Nathan Craig 2021-06-22
This repository is composed of data that are initially reported on the Immigration and Customs Enforcement Guidance on COVID-19 website. The way that ICE reports the data do not lend themselves well to examining trends over time. To remedy this, web scraping methods were employed to regularly capture the data and compile it into summary tables. This repository contains those data tables as well as some initial exploratory analysis of these data. Some of the initial plots and insights are provided in this file. Before embarking on that analysis I want to first acknowledge some of the other fine work begin done to track COVID-19 data reported by ICE.
It is important to note that on November 18, 2020, Smart and Garcia [-@smart2020] through the Vera Institute of Justice launched an exceptional dashboard of ICE data entitled “Tracking COVID-19 in Immigration Detention.” The dashboard includes some plots of summary trends as well as facet plots of each facility. The report that goes with the dashboard details some of the changes in how ICE has reported the data over time and some of the issues with the data ICE supplied. Smart and Garcia’s [-@smart2020] work also includes a GitHub repository.
The UCLA Law COVID-19 Behind Bars Data Project, which contains a great
deal of information on COVID-19 in prisons, also includes a section on
“COVID-19 in ICE Detention.” They
provide some useful maps and summary plots. The UCLA Law COVID-19 Behind
Bars Data Project developed an R package entitled
behindbarstools
that represents functions used by the project for pulling, cleaning,
wrangling, and visualizing the data they are collecting. Ishan Vedantham
Nagpal, who is part of the UCLA project, also has a
repository that uses python
to scrape ICE COVID data.
On Friday March 13, 2020 ICE suspended family and social visitation. I visited a friend who was detained that the Otero County Processing Center on that day. He had been held at the facility for nearly a year. On March 27, 2020 ICE established the Guidance on COVID-19 web page and indicated the site would be updated frequently. Shortly afterwards I began logging the data on a daily basis, occasionally relying on the Internet Archive’s Wayback Machine’s regular scrapes of the page to pick up days that I missed.
In April of 2020, Advocate Visitors with Immigrants in Detention (AVID) in the Chihuahuan Desert began raising concerns about the spread of COVID-19 [@brownvega2020]. On April 30, AVID joined other immigrant advocacy groups calling for the release of people from ICE detention due to COVID-19 health risks. On May 14, 2020 AVID sent a letter to various public officials raising concerns about the spread of COVID-19 in detention and calling for releases. On May 28, 2020 AVID sent an additional letter to public officials again raising concerns about the spread of COVID-19 and medical negligence on the part of private detention contractors. This letter contained one of the first graphs of ICE COVID-19 data produced by AVID, at that point data were being manually copied and pasted while the graph was constructed in Excel.
On June 17, 2020 AVID sent a letter to Governor Lujan Grisham calling for her to prevent ICE from transferring individuals into immigration detention centers located in the state of New Mexico. In this letter, AVID again relied on data scraped from the ICE Guidance on COVID-19 web page. AVID illustrated how COVID-19 rates increased in the Otero County Processing Center after a May 1, 2020 transfer of individuals from Otay Mesa to OCPC and how there was a new increase in COVID-19 cases at OCPC after transfers resumed again in mid-July of 2020. By this time, AVID was using python to scrape the ICE web site and for constructing graphs of the data.
On June 30, 2020 AVID generated a wrap up of COVID-19 in ICE detention and as part of that provided a bar graph illustrating the number of cumulative COVID-19 cases by contractor. The graph showed that the largest private prison contractors had the highest number of COVID-19 cases. While this graph did not account for the fact that private prison contractors constitute the majority of ICE detention beds it does confirm that the majority of COVID-19 cases in ICE detention occurred in privately contracted facilities.
On July 16, 2020 AVID released a plotly graph of Cumulative COVID-19 cases at all facilities as well as an initial summary of several anomalies present in the ICE data. That graphic continued to be updated until February 28, 2021. On November 25, 2020 AVID released a report developed in collaboration with faculty from NMSU that addressed concerns raised by migrants detained at the Otero County Processing Center during the early part of the COVID-19 pandemic [@brownvega2020a]. On January 2, 2021 AVID released an additional report this time in conjunction with Innovation Law Lab that detailed in part concerns expressed by migrants at the Otero County Processing Center regarding COVID-19 and specifically the use of prolonged solitary confinement for people who tested positive [@craig2021].
# Load Libraries
library(readr)
library(summarytools)## Registered S3 method overwritten by 'pryr':
## method from
## print.bytes Rcpp
## For best results, restart R session and update pander using devtools:: or remotes::install_github('rapporter/pander')
st_options(plain.ascii = FALSE,
footnote = NA,
subtitle.emphasis = FALSE,
round.digits = 2)
library(tidyverse)## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --
## v ggplot2 3.3.3 v dplyr 1.0.6
## v tibble 3.1.1 v stringr 1.4.0
## v tidyr 1.1.3 v forcats 0.5.1
## v purrr 0.3.4
## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
## x tibble::view() masks summarytools::view()
library(lubridate)##
## Attaching package: 'lubridate'
## The following objects are masked from 'package:base':
##
## date, intersect, setdiff, union
library(ggplot2)
library(ggstatsplot)## You can cite this package as:
## Patil, I. (2021). Visualizations with statistical details: The 'ggstatsplot' approach.
## PsyArxiv. doi:10.31234/osf.io/p7mku
library(knitr)
options(digits=3)
opts_chunk$set(results = 'asis',
comment = NA,
prompt = FALSE,
cache = FALSE)
# Turn off scientific notation
options(digits=5, scipen=15)# Load Dataset
df_summary <- read_csv("./data/covid_summaries.csv",
col_types = cols(Date = col_date(format = "%m/%d/%Y"),
`Total Deaths` = col_double(), `Total COVID-19 Confirmed in Custody` = col_double()))# ICE Detention Population Over Time
ggplot(df_summary, aes(x=Date, y=`Total Detained`))+
geom_line()+
geom_point()+
geom_smooth()+
labs(
title = "ICE Detention Population Over Time",
subtitle = "Smoothed trend line added",
caption = "Data source: https://www.ice.gov/coronavirus"
)`geom_smooth()` using method = 'loess' and formula 'y ~ x'
Warning: Removed 27 rows containing non-finite values (stat_smooth).
Warning: Removed 27 row(s) containing missing values (geom_path).
Warning: Removed 27 rows containing missing values (geom_point).
# Total Confirmed COVID-19 Over Time
ggplot(df_summary, aes(x=Date, y=`Total COVID-19 Confirmed in Custody`))+
geom_line()+
geom_smooth()+
labs(
title = "Total Number of Confirmed COVID-19 Cases in ICE Detention Over Time",
subtitle = "Smoothed trend line added",
caption = "Data source: https://www.ice.gov/coronavirus"
)`geom_smooth()` using method = 'loess' and formula 'y ~ x'
Warning: Removed 63 rows containing non-finite values (stat_smooth).
Warning: Removed 63 row(s) containing missing values (geom_path).
# Ratio Confirmed by Total Detained
df_summary %>%
mutate(`Confirmed Detained Ratio` = `Total COVID-19 Confirmed in Custody`/`Total Detained`) %>%
ggplot(aes(x=Date, y=`Confirmed Detained Ratio`))+
geom_line()+
geom_smooth()+
labs(
title = "Ratio of Confirmed COVID-19 Cases in ICE Detention by \nTotal ICE Detention Population Over Time",
subtitle = "Smoothed trend line added",
caption = "Data source: https://www.ice.gov/coronavirus"
)`geom_smooth()` using method = 'loess' and formula 'y ~ x'
Warning: Removed 63 rows containing non-finite values (stat_smooth).
Warning: Removed 63 row(s) containing missing values (geom_path).
# Cumulative COVID-19
ggplot(df_summary, aes(x=Date, y=`Total Cumulative COVID-19`))+
geom_line()+
geom_smooth()+
labs(
title = "Cumulative COVID-19 Cases in ICE Detention Over Time",
subtitle = "Smoothed trend line added",
caption = "Data source: https://www.ice.gov/coronavirus"
)`geom_smooth()` using method = 'loess' and formula 'y ~ x'
# Total COVID-19 Tests
ggplot(df_summary, aes(x=Date, y=`Total Tested`))+
geom_line()+
geom_smooth()+
labs(
title = "Total Number of COVID-19 Tests in ICE Detention Population Over Time",
subtitle = "Smoothed trend line added",
caption = "Data source: https://www.ice.gov/coronavirus"
)`geom_smooth()` using method = 'loess' and formula 'y ~ x'
Warning: Removed 31 rows containing non-finite values (stat_smooth).
Warning: Removed 31 row(s) containing missing values (geom_path).
# Total Deaths in ICE Custody
ggplot(df_summary, aes(x=Date, y=`Total Deaths`))+
geom_line()+
geom_smooth()+
labs(
title = "Total Number of Deaths in ICE Detention Over Time",
subtitle = "Smoothed trend line added",
caption = "Data source: https://www.ice.gov/coronavirus"
)`geom_smooth()` using method = 'loess' and formula 'y ~ x'
Warning: Removed 63 rows containing non-finite values (stat_smooth).
Warning: Removed 63 row(s) containing missing values (geom_path).
