How Oakland Police is Collecting Your Sensitive Location Data


Police cars mounted with automatic license plate readers (ALPRs) are always winding their way through the streets of Oakland. The cameras are recording the plate numbers of vehicles in the front and behind, as well as collecting footage on the surrounding areas. The policy of the Oakland Police Department (PD) is to utilize ALPR technology to capture and store digital license plate data and images in order to track stolen vehicles, monitor hit-and-run cases and record suspicious activity. Each morning, before the patrol gets into the car, they receive a “hit list” of plate numbers that are loaded into the readers and fires off an alert if any number matches plates of vehicles on the street. In the revised policy, Oakland PD has established a retention policy of 6 months, after which records will be destroyed.

In a 2012 report, American Civil Liberties Union (ACLU) identified that less than 1% of the data collected using ALPR technology matched the “hit list”. This poses an important question: what is being done with our sensitive, private location information? The technology is automated, but a retention policy of up to 6 months means public agencies like the Oakland PD can put together pieces of location information. This reveals personal information such as political or political associations that can be abused and breaches privacy laws.

Data sources

The data collected for this analysis was received as a result of public records request, leading to the release of over 2.7 million readings between 2010 and 2014 in Oakland Open Data. Additional data sources include American Fact Finder (race and income) and Oakland Police Department’s Crime Data. The following visualizations should provide more insight into how plate numbers are being collected, what neighborhoods are disparately targeted and further analysis on causality.

An overview of ALPR records

Mapping the raw data of ALPR readings over time reveals specific neighborhoods encountered more surveillance than others, as seen in the image below. The heatmap provides an overview of neighborhoods with repeated surveillance by plotting the raw readings against time (between 2010 and 2014) and number of records collected monthly.

Creating a time stamped graph of ALPR readings

The finding gets more interesting as we gauge deeper into the data. I aggregated the number of license plate readings per day and plotted it against time to show the trend.

The flat lines on the time axis showing zero readings corroborate to missing data in the released records. The data retention policy cited earlier was only recently determined by Oakland PD, until which point all the data was recorded and retained infinitely. I spoke to Mike Katz-Lacabe from the Center for Human Rights and Privacy, and a well-informed privacy expert from the Bay Area to understand the piece about missing data. He explained to me how the lack of a defined retention policy meant data was being stored in external hard drives and damaged. There is a possibility that Oakland PD was not able to release the records because the data is lost.

We can see from the time stamped graph the are tallest spikes are in August 29, 2012 and August 2, 2014. Once we dive deeper into the data, we can find out if that corresponds to an important political event, which could point to why more records were collected during certain time periods.

Analyzing disparate surveillance on racialĀ groups

In the second part of the analysis, I tried to find out if minority groups, such as African-Americans and Hispanics are being disparately targeted. This means there will be more patrol equipped with ALPR technology in neighborhoods with higher percentages of the African-Americans or Hispanics. In order to make the analysis accurate, I normalized total readings in a given neighborhood by population. This becomes tricky because neighborhoods near Oakland Airport for example, will have a lower population and a high number of records owing to the fact that surveillance technology is historically more prevalent near and inside airports. This makes the processed data slightly inflated and not necessarily always useful in making calculated assumptions about what is actually happening. The data nevertheless should not be entirely discounted. The map shows areas like Downtown Oakland having a higher intensity of ALPR records which is accurate given it is a central location.

To understand the implication of surveillance technologies such as ALPR, we dived deeper into the data and mapped records on two minority groups, Hispanics and African-Americans. When compared with percentages of African-American or Hispanic population, we find areas such as Oakland Airport have high concentrations of African-American population and high ALPR intensity. Deriving any positive correlation between readings and concentration of racial groups can throw us off; the intensity of ALPR is misleading. In addition to more surveillance technology, the high intensity of readings near airports can be explained by the fact there are more vehicles near the airport. On the other hand, neighborhoods with high percentage of Hispanics show relatively high intensity of ALPR readings consistently, ranging between 0.4 to 7.0 units.

In order to draw a statistically significant relationship, I plotted the intensity of ALPR readings against the percentage of Hispanics and African-Americans in the population. Contrary to what the maps showed, the graphs did not reveal any significant correlation.


Understanding disparate surveillance on low-income households

A similar comparison can be drawn between the intensity of ALPR readings and household income. Temescal and Oakland Airport are both low-income areas with two of the highest brackets in ALPR intensity. In San Leandro, Castro Valley and Lakeshore, where 12-18 percent of households have an annual income of over $200,000 have lower intensity of ALPR records. Cyrus Favrier, senior business editor at Ars Technica resonates my finding in his investigative report from March 2015. He refers to over 4.6 million records collected between the same time frame as mine where patrol cars mounted with ALPRs were seen more frequently in low-income neighborhoods.

Correlation between crime rate and ALPR readings

When Oakland PD’s crime data is mapped and compared against the intensity of ALPR readings, it is seen that low-income neighborhoods are also hotspots for crime, which explains why there is more police patrol in these areas. Because ALPR technology is mounted on police cars and pre-loaded with a ‘hit list’, it is not unlikely that there is an assumption areas with a higher crime rate will have a greater chance of matching license plate numbers with missing vehicles.

Conclusion and Next Steps

While the analysis has provided depth in insights and understanding of ALPR technology, it has however not shown any causality between ALPR readings and racially discriminated groups or low-income populations. There is some correlation, but it is too early to attribute it to implicit bias and disparate surveillance.

The missing data piece in ALPR readings because of damages to the hard drive makes it challenging to draw concrete conclusions. In the next round of analysis, it will be interesting to assess the number of vehicles on the road with ALPR readings or potential national security threats following particular events to understand the causality between number of records with other factors.

Crime Types in Berkeley (the Leaflet Way!)

One thing that strikes me particularly about Berkeley is how crime-intensive it is in spite of being the most liberal, relatively densely populated and student-centric cities in the U.S. Therefore, to get a better sense of the crime type, I used the leaflet to make a web map of different types of crime in Berkeley. I pulled the CSV file from Open Berkeley, cleaned it by pulling out the coordinates and type of crime, and finally converted it to the GeoJSON file. The geojson was then plugged within leaflet to make the following web map.

The challenging part of the exercise was to find 18 different colors to code the different kinds of crimes in Berkeley. Most colors aren’t vastly unique – which is why the color ‘palette’ for my map might not be as tasteful as I’d like it to be.

Is it safe to live in Berkeley?

Having lived in the Berkeley for the past 1 year, I am beginning to realize the city of liberals and pot-heads isn’t necessarily as safe as I’d assume it to be. The latest app, Nixle from Berkeley Police Department and the University of California, Berkeley seems to be sending me emails about sexual assault, armed robbery and break-ins constantly — but I can’t seem to figure out where crime occurs most.

I pulled data on calls made to the Berkeley Police Department over the past 6 months (180 days) from the open data portal of City of Berkeley to see if I could understand where crime occurs the most. I used Python to isolate the location coordinates and type of crime over the past 180 days, and then imported it to Carto to create the map.

Choosing what kind of map to place it in was challenging, because I was trying to respond to two questions:

  • Where crime occurs the most?
  • What kind of crimes are most common and where?

Having thought this through, I decided to show this in two maps instead of one. In the first map seen below, we are able to identify that crime is most common in central Berkeley and closer to the university campus. It becomes less frequent as we move towards North or South Berkeley, and almost becomes zero near Albany and El Cerrito. This could be because of the fact that residents of Albany and El Cerrito do not call BPD to report crimes.

In the second map, I was keen to understand what kind of crime is more common, and therefore, decided to map crimes by category. I was able to use the same dataset that resulted in the map below.

Although the second map is slightly more difficult to understand, it was clear that the most common type of crimes are motor vehicle theft and burglary. This was great information — I realized I definitely need to be more careful about locking up my bike and parking my car!