Data Documentation

Meta Data Name: USAFacts

Last Modified: 2/28/2021

Author: Stephanie Yang

Data Location:

GeoDaCenter/covid/public/csv

• County
  • Cases: covid_confirmed_usafacts.csv
  • Deaths: covid_deaths_usafacts.csv
• State
  • Cases: covid_confirmed_usafacts_state.csv
  • Deaths: covid_deaths_usafacts_state.csv

Data Source(s) Description:

USAFacts publishes COVID-19 data confirmed cases and death data on county and state level. All data are updated on a daily basis. USAFacts also provide various data visualization here.

Direct links: Confirmed Cases | Deaths

Description of Data Processing:

• Data are directly downloaded from USAFacts.

Key Variable and Definitions:

covid_confirmed_usafacts_state.csv

covid_deaths_usafacts_state.csv

covid_confirmed_usafacts.csv

covid_deaths_usafacts.csv

Description of Data Source Tables:

See the Detailed Methodology and Sources: COVID-19 Data for additional information.

Data Limitations:

No limitations to report.

Comments/Notes:

n/a

Meta Data Name: New York Times

Last Modified: 2/23/2021

Author: Dylan Halpern

Data Location:

GeoDaCenter/covid/public/csv

• County
  • Cases: covid_confirmed_nyt.csv
  • Deaths: covid_deaths_nyt.csv
• State
  • Cases: covid_confirmed_nyt_state.csv
  • Deaths: covid_deaths_nyt_state.csv

Data Source(s) Description:

The New York Times is publishing their on-going COVID-19 data, available here.

Direct links: States | Counties

Description of Data Processing:

• State and county confirmed case and death data are taken directly from the NYT data and transposed into time-series.

Key Variable and Definitions:

covid_confirmed_nyt.csv

covid_confirmed_nyt_state.csv

covid_deaths_nyt.csv

covid_confirmed_nyt_state.csv

Description of Data Source Tables:

See the New York Times Repo for additional information.

Data Limitations:

No limitations to report.

Comments/Notes:

n/a

Meta Data Name: 1 Point 3 Acres (1P3A)

Last Modified: 2/28/2021

Author: Stephanie Yang

Data Source(s) Description:

1P3A is one of the earliest organizations that collect and publish COVID-19 data. The data is not publicly available, but researchers can fill a request for access here.

No direct links available.

Description of Data Processing:

• Data are directly downloaded from 1P3A

Key Variable and Definitions:

covid_confirmed_1p3a_state.csv

covid_deaths_1p3a_state.csv

covid_confirmed_1p3a.csv

covid_deaths_1p3a.csv

Description of Data Source Tables:

See 1P3A's FAQ Board for additional information.

Data Limitations:

Researchers should request data access directly on 1P3A's page.

Comments/Notes:

n/a

Meta Data Name: Center for Disease Control COVID Data

Last Modified: 2/23/2021

Author: Dylan Halpern

Data Location:

GeoDaCenter/covid/public/csv

• Cases
  • County Cases: covid_confirmed_cdc.csv
• Deaths
  • County Deaths: covid_deaths_cdc.csv
• Testing
  • County Testing Count: covid_testing_cdc.csv
  • County Testing Capacity Per 100k Population: covid_tcap_cdc.csv
  • County Weekly Positivity: covid_wk_pos_cdc.csv
  • County Confirmed Cases Per Testing: covid_ccpt_cdc.csv
• Vaccination (State-level)
  • First Dose Administered: vaccine_admin1_cdc.csv
  • Second Dose Administered: vaccine_admin2_cdc.csv
  • Doses Distributed but not administered: vaccine_dist_cdc.csv

Data Source(s) Description:

This data is sourced from the CDC's Covid Data Tracker on the County and Vaccination views. The CDC publishes 7-day rolling average aggregations of testing, case, and death data and daily snapshots of vaccination data.

Both state and county datasets can be joined to Census Cartographic Boundary Files. The Atlas uses a resolution of 20M.

Key Variable and Definitions:

County

covid_testing_cdc.csv

covid_tcap_cdc.csv

covid_wk_pos_cdc.csv

covid_ccpt_cdc.csv

covid_confirmed_cdc.csv

covid_deaths_cdc.csv

Vaccination vaccine_admin1_cdc.csv

vaccine_admin2_cdc.csv

vaccine_dist_cdc.csv

Description of Data Processing:

• Cases and Deaths: 7-day rolling averages of Cases and Deaths are taken directly from the CDC endpoint and transposed into time-series data.
• Testing Count: Testing count is taken from the 7-day rolling average of new test results (new_test_results_reported_7_day_rolling_average)
• Testing Capacity: Testing capacity is taken from the testing county above divided by the county population.
• Testing Positivity: Testing positivity is taken from the 7-day rolling average of positive of new test results (percent_new_test_results_reported_positive_7_day_rolling_average)
• Testing Confirmed Cases Per Testing(CCPT): Testing CCPT is taken from the 7-day average of new confirmed cases divided by the 7-day average testing count.
• Vaccination First and Second Doses Administered: The first and second doses administered are taken directly from the Administered_Dose1 field and transposed into time-series. On the frontend of the Atlas, this is presented as a numerator on top of the State population.
• Vaccination Dose Distributed but not administered: This metric is taken from the total doses distributed (Doses_Distributed) subtracted by the total doses administered (Doses_Administered). This gives an estimation of the number of doses "on hand" for each state. On the frontend of the Atlas, this is presented as a numerator per 100,000 population.

Description of Data Source Tables:

County Data: CDC County data is available from two API endpoints. One for the latest county snapshot and another for state-specific historical time-series data. The URL format for the second endpoint is https://covid.cdc.gov/covid-data-tracker/COVIDData/getAjaxData?id=integrated_county_timeseries_state_{**STATE-2-LETTER-CODE**}_external.

The below data fields are available in the historical data from each state endpoint.

• fips_code County FIPS code geographic identifier
• state Two letter state name
• state_name Full state name
• cbsa_code Core-Based Statistical Areas name
• county County name
• new_cases_week_over_week_percent_change Week over week percent change in cases
• new_cases_7_day_rolling_average 7-day rolling average of new cases
• new_cases_per_100k_7_day_rolling_average 7-day rolling average of new cases per 100,000 population
• new_deaths_7_day_rolling_average 7-day rolling average of new deaths
• new_deaths_week_over_week_percent_change Week over week percent change in deaths
• new_deaths_per_100k_7_day_rolling_average 7-day rolling average of new deaths per 100,000 population
• daily_cli_7_day_rolling_average Daily, 7-day rolling average of "COVID like illnesses"
• daily_cli_percentage_7_day_rolling_average Usage Unclear. Percentage of daily, 7-day rolling average of "COVID like illnesses"
• daily_ili_percentage_7_day_rolling_average Usage Unclear. Percentage of daily, 7-day rolling average of "Influenza like illnesses"
• new_test_results_reported New test results reported in this data window
• new_test_results_reported_7_day_rolling_average 7-day rolling average of new tests reported
• percent_new_test_results_reported_positive_7_day_rolling_average 7-day rolling average of positive test results reported on new tests
• percent_positive_7_day Overall positivity for tests in the past 7 day
• total_test_results_reported_week_over_week_count_change Week over week change in test results reported
• testing_suppressed Usage Unclear. Potentially tests suppressed for anonymity or inconclusive tests.
• total_hospitals_reporting Number of hospital facilities reporting data in this county
• admissions_covid_confirmed_last_7_days Total hospital admissions for COVID-19 in the past 7 day
• admissions_covid_confirmed_7_day_rolling_average 7-day rolling average of confirmed hospital COVID admissions
• admissions_covid_confirmed_last_7_days_per_100_beds Number of confirmed hospital COVID admissions in the past 7 days per 100 hospital beds
• admissions_covid_confirmed_week_over_week_percent_change Week over week percent change of COVID admissions to hospitals
• percent_adult_inpatient_beds_used_confirmed_covid Percentage of adult inpatient hospital beds confirmed occupied by COVID patients
• percent_adult_inpatient_beds_used_confirmed_covid_week_over_week_absolute_change Change in number of hospital beds used for COVID-19 patients
• hospitals_included_in_percent_adult_inpatient_beds_used_confirmed_covid Data coverage for number of hospitals reporting data adult inpatient bed usage
• percent_adult_icu_beds_used_confirmed_covid Percent of adult ICU beds used for COVID-19 patients
• percent_adult_icu_beds_used_confirmed_covid_week_over_week_absolute_change Week over week change in number of ICU hospital beds used for COVID-19 patients
• hospitals_included_in_percent_adult_icu_beds_used_confirmed_covid Data coverage for number of hospitals reporting data adult inpatient bed usage
• cbsa_daily_cli_7_day_rolling_average 7-day rolling average of Covid-like illnesses reported in the Core-Based Statistical Areas
• cbsa_daily_cli_percentage_7_day_rolling_average Usage Unclear. Percent of 7-day rolling average of Covid-like illnesses reported in the Core-Based Statistical Areas
• cbsa_daily_ili_7_day_rolling_average 7-day rolling average of Influenza-like illnesses reported in the Core-Based Statistical Areas
• cbsa_daily_ili_percentage_7_day_rolling_average Usage Unclear. Percent of 7-day rolling average of influenza-like illnesses reported in the Core-Based Statistical Areas
• date Date reported
• report_date_window_start ISO date format start of reporting window
• report_date_window_end ISO date format end of reporting window

Vaccination Data: The most recent CDC Vaccination data reports across 4 dimensions. They report:

• Total Doses vs People: Total Doses includes doses administered (shots given) or distributed (delivered) in that state, but not necessarily to residents of that state. People includes only people from that state.
• 1st or 2nd dose: Number of first or second doses administered.
• Total Population or 18 years or older: Population normalization for the whole population or only individuals 18 years or older.
• Count of doses vs percent of population: Doses administered as a count (or population normalized count) or as a percentage of the state population.

Field descriptions are inferred from CDC descriptions on the Covid Data Tracker and variable names in the page's source bundle.

For direct access to the data, see the CDC Api Endpoint.

Note: In the future, the CDC may make available time-series vaccination data, which should be used instead of these snapshots. See also Our World in Data's repo here.

Current Field and Descriptions

• Date Date for data report in ISO Format
• Location Two letter state name
• ShortName Three letter state name
• LongName Full state name
• Census2019 2019 Census population count
• Doses_Distributed Total doses distributed to this state
• Doses_Administered Total doses administered in this state
• Dist_Per_100K Doses distributed in this state per 100,000 population
• Admin_Per_100K Doses administered in this state per 100,000 population
• Administered_Dose1 Total number of first doses administered in this state
• Administered_Dose1_Per_100K First doses administered in this state per 100,000 population
• Administered_Dose2Total number of second doses administered in this state
• Administered_Dose2_Per_100K Second doses administered in this state per 100,000 population
• Administered_Dose1_Pop_Pct Percent of population in this state who have received the first dose
• Administered_Dose2_Pop_Pct Percent of population in this state who have received the second dose
• date_type Type of date for this entry, usually "Report"
• Recip_Administered Doses administered to people from this state
• Administered_Dose1_Recip First doses administered to people from this state
• Administered_Dose2_Recip Second doses administered to people from this state
• Administered_Dose1_Recip_18Plus First doses administered to people from this state 18 years or older
• Administered_Dose2_Recip_18Plus Second doses administered to people from this state 18 years or older
• Administered_Dose1_Recip_18PlusPop_Pct Percent of population of this state who have received a first dose aged 18 years or older
• Administered_Dose2_Recip_18PlusPop_Pct Percent of population of this state who have received a second dose aged 18 years or older
• Census2019_18PlusPop Population in this state 18 years or older as of the 2019 Census
• Distributed_Per_100k_18Plus Doses distributed to this state per 100,000 population 18 years or older
• Administered_18Plus Doses administered in this state to people 18 years or older
• Admin_Per_100k_18Plus Doses distributed in this state per 100,000 population 18 years or older

Data Limitations:

The data is pre-aggregated to 7-day rolling averages. Currently, we utilize the state dose totals and not the people totals, as the available data history is longer.

Comments/Notes:

n/a

• Meta Data Name: Health and Human Services Data

Last Modified: 2/23/2021

Author: Dylan Halpern

Data Location:

GeoDaCenter/covid/public/csv

• Testing: covid_testing_cdc_state.csv
• Testing Capacity: covid_tcap_cdc_state.csv
• Testing Positivity: covid_wk_pos_cdc_state.csv
• Testing Confirmed Cases Per Testing: covic_ccpt_cdc_state.csv

Data Source(s) Description:

HHS State Level COVID-19 Diagnostic Laboratory Testing (PCR Testing) Time Series is available here. It is updated daily and sourced from CDC COVID-19 Electronic Laboratory Reporting (CELR), Commercial Laboratories, State Public Health Labs, In-House Hospital Labs. A full data dictionary is available here.

Description of Data Processing:

• Testing: Total testing volume is taken from a 7-day rolling average of total testing figures each day.
• Testing Capacity: Testing capacity is calculated based on testing divided by population, then multiplied by 100,000.
• Testing Positivity: Testing positivity is taken from the reported positive tests divided by total tests.
• Testing Confirmed Cases Per Testing: USA Facts State-level confirmed cases are divided by total testing volume.

Key Variable and Definitions:

covid_testing_cdc_state.csv

covid_tcap_cdc_state.csv

covid_wk_pos_cdc_state.csv

covic_ccpt_cdc_state.csv

Description of Data Source Tables:

Descriptions via HHS / HealthData.gov.

• state (string) - Abbreviation of state associated with the test. Typically patient's state of residence, but provider or lab state used when patient is unavailable.
• state_name (string) - Name of state associated with the test. Typically patient's state of residence, but provider or lab state used when patient is unavailable.
• state_fips (string) - Numerical identifier of state associated with the test. Typically patient's state of residence, but provider or lab state used when patient is unavailable.
• fema_region (string) - Region associated with the test. Typically that of patient's state of residence, but provider or lab state used when patient is unavailable.
• overall_outcome (string) - Outcome of test -- Positive, Negative or Inconclusive.
• date (date) - Typically the date the test completed or the date that the result was reported back to the patient. If neither are available, it can be the date the specimen was collected, arrived at the testing facility, or the date the test was ordered.
• new_results_reported (long) - The number of tests completed with the specified outcome in the specified state on the listed date. (Large spikes may result from states submitting tests for several proceeding days at once with a single date).
• total_results_reported (long) - The cumulative number of tests completed with the specified outcome in the specified state up through the listed date.

Data Limitations:

No limitations to report.

Comments/Notes:

n/a

Meta Data Name: County Health Rankings & Roadmaps

Last Modified: 2/23/2021

Author: Dylan Halpern

Data Location:

GeoDaCenter/covid/public/csv

• County:
  • Health Context: chr_health_context.csv
  • Health Factors: chr_health_factors.csv
  • Life Expectancy: chr_life.csv
• State:
  • Health Context State: chr_health_context_state.csv
  • Health Factors State: chr_health_factors_state.csv
  • Life Expectancy State: chr_life_state.csv

Data Source(s) Description:

County Health Rankings and Roadmaps publishes data at the state and county level including "how health is influenced by where we live, learn, work, and play." Data can be accessed via their website.

Description of Data Processing:

n/a

Key Variable and Definitions:

chr_health_context / chr_health_context_state

chr_health_factors / chr_health_factors_state

chr_life / chr_life_state

Description of Data Source Tables:

Detailed descriptions of the included variables with methodology are available from County Health Rankings & Roadmaps:

• Health Factors and Context
• Health Outcomes / Life Expectancy

Data Limitations:

No limitations to report.

Comments/Notes:

n/a

Meta Data Name: Yu Group at UC Berkeley

Last Modified: 02/28/2021

Author: Laura Chen

Data Location:

• Berkeley Severity Forecasting berkeley_predictions.csv

Data Source(s) Description:

The Yu group at UC Berkeley Statistics and EECS has compiled, cleaned and documented a large corpus of hospital- and county-level data from a variety of public sources to aid data science efforts to combat COVID-19.

Description of Data Processing:

Data are taken directly from the output of the Yu Group at UC Berkeley's model.

Key Variable and Definitions:

berkeley_predictions.csv

Description of Data Source Tables:

At the hospital level, the data includes the location of the hospital, the number of ICU beds, the total number of employees, and the hospital type. At the county level, the data includes COVID-19 cases/deaths from USA Facts and NYT, automatically updated every day, along with demographic information, health resource availability, COVID-19 health risk factors, and social mobility information.

See the Yu-Group/covid19-severity-prediction for more information.

• Hospital Level Data
  • cms_cmi: Case Mix Index for hospitals from CMS
  • cms_hospitalpayment: Teaching Hospital info from CMS
  • DH_hospital: US Hospital info from Definitive Healthcare
  • dhhs_hospitalcapacity: US hopsital capcity data, updated weekly, from US DHHS
  • hifld_hospital: Hospital info from homeland infrastructue foundation level data
• Nursing Homes Level Data
  • nyt_nursinghomes: number of COVID-19-related cases and deaths from nursing homes, as reported by NYT
  • hifld_nursinghomes: database of nursing homes/assisted living facilities, populated via open source authoritative sources
• County Level Data
  • COVID-19 Cases/Deaths Data
    • nytimes_infections: COVID-19-related death/case counts per day per county from NYT
    • usafacts_infections: COVID-19-related death/case counts per day per county from USA Facts
    • ccd_daily: COVID-19-related deaths, cases, hospitalizations, and testing statistics
  • Demographics and Health Resource Availability
    • ahrf_health: contains county-level information on health facilities, health professions, measures of resource scarcity, health status, economic activity, health training programs, and socioeconomic and environmental characteristics from Area Health Resources Files
    • cdc_svi: Social Vulnerability Index for counties from CDC
    • hpsa_shortage: information on areas with shortages of primary care, as designated by the Health Resources & Services Administration (HRSA)
    • khn_icu: information on number of ICU beds and hospitals per county from Kaiser Health News
    • usda_poverty: county-level poverty estimates from the United States Department of Agriculture, Economic Research Service
  • Health Risk Factors
    • chrr_health: contains estimates of various health outcomes and health behaviors (e.g., percentage of adult smokers) for each county from County Health Rankings & Roadmaps
    • dhdsp_heart: cardiovascular disease mortality rates from CDC DHDSP
    • dhdsp_stroke: stroke mortality rates from CDC DHDSP
    • ihme_respiratory: chronic respiratory disease mortality rates from IHME
    • medicare_chronic: Medicare claims data for 21 chronic conditions
    • nchs_mortality: overall mortality rates for each county from National Center for Health Statistics
    • usdss_diabetes: diagnosed diabetes in each county from CDC USDSS
    • kinsa_ili: measures of anomalous influenza-like illness incidence (ILI) outbreaks in real-time using Kinsa’s county-level illness signals, developed from real-time geospatial thermometer data (private data)
    • cmu_covidcast: epidemiological data from the CMU Delphi COVIDcast, which includes data on COVID-like symptoms from Facebook surveys, estimated COVID-related doctor visits and hospital admissions, and other indicators
  • Social Distancing and Mobility/Miscellaneous
    • nytimes_masks: mask-wearing survey data from NYT and Dynata
    • google_mobility: community mobility reports from Google
    • apple_mobility: mobility trends from Apple maps direction requests
    • unacast_mobility: county-level estimates of the change in mobility from pre-COVID-19 baseline from Unacast (private data)
    • streetlight_vmt: estimates of total vehicle miles travelled (VMT) by residents of each county, each day; provided by Streetlight Data (private data)
    • safegraph_socialdistancing: aggregated daily views of USA foot-traffic summarizing movement between counties from SafeGraph (private data)
    • safegraph_weeklypatterns: place foot-traffic and demographic aggregations that answer: how often people visit, where they came from, where else they go, and more; from SafeGraph (private data)
    • jhu_interventions: contains the dates that counties (or states governing them) took measures to mitigate the spread by restricting gatherings (e.g., travel bans, stay at home orders)
    • mit_voting: county-level returns for presidential elections from 2000 to 2016 according to official state election data records
• Miscellaneous Data
  • bts_airtravel: survey data including origin, destination, and itinerary details from a 10% sample of airline tickets from the Bureau of Transportation Statistics
  • fb_socialconnectedness: an anonymized snapshot of all active Facebook users and their friendship networks as a measure of social connectedness between two different places

Data Limitations:

No limitations to report.

Comments/Notes:

n/a

Meta Data Name: Safegraph Social Distancing Data

Last Modified: 3/03/2021

Author: Andres Crucetta Nieto

Data Location:

Safegraph Social Distancing data can be accessed via their COVID-19 data consortium signup.

Data Source(s) Description:

Source is here via Safegraph.

Description of Data Source Tables:

The data was generated using a panel of GPS pings from anonymous mobile devices. We determine the common nighttime location of each mobile device over a 6 week period to a Geohash-7 granularity (~153m x ~153m). For ease of reference, we call this common nighttime location, the device's "home". We then aggregate the devices by home census block group and provide the metrics set out below for each census block group. [1]

Description of Data Processing:

The data for Social Distancing was distilled from its raw status into daily and weekday format. We also created a percent change from 2019 dataset to compare trends in the workplace.

Key Variable and Definitions:

Percentage of Delivery Workers (Daily)

Percentage of Full-Time Workers (Daily)

Percentage of Full-Time Workers (Weekday)

Percentage of Home Dwellers (Daily)

Percentage of Part-Time Workers (Daily)

Percentage of Part-Time Workers (Weekday)

Change from 2019 - Full-Time

Change from 2019 - Home Dwellers

Change from 2019 - Part-Time

Percent of People at Home

Percent of People Full-Time

Percent of People Part-Time

Data Limitations:

To preserve privacy, we apply differential privacy to all of the device count metrics other than the device_count. This may cause the exact sum of devices to not equal device_count, especially for sparsely populated origin_census_block_group. Differential privacy is applied to all of the following columns: completely_home_device_count, part_time_work_behavior_devices, full_time_work_behavior_devices, delivery_behavior_devices, at_home_by_each_hour, bucketed_away_from_home_time, bucketed_distance_traveled, bucketed_home_dwell_time, bucketed_percentage_time_home. [1]

If as a result of the differential privacy applied:

device_count < part_time_work_behavior_devices + full_time_work_behavior_devices +completely_home_device_count or
device_count < sum(counts in bucketed_distance_traveled) or
device_count < sum(counts in bucketed_home_dwell_count),

we then increase the device_count to the applicable sum (this only occurs in census_block_groups with small device_counts).

Comments/Notes:

References:

[1] https://docs.safegraph.com/docs/social-distancing-metrics

Meta Data Name: American Community Survey

Last Modified: 2/28/21

Author: Kenna Camper

Data Location:

GeoDaCenter/covid/data

• County Population county_pop.csv
• Essential Workers context_essential_workers_acs.csv

Data Source(s) Description:

Source is here via the United States Census Bureau.

Description of Data Processing:

Population counts are taken directly from 2019 ACS 5-year estimates and joined to geospatial data. Essential worker estimates are generated from 2019 ACS 5-year estimates of workers by "essential" occupations over total workers in each county. We currently use the occupation categories from the Chicago Metropolitan Agency for Planning.

Key Variable and Definitions:

context_essential_workers_acs

county_pop.csv

Description of Data Source Tables:

See the American Community Survey Data for additional information.

Data Limitations:

No limitations to report.

Comments/Notes:

n/a

Meta Data Name: Hospital and Clinic Locations

Last Modified: 3/3/2021

Author: Dylan Halpern

Data Location:

GeoDaCenter/covid/public/csv

• Federally Qualified Health Clinics (FQHCs) context_fqhc_clinics_hrsa.csv
• National Hospital Locations context_hospitals_covidcaremap.csv

Data Source(s) Description:

Federally qualitifed health clinic locations and testing status are sourced from HRSA's online location finder. Hospital location and information are sourced from the CovidCareMap project.

Description of Data Source Tables:

n/a

Description of Data Processing:

Data from HRSA are taken directly and filtered for the columns listed below. CovidCareMap hospital data is included completely.

Key Variable and Definitions:

context_fqhc_clinics_hrsa.csv

context_hospitals_covidcaremap.cdc

Additional fields describe hospital bed capacity and occupancy.

Data Limitations:

n/a

Comments/Notes:

n/a

Meta Data Name: Geographies

Last Modified: 3/3/2021

Author: Dylan Halpern

Data Location:

All geospatial data used in the Atlas are available under GeoDaCenter/covid/public/geojson.

• County Geographies
  • 1 Point 3 Acres county_1p3a.geojson
  • New York Times county_nyt.geojson
  • USA Facts county_usfacts.geojson
  • Centers for Disease Control cdc.geojson
• State Geographies
  • 1 Point 3 Acres state_1p3a.geojson
  • New York Times state_nyt.geojson
  • USA Facts state_usafacts.geojson
• Congressional District Geographies (2018)
  • District Centroids (Labels) district_centroids.geojson
  • District Boundaries districts.geojson
• Overlay Highlights
  • Important note: These highlight layers are created by subtracting the original data sources (see below) from a single dissolved US geogaphy.
  • Black Belt Counties blackbelt_highlight.geojson
  • Native American or American Indian Reservations reservations.geojson
  • Hypersegregated City Counties segregated_cities.geojson

Data Source(s) Description:

County and state boundaries are sourced from the US Census Cartographic Boundary Files at the 20m resolution.

Native American or American Indian reservation boundaries come from the TIGER/line 2017 dataset.

Congressional district boundaires come from the 2018 National Congressional District Boundaries.

Hypersegregated cities are based on work by Massey and Tannen, 2015 (press release, article)

Description of Data Source Tables:

Source geospatial data provide boundaries and a geospatial identifier (GEOID or FIPS code).

Description of Data Processing:

State and county boundaries are joined with basic information for normalization (population and beds). Highlight layers are generated using a symmetrical difference operation against a dissolved US geography.

Key Variable and Definitions:

Data Limitations:

Comments/Notes:

n/a