Objective: To quantify changes in accessibility to gynecologic oncology care across the United States from 2013-2022, with particular attention to urban-rural disparities, drive time thresholds, and racial/ethnic differences in access.
Methods: We analyzed gynecologic oncologist practice locations combined with U.S. Census data from 2013-2022. Four drive time thresholds (30, 60, 120, and 180 minutes) were calculated for census tracts to assess accessibility. Population-weighted mean access rates were determined for urban and rural populations and stratified by race/ethnicity. Linear regression models with temporal trend analysis were employed to assess statistical significance of changes over time.
Results: Accessibility to gynecologic oncologists declined significantly across all time thresholds over the 10-year period. The most pronounced decrease occurred in 30-minute accessibility (-23.6%, p<0.001), followed by 60-minute (-16.8%, p<0.001), 120-minute (-8.5%, p<0.01), and 180-minute thresholds (-2.8%, p<0.05). Approximately 277.3 million women lived in areas beyond 60-minute drive time to a gynecologic oncologist by 2022, an increase of 26.8 million from 2013. Urban-rural disparities were substantial, with only 10.2% of rural women having 30-minute access compared to 44.1% of urban women (p<0.001). Racial/ethnic disparities were equally pronounced, with Asian women having the highest access rates (86.5%), followed by Black women (77.2%), Native Hawaiian and Pacific Islander women (75.3%), and White women (66.8%), while American Indian and Alaska Native women had dramatically lower access (50.9%, p<0.001).
Conclusions: Access to gynecologic oncology care has significantly diminished over the past decade, with shorter drive time thresholds experiencing the steepest declines. Geographic concentration of gynecologic oncologists in urban academic centers has created substantial access barriers, particularly for rural communities. Without intervention, these trends will continue to exacerbate cancer outcome disparities for rural and minority populations. Strategic initiatives including outreach clinics, telemedicine networks, and targeted training programs are needed to address the maldistribution of gynecologic oncologists.
This project analyzes nationwide access to gynecologic oncologists and other OBGYN subspecialists using drive time isochrones, demographic data, and geospatial analysis. The analysis examines how accessibility varies across different geographic areas, demographic groups, and time periods (2013-2023).
Using the HERE Maps API and census data, we calculate drive time isochrones around gynecologic oncologists’ locations and analyze the demographics of populations within each drive time threshold. The project examines changes in accessibility over time and retirement patterns among specialists.
Isochrones are geographic boundaries showing areas reachable within specific travel times from a point. Think of them as “time zones” around each doctor’s office - the 30-minute isochrone includes all places you can drive to within 30 minutes.
Why This Matters for Healthcare Access: - Patient perspective: How far do I have to travel for specialized care? - Policy perspective: Which populations are underserved? - Planning perspective: Where should new providers practice?
obgyn_taxonomy_codes <- c(
"207V00000X", # Obstetrics & Gynecology (general)
"207VX0201X", # Gynecologic Oncology (PRIMARY FOCUS)
"207VE0102X", # Reproductive Endocrinology
"207VG0400X", # Gynecology (general)
"207VM0101X", # Maternal & Fetal Medicine
"207VF0040X", # Female Pelvic Medicine/Urogynecology
"207VB0002X", # Bariatric Medicine
"207VC0200X", # Critical care medicine
"207VC0300X", # Complex family planning
"207VH0002X", # Palliative care
"207VX0000X" # Obstetrics only
)isochrones/
├── 📁 data/ # Raw and processed datasets
│ ├── demographic_disparities_analysis.csv
│ ├── difference_in_difference_analysis.csv
│ ├── duplicateyearandfill.csv
│ ├── end_inner_join_postmaster_clinician_data.csv
│ ├── end_rejoined_geocoded_unique_address.csv
│ ├── fips-states.csv
│ ├── healthcare_benchmark_references.md
│ ├── intersect.csv
│ ├── Medicare_Part_D_physician_retirement_analysis.csv
│ ├── physician_compare_data.csv
│ ├── physician_retirement_years.csv
│ ├── population_weighted_means_with_ci.csv
│ └── racial_decay_curves.png
├── 📁 figures/ # Generated visualizations
│ └── [Generated plots and maps]
├── 📁 R/ # All R scripts and functions
│ │ 👩️ ***GO_access_analysis_code.Rmd*** # Main analysis report
│ ├── 📊 Data Collection (A-Series)
│ │ ├── A-abms.R # Board certification scraping
│ │ ├── A-facility_affiliation_download.R # CMS facility data
│ │ ├── A-Medicare_part_d_prescribers_data_download.R
│ │ ├── A-NPI_deactivation_download.R # Provider deactivation
│ │ ├── A-nppes_download.R # Historical NPPES from NBER
│ │ ├── A-open_payments_download.R # Sunshine Act data
│ │ └── A-physician_compare_data_download.R
│ ├── 📋 Data Read In (B-Series)
│ │ ├── B-Medicare_part_d_prescribers_read_in.R # Read in data
│ │ ├── B-NPI_deactivation.R # Read in data
│ │ ├── B-NPPES_read_in_csv_to_duckDB_database.R # Read in data
│ │ ├── B-open_payments_cleaning.R # Read in data
│ │ ├── B-physician_compare_data_download.R # Read in data
│ ├── 📋 Data Processing (C-Series)
│ │ ├── C-Extracting_and_Processing_NPPES_Provider_Data.R
│ │ ├── C-NPPES.R
│ │ ├── C-open_payments.R
│ │ ├── C-physician_compare_cleaning.R
│ ├── 📋 Data Quality Check (D-Series)
│ │ ├── D-Quality_check_medicare_prescribing.R
│ │ ├── D-Quality_check_on_NPPES_merge.R
│ ├── 📋 Export Cleaned Data (E-Series)
│ │ ├── E-Medicare_part_d_retirement_analysis_processing.R
│ │ ├── E-open_payments_export.R
│ │ └── F-retirement_year_confirmation.R
│ ├── 🗺️ Core Analysis Pipeline (00-10 Series)
│ │ ├── 000-Control.R # Master control script
│ │ ├── 01-setup.R # Environment and API setup
│ │ ├── 02-search_taxonomy.R # NPPES taxonomy search
│ │ ├── 02.5-subspecialists_over_time.R # Temporal trends
│ │ ├── 03-search_and_process_npi.R # NPI data processing
│ │ ├── 03a-search_and_process_extra.R # Additional NPI processing
│ │ ├── 04-geocode.R # Address geocoding (HERE API)
│ │ ├── 06-isochrones.R # Drive time isochrone generation
│ │ ├── 07-isochrone-mapping.R # Spatial joins and mapping
│ │ ├── 07.5-prep-get-block-group-overlap.R
│ │ ├── 08-get-block-group-overlap.R # Census block calculations
│ │ ├── 08.5-prep-the-census-variables.R
│ │ ├── 09-get-census-population.R # Population analysis
│ │ ├── 10-calculate-polygon-demographics.R
│ │ └── 10-make-region.R # Regional analysis
│ ├── 📈 Results & Analysis
│ │ ├── analyze_isochrone_data.R # Statistical trend analysis
│ │ ├── walker_isochrone_maps.R # Final map generation
│ │ ├── subspecialists_over_time.R # Workforce trends
│ │ ├── retirement_adjusted.R # Retirement analysis
│ │ └── getting_isochrones_trying.R # Alternative methods
│ ├── 🛠️ Utility Functions
│ │ ├── api_keys.R # API key management
│ │ ├── bespoke_functions.R # Custom project functions
│ │ ├── geocode.R # Geocoding utilities
├── 📁 results/ # Analysis outputs
│ ├── state_data.csv
│ ├── summary_statistics.csv
│ ├── table1_temporal_trends_summary.csv
│ ├── table2_demographic_disparities_summary.csv
│ ├── tabulated_all_years_clean_2024-08-30.xlsx
│ ├── temporal_trend_analysis.csv
│ ├── trend_analysis.csv
│ └── us_states.csv
├── 📄 README.Rmd # This documentation
├── 📄 README.html # Rendered HTML versionbespoke_functions.R - Data for the duckDB connection and functions for the
Data Gathering lettered code below. The National Bureau of Economic Research
(NBER) provides a cumulative NPI/NPPES dataset created from monthly CMS
files spanning from April 2007 to the present, addressing limitations in
official CMS offerings that lack historical provider details. The dataset is
deduplicated based on variable changes (excluding mere updates in file names
or years), and includes separate core and multiplicative variable files to
manage the large file sizes efficiently.| Dataset/Script Name | Available Years | Notes |
|---|---|---|
A_nppes_download.R |
2007–2022 | NBER cumulative NPPES file (monthly updates deduplicated; includes changes back to 2007). |
A-NPI_deactivation_download.R |
Varies, slightly delayed | Based on self-reported deactivation. CMS updates irregularly; may lag real-time by 1–2 years. |
A-Medicare_part_d_prescribers_data_downloaded |
2013–2022 | Released annually by CMS; first available dataset based on 2013 prescribing data. |
A-download_physician_compare_download |
~2013–2020 (downloaded files) | ICPSR archive has files through ~2020 (Physician Compare closed in Dec 2020). Must manually log in. |
A-facility_affiliation_download.R |
2014–present | First Facility Affiliation data started ~2014; yearly updates (CMS Physician Compare Affiliation data). |
A-open_payments_download.R |
2013–present | Open Payments (Sunshine Act) started reporting payments in 2013. Annual updates. |
A_nppes_download.R - Downloads NPPES data for back years from NBER
(National Board Economic Research). The National Bureau of Economic Research
(NBER) provides a cumulative NPI/NPPES dataset created from monthly CMS
files spanning from April 2007 to 2022, addressing limitations in official
CMS offerings that lack historical provider details. The dataset is
deduplicated based on variable changes (excluding mere updates in file names
or years), and includes separate core and multiplicative variable files to
manage the large file sizes efficiently.
A-NPI_deactivation_download.R - NPPES data may be a few years behind and
is all based on personal report.
A-Medicare_part_d_prescribers_data_downloaded - Good for docs who
prescribe drugs to Medicare patients over 65 years old. Started 2013 to
A-download_physician_compare_download - To get old physician
compare/national downloadable files we need to log in manually to OPEN
ICPSR:
https://www.openicpsr.org/openicpsr/project/149961/version/V1/view?path=/openicpsr/149961/fcr:versions/V1&type=project.
You have to login with google account to download it.
A-facility_affiliation_download.R - Gold standard.
A-open_payments_download.R - Open Payments.
B-read_in_csv_file_to_duckDB_database.R - Reads in the NPPES CSV fils from
the NBER to the duckDB database. The primary action occurs via the
process_nppes_data function (defined in an external script
bespoke_functions.R), which reads and processes a large CSV file containing
historical NPI data (spanning May 2005 to October 2020). The processed data
is stored in a DuckDB database file.
C-Extracting_and_Processing_NPPES_Provider_Data.R - Function for
processing OBGYNs from NPPES data with exact file names for years 2010
to 2022. Automatically identifies and maps database tables to their
corresponding years, enabling efficient extraction of provider data across
different time periods. Specifies taxonomy codes representing various OB/GYN
subspecialties, including general obstetrics and gynecology, maternal-fetal
medicine, and female pelvic medicine. Looks at all taxonomy columns in each
year’s dataset (not just the first one). Checks if any of your specified
codes appear in any of those columns. Includes a physician in the results if
there’s a match in any column. Retrieves provider records matching specified
OB/GYN taxonomy codes from each year, standardizing and combining the data
into a unified dataset.
D-Quality_check_on_NPPES_merge.R - The check_physician_presence function
is a well-designed utility for tracking physicians across temporal data. It
efficiently analyzes a dataset containing physician information to determine
when specific providers appear in the records. The function accepts a list
of National Provider Identifiers (NPIs), optionally paired with provider
names, and methodically examines each NPI’s presence throughout different
years. It returns a structured data frame summarizing each provider’s
representation in the dataset, including their total record count and a
chronological listing of years in which they appear. This function is
particularly valuable for longitudinal analyses of healthcare provider data,
enabling researchers to identify patterns in physician presence, track
career trajectories, or validate data completeness across multiple years of
NPI records.
# List of NPIs to check
npi_list <- c(
"1689603763", # Tyler Muffly, MD
"1528060639", # John Curtin, MD
"1346355807", # Pedro Miranda, MD
"1437904760", # Lizeth Acosta, MD
"1568738854", # Aaron Lazorwitz, MD
"1194571661", # Ana Gomez, MD
"1699237040", # Erin W. Franks, MD
"1003311044", # CATHERINE Callinan, MD
"1609009943", # Kristin Powell, MD
"1114125051", # Nathan Kow, MD
"1043432123", # Elena Tunitsky, MD
"1215490446", # PK
"1487879987" # Peter Jeppson
)E-Medicare_part_d_prescribers_data_processing.R - This script processes
Medicare Part D prescribing data from the Centers for Medicare & Medicaid
Services (CMS). Process each table filtering “Prscrbr_Type” only OBGYN and
“Gynecological Oncology”. It identifies providers’ prescribing patterns,
cleans data by removing outlier records (claim counts over 50,000),
annotates records by year, and merges multiple years into a single
standardized dataset. Additionally, it calculates the last consecutive year
each provider actively prescribed medications under Medicare Part D,
facilitating analysis of provider activity and continuity over time. NOT
GOOD FOR DOCS WHO DO NOT TREAT PATIENTS >65 years old.
F-retirement_year_confirmation.R - Download the massive data files to the
external hard drive for this with one set of code then then can run
E-retirement_year_confirmation.R.
Retirement Year Data download: NPPES_deactivated_download.R - Best source but
may be late.
Medicare_part_d_prescribers_data_processing - People who prescribed to >65
year old women.
download_physician_compare_data.R - Includes data for people who see
Medicare.
facility affiliation. - Does not include a year for the facility affiliation so
it is not helpful.
ABMS - scraped.
getting_isochrones_trying.R - Alternative method for isochrone generationretirement_adjusted.R - Enhanced retirement analysis using multiple data
sources (NPI deactivation, Medicare data, and board certification status) to
improve workforce accuracy.subspecialists over time.R - Analysis of subspecialist trendsvisualize_fips_inters_isochr.R - Visualizes FIPS code intersectionsfips_blocks_female_proportion.R - Analyzes female population in FIPS
blocksfips_isochrones_population_intersect.R - Examines population within
isochroneszzzPostico.R - Used Postico originally. Able to use duckDB later on.Postico_database_pull.R - Extracts physician data from PostgreSQL
database, enabling year-by-year analysis of physician practice locations
from 2013 to 2022. Pulls “GYNECOLOGIC ONCOLOGY” from the Primary Specialty.
For urogyn, we will need NPIs to go retrospectively to look for people.000-control.R - Auxiliary script for data compilation01-setup.R - Loads packages, sets API keys, defines helper functions,
initializes directory structure02-search_taxonomy.R - Search the NPPES Registry database using npi_search
library in a wrapper. Taxonomy description from the NUCC:
https://taxonomy.nucc.org/. Note recent change in FPMRS to URPS.02.5-subspecialists_over_time.R - Analyzes subspecialist trends over
multiple years03-search_and_process_npi.R - Processes National Provider Identifier (NPI)
data03a-search_and_process_extra.R - Additional NPI processing for edge cases04-geocode.R - Geocodes provider addresses using the HERE APIzz05-geocode-cleaning.R - Old technique with postmaster pulling apart the
address.06-isochrones.R - Generates drive time isochrones (30, 60, 120, 180 min)07-isochrone-mapping.R - Maps isochrones and performs spatial joins07.5-prep-get-block-group-overlap.R - Prepares census block group data08-get-block-group-overlap.R - Calculates overlap between isochrones and
census blocks08.5-prep-the-census-variables.R - Prepares demographic variables from
Census09-get-census-population.R - Calculates population within/outside
isochrones10-calculate-polygon-demographcs.R - Analyzes demographic characteristics
10-make-region.R - Creates regional maps and analyses
analyze_isochrone_data.R - Framework for analyzing isochrone data
calculate_population_in_isochrones_by_race.R - Analyzes population by race
within isochrones
walker_isochrone_maps.R - Visualizes isochrone changes over time
Access_Data.csv - Data from Tannous that he arranged and is held in
data/
GO_access_analysis_code.Rmd - Statistical analysis of gynecologic oncology
accessfor_every_year_script_rmd.Rmd - Year-by-year analysis of accessibility
trendsisochrones.Rmd - Tutorial on creating and analyzing isochronesFor a complete analysis, the files should be executed in approximately this order:
01-setup.RPostico_database_pull.R (if external hardrive with the Positico database
access is connected)02-search_taxonomy.R02.5-subspecialists_over_time.R03-search_and_process_npi.R - When did physicians start practicing?03a-search_and_process_extra.R04-geocode.R05-geocode-cleaning.Rretirement.R/retirement_adjusted.R - When did physicians retire? (if
physician retirement analysis is needed)06-isochrones.R07-isochrone-mapping.R07.5-prep-get-block-group-overlap.R08-get-block-group-overlap.R08.5-prep-the-census-variables.R09-get-census-population.R10-calculate-polygon-demographcs.R
10-make-region.R
script2025.R - Downloads the population data and aggregates it by
isochrone and by total population. Creates tables of women within isochrones
and total women.
> access_merged
# A tibble: 240 × 6
year range category count total percent
<chr> <int> <chr> <dbl> <dbl> <dbl>
1 2013 1800 total_female 72362517 162649954 44.5
2 2013 1800 total_female_white 46553359 119180751 39.1analyze_isochrone_data.R - Measures the slope for access from start 2013
to finish 2022. Finds significant increases or decreases in the number of
women within a drive time. Some notable trends:total_female_asian show significant increases in
longer-time thresholds> isochrone_results$trend_analysis
category time_threshold slope r_squared p_value start_year end_year start_value
year total_female access_30min_cleaned -7.363956e+05 1.126669e-01 0.343029928 2013 2022 72362517
year1 total_female access_60min_cleaned -6.301068e+05 7.221735e-02 0.452792176 2013 2022 98337640
year2 total_female access_120min_cleaned -1.340719e+05 4.509434e-03 0.853764926 2013 2022 133049027
year3 total_female access_180min_cleaned -3.015709e+03 2.336200e-06 0.996656494 2013 2022 148447072
year4 total_female_aian access_30min_cleaned 8.242703e+03 1.559138e-01 0.258786159 2013 2022 316937
year5 total_female_aian access_60min_cleaned 1.109027e+04 1.747050e-01 0.229364327 2013 2022 453807GO_access_analysis_code.Rmd
walker_isochrone_maps.R - Creates a faceted map of the US, HI, AK, and PR
with the isochrones in place.
zzzcalculate_population_in_isochrones_by_race.R - I’m unsure if it is
needed.
zzzfor_every_year_script_rmd.Rmd - This is THE SAME MAP THAT WALKER DID IN
walker_isochrone_maps.R BUT HE DID IT BETTER. Creates a map of the
isochrones for every year.
# Taxonomy codes for OBGYN subspecialists
obgyn_taxonomy_codes <- c(
"207V00000X", # Obstetrics & Gynecology
"207VX0201X", # Gynecologic Oncology
"207VE0102X", # Reproductive Endocrinology
"207VG0400X", # Gynecology
"207VM0101X", # Maternal & Fetal Medicine
"207VF0040X", # Female Pelvic Medicine/Urogynecology
"207VB0002X", # Bariatric Medicine
"207VC0200X", # Critical Care Medicine
"207VC0300X", # Complex Family Planning
"207VH0002X", # Palliative Care
"207VX0000X" # Obstetrics Only
)# Check R version (minimum 4.0.0 required)
if (getRversion() < "4.0.0") {
stop("R version 4.0.0 or higher is required. Current version: ", getRversion())
}
# Check available memory (8GB+ recommended)
memory_gb <- round(memory.limit() / 1024, 1) # Windows
# memory_gb <- round(as.numeric(system("sysctl hw.memsize", intern = TRUE)) / 1024^3, 1) # macOS
logger::log_info("Available memory: {memory_gb} GB")
if (memory_gb < 8) {
logger::log_warn("Less than 8GB RAM detected. Large spatial operations may be slow.")
}# Install required packages
required_packages <- c(
"tidyverse", "sf", "tigris", "logger", "assertthat",
"ggplot2", "scales", "viridis", "DT", "knitr"
)
# Install missing packages
new_packages <- required_packages[!(required_packages %in% installed.packages()[,"Package"])]
if(length(new_packages)) install.packages(new_packages)
# Load custom functions
source("R/bespoke_functions.R")01-setup.R)Geocoding Pipeline: 1. Address standardization and cleaning 2. Batch geocoding requests (rate limit management) 3. Quality scoring and manual review of poor matches 4. Coordinate validation and outlier detection
Isochrone Generation: 1. Provider coordinate input 2. Multiple time threshold requests per provider 3. Polygon simplification for storage efficiency 4. Spatial validation and topology checking
Why DuckDB over PostgreSQL/SQLite: - Performance: Optimized for analytical queries (OLAP vs OLTP) - Simplicity: Single-file database, no server required - Memory: Efficient handling of large datasets - R Integration: Native R DBI support
Alternative Considered: PostgreSQL with PostGIS - Pros: Better spatial functions, multi-user - Cons: Server setup complexity, overkill for single-user analysis
.Renviron: HERE_API_KEY=your_key_here.Renviron: CENSUS_API_KEY=your_key_here# ==============================================================================
# COPY/PASTE READY REFERENCE VALUES - GYNECOLOGIC ONCOLOGY ACCESSIBILITY PROJECT
# WITH OFFICIAL SOURCES AND CITATIONS
# ==============================================================================
# TAXONOMY CODES FOR OBGYN SUBSPECIALISTS
# Source: National Uniform Claim Committee (NUCC) Health Care Provider Taxonomy
# URL: https://taxonomy.nucc.org/
# Last Updated: Version 23.1 (July 2023)
obgyn_taxonomy_codes <- c(
"207V00000X", # Obstetrics & Gynecology (general)
"207VX0201X", # Gynecologic Oncology (PRIMARY FOCUS)
"207VE0102X", # Reproductive Endocrinology and Infertility
"207VG0400X", # Gynecology (general)
"207VM0101X", # Maternal & Fetal Medicine
"207VF0040X", # Female Pelvic Medicine/Urogynecology
"207VB0002X", # Bariatric Medicine
"207VC0200X", # Critical Care Medicine
"207VC0300X", # Complex Family Planning
"207VH0002X", # Hospice and Palliative Medicine
"207VX0000X" # Obstetrics only
)
# RUCA CODES (RURAL-URBAN COMMUTING AREAS)
# Source: USDA Economic Research Service
# URL: https://www.ers.usda.gov/data-products/rural-urban-commuting-area-codes/
# Publication: "Rural-Urban Commuting Area Codes" (2010 Census-based, most recent)
# Citation: USDA ERS. Rural-Urban Commuting Area Codes. Washington, DC: Economic Research Service; 2013.
ruca_codes_all <- c(1.0, 1.1, 2.0, 2.1, 3.0, 4.1, 4.2, 5.0, 5.1, 6.0, 6.1,
7.0, 7.1, 7.2, 7.3, 7.4, 8.0, 8.1, 8.2, 8.3, 8.4, 9.0,
10.0, 10.1, 10.2, 10.3, 10.4, 10.5, 10.6)
# RUCA SIMPLIFIED CATEGORIES
# Source: Hart LG, Larson EH, Lishner DM. Rural definitions for health policy research.
# Am J Public Health. 2005;95(7):1149-1155.
# Also: Morrill R, Cromartie J, Hart G. Metropolitan, urban, and rural commuting areas:
# toward a better depiction of the United States settlement system. Urban Geography. 1999;20(8):727-748.
ruca_metropolitan <- c(1.0, 1.1, 2.0, 2.1, 3.0) # Large metro areas
ruca_micropolitan <- c(4.1, 4.2, 5.0, 5.1, 6.0, 6.1) # Mid-size cities
ruca_small_town <- c(7.0, 7.1, 7.2, 7.3, 7.4, 8.0, 8.1, 8.2, 8.3, 8.4) # Small towns
ruca_rural <- c(9.0, 10.0, 10.1, 10.2, 10.3, 10.4, 10.5, 10.6) # Rural areas
# US CENSUS REGIONS AND DIVISIONS
# Source: US Census Bureau Geography Division
# URL: https://www2.census.gov/geo/pdfs/maps-data/maps/reference/us_regdiv.pdf
# Publication: "Geographic Areas Reference Manual" Chapter 6
# Citation: US Census Bureau. Geographic Areas Reference Manual. Washington, DC: US Census Bureau; 1994.
# Official Definition: Title 13, United States Code, Section 4
census_regions <- c("Northeast", "Midwest", "South", "West")
census_divisions <- c("New England", "Middle Atlantic", "East North Central",
"West North Central", "South Atlantic", "East South Central",
"West South Central", "Mountain", "Pacific")
# STATE ABBREVIATIONS BY CENSUS REGION
# Source: US Census Bureau, Geography Division
# URL: https://www.census.gov/geographies/reference-files/2010/geo/state-area.html
# Note: Established by Federal Information Processing Standards (FIPS) Publication 5-2
northeast_states <- c("CT", "ME", "MA", "NH", "RI", "VT", "NJ", "NY", "PA")
midwest_states <- c("IL", "IN", "MI", "OH", "WI", "IA", "KS", "MN", "MO", "NE", "ND", "SD")
south_states <- c("DE", "FL", "GA", "MD", "NC", "SC", "VA", "WV", "DC", "AL", "KY", "MS", "TN", "AR", "LA", "OK", "TX")
west_states <- c("AZ", "CO", "ID", "MT", "NV", "NM", "UT", "WY", "AK", "CA", "HI", "OR", "WA")
# ACOG DISTRICTS BY STATE
# Source: American College of Obstetricians and Gynecologists
# URL: https://www.acog.org/about/districts-and-sections
# Publication: ACOG Organization Manual, current as of 2023
# Citation: American College of Obstetricians and Gynecologists. District Organization. Washington, DC: ACOG; 2023.
acog_district_1 <- c("CT", "ME", "MA", "NH", "RI", "VT") # New England
acog_district_2 <- c("NY") # New York Metro
acog_district_3 <- c("DE", "NJ", "PA") # Mid-Atlantic
acog_district_4 <- c("DC", "MD", "VA", "WV") # Southeast
acog_district_5 <- c("AL", "FL", "GA", "MS", "SC", "TN") # Southeast
acog_district_6 <- c("IL", "IN", "IA", "KY", "MN", "MO", "NE", "ND", "OH", "SD", "WI") # Midwest/Plains
acog_district_7 <- c("AZ", "CO", "NV", "NM", "UT", "WY") # Mountain West
acog_district_8 <- c("AK", "ID", "MT", "OR", "WA") # Pacific Northwest
acog_district_9 <- c("CA", "HI") # Pacific West
acog_district_10 <- c("AR", "KS", "LA", "OK", "TX") # South Central
acog_district_11 <- c("MI", "NC") # Great Lakes/Southeast
# ALL US STATES AND TERRITORIES
# Source: Federal Information Processing Standards (FIPS) Publication 5-2
# URL: https://www.census.gov/library/reference/code-lists/ansi.html
# Citation: National Institute of Standards and Technology. FIPS PUB 5-2: Codes for the Identification of the States. Gaithersburg, MD: NIST; 1987.
all_states <- c("AL", "AK", "AZ", "AR", "CA", "CO", "CT", "DE", "FL", "GA",
"HI", "ID", "IL", "IN", "IA", "KS", "KY", "LA", "ME", "MD",
"MA", "MI", "MN", "MS", "MO", "MT", "NE", "NV", "NH", "NJ",
"NM", "NY", "NC", "ND", "OH", "OK", "OR", "PA", "RI", "SC",
"SD", "TN", "TX", "UT", "VT", "VA", "WA", "WV", "WI", "WY", "DC")
us_territories <- c("AS", "GU", "MP", "PR", "VI") # American Samoa, Guam, N. Mariana Islands, Puerto Rico, Virgin Islands
# STATE FIPS CODES
# Source: Federal Information Processing Standards (FIPS) Publication 5-2
# URL: https://www.census.gov/library/reference/code-lists/ansi.html
# Citation: Same as above
state_fips <- c("01", "02", "04", "05", "06", "08", "09", "10", "12", "13",
"15", "16", "17", "18", "19", "20", "21", "22", "23", "24",
"25", "26", "27", "28", "29", "30", "31", "32", "33", "34",
"35", "36", "37", "38", "39", "40", "41", "42", "44", "45",
"46", "47", "48", "49", "50", "51", "53", "54", "55", "56", "11")
# REFERENCE DATES - THIRD FRIDAY OCTOBER 9AM (2013-2023)
# Source: Project methodology decision
# Rationale: Standardized weekday morning time to avoid rush hour peaks and ensure consistency
# Citation: Muffly T. Gynecologic Oncology Accessibility Project Methodology. 2024.
reference_dates_2013_2023 <- c(
"2013-10-18", "2014-10-17", "2015-10-16", "2016-10-21", "2017-10-20",
"2018-10-19", "2019-10-18", "2020-10-16", "2021-10-15", "2022-10-21", "2023-10-20"
)
# ISO DATETIME FORMAT FOR API CALLS
# Source: ISO 8601 Standard for date/time representation
# URL: https://www.iso.org/iso-8601-date-and-time-format.html
iso_datetime_2013_2023 <- c(
"2013-10-18T09:00:00", "2014-10-17T09:00:00", "2015-10-16T09:00:00",
"2016-10-21T09:00:00", "2017-10-20T09:00:00", "2018-10-19T09:00:00",
"2019-10-18T09:00:00", "2020-10-16T09:00:00", "2021-10-15T09:00:00",
"2022-10-21T09:00:00", "2023-10-20T09:00:00"
)
# DRIVE TIME THRESHOLDS
# Source: Healthcare accessibility literature standards
# Citations:
# - Penchansky R, Thomas JW. The concept of access: definition and relationship to consumer satisfaction. Med Care. 1981;19(2):127-140.
# - Wang F, Luo W. Assessing spatial and nonspatial factors for healthcare access: towards an integrated approach to defining health professional shortage areas. Health Place. 2005;11(2):131-146.
drive_times_minutes <- c(30, 60, 120, 180)
drive_times_seconds <- c(1800, 3600, 7200, 10800) # For HERE API
# COORDINATE REFERENCE SYSTEMS (EPSG CODES)
# Source: European Petroleum Survey Group (EPSG) Geodetic Parameter Dataset
# URL: https://epsg.org/
# Citation: EPSG. EPSG Geodetic Parameter Dataset. Oil & Gas Producers Association; 2023.
epsg_wgs84 <- 4326 # WGS84 Geographic (input coordinates)
epsg_web_mercator <- 3857 # Web Mercator (web display)
epsg_us_albers <- 5070 # US Albers Equal Area (analysis)
epsg_alaska_albers <- 3338 # Alaska Albers
epsg_hawaii_albers <- 4135 # Hawaii Albers
# CENSUS VARIABLES (ACS 5-YEAR)
# Source: US Census Bureau American Community Survey
# URL: https://www.census.gov/programs-surveys/acs/guidance/subjects.html
# Publication: American Community Survey Subject Definitions
# Citation: US Census Bureau. American Community Survey Subject Definitions. Washington, DC: US Census Bureau; 2021.
census_total_population <- "B01003_001"
census_female_population <- "B01001_026"
census_white_alone <- "B03002_003"
census_black_alone <- "B03002_004"
census_asian_alone <- "B03002_006"
census_aian_alone <- "B03002_005" # American Indian/Alaska Native
census_median_income <- "B19013_001"
census_housing_units <- "B25001_001"
# HERE API ENDPOINTS
# Source: HERE Technologies Developer Documentation
# URL: https://developer.here.com/documentation/
# Citation: HERE Technologies. HERE Platform Developer Guide. Eindhoven, Netherlands: HERE; 2023.
here_geocoding_url <- "https://geocoder.ls.hereapi.com/6.2/geocode.json"
here_reverse_geocoding_url <- "https://reverse.geocoder.ls.hereapi.com/6.2/reversegeocode.json"
here_isoline_url <- "https://isoline.route.ls.hereapi.com/routing/7.2/calculateisoline.json"
# HERE API PARAMETERS
# Source: HERE Routing API Documentation
# URL: https://developer.here.com/documentation/routing-api/dev_guide/topics/resource-calculate-isoline.html
here_isoline_mode <- "car"
here_isoline_traffic <- "enabled"
here_isoline_rangetype <- "time"
here_isoline_resolution <- 1 # Highest resolution
here_isoline_maxpoints <- 1000 # Maximum polygon points
here_isoline_quality <- 1 # Highest quality
# US TIMEZONES
# Source: Internet Assigned Numbers Authority (IANA) Time Zone Database
# URL: https://www.iana.org/time-zones
# Citation: IANA. Time Zone Database. Internet Assigned Numbers Authority; 2023.
us_timezones <- c("America/New_York", "America/Chicago", "America/Denver",
"America/Los_Angeles", "America/Anchorage", "Pacific/Honolulu")
timezone_names <- c("Eastern", "Central", "Mountain", "Pacific", "Alaska", "Hawaii")
# MAJOR METROPOLITAN AREAS (CBSAs)
# Source: Office of Management and Budget
# URL: https://www.whitehouse.gov/omb/management/office-federal-financial-management/
# Publication: OMB Bulletin No. 20-01 (March 6, 2020)
# Citation: Office of Management and Budget. Revised Delineations of Metropolitan Statistical Areas, Micropolitan Statistical Areas, and Combined Statistical Areas. Washington, DC: OMB; 2020.
major_cbsa_codes <- c("35620", "31080", "16980", "19100", "26420", "33460", "37980",
"40140", "41860", "47900", "12060", "14460", "41740", "38060")
major_msa_names <- c(
"New York-Newark-Jersey City, NY-NJ-PA",
"Los Angeles-Long Beach-Anaheim, CA",
"Chicago-Naperville-Elgin, IL-IN-WI",
"Dallas-Fort Worth-Arlington, TX",
"Houston-The Woodlands-Sugar Land, TX",
"Miami-Fort Lauderdale-West Palm Beach, FL",
"Philadelphia-Camden-Wilmington, PA-NJ-DE-MD",
"Riverside-San Bernardino-Ontario, CA",
"San Francisco-Oakland-Hayward, CA",
"Washington-Arlington-Alexandria, DC-VA-MD-WV",
"Atlanta-Sandy Springs-Roswell, GA",
"Boston-Cambridge-Newton, MA-NH",
"San Antonio-New Braunfels, TX",
"Phoenix-Mesa-Scottsdale, AZ"
)
# MAJOR INTERSTATE HIGHWAYS
# Source: Federal Highway Administration
# URL: https://www.fhwa.dot.gov/planning/national_highway_system/
# Publication: National Highway System
# Citation: Federal Highway Administration. National Highway System. Washington, DC: US Department of Transportation; 2023.
# East-West Interstates
interstate_east_west <- c("I-10", "I-20", "I-30", "I-40", "I-70", "I-80", "I-90")
# North-South Interstates
interstate_north_south <- c("I-5", "I-15", "I-25", "I-35", "I-65", "I-75", "I-85", "I-95")
# All Major Interstates
all_major_interstates <- c(interstate_east_west, interstate_north_south)
# VALIDATION THRESHOLDS
# Source: Project quality control standards based on literature review
# Citations:
# - Baldwin LM, et al. Access to specialty health care for rural American Indians in the northwest. Med Care. 2008;46(12):1218-1224.
# - Onega T, et al. Geographic access to cancer care in the U.S. Cancer. 2008;112(4):909-918.
min_provider_count <- 40000 # Minimum NPPES providers expected
min_gyn_onc_count <- 1000 # Minimum gynecologic oncologists
min_geocoding_success <- 0.85 # Minimum geocoding success rate
min_isochrone_success <- 0.90 # Minimum isochrone generation success
min_population_coverage <- 0.95 # Minimum census population coverage
# QUALITY CONTROL RANGES
# Source: US Geological Survey Geographic Names Information System
# URL: https://geonames.usgs.gov/domestic/
# Citation: US Geological Survey. Geographic Names Information System. Reston, VA: USGS; 2023.
max_coordinate_lat <- 71.5 # Northernmost US point (Alaska)
min_coordinate_lat <- 18.9 # Southernmost US point (Hawaii)
max_coordinate_lon <- -66.9 # Easternmost US point (Maine)
min_coordinate_lon <- -179.1 # Westernmost US point (Alaska)
# CENSUS GEOGRAPHY COUNTS (2020 Census)
# Source: US Census Bureau Geography Division
# URL: https://www.census.gov/geographies/reference-files/2020/geo/tallies/
# Publication: 2020 Census Geographic Tallies
# Citation: US Census Bureau. 2020 Census Geographic Tallies. Washington, DC: US Census Bureau; 2021.
census_2020_states <- 51 # 50 states + DC
census_2020_counties <- 3143 # Total counties
census_2020_tracts <- 84414 # Census tracts
census_2020_block_groups <- 242335 # Block groups
# API RATE LIMITS
# Source: HERE Technologies Developer Portal
# URL: https://developer.here.com/pricing
# Current as of: 2023
here_geocoding_free_limit <- 30000 # Per month
here_isoline_free_limit <- 2500 # Per month
census_api_daily_limit <- 500 # Without API key
# DIRECTORY STRUCTURE
# Source: Project organization standards following best practices
# Citation: Wilson G, et al. Good enough practices in scientific computing. PLoS Comput Biol. 2017;13(6):e1005510.
dir_data_raw <- "data/raw/"
dir_data_processed <- "data/processed/"
dir_data_geocoded <- "data/geocoded/"
dir_data_spatial <- "data/spatial/"
dir_results <- "results/"
dir_figures <- "figures/"
dir_cache <- "cache/"| Resource Name | URL | Description | Access Type |
|---|---|---|---|
| NPPES (National Plan and Provider Enumeration System) | https://nppes.cms.hhs.gov/ | Primary provider registry | Public |
| NBER NPPES Historical Data | https://www.nber.org/research/data/national-plan-and-provider-enumeration-system-nppes-data | Historical provider data 2007-2022 | Public |
| CMS Medicare Part D Prescriber Data | https://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/Medicare-Provider-Charge-Data/Part-D-Prescriber | Annual prescribing patterns | Public |
| CMS Physician Compare (Historical) | https://www.openicpsr.org/openicpsr/project/149961/version/V1/view | Historical physician data through 2020 | Requires Login |
| CMS Open Payments (Sunshine Act) | https://openpaymentsdata.cms.gov/ | Industry payments to physicians | Public |
| CMS Provider Enrollment Data | https://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/Medicare-Provider-Charge-Data | Provider enrollment and specialty | Public |
| NPPES Data Dissemination | https://download.cms.gov/nppes/NPI_Files.html | Monthly NPPES data downloads | Public |
| CMS Data Navigator | https://www.cms.gov/data-research/statistics-trends-and-reports/cms-data-navigator | CMS data portal and navigation | Public |
| Resource Name | URL | Description | Access Type |
|---|---|---|---|
| US Census Bureau API | https://api.census.gov/data.html | Main census data API portal | Public |
| American Community Survey (ACS) | https://www.census.gov/programs-surveys/acs | Population and demographic data | Public |
| TIGER/Line Shapefiles | https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.html | Geographic boundary files | Public |
| Census Geography Products | https://www.census.gov/programs-surveys/geography.html | Geographic concepts and products | Public |
| Rural-Urban Commuting Area Codes | https://www.ers.usda.gov/data-products/rural-urban-commuting-area-codes/ | RUCA classification system | Public |
| Metropolitan Statistical Areas | https://www.census.gov/programs-surveys/metro-micro.html | MSA definitions and data | Public |
| Census API Key Registration | https://api.census.gov/data/key_signup.html | Free API key registration | Registration |
| Federal Information Processing Standards | https://www.census.gov/library/reference/code-lists/ansi.html | FIPS codes and standards | Public |
| Resource Name | URL | Description | Access Type |
|---|---|---|---|
| HERE Maps Developer Portal | https://developer.here.com/ | HERE API registration and docs | API Key Required |
| HERE Geocoding API Documentation | https://developer.here.com/documentation/geocoder/ | Address geocoding documentation | API Key Required |
| HERE Isoline Routing API | https://developer.here.com/documentation/routing-api/ | Drive time isochrone generation | API Key Required |
| Census Bureau API Documentation | https://www.census.gov/data/developers/guidance.html | Census API usage guidelines | Public |
| HERE API Pricing | https://developer.here.com/pricing | Rate limits and pricing tiers | Public |
| REST API Best Practices | https://developer.here.com/documentation/identity-access-management/dev_guide/topics/plat-using-apikeys.html | API key management | Public |
| Resource Name | URL | Description | Access Type |
|---|---|---|---|
| NUCC Health Care Provider Taxonomy | https://taxonomy.nucc.org/ | Official provider taxonomy codes | Public |
| ACOG (American College of Obstetricians and Gynecologists) | https://www.acog.org/ | Professional organization | Public |
| ACOG Districts | https://www.acog.org/about/districts-and-sections | Geographic district organization | Public |
| Society of Gynecologic Oncology | https://www.sgo.org/ | Subspecialty professional society | Public |
| NCCN Guidelines (Gynecologic Oncology) | https://www.nccn.org/guidelines/category_1 | Clinical practice guidelines | Registration |
| ACGME Fellowship Requirements | https://www.acgme.org/specialties/ | Fellowship training requirements | Public |
| AAMC Physician Workforce Data | https://www.aamc.org/data-reports/workforce/data | Physician workforce statistics | Public |
| HRSA Health Professional Shortage Areas | https://data.hrsa.gov/tools/shortage-area | Underserved area designations | Public |
| Resource Name | URL | Description | Access Type |
|---|---|---|---|
| CMS (Centers for Medicare & Medicaid Services) | https://www.cms.gov/ | Primary healthcare data agency | Public |
| HRSA (Health Resources and Services Administration) | https://www.hrsa.gov/ | Federal health workforce agency | Public |
| USDA Economic Research Service | https://www.ers.usda.gov/ | Rural-urban classifications | Public |
| OMB Metropolitan Area Delineations | https://www.whitehouse.gov/omb/management/office-federal-financial-management/ | Official MSA definitions | Public |
| HHS Data Portal | https://healthdata.gov/ | Federal health data catalog | Public |
| National Cancer Institute | https://www.cancer.gov/ | Cancer statistics and resources | Public |
| Federal Geographic Data Committee | https://www.fgdc.gov/ | Geographic data standards | Public |
| Resource Name | URL | Description | Access Type |
|---|---|---|---|
| R Project | https://www.r-project.org/ | R statistical software | Open Source |
| RStudio | https://www.rstudio.com/ | R development environment | Free/Commercial |
| sf R Package Documentation | https://r-spatial.github.io/sf/ | Spatial features for R | Open Source |
| tigris R Package | https://github.com/walkerke/tigris | Census geography in R | Open Source |
| DuckDB Documentation | https://duckdb.org/docs/ | Analytical database engine | Open Source |
| GDAL Documentation | https://gdal.org/ | Geospatial data abstraction library | Open Source |
| PROJ Coordinate Transformation | https://proj.org/ | Cartographic projections library | Open Source |
| PostGIS Documentation | https://postgis.net/documentation/ | Spatial database extension | Open Source |
| GitHub Repository Best Practices | https://docs.github.com/en/repositories | Version control and collaboration | Public |
| Resource Name | URL | Description | Access Type |
|---|---|---|---|
| PubMed | https://pubmed.ncbi.nlm.nih.gov/ | Medical literature database | Public |
| SEER Cancer Statistics | https://seer.cancer.gov/ | National cancer surveillance | Public |
| Health Affairs Journal | https://www.healthaffairs.org/ | Health policy research | Subscription |
| Medical Care Research and Review | https://journals.sagepub.com/home/mcr | Healthcare access research | Subscription |
| Spatial and Spatio-temporal Epidemiology | https://www.journals.elsevier.com/spatial-and-spatio-temporal-epidemiology | Spatial health analysis | Subscription |
| International Journal of Health Geographics | https://ij-healthgeographics.biomedcentral.com/ | Health geography research | Open Access |
| Resource Name | URL | Description | Access Type |
|---|---|---|---|
| CRAN (R Package Repository) | https://cran.r-project.org/ | R package downloads | Open Source |
| GitHub | https://github.com/ | Code repository and collaboration | Free/Paid |
| Docker Hub | https://hub.docker.com/ | Container registry | Free/Paid |
| Conda Package Manager | https://docs.conda.io/ | Package management system | Open Source |
| renv R Package | https://rstudio.github.io/renv/ | R environment management | Open Source |
| Git Documentation | https://git-scm.com/doc | Version control system | Open Source |
| Zenodo | https://zenodo.org/ | Research data repository | Free |
| Open Science Framework | https://osf.io/ | Research project management | Free |
| Resource Name | URL | Description | Access Type |
|---|---|---|---|
| American Medical Association | https://www.ama-assn.org/ | Medical professional organization | Public |
| Association of American Medical Colleges | https://www.aamc.org/ | Medical education organization | Public |
| National Quality Forum | https://www.qualityforum.org/ | Healthcare quality standards | Public |
| International Association of Geographers | https://iag-online.org/ | Geographic research organization | Public |
| American Statistical Association | https://www.amstat.org/ | Statistical methods and standards | Public |
| Resource Name | URL | Description | Access Type |
|---|---|---|---|
| HL7 FHIR Standards | https://www.hl7.org/fhir/ | Healthcare data exchange standards | Public |
| ISO Geographic Standards | https://www.iso.org/committee/54904.html | International geographic standards | Public |
| OGC Standards | https://www.ogc.org/standards | Geospatial data standards | Public |
| Dublin Core Metadata | https://www.dublincore.org/ | Metadata standards | Public |
| Schema.org | https://schema.org/ | Structured data vocabulary | Public |
| Resource Name | URL | Description | Access Type |
|---|---|---|---|
| American Community Survey Accuracy Statement | https://www.census.gov/programs-surveys/acs/guidance/statistical-testing-tool.html | ACS data accuracy guidelines | Public |
| HERE Map Quality | https://developer.here.com/documentation/routing-api/dev_guide/topics/resource-calculate-isoline.html | API data quality specifications | API Documentation |
| CMS Data Quality Assurance | https://www.cms.gov/Research-Statistics-Data-and-Systems/CMS-Information-Technology/AccesstoDataApplication/DataUseAgreements | Data quality standards | Public |
| Spatial Data Quality Standards | https://www.fgdc.gov/standards/projects/framework-data-standard | Federal spatial data quality | Public |
| Resolution | File Name Pattern | Download URL | File Size | Use Case |
|---|---|---|---|---|
| 20m (High Detail) | cb_2021_us_nation_20m.zip |
https://www2.census.gov/geo/tiger/GENZ2021/shp/ | ~50MB | Detailed national analysis |
| 5m (Medium Detail) | cb_2021_us_nation_5m.zip |
https://www2.census.gov/geo/tiger/GENZ2021/shp/ | ~20MB | General national mapping |
| 500k (Low Detail) | cb_2021_us_nation_500k.zip |
https://www2.census.gov/geo/tiger/GENZ2021/shp/ | ~5MB | Web mapping, overview maps |
| Resolution | File Name Pattern | Download URL | File Size | Use Case |
|---|---|---|---|---|
| 20m (High Detail) | cb_2021_us_state_20m.zip |
https://www2.census.gov/geo/tiger/GENZ2021/shp/ | ~25MB | Detailed state analysis |
| 5m (Medium Detail) | cb_2021_us_state_5m.zip |
https://www2.census.gov/geo/tiger/GENZ2021/shp/ | ~8MB | RECOMMENDED for most analysis |
| 500k (Low Detail) | cb_2021_us_state_500k.zip |
https://www2.census.gov/geo/tiger/GENZ2021/shp/ | ~2MB | Web display, overview maps |
| Resolution | File Name Pattern | Download URL | File Size | Use Case |
|---|---|---|---|---|
| 20m | cb_2021_us_county_20m.zip |
https://www2.census.gov/geo/tiger/GENZ2021/shp/ | ~45MB | Detailed county analysis |
| 5m | cb_2021_us_county_5m.zip |
https://www2.census.gov/geo/tiger/GENZ2021/shp/ | ~15MB | RECOMMENDED |
| 500k | cb_2021_us_county_500k.zip |
https://www2.census.gov/geo/tiger/GENZ2021/shp/ | ~4MB | Web mapping |
| Resolution | File Name Pattern | Download URL | File Size | Use Case |
|---|---|---|---|---|
| National 500k | cb_2021_us_tract_500k.zip |
https://www2.census.gov/geo/tiger/GENZ2021/shp/ | ~180MB | National tract analysis |
| State-by-State 500k | cb_2021_[FIPS]_tract_500k.zip |
https://www2.census.gov/geo/tiger/GENZ2021/shp/ | 1-20MB each | State-specific analysis |
Example State Files: - Colorado: cb_2021_08_tract_500k.zip - California:
cb_2021_06_tract_500k.zip
- Texas: cb_2021_48_tract_500k.zip - New York: cb_2021_36_tract_500k.zip
| File Type | File Name Pattern | Download URL | File Size | Notes |
|---|---|---|---|---|
| National | cb_2021_us_bg_500k.zip |
https://www2.census.gov/geo/tiger/GENZ2021/shp/ | ~350MB | Similar to your simplified_us_lck_grp_2021.shp |
| State-by-State | cb_2021_[FIPS]_bg_500k.zip |
https://www2.census.gov/geo/tiger/GENZ2021/shp/ | 2-40MB each | Smaller, manageable files |
# Simplified boundaries with cb = TRUE parameter
library(tigris)
# US States (simplified)
states_simplified <- tigris::states(cb = TRUE, resolution = "20m") # or "5m", "500k"
# US Counties (simplified)
counties_simplified <- tigris::counties(cb = TRUE, resolution = "20m")
# Census Tracts (simplified) - by state
colorado_tracts <- tigris::tracts(state = "CO", cb = TRUE)
# Block Groups (simplified) - by state
colorado_bg <- tigris::block_groups(state = "CO", cb = TRUE)
# National boundaries
us_nation <- tigris::nation(cb = TRUE, resolution = "5m")library(USAboundaries)
# Current simplified state boundaries
states_2020 <- us_states(resolution = "low") # Simplified
states_2020_hi <- us_states(resolution = "high") # Detailed
# Current simplified county boundaries
counties_2020 <- us_counties(resolution = "low")
# Historical boundaries (simplified)
states_1990 <- us_states(map_date = "1990-01-01", resolution = "low")| Scale | Resolution | Download URL | US Coverage |
|---|---|---|---|
| 1:10m | High detail | https://www.naturalearthdata.com/downloads/10m-cultural-vectors/ | Detailed US boundaries |
| 1:50m | Medium detail | https://www.naturalearthdata.com/downloads/50m-cultural-vectors/ | RECOMMENDED |
| 1:110m | Low detail | https://www.naturalearthdata.com/downloads/110m-cultural-vectors/ | Overview mapping |
Specific Files: - Countries: ne_50m_admin_0_countries.zip -
States/Provinces: ne_50m_admin_1_states_provinces.zip
| Source | Description | Download URL | Format |
|---|---|---|---|
| Geofabrik | US state extracts | https://download.geofabrik.de/north-america/us/ | Various formats |
| BBBike | Custom extracts | https://extract.bbbike.org/ | Shapefile, GeoJSON |
library(sf)
# Load detailed boundary
detailed_boundary <- st_read("detailed_boundary.shp")
# Simplify geometry (tolerance in map units)
simplified_boundary <- st_simplify(detailed_boundary, dTolerance = 1000) # 1km tolerance
# Alternative: preserve topology
simplified_boundary <- st_simplify(detailed_boundary, preserveTopology = TRUE, dTolerance = 500)
# Save simplified version
st_write(simplified_boundary, "simplified_boundary.shp")library(tigris)
library(sf)
# Set options for simplified boundaries
options(tigris_use_cache = TRUE) # Cache for faster repeated access
# Load simplified boundaries
us_states <- tigris::states(cb = TRUE, resolution = "5m")
us_counties <- tigris::counties(cb = TRUE, resolution = "5m")
us_nation <- tigris::nation(cb = TRUE, resolution = "5m")
# Load census tracts for specific states (simplified)
colorado_tracts <- tigris::tracts(state = "CO", cb = TRUE)
california_tracts <- tigris::tracts(state = "CA", cb = TRUE)
# Load block groups for analysis (similar to your file)
colorado_bg <- tigris::block_groups(state = "CO", cb = TRUE)| Boundary Type | Detailed Size | Simplified Size | Speed Improvement |
|---|---|---|---|
| US States | ~15MB | ~8MB | 2x faster rendering |
| US Counties | ~80MB | ~15MB | 5x faster rendering |
| Census Tracts | ~1.2GB | ~180MB | 10x faster rendering |
| Block Groups | ~2.5GB | ~350MB | 15x faster rendering |
# Check validity of simplified geometries
st_is_valid(simplified_boundary)
# Fix invalid geometries if needed
simplified_boundary <- st_make_valid(simplified_boundary)
# Check area preservation (should be close to original)
original_area <- sum(st_area(detailed_boundary))
simplified_area <- sum(st_area(simplified_boundary))
area_difference <- abs(simplified_area - original_area) / original_area
print(paste("Area difference:", scales::percent(area_difference)))cb_[YEAR]_[GEOGRAPHY]_[ENTITY]_[RESOLUTION].zipcb_2021_us_state_5m.zip (US states, 5m resolution)cb_2021_08_tract_500k.zip (Colorado tracts, 500k resolution)cb_2021_us_bg_500k.zip (US block groups, 500k resolution)The 500k resolution files are closest to your
simplified_us_lck_grp_2021.shp pattern and are optimized for analytical work
while maintaining essential geographic accuracy.
| Geographic Unit | Data Source | Download URL | File Format | Coverage | Update Frequency | Notes |
|---|---|---|---|---|---|---|
| Census Tracts | US Census Bureau | https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.html | Shapefile, KML, GeoJSON | National, State, County | Annual | Primary analysis unit for demographics |
| Census Block Groups | US Census Bureau | https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.html | Shapefile, KML, GeoJSON | National, State, County | Annual | Highest resolution demographic data |
| Census Blocks | US Census Bureau | https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.html | Shapefile, KML, GeoJSON | National, State, County | Annual | Most detailed geographic unit |
| State Boundaries | US Census Bureau | https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.html | Shapefile, KML, GeoJSON | National | Annual | State-level analysis |
| County Boundaries | US Census Bureau | https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.html | Shapefile, KML, GeoJSON | National, State | Annual | County-level aggregation |
| ZIP Code Tabulation Areas (ZCTAs) | US Census Bureau | https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.html | Shapefile, KML, GeoJSON | National | Decennial Census | Approximate ZIP code areas |
| Urban Areas | US Census Bureau | https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.html | Shapefile, KML, GeoJSON | National | Decennial Census | Urban vs rural classification |
| Geographic Unit | Data Source | Download URL | File Format | Coverage | Update Frequency | Notes |
|---|---|---|---|---|---|---|
| Health Professional Shortage Areas (HPSAs) | HRSA | https://data.hrsa.gov/data/download | Shapefile, GeoJSON, CSV | National | Quarterly | Primary care, dental, mental health |
| Medically Underserved Areas (MUAs) | HRSA | https://data.hrsa.gov/data/download | Shapefile, GeoJSON, CSV | National | Quarterly | Areas lacking medical care |
| Medically Underserved Populations (MUPs) | HRSA | https://data.hrsa.gov/data/download | Shapefile, GeoJSON, CSV | National | Quarterly | Population-based designations |
| Federally Qualified Health Centers (FQHCs) | HRSA | https://data.hrsa.gov/data/download | Point data, CSV | National | Monthly | FQHC locations |
| Rural Health Clinics | HRSA | https://data.hrsa.gov/data/download | Point data, CSV | National | Monthly | RHC locations |
| Critical Access Hospitals | HRSA | https://data.hrsa.gov/data/download | Point data, CSV | National | Monthly | CAH locations |
| Geographic Unit | Data Source | Download URL | File Format | Coverage | Update Frequency | Notes |
|---|---|---|---|---|---|---|
| Rural-Urban Commuting Areas (RUCAs) | USDA ERS | https://www.ers.usda.gov/data-products/rural-urban-commuting-area-codes/documentation/ | CSV with FIPS codes | National (Census Tracts) | ~10 years | 2010 Census-based (most recent) |
| Urban Influence Codes | USDA ERS | https://www.ers.usda.gov/data-products/urban-influence-codes/ | CSV with FIPS codes | National (Counties) | ~10 years | County-level rural-urban |
| Rural-Urban Continuum Codes | USDA ERS | https://www.ers.usda.gov/data-products/rural-urban-continuum-codes/ | CSV with FIPS codes | National (Counties) | ~10 years | Metro and non-metro counties |
| Geographic Unit | Data Source | Download URL | File Format | Coverage | Update Frequency | Notes |
|---|---|---|---|---|---|---|
| Hospital Referral Regions (HRRs) | Dartmouth Atlas | https://www.dartmouthatlas.org/tools/downloads.aspx | Shapefile | National | ~10 years | Healthcare market areas |
| Hospital Service Areas (HSAs) | Dartmouth Atlas | https://www.dartmouthatlas.org/tools/downloads.aspx | Shapefile | National | ~10 years | Local hospital markets |
| Geographic Unit | Data Source | Download URL | File Format | Coverage | Update Frequency | Notes |
|---|---|---|---|---|---|---|
| Primary Roads | US Census Bureau | https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.html | Shapefile | National, State | Annual | Major highways and roads |
| All Roads | US Census Bureau | https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.html | Shapefile | National, State, County | Annual | Complete road network |
| Highway System | Data Source | Download URL | File Format | Coverage | Update Frequency | Specific Routes Included |
|---|---|---|---|---|---|---|
| Interstate Highway System | US Census Bureau TIGER | https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.html | Shapefile | National | Annual | I-95, I-10, I-5, I-75, I-80, I-40, I-35, I-25, I-70, I-90, etc. |
| US Highway System | US Census Bureau TIGER | https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.html | Shapefile | National | Annual | US-1, US-50, US-101, US-Route 66 (historic), etc. |
| Primary Roads (Interstate + US) | US Census Bureau TIGER | https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.html | Shapefile | National | Annual | All Interstate and US highways combined |
| National Highway System (NHS) | FHWA | https://www.fhwa.dot.gov/planning/national_highway_system/ | Shapefile, GeoJSON | National | Annual | Congressionally designated strategic highways |
| Route | Description | States Traversed | Length (Miles) | Key Cities Connected | Healthcare Relevance |
|---|---|---|---|---|---|
| I-10 | Southern transcontinental | CA, AZ, NM, TX, LA, MS, AL, FL | 2,460 | Los Angeles - Houston - New Orleans - Jacksonville | Major Sun Belt corridor |
| I-20 | Southern route | TX, LA, MS, AL, GA, SC | 1,535 | Dallas - Atlanta - Columbia | Connects major Southern medical centers |
| I-30 | Arkansas-Texas | TX, AR | 367 | Dallas - Little Rock | Links Dallas medical district |
| I-40 | Central transcontinental | CA, AZ, NM, TX, OK, AR, TN, NC | 2,555 | Los Angeles - Nashville - Raleigh | Major cross-country medical corridor |
| I-70 | Central route | UT, CO, KS, MO, IL, IN, OH, WV, PA, MD | 2,151 | Denver - Kansas City - St. Louis - Columbus | Mountain West to Mid-Atlantic |
| I-80 | Northern transcontinental | CA, NV, UT, WY, NE, IA, IL, IN, OH, PA, NJ, NY | 2,899 | San Francisco - Chicago - New York | Major Northern medical corridor |
| I-90 | Northern border route | WA, ID, MT, WY, SD, MN, WI, IL, IN, OH, PA, NY, MA | 3,020 | Seattle - Chicago - Boston | Longest interstate, connects major Northern cities |
| Route | Description | States Traversed | Length (Miles) | Key Cities Connected | Healthcare Relevance |
|---|---|---|---|---|---|
| I-5 | West Coast | CA, OR, WA | 1,381 | San Diego - Los Angeles - San Francisco - Portland - Seattle | Pacific Coast medical corridor |
| I-15 | Southwestern | CA, NV, AZ, UT, ID, MT | 1,433 | San Diego - Las Vegas - Salt Lake City | Desert Southwest access |
| I-25 | Mountain corridor | NM, CO, WY | 1,062 | Albuquerque - Denver - Cheyenne | Front Range medical access |
| I-35 | Central Plains | TX, OK, KS, MO, IA, MN | 1,568 | Laredo - San Antonio - Austin - Dallas - Minneapolis | Texas to Twin Cities corridor |
| I-65 | South Central | AL, TN, KY, IN | 887 | Mobile - Birmingham - Nashville - Louisville - Indianapolis | Connects Southern to Midwest medical centers |
| I-75 | Eastern corridor | FL, GA, TN, KY, OH, MI | 1,786 | Miami - Atlanta - Cincinnati - Detroit | Major Eastern medical corridor |
| I-85 | Southeastern | AL, GA, SC, NC, VA | 666 | Montgomery - Atlanta - Charlotte - Richmond | Southeast medical corridor |
| I-95 | East Coast | ME, NH, MA, RI, CT, NY, NJ, PA, DE, MD, DC, VA, NC, SC, GA, FL | 1,908 | Miami - Savannah - Richmond - Washington - Philadelphia - New York - Boston | Primary East Coast medical corridor |
| Data Source | Download URL | File Format | Coverage | Cost | Licensing | Quality Level |
|---|---|---|---|---|---|---|
| OpenStreetMap (OSM) | https://www.openstreetmap.org/ | Various | Global | Free | Open Data License | High, community maintained |
| HERE Maps Road Data | https://developer.here.com/ | API/Download | Global | Commercial | HERE License | Very High, commercial grade |
| TomTom Road Network | https://developer.tomtom.com/ | API | Global | Commercial | TomTom License | Very High, commercial grade |
| Package | Function | Data Included | Usage Example | Notes |
|---|---|---|---|---|
| tigris | primary_roads(), primary_secondary_roads() |
Census TIGER roads | tigris::primary_roads(cb = TRUE) |
Automatically downloads highways |
| osmdata | opq(), add_osm_feature() |
OpenStreetMap data | osmdata::opq("USA") %>% add_osm_feature("highway", "motorway") |
Query specific highway types |
| tidytransit | Transit and road network analysis | GTFS + road networks | Various | Public transportation integration |
| dodgr | Distance calculations on road networks | OSM road networks | Network analysis | Route optimization |
# Download all primary roads (includes Interstates)
primary_roads <- tigris::primary_roads(cb = TRUE)
# Filter for Interstate highways only
interstates <- primary_roads %>%
filter(stringr::str_detect(FULLNAME, "^I "))
# Specific Interstate (example: I-25)
i25 <- primary_roads %>%
filter(stringr::str_detect(FULLNAME, "I 25"))library(osmdata)
# Download Interstate highways
interstates_osm <- opq("United States") %>%
add_osm_feature(key = "highway", value = "motorway") %>%
add_osm_feature(key = "ref", value = c("I 95", "I 25", "I 70")) %>%
osmdata_sf()
# Download US highways
us_highways <- opq("United States") %>%
add_osm_feature(key = "highway", value = "trunk") %>%
add_osm_feature(key = "ref", value = c("US 50", "US 101")) %>%
osmdata_sf()| Geographic Unit | Data Source | Download URL | File Format | Coverage | Update Frequency | Notes |
|---|---|---|---|---|---|---|
| NCI-Designated Cancer Centers | National Cancer Institute | https://www.cancer.gov/research/infrastructure/cancer-centers/find | Point data (geocode addresses) | National | As designated | Comprehensive cancer centers |
| CoC-Accredited Cancer Programs | Commission on Cancer | https://www.facs.org/quality-programs/cancer-programs/commission-cancer/coc-accredited-programs/ | Point data (geocode addresses) | National | Annual | Accredited cancer programs |
| NCCN Member Institutions | NCCN | https://www.nccn.org/about/member-institutions | Point data (geocode addresses) | National | As membership changes | Leading cancer centers |
| Package | Function | Data Included | Usage | Notes |
|---|---|---|---|---|
| tigris | states(), counties(), tracts(), block_groups() |
All Census boundaries | tigris::states(cb = TRUE) |
Automatically downloads and caches |
| tidycensus | get_acs(..., geometry = TRUE) |
Census data + boundaries | get_acs(geography = "tract", geometry = TRUE) |
Data and geography combined |
| sf | Spatial data processing | - | st_read("shapefile.shp") |
Core spatial analysis |
| USAboundaries | Historical US boundaries | Counties, states (1629-2000) | us_states(map_date = "2000-01-01") |
Historical analysis |
| spData | Spatial datasets | Various example datasets | data(us_states) |
Teaching and examples |
| Geographic Unit | 2010 Census Vintage | 2020 Census Vintage | Compatibility Issues |
|---|---|---|---|
| Census Tracts | ~74,000 tracts | ~84,000 tracts | Tract boundaries changed significantly |
| Block Groups | ~217,000 BGs | ~242,000 BGs | Many boundary changes |
| Counties | 3,143 counties | 3,143 counties | Stable boundaries |
| States | 50 + DC | 50 + DC | No changes |
| ZCTAs | 2010 ZIP patterns | 2020 ZIP patterns | Significant changes in suburban areas |
MIT (allows commercial and academic use with attribution)
We welcome contributions!
If you use this work in your research, please cite:
Primary Contact: Tyler Muffly, MD
Email: tyler.muffly@dhha.org
GitHub: https://github.com/mufflyt/isochrones