UrbanFinal

The Relationship Between Eviction and Incarceration in NYC

Introduction/Background

This project investigates the relationship between housing instability and criminal legal system involvement in New York City, New York. Specifically, it asks whether higher rates of eviction filings in New York City neighborhoods correlate with increased police contact, arrests, or jail admissions, and how these patterns intersect with race and income. One hypothesis is that eviction functions as a destabilizing force that may increase the likelihood of shelter entry and criminal justice contact, which is known as the public housing to prison pipeline [Columbia].

New York City is one of America’s most populous and major cities, and understanding this relationship there is certainly urgent, as it is one of the most prominent nexuses of the affordable housing crisis, shelter overcrowding, and policing of low-income communities. By focusing on evictions, low-level arrests, and shelter entries, this project seeks to show how housing policy and criminal justice outcomes are deeply intertwined. Data from this analysis comes from several official sources from the NYC Open Data site and the census (ACS). This report puts forth a set of initial findings focused on eviction filings in NYC and how they relate to neighborhood racial composition and income.

Figures and Tables

library(dplyr)

Attaching package: 'dplyr'

The following objects are masked from 'package:stats':

    filter, lag

The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union

library(lubridate)

Attaching package: 'lubridate'

The following objects are masked from 'package:base':

    date, intersect, setdiff, union

library(tidyr)
library(tidycensus)
library(stringr)

evictions <- read.csv("newevictions.csv")
arrests <- read.csv("nycarrests.csv")
shelter <- read.csv("sheltercensus.csv")

census_api_key("e56623b11ab31e749244318a1e292fb8e7ce936b", install = TRUE, overwrite = TRUE)

Your original .Renviron will be backed up and stored in your R HOME directory if needed.

Your API key has been stored in your .Renviron and can be accessed by Sys.getenv("CENSUS_API_KEY"). 
To use now, restart R or run `readRenviron("~/.Renviron")`

[1] "e56623b11ab31e749244318a1e292fb8e7ce936b"

evictions_clean <- evictions %>%
  mutate (Executed.Date = mdy(Executed.Date),                         
    year = year(Executed.Date),                                 
    month = month(Executed.Date, label = TRUE, abbr = TRUE), 
    Borough = toupper(BOROUGH)) %>%
  select(Executed.Date, year, month, Borough, Eviction.Postcode,
         Latitude, Longitude, Census.Tract, NTA)

arrests_clean <- arrests %>%
  filter(JURISDICTION_CODE %in% c(0,1,2)) %>%           
  mutate (ARREST_DATE = mdy(ARREST_DATE),
    year = year(ARREST_DATE),
    month = month(ARREST_DATE, label = TRUE, abbr = TRUE),
    Borough = case_when(
      ARREST_BORO == "B" ~ "BRONX",
      ARREST_BORO == "K" ~ "BROOKLYN",
      ARREST_BORO == "M" ~ "MANHATTAN",
      ARREST_BORO == "Q" ~ "QUEENS",
      ARREST_BORO == "S" ~ "STATEN ISLAND")) %>%
  filter(!is.na(Latitude), Latitude != 0) %>%
  filter(!is.na(ARREST_DATE))

shelter_clean <- shelter %>%
  mutate (Date = mdy(Date.of.Census),
    year = year(Date),
    month = month(Date, label = TRUE, abbr = TRUE)) %>%
  group_by(year, month) %>%
  summarize (avg_total_sheltered = mean(Total.Individuals.in.Shelter),
    .groups = "drop")

nyc_census <- get_acs (geography = "tract",
  variables = c (median_income = "B19013_001",
    total_pop = "B01003_001",
    black_pop = "B02001_003",
    latinx_pop = "B03003_003"),
  state = "NY",
  county = c("New York", "Kings", "Bronx", "Queens", "Richmond"),
  year = 2022,
  survey = "acs5",
  geometry = FALSE) %>%
  select(-moe) %>%
  pivot_wider(names_from = variable, values_from = estimate) %>%
  mutate(pct_black = black_pop / total_pop,
    pct_latinx = latinx_pop / total_pop,
    tract_id = str_sub(GEOID, nchar(GEOID) -5) ) # trying to match tract_id format

Getting data from the 2018-2022 5-year ACS

evictions_clean <-evictions_clean %>%
  mutate(tract_id = str_pad(as.character(Census.Tract), width = 6, pad = "0"))

evictions_census <- evictions_clean %>%
  inner_join(nyc_census, by = "tract_id", relationship = "many-to-many")

evictions_by_tract <- evictions_census %>%
  group_by(tract_id) %>%
  summarize(evictions = n(),
    median_income = median(median_income, na.rm = TRUE),
    pct_black = mean(pct_black, na.rm = TRUE),
    pct_latinx = mean(pct_latinx, na.rm = TRUE))

Table of Summary Statistics by Race/Income Quartile

Table 1: Evictions by Income Quartile and Demographics

income_quartile	mean_evictions	mean_pct_black	mean_pct_latinx
1	106.75281	0.3077011	0.4812977
2	99.04494	0.2208775	0.3997142
3	46.85393	0.1389895	0.2157302
4	35.64045	0.0696088	0.1385671

Evictions and Percent Black Population by Census Tract

`geom_smooth()` using formula = 'y ~ x'

Warning: Removed 3 rows containing non-finite outside the scale range
(`stat_smooth()`).

Warning: Removed 3 rows containing missing values or values outside the scale range
(`geom_point()`).

Figure 1: Evictions and % Black Population (NYC Tracts)

Evictions and Demographics Across Income Quartiles

Figure 2: Evictions and Demographics across Income Quartiles

Discussion & Conclusion

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