Merced, 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 Merced, 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 Merced, Calif., overall, the most-targeted block groups had:

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

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

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

Targeting Level Demographic Proportion of Block Group pop.
Most Targeted Block Groups Asian 0.8
Most Targeted Block Groups Black 4.1
Most Targeted Block Groups Latino 46.1
Most Targeted Block Groups White 20.0
Median Targeted Block Groups Asian 0.0
Median Targeted Block Groups Black 1.1
Median Targeted Block Groups Latino 47.3
Median Targeted Block Groups White 23.5
Least Targeted Block Groups Asian 3.5
Least Targeted Block Groups Black 0.0
Least Targeted Block Groups Latino 25.4
Least Targeted Block Groups White 41.1
Jurisdiction Total Asian 6.4
Jurisdiction Total Black 1.8
Jurisdiction Total Latino 43.1
Jurisdiction Total White 23.0

Household Income

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

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

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

  • A smaller proportion of households that made less than $45K a year.
  • A greater 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 0.0
Most Targeted Block Groups $75k - 100k 0.0
Most Targeted Block Groups $200k and above 0.0
Most Targeted Block Groups Less than 45k 67.9
Median Targeted Block Groups $120k - 150k 0.0
Median Targeted Block Groups $75k - 100k 2.4
Median Targeted Block Groups $200k and above 0.0
Median Targeted Block Groups Less than 45k 51.5
Least Targeted Block Groups $120k - 150k 1.7
Least Targeted Block Groups $75k - 100k 6.0
Least Targeted Block Groups $200k and above 3.2
Least Targeted Block Groups Less than 45k 15.4
Jurisdiction Total $120k - 150k 0.9
Jurisdiction Total $75k - 100k 4.0
Jurisdiction Total $200k and above 1.6
Jurisdiction Total Less than 45k 37.3

Public Housing

In Merced, Calif. 13 percent of public housing was on block groups the software targeted the most, 99 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 22, 2018 and Jul 01, 2020. We confirmed these dates with the Merced, Calif., police department.

Census GEOID Block Predictions Num. Public Housing Units Pct. days w/ Predictions
060470010043 3005 1929 3 93.728223
060470010033 3001 1827 1 93.728223
060470016022 2000 1691 2 93.147503
060470016011 1019 1854 2 90.243902
060470010041 1001 1825 1 87.921022
060470017001 1002 1767 1 81.881533
060470015033 3023 1170 2 68.989547
060470010051 1020 1121 2 66.434379
060470015031 1026 405 1 46.689895
060470015031 1024 459 1 42.857143
060470013013 3052 386 1 38.327526
060470015031 1014 319 12 36.817654
060470010051 1005 349 1 35.772358
060470015032 2020 337 4 34.030197
060470015031 1021 303 21 33.449477
060470015031 1023 271 1 30.545877
060470014012 2005 254 1 29.500581
060470015031 1022 229 8 25.783972
060470015031 1015 175 10 19.512195
060470015032 2011 161 12 18.699187
060470015031 1019 148 1 14.401858
060470013013 3049 144 1 13.937282
060470016021 1011 114 3 11.846690
060470016022 2013 59 1 6.852497

Maps

Predictions

Density Map

The map below aggregates all the predictions Merced, 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 - 245 Least Predictions
245 - 489
489 - 734
734 - 978
978 - 1220
1220 - 1470 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 Merced, 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 50 block groups in Merced, Calif., the smallest block group had a population of approximately 478 and the largest had a population of approximately 9,646.

In Merced, Calif., we analyzed 176,698 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 Merced, Calif. overall for comparison. The predictions we analyzed were between Feb 22, 2018 and Jul 01, 2020 , we received confirmation that Merced, Calif. department used the software between Jul 01, 2015 and Jul 01, 2020.

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 Merced, 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.