Tracy, Calif.

The Markup and Gizmodo have obtained and analyzed actual predictions for more than three dozen departments that used PredPol predictive policing software for at least six months between 2018 and 2020. This data sheet provides the findings from our disparate impact analysis and public housing analysis for Tracy, Calif. To learn more about the project read, our investigation. For more details on how we did this analysis, read our methodology.

Findings

Overview

  • Predpol’s algorithm relentlessly targeted the block groups in each jurisdiction that were most heavily populated by people of color and the poor, particularly those containing public housing. The algorithm spared block groups with more White residents the same level of scrutiny.

  • The proportion of each jurisdiction’s Black and Latino residents was higher in the most-targeted block groups and lower in the least-targeted block groups compared to the jurisdiction overall. The opposite was true for the White population: The least-targeted block groups contained a higher proportion of White residents, and the most-targeted block groups contained a lower proportion.

  • For the majority of jurisdictions in our data set (27 jurisdictions), a higher proportion of their low-income households lived in the block groups that were targeted the most. In some jurisdictions, all of their subsidized and public housing was located in block groups PredPol targeted more than the median.

  • These vast disparities were caused by the algorithm relentlessly predicting crime in the block groups in each jurisdiction that contained a higher proportion of the low-income residents and Black and Latino residents. They were the subject of crime predictions every shift, every day, and in multiple locations in the same block group.

  • We also analyzed arrest statistics by race from the FBI’s Uniform Crime Reporting (UCR) Project for 29 of the agencies in our data that were in UCR. In 90 percent of them, per capita arrests were higher for Black people than White people—or any other racial group included in the dataset, mirroring the characteristics of the neighborhoods that the algorithm targeted.

  • We analyzed arrest data provided by 10 law enforcement agencies in our data and the rates of arrest in predicted areas remained the same whether PredPol predicted a crime that day or not.

Race and Ethnicity

Compared to Tracy, Calif., overall, the most-targeted block groups had:

  • A greater proportion of Asians residents.
  • A greater proportion of Black residents.
  • A smaller proportion of Latino residents.
  • A smaller proportion of White residents.

Compared to Tracy, Calif. overall, the least-targeted block groups had:

  • A smaller proportion of Asian residents.
  • A smaller proportion of Black residents.
  • A greater proportion of Latino residents.
  • A smaller proportion of White residents.

Targeting Level Demographic Proportion of Block Group pop.
Most Targeted Block Groups Asian 29.7
Most Targeted Block Groups Black 2.6
Most Targeted Block Groups Latino 24.0
Most Targeted Block Groups White 22.9
Median Targeted Block Groups Asian 0.0
Median Targeted Block Groups Black 0.0
Median Targeted Block Groups Latino 24.8
Median Targeted Block Groups White 29.1
Least Targeted Block Groups Asian 4.4
Least Targeted Block Groups Black 1.3
Least Targeted Block Groups Latino 36.5
Least Targeted Block Groups White 28.1
Jurisdiction Total Asian 10.7
Jurisdiction Total Black 1.5
Jurisdiction Total Latino 29.0
Jurisdiction Total White 28.2

Household Income

Compared to Tracy, Calif. overall, the most-targeted block groups had:

  • A smaller proportion of households that made less than $45K a year.
  • A greater proportion of households that made between between $75K and 100k a year.
  • A greater proportion of households that made between $120k and 150K a year.
  • A greater proportion of households that made $200K and above a year.

Compared to the Tracy, Calif. overall, the least-targeted block groups had:

  • A smaller proportion of households that made less than $45K a year.
  • A smaller proportion of households that made between $75K and 100K a year.
  • A greater proportion of households that made between $120K and 150K a year.
  • A greater proportion of households that made $200K and above a year.

Targeting Level Demographic Proportion of Block Group pop.
Most Targeted Block Groups $120k - 150k 5.6
Most Targeted Block Groups $75k - 100k 8.5
Most Targeted Block Groups $200k and above 9.1
Most Targeted Block Groups Less than 45k 7.8
Median Targeted Block Groups $120k - 150k 0.0
Median Targeted Block Groups $75k - 100k 3.8
Median Targeted Block Groups $200k and above 0.0
Median Targeted Block Groups Less than 45k 19.8
Least Targeted Block Groups $120k - 150k 4.6
Least Targeted Block Groups $75k - 100k 7.7
Least Targeted Block Groups $200k and above 7.9
Least Targeted Block Groups Less than 45k 6.8
Jurisdiction Total $120k - 150k 3.2
Jurisdiction Total $75k - 100k 8.1
Jurisdiction Total $200k and above 4.2
Jurisdiction Total Less than 45k 12.0

Public Housing

In Tracy, Calif. 22 percent of public housing was on block groups the software targeted the most, 73 percent of public housing was on block groups the software targeted more than the median.

The table below provides how many predictions each block with public housing received. The final column tells us the percentage of days a block received predictions from PredPol’s software between Feb 23, 2018 and Jan 30, 2021. We confirmed these dates with the Tracy, Calif., police department.

Census GEOID Block Predictions Num. Public Housing Units Pct. days w/ Predictions
060770054031 1006 960 19 76.7940354
060770054061 1013 389 1 34.4827586
060770054051 1019 331 1 30.1957130
060770054051 1014 186 4 17.2413793
060770054061 1012 204 25 16.3094129
060770053051 1009 188 2 15.5638397
060770053081 1014 205 1 15.1910531
060770053035 5000 177 2 14.9114632
060770054031 1004 134 5 12.3951538
060770053031 1002 143 1 10.1584343
060770054052 2028 96 2 8.1081081
060770053032 2004 31 1 2.8890960
060770053025 5022 18 1 1.6775396
060770054052 2027 16 1 1.3979497
060770054031 1005 13 16 1.2115564
060770054051 1015 8 1 0.7455732
060770054061 1022 5 3 0.4659832

Maps

Predictions

Density Map

The map below aggregates all the predictions Tracy, Calif., received in our analysis window into a 2D grid. Each square of the grid represents an area approximately 500 ft. x 500 ft., the size of the PredPol prediction box. The color represents the number of predictions that occurred within the square. The more predictions, the darker the square.

Prediction Count Description
0 - 211 Least Predictions
211 - 422
422 - 632
632 - 842
1050 - 1260 Most Predictions

Sources: Markup, Predpol

The grid drawn on this map provides an approximate aggregation of the prediction data. The actual prediction box in the reports provided to departments will vary from the ones shown above.

Choropleth

This map shows the predictions aggregated to the level of the Census block group. Aggregating prediction data to the geographic area of a Census block group introduces additional complexity to the analysis, and hence this map should be interpreted with some caution. See the limitations section of the methodology for more details.

Source: Markup, Predpol

Race and Ethnicity

Black

Source: 2018 five-year ACS.

Latino

Source: 2018 five-year ACS.

White

Source: 2018 five-year ACS.

Household Income

Less than $45k

Source: 2018 five-year ACS.

$75k - $100k

Source: 2018 five-year ACS.

$125k - $150k

Source: 2018 five-year ACS.

$200k and above

Source: 2018 five-year ACS.

Methods

We analyzed the distribution of PredPol predictions for Tracy, Calif. at the geographic level of a Census block group, which is a cluster of blocks with a population of between a few hundred to a few thousand people, generally. There are 45 block groups in Tracy, Calif., the smallest block group had a population of approximately 567 and the largest had a population of approximately 11,621.

In Tracy, Calif., we analyzed 77,756 predictions and used there locations to determine the block groups that were targeted the most, the median and the least. This data sheet presents the breakdown of the racial groups and household income ranges of the people who lived in those block groups. We also present the breakdowns for Tracy, Calif. overall for comparison. The predictions we analyzed were between Feb 23, 2018 and Jan 30, 2021 , we received confirmation that Tracy, Calif. department used the software between Aug 14, 2015 and Jan 30, 2021.

For the race/ethnicity and income analyses, we merged 2018 five-Year American Community Survey data and prediction data and observed the makeup of block groups that were targeted above and below the median, those targeted the most and those targeted the least. For the sake of consistency in our analysis we only used demographic groups for which we had reliable population estimates for all the jurisdictions in our data set. These are:

  • Racial Groups
    • Black
    • Asian
    • Latino
    • White
  • Household Income
    • Less than $45K
    • Between $75K-$100K
    • Between $125K-$150K
    • Greater than $200K

Definitions

We used the Census’ “designated place” boundaries as the boundaries for most jurisdictions. For Sheriff’s departments we confirmed the boundaries with the department.

We defined the most-targeted block groups as those in Tracy, Calif. which encompassed the highest five percent of predictions. We defined the median-targeted block groups as the five percent around the median block group for predictions. And we defined the least-targeted block groups as those with the bottom five percent of predictions.

In some of the larger jurisdictions, more than five percent of block groups got zero predictions. In those cases, we chose the most populated block groups with no predictions for the five percent. Learn more about how we did this in our methodology.

We identified public housing through HUD’s online lookup tool available at https://resources.hud.gov

Data

The data used to generate this analysis can be found in our GitHub repository. It also contains the URLs for the rest of the data sheets from our analysis.