Ocala, Fla.

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 Ocala, Fla. 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 Ocala, Fla., 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 Ocala, Fla. overall, the least-targeted block groups had:

  • A greater 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 0.0
Most Targeted Block Groups Black 22.1
Most Targeted Block Groups Latino 9.4
Most Targeted Block Groups White 46.7
Median Targeted Block Groups Asian 0.0
Median Targeted Block Groups Black 0.0
Median Targeted Block Groups Latino 1.6
Median Targeted Block Groups White 87.0
Least Targeted Block Groups Asian 3.6
Least Targeted Block Groups Black 2.4
Least Targeted Block Groups Latino 3.0
Least Targeted Block Groups White 71.8
Jurisdiction Total Asian 1.1
Jurisdiction Total Black 9.9
Jurisdiction Total Latino 4.6
Jurisdiction Total White 63.3

Household Income

Compared to Ocala, Fla. 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 Ocala, Fla. 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 0.9
Most Targeted Block Groups $200k and above 0.0
Most Targeted Block Groups Less than 45k 56.0
Median Targeted Block Groups $120k - 150k 1.2
Median Targeted Block Groups $75k - 100k 6.0
Median Targeted Block Groups $200k and above 1.8
Median Targeted Block Groups Less than 45k 32.5
Least Targeted Block Groups $120k - 150k 1.4
Least Targeted Block Groups $75k - 100k 4.8
Least Targeted Block Groups $200k and above 3.0
Least Targeted Block Groups Less than 45k 22.4
Jurisdiction Total $120k - 150k 0.3
Jurisdiction Total $75k - 100k 3.1
Jurisdiction Total $200k and above 0.8
Jurisdiction Total Less than 45k 42.7

Public Housing

In Ocala, Fla. 24 percent of public housing was on block groups the software targeted the most, 74 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 24, 2018 and Mar 01, 2019. We confirmed these dates with the Ocala, Fla., police department.

Census GEOID Block Predictions Num. Public Housing Units Pct. days w/ Predictions
120830014013 3025 643 17 91.1051213
120830020021 1006 429 2 78.7061995
120830017002 2031 439 2 77.6280323
120830024012 2039 435 1 74.6630728
120830018002 2000 178 2 47.9784367
120830014024 4035 180 1 36.6576819
120830022011 1041 135 1 32.0754717
120830017001 1003 63 1 15.9029650
120830017002 2029 23 31 5.3908356
120830017002 2022 17 2 4.5822102
120830019001 1137 17 1 4.5822102
120830020022 2000 17 4 4.5822102
120830017001 1047 15 1 4.0431267
120830017002 2032 14 2 3.7735849
120830017002 2035 10 1 2.6954178
120830017001 1014 3 2 0.8086253
120830014013 3000 3 21 0.8086253

Maps

Predictions

Density Map

The map below aggregates all the predictions Ocala, Fla., 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
0.555 - 75.2 Least Predictions
75.2 - 149
149 - 224
224 - 298
298 - 372
372 - 446 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 Ocala, Fla. 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 53 block groups in Ocala, Fla., the smallest block group had a population of approximately 100 and the largest had a population of approximately 6,048.

In Ocala, Fla., we analyzed 23,081 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 Ocala, Fla. overall for comparison. The predictions we analyzed were between Feb 24, 2018 and Mar 01, 2019 , we received confirmation that Ocala, Fla. department used the software between Feb 01, 2016 and Mar 01, 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 Ocala, Fla. 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.