Frederick, Md.

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 Frederick, Md. 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 Frederick, Md., 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 Frederick, Md. 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 1.1
Most Targeted Block Groups Black 17.7
Most Targeted Block Groups Latino 15.2
Most Targeted Block Groups White 33.4
Median Targeted Block Groups Asian 0.0
Median Targeted Block Groups Black 3.4
Median Targeted Block Groups Latino 8.8
Median Targeted Block Groups White 50.4
Least Targeted Block Groups Asian 0.0
Least Targeted Block Groups Black 0.0
Least Targeted Block Groups Latino 0.1
Least Targeted Block Groups White 79.2
Jurisdiction Total Asian 1.7
Jurisdiction Total Black 9.3
Jurisdiction Total Latino 5.5
Jurisdiction Total White 56.0

Household Income

Compared to Frederick, Md. 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 Frederick, Md. overall, the least-targeted block groups had:

  • A greater proportion of households that made less than $45K a year.
  • A smaller proportion of households that made between $75K and 100K a year.
  • A smaller 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 5.4
Most Targeted Block Groups $200k and above 0.0
Most Targeted Block Groups Less than 45k 28.6
Median Targeted Block Groups $120k - 150k 1.5
Median Targeted Block Groups $75k - 100k 7.3
Median Targeted Block Groups $200k and above 3.0
Median Targeted Block Groups Less than 45k 15.3
Least Targeted Block Groups $120k - 150k 0.2
Least Targeted Block Groups $75k - 100k 5.1
Least Targeted Block Groups $200k and above 3.9
Least Targeted Block Groups Less than 45k 18.9
Jurisdiction Total $120k - 150k 2.2
Jurisdiction Total $75k - 100k 7.1
Jurisdiction Total $200k and above 3.6
Jurisdiction Total Less than 45k 15.8

Public Housing

In Frederick, Md. 11 percent of public housing was on block groups the software targeted the most, 85 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 Apr 01, 2018 and Apr 08, 2019. We confirmed these dates with the Frederick, Md., police department.

Census GEOID Block Predictions Num. Public Housing Units Pct. days w/ Predictions
240217505053 3013 1427 6 99.1957105
240217507021 1001 619 1 92.2252011
240217503001 1003 549 2 87.1313673
240217651001 1006 353 1 76.9436997
240217505035 5006 565 1 57.1045576
240217503001 1004 202 4 51.2064343
240217505041 1004 208 1 43.4316354
240217501001 1001 140 2 37.5335121
240217505051 1019 114 2 27.8820375
240217651002 2022 76 2 18.7667560
240217505035 5004 103 1 17.4262735
240217722002 2045 87 5 16.6219839
240217508031 1034 117 1 15.5495979
240217505031 1012 112 1 15.0134048
240217505062 2002 65 2 13.6729223
240217722002 2063 50 29 13.1367292
240217507013 3011 56 1 11.7962466
240217651001 1020 37 1 9.9195710
240217502001 1009 37 2 9.9195710
240217505062 2003 49 1 9.9195710
240217651002 2005 42 1 9.6514745
240217503001 1002 24 1 5.6300268
240217508011 1046 24 2 5.6300268
240217506003 3023 19 1 5.0938338
240217651002 2004 19 1 5.0938338
240217501001 1009 23 4 4.5576408
240217502002 2004 15 1 4.0214477
240217651001 1008 21 1 3.7533512
240217505051 1031 19 5 3.7533512
240217503002 2008 12 1 3.2171582
240217508031 1031 23 2 3.2171582
240217507021 1018 15 2 2.6809651
240217501001 1000 9 3 2.4128686
240217505043 3012 7 1 1.8766756
240217501002 2012 7 1 1.8766756
240217651002 2015 5 1 1.3404826
240217503002 2005 4 1 1.0723861
240217508013 3013 4 1 0.5361930
240217506003 3021 1 1 0.2680965
240217651002 2018 1 1 0.2680965
240217505062 2000 1 1 0.2680965

Maps

Predictions

Density Map

The map below aggregates all the predictions Frederick, Md., 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
0.077 - 155 Least Predictions
155 - 309
309 - 462
462 - 616
616 - 770
770 - 925 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 Frederick, Md. 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 58 block groups in Frederick, Md., the smallest block group had a population of approximately 156 and the largest had a population of approximately 3,287.

In Frederick, Md., we analyzed 24,414 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 Frederick, Md. overall for comparison. The predictions we analyzed were between Apr 01, 2018 and Apr 08, 2019 , we received confirmation that Frederick, Md. department used the software between Apr 01, 2018 and Apr 08, 2019.

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 Frederick, Md. 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.