Jefferson County, Ala.

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 Jefferson County, Ala. 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 Jefferson County, Ala., 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 Jefferson County, Ala. overall, the least-targeted block groups had:

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

Targeting Level Demographic Proportion of Block Group pop.
Most Targeted Block Groups Asian 0.0
Most Targeted Block Groups Black 41.3
Most Targeted Block Groups Latino 0.9
Most Targeted Block Groups White 41.3
Median Targeted Block Groups Asian 0.0
Median Targeted Block Groups Black 91.6
Median Targeted Block Groups Latino 0.0
Median Targeted Block Groups White 1.7
Least Targeted Block Groups Asian 0.0
Least Targeted Block Groups Black 86.3
Least Targeted Block Groups Latino 0.0
Least Targeted Block Groups White 1.3
Jurisdiction Total Asian 0.3
Jurisdiction Total Black 34.1
Jurisdiction Total Latino 0.6
Jurisdiction Total White 49.0

Household Income

Compared to Jefferson County, Ala. overall, the most-targeted block groups had:

  • A greater 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 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 Jefferson County, Ala. 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 smaller proportion of households that made $200K and above a year.

Targeting Level Demographic Proportion of Block Group pop.
Most Targeted Block Groups $120k - 150k 0.6
Most Targeted Block Groups $75k - 100k 5.8
Most Targeted Block Groups $200k and above 0.4
Most Targeted Block Groups Less than 45k 33.4
Median Targeted Block Groups $120k - 150k 0.0
Median Targeted Block Groups $75k - 100k 0.2
Median Targeted Block Groups $200k and above 0.0
Median Targeted Block Groups Less than 45k 57.7
Least Targeted Block Groups $120k - 150k 0.0
Least Targeted Block Groups $75k - 100k 0.2
Least Targeted Block Groups $200k and above 0.0
Least Targeted Block Groups Less than 45k 54.3
Jurisdiction Total $120k - 150k 1.4
Jurisdiction Total $75k - 100k 4.6
Jurisdiction Total $200k and above 2.9
Jurisdiction Total Less than 45k 31.3

Public Housing

In Jefferson County, Ala. 13 percent of public housing was on block groups the software targeted the most, 56 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 26, 2021. We confirmed these dates with the Jefferson County, Ala., police department.

Census GEOID Block Predictions Num. Public Housing Units Pct. days w/ Predictions
010730119011 1000 5235 1 99.9064546
010730118023 3004 3577 1 97.0065482
010730129121 1005 2257 1 94.0130964
010730112072 2000 2174 1 92.7034612
010730112101 1033 1535 2 84.1908326
010730116001 1075 1323 10 82.5070159
010730112072 2001 1742 2 79.7006548
010730117061 1016 1241 4 73.9943873
010730113012 2009 1022 13 66.6043031
010730113011 1057 820 13 43.6856876
010730136015 5052 493 5 42.8437792
010730133002 2091 426 1 35.7343312
010730106022 2029 331 7 27.5023386
010730133001 1019 320 7 25.9120674
010730113012 2016 243 11 22.6379794
010730106022 2049 229 12 18.6155285
010730040005 5020 160 1 14.4059869
010730106022 2027 113 23 10.0093545
010730045002 2001 105 29 9.8222638
010730119012 2017 95 2 8.8868101
010730106022 2031 91 3 8.5126286
010730131001 1015 64 3 5.9869036
010730136015 5056 63 7 5.8933583
010730106022 2030 63 9 5.7062675
010730105002 2036 78 1 5.6127222
010730106023 3014 61 10 5.1449953
010730106031 1019 60 1 4.4901777
010730129084 4031 51 1 4.3030870
010730120012 2107 42 1 3.9289055
010730110021 1096 48 1 3.5547240
010730106022 2028 40 22 3.3676333
010730106026 6056 34 1 3.1805426
010730106026 6049 32 1 2.9934518
010730117061 1052 46 8 2.8063611
010730104021 1057 27 1 2.5257250
010730143021 1031 29 1 1.8709074
010730118032 2014 21 1 1.8709074
010730136015 5055 18 8 1.6838167
010730131001 1014 16 6 1.4967259
010730106026 6050 15 1 1.4031805
010730106022 2003 14 1 1.3096352
010730129072 2016 13 1 1.2160898
010730106026 6055 13 1 1.2160898
010730119012 2022 11 13 1.0289991
010730113021 1002 9 16 0.8419083
010730106022 2025 7 1 0.6548176
010730106022 2018 5 2 0.4677268
010730106022 2041 1 1 0.0935454

Maps

Predictions

Density Map

The map below aggregates all the predictions Jefferson County, Ala., 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 - 460 Least Predictions
460 - 918
918 - 1380
1380 - 1840
1840 - 2290
2290 - 2760 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 Jefferson County, Ala. 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 518 block groups in Jefferson County, Ala., the smallest block group had a population of approximately 141 and the largest had a population of approximately 8,344.

In Jefferson County, Ala., we analyzed 471,403 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 Jefferson County, Ala. overall for comparison. The predictions we analyzed were between Feb 23, 2018 and Jan 26, 2021 , we received confirmation that Jefferson County, Ala. department used the software between Feb 23, 2018 and Jan 26, 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 Jefferson County, Ala. 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.