Homeland Infrastructure Foundation Level Data

Author

Manjul Balayar

Published

July 11, 2024

Brief Description

The Homeland Infrastructure Foundation Level Dataset (HIFLD) is a comprehensive collection of geospatial data critical for infrastructure planning and management, focusing on essential public services and facilities. Its datasets, which are integral to the built capital, include measures such as Cellular Towers, Colleges and Universities, Convention Centers, Fire and Emergency Stations, Hospitals, Law Enforcement Locations, Sports Venues, Private and Public Schools, Community Colleges, and Child Care Centers.

Source: HIFLD

Measures

  1. Cellular_Towers_per_10k
  2. College_Universities_per_10k
  3. Convention_Centers_per_10k
  4. Fire_Emergency_Stations_per_10k
  5. Hospitals_per_10k
  6. Local_Law_Enforcement_Locations_per_10k
  7. Sports_Venues_per_10k
  8. Private_Schools_per_10k
  9. Public_Schools_per_10k
  10. Community_Colleges_per_10k
  11. Child_Care_Centers_per_10k (ONLY County level)

All these measurements were obtained from the Homeland Infrastructure Foundation Level Dataset, except for the childcare data, which was sourced from the County Health Rankings Dataset. Initially, all datasets were in geojson format so after extraction we performed a spatial join with the ACS population data at both the city and county levels to obtain the GEOID, population, and accurate county and city names, ensuring each location was correctly correlated.

For the per_10k calculations, I used ACS population data, dividing the total number of venues by the population and then multiplying by 10,000:

per10k = [
    'Cellular_Towers', 'College_Universities', 'Convention_Centers', 'Fire_Emergency_Stations',
    'Hospitals', 'Local_Law_Enforcement_Locations', 'Sports_Venues', 'Private_Schools', 'Public_Schools',
    'Community_Colleges', 'Child_Care_Centers'
]

for count in per10k:
  county[f'{count}_per_10k'] = ((county[count] / county['Population']) * 10000)