The SWITRS data is not as useful to this inquiry as it could be, unfortunately, since the “city of residence” is not a part of the publicly available traffic accident data. Nonetheless, this data can be expected to demonstrate reasonably credible data involving driver behavior, relative to race, that can be used as another baseline for detecting “bias”.

This problem of how to prove “racial profiling/bias” on the part of the police seems to be well stated in the Rhode Island Study Final Report (2003). Those points are included to reinforce the idea that this problem is essential a national problem, and the solutions, relative to monitoring and resolution, affect every community more-or-less equally:

Although there are numerous questions that can be raised about the relationship between race and traffic stop practices within police departments, four primary questions are addressed in this report.

1. What is the general pattern of traffic stop activity in Rhode Island?

2. Do disparities exist between the demographics of those estimated to be driving on roadways of Rhode Island and the demographics of those who are stopped for traffic violations? If so, in which jurisdictions are racial disparities the greatest?

3. Are racial disparities between the driving population and the stopped population explained or mitigated by race-neutral factors?

4. Are there racial disparities in the proportion of drivers who are searched once they are stopped? If so, are there race-neutral factors that might explain such differences in post-stop activity?

Every traffic stop monitoring program should be designed to collect data that will lead to the successful answering of at least the questions posed above. There is little evidence that the Palo Alto Police understood this key issue in their overall management of this data collection exercise.

Red Flags have clearly been raised in every traffic study published, including this one. There is no doubt racial disparities exist between Whites/Asians and “Blacks”/Hispanics in the Palo Alto traffic stop data, as well as virtually every other published study of such data. This disparity is not difficult to demonstrate. The question as to why this disparity exists constitutes a failure on the part of the police (everywhere) to collect sufficient data to justify their actions via analytic review. If each of these traffic stops in Palo Alto were justified, then the police have simply been doing their jobs. However, there are sufficient “red flags” raised in this review to demonstrate that quite likely in Palo Alto some motorists—“Blacks” and Hispanics, in particular--are being unnecessarily stopped, and some, unnecessarily searched. 4.9 Linkages Between Traffic Stops And Traffic Accidents

One of the most obvious “benefits” of an aggressive traffic monitoring/policing program would be the demonstration of a clear linkage between traffic stops and traffic accidents. Given substantially more information (such as driver identify, stop history and accident history), there might be a way to establish such linkages. However, that sort of exercise could only be performed at the State level, requiring extensive research to obtain valid information needed to establish such linkages.

Linkages between traffic stops, and traffic accidents, are not mentioned in any of the traffic stop data collection studies. Therefore, questions about the long-term need for traffic monitoring/policing should be asked, given the increased cost of police personnel, and the emergence of on-vehicle event data recorders (EDRs), which will become required equipment after 2012 on new vehicles.

This review makes some initial linkages between traffic stops and traffic accidents, although no conclusions about the reduction of traffic accidents can be inferred from the presentation of this data at this time.

4.10 Alcohol-related Accident Locations And Arrest Stop Locations

Reducing Alcohol-related accidents, which have caused hundreds of thousands of deaths and injuries in the US (and world-wide) have been the focus of police and society for some time now. National statistics show that there has been a decline in such accidents over the past twenty years. Locally, SWITRS data shows that there are not very many alcohol-related accidents in Palo Alto, although the police report that the number of arrests resulting from “Driving Under The Influence” (DUIs) ranges from 150 to 250, on a yearly basis.

While the SWITRS data provides information about race, location, time-of-day, alcohol-involved (and more), the Palo Alto traffic stop data does not seem to provide any clues to identify those stops resulting in “arrests” which were due to DUIs. Therefore, a definite linkage between traffic monitoring and alcohol-related accidents can not be drawn at this time. However, this is a fairly clear correlation between the number of traffic stops in the downtown area of Palo Alto, and the number of alcohol-related accidents that occur in that area. With the number of restaurants and bars serving alcohol, and the large number of people working in this area, the need for traffic monitoring, and the expectation of alcohol-related accidents, can be asserted from a review of all publicly-accessible data.

4.11 Costs vs Benefits of “Traffic Services”

As the City of Palo Alto has spent most of the last decade reviewing its financial situation, departments have been required to model their operations so that actual costs of “services” could be better identified. This budget year, the cost of “traffic services” is now documented to be over $2+M a year. Given the rare opportunity offered the public via the publication of this traffic stop data to review this aspect of police activities, questions about “how effective” “traffic services” might be can be asked, with some hope of having answers to this question couched in “hard numbers”--rather than emotional appeals about “public safety” that often hold sway in such discussions.

4.12 Yearly Contacts--A Metric Of “Service Levels”

Data released by the Palo Alto Police show between 65,000 and 70,000 yearly “calls-for- service”, for the past five years. It is not clear from the presentation of this data if traffic stops and accident responses are included in these “calls-for-service” numbers, or how many unique people are involved in these calls-for-service.

The Bureau of Justice Statistics report on Police/Public Contacts (Appendix.H), referenced in Section 4.1 (above), reports that about 40% of the public’s contact with “the police” involves traffic stops. From the data released so far by the Palo Alto Police involving traffic stops, or other activities involving public contact, it is difficult to make a similar determination.

For the most part, the actual breakdown of “calls-for-service” is generally not published in any meaningful way by the Palo Alto Police, as would be expected if a yearly performance report were issued by the Police (and Fire Department). Being able to fully understand the activities of each of these City government functions, as well as model these activities in terms of costs/benefits, is necessary to manage these two organizations which consume over 40% of the City’s spending.

While the Bureau of Justice Statistics national traffic stop data can not be directly applied to Palo Alto, there are some similarities that merit note: the national average for all US motorists stopped seems to be about 9%. The average for Palo Alto residents seems to be about 9%, also. The national average for searches is about 5%, whereas it is about 9% in Palo Alto. Given this significant difference, and with little evidence of “search productivity” published by the Palo Alto Police, the number of searches would seem to be unnecessarily high. Until better documentation is available from the Palo Alto Police, this “service metric” needs to be considered as a “red flag”.

It would certainly be worthy of future study to obtain the police calls-for-service numbers, as well as comparative traffic stop data, from all of the metropolitan police agencies in the San Francisco Bay Area to determine if the police “service levels” are comparable, or if some communities might be “under-policed”, or “over-policed”.

4.13 Costs of Traffic Stops For County/State/Federal Governments

Other cost issues which exist, but may not be easily appreciated, arise from the costs of operating the local courts system, that must deal with traffic-generated violations, State and Federal oversight agencies, like the CHP, the California Department of Justice, and the Federal Department of Justice, that have been required to enjoin local law enforcement agencies around the country in order to ensure fairness for minorities using the highways. All of these costs need to be seen as “externalities”, relative to local law enforcement, but none the less run into the billions of dollars, when the finances of operating these agencies is considered.

At some point in time, these “chained costs” that are related to traffic stops need to be modeled. As the costs mount, the question about the benefit of the use of overly aggressive traffic stops needs a sober review.

4.14 Issues Suggesting Regionalization Of Public Safety Agencies

Projections of local government costs, the clearly redundant nature of local government services involving contiguous geographic locations, like Palo Alto, Menlo Park, Los Altos, and Mountain View call for a reorganization of local area governments into some sort of regional entity to deliver public services. Public safety agencies necessarily must be considered in such a rethinking of local governments’ service delivery model.

Complaints by the Palo Alto Police that the cost of collection of this data, in terms of time and/or dollars, is excessive could be answered by the application of technology to the problem of traffic stop data collection. Virtually all of the information that the Palo Alto Police collected was available on some computer system somewhere, and the data unique to a given traffic stop could be much more easily captured if tablet PCs were used, with application software written that would reduce the data required of the officer to a

minimum. Small departments like Palo Alto’s can not be expected to develop such hardware/software, whereas larger departments would be able to fund the specification, and development activities, given the results would benefit the residents, and police, served in the larger (reorganized) jurisdictions. (A short concept paper is offered via Appendix.G.63 which provides offers some possibilities for the use of Tablet PCs for traffic stop information/data collection.)

Many of the ideas about the future of “traffic services” (cost, necessity, reporting, etc.) in Palo Alto are common to every large urban area in the US. Computer-based decision support and information management systems for police use have been under development for some time now. Increasingly the term “predictive analytics” can be seen in the trade literature about police management issues. Such systems would, it is suggested, have the kinds of information about local crime occurrences, which would be helpful when trying to analyze, evaluate and understand the stop/search rates of a local police traffic unit.

It would not be hard to suggest that more than enough information is available to police to provide additional data along the following lines for traffic stop analysis:

Location/Time of Burglaries/Larcenies Location/Time of Traffic Stops Vehicle Search Rates Search Productivities DUI vs Non-DUI Arrest Rates Citation vs No-Citation Stop Rates

This additional information would help to create an understanding of police activities that could better justify their actions to the public.

While the cost of such systems might not be easily absorbed by local agencies, regionalizing such costs (to include support personnel), would be easily done in the regional police scenario.

5.0 Presentation of US Census Data

The material following introduces 201 US Census data, which will be used to establish racial “baselines” needed to make “adjustments” to the raw stop data, allowing meaningful comparisons between racial subgroups to be attempted. (Other Census data is included via appendices.)

5.1 SF.BayArea 2010 Census Data

The following table, taken from data on the US Census web-site (2010 census), provides the racial makeup of the cities found in the Q4/2009 demographic data released by the Palo Alto Police:

Table.5.1—San Francisco Bay Area 2010 Census Data.

    Unadjusted PercentageCity Population White Black Asian Hispanic OtherBELMONT 25835 67.6 1.6 19.9 3.7 7.2BERKELEY 112580 59.5 10.0 19.3 4.4 6.8BURLINGAME 28806 67.7 1.2 20.3 5.0 5.8CAMPBELL 39349 66.9 2.9 16.1 6.9 7.2CUPERTINO 47802 54.4 3.0 31.3 5.2 6.0DALY CITY 101123 23.6 3.6 55.5 11.1 6.1EAST PALO ALTO 28155 28.8 16.7 3.8 38.0 12.5FREMONT 214089 32.8 3.3 50.6 6.4 6.9GILROY 48821 58.7 1.9 7.1 25.2 7.5HAYWARD 144186 34.2 11.9 22.0 20.8 11.2LOS ALTOS 28976 70.6 0.5 23.5 0.7 4.7MENLO PARK 32026 70.2 4.8 9.9 8.7 6.4MILPITAS 66790 20.5 2.9 62.2 8.7 5.6MORGAN HILL 37882 65.2 2.0 10.2 15.3 15.3MOUNTAIN VIEW 74066 56.0 2.2 26.0 9.8 6.1NEWARK 42573 41.3 4.7 27.2 18.2 8.8OAKLAND 390734 34.5 28.0 16.8 13.7 7.0PALO ALTO 64403 64.2 1.9 27.1 2.2 4.6REDWOOD CITY 76815 60.2 2.4 10.7 19.5 6.7SAN BRUNO 41114 49.5 2.3 25.4 12.3 10.5SAN FRANCISCO 805235 48.5 6.1 33.3 6.6 5.9SAN JOSE 945942 42.8 3.2 32.0 15.7 6.3SAN MATEO 97207 57.8 2.4 18.9 12.6 8.3SANTA CLARA 116468 45.0 2.7 37.7 8.3 6.4SOUTH SAN FRANCISCO 63362 37.3 2.6 36.6 15.1 8.4SUNNYVALE 140081 43.0 2.0 40.9 8.7 5.5UNION CITY 69516 23.9 6.3 50.9 10.4 8.5

5.2 Regional/State/US Census Data

The following data, obtained from the 2010 Census, provides the racial components of the regional, California, and US populations, at large:

Table.5.2—San Francisco Bay Area Regional 2010 Census Data.

EntityGov. Type Population White Black Asian Hispanic Other

Alameda County County 1,510,271 44.0 12.6 26.1 10.8 7.4Contra Costa County County 1,049,025 58.6 9.3 14.4 10.7 7.0San Francisco County County 805,235 48.5 6.1 33.3 6.6 5.6San Mateo County County 781,451 53.4 2.8 24.8 11.8 7.2Santa Clara County County 1,781,642 47.0 2.6 32.0 12.4 6.0Santa Cruz County County 262,282 72.5 1.1 4.2 16.5 5.7Regional Averages Region   6,100,000** 54.0 5.8 22.5 11.5 6.5California State 37,871,648 57.6 6.2 13.0 37.6 11.5

USA Country308,745,53

6 72.4 12.6 4.8 16.3 4.0

Table.5.2 (above) provides a key insight into the racial components of the SF.Bay Area, compared to California, and the US, as a whole. Of particular note is the fact that “Blacks” are under-represented regionally, and in California, compared to the national average. “Asians” are over-represented in the SF.BayArea compared to California, and the US population, at large. “Hispanics” seem to be over-represented in California, compared to the US, at large. “Whites”, while significantly under-represented compared to the US at large, are the constitute the dominant racial subgroup, and will be used for normalizing other racial subgroups, unless otherwise noted.

Table.5.3—Census Tract Data For East Palo Alto (2010)

CityTract No. Population White Black Hispanic Asian Other Income

MP 6117 5006 3% 25% 68% 3% 1% $48,654EPA 6118 5555 1% 27% 57% 6% 8% $54,036EPA 6119 10904 14% 25% 48% 7% 7% $48,450EPA 6120 8727 4% 22% 62% 2% 10% $52,917EPA 6121 8312 27% 5% 51% 4% 12% $48,723

Census tract data provides a level of insight into key demographic information that aggregation at the City-level, or zipcode-level does not. While this level of information can not be utilized in this review, it is presented to facilitate familiarization with this data. For small geographic zones, specialized reference baseline data could be developed which could allow future analyses to focus on neighborhoods, both in terms of crime, and traffic stops.

6.0 Presentation of SWITRS Traffic Accident Data (By Race)

In addition to US Census data, implications of racial subgroup representation in traffic related activities can be found in the CHP/SWITRS databases for statewide traffic accidents. The SWITRS data is not as “credible” where racial components are concerned, as is the US Census, but does provide very meaningful clues as to variations in driving behaviors that differ between racial subgroups. The SWITRS accident data goes a long way to demonstrate the general representation of racial subgroups involved in traffic accidents. The SWITRS data released to the public does not include the city-of-residence of the parties involved in traffic accidents. Having this information would make the SWITRS data even more valuable than currently it is. Moreover, accident reports originating at the local level often do not specify the race of participants, making the actual racial representations (of the whole) subject to some error.

While no direct linkages between traffic stops and traffic accidents can be deduced, certainly the door is opened to further investigation of such linkages.

The following material introduces SWITRS accident data that will be used to make racial adjustments of raw traffic stop data--

Table.6.1 CHP/SWITRS State Wide Race-Based Traffic Accident Data

Race 2005 2006 2007 2008 20092010

(Census)Unspecified 17.7 16.7 16.3 16.1 15.6 N/AAsian 5.4 5.5 5.5 5.8 6 13.0%Black 7.1 7.2 7.1 7 7 6.2%Hispanic 28.2 29.3 29.7 29.5 29.3 37.6%Other 4.4 4.6 4.6 4.6 4.8 11.5%White 37.3 36.7 36.7 37 37.3 57.6%

Note—The “Unspecified race” comes from traffic officers not obtaining this information prior to completing the accident report, leaving the field blank. The reasonably large percentage of these “unspecifieds” necessarily depresses the actual percentages of the other races.

6.1 Traffic Accidents In Palo Alto For 2009, By Race.

One of the problems of any inquiry into possible variances in police actions, which might be linked to race, is the establishment of “baselines” that can be used to demonstrate statistically significant differences in police behavior—based on the race of those involved. Given the highly variable nature of vehicular traffic in any community/police jurisdiction, establishing reliable baselines has proven to be a daunting task for researchers across the country. However, there are interesting datapoints that emerge

when looking at the races of parties “at fault” in traffic accidents, in California, and cities neighboring Palo Alto.

Table.6.2—SWITRS 2009: Palo Alto Reported Accidents, By Race.

Race Number% of Total

Regional Percent-

ages

Relative Racial Repre-

sentationNot Specified 57 8.6% N/A N/AAsian 85 12.9% 22.5% 0.6Black 22 3.3% 5.8% 0.6Hispanic 89 13.5% 11.5% 1.2Other 92 13.9% 6.5% 2.1White 316 47.8% 54.0% 0.9

Total 661 100.0% 100.3%  

Note—Police investigators are not always able to determine which of the parties involved in an accident is “at fault”. Therefore, the number of accidents available for use in this sort of comparison is necessarily smaller than the total number of accidents reported for a given locality/jurisdiction, and timeframe.

Table.6.3 Accidents—East Palo Alto Reported Accidents (By Race)

Race 2005 2006 2007 2008 20092010

(Census)Unspecified 39.5 37.9 34.5 35.4 37.4 0.0%Asian 0.6 0.6 0.6 1.1 16.8 3.8%Black 20.9 17.2 17.8 22.9 32.8 16.7%Hispanic 25.3 28.2 39.1 30.3 3.1 38.0%Other 6.4 6.9 2.9 5.1 9.9 12.5%White 7.3 9.1 5.2 5.1 0.0 28.8%

Table.6.4 Accidents—Menlo Park Reported (By Race)

Race 2005 2006 2007 2008 20092010

(Census)Unspecified 21.5 21 21.6 23.2 25.2 N/AAsian 1.6 1.1 3.1 2.4 1.5 9.9%Black 6.6 7.2 8.3 7.3 5.8 4.8%Hispanic 25.5 22.1 26.2 25.3 23.4 8.7%Other 3.9 3.5 3.1 0.9 4.4 6.4%White 40.9 45.2 37.7 40.9 39.8 70.2%

Table.6.5 Accidents—Mountain View Reported (By Race)

Race 2005 2006 2007 2008 20092010

(Census)Unspecified 22.7 18.3 19.3 17.2 18.6 N/AAsian 9.5 8.4 9.1 9.6 9.6 26.0%Black 4.3 5.6 3.8 4 3.4 2.2%Hispanic 21.1 22.6 22.4 26.2 24.2 9.8%Other 4.8 4.2 6.5 5.7 6.4 6.1%White 37.8 40.9 39 37.4 37.7 56.0%

Table.6.6--Accidents: San Jose Reported (By Race)

Race 2005 2006 2007 2008 20092010

(Census)Unspecified 17.6 19.4 17.9 15.0 15.6 N/AAsian 13.5 12.9 14.1 14.1 15.3 32.0%Black 4.4 4.3 4.4 4.1 4.6 3.2%Hispanic 32.2 31.7 32.1 33.8 32.6 15.7%Other 2.2 2.3 2.5 2.6 2.8 6.3%White 30.0 29.4 28.9 30.5 29.1 42.8%

Table.6.7--Alcohol-related Accidents In Palo Alto (2009)

Race NumberUnadjusted

PercentN/A 7 13.5A 3 5.8B 1 1.9H 9 17.3O 9 17.3W 23 44.2

Total 52

Table.6.8--Alcohol-related Accidents in Palo Alto During Q4/2009

Date Race20091010 W20091029 H20091101 N/A20091113 H20091129 N/A20091203 O20091209 H20091212 W20091228 O

Total 9

Table.6.9—Racial Components of 2009/Q4 Alcohol-Related Accidents.

Race Number PercentW 2 22.2%B 0 0.0%H 3 33.3%O 2 22.2%

N/A 2 22.2%Total 9  

Comments

Although the published traffic stop data does not offer specifics as to whether a given stop resulted in a DUI arrest, other data published by the Palo Alto Police states that DUI arrests over the years have ranged from 150 to 250 a year. This would suggest, on a quarterly basis, that there would be between 45-65 DUI stops per quarter (one every two to three days).

“Red Flag”--Of particular interest to this review is that the unadjusted percentage of “Blacks” involved in alcohol-related accidents in Palo Alto is virtually the same as the Census representation. While there is no suggestion that a relationship between “driving under the influence” can be related to other driving behaviors that might result in a traffic stop, this particular data point is worth pondering, in light of the other racial disparities found in this review.

7.0 Presentation of Palo Alto Traffic Stop Data

Key elements of traffic stop “transactions” include: 1) the stop reason, 2) the stop disposition (issuance of a citation/warning and release, or arrest/search and detention). Additionally, racial data is linked to the city-of-residence of stopped motorists, so these two data elements will be included in the traffic stop data, where relevant. The traffic stop data will be presented along the following lines:

Basic Traffic Stops Data City-of-residence of Drivers Stopped Traffic Accidents vs Traffic Stops (By Streets)

Detailed Traffic Stops Datao City-of-Residenceo Racial Components, unadjusted and adjustedo By Time-of-Dayo By Age

Reasons For Stopo City-of-Residenceo Racial Components, unadjusted and adjusted

Citationso City-of-Residenceo Racial Components, unadjusted and adjustedo By Ageo By Gender

Warnings/No Actiono City-of-Residenceo Racial Components, unadjusted and adjustedo By Ageo By Gender

Searcheso City-of-Residenceo Racial Components, unadjusted and adjustedo By Ageo By Gender

Arrestso City-of-Residenceo Racial Components, unadjusted and adjustedo By Gendero By Age

Where appropriate, the racial components will be expressed as “adjusted percentages”, based on local, or regional, Census data. Otherwise, “unadjusted percentages” (simple percentage of the whole) will be expressed.

7.1 Basic Traffic Stops Data

The following tables provide a very basic presentation of demographic data from the 2009/Q4 reporting period:

Table.7.1—Stops, By Gender.

Gender Number PercentF 1282 31.8M 2742 68.0X 6 0.1Total 4030  

Table.7.2—Stops, By Reason.

Stop Reason Number PercentOTHER CRIMINAL CODE (SPECIFY) 133 3.3PENAL CODE 103 2.6PRE-EXISTING KNOWLEDGE/INFO 156 3.9VEHICLE CODE: EQUIPMENT/REG VIOLATION 1699 42.2VEHICLE CODE: MOVING/HAZARD 1939 48.1

Totals 4030

Table.7.3—Stops, By Disposition.

Stop Disposition Number PercentARREST 263 6.5CITE 1501 37.2NO ACTION 1048 26.0OTHER 29 0.7WARNING 1189 29.5

Total 4030

Table.7.4—Stops, By Race (All Cities, Unadjusted).

Race NumberUnadj.

PercentA 460 11.4B 444 11.0H 738 18.3W 1823 45.2X 565 14.0

Total 4030

Table.7.5—Stops: Vehicle Code Violations (All Cities, By Race)

Race NumberUnadj.

PercentCensus

AdjustedAct. Vs Census

A 442 10.9 27.1 0.4B 346 8.6 4.9 1.7H 670 16.6 11.5 1.4W 1648 40.9 49.4 0.8X 532 13.2 7.6 1.7

Total 3638 90.2

Comments

“Red Flag”: “Blacks” and Hispanic stops show these racial sub-groups as over-stopped, relative to their regional census representation, and Asians under-stopped relative to their regional representation.

“X” (Unspecified) probably not White, meaning that the actual percentages for Not-Whites are higher than shown here. “X” also can mean that the traffic officers failed to obtain this information during the stop, and filled in an ‘X’ after the motorist had been released from detention just to complete the data record.

In a “perfect world”--assuming no “Racial Profiling” on the part of the police--the “Actual vs Census” ratio would be close to 1.0 for all racial sub-groups. This theme of “Blacks”/Hispanics being over-represented in the traffic stop data, relative to their regional Census representation, and Asians under-represented in the traffic stop data reappears throughout this study. The data suggests strongly that the race, or economic status, of those stopped might be an issue in the disposition of the stop.

7.2 City-of-Residence For Majority of Traffic Stops ( ~80%)

The following table lists the cities-of-residence of the drivers stopped, ranked as a percentage of the total number of stops made by the police during that reporting period:

Table.7.6—Stops: By City-of-Residence

CityNumber of

Stops PercentPALO ALTO 1581 39.23EAST PALO ALTO 343 8.51SAN JOSE 292 7.25MOUNTAIN VIEW 247 6.13MENLO PARK 165 4.09REDWOOD CITY 146 3.62SUNNYVALE 134 3.33LOS ALTOS 89 2.21SAN FRANCISCO 89 2.21FREMONT 65 1.61SANTA CLARA 54 1.34STANFORD 53 1.32SAN MATEO 45 1.12  SubTotals: 81.97

Table.7.7—Stops: Citations vs No Action/Warnings, By City-of-Residence

City of Residence Population Stops CitationsCite %

No-Citations

No-Cite %

PALO ALTO 64403 1581 598 37.8 865 54.7EAST PALO ALTO 28155 343 53 15.5 252 73.5SAN JOSE 945942 292 120 41.1 153 52.4MOUNTAIN VIEW 74066 247 103 41.7 128 51.8MENLO PARK 32026 165 63 38.2 92 55.8REDWOOD CITY 76815 146 51 34.9 81 55.5SUNNYVALE 140081 134 58 43.3 70 52.2LOS ALTOS 28976 89 42 47.2 43 48.3SAN FRANCISCO 805235 89 40 44.9 40 44.9FREMONT 214089 65 30 46.2 28 43.1SANTA CLARA 116468 54 34 63.0 14 25.9STANFORD 11560 53 22 41.5 29 54.7

“Red Flag”—The Cite/No-Cite Ratio For East Palo Alto is very low, compared to other cities. While the Cite/No-Cite Ratio for residents of the City of Santa Clara is high, the very low number of stops makes this not an issue of concern for a reporting period of only three months.

Table.7.8—Stops: Drivers From Remotely Located Cities-Of-Residence (By Race)

Race Number Unadj.

PercentA 115 12.1B 95 10.0H 127 13.4W 465 48.9X 148 15.6

Total 950  

Table.7.9— Stops: By Sex

Sex Number PercentF 308 32.4M 639 67.3X 3 0.3

Total 950  

Comments

Table.7.6 shows that about 80% of the drivers stopped in Palo Alto reside in Palo Alto, or eleven nearby cities/towns (and Stanford), with the remaining 20% of the drivers claiming residence all over the Bay Area, State and the World (for a total of 180 other Cities-of-Residence/locations).

It should come as no surprise that the largest number of the traffic stops involve drivers residing in Palo Alto, and the surrounding towns of East Palo Alto, Menlo Park, Mountain View and Stanford. While the exact number of daily vehicle trips on Palo Alto streets is not precisely known, the Transportation Department has stated in the past that as many as 600,000 trips occur daily. Given the likelihood that the number of vehicle trips is in the 500,000 to 600,000 range (per day), 30-60 traffic stops a day is not very many--calling into question the value of the activity, particularly given its increasing cost.

7.3 Comparison of Accidents Locations vs Traffic Stops Locations (By Streets)

The following table compares the streets/roads with the highest number of traffic accidents with the streets/roads where the highest number of traffic stops occurred, during 2009/Q4:

Table.7.10—Comparison of Palo Alto Traffic Accidents vs Traffic Stops (By Street)

Accidents By Street (2009)   Traffic Stops By Street (2009/Q4)             Primary Road Number % Primary Road Number %EL CAMINO REAL/RT 82 97 12.5% EL CAMINO REAL 579 14.4%UNIVERSITY AV 68 8.8% UNIVERSITY AV 344 8.5%ALMA ST 54 7.0% EMBARCADERO RD 223 5.5%MIDDLEFIELD RD 50 6.4% MIDDLEFIELD RD 206 5.1%EMBARCADERO RD 45 5.8% SAN ANTONIO RD 201 5.0%PAGE MILL RD 41 5.3% ALMA ST 172 4.3%OREGON EXPWY 41 5.3% LYTTON AV 122 3.0%SAN ANTONIO RD 23 3.0% HAMILTON AV 103 2.6%HAMILTON AV 20 2.6% E CHARLESTON RD 101 2.5%E CHARLESTON RD 20 2.6% E BAYSHORE RD 84 2.1%ARASTRADERO RD 18 2.3% BRYANT ST 79 2.0%

LYTTON AV 15 1.9% EMERSON ST 77 1.9%BRYANT ST 15 1.9% HY 101 77 1.9%SAND HILL RD 12 1.5% PAGE MILL RD 72 1.8%CALIFORNIA AV 11 1.4% OREGON EXPWY 60 1.5%EMERSON ST 10 1.3% E MEADOW DR 51 1.3%RAMONA ST 10 1.3% WAVERLEY ST 50 1.2%E BAYSHORE RD 10 1.3% ARASTRADERO RD 47 1.2%

Totals: 776 72.2% W MEADOW DR 46 1.1%CHANNING AV 45 1.1%CALIFORNIA AV 42 1.0%PARK BL 42 1.0%RAMONA ST 40 1.0%HIGH ST 40 1.0%

Total   72.0%

Table.7.11—Streets Where Traffic Stops Arrests Occurred vs Streets Where Alcohol-Involved Accidents Occurred

Street Stops Percent   StreetAcci-dents Percent

UNIVERSITY AV 39 14.8   LYTTON AV 5 9.1EL CAMINO REAL 38 14.4   OREGON EXPWY 4 7.3LYTTON AV 17 6.5   ALMA ST 3 5.5HAMILTON AV 16 6.1   EMERSON ST 3 5.5

EMERSON ST 15 5.7  EL CAMINO REAL/RT 82 3 5.5

SAN ANTONIO RD 14 5.3   SAND HILL RD 2 3.6EMBARCADERO RD 10 3.8   MIDDLEFIELD RD 2 3.6MIDDLEFIELD RD 9 3.4   PAGE MILL RD 2 3.6BRYANT ST 8 3.0   HIGH ST 2 3.6ALMA ST 7 2.7   HAMILTON AV 2 3.6E BAYSHORE RD 7 2.7   UNIVERSITY AV 2 3.6W BAYSHORE RD 6 2.3   EMBARCADERO RD 2 3.6CALIFORNIA AV 4 1.5   RAMONA ST 2 3.6HY 101 4 1.5   PORTAGE AV 1 1.8RAMONA ST 4 1.5   STANFORD AV 1 1.8WAVERLEY ST 3 1.1   PARK AV 1 1.8SAND HILL RD 3 1.1   VENTURA AV 1 1.8HIGH ST 3 1.1   SAN ANTONIO RD 1 1.8CHANNING AV 2 0.8   W CHARLESTON RD 1 1.8KIPLING ST 2 0.8   MATADERO AV 1 1.8MAIN SP 2 0.8   MADDUX DR 1 1.8TASSO ST 2 0.8   BLAKE WILBUR DR 1 1.8SENECA ST 2 0.8   CALIFORNIA AV 1 1.8WEBSTER ST 2 0.8   CHANNING AV 1 1.8E CHARLESTON RD 2 0.8   CHURCHILL AV 1 1.8W CHARLESTON RD 2 0.8   COWPER ST 1 1.8ADDISON AV 2 0.8   CRESCENT DR 1 1.8PORTAGE AV 1 0.4   E CHARLESTON RD 1 1.8SHERIDAN AV 1 0.4   GREER RD 1 1.8

PRIMROSE WY 1 0.4   JACARANDA LN 1 1.8SHERMAN AV 1 0.4   LINCOLN AV 1 1.8WILKIE WY 1 0.4   LOMA VERDE AV 1 1.8W MEADOW DR 1 0.4   LOUIS RD 1 1.8VILLA REAL 1 0.4   WAVERLEY ST 1 1.8

Comments

There have been many complaints about the lack of traffic enforcement in Palo Alto over the years (particularly in South Palo Alto). Table.7.11 shows that for streets/roads with high accident occurrences, traffic enforcement tracks accident rates quite closely. This would seem to be a positive finding from this review, at least from a police resource allocation point-of-view.

Alcohol-related accidents might seem to be more random, but a review of the SWITRS traffic accident data for 1995-2009 shows that most of these accidents occur in the downtown section of Palo Alto, and on roads such as El Camino and Oregon Expressway, which see high traffic volumes. Unfortunately, the SWITRS data does not provide the city-of-residence for the vehicle operators, so the analysis of these accidents does not offer any insight as to whether these accidents are caused by Palo Alto residents, or non-residents.

7.4 Stops On Major Streets, By Race

The following tables provide a breakdown, by race, of motorists stopped on major Palo Alto streets during the 2009/Q4 reporting period:

Table.7.12—Stops On Major Streets, By Race.

Stops On Major Streets, By Race

Primary Road Stops White Black Hispanic Asian Other Year QuarterEL CAMINO REAL 579 287 58 99 64 71 2009 Q4UNIVERSITY AV 344 144 46 69 33 52 2009 Q4W BAYSHORE RD 26 7 8 6 2 3 2009 Q4E BAYSHORE RD 84 8 33 29 6 8 2009 Q4E CHARLESTON RD 101 41 10 16 17 17 2009 Q4W CHARLESTON RD 18 6 3 1 5 3 2009 Q4ARASTRADERO RD 47 20 3 5 12 7 2009 Q4OREGON EXPWY 60 25 7 8 10 10 2009 Q4MIDDLEFIELD RD 206 97 22 37 22 28 2009 Q4

Table.7.13—Stops On Major Streets, By Race (Expressed In Percentages)

Stops On Major Streets, By Race (In Percentages)

Primary Road StopsWhite

%Black

%Hispanic

%Asian

%Other

% Year QuarterEL CAMINO REAL 579 49.6 10.0 17.1 11.1 12.3 2009 Q4

UNIVERSITY AV 344 41.9 13.4 20.1 9.6 15.1 2009 Q4W BAYSHORE RD 26 26.9 30.8 23.1 7.7 11.5 2009 Q4E BAYSHORE RD 84 9.5 39.3 34.5 7.1 9.5 2009 Q4E CHARLESTON RD 101 40.6 9.9 15.8 16.8 16.8 2009 Q4W CHARLESTON RD 18 33.3 16.7 5.6 27.8 16.7 2009 Q4ARASTRADERO RD 47 42.6 6.4 10.6 25.5 14.9 2009 Q4OREGON EXPWY 60 41.7 11.7 13.3 16.7 16.7 2009 Q4MIDDLEFIELD RD 206 47.1 10.7 18.0 10.7 13.6 2009 Q4

Comments

Table.7.14 offers interesting insights into the difficulties of depending on raw stop data without adequate baseline data to come to conclusions about possible “racial profiling”. The raw data shows that “Blacks” and Hispanics are stopped about 64% of the time, whereas Whites are stopped only 10% of the time. This seeming disparity needs to be offset by the fact that “Blacks” and Hispanics comprise about 54% of the population of East Palo Alto.

Given that East/West Bayshore Roads are limited access roads, baseline data which establishes the number of car trips, by race, could provide the context necessary to determine if the police were over-stopping, or under-stopping, any particular racial group. Given the capabilities of automatic license plate readers, road use profiles could fairly easily be produced that would augment the driver pool data determined from the Census data for both Palo Alto, and East Palo Alto.

Assuming that there were no “racial profiling” by the police, and also assuming a more-or-less uniform distribution of opportunities for vehicles to be subject to a traffic stop, the distribution by races stopped would be reflective of the number of vehicle trips made by each racial subgroup. Unfortunately, such baseline data does not exist.

In this case, given that Whites are the racial minority in East Palo Alto, the low percentage of White stops is to be expected, and the higher percentage of stops for “Blacks”/Hispanics is to be expected. This logic bears out also by the low percentage of “Blacks” stopped on East Charleston Road, West Charleston Road, and Arastradero Road, in conjunction with the high percentage of Asians stopped. (Asians have moved into the Gunn High School section of Palo Alto, as well as Los Altos and Los Altos Hills, in great numbers over the past two decades.)

Lastly, given that the total stops on East/West Bayshore Road was only 110 stops out of the total 4030 Palo Alto stops (or 2.7%), this situation can not be considered as a “Red Flag” by itself.

Future traffic stops data collection should attempt to increase the locality of the driver’s residence from “City” to “Neighborhood” (at least ZipCode, and perhaps Census Tract). Traffic accidents occur often within five miles of a motorist’s home. Therefore, it is not hard to speculate that traffic stops also happen fairly close to home. Having this locality data in the traffic stop record would help to better detect patterns of overstopping, or understopping, of various racial groups.

7.5 Stops--By City/By Race

Given the significant variance in the racial components of each community in the SF.BayArea, any attempt to compare the number of traffic stops as a function of race is not meaningful, unless a normalization procedure applied--which will then allow a more “apples-to-apples” comparison to result.

Table.7.14—Stops Based On Race and City, Adjusted By Census Representation

City-Of-Residence Race StopsRel. To “White”

EAST PALO ALTO A 10 3EAST PALO ALTO B 102 7EAST PALO ALTO H 172 5EAST PALO ALTO W 27 1EAST PALO ALTO X 32 3FREMONT A 16 <1FREMONT B 8 4FREMONT H 3 1FREMONT W 24 1FREMONT X 14 3LOS ALTOS A 20 1LOS ALTOS B 1 2LOS ALTOS H 1 2LOS ALTOS W 58 1LOS ALTOS X 9 2MENLO PARK A 6 <1MENLO PARK B 21 3MENLO PARK H 22 2MENLO PARK W 95 1MENLO PARK X 21 2MOUNTAIN VIEW A 27 1MOUNTAIN VIEW B 13 3MOUNTAIN VIEW H 56 3MOUNTAIN VIEW W 110 1MOUNTAIN VIEW X 41 3PALO ALTO A 196 1PALO ALTO B 152 6PALO ALTO H 168 6PALO ALTO W 869 1PALO ALTO X 196 3REDWOOD CITY A 6 1

REDWOOD CITY B 11 6REDWOOD CITY H 67 4REDWOOD CITY W 45 1REDWOOD CITY X 17 3SAN FRANCISCO A 15 1SAN FRANCISCO B 12 2SAN FRANCISCO H 6 1SAN FRANCISCO W 41 1SAN FRANCISCO X 15 3SAN JOSE A 37 0SAN JOSE B 28 3SAN JOSE H 78 2SAN JOSE W 108 1SAN JOSE X 41 2SAN MATEO A 3 1SAN MATEO B 4 4SAN MATEO H 10 2SAN MATEO W 22 1SAN MATEO X 6 2SANTA CLARA A 8 1SANTA CLARA B 3 3SANTA CLARA H 11 4SANTA CLARA W 18 1SANTA CLARA X 14 6SUNNYVALE A 21 1SUNNYVALE B 8 4SUNNYVALE H 32 4SUNNYVALE W 40 1SUNNYVALE X 33 6

Note #1—Yellow Bands in Table.7.14 indicate “White” baseline—used for racial adjustments to stops data.

Note #2—Should the actual number of drivers within each racial subgroup become available, these results are subject to refinement.

Table.7.15--Stops/City of Residence: Palo Alto

Unadjusted Percent By RaceDisposition White Black Asian Hispanic OtherARREST 3.0 1.6 0.1 1.3 0.4CITE 22.4 1.6 6.7 2.0 5.2NO ACTION 11.6 3.2 2.3 2.8 2.2OTHER 0.6 0.3 0.1 0.1 0.1WARNING 17.4 3.0 3.3 4.4 4.4

Totals: 55.0 9.7 12.5 10.6 12.3Census

Representation (%): 64.2 1.9 27.1 2.2 4.6

Table.7.16—Adjusted Racial Components Of Traffic Stops Involving Palo Alto Residents.

Race

Census Adjusted

PercentageBlacks Stopped: 64.10%Whites Stopped: 10.80%Asians Stopped: 5.80%Hispanics Stopped: 61.20%Others Stopped: 34.10%

Table.7.17--Stops/City of Residence: East Palo Alto

Unadjusted Percent By RaceDisposition White Black Asian Hispanic OtherARREST 0.9 1.5 0.3 5.8 0.9CITE 2 4.1 1.2 6.4 1.7NO ACTION 2.6 12 0.3 22.4 3.2OTHER 0.3 0.6 1.2 0.9 3.5WARNING 2 11.7 0 14.6 0

Percent of Stops 7.8 29.9 3 50.1 9.3Census

Percentages 28.8 16.7 3.8 38 12.5

Table.7.18—Stops/City of Residence: Menlo Park

Unadjusted Percent By RaceDisposition White Black Asian Hispanic OtherARREST 3.0 1.2 1.2 0.6 1.2CITE 25.5 2.4 1.2 3.6 5.5NO ACTION 13.3 5.5 1.2 5.5 4.2OTHER 15.8 3.6 0.0 3.6 1.8

Percent of Stops 57.6 12.7 3.6 13.3 12.7Census

Percentages 70.2 4.8 9.9 8.7 6.4

Table.7.19—Stops/City of Residence: San Jose

Unadjusted Percent By RaceDisposition White Black Asian Hispanic OtherARREST 2.4 0.7 0.3 1.7 1.0CITE 14.0 2.4 7.2 10.3 7.2NO ACTION 7.5 4.1 1.4 8.6 1.7OTHER 0.3 2.4 3.8 6.2 4.1WARNING 12.7 0.0 0.0 0.0 0.0

Percent of Stops 36.9 9.6 12.7 26.8 14.0Census

Percentages 42.8 3.2 32.0 15.7 6.3

7.6 Stops---By Month: All Cities, All Races

Table.7.20—Stops: By Month (All Cities, All Races)

  2009      Q4 October November DecemberStops 4030 1157 1377 1496Average 43.8 37.3 45.9 48.3Minimum 14 21 18 14Maximum 93 61 80 93

  2010      Q1 January February MarchStops 4004 1485 1124 1394Average TBD TBD TBD TBDMinimum TBD TBD TBD TBDMaximum TBD TBD TBD TBD

  2010      Q2 April May JuneStops 4008 1410 1452 1146Average TBD TBD TBD TBDMinimum TBD TBD TBD TBDMaximum TBD TBD TBD TBD

Note: TBD—To Be Determined.

Comments

If traffic stops were strictly random, than any variance from norm (in this case “Whites”) would tend to suggest “bias” on the part of the police. However, traffic stops are not random events; traffic stops are supposed to be triggered because of some action on the part of the driver, such as a speed limit violation, or obvious problem with a required safety equipment, such as head lights/tail lights . Variances in this data then represent

difference in the driving characteristics of specific racial subgroups, or bias by the police in initiation of traffic stops when these sorts of situations are detected.

7.7 Traffic Stops—All Cities/By Race

Table.7.21—Stops: All Cities, All Races.

RaceStop

Disposition Number

Unadj. Percent of

TotalA ARREST 11 0.3%A CITE 243 6.0%A NO ACTION 89 2.2%A OTHER 2 0.0%A WARNING 115 2.9%  SubTotals: 460 11.4%       B ARREST 49 1.2%B CITE 76 1.9%B NO ACTION 180 4.5%B OTHER 6 0.1%B WARNING 133 3.3%  SubTotals: 444 11.0%       H ARREST 82 2.0%H CITE 182 4.5%H NO ACTION 255 6.3%H OTHER 6 0.1%H WARNING 213 5.3%  SubTotals: 738 18.3%       

W ARREST 97 2.4%W CITE 754 18.7%W NO ACTION 414 10.3%W OTHER 12 0.3%W WARNING 546 13.5%  SubTotals: 1823 45.2%       X ARREST 24 0.6%X CITE 246 6.1%X NO ACTION 110 2.7%X OTHER 3 0.1%X WARNING 182 4.5%

  SubTotals: 565 14.0%  Total: 4030 100.0%

Table.7.22—All Cities, All Races, Adjusted To Regional Census Baseline.

Stop Disposition Race Number Percent

Regional Percent Difference

Adjusted To

Census Baseline

ARREST A 11 4.2% 22.5% -18.3% 0.2ARREST B 49 18.6% 5.8% 12.8% 3.2ARREST H 82 31.2% 11.5% 19.7% 2.7ARREST W 97 36.9% 54.0% -17.1% 0.7ARREST X 24 9.1% 6.5% 2.6% 1.4    263                     CITE A 243 16.2% 22.5% -6.3% 0.7CITE B 76 5.1% 5.8% -0.7% 0.9CITE H 182 12.1% 11.5% 0.6% 1.1CITE W 754 50.2% 54.0% -3.8% 0.9CITE X 246 16.4% 6.5% 9.9% 2.5    1501                     NO ACTION A 89 8.5% 22.5% -14.0% 0.4NO ACTION B 180 17.2% 5.8% 11.4% 3.0NO ACTION H 255 24.3% 11.5% 12.8% 2.1NO ACTION W 414 39.5% 54.0% -14.5% 0.7NO ACTION X 110 10.5% 6.5% 4.0% 1.6    1048                     OTHER A 2 6.9% 22.5% -15.6% 0.3OTHER B 6 20.7% 5.8% 14.9% 3.6OTHER H 6 20.7% 11.5% 9.2% 1.8OTHER W 12 41.4% 54.0% -12.6% 0.8OTHER X 3 10.3% 6.5% 3.8% 1.6    29                     WARNING A 115 9.7% 22.5% -12.8% 0.4WARNING B 133 11.2% 5.8% 5.4% 1.9WARNING H 213 17.9% 11.5% 6.4% 1.6WARNING W 546 45.9% 54.0% -8.1% 0.9WARNING X 182 15.3% 6.5% 8.8% 2.4    1189        

Comments

The column header “Adjusted to Census Baseline” is the ratio of the actual police stop statistic/number as compared to the Census representation for that racial subgroup. As this number moves away from 1.0, this means that there is an oversampling when the number is grater than 1.0 and an under-sampling when the number is less than 1.0. In this use, ratios significantly larger than 1.0 become “red flags” that would need investigation and monitoring in the future.

7.8 Stops—By Time-of-Day

The following table provides the counts of traffic stops, based on the time of day—

Table.7.23—Stops: Time-of-Day/All Cities/All Races

Stops By Time-of-Day All Races/All Cities.

       Time-of-Day Number Hours Percent00:00-05:59 632 6 15.8%06:00-07:59 98 2 2.5%08:00-11:59 925 4 23.2%12:00-13:59 398 2 10.0%14:00-16:59 619 3 15.5%17:00-18:59 247 2 6.2%19:00-20:59 301 2 7.5%21:00-23:59 768 3 19.3%

Note—Table.7.23 employs a “drive time” frame of reference, identifying time-of-day when high traffic volumes are to be expected.

Comments

Some traffic stop studies/analyses have presented time-of-day traffic stops data in terms of three, eight-hour, shifts (00:00-08:00, 08-16:00 and 16:00 to 24:00). The presentation used in this review is intended to more closely approximate traffic patterns in Palo Alto, and the Silicon Valley, rather than a backdrop of “police department shifts”, which may well not provide insights to the patterns of road use of local drivers.

The data shows that about two-thirds of the stops are conducted during daylight, when the bulk of the daily vehicle trips occur. The remaining one-third of the stops are during non-daylight hours, when vehicle use of the roads is at its lowest.

Some of the literature on traffic stops deals with problems of modeling “after-dark” traffic stops. The authors of these papers generally demonstrate that any behavior models of police activity that might be valid during the day, are not nearly as valid at night—requiring a separate set of models for a night time behavior prediction. This need for a second set of models is not generally understood, or promoted, by all researchers, however.

7.9 Stops Resulting In Searches

The literature on traffic stops very clearly reflects the concerns of minority motorists expressing their frustration of being stopped “driving while Black”--particularly where vehicle searches occurred after being stopped for reasons often alleged to be more “pretext” than not. Given the ever-changing field of legal opinion and practice where police power and traffic stops are involved, being searched for no obvious reason should be a concern to every American. The 2009/Q4 traffic stop data shows that of the 4030 traffic stops executed, the total number of searches that resulted was 373, or about 9% (1 search per 11 stops). The following tables provide details about these searches: Table.7.24—Traffic Stops Resulting in Searches (All Cities/All Races)

Searches: All Races/All Cities

Search Reason Number PercentCONSENT 48 1.20%INCIDENT TO ARREST 100 2.50%NO SEARCH 3657 90.70%PAROLE/PROBATION 97 2.40%PROBABLE CAUSE 51 1.30%VEHICLE IMPOUND INVENTORY 77 1.90%

Table.7.25—Stops Resulting In Searches, By Gender.

Gender Number PercentF 56 15M 317 85

Table.7.26--Stops Resulting In Searches, By Race (Aggregate)

Race NumberUnadj.

Percent

Regional Census

Rep.

Unadj. Vs Census

RepresentationA 17 4.6 13 35.4%B 90 24.1 6.2 388.7%H 109 29.2 37.6 77.7%W 129 34.6 57.6 60.1%X 28 7.5 11.5 65.2%

Total 373

Table.7.27-- Stop Dispositions, by Search Reasons

Stop Disposition Search Reason Number PercentARREST CONSENT 9 0.2ARREST INCIDENT TO ARREST 97 2.4ARREST NO SEARCH 64 1.6ARREST PAROLE/PROBATION 15 0.4ARREST PROBABLE CAUSE 21 0.5ARREST VEHICLE IMPOUND INVENTORY 57 1.4       CITE CONSENT 5 0.1CITE NO SEARCH 1463 36.3CITE PAROLE/PROBATION 10 0.2CITE PROBABLE CAUSE 3 0.1CITE VEHICLE IMPOUND INVENTORY 20 0.5       NO ACTION CONSENT 18 0.4NO ACTION NO SEARCH 975 24.2NO ACTION PAROLE/PROBATION 43 1.1NO ACTION PROBABLE CAUSE 12 0.3       OTHER CONSENT 6 0.1OTHER INCIDENT TO ARREST 2 0OTHER NO SEARCH 16 0.4OTHER PAROLE/PROBATION 3 0.1OTHER PROBABLE CAUSE 2 0       WARNING CONSENT 10 0.2WARNING INCIDENT TO ARREST 1 0WARNING NO SEARCH 1139 28.3WARNING PAROLE/PROBATION 26 0.6WARNING PROBABLE CAUSE 13 0.3

Table.7.28–Stops Resulting In Searches, By Age.

Age Range Number Percent16-19 30 8.020-29 173 46.430-39 57 15.340-49 55 14.750-59 46 12.460 or older 12 3.3

Total 373

Table.7.29—Stops Resulting In Searches, By City.

City-of-Residence Stops SearchesSearch Percent

Stop Percent

PALO ALTO 1581 142 38.1 9.0EAST PALO ALTO 343 59 15.8 17.2SAN JOSE 292 25 6.7 8.6MOUNTAIN VIEW 247 22 5.9 8.9REDWOOD CITY 146 20 5.4 13.7MENLO PARK 165 13 3.5 7.9SUNNYVALE 134 8 2.1 6.0SAN FRANCISCO 89 8 2.1 9.0SANTA CLARA 54 8 2.1 14.8TRANSIENT 18 6 1.6 33.3OAKLAND 25 6 1.6 24.0NEWARK 39 5 1.3 12.8LOS ALTOS 89 5 1.3 5.6FREMONT 65 4 1.1 6.2SAN MATEO 45 4 1.1 8.9STOCKTON 11 2 0.5 18.2SAN BRUNO 8 2 0.5 25.0SAN LEANDRO 12 2 0.5 16.7SACRAMENTO 12 2 0.5 16.7UNION CITY 26 2 0.5 7.7HAYWARD 36 2 0.5 5.6STANFORD 53 2 0.5 3.8LOS BANOS 4 2 0.5 50.0SOUTH SAN FRANCISCO 7 2 0.5 28.6DALY CITY 10 2 0.5 20.0SANTA BARBARA 2 1 0.3 50.0ANTIOCH 4 1 0.3 25.0WOODSIDE 12 1 0.3 8.3SAN LORENZO 3 1 0.3 33.3TEXAS 12 1 0.3 8.3SALINAS 6 1 0.3 16.7BURLINGAME 18 1 0.3 5.6COLORADO 5 1 0.3 20.0CUPERTINO 17 1 0.3 5.9DENAIR 1 1 0.3 100.0FAIRFIELD 1 1 0.3 100.0GILROY 5 1 0.3 20.0LOS ALTOS HILLS 12 1 0.3 8.3MILPITAS 21 1 0.3 4.8NEW YORK 7 1 0.3 14.3MILBRAE 3 1 0.3 33.3RICHMOND 4 1 0.3 25.0BERKELEY 5 1 0.3 20.0

Table.7.30—Stops Resulting In Searches, By City, and By Race.

City Stops Searches White Black Asian Hispanic OtherPALO ALTO 1581 142 70 39 2 22 9EAST PALO ALTO 343 59 3 18 1 34 3SAN JOSE 292 25 6 6 3 8 2MOUNTAIN VIEW 247 22 8 3 2 8 1REDWOOD CITY 146 20 5 2 1 11 1MENLO PARK 165 13 6 5 0 1 1SUNNYVALE 134 8 4 0 0 4 0SAN FRANCISCO 89 8 3 2 0 0 3SANTA CLARA 54 8 3 1 0 4 0TRANSIENT 18 6 4 1 0 1 0OAKLAND 25 6 0 5 0 1 0NEWARK 39 5 0 1 0 4 0LOS ALTOS 89 5 3 0 2 0 0FREMONT 65 4 3 0 0 0 1SAN MATEO 45 4 1 1 0 2 0STOCKTON 11 2 1 1 0 0 0SAN BRUNO 8 2 2 0 0 0 0SAN LEANDRO 12 2 0 0 0 0 2SACRAMENTO 12 2 0 0 0 2 0UNION CITY 26 2 0 1 1 0 0HAYWARD 36 2 0 0 0 1 1STANFORD 53 2 0 0 0 1 1LOS BANOS 4 2 0 1 0 1 0SOUTH SAN FRANCISCO 7 2 0 0 0 1 1DALY CITY 10 2 0 0 2 0 0SANTA BARBARA 2 1 1 0 0 0 0ANTIOCH 4 1 0 0 1 0 0WOODSIDE 12 1 1 0 0 0 0SAN LORENZO 3 1 0 1 0 0 0TEXAS 12 1 0 1 0 0 0SALINAS 6 1 0 0 0 1 0BURLINGAME 18 1 0 0 0 1 0COLORADO 5 1 1 0 0 0 0CUPERTINO 17 1 0 0 0 0 1DENAIR 1 1 1 0 0 0 0FAIRFIELD 1 1 1 0 0 0 0GILROY 5 1 0 0 0 1 0LOS ALTOS HILLS 12 1 0 0 0 0 1MILPITAS 21 1 1 0 0 0 0NEW YORK 7 1 0 0 1 0 0MILBRAE 3 1 1 0 0 0 0RICHMOND 4 1 0 0 1 0 0BERKELEY 5 1 0 1 0 0 0

Comments

While racial disparities quickly emerged in the data collection exercises around the country, there seemed to be little effort to revise the data collection templates so that sufficient information was collected to prove the justification of individual police officers decisions and actions. This same problem exists in the Palo Alto data.

The following problems exist in the current data, making analysis difficult:

Search Initiation Reason Not Recorded. Search Productivity Can Not Be Determined From Published Data.

o No Indication Of “Contraband” Discovered In Published Data.o No Way to Link Arrests To Searches In Current Data.o Time Required For Searches Not Recorded

Even though there is clear evidence of higher stop rates and higher search rates for “Blacks” and Hispanic who are residents of Palo Alto and East Palo Alto than is suggested from their Census representation, there might well be totally justifiable reasons for these stops and searches. Unfortunately, those reasons were not recorded for later evaluation by the Palo Alto Police. Since the number of stops/searches for motorists whose city-of-residence is outside the immediate vicinity of Palo Alto becomes quite small as the distance between Palo Alto and those cities increases, any evidence of “racial profiling”/”bias” of such drivers simply can not be found in any statistically significant way.

While there are no known traffic surveys that have been conducted by the City of Palo Alto Traffic Engineers, or Police Department, that would provide baseline information about the city-of-residence of those using Palo Alto streets, it stands to reason that people tend to drive in their own city, and those nearby, more often than they drive in cities that are located at any great distance from their homes. This logic is reinforced by Table.30, which lists the cities-of-residence of those stopped and searched.

Stop/search data/rates can only be appreciated when compared to other (preferably local) police jurisdictions. At this writing, little such data exists. However, from the data that is readily available, the following information about “searches” can be gleaned:

From the Rhode Island Study:

Statewide, discretionary searches are rare events.

Only 4.5% of traffic stops resulted in a discretionary search of the driver, passenger or vehicle.

Statewide, non-white motorists are 2.5 times more likely to be searched than white motorists.

From Menlo Park (2002) data:

Of 5340 stops (July thru Dec 2002), only 4% warranted a search.

From California Highway Patrol (2003):

The patrol said just 2 percent of traffic stops and other ''public contacts'' resulted in searches. Of those, it said, 99.9 percent occur after an arrest or the impounding of a vehicle involved in a crime.

“Red Flag”—The number of searches conducted by the Palo Alto Police seems higher than those conducted by other departments. Due to the lack of consistent data collection terminology and methodology, as well as very little data from neighboring communities, this issue of high search rates is difficult to discuss at this time. As such, it is being noted as a “red flag”, with the hope that the Palo Alto Police will revise their traffic stop terminologies, and better document their data collection templates, so that discretionary searches can be more easily identified in the future.

The lack of stop duration time makes modeling the effectiveness of searches, particularly “stop productivity”, difficult. Moreover, the loss of the public’s time being consumed by unnecessary/unproductive stops/searches is also not possible to determine from the current data.

Given that traffic stops/searches are integral to the more general activity of “fighting crime”, linking traffic stops to the background of on-going investigations, as well as geographic/neighborhood crime rates, would be necessary to fully appreciate the actions of police initiating “unproductive” stops/searches. Making this sort of linkage would not be difficult if local law enforcement agencies were to use computer systems that made such information easily available when needed for analyses/reviews, such as this one. Given sufficient information about events involving police activities in given locations/neighborhoods, these racial disparities in search rates might become justifiable, or at least explained.

(Note—This area requires much more investigation.)

7.10 Stops Resulting In Arrests

Of the 4030 stops reported for 2009/Q4, 263 resulted in an arrest. Unfortunately, there is no information in the traffic stop records providing details for these arrests. While published City Budget states that DUIs for years in/about 2009 fall in the 180-200 range, the exact number of DUIs, with a racial breakdown of those arrested, is reported by the police to the Bureau of Justice Statistics. The actual DUI statistics are listed in Table.7.31, following:

Table.7.31—Five-Year History Of Palo Alto DUI Attests, By Race.

Year DUIs Whites Blacks Asians2005 151 141 8 22006 161 146 14 12007 216 209 7 02008 211 192 18 12009 127 123 4 0

Yearly Avg: 173.2 162.2 10.2 0.8% of

Total:   93.6% 5.9% 0.5%

Given the reported number of DUIs for 2005-2009 in Table.7.31, then there would likely be 45-50 DUI arrests per quarter (on average). While other reasons for arrests include possession of illegal drugs/weapons, parole violations, etc., the exact reason for traffic stop arrests is not made available in the 2009/Q4 data, unfortunately.

“Red Flag”—The low number of “Blacks” arrested for DUIs, when compared to the relatively high number of “Blacks” (relative to their Census representation) stopped in Palo Alto should be considered in any review of possible “bias” on the part of the Palo Alto Police towards “Blacks” involved in traffic stops, at least.

7.11 Stops Resulting In Arrests (By City)

The following table lists the city-of-residence for the traffic stops resulting in an arrest of the driver, ranked by the percentage of the total number of stops:

Table.7.32—Stops Resulting In Arrests (By City)

City Of Residence Number PercentPALO ALTO 101 38.4EAST PALO ALTO 32 12.2SAN JOSE 18 6.8MOUNTAIN VIEW 15 5.7REDWOOD CITY 14 5.3MENLO PARK 10 3.8SAN FRANCISCO 9 3.4FREMONT 6 2.3SUNNYVALE 6 2.3NEWARK 5 1.9SANTA CLARA 5 1.9OAKLAND 4 1.5TRANSIENT 4 1.5DALY CITY 3 1.1LOS ALTOS 3 1.1HAYWARD 2 0.8LOS BANOS 2 0.8STANFORD 2 0.8

Table.7.33—Stops Resulting In Arrest (By City, and By Race)

City Stops Arrests Percent White Black Asian Hispanic OtherPALO ALTO 1581 101 6.4% 47 25 1 21 7EAST PALO ALTO 343 32 9.3% 3 5 1 20 3SAN JOSE 292 18 6.2% 7 2 1 5 3MOUNTAIN VIEW 247 15 6.1% 7 1 0 6 1REDWOOD CITY 146 14 9.6% 3 1 1 9 0MENLO PARK 165 10 6.1% 5 2 0 1 2SAN FRANCISCO 89 9 10.1% 2 3 1 1 2FREMONT 65 6 9.2% 3 1 0 1 1SUNNYVALE 134 6 4.5% 4 0 0 2 0NEWARK 39 5 12.8% 0 1 0 4 0SANTA CLARA 54 5 9.3% 2 0 0 3 0OAKLAND 25 4 16.0% 0 3 0 1 0TRANSIENT 18 4 22.2% 2 1 0 1 0LOS ALTOS 89 3 3.4% 2 0 1 0 0DALY CITY 10 3 30.0% 0 0 2 0 1LOS BANOS 4 2 50.0% 0 1 0 1 0STANFORD 53 2 3.8% 0 0 0 1 1HAYWARD 36 2 5.6% 0 1 0 0 1SAN PABLO 1 1 100.0% 0 0 0 1 0SAN RAMON 2 1 50.0% 0 0 0 0 1ANTIOCH 4 1 25.0% 1 0 0 0 0SANTA BARBARA 2 1 50.0% 1 0 0 0 0SANTA NELLA 1 1 100.0% 0 1 0 0 0SOUTH SAN FRANCISCO 7 1 14.3% 0 0 0 1 0STOCKTON 11 1 9.1% 1 0 0 0 0UNION CITY 26 1 3.8% 0 0 1 0 0SAN MATEO 45 1 2.2% 1 0 0 0 0SAN LEANDRO 12 1 8.3% 0 0 0 0 1AUSTRALIA 1 1 100.0% 1 0 0 0 0BURLINGAME 18 1 5.6% 0 0 0 1 0COLORADO 5 1 20.0% 1 0 0 0 0FAIRFIELD 1 1 100.0% 1 0 0 0 0KANSAS 2 1 50.0% 1 0 0 0 0MILPITAS 21 1 4.8% 1 0 0 0 0NEW YORK 7 1 14.3% 0 0 1 0 0RICHMOND 4 1 25.0% 0 0 1 0 0SACRAMENTO 12 1 8.3% 0 0 0 1 0SALINAS 6 1 16.7% 0 0 0 1 0SAN BRUNO 8 1 12.5% 1 0 0 0 0VALLEJO 1 1 100.0% 0 1 0 0 0

Totals 3587 263   97 49 11 82 24

Comments

With arrest reasons (such as DUIs) not recorded in the stop data available to the public, the raw arrest numbers do not provide the clarity that would be hoped for, given the effort of this sort of this data collection process. Presumably about fifty (50) of these arrests were DUIs (from Table.11.1), leaving the remaining arrests to be explained. Any questions about “bias” on the part of the traffic officers in disposing of these stops exceeds the available data to even suggest such behavior on the part of the police. While variations in “Cite/No-Cite” ratios might lead to the possibilities of “bias” on the part of individual traffic officers, it would be difficult to believe that claims of “bias” on the part of individual officers would emerge when trying to understand racial disparities in the number of arrests in the Palo Alto traffic stop data.

Based on the available data, there is little evidence that arrests arising from traffic stops were not valid. Missing from this conjecture, however, is the disposition of the court cases that result from the referrals of these arrests to the Santa Clara County District Attorney’s Office. It would be most interesting to be able to include the results of these arrests, once referred to the District Attorney’s Office, with the arrest data as a validity check on the original arrests.

7.12 Stops Resulting In Arrests (By Time-of-Day)

Table.7.34—Stops Resulting In Arrests, By Time-of-Day.

Time Number Percent00:00-05:59: 77 29.306:00-07:59: 8 3.008:00-11:59: 40 15.212:00-13:59: 18 6.814:00-16:59: 40 15.217:00-18:59: 13 4.919:00-20:59: 21 8.021:00-23:59: 44 16.7

Total 261  .Comments:

Table.7.34 shows that just less than 50% of the traffic stops resulting in arrests initiated in Palo Alto during the 2009/Q4 reporting period were between the nighttime hours of 19:00 (PM) and 06:00 (AM), when the expectation that the race of a motorists could be known by a traffic officer during the initiation phase of a traffic stop would be very low.

The lack of reasons for arrests diminishes the value of this information, particularly since the arrest percentages are so significant. Future stop data should most definitely include the reason for the arrest, as well as the number of people arrested in the stopped vehicle.

7.13 Stops Resulting In Arrests (By Age, By Race)

The following three tables provide an age breakdown of those arrested, by the three major racial categories:

Table.7.35—Age Ranges For Whites Arrested By Palo Alto Police.

Age Range Number

Percent of Total

16-19 3 1.120-29 37 1430-39 11 4.240-49 20 7.850-59 20 7.660 or older 6 2.2

Total: 97  

Table.7.36—Age Ranges For Blacks Arrested By Palo Alto Police.

Age Range Number

Percent of Total

16-19 3 1.220-29 12 4.530-39 11 4.440-49 10 3.950-59 11 4.360 or older 2 0.8

Total 49  

Table.7.37—Age Ranges For Hispanics Arrested By Palo Alto Police.

Age Range Number

Percent of Total

16-19 7 2.720-29 46 17.530-39 13 5.140-49 9 3.550-59 5 2

60 or older 2 0.8

Total: 82  

Comments

Reasons for arrests not included in current records. Table.A9 provides details about DUI arrests which are believed to comprise about twenty to twenty-five percent of the arrests resulting from traffic stops.

7.14 East Palo Alto Stops—A Closer Look.

Since East Palo Alto residents are involved in about nine percent (9%) of all traffic stops in Palo Alto, and twelve percent (12%) of the arrests, a detailed look at East Palo Alto traffic stop statistics is warranted. The following tables provide the reasons, and dispositions, of the traffic stops involving Whites, “Blacks” and Hispanic residents:

Table.7.37--EPA Stops: “Blacks”: By Stop Reason.

Stop Reason Number PercentPENAL CODE 1 1.0%PRE-EXISTING KNOWLEDGE/INFO 5 4.9%VEHICLE CODE: EQUIPMENT/REG VIOLATION 69 67.6%VEHICLE CODE: MOVING/HAZARD 27 26.5%

Total: 102  

Table.7.38—EPA Stops: “Blacks”, By Disposition.

Stop Disposition Number PercentARREST 5 4.9%CITE 14 13.7%NO ACTION 41 40.2%OTHER 2 2.0%WARNING 40 39.2%

Totals: 102  

“Red Flag”: Cite vs No-cites: 13.7% vs 79.4%

“Red Flag”--When only Cite vs No-Cite (No Action+Warning) police actions are considered, then 79.4% of the traffic stops involving East Palo Alto “Blacks” result in a “No-Cite” situation, with only 14% of those stopped being cited.

This somewhat suspicious Cite vs No-Cite ratio is no doubt the cause of many East Palo Alto residents claiming “racial profiling” is being practiced by the Palo Alto Police, even though the number of East Palo Alto “Blacks” actually ticketed is quite low.

Table.7.39 Stops: EPA Hispanics, By Stop Reason

Stop Reason Number PercentOTHER CRIMINAL CODE (SPECIFY) 2 1.2%PENAL CODE 3 1.7%PRE-EXISTING KNOWLEDGE/INFO 9 5.2%VEHICLE CODE: EQUIPMENT/REG VIOLATION 109 63.4%VEHICLE CODE: MOVING/HAZARD 49 28.5%

Total 172  

Table.7.40—EPA Stops: Hispanics, By Disposition

Stop Disposition Number PercentARREST 20 11.6%CITE 22 12.8%NO ACTION 77 44.8%OTHER 3 1.7%WARNING 50 29.1%

Total 172  

“Red Flag”: Cite vs No-cites: 12.8% vs 73.9%

“Red Flag”--When only Cite vs No-Cite (No Action+Warning) police actions are considered, then 73.9% of the traffic stops involving East Palo Alto Hispanics result in a “No-Cite” situation, with only 12.8% of those stopped being cited.

Table.7.41 Stops: EPA Whites, By Stop Reason

Stop Reason Number PercentPENAL CODE 2 7.4%PRE-EXISTING KNOWLEDGE/INFO 1 3.7%VEHICLE CODE: EQUIPMENT/REG VIOLATION 12 44.4%VEHICLE CODE: MOVING/HAZARD 12 44.4%

Total 27  

Table.7.42 Stops: EPA Whites, By Disposition

Stop Disposition Number PercentARREST 3 11.1%CITE 7 25.9%NO ACTION 9 33.3%OTHER 1 3.7%WARNING 7 25.9%

Total 27  

Note: Cite vs No-cites: 25% vs 59.2%

Table.7.43 Stops EPA: Whites/Blacks/Hispanics, By Time-of-Day

Stop Time White Black Hispanic00:00-05:59 5 16 3506:00-07:59 0 2 708:00-11:59 5 18 2412:00-13:59 4 8 914:00-16:59 2 31 3017:00-18:59 2 4 419:00-20:59 2 4 1321:00-23:59 7 18 49

Total 27 101 171% of Stops 9.0% 33.8% 57.2%% of Pop. 28.8% 16.6% 38.0%

Table.7.44 EPA Stops, By Race, Stop Reason and Stop Disposition

Race

% of Pop-

ulation Stops Equipment Moving CiteNo-

ActionWhites 28.8% 27 44.4% 44.4% 25.9% 59.2%Blacks 16.7% 102 67.6% 26.5% 13.7% 81.4%Hispanics 38.0% 127 63.4% 28.5% 12.8% 73.9%

Total   256        

Note—T.44 only includes stops based on moving and equipment violations.

Table.7.45—Stop Statistics, By Major Streets (2009/Q4).

Primary Road Stops%

Total CitesNo-

Cites

Cites vs. No-Cites Arrests Searches

% Sear-ches

EL CAMINO REAL 579 14.4% 136 400 34.0% 38 45 7.8UNIVERSITY AV 344 8.5% 92 213 43.2% 39 40 11.6EMBARCADERO RD 223 5.5% 105 106 99.1% 10 19 8.5MIDDLEFIELD RD 206 5.1% 92 103 89.3% 9 13 6.3SAN ANTONIO RD 203 5.0% 64 124 51.6% 14 21 10.3LYTTON AV 123 3.1% 22 83 26.5% 17 18 14.6HAMILTON AV 103 2.6% 22 64 34.4% 16 16 15.5E CHARLESTON RD 101 2.5% 51 46 110.9% 2 6 5.9E BAYSHORE RD 84 2.1% 8 68 11.8% 7 21 25.0OREGON EXPWY 60 1.5% 17 41 41.5% 1 4 6.7ARASTRADERO RD 47 1.2% 25 22 113.6% 0 0 0.0W BAYSHORE RD 26 0.6% 7 13 53.8% 6 7 26.9W CHARLESTON RD 18 0.4% 9 7 128.6% 2 1 5.6

Note: Blue denotes streets where the number of stops with citations (Cites) written closely equals the number of stops without citations written (No-Cites).

Red denotes streets where the number of stops with citations (Cites) written is markedly lower than the number of stops without citations written (No-Cites).

Table.7.46—Stops/Searches On East Bayshore Road, By City-of-Residence

City of Residence Search PercentEAST PALO ALTO 10 47.6LOS BANOS 1 4.8MENLO PARK 1 4.8NEWARK 1 4.8PALO ALTO 5 23.8SAN JOSE 2 9.5SANTA CLARA 1 4.8

Total: 21

Note—Since East Bay Shore Road is a conduit to East Palo Alto, servicing only a limited number of commercial/government buildings , the high number of East Palo Alto residents stopped is to be expected, and does not, by itself, constitute a “Red Flag”. Table.7.47—Stops/Searches On East Bayshore Road, By Race

Race Number PercentB 10 47.6H 9 42.9W 2 9.5

Table.7.48—Stops/Searches On West Bayshore Road, By City-of-Residence

City of Residence Search PercentMENLO PARK 1 14.3PALO ALTO 4 57.1REDWOOD CITY 1 14.3SAN JOSE 1 14.3

Total: 7

Table.7.49—Stops/Searchers On West Bayshore Road, By Race.

Race Number PercentA 1 14.3B 2 28.6H 1 14.3W 3 42.9

Table.7.50—Stops/Searches On University Avenue, By City-of-Residence

City of Residence Searches PercentPALO ALTO 13 32.5EAST PALO ALTO 9 22.5FREMONT 3 7.5SUNNYVALE 3 7.5MENLO PARK 2 5.0REDWOOD CITY 2 5.0SAN JOSE 2 5.0LOS ALTOS 1 2.5NEWARK 1 2.5OAKLAND 1 2.5SAN MATEO 1 2.5TRANSIENT 1 2.5UNION CITY 1 2.5

Total: 39

Table.7.51—Stops/Searches On University Avenue, By Race.

Race Number PercentB 11 27.5H 12 30W 14 35X 3 7.5

Comments

Without traffic volume data for each of these streets, there is no easy way to make a determination as to whether motorists are being overstopped, or not, on East Bayshore Road, and West Bayshore Road. While the Stop/Search numbers do not seem particularly large, for this reporting period, without more information in the traffic stop data record, which would lead to some sense of “search productivity”, the “Cite vs No-Cite” ratio of less than 12% certainly raises a “Red Flag” as to possible overstopping over motorists by the Palo Alto Police on East Bayshore Road.

The Palo Alto Police should be tasked to explain these stops, in one way or another. Resolving this question might possibly require similar data from the East Palo Alto police to make meaningful inferences, or to understand the level of traffic monitoring performed by the East Palo Alto Police.

Traffic Stop Productivity—Citations vs. No-Citation Stops(Courtesy Stops Vs. Racial Profiling/”Pretext” Stops)

One simple definition of “traffic stop productivity” would be the ratio of citations generated from the traffic stops for a geographic area (city, neighborhood, or street) versus the total stops for that given area. Such a metric can be seen as an extremely important measure of the effectiveness of a police department’s traffic enforcement unit, as well as a key indicator of “racial profiling”/bias on the part of that police department.

Perhaps, in a “perfect world”, traffic stops would be only executed when justified, and for reasons that would result in a citation being written for every traffic stop executed. The citation/stop ratio would then be very close to 1.0 for all races. As the number of stops where no citations were issued increases, the Cite/No-Cite ratio decreases, ultimately approaching zero. Ratios significantly lower than 1.0 become “red flags” to possible “racial profiling”/bias, or other police action that very well may not have a valid reason to justify the stop (such as “pre-text stops”). In reality, not all traffic stops result in a citation, even though they may be initiated for valid reasons. Hence, the Cite/Stop ratio, in general, will always be lower than 1.0. The focus of traffic stop data then shifts to whether the Cite/Stop ratio, and the No-Cite/Cite ratios, are constant across all races, or if there are clear differences that might be suggest “Racial Profiling”/Bias as the source of these differences.

Table.11.1 lists the historical Stops-Made/Citations-Issued by the Palo Alto Police, showing the Citation/Stop ratios have ranged from 33% to 85%, over recent years. This information has been made public for almost ten years now, via the City Auditor’s Service and Accomplishment Report, and presumably has been presented, in one form or another, to the City Council during the City’s yearly finance cycle. Therefore it must be considered “common knowledge”.

The high percentage of Equipment/No Action stops involving many “Blacks” and Hispanics from East Palo Alto would suggest that these motorists are not operating their vehicles with the required registration and safety equipment. However, the very high level of “NO ACTION/WARNING” stop dispositions, coupled with the very low level of citations issued to East Palo Alto “Blacks” and Hispanics, raises the clear question: “why are these stops being instigated, and why are there so few citations issued?”

Given the data at hand (2009/Q4), it would appear that East Palo Alto “Black” and Hispanic motorists are being given a “pass” by Palo Alto Police, where Vehicle Code violations are concerned, or are the targets of “pretext” stops? We must take on faith that the traffic officers have honestly accessed the situation that caused them to initiate each stop, and have accurately recorded the stop data. Unfortunately, as noted elsewhere, the design of the Palo Alto data collection template does not seem to provide sufficient information to allow and after-the-fact review of the stop records to come to the conclusion that every stop was justified. Perhaps in the future, given how inexpensive digital photography has become, police will make a digital image of the vehicle that will provide evidence that stops for equipment deficiencies were justified, at least.

7.15.1.1 Citation vs No-Citation Ratios As Indicators of Pretext Stops

For the 4030 stops reported for the 2009/Q4 period, the Palo Alto Police issued citations to 37.3% of the motorists, while not issuing citations to 55.1% of those stopped. The Cite vs No-Cite (C/NC) then becomes a baseline for comparing how motorists of differing races are handled. Assuming equal treatment across the races, then the C/NC ratio should be the same. If this ratio is not roughly equivalent across the races, then this is a clear “red flag” that can be seen as an indicator for “Pretext” stops based on race.

Table.7.52—Citation vs No-Citation Ratios As Indicators of Pretext Stops

Citation Vs No-Citation Ratios As Indicator Of Pretext Stops

             Primary Road Stops White Black Asian Hispanic OtherEL CAMINO REAL 579 0.41 0.08 0.49 0.24 0.36UNIVERSITY AV 344 0.61 0.14 0.57 0.24 0.50W BAYSHORE RD 26 3.00 0.40 N/A 0.00 0.50E BAYSHORE RD 84 0.14 0.08 0.20 0.13 0.14E CHARLESTON RD 101 1.11 1.25 2.40 0.23 1.83W CHARLESTON RD 18 0.50 N/A 4.00 N/A 0.50ARASTRADERO RD 47 1.86 0.50 1.00 0.00 2.50OREGON EXPWY 60 0.50 0.50 0.43 0.33 0.25SAN ANTONIO RD 203 0.65 0.35 1.10 0.20 1.00EMBARCADERO RD 223 1.00 0.21 1.40 0.63 3.22LYTTON AV 123 0.33 0.17 0.33 0.36 0.11HAMILTON AV 103 0.44 0.00 1.67 0.13 0.14MIDDLEFIELD RD 206 0.96 0.54 2.00 0.65 0.79

Note—N/A denotes zero stops for this reporting period on this street.

Note—The threshold for the determination of “red flags” in the table above is somewhat arbitrary in this preliminary review, and is subject to change in the future. Graphic.7.1—Cite vs No-Cite Ratios As Indicator of Pretext Stops, By Race.

Cite vs No-Cite RatiosAs Indicator of Pretext Stops

0.00

1.00

2.00

3.00

4.00

5.00

EL CAMINOREAL

ECHARLESTON

RD

SAN ANTONIORD

MIDDLEFIELDRD

Major Palo Alto Streets

Cite

/No-

Cite

Rat

io

WhiteBlackAsianHispanicOther

“Red Flag”—Given the Palo Alto Police Department published targets for citations, it would seem that the Palo Alto Police are not treating East Palo Alto “Blacks” and Hispanics in a way needed to meet these targets.

7.15.2 Stop Productivity On Major Streets, By Race.

Given that the 2009/Q4 aggregate Citation vs Stops are about 37.5% to 55%, then it becomes instructive to look at the “stop productivity” on Palo Alto’s major roads/streets/highways, and also as a function of race.

Table.7.53—Stops vs Citations (On Major Streets), By Street and By Race.

    Citations vs Stops (As Percentages)Primary Road Stops White Black Asian Hispanic OtherEL CAMINO REAL 579 26.8 6.9 31.3 17.2 25.4UNIVERSITY AV 344 33.3 10.9 36.4 15.9 30.8W BAYSHORE RD 26 42.9 25.0 50.0 0.0 33.3E BAYSHORE RD 84 12.5 6.1 16.7 10.3 12.5E CHARLESTON RD 101 48.8 50.0 70.6 18.8 64.7W CHARLESTON RD 18 33.3 33.3 80.0 100.0 33.3ARASTRADERO RD 47 65.0 33.3 50.0 0.0 71.4OREGON EXPWY 60 32.0 28.6 30.0 25.0 20.0SAN ANTONIO RD 203 37.7 23.1 50.0 14.8 48.0EMBARCADERO RD 223 48.8 17.4 58.3 32.7 72.5

LYTTON AV 123 21.8 12.5 25.0 21.7 8.0HAMILTON AV 103 26.4 0.0 50.0 8.3 11.1SAN ANTONIO RD 206 48.5 31.8 63.6 35.1 39.3

Total Stops : 2117          Averages:   36.8 21.5 47.1 23.1 36.2

Note—In the review of “Stop Productivity” data, the actual number of stops is not considered as the basis for a “red flag” situation.

Note—The threshold for the determination of “red flags” in the table above is somewhat arbitrary in this preliminary review, and is subject to change in the future.

Graphic.7.2—Citation Rate For Stops On Major Palo Alto Streets.

0%20%40%60%80%

100%

Cite

/Sto

p Pe

rcen

t

EL CAMINOREAL

EMBARCADERO RD

Major Streets

Citation Rate For Stops On Major Palo Alto Streets

OtherHispanicAsianBlackWhite

Graphic.7.2 demonstrates that Asians, while typically under-stopped, are over-cited, when compared to other races.

7.15.3 “Stop Productivity”, By Stop Reason, And Race.

Stop productivity is effectively the value of a traffic stop in terms of a citation and/or arrest. Since arrests generally are associated with DUIs (Driving Under the Influence), or an outstanding warrants, issues associated with “Racial Profiling/Bias” generally are not raised. However, when motorists are stopped and not cited, then this police activity can be readily detected (after the fact) via “Stop Productivity” calculations.

Exactly what numeric values the “Stop Productivity” would depend on any number of variables, such as department “quotas/targets”, training level of traffic enforcement unit, attitudes/mindsets about citing motorists by individual officers in traffic enforcement, etc.

Table.11.1 shows that the Palo Alto Police Department has published targets for citations and total number of stops, over the years. The ratio of the citations to stops becomes one metric for modeling traffic enforcement.

The next metric to be developed from this data would be the “Stop Productivity”, which would provide a clear sense as to whether stops were justified, or not. When the “Stop Productivity” ratio varies too much from the aggregate average (and the published targets), then this simple set of numbers can provide a window into the management of the traffic enforcement unit, as well as an indicator as to whether the police are utilizing “pretext stops” as a part of a large policing effort, which might involve “Racial Profiling/Bias”. The key number for the 2009/Q4 reporting period’s No-Cite/Cite ratio is 1.47, rounded to 1.5. Data in the following two tables will be “red-flagged” as the “by-race” NC/C ratios vary from the aggregate NC/C ratio.

Table.7.54—Equipment/Registration Stops: No-Citation vs Citation Ratios, By Race.

Equipment/Registration Stops: No-Cite Vs Cite Ratios         

Stop Reason: Vehicle Code Race No-Cite Stops Cite-Stops NC v C EQUIPMENT/REG VIOLATION A 99 8.50% 50 11.80% 2.0EQUIPMENT/REG VIOLATION B 178 15.20% 30 7.10% 5.9EQUIPMENT/REG VIOLATION H 300 25.60% 69 16.30% 4.3EQUIPMENT/REG VIOLATION W 447 38.20% 215 50.70% 2.1EQUIPMENT/REG VIOLATION X 146 12.50% 60 14.20% 2.4

Totals:   1170   424    

Note: “NC v C” is the No-Cite/Cite ratio (Aggregate Value: 1.5)

Note—The threshold for the determination of “red flags” in the table above is somewhat arbitrary in this preliminary review, and is subject to change in the future.

Table.7.55—Moving/Hazard Stops: No-Citation vs Citation Ratios, By Race.

Moving/Hazard Stops: No-Cite Vs Cite Ratios         

Stop Reason: Vehicle Code Race No-Cite Stops Cite-Stops NC v C MOVING/HAZARD A 93 11.40% 191 18.30% 0.5MOVING/HAZARD B 74 9.10% 36 3.50% 2.1MOVING/HAZARD H 126 15.40% 108 10.40% 1.2MOVING/HAZARD W 399 48.80% 526 50.40% 0.8MOVING/HAZARD X 125 15.30% 182 17.40% 0.7

Totals:   817   1043    

Note: “NC v C” is the No-Cite/Cite ratio (Aggregate Value: 1.5)

Note—The threshold for the determination of “red flags” in the table above is somewhat arbitrary in this preliminary review, and is subject to change in the future.

Discussion

Tables.7.52, 7.53, 7.54 and 7.55 offer the following insights into the 2009/Q4 traffic stop data--

Stop Productivity perhaps most important metric introduced yet. Demonstrates significant differences in Stop Dispositions, by race. Certainly demonstrates likelihood of “pretext stops”, based on race. Demonstrates that “Citation targets” are not being met. Calls into question need for/cost of unnecessary (“courtesy”) traffic stops. Suggests need for better management/oversight of traffic teams.

As with all of views of the traffic stop data presented in this review, the lack of detail for stops and dispositions in the data collection templates hinders developing this metric to any greater extent. It is believed that this metric should be considered as one of the key metrics for monitoring traffic stop team performance in general, as well as conducting oversight where possible issues of “racial profiling”/bias might be concerned.

8.0 “Data Not Available”

From reviewing traffic data collected in other jurisdictions, as well as considering the data not collected in the Palo Alto experience, the following list of stop-related data that should be collected in the future is presented:

Officer IDIncident Tracking NumberCitation NumberUnique Driver IDZip Code Of Driver’s Primary ResidenceStop DurationGPS Location Of StopRecords Check Performed?Vehicle Type (Car, Truck, Bicycle, Bus)Vehicle Personal or Commercial (delivery, trade, etc).Make/Model/Year of VehiclePosted Speed LimitActual Vehicle Speed When Stop Initiated (if available).Arrest ReasonArrest Based On SearchPassengers ArrestedMotorist Opinion If Stop Was Valid.Minor Children As Passengers.Motorist Possessed Invalid/No Driver’s License.Motorist Attempted To Flee Police—Chase Involved.Immigration Status Of DriverUse-of-Force Involved During Stop?Taser Used During Stop?Stop Reasons:

Calls for Service/APB City Ordinance Violation Equipment Violation High Speeding (15 mph and over) Low Speeding (Under 15 mph) Motorist Assist Other Traffic Violation Registration Violation Special Detail Outstanding WarrantBe On The Look Out For (BOLO)Possible DUISeatbelt ViolationHandheld Cell Phone Use/Inattention ViolationToo Many People In VehiclePeople Riding In Back Of Open Truck

“Suspicious Driving”Search Results

None Weapons Money Drugs Alcohol Other Multiple

Note—The last data set made available to the public (2010/Q2) does have fields for incident numbers, and comments. The comments for every record are not uniformly completed so as to be useful for analysis. This modification to the data set collected possibly shows some evolution in the view of the data needed to be collected. (The possibility also exists that this information had been collected by the police and not released to the public until this reporting period.)

Comments

The list above is quite extensive, to be sure. However, the stop data released by the police (to date) has proven to be insufficient for a full recreation of most traffic stops, so that an independent review of the stop record (at a later time) would facilitate the determination that the stop and its disposition followed department policy and was, therefore, fully valid in its execution. The information above, if available, would provide information needed to facilitate a review of the stop, leading to the complete understanding of need for, and resolution of, each traffic stop.

It is suggested that automating this information collection (via a Table/PC of some sort, coupled with “back-end” data bases) would not increase the time needed to process the typical traffic stop, and would increase the amount, and quality, of data collected during traffic stops.

9.0 Comparison Of Palo Alto Stop Data With Other Communities’ Stop Data

Examination of the behavior of the Palo Alto police, relative to the execution of traffic stops becomes difficult without baselines of similar behavior from other police departments in the neighboring cities. Even though this issue of “racial profiling” has been discussed since at least 1999, there is very little traffic stop data on-line from local communities, and almost none is current. Menlo Park, and the Alameda County Sheriff’s Office does have some data on-line, so these comparisons between these two jurisdictions has be attempted in this review.

Menlo Park—See Appendix.K.

Alameda Country Sheriff’s Office—See Appendix M.

Comments

While clearly important in determining many aspects of police department behavior at the local and regional level, the lack of a common data collection template, and common format for publication of this data, makes comparison of each police jurisdictions data problematic, at best. Only Palo Alto seems to have included the city-of-reference of the vehicle operators, which has proven very helpful in the review of the disposition phase of each traffic stop, as well as helping to deal with claims of “racial profiling” and “bias”.

10.0List Of “Red Flags”

The following list provides an index to those data presentations where “red flags” indicating racial disparities can be seen:

Table.10.1—List of “Red Flags”

Section Topic4.2 Crime and Race In Palo Alto6.1 Traffic Accidents In Palo Alto For 2009, By Race7.1 Basic Traffic Stops Data7.2 City-of-Residence For Majority of Traffic Stops (~80%)7.7 Traffic Stops—All Cities/By Race7.9 Stops Resulting In Searches7.13 Stops Resulting In Arrests7.14 East Palo Alto Stops—A Closer Look.7.15 Stop Productivity--Citation vs No-Citation Stops11.6 Evidence of Traffic Stop “Quotas” In Palo Alto11.7 Probability of Being Stopped While Driving In Palo Alto, On Yearly Basis13.8 Red Flags

Table,10.1 is intended to help readers easily find, and review those sections of this document where data, and analysis, demonstrate concern over police actions during traffic stops.

11.0 General Discussion

While the data presented throughout this review hopefully speaks for itself, there are many issues raised in this study that can not be directly addressed by traffic stop data analysis, needing additional discussion to fully appreciate. The sections that follow address the most important of those issues:

11.1 Monitoring Police Performance Requires Collecting Traffic Stop Data.

A recurring problem with the management of the Palo Alto Police emerges in the analysis of the administration of the recording of traffic stop data. Various sources suggest that a significant percentage of police contact with the public is through traffic stops—perhaps as high as 40% at the national level. Yet, the Palo Alto Police Management has failed to recognize the importance of documenting the activities of the police via a yearly performance report. Given that the number of traffic stops over the years has ranged from 8800+-16,000+ a year (and could easily be more), police/public contact is also significant in Palo Alto.

The somewhat abstract issue of “checks-and-balances” between the various levels of government enters the discussion at times like these. In Palo Alto, there is no specific call for the existence of a police department in the City’s Charter, and as such, no specific control over the police via the City Council has been established in the Charter, either. Discussion of police issues, including performance, has been historically more “taboo”, than not, by the Palo Alto City Council. While there are Charter restrictions limiting City Council involvement in the day-to-day operations of the City, there is nothing in the Charter restricting the Council from demanding full reporting of the various departments by the City Manager. Unfortunately, this level of oversight has been denied Palo Alto residents, and property owners, over the decades, by those elected to Palo Alto’s primary oversight body to call for such accounting of the City’s Police Department.

The collection of traffic stop data is an important aspect of the information needed by the public to fully appreciate the activities, and the cost/justification, of the police. As such, the traffic stop data collection activity should be reinstated by the Palo Alto Police immediately.

11.2 Future Traffic Stop Data Review Need Only Focus On Local Cities.

Costs associated with collecting/analyzing/distributing traffic stop data have been voiced by the Palo Alto Police since the early days of the collection of this data. While the details of the analysis phase has not been investigated for this review, it is unlikely that the use of database technology, such as employed in this study, was not utilized in the past by the Palo Alto Police, which would reduce the data processing time to less than five minutes on a laptop. However, the presentation of this data, in a format that is “digestable” for ordinary people, remains a bit of a challenge, since this is both time consuming to produce, and time consuming to read.

Table.7.6 reveals that residents of Palo Alto and East Palo Alto are involved in over fifty percent of the traffic stops, searches and arrests. Even with the other ten local cities, and Stanford included, the data generated tracking the other 20% of the traffic stops provides

little of interest, and could easily not be considered for future reports-- unless there were to be shifts in the underlying demographics that computer-based monitoring detected. Given the amount of data that can be generated from a traffic stop data set, being able to identify the most relevant information, and make only that available to the public would not only make the traffic stop operation more transparent to the general public, but would reduce the amount of unnecessary data that makes this a difficult issue to document, understand, and discuss in a public way.

11.3 Surprises—Good and Bad

Two positive points are clearly established by this study—1) traffic enforcement is proportional to traffic accidents on those streets/roads with the highest number of accidents, and that, on average, three arrests a day are made by the Police. The reasons for these arrests are not clear from the data, however. With the increasing intolerance for drunk driving, presumably a significant number of these arrests are DUIs (Driving Under the Influence), with the remainder being for outstanding warrants, parole violations, or other violations of the penal code. Unfortunately, the released stop data does not provide any insight into the reasons for each arrest.

The disproportionate number of stops of “Blacks” and Hispanics, at levels that see (statistically) every “Black” and Hispanic Palo Alto resident stopped every eighteen months, or so, is most surprising, and very troubling. The Palo Alto Police should be directed to explain these facts to the City Council, and the public.

Also, the fact that 55% of Palo Alto traffic stops seem to be “courtesy stops”, with little/no information in the individual stop records to justify the reasoning of each officer for initiating a stop, and then not issuing a citation, raises the somewhat troubling issue about the level of “discretionary stops” that are conducted by the Palo Alto Police.

11.4 Comparison Of Stops Data With Other Jurisdictions.

Study after study concludes that the various racial components of communities do not necessarily reflect the racial components of the transportation paths in a given locale, making straightforward analyses of the collected demographic data difficult. This difficulty extends to making comparisons between data collected from police jurisdictions that are not contiguous to the jurisdiction under study.

However, if the racial component of the issue were removed, and the general issues of performance, and cost modeling were considered as important, then the following comparisons between jurisdictions would provide information that would prove invaluable in understanding police effectiveness:

Male vs Female Stop/Search Ratios White vs “Black” vs Hispanic vs Asian Ratios Cite vs No Cite Ratios Motorist’s City-of-Residence Cite Reasons Arrest Rates Search Rates Search Productivity City-of-Residence Stop Duration Times Stops/10000 Vehicle Trips

Difficulty obtaining traffic stop data from local police departments has frustrated efforts to compare the Palo Alto stop data, and police actions, with other, similar, municipalities. Hopefully, this data will be made available in the future, and these comparisons can be made.

11.5 Evidence of Traffic Stop “Quotas” In Palo Alto

Table.11.1 below lists basic traffic stop data for Palo Alto for the past decade:

Table.11.1—Historic Traffic Stop Data

Fiscal Year DUIs

Traffic Stops

Yearly Diff.

Citations Issued

Citations vs Stops

Traffic Services Expend-

itures1998-99   -   12,455 N/A  1999-00   11,938   15,146 126.9%  2000-01   15,165 21.3% 12,831 84.6%  2001-02   13,670 -10.9% 11,001 80.5%  2002-03 191 9,956 -37.3% 8,279 83.2% $2.12003-04 172 9,973 0.2% 7,301 73.2% $1.4*2004-05 111 8,822 -13.0% 5,671 64.3% $1.52005-06 247 11,827 25.4% 7,687 65.0% $1.5

2006-07 257 15,563 24.0% 6,232 40.0% $1.72007-08 343 19,177 18.8% 6,326 33.0% $1.72008-09 192 14,152 -35.5% 5,766 40.7% $1.92009-10 181 13,344 -6.1% 7,520 56.4% $2.02010-11 250*     7,000*    

Source: Auditor’s Yearly Services and Accomplishments Reports

Notes:1) *Denotes Change In Accounting Methodology2) Red Denotes Targets for year (per Auditor’s Report).3) Value of “Citations” for 1999-00 may be in error.

Comments

Review/analysis of traffic stop data would normally look at the following metrics in order to look for evidence of traffic stop “quotas” being in place by a given police department:

Monthly variations Yearly Variations Traffic volumes Differences Between Neighboring Cities In Stops/Citations

Obtained from credible City sources, Table.11.1 (above) provides clear evidence of yearly variations in traffic stops, and citations written, by the Palo Alto police. The published budget has, for some time now, used the term “targets” to describe the expenditures, and expected results (performance) for each department. Therefore, given that the Palo Alto Police are projecting the number of traffic (and parking) citations into the coming fiscal year, it then is patently clear that something akin to a “quota” system is being practiced by the Palo Alto Police--with the approval of the City Council.

Table.11.1 also calls into question why the large variability in traffic stops and citations over the years. While budget data tracking “traffic services” expenditures are expressed in terms of dollars, actual head count and man-hours allocated to this police function are needed to fully appreciate the high variation in stops. Short of this man-hour data, this variation must be seen as another “red flag” that needs explanation by the Palo Alto Police.

11.7 Probability of Being Stopped While Driving In Palo Alto, On Yearly Basis

The following table lists the towns whose residents are stopped most frequently in Palo Alto, providing the likelihood that motorist residing in one of these local cities will be stopped at least once during a given year:

Table.11.2—General Likelihood of Traffic Stop In Palo Alto, By City-of-Residence.

City Stops

Estimated Driver-Age Population

Probability of Yearly

Stop

Stop Likely Once Every

Palo Alto 6000 50,000 12.0% 8 yearsEast Palo Alto 1300 20,500 6.3% 16 yearsMenlo Park 700 24,500 2.9% 36 yearsMountain View 1000 60,290 1.7% 60 yearsLos Altos 360 21,600 1.7% 60 yearsSunnyvale 600 110400 0.5% 184 years

Note—Stops-per-year estimates based on extrapolation of number of 2009/Q4 stops.

Note—Estimated Driver Population is based on Y.2010 Census data, not DMV-provided data. The actual number of licensed, and active, drivers for each town is certainly smaller than that determined from the Census data.

Comments

Since most people are likely to drive outside their city-of-residence to get to/from their jobs, and to spend most of their time closer to home, for shopping, and recreation, it is easy to understand that the farther people live from Palo Alto, the less likely they are to drive in Palo Alto. As such, the numbers of people from remote locations stopped for traffic violations becomes quite small. Given the low number of traffic stops involving motorists from cities outside the Palo Alto/East Palo Alto/Mountain View/Menlo Park/Stanford cluster, attempting to correlate Census data with stop data becomes futile.

Table.11.2 suggests that future traffic stop reports should be refocused on the Palo Alto/East Palo Alto/Menlo Park/Stanford cluster (essentially the significant 80% of the stops), with the remaining 20% of the stops aggregated. Census data correlations with this group should use Regional/California data for baselines.

11.8 Racial/Cultural Differences In Driver Behavior

National, and Palo Alto, traffic stop data, typically reveal racial disparities in the stop rates for “Blacks” and Hispanics versus Whites and Asians. “Blacks” and Hispanics are generally over-sampled in terms of their local, and regional, Census representations; whereas, Asians seem to be under-sampled. This begs the question as to whether these racial designators really are cultural designators, and are there significant differences in driving behaviors between cultural groups that justify these disparities in the number of traffic stops for each group?

In addition to “culture”, immigration issues enter the discussion at this point. The racial designator “Hispanic” has little real meaning, when it describes anyone who personally, or whose family, originated in Mexico, Central/South America. As noted above, recent immigrants are not likely to fully appreciate the magnitude of the California Vehicle Code, nor to have much experience driving in the US. Hence, it is not difficult to believe that such immigrants would make mistakes (including not possessing a valid driver’s license), while driving in a high-enforcement environment that could result in disparities in the racial components of total number of drivers stopped. If the police were to make public all available information about each traffic stop, such as vehicle type, and whether the driver possessed a valid license (for example), it would go a long way towards helping to develop clear understandings of not only the details of each traffic stop, but to better determine, analytically, the validity of each traffic stop, and ultimately, all of the stops for a given reporting period.

Too often, there is a public perception that the police are in the wrong when there are public complaints, such as was the case when this matter first emerged about ten years ago. It falls to the police to operate in the most transparent manner they can, in order to insure the public has the information/data it needs to ensure that police actions are justified, and appropriate to the problem at hand. To that end, the disparities identified in this study strongly call for more detailed traffic stop data to better explain these disparities. There is no reason not to believe that all of these traffic stops were valid. However, that point is hard to make at this time, from the data released to the public.

11.9 Racial Components Of Traffic Stops--A Reflection of Immigration Trends,

and/or Socio-Economics?

The under-stopping of Asians, relative to their Census levels, and the over-stopping of Hispanics relative to their levels in the Census, coupled with high NC/C ratios (see Section 7.15) for Hispanics and the low NC/C ratios for Asians begs the question if trends in immigration policy can be seen in the traffic stop (and traffic accident) data?

The following questions are naturally raised:

Number of people in racial sub-group with driver’s licenses Equal Distribution of Male/Female Licensed Drivers. Older immigrants not licensed at same rate as older native-born drivers. Ability of immigrants in “the professions” to better maintain their vehicles.

These questions can not be easily answered in this review, but should be investigated at some point in time.

11.10 Traffic Stop Search Productivity

Given the concerns expressed nationally, and locally, about unwarranted traffic stops, and searches, it is curious that so few of the traffic stop collection exercises provide much in terms of insight into their operations from the data collected, and provided to the public. (In retrospect, perhaps the concerns about “racial profiling” were more politically important at the time, that the issues of cost controls that are facing governments around the world, now.)

While the data in this report does identify some racial disparities in stops and searches, the bulk of the people stopped, searched, and arrested in Palo Alto are White, and residents of Palo Alto. This opens the door to the questions:

Why so many stops? Why so many searches? What was the productivity of these searches?

The review of the Palo Alto traffic stop data reveals that:

9.3% of all traffic stops resulted in a vehicle search. 7.3% of all traffic stops resulted in an arrest.

A nine-ten percent (9%-10%) search rate for traffic stops in a town like Palo Alto seems curiously high; however, without additional context, we are left with no clear understanding as to why so many people are being searched by the Palo Alto Police. As mentioned in Section.4.8 (above), the Rhode Island Study determined that statewide only 4.5% of the traffic stops resulted in an arrest and 7.9% of all traffic stops resulted in the driver, passenger or vehicle being searched. Locally, Menlo Park data (Appendix.K) shows that only about 4.5% of the vehicles stopped were searched.

Assuming that the searches were initiated for good “police reasons”, then publishing the results of these searches, in terms of “productivity” provides the public assurances that the decisions made by the police were justified, resulting in the discovery of items that led to seizure, and arrest, of those transporting these items. Without any published data on “search productivity”, we are left with no assurances that these searches, or stops, were justified.

Without current traffic stop data from every town in the area, it is impossible to make any specific claims about the search rate being too high in Palo Alto. Coupled with a lack of “search productivity” data, there is every reason to be concerned that the police are pushing the “envelope of necessity” beyond a reasonable level, where stops and searches are concerned.

Note—This topic that needs more research.

11.11 Value of “No Action/Warning” Stops

Given the high percentage of “NO ACTION/WARNING” traffic stops in Palo Alto (~55%), the question as to why over 2,000 motorists per quarter (650 motorists per month) are being stopped by the Palo Alto Police for no clear reason should be a concern for all Palo Altans.

Certainly better documentation on each stop would go a long way towards reducing concerns about individual police officers, without any clear management oversight of their behavior and actions. But, at this time, the traffic stop data recording has been terminated. So, there is absolutely no apparent check-and-balance in place in the Palo Alto Police Department that allows the public any insight into the actual number of stops that may not prove any purpose, other than to establish a “police presence” on the streets and roads.

Having quarterly reports generated by the Police Departments of every San Francisco BayArea police jurisdiction, in a common format, with machine readable data available, should be the goal of the civilian governments of this major metropolitan area. By comparing the Cite vs No-Cite ratios alone, different levels of performance by each police agency would readily become obvious. With traffic officer IDs included in the data, then it would be straightforward to determine if any officer in the area were processing stopped motorists in a way that might be considered as “too harsh” to “too lenient”.

From a cost point-of-view, 55% of the cost of “traffic services” (perhaps $1M/year) is generating little, or nothing, of value, to Palo Alto residents, and/or taxpayers. It would seem that even if this 55% of the total stops were justified, and considered “courtesy stops” (because no citation were issued), then the result has generated a lot of concern, and fear of “racial profiling” in this, and surrounding, communities.

It could not hurt to have the police better explain their intentions, and cost-model their activities, when it comes to these “NO ACTION” stops.

11.12 Issues With Current Review

The following issues exist with the Palo Alto Police demographic data collection exercise, which necessarily impact this review of that data:

Not Enough Data Collected To Recreate Stop Events Very small sample size (Only 4000 Stops) Raw Data For Only Three Quarters (< One Year) Available Online. No Direct Contact With Police Department. No Officer IDs Available No Unique Driver IDs Available. No Data Available Reflecting Actual Drivers Per Racial Cohort No Data From Court Actions Resulting From Stops/Arrests

Total Revenue Generated From Traffic Stops Not Readily Available Cost of Traffic Stops Not Easily Determined. Benefit of Traffic Stops Not Easily Determined. Little Data Available For Comparisons With Neighboring/Regional Cities. Problems With Original Data Collection Design. No Feedback From Stopped Motorists About Validity Of Stops.

Comments

This scope of this review is limited to data available to the public, and easily converted to machine-readable formats. As such, there is very little data available to the public to make the necessary comparisons that would lead to meaningful results that would actually prove, or disprove, claims of “racial profiling” and/or “bias” on the part of the local police agencies.

Since the Palo Alto Police has been collecting data for many years, having only three quarters of raw data available on-line frustrates any attempts to identify trends in the stop data that might reflect differences in the way the Palo Alto Police has managed “traffic services” over the years. One quarter’s data is simply too small to prove, or disprove, much of anything. However, the work done to develop the data base queries demonstrate the level of effort that the Palo Alto Police Department would need to have expended to produce this sort of analysis.

11.13 Areas For Future Inquiry

The following areas of further inquiry would add depth to future reviews, and are presented without discussion:

Future Inquiry: Officer IDs Available In Traffic Stop Records Why High Percentage Of Stops Resulting In “NO Action” or “Warning” High Black/Hispanic Stop Rate Compared To Whites/Asians Tracking Racial Components Through Complete Traffic Stop Review Of All Historic Traffic Stop Data Review Of 911 Emergency Center/Dispatch Logs Use of Actual Number of Drivers, by race, by City, (from DMV) Map of traffic stop locations (demonstrating possible “Hunt Zones”) Investigation of High Yearly Variation In Number of Traffic Stops Review of videos from traffic stops Ride-Along With PA Police Ride-Along With MV Police Ride-Along With EPA Police Ride-Along With MP Police Inclusion of Car Make/Model/Year Data Interviews With Blacks/Hispanics Who Feel They Were Unfairly Stopped Use of DMV Data For Actual Driver Counts, By City, By Race. Redesign of the Demographic Data Collection Template. Review of Traffic Citation History Of Cited Drivers. Video of Streets With High Number of Stops/Accidents For Baselines Review of Court Challenges To Palo Alto Traffic Tickets Review of Unpublished Traffic Stop Data For Palo Alto and East Palo Alto

“Blacks and Hispanics For Better Stop Reasons/Disposition Analysis. Failure Of Individual Officers To File Complete Traffic Stop Reports. Stops Based On Traffic Volume, By Street. Better Integration Of Crime/Traffic Statistics. Analysis of traffic stops at the street/neighborhood level using Census tract data. Use of Automatic License Plate Readers To Develop Realistic Baseline Data For

Road/Street Use. Better Comparison Of Traffic Stop Data With That Of Other Jurisdictions. Automating Traffic Stop Data Collection Implementation of “Data Not Collected” List. Increase Role of City Auditor In Monitoring Police Activities. Promote Regional Collection of Traffic Stop Data. Investigate High Variability of Traffic Stops From Year-to-Year. Determination of Actual Cost Of Traffic Stop Data Collection/Processing.

Comments

Many of the items suggested above would increase the depth, and breadth, of the baselines needed to interpret the quarterly traffic stop data. Many of these items also suggest the accessibility and/or integration of various government/law enforcement data bases, allowing for the collection of data not now available, such as city-of-residence information that is needed to make better demographic correlations. These integrations would only be possible with a commitment to more “sunshine” on the part of all local governments, and local law enforcement agencies.

11.14 Problems With Independent Police Auditor’s Review On “Bias-Based Policing” (February, 2010).

In February, 2010, the so-called Independent Police Auditor issued a report entitled “Bias-Based Police”, which was intended to provide the City Council, and the public, his opinion as to any evidence of “Racial Profiling/Bias” on the part of the Palo Alto Police (Appendix.G.5 and Appendix.G.8)

This “study” seems to avoid any meaningful analysis of the data collected by the Palo Alto Police, providing, instead, an overview of the difficulties encountered by other law enforcement agencies in dealing with this issue. While this review of the literature was certainly appropriate, missing notably from the Police Auditor’s report is a reading list, such as found in Appendix.G in this document.

The Independent Police Auditor’s review spends most of its time walking around the issues of the actual data, and tries instead to paint a “pretty picture” of the Palo Alto Police Department’s efforts to be “sensitive” to (presumably) non-white motorists, while missing the key issues that “Blacks” and Hispanic Palo Alto/East Palo Alto residents are stopped quite disproportionately to their numbers in the Census.

The Police Auditor did not mention the possibility of the use of “pretext stops” by the Palo Alto police, or the use of traffic stops as a tool in dealing with, say, property crimes—which are somewhat higher in Palo Alto than other nearby cities. Also, the Auditor did not seem to provide any suggestions about how to utilize “technology” to reduce the impact of this data collection on the traffic officers, and the Department, as a whole.

To his credit, the Police Auditor did recognize the value of this data, and did not suggest that the data collection program be terminated. His suggestions that this data “sends a message” to the community is true—when the data is properly analyzed, and included in a comprehensive performance report.

Appendix.G.8 suggests that the City paid over $200/hour for this “report”. Given the lack of substantive information in this review, the continued use of outside sources for any future reviews of police activities that do not involve data-based analysis, does not seem to be a justified use of City funds (particularly at such extravagant hourly rates).

12.0Conclusion

Given the width, and breadth, of this review of traffic stop data for 2009/Q4, conclusions based on the data analysis can be found in Section 12.1, and some thoughts about “process” can be found in Section 12.2.

12.1 Data Review

This review of the 2009/Q4 Palo Alto traffic stop data, using Relational Database technology to identify relationships of the data elements in the raw data made available to the public, and other published data, demonstrates:

1) Clear evidence that a “racial disparity” exists between stops in Palo Alto involving Whites/Asians and “Blacks”/Hispanics, particularly “Blacks”/Hispanics who are residents of Palo Alto, and East Palo Alto.. While there is insufficient information in the raw data to support any interpretation that the Palo Alto Police are engaging in “racial profiling”, there are a goodly number of “red flags” involving the large percentage of “Black” and Hispanic Palo Alto residents who are stopped every year raised by this review. There may be valid reasons for every traffic stop conducted by traffic patrols, but there is simply not enough data in the public record to discern those reasons. This point needs to be seen as a failure of Police Management to properly understand the issues, and to utilize the resources available to it to insure that data necessary to provide the public the information necessary to prove, or disprove, the allegations that were directed towards the police at the time was collected, and properly analyzed.

2) Clear evidence that the Palo Alto Police are stopping motorists of all races for what seems to be “courtesy stops” at an unnecessarily high level—without providing justifiable reasons for such stops. Unfortunately, without similar, and current, stop data from other police jurisdictions, there is no easy way to determine if Palo Alto is “over-stopping” motorists, or not.

3) Clear evidence that the Palo Police have published “targets” for the performance of its “traffic services” unit, and that these “targets” vary considerably, from year-to-year, suggesting strongly that the level of traffic “enforcement” in Palo Alto is more driven by money, than public safety—AND demonstrating that there are “quotas” in place for stopping motorists in Palo Alto.

4) The low number of traffic stops involving people not living in the immediate vicinity (Palo Alto, East Palo Alto, San Jose, Redwood City, Menlo Park, Mountain View and Stanford) may not be of interest, in terms of long-term, detailed, reporting of traffic stops.

5) Traffic stops tend to track accident rates, on Palo Alto’s streets.

6) Other than in the downtown business area, there does not seem to be any meaningful clustering of stop locations.

7) Twenty percent (20%) of all arrests in Palo Alto involve “Blacks”, even though the Census representation for “Blacks” is only 1.6%.

While this review has attempted to blend data from various sources to better understand the behavior of the Palo Alto Police conducting traffic stops, it becomes clear that this data, while interesting, is too fragmented as it is released to facilitate a fully integrated model of police activity that could be constructed with access to all the data available to the Police.

Even if the 2010/Q1 and 2010/Q2 data were to added to this review, it is difficult to expect the results would be very different for such a short timeframe. For this sort of analysis to be productive to the public, access to all of the data held by the Palo Alto police would be required.

12.2 Process Review

This review has introduced topics peripheral to the direct examination of the raw traffic stop data in order to promote the concepts of:

1) Police agency transparency 2) Advanced uses of “technology”3) Consolidation of the data collection/management functions through “regionalization”.

Discussion of these issues of “processes” might reduce the size of future reviews of traffic stop data if handled in one, or more, separate documents, or if the suggestions/recommendations were to be adopted by police so that the issues no longer existed.

The following topics are suggested as important issues, where future “police process” is concerned:

Reducing the Size/Cost of “Traffic Services” Need For Traffic Stop Data From All CA Cities Cost/Benefits of “Traffic Services” Need For Use Of Automated License Plate Readers Automated Speed/Red Light Cameras Other Speed Monitoring Capabilities Automation of Traffic Stop Data Collection Regionalization of Traffic Stop Data Collection Standardize Data Collection Template/Process. Adopting Tablet/PCs and software to provide standardized data collection. Traffic Stop Analysis/Statistics Provided at Central location. All Data On-line, and available to Public. Follow-Thru to Court Appearance For Traffic Stop Analysis

Comments

As most reviews of police traffic stop data have identified that “racial disparities” exist between “Blacks/Hispanics”, and “Whites/Asians”, so too does this review find the same results here in Palo Alto. Unlike other reviews of this matter, this review attempts to identify other aspects of police activity, so as the “background crime rates”, as important in understanding traffic stop rates. This approach, given the limited amount of data available to the public, at best only opens doors into the generally opaque world of the police, and does not provide any meaningful answers at this point. Cost issues associated with Palo Alto’s “traffic services” are more easily questioned, and given the staggering costs of providing governmental services in the future, questions about the continuation of this police “service” that seems to have upwards of ten sworn officers assigned to this task, and some number of civilians “heads” should be questioned by the City Council, and the public, immediately. This review of the 2009/Q4 traffic stop data finds that it is unlikely that “racial profiling” during traffic stop initiation (Phase I) exists. However, the data clearly suggests that there is some “bias” exhibited towards “Blacks/Hispanics” during the disposition phase of the stops (Phase II). Unfortunately, as discussed in detail throughout this paper, there is just insufficient information recorded in the stops data, and no other baseline data exists, needed to make definitive conclusions about this police behavior.

13.0Recommendations

The following recommendations about future traffic stop data collections, and other matters involving “traffic services” and the police, in general, are offered:

City Council Direct City Manager To Produce Yearly Police Performance Reports.

City Manager/Police Chief Initiate A Review Of “Traffic Services”, To Determine:

o Disproportionate Stopping Of Palo Alto “Blacks” and Hispanicso Actual Relationship Of “Traffic Services” To Public Safetyo If Number Of Stops In Palo Alto Is Higher Than Surrounding

Communitieso If Number of Searchers Is Higher Than Surrounding Communitieso If Search Productivity Justifies Number of Searcheso If Number of “Courtesy Stops” Justified By Cost Of Traffic Officers.o If “Traffic Services” Can Be “Downsized” To Reduce Costs.

That All Police Officers Be Required To Read Traffic Stop Studies (such as listed in Appendix.G).

Investigate Tablet/PCs With Integrated Data Management Services for use in Traffic Stops

Police Be Required to Create A Technology Plan With A Ten-Year Horizon. Police Be Required To Coordinate with Palo Alto Traffic Engineering For:

o Up-To-Date Traffic Volume Datao Automated License Plate Readers For Traffic Surveyso Integrated Database Management for Data Sharing

Police Adopt an “Open Door”/”Lots of Sunshine” Policy For Dealing With Public.

A Retention Schedule For Police Data Should Be Created. All Data About Police Activities, Not Restricted By Law, Should Be Placed On-

line In As Many Formats As Possible (.txt., xls, .csv, .doc, etc.) Redesign Collection Template For Use With Optical Scanners (Unnecessary If

Tablet/PCs Used) Local Police Departments Create/Adopt Regional Collection Template Local Police Department Form Consortium With Other Police Departments For

Centralized Reporting Generation using Common Methodologies and external data sources

Move towards integrating all Police/Court/DMV databases In Greater Bay Area. Once Database Integration Achieved, Monitor Police Activity Continuously. City of Palo Alto Install a “Mesh Network” To Support Public Safety Data

Communications Needs. Videos Of Traffic Stops Be Posted On The Palo Alto Police web-site. A Web-Page For Motorists To Complain/Comment On Their Experiences During

Traffic Stops In Palo Alto Be Created.

Comments

While concerns about “race” initiated this review of Palo Alto’s traffic stop data, clearly, the general thrust of these recommendations is towards more: 1) increased police transparency, 2) automation, 3) regionalization and 4) cost reduction. The issues associated with costs and the automation of police information soon emerged as far bigger problems for Palo Alto than any problems that unnecessary stops might be causing. The level of funding, and better management of those officers assigned to “traffic services” could quickly reduce the over-stopping of motors.

Complaints/concerns about the costs of data collection/processing seem to permeate the traffic studies that have been generated from this nation-wide data collection effort over the past decade. However, few of those publishing their analyses of the data seem to have been “technologists”, who were able to offer low-cost solutions to what has been considered to be “high-cost” data, which has historically required a significant amount of human labor to collect. Advances in low-cost computing, and wireless data communications technologies, as well as the now-ubiquitous Internet, offer many solutions that were not conceivable only a decade ago.

Given that the costs of public safety now consume as much as forty-fifty percent (40%-50%) of many local government’ general funds, activities such as monitoring for “racial profiling” can easily be seen as unnecessary expenditures, and curtailed at during times when funds are scarce. However, these data collection costs can be reduced by increased use of low-cost “technology”, and the processing and analysis costs can be spread across a region’s law enforcement agencies. There then would seem to be no reason not to collect this sort of information to provide comprehensive, and meaningful, performance reports on local police agencies’ activities.

14.0Final Thoughts/Comments.

Various thoughts, and comments, needed to help bring this review to closure, follow.

14.1 Point-of-View Of This Review.

This review of the Palo Alto Traffic Stop/Demographic data is different that other reviews, in that it has been based on a “systems” point-of-view that attempts to deconstruct the problem at hand, and review both the “design” of the data collection experiment, but also the results of this experiment. Other reviews of this data seem to only focus on the data, and not the total problem of traffic stops, on-going crime in the city, the role of traffic stops as a general crime-fighting tool, and issues associated with the long-term monitoring of police activities. Rather than trying to “find fault” with the police conducting traffic stops, this review attempts to understand their actions, more-or-less assuming that the majority of those actions are valid.

14.2 Problems With Data Availability.

Investigating, and producing, this review has been both difficult and time-consuming; while some of the raw data needed to perform these evaluations is available on-line--most is not. Once the evidence of “racial disparities” in traffic stops could be demonstrated from an analysis of data, the focus of this review shifted from concerns about “race” to concerns about cost, and to the larger question of the management of “traffic services”. This secondary focus involves identifying historical trends from existing data, and asking two fundamental questions: 1) “Why didn’t Police Management design the data collection templates so that the data could be fully analyzed?”, and 2) “is there any benefit to the current level of traffic monitoring, stops and searches in Palo Alto?” Looking at the “bigger picture”, issues involving “race” became secondary to the larger issues of the need, and cost, of “traffic services”, and the transparency of the Palo Alto Police—two issues which appear frequently in this paper.

14.3 Evidence For Terminating/Reducing “Street Teams”/”Traffic Services”

During the discussions of the 2011-2012 budget cycle, the Palo Alto City Manager proposed discontinuing “traffic services”. At that time, the City Manager did not provide any data to justify his proposals, such as found in this study. With the cost-to-employ a Palo Alto Police Officer, now over $185,000 per year, it is understandable why the City Manager might make such suggestions. Hopefully, the results of this study call into question the need for the current level of police presence on the streets and the high level of stops resulting in searches, compared with other communities, can provide the City Manager, and the public, information to continue this debate about the need for “traffic services” at its current level during the next budget cycle.

14.4 Complexity Of Modeling Traffic Stops

A comprehensive review of traffic stops, and traffic stop data, causes one to appreciate that traffic stops are not a “black and white” matter, readily amenable to statistical analysis. Although not clearly discussed in this paper, it would not be hard to show that as many as thirty to fifty “variables” are involved in initiating, and processing, a traffic stop. Recognizing these “variables”, and recording them in a meaningful way, has not been achieved successfully by the nation’s police agencies, as the numerous studies of this issue have concluded. Moreover, there seems to be a reluctance on the part of the nation’s police to be scrutinized in the execution of their duties. This mindset, on the part of the police, has resulted in many lost opportunities to collect meaningful information that could be used to better monitor the behavior of not only individual officers, but whole departments, as well.

14.5 Lack of Police Support

It is the belief of this long-time resident that the Palo Alto Police have not been genuinely supportive of the idea of making this data available to the public from the time they were

first tasked to collect the data. Their limited analysis of the data has left the public without a clear view of the productivity of “traffic services”, in general--something which this paper has attempted to investigate. Their failure to compare Palo Alto statistics with similar data from other communities has left Palo Alto residents, and motoring visitors, without a clear vision of the effectiveness of the Police on the city’s roads and streets, from a safety, or cost, point-of-view.

From reading the reports from various sources around the US that have been published to deal with the results of these demographic traffic stop data collections, it becomes clear that the police have not seemed overly interested in participating in these exercises. Locally, that same attitude seems to have prevailed. During the course of working on this paper, requests for information from the police of the Palo Alto, Menlo Park, San Jose, and the Sheriff's office of Alameda County have been ignored. While none of these requests have been made under the provisions of the California Public Records Act, e-mails to various police officers, even the Chief of Police of Palo Alto, have received no response. Gaining access to this basic to the raw data seems to have only been available here in Palo Alto, but all of the information needed to interpret the data has not been placed on the list police web-pages.

Clearly, getting this sort of information into the public domain does not seem to be something that most police seem comfortable facilitating. Therefore, City Councils and other civilian oversight groups, such as county supervisors, should be aware that this fundamental information is generally unavailable to the public. Moreover, proper analysis would help to clarify the public's vision of their police force. Unfortunately the necessary data is not available, and it does not seem likely to be available in the immediate future. For reasons such as this, the future of this sort of activity should not be in the hands of local police departments, that have little interest in actually investigating their own performance, or having to face the risk of actually identifying misconduct amongst their own officers. Perhaps some outside group, or agency, should be tasked to develop the software and data collection templates that individual police forces should be required to use in the future that will allow the time and effort expended in collecting such data to be justified by meaningful results from analysis of that data..

14.6 Costs Associated With This Review

Costs associated with this data collection are alluded to in virtually every document published. Most of the concerns have to do with issues associated with the data collection in the field. Few of the papers discuss the analysis costs, however. The following are the “time costs” associated with this review:

Review Costs Time:

Over 300 Hours Of Personal Time Expended Activities:

Topic FamiliarizationRaw Police-Provided Data “Clean Up”Background AnalysisDataBase Query development (7,000-8,000 lines of SQL code)Data AnalysisReport Writing

Although this author had commented on the quarterly reports issued by the Palo Alto Police in the past, the personal time required for conducting an in-depth review was not available until recently. Given the amount of time required to develop the SQL code, and to analyze the results, it is not likely that individuals in Palo Alto will be willing to “donate” this much personal time to doing the job that the Police, or City Auditor, should be doing. Following this train-of-thought leads to the conclusion that these sorts of reviews of police activity are very important, and should be facilitated at a regional level, reducing the cost of developing the analysis and data collection templates for all local law enforcement agencies.

14.7 Report Generation Tools Needed.

This report has been primarily generated, and assembled, by hand. The output of the database queries is in a format called "text", which is then "cut-and-pasted" into an Excel spreadsheet, and then manipulated in one way or another with formatting, or additional columns, for readability and/or additional content. Then once properly formatted each table is again cut and pasted into the Microsoft Word document that where the final report/document was assembled.

While none of this “finger work” is particularly complicated, it is, however, exceedingly tedious and time consuming. There is also a certain amount of synchronization needed between tables in the document, and SQL scripts generating the data. What is needed is a way that each document table can link to a segment of code in the script that generates the data needed for that particular table. Currently, this synchronization is being done manually. Tools need to be developed that provide something formal, that can allow much of the report to be generated automatically.

While the level of complexity of this report comment the moment, does are a good number of hours of "handwork", that no doubt would seem onerous to an organization that was required to put out this level of support every order. Needed, are document generation tools which would allow database queries to be triggered from the document in some way, and the results of those queries formatted as cable structures table objects that would then be easily imported back into the into the Microsoft Word document. There are a number of different ways to do this, of all of which would require some sort of software development to automate, and simplify the process of generating these reports.

The extent of the software development suggested by such to is by the need for such tools while not extensive, would necessarily add expense to the data collection and distribution across collection and distribution to the cost of distribution of the data was collected. These sorts of tools, which can be useful within an entire crossing of a complete municipal organization, however so at it since there's nothing generic about there's nothing specific about the need for these kinds of reports to be generated from using backend hurries to databases. Again, the sort of automation of government function calls out for a regional approach, since the cost for these for this tool development, and could be allocated across the governments of regions, with. It's these kinds of tools can also be developed at the state level, and distribute to governments around the state also.

14.8 Concern Over “Red Flags”

The “red flags” raised should be of concern to all Palo Alto residents, and people who drive the streets of Palo Alto. Hopefully, the Palo Alto City Council will take note, and direct the Palo Alto Police to continue this data collection of traffic stops, and to rethink the data collection template so that more meaningful analytic results can be obtained when the data is reviewed.

Submitted By:

Wayne MartinPalo Alto, CAwww.twitter.com/wmartin46www.youtube.com/wmartin46www.scribd.com/wmartin46October, 2011

Appendix.A—Glossary

Traffic ServicesStreet TeamsTraffic Enforcement Units

Terms used to identify the activity of a local police department dedicated to traffic monitoring, and speed limit enforcement, and vehicle code enforcement. Only those officers generating “demographic stop data” based on their interactions with the public are being considered for this review.

Traffic Stop

The Ramirez-DOJ guide deals only with traffic stops, and it offers the following definition:

“A ‘stop’ is defined as any time an officer initiates contact with a vehicle resulting in the detention of an individual and/or vehicle.”

Discretionary Search

A search that is not instigated incident to a lawful arrest

Normalization

Normalization is a procedure used in statistical analysis that allows entities that are similar (such as the crime rate in different cities) to be compared fairly with each other, by bringing them to a common scale. Typically, a baseline is chosen, and all of the measured data is adjusted to that baseline (ie—crimes per 100,000 residents). Raw data that involves set members with different characteristics (such as race) can not be successfully compared, without being normalized first.

Census Representation (Overrepresentation/Underrepresentation)

Subgroups identified by the US Census are most often listed as a percentage of the whole. This percentage can then be considered as the nominal “subgroup representation”, which could then be used as a baseline for predicting that subgroup’s participation in activities involving the general public. Variations from this Census-measured baseline can then be used to demonstrate “bias” towards that subgroup, in one way or another. Overrepresentation would mean that there are more people of a given subgroup in a given sampling, and underrepresentation would mean that there are fewer members of a given subgroup in a sampling than would be expected from the Census measurements of that subgroups representation compared to the whole population.

Proactive vs Reactive Policing

Proactive policing occurs when the police act in ways that reduce crime, by (most often) interacting with individuals who are believed to be about to commit crimes in ways that discourage said individuals from actually engaging in illegal activities. Proactive policing can easily result in “pretext traffic stops”. Reactive policing means that the police only get involved when an actual crime has been committed. Proactive policing tends to restrict the “liberty” of some people, particularly people who might be inclined to commit crimes, but since a “wide net” is often used, people not inclined towards criminal activities find themselves under police scrutiny, in one way or another.

“Pretext” Traffic Stops

A pretext stop or arrest is an objectively valid stop for an improper reason. It occurs where the police employ a stop based on probable cause or reasonable suspicion as a device to search for evidence of an unrelated offense for which probable cause is lacking. Pretext stops commonly involve citizens traveling in a motor vehicle who are stopped by police for a minor traffic violation just so the officers can investigate drug trafficking or other more serious crimes.

Search Productivity

Searches that result in the discovery of evidence of illegal activities on the part of the vehicle’s driver, or passenger, that leads to a subsequent arrest, impound of the vehicle, or discovered “contraband”, can be considered as a “productive search”. The ratio of searches that meet these criteria vs the total number of searches performed can then be considered as the “search productivity” for a given reporting period.

SWITRS-Software Integrated Traffic Management System.

The California Highway Patrol (CHP) has collected traffic accident data from the California local law enforcement agencies since the 1970s. This data is online, in machine-readable formats, for analysis and review.

Vehicular Telemetrics

Vehicular telemetrics can most easily be explained as the extension to all vehicles of the current “OnStar” capabilities that are available on some high-end automobiles in the US. As envisioned, vehicles will be configured with censors that will records events involved with normal, and accident-involved, operations. In the case that vehicles configured with a telemetric monitoring system is involved in an accident, the system would determine the extent of the damages, and send via local mesh networks, or satellite communications, this information to the nearest emergency dispatch center, that would then determine the appropriate first response needed for the safety of the parties injured, and a timely disposition of the accident.

Event Data Recorders (EDRs)

Event Data Recorders are best described as “black boxes” for vehicles, which provide the same function that “black boxes” provide for aircraft. Sensors on the vehicles transmit continuous data streams, which are stored, and used to reconstruct accidents, such the vehicle become involved in a collision. The Federal Transportation Agency (FTA) has directed that all vehicles manufactured in the US be outfitted with EDRs starting in 2011.

Census Tracts and Block Numbering Areas

Census tracts are small, relatively permanent statistical subdivisions of a county. Census tracts are delineated for most metropolitan areas (MA's) and other densely populated counties by local census statistical areas committees following Census Bureau guidelines (more than 3,000 census tracts have been established in 221 counties outside MA's). Six States (California, Connecticut, Delaware, Hawaii, New Jersey, and Rhode Island) and the District of Columbia are covered entirely by census tracts. Census tracts usually have between 2,500 and 8,000 persons and, when first delineated, are designed to be homogeneous with respect to population characteristics, economic status, and living conditions. Census tracts do not cross county boundaries. The spatial size of census tracts varies widely depending on the density of settlement. Census tract boundaries are delineated with the intention of being maintained over a long time so that statistical comparisons can be made from census to census. However, physical changes in street patterns caused by highway construction, new development, etc., may require occasional revisions; census tracts occasionally are split due to large population growth, or combined as a result of substantial population decline. Census tracts are referred to as "tracts" in all 1990 data products. Block numbering areas (BNA's) are small statistical subdivisions of a county for grouping and

numbering blocks in non-metropolitan counties where local census statistical area committees have not established census tracts.

Relational Data Base Technology

From Wikipedia:

A relational database matches data by using common characteristics found within the data set. The resulting groups of data uses the relational model (a technical term for this is schema).

The software used in relational database is called a relational database management system (RDBMS). The term "relational database" often refers to RDBMS software, not the database itself.

Relational databases are currently the predominant choice in storing data like financial records, medical records, personal information and manufacturing and logistical data.

Appendix.B-- Example of Palo Alto Traffic Stop Demographic Raw Data Record

The following is an example of a typical traffic stop record, released by the Palo Alto Police Department:

Date, Time , Stop Reason, RD, Location,04/2009,16:00,OTHER CRIMINAL CODE (SPECIFY),16,3900BL LA DONNA AV

Race,Sex,Age, City of Residence, Stop Disposition, Why SearchB, M, 24, ALABAMA, NO ACTION, NO SEARCH

Appendix.C—Age Cohorts for Palo Alto and Surrounding Cities

The Table.A1 (below) provides a breakdown by “age cohorts” in cities neighboring Palo Alto. This data bas been used to better estimate the pool of possible drivers in each of these cities. People under fifteen years of age, and over eighty-five years of age have been removed from the pool of drivers, so that the demographic “adjustment” can provide the most accurate numeric values as to the number of people in each racial subgroup.

Table.A1 US Census Provided Cohort Ages For Nearby Cities

CityPop-

ulation

Driver Age Pop-ulation Under 5 5_to_9 10_to_14 15_to_19 20_to_24 25_to_29

PALO ALTO 64403 49912 5.4 6.8 6.8 5.6 3.6 5.9EAST PALO ALTO 28155 20581 9.3 8.6 8.4 8.8 9.2 9.2MENLO PARK 32206 24509 7.7 7.2 6.1 4.8 4.2 6.9MONTAIN VIEW 74066 60290 7.1 5.5 4.4 4.4 5.6 10.4                       30_to_34 35_to_39 40_to_44 45_to_49 50_to_54 55_to_59PALO ALTO     5.9 6.9 7.9 8.7 7.4 6.4EAST PALO ALTO     8.3 7.4 6.7 5.9 5.2 4MENLO PARK     7.1 8 7.8 7.9 6.8 5.7MONTAIN VIEW     10.7 9.3 8.1 7.3 6.6 5.5                       60_to_64 65_to_69 70_to_74 75_to_79 80_to_84 over_85PALO ALTO     5.5 4.5 3.4 3 2.7 3.5EAST PALO ALTO     3.1 2 1.5 1.1 0.9 0.6MENLO PARK     5.4 3.7 3 2.5 2.2 2.9MONTAIN VIEW     4.5 3.1 2.4 1.8 1.6 1.6

The pool of possible drivers for each of the nearby cities is determined by subtracting the cohorts that are under fifteen years of age, and over eighty-five years of age. This approach still over-represents the number of drivers in each cohort, but provides the best estimate available, short of access to DMV data.

Appendix.D—Police Costs/Performance Data

The following data, drawn from various City sources, provides background information about public expenditures to operate the Palo Alto Police Department, and its effectiveness:

Table.A2—Published Police Budget Data.

Police Budget 2012   Expenditures $31M% Of General Fund 22%Number of Full Time Positions 7.95Number of Temporary Positions 1.44Traffic Services  

Expenditures $2.5MRevenues $.5M

The following police performance data, drawn from various sources, provides only a very limited insight into the effectiveness of the Palo Alto Police:

Table.A3—Published Palo Alto Police Performance Data (And Targets).

Statistic 2010 2012Traffic Citations 7520 7000Injury Accidents 368 375Number DUI Arrests 181 250Number of Bicycle/Pedestrian Accidents 81 100     Number of Traffic Citations per Officer 720 650Number of Injury Accidents 368 375Number of DUI arrests 181 250

Note—Changes in Performance Data appear in budget document without any justifications.

Note—The Palo Alto Police does not accept reports of minor accidents. Accident counts therefore only reflected reported accidents, which involve injuries, or significant vehicle damage.

Table.A4—Police Effectiveness.

Ratio of traffic citations issued and DUI Arrests to total injury accidents: 20:01         2010 2011

Percent reduction in collisions in “super block” during school hours 0% 15%Percent reduction in bicycle/pedestrian accidents 24% 10%

Source: PA City Budget/Police Section

Note: Police effectiveness data routinely is published as “percentages”, but without any hard base values to determine the actual meaning of the percentage. For example, if there were four bicycle/pedestrian accidents one year, and three the next, then this would represent a 25% reduction in this sort of accident. While seemingly impressive, a base of only four accidents would not represent a large danger to the public, in general. For percentages to be of value to the public, base values must be published also.

Table.A5—Police Data From Auditor’s Report.

Auditors Year Service and Accomplishments Report

Year 2010Department PoliceCost/Resident For Police Services 3665-Year Increase In Spending 34%

Police Deparment Spending

2nd Out of 10 Local

CitiesTraffic Stops 13,300

Traffic Services Percentage of Police Budget 6%

Note: The Palo Alto Police routinely fail to acknowledge traffic volumes as a primary variable in number of traffic accidents.

Appendix.E—Break Down Of Traffic Accident Participants

Table.A6—Statewide Accidents (By Race)

  Racial Breakdown of State-Wide Traffic Accidents

Race 2005 2006 2007 2008 20092010

(Census)Unspecified 17.7 16.7 16.3 16.1 15.6 N/AAsian 5.4 5.5 5.5 5.8 6.0 13.0Black 7.1 7.2 7.1 7.0 7.0 6.2Hispanic 28.2 29.3 29.7 29.5 29.3 37.6Other 4.4 4.6 4.6 4.6 4.8 11.5White 37.3 36.7 36.7 37.0 37.3 57.6

Table.A7--City of Palo Alto (Jurisdiction=4312)

Racial Breakdown Of Parties Involved In Vehicular Accidents             

Race 2005 2006 2007 2008 20092010

(Census)Unspecified 20.2 14.1 10.1 11.8 8.6 N/AAsian 11.1 11.3 10.0 12.7 12.9 27.1Black 3.8 5.5 5.5 3.9 3.3 1.9Hispanic 12.4 13.6 14.8 15.6 13.5 2.2Other 7.9 7.6 9.9 9.5 13.9 4.6White 44.5 48.0 49.7 46.4 47.8 64.2

Appendix.F--Uniform Crime Reporting Stats For Palo Alto (2006-2010)

The following information was obtained from the Palo Alto Police web-site:

Uniform Crime Reporting is a statewide law enforcement program designed to provide a nationwide view of crime based on the submission of statistics by law enforcement agencies throughout the country. Each agency is required to report monthly crime statistics to the California Department of Justice, which will in turn forward the information to the FBI. The FBI then uses this information to publish their annual Uniform Crime Report.

Table.A8—Palo Alto Part I Crimes.

Part 1 Crimes 2006 2007 2008 2009    2010

Homicide 0 1 2 1          0Rape* 3 2 5 9          4Robbery* 36 47 43 29        39Assault** 20 18 34 63        70Burglary Total* 404 286 366 267       217

Larceny-Theft* 1352 1104 1307 1350    1083

Motor Vehicle Theft* 148 82 80 50        49Arson* 19 15 14 33        23

Totals 1982 1555 1851 1802    1485

Notes:* includes attempts** Does not include simple assaults

Below is a 5 year count of all Police Calls for Service, Offenses and Accident reports.

Table.A8—Calls For Police Service.

Year Calls For Service Offenses Accidents

        2010          64,377 5,463 1,037

2009 63,950 6,171 1,011

2008 66,635 7,358 1,0522007 69,307 7,208 1,2202006 71,867 7,398 1,275

Source:http://www.cityofpaloalto.org/depts/pol/police_information/statistics.asp

Note—This data does not provide any information as to whether traffic stops are included in these performance-related metrics.

Appendix.G—WEB-based Resources

1 City of Palo Alto Police:  www.papd.org   2 Demographic Data Report for July 1 to September 30, 2008:  http://www.cityofpaloalto.org/civica/filebank/blobdload.asp?BlobID=14051   3 Demographic Data Report for January 1 thru March 31, 2009:  http://www.cityofpaloalto.org/civica/filebank/blobdload.asp?BlobID=16495   4 Demographic Data Report for October 1, thru December 31, 2009     http://www.cityofpaloalto.org/civica/filebank/blobdload.asp?BlobID=18755   5 Independent Police Auditor: Biased-Based Policing:  http://www.cityofpaloalto.org/civica/filebank/blobdload.asp?BlobID=18904   6 Articles About “Racial Profiling” In Palo Alto:   7 POLICE: Racial profiling to start Saturday:  http://www.paloaltoonline.com/weekly/morgue/news/2000_Jun_28.DWB3.html   8 Palo Alto officially condemns racial profiling:  http://www.paloaltoonline.com/news/show_story.php?id=10002   8 $205-per-hour fee for 'racial-profiling' audit:  http://www.paloaltoonline.com/news/show_story.php?id=10215   

10 March against racial profiling planned Sunday:  http://www.paloaltoonline.com/news/show_story.php?id=9942   

11 Expert helps Palo Alto police fight racial bias   http://www.paloaltoonline.com/news/show_story.php?id=12470   

12 End of PA Police Demographic Data Collection:  http://www.paloaltoonline.com/weekly/story.php?story_id=13054   

13 US Census:  www.census.gov   

14 CHP/SWITRS Traffic Accident Data:  http://www.chp.ca.gov/switrs/   

15 CHP Public Contact Demographic Data Summary:  http://www.chp.ca.gov/pdf/report.pdf   

16 US DoJ--Traffic Stops:  http://bjs.ojp.usdoj.gov/index.cfm?ty=tp&tid=702   

17 US DoJ--Contacts between Police and the Public, 2005:  http://www.bjs.gov/content/pub/pdf/cpp05.pdf   

18ANALYSIS OF DEMOGRAPHIC DATA FROM THE FIRST AND SECOND QUARTERS OF FISCAL YEAR 2006-07:

  http://www.cityofpaloalto.org/cityagenda/publish/cmrs/documents/CMR180-07.pdf   

19 City of San Jose Open Government/“Sunshine” Project:  [http://www.sanjoseca.gov/clerk/taskforce/srtf/srtf.asp   

20 City of San Jose Office of Independent Police Auditor:  http://www.sanjoseca.gov/ipa/   

21 San Jose Traffic Stops:  http://www.sanjoseca.gov/ipa/reports/00/chapter%204.pdf   

22Statistical Reports Produced By San Jose Police Department:

  http://goo.gl/MnCPG   

23 Oakland Stops Data Produces Mixed Results:  http://www.scribd.com/doc/44849699/Oakland-Racial-Profiling-2004-RAND-Release   

24Alameda County Sheriff’s Department/2010 Traffic Stop Report:

  http://www.alamedacountysheriff.org/LES/2010Traffic_Stop_Analysis.html   

25 Traffic Stops Data Collection:  http://www.portlandonline.com/police/index.cfm?c=42284   

26 Administrative Review of Traffic Stop Statistical Study:  http://goo.gl/AnLwz   

27 Myth of Racial Profiling:  http://www.city-journal.org/html/11_2_the_myth.html   

28 Best Practices During Traffic Stops:  http://www.fairfield-city.org/police/Traffic%20Stop%20Final%20Report.pdf

   29 CA Jurisdictions Currently Collecting Data:  http://www.racialprofilinganalysis.neu.edu/background/jurisdictions.php?state=CA   

30CA Legislative Analyst’s Office (LAO) Report: Racial Profiling:

  http://www.lao.ca.gov/2002/racial_profiling/8-02_racial_profiling.pdf   

31 San Jose Racial Profiling:  http://www.aele.org/data.html  http://www.sanjoseca.gov/clerk/CommitteeAgenda/Rules/080206/Rules080206_E.pdf   

32 Racial Profiling In SC:  http://www.scpronet.com/wp-content/uploads/2010/01/Racial-Profiling-Study-2010.pdf   

33 Roots of Racial Profiling:  http://reason.com/archives/2001/08/01/the-roots-of-racial-profiling   

34 Examining the Generality of Citizens' Views on Racial   Profiling in Diverse Situational Contexts:  http://cjb.sagepub.com/content/35/12/1527.refs   

35 Theory and Racial Profiling:  http://goo.gl/UEOh7   

36 Testing For Racial Profiling In Traffic Stops:  http://harrisschool.uchicago.edu/about/publications/working-papers/pdf/wp_05_07.pdf   

37 Rhode Island Traffic Stop Study:  http://www.riag.ri.gov/documents/reports/traffic/final.pdf   

38Racial Profiling Studies In Law Enforcement: Issues and Methodology:

  http://www.house.leg.state.mn.us/hrd/pubs/raceprof.pdf   

39Minneapolis Police Traffic Stops and Driver’s Race Analysis

  and Recommendations:  http://goo.gl/vuJYc   

40 Towns say Traffic Stop Stats Skewed:  http://stlouis.cbslocal.com/2011/06/02/towns-say-traffic-stop-stats-skewed/   

41 Rhode Island Traffic Stops Act Final Report (2003):

  http://www.racialprofilinganalysis.neu.edu/IRJ_docs/RIFinalReportExecSummary.pdf  http://www.racialprofilinganalysis.neu.edu/reporting/benchmarks.php   

42 North Caroline On-line Traffic Stop Statistics:  http://www.ncdoj.com/Crime/View-Traffic-Stop-Statistics.aspx   

43A Collection of Traffic Stop Information and Biased Enforcement:

  The Research and Legal Perspective:  http://goo.gl/rbYkG   

44 “Suspicious Driving” (Driving While Black):  http://goo.gl/8L0fU   

45 Defending “Pretext” Traffic Stops:  http://thevig.portlandtribune.com/news/story.php?story_id=24224   

46 Pretext Traffic Stops:  http://findarticles.com/p/articles/mi_m2194/is_n11_v65/ai_19017623/   

47Supreme Court Upholds Use of Traffic Stops as Pretext for Drug Searches:

  http://www.ndsn.org/sept96/traffic.html   

48 Pretext, Traffic Stops, Arrests, and Hinojosa v. State:  http://goo.gl/JMt9x   

49 Traffic Stops:  http://le.alcoda.org/publications/point_of_view/files/traffic_stops.pdf   

50 A Traffic Stop Primer:  http://goo.gl/WQNe6   

51 Pretexts for Traffic Stops: Weaving Within a Lane:  http://goo.gl/RNnxA   

52 ACLU Traffic Stop Review:  http://www.policyarchive.org/handle/10207/bitstreams/96062.pdf   

53 Racial Profiling in California Law Enforcement   California Civil Rights Lawyers:  http://www.shouselaw.com/racial-profiling.html   

54 The Law of Pretext Stops:

  http://goo.gl/YSxRZ   

55California Ending Use of Minor Traffic Stops as Search Pretext:

  http://goo.gl/JrC84   

56 Racial Profiling:  http://www1.umn.edu/irp/publications/ARB/ARB%20.html   

57 50 Criminals Arrested Over 2,700 Times:  http://mynorthwest.com/?nid=11&sid=551174   

58 Increasing Police Productivity Through Technology:  http://goo.gl/T04SF   

59 Traffic Stop Quotas:  http://goo.gl/Hd4fM   

60 Age and Crime - Age-crime Patterns For The U.S:  http://goo.gl/wY2QW   

61 Arrest Trends/Cities, 2009–2010:  http://goo.gl/xebZw   

62Percent of Offenses Cleared by Arrest or Exceptional Means:

  http://goo.gl/KYz2p     Technology-Based Resources—   

63 Police Tablet PCs—Going Paperless:  http://www.scribd.com/doc/60385905/Police-Tablet-PCs-Going-Paperless   

64 Automatic License Plate Recognition--     http://en.wikipedia.org/wiki/Automatic_number_plate_recognition     http://www.youtube.com/watch?v=WA5Gy32aqdo     http://www.elsag.com/mobile.htm?gclid=CJmkurj31KsCFSVpgwodYQbpPg     http://pipstechnology.com/home_us/     Predictive Analytics:

   

65

Connecting the Dots: Data Mining and Predictive Analytics in Law Enforcement and Intelligence Analysis:

  http://goo.gl/5HsSI   

66Chicago Police Using 'Predictive Analytics' to Stop Crime Before it Starts:

  http://goo.gl/7eWHe   

67Brett Goldstein | 36 | Director, Predictive analytics group, Chicago Police Department:

  http://www.youtube.com/watch?v=diqACQPvSNQ   

68 Police warm to predictive analysis crime fighting tools:  http://goo.gl/Cb1yc   

69 Event Data Recorders:  http://www.nhtsa.gov/EDR     http://en.wikipedia.org/wiki/Event_data_recorder     http://www.iihs.org/research/qanda/edr.html     http://www.accidentreconstruction.com/research/edr/index.asp        ** Vehicular Telemetrics   

70 Telemetrics:  http://en.wikipedia.org/wiki/Telematics   

71 TR-48 Vehicular Telematics:  http://www.tiaonline.org/standards/committees/committee.cfm?comm=tr-48   

72 Vehicular Telemetrics News:  http://bx.businessweek.com/vehicle-telematics/news/

73 Mapping Armerica—Block-By-Blockhttp://equity.lsnc.net/2010/12/instant-maps-for-every-u-s-census-tract/http://projects.nytimes.com/census/2010/explorer?hp

74 Census Data On Google Mapshttp://www.gcensus.com/

California Census Tracks (By County)75 http://goo.gl/G3cr4

http://goo.gl/LxJnv

76 Palo Alto California Speed Traps:http://www.speedtrap.org/city/1144/Palo%20Alto

77Automatic Speed Detection And Ticket Writing Technology:http://goo.gl/bPmIg

78 Overview of Relational DataBases:http://en.wikipedia.org/wiki/Relational_database

Appendix.H—Bureau of Justice Statistic Traffic Stop Data

http://bjs.ojp.usdoj.gov/index.cfm?ty=tp&tid=702

Traffic Stops

The most common reason for contact with the police is some form of traffic stop or traffic related incident.  In 2005, 41% of all face-to-face contacts with police involved traffic stops and 12% involved traffic accidents.  About half of all traffic stops resulted in a traffic ticket.  Approximately 5% of all stopped drivers were searched by police during a traffic stop.  In addition to the Police Public Contact Survey (PPCS), BJS collects data on state police policies regarding the race and ethnicity of persons involved in traffic stops. These findings were based on the 2005 PPCS. 

Summary findings

In 2004, 22 state police agencies required their officers to collect race or ethnicity data for all traffic stops, an increase of 6 agencies since 2001 and 13 agencies since 1999.

An estimated 17.8 million persons age 16 or older indicated that their most recent contact with the police in 2005 was as a driver pulled over in a traffic stop.  These drivers represented 8.8% of the nation’s 203 million drivers.

Stopped drivers reported speeding as the most common reason for being pulled over in 2005. 

Approximately 86% of stopped drivers felt they were pulled over for a legitimate reason.  While the majority of stopped drivers felt police had a legitimate reason for stopping them, driver opinion was not consistent across racial/ethnic categories.  White (87.6%) and Hispanic drivers (85.1%) were more likely than black drivers (76.8) to feel the stop was legitimate.

In 2005, white, black, and Hispanic drivers were stopped by police at similar rates, while blacks (9.5%) and Hispanics (8.8%) motorists were more likely than whites (3.6%) to be searched by police. 

Drivers under the age of 30 (8.4%) had a greater likelihood than drivers age 30 or older (2.7%) of being frisked or having their vehicle searched.

Appendix.I—Traffic Stop Data For Greensville, NC (Pop: 68,000)

http://trafficstops.ncdoj.gov/Default.aspx?pageid=2

Greenville Police DepartmentInitial Purpose of Traffic Stop by Driver's Age

Thursday, September 29, 2011Report From 1/1/2009 through 1/31/2010

Purpose Under 20 20-24 25-29 30-34 35-39 40-49 50-59 Over 59 TotalDriving While Impaired 5 9 8 2 3 4 0 0 31

Investigation 28 82 52 33 27 40 22 20 304

Other Motor Vehicle Violation 54 97 50 26 41 34 32 12 346

Safe Movement Violation 45 71 34 36 19 37 22 19 283

Seat Belt Violation 126 233 105 48 43 68 31 13 667

Speed Limit Violation 387 806 348 286 202 386 205 130 2750

Stop Light/Sign Violation 52 159 82 56 50 58 55 38 550

Vehicle Equipment Violation 50 140 64 44 32 59 32 13 434

Vehicle Regulatory Violation 72 290 194 141 122 147 89 46 1101

Checkpoint 0 0 0 0 0 0 0 0 0

Total 819 1887 937 672 539 833 488 291 6466

Enforcement Action Taken by Driver's Sex, Race, and EthnicityThursday, September 29, 2011

Report From 1/1/2009 through 1/31/2011Action Gender White Black Native

American Asian Other Total By Race Hispanic Non

HispanicTotal By Ethnicity

Citation Issued Female 3281 2613 3 46 130 6073 141 5932 6073

No Action Taken Female 154 233 0 1 4 392 7 385 392

On-View Arrest Female 35 41 0 0 1 77 2 75 77

Verbal Warning Female 507 588 1 6 22 1124 33 1091 1124

Written Warning Female 363 248 0 1 4 616 4 612 616

Written Warning Male 428 309 1 3 14 755 20 735 755

Verbal Warning Male 636 857 4 7 48 1552 57 1495 1552

On-View Arrest Male 86 106 0 0 0 192 6 186 192

No Action Taken Male 271 353 0 2 13 639 16 623 639

Citation Male 3948 3083 8 42 259 7340 282 7058 7340

Issued

Female Total Female 4340 3723 4 54 161 8282 187 8095 8282

Male Total Male 5369 4708 13 54 334 10478 381 10097 10478

Total   9709 8431 17 108 495 18760 568 18192 18760

Appendix.J—Palo Alto Arrests (20xx), All Ages, All Races, All Cities

Table.A9—Palo Alto Arrest Data (2009).

  Total Whites Blacks AIAN API

All Offenses 1,528 1,224 286 3 15Murder and Non-Negligent Manslaughter 1 1 0 0 0Forcible Rape 0 0 0 0 0Robbery 13 4 9 0 0Aggravated Assault 61 51 9 0 1Burglary 96 78 16 1 1Larceny-Theft 212 175 36 0 1Motor Vehicle Theft 12 11 1 0 0Arson 3 3 0 0 0Other Assaults 113 93 17 0 3Forgery and Counterfeiting 4 3 1 0 0Fraud 7 5 2 0 0Embezzlement 2 2 0 0 0Stolen Property; Buying, Receiving, Possessing 15 12 2 0 1Vandalism 25 22 3 0 0Weapons; Carrying, Possessing, etc. 21 18 3 0 0Prostitution and Commercialized Vice 1 1 0 0 0

Sex Offense (except forcible rape and prostitution) 8 8 0 0 0

Drug Abuse Violations -Total 226 154 71 0 1

     Sale-Manufacturing-Total 18 14 4 0 0     Possession-SubTotal 208 140 67 0 1Gambling 0 0 0 0 0Offenses Against the Family and Children 0 0 0 0 0Driving Under the Influence 127 123 4 0 0Liquor Laws 9 8 1 0 0Drunkenness 228 208 19 0 1Disorderly Conduct 14 9 4 0 1Vagrancy 30 27 2 0 1All Other Offenses (except traffic) 300 208 86 2 4Suspicion 0 0 0 0 0Curfew and Loitering Law Violations 0 0 0 0 0Runaways 0 0 0 0 0

Violent Crime Index 75 56 18 0 1Property Crime Index 323 267 53 1 2

BJS Arrest Data Analysis Tool:http://bjs.ojp.usdoj.gov/index.cfm?ty=datool&surl=/arrests/index.cfm

Appendix.K—Menlo Park (CA) Traffic Stop Data, July 2002-December 2002

Table.A10—Menlo Park Traffic Stop Data.

Menlo Park Police DepartmentRacial Profiling Statistics July 2002 to December 2002

 Overall Totals 5340 Stops

PC for stop # % Race # % Gender # % Age # %P - Penal code 29 0% Black 717 11% M 4166 66% 1 0-18 328 5%V - Vehicle code 6030 95% White 2831 45% F 2177 34% 2 19-29 2202 35%M – Match suspect 87 1% Hispanic 1832 29%       3 30-39 1624 26%O - Other code violation 194 3% Asian/PI 651 10%       4 40 - + 2174 34%

S - Suspicious 0 0%Mdl E./Indian 6 0%            

      Other 303 5%            Cited/warned # % Beat # % Searched # % Comments    C cited 5066 80% 1 & 2 4149 65% Y Yes 260 4% Contraband 52 20%W warned 1203 19% 3 2191 35% N No 6080 96%      

Source:http://www.menlopark.org/council/staffreport/2003/04/040803_h1a.pdfhttp://service.govdelivery.com/service/docs/CAMENLO/CAMENLO_107/CAMENLO_107_20030826_020000_en.pdf

Appendix.L—Details Of Essential Traffic Stop Data For East Palo Alto “Blacks”.

Time Addr Primary Road Secondary Road Stop Reason Stop Dispo-sition Search Reason0:00   UNIVERSITY AV WEBSTER ST VEHICLE CODE: MOVING/HAZARD WARNING NO SEARCH0:09   MIDDLEFIELD RD HAMILTON AV VEHICLE CODE: MOVING/HAZARD WARNING NO SEARCH0:10   PAGE MILL RD ASH ST VEHICLE CODE: MOVING/HAZARD NO ACTION NO SEARCH

0:17   EL CAMINO REAL SAND HILL RDVEHICLE CODE: EQUIPMENT/REG VIOLATION WARNING NO SEARCH

0:25 700BL UNIVERSITY AV  VEHICLE CODE: EQUIPMENT/REG VIOLATION ARREST

INCIDENT TO ARREST

0:30 900BL UNIVERSITY AV   VEHICLE CODE: MOVING/HAZARD CITE NO SEARCH

0:39 2000BL E BAYSHORE RD  VEHICLE CODE: EQUIPMENT/REG VIOLATION NO ACTION NO SEARCH

0:49 4000BL FABIAN ST  VEHICLE CODE: EQUIPMENT/REG VIOLATION NO ACTION NO SEARCH

0:54   LYTTON AV ALMA ST VEHICLE CODE: MOVING/HAZARD WARNING NO SEARCH

0:56 500BL RAMONA ST  VEHICLE CODE: EQUIPMENT/REG VIOLATION WARNING NO SEARCH

1:08  W BAYSHORE RD EMBARCADERO RD

VEHICLE CODE: EQUIPMENT/REG VIOLATION WARNING NO SEARCH

1:20   E BAYSHORE RD PULGASVEHICLE CODE: EQUIPMENT/REG VIOLATION NO ACTION NO SEARCH

1:39 900BLKSAN ANTONIO RD  

VEHICLE CODE: EQUIPMENT/REG VIOLATION CITE PAROLE/PROBATION

1:59 400BL EMERSON ST   PRE-EXISTING KNOWLEDGE/INFO WARNING NO SEARCH

2:06   MIDDLEFIELD RDOLD MIDDLEFIELD WY

VEHICLE CODE: EQUIPMENT/REG VIOLATION WARNING PAROLE/PROBATION

2:14   EL CAMINO REAL SERVEHICLE CODE: EQUIPMENT/REG VIOLATION WARNING NO SEARCH

3:44   OREGON EXPWY LOUIS RDVEHICLE CODE: EQUIPMENT/REG VIOLATION CITE NO SEARCH

7:43 2300 E BAYSHORE RD   PRE-EXISTING KNOWLEDGE/INFO OTHER CONSENT7:49 100BL KELLOGG AV   VEHICLE CODE: MOVING/HAZARD CITE NO SEARCH

8:11   EL CAMINO REAL CHURCHILL AVVEHICLE CODE: EQUIPMENT/REG VIOLATION NO ACTION NO SEARCH

8:31   UNIVERSITY AV E CRESCENT DR VEHICLE CODE: MOVING/HAZARD NO ACTION PROBABLE CAUSE

9:08  EMBARCADERO RD NEWELL RD

VEHICLE CODE: EQUIPMENT/REG VIOLATION NO ACTION NO SEARCH

9:26  EMBARCADERO RD LOUIS RD

VEHICLE CODE: EQUIPMENT/REG VIOLATION CITE PAROLE/PROBATION

10:03   LAURA LN E BAYSHORE RDVEHICLE CODE: EQUIPMENT/REG VIOLATION NO ACTION NO SEARCH

10:07   LAURA LN E BAYSHORE RDVEHICLE CODE: EQUIPMENT/REG VIOLATION NO ACTION NO SEARCH

10:09 500BK UNIVERSITY AV  VEHICLE CODE: EQUIPMENT/REG VIOLATION CITE NO SEARCH

10:26 700BLK LYTTON AV  VEHICLE CODE: EQUIPMENT/REG VIOLATION NO ACTION NO SEARCH

10:38 180 EL CAMINO REAL  VEHICLE CODE: EQUIPMENT/REG VIOLATION NO ACTION NO SEARCH

10:44 2200E E BAYSHORE RD  VEHICLE CODE: EQUIPMENT/REG VIOLATION WARNING NO SEARCH

10:58 2200BL E BAYSHORE RD   VEHICLE CODE: MOVING/HAZARD NO ACTION NO SEARCH

11:19   UNIVERSITY AV FULTON STVEHICLE CODE: EQUIPMENT/REG VIOLATION WARNING NO SEARCH

11:24   LEGHORN ST SAN ANTONIO RD VEHICLE CODE: MOVING/HAZARD NO ACTION NO SEARCH

11:24   ST FRANCIS DR CHANNING AVVEHICLE CODE: EQUIPMENT/REG VIOLATION WARNING PAROLE/PROBATION

11:50 2225 E BAYSHORE RD   VEHICLE CODE: MOVING/HAZARD WARNING NO SEARCH11:54   HIGH ST HOMER ST PRE-EXISTING KNOWLEDGE/INFO NO ACTION NO SEARCH

11:57 2200BL E BAYSHORE RD  VEHICLE CODE: EQUIPMENT/REG VIOLATION WARNING NO SEARCH

11:58 400BL PAGE MILL RD   VEHICLE CODE: MOVING/HAZARD CITE NO SEARCH

12:28 3300BL EL CAMINO REAL  VEHICLE CODE: EQUIPMENT/REG VIOLATION CITE NO SEARCH

13:05 855 EL CAMINO REAL  VEHICLE CODE: EQUIPMENT/REG VIOLATION NO ACTION NO SEARCH

13:22 800BLKE CHARLESTON RD  

VEHICLE CODE: EQUIPMENT/REG VIOLATION CITE NO SEARCH

13:35 830E CHARLESTON RD  

VEHICLE CODE: EQUIPMENT/REG VIOLATION CITE NO SEARCH

13:36   UNIVERSITY AV WEBSTER ST VEHICLE CODE: MOVING/HAZARD NO ACTION NO SEARCH

13:41  EMBARCADERO RD NEWELL RD

VEHICLE CODE: EQUIPMENT/REG VIOLATION WARNING NO SEARCH

13:48   WEBSTER ST HAMILTON AV VEHICLE CODE: MOVING/HAZARD WARNING NO SEARCH

13:49   MIDDLEFIELD RD GARLAND DRVEHICLE CODE: EQUIPMENT/REG VIOLATION NO ACTION NO SEARCH

14:16 2080 CHANNING AV  VEHICLE CODE: EQUIPMENT/REG VIOLATION NO ACTION NO SEARCH

14:24   HY 101 EMBARCADERO RD VEHICLE CODE: MOVING/HAZARD NO ACTION NO SEARCH14:49   E BAYSHORE RD LAURA LN VEHICLE CODE: MOVING/HAZARD WARNING NO SEARCH

14:50  EMBARCADERO RD NEWELL RD

VEHICLE CODE: EQUIPMENT/REG VIOLATION NO ACTION NO SEARCH

14:56   LEGHORN ST SAN ANTONIO RDVEHICLE CODE: EQUIPMENT/REG VIOLATION ARREST

VEHICLE IMPOUND INVENTORY

14:59   MIDDLEFIELD RD ASHTON AV VEHICLE CODE: MOVING/HAZARD WARNING NO SEARCH15:07   ALMA ST W MEADOW DR VEHICLE CODE: MOVING/HAZARD WARNING NO SEARCH

15:08   NEWELL RD WOODLANDVEHICLE CODE: EQUIPMENT/REG VIOLATION WARNING PAROLE/PROBATION

15:09   UNIVERSITY AV WOODLAND AV VEHICLE CODE: MOVING/HAZARD ARRESTINCIDENT TO ARREST

15:12   HY 101 OREGON EXPWYVEHICLE CODE: EQUIPMENT/REG VIOLATION NO ACTION NO SEARCH

15:19   HY 101 EMBARCADERO RDVEHICLE CODE: EQUIPMENT/REG VIOLATION NO ACTION NO SEARCH

15:28  SAN ANTONIO RD MIDDLEFIELD RD

VEHICLE CODE: EQUIPMENT/REG VIOLATION ARREST PROBABLE CAUSE

15:33 400BLK FLORENCE ST   VEHICLE CODE: MOVING/HAZARD WARNING NO SEARCH

15:33   UNIVERSITY AV FLORENCE STVEHICLE CODE: EQUIPMENT/REG VIOLATION WARNING NO SEARCH

15:38   MIDDLEFIELD RD N CALIFORNIAVEHICLE CODE: EQUIPMENT/REG VIOLATION OTHER NO SEARCH

15:40  SAN ANTONIO RD CALIFORNIA AV

VEHICLE CODE: EQUIPMENT/REG VIOLATION WARNING NO SEARCH

15:52 2200BLK E BAYSHORE RD  VEHICLE CODE: EQUIPMENT/REG VIOLATION WARNING NO SEARCH

15:54   HIGH ST EVERETT AV VEHICLE CODE: MOVING/HAZARD CITE NO SEARCH

16:01   MIDDLEFIELD RD  VEHICLE CODE: EQUIPMENT/REG VIOLATION NO ACTION NO SEARCH

16:06 2200BLK E BAYSHORE RD  VEHICLE CODE: EQUIPMENT/REG VIOLATION WARNING PAROLE/PROBATION

16:08  EMBARCADERO RD GREER RD PENAL CODE CITE CONSENT

16:09   NEWELL RD CHANNING AVVEHICLE CODE: EQUIPMENT/REG VIOLATION WARNING NO SEARCH

16:12   E BAYSHORE RD LAURA LNVEHICLE CODE: EQUIPMENT/REG VIOLATION NO ACTION NO SEARCH

16:29 2100BL E BAYSHORE RD  VEHICLE CODE: EQUIPMENT/REG VIOLATION NO ACTION PAROLE/PROBATION

16:36 2300BK MIDDLEFIELD RD  VEHICLE CODE: EQUIPMENT/REG VIOLATION NO ACTION NO SEARCH

16:40  SAN ANTONIO RD BRIARWOOD WY

VEHICLE CODE: EQUIPMENT/REG VIOLATION NO ACTION NO SEARCH

16:46 500BLK EMERSON ST   VEHICLE CODE: MOVING/HAZARD CITE NO SEARCH16:51   ARBORETUM RD PALM DR VEHICLE CODE: EQUIPMENT/REG CITE NO SEARCH

VIOLATION

16:51   LYTTON AV BRYANT STVEHICLE CODE: EQUIPMENT/REG VIOLATION NO ACTION NO SEARCH

16:52   HY 101 UNIVERSITY AV PRE-EXISTING KNOWLEDGE/INFO WARNING NO SEARCH16:56 2400BL ROSS RD   VEHICLE CODE: MOVING/HAZARD WARNING NO SEARCH

17:03 2400BL PARK BL  VEHICLE CODE: EQUIPMENT/REG VIOLATION NO ACTION NO SEARCH

17:22 2200BLK E BAYSHORE RD   PRE-EXISTING KNOWLEDGE/INFO NO ACTION NO SEARCH18:29   FULTON ST EMBARCADERO RD VEHICLE CODE: MOVING/HAZARD WARNING NO SEARCH

18:54 900BLKSAN ANTONIO RD  

VEHICLE CODE: EQUIPMENT/REG VIOLATION ARREST CONSENT

19:45   UNIVERSITY AV COWPER STVEHICLE CODE: EQUIPMENT/REG VIOLATION NO ACTION NO SEARCH

20:04   UNIVERSITY AV LINCOLN AVVEHICLE CODE: EQUIPMENT/REG VIOLATION NO ACTION NO SEARCH

20:29   E BAYSHORE RD WATSON CT VEHICLE CODE: MOVING/HAZARD NO ACTION NO SEARCH

20:57   COWPER ST LYTTON AVVEHICLE CODE: EQUIPMENT/REG VIOLATION WARNING NO SEARCH

21:25   HY 101 EMBARCADERO RDVEHICLE CODE: EQUIPMENT/REG VIOLATION WARNING NO SEARCH

21:40 2200BL E BAYSHORE RD   VEHICLE CODE: MOVING/HAZARD WARNING NO SEARCH

22:02   CHANNING AV W BAYSHORE RDVEHICLE CODE: EQUIPMENT/REG VIOLATION NO ACTION NO SEARCH

22:28   E BAYSHORE RD LAURA LNVEHICLE CODE: EQUIPMENT/REG VIOLATION WARNING NO SEARCH

22:29 200BK HAMILTON AV  VEHICLE CODE: EQUIPMENT/REG VIOLATION NO ACTION NO SEARCH

22:44 500BL UNIVERSITY AV  VEHICLE CODE: EQUIPMENT/REG VIOLATION NO ACTION PROBABLE CAUSE

22:55 800BL UNIVERSITY AV  VEHICLE CODE: EQUIPMENT/REG VIOLATION WARNING NO SEARCH

22:57  W BAYSHORE RD CHANNING AV

VEHICLE CODE: EQUIPMENT/REG VIOLATION NO ACTION NO SEARCH

22:59  SAN ANTONIO RD MIDDLEFIELD RD

VEHICLE CODE: EQUIPMENT/REG VIOLATION WARNING NO SEARCH

22:59   UNIVERSITY AV MIDDLEFIELD RDVEHICLE CODE: EQUIPMENT/REG VIOLATION WARNING PROBABLE CAUSE

23:13   E BAYSHORE RD EMBARCADERO RDVEHICLE CODE: EQUIPMENT/REG VIOLATION NO ACTION NO SEARCH

23:16  E CHARLESTON RD FABIAN WY

VEHICLE CODE: EQUIPMENT/REG VIOLATION WARNING NO SEARCH

23:19   OREGON EXPWY MIDDLEFIELD RD VEHICLE CODE: MOVING/HAZARD WARNING NO SEARCH

23:20  SAN ANTONIO RD E CHARLESTON RD

VEHICLE CODE: EQUIPMENT/REG VIOLATION WARNING NO SEARCH

23:46   UNIVERSITY AV WOODLAND AVVEHICLE CODE: EQUIPMENT/REG VIOLATION NO ACTION NO SEARCH

23:48   ORG AHS VEHICLE CODE: MOVING/HAZARD NO ACTION NO SEARCH

23:51 2100BLK E BAYSHORE RD  VEHICLE CODE: EQUIPMENT/REG VIOLATION WARNING NO SEARCH

23:56   E BAYSHORE RD EMBARCADERO RDVEHICLE CODE: EQUIPMENT/REG VIOLATION NO ACTION PAROLE/PROBATION

Appendix.M—Alameda County Sheriff’s Office Stop Data

http://www.alamedacountysheriff.org/about_us.htm

Sheriff's Office has a current adjusted net budget of approximately $185.7 million and has over 1500 authorized positions, including in excess of 1000 sworn personnel.

Providing patrol and investigative services to the unincorporated areas of Alameda County Pursuant to contractual agreements, providing patrol and investigative services to the City of Dublin, Peralta Community College District, Oakland-Alameda County Coliseum complex, Oakland International Airport, Highland County Hospital, Social Services, and to the Alameda-Contra Costa Transit District.

Dublin, CA, has a population of over 46,000, per the 2010 Census.

Provides patrol services for nearly 150,000+ citizens within Unincorporated Alameda County - Ashland, Castro Valley, Cherryland, San Lorenzo, Sunol, Livermore Valley

http://www.alamedacountysheriff.org/LES/2010Traffic_Stop_Analysis.html