⚙️ How-To Aircraft registrations Maps & visualisation Notebook By · Updated

How to Map Registered Aircraft per Resident by County

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Problem statement

Which US counties have the most aircraft for their population? The FAA registry gives each aircraft's mailing county, and the Census Bureau's estimates give residents, so the rate is one join away. But the address on a registration is the owner's, and many owners are companies with an address of convenience, so a raw map of aircraft per resident picks out a few trust and registration-agent addresses as aviation capitals. This page builds the rate for every county with the 16 October 2026 registry, finds what drives the outliers, and maps individually owned aircraft instead.

Quick answer

Keep valid registrations with a US address, build a five-digit county code from the state and the FAA's three-digit county, count, and divide by the 2025 population:

us = reg[(reg.COUNTRY == "US") & (reg["STATUS CODE"] == "V")].copy()
us["GEOID"] = us.STATE.map(STATEFP) + us.COUNTY
c = pop.set_index("GEOID")[["STNAME", "CTYNAME", "POPESTIMATE2025"]].join(us.GEOID.value_counts().rename("aircraft"))
c["aircraft"] = c.aircraft.fillna(0)
c["per_1000"] = c.aircraft / c.POPESTIMATE2025 * 1000
print(f"US: {c.aircraft.sum() / c.POPESTIMATE2025.sum() * 1000:.2f} valid registrations per 1,000 residents; county median {c.per_1000.median():.2f}")
US: 0.88 valid registrations per 1,000 residents; county median 1.05

The US has 0.88 valid registrations per 1,000 residents, while the median county has 1.05: aircraft are more common, relative to people, in thinly populated counties.

Horizontal bars of the ten counties of 20,000 or more residents with the most valid registrations per 1,000 residents, led by Duchesne County, Utah at 80.9, with Duchesne and New Castle County, Delaware highlighted in red as registration-address effects. Horizontal bars of the ten counties of 20,000 or more residents with the most valid registrations per 1,000 residents, led by Duchesne County, Utah at 80.9, with Duchesne and New Castle County, Delaware highlighted in red as registration-address effects.
Highest registration rates: the top one is an address of convenience.

Counties, owners and the outliers

1. The highest rates

print(c[c.POPESTIMATE2025 >= 20_000].nlargest(8, "per_1000")[["CTYNAME", "STNAME", "aircraft", "per_1000"]].round(1).to_string())
d = us[us.GEOID == "49013"]
print("Duchesne County, UT:", d.CITY.value_counts().head(2).to_dict(), d["TYPE REGISTRANT"].map(REGISTRANT).value_counts().head(2).to_dict())
                         CTYNAME    STNAME  aircraft  per_1000
GEOID                                                         
49013            Duchesne County      Utah    1688.0      80.9
02122    Kenai Peninsula Borough    Alaska    1012.0      16.3
32510                Carson City    Nevada     954.0      16.3
56025             Natrona County   Wyoming    1218.0      15.1
02170  Matanuska-Susitna Borough    Alaska    1758.0      14.8
41027          Hood River County    Oregon     343.0      14.5
10003          New Castle County  Delaware    8401.0      14.3
20059            Franklin County    Kansas     355.0      13.5
Duchesne County, UT: {'NORTH SALT LAKE': 1648, 'ROOSEVELT': 13} {'corporation': 1550, 'LLC': 91}

Duchesne County in eastern Utah tops the list at 80.9 aircraft per 1,000 residents, five times the next county. Its 1,688 aircraft are almost all corporation-owned, with a mailing city of North Salt Lake, which is not in Duchesne County at all: an address of convenience with a county code that does not even match it. New Castle County, Delaware, is in the list for the same kind of reason. Each of the others needs the same check of owner type and mailing city before it is read as a local fleet.

2. Individuals only

ind = us[us["TYPE REGISTRANT"] == "1"].GEOID.value_counts()
c["individual_per_1000"] = ind.reindex(c.index).fillna(0) / c.POPESTIMATE2025 * 1000
print(c[c.POPESTIMATE2025 >= 20_000].nlargest(8, "individual_per_1000")[["CTYNAME", "STNAME", "individual_per_1000"]].round(1).to_string())
print(f"rank correlation, all owners against individuals (20,000+ residents): "
      f"{c[c.POPESTIMATE2025 >= 20_000][['per_1000', 'individual_per_1000']].corr(method='spearman').iloc[0, 1]:.2f}")
                            CTYNAME   STNAME  individual_per_1000
GEOID                                                            
02122       Kenai Peninsula Borough   Alaska                 10.0
02170     Matanuska-Susitna Borough   Alaska                  8.9
02020        Anchorage Municipality   Alaska                  5.7
02090  Fairbanks North Star Borough   Alaska                  5.2
48171              Gillespie County    Texas                  4.7
48221                   Hood County    Texas                  4.1
13231                   Pike County  Georgia                  3.8
12121               Suwannee County  Florida                  3.6
rank correlation, all owners against individuals (20,000+ residents): 0.86

Counting only aircraft registered to individuals, who use their home address, the top of the list is Alaska: the Kenai Peninsula and Matanuska-Susitna boroughs at 10.0 and 8.9 per 1,000, then Anchorage and Fairbanks. Gillespie and Hood counties in Texas and Pike County, Georgia, follow. The two rankings still correlate at 0.86, but the outliers are gone.

Horizontal bars of the ten counties with the most individually owned aircraft per 1,000 residents, led by the Kenai Peninsula Borough at 10.0 and Matanuska-Susitna Borough at 8.9, Alaska. Horizontal bars of the ten counties with the most individually owned aircraft per 1,000 residents, led by the Kenai Peninsula Borough at 10.0 and Matanuska-Susitna Borough at 8.9, Alaska.
Counting individual owners only, Alaska boroughs lead and the outliers disappear.

3. The rows that did not join

lost = us[~us.GEOID.isin(c.index)]
print(f"valid US registrations with no county: {len(lost):,} ({len(lost) / len(us):.1%}); by state: {lost.STATE.value_counts().head(4).to_dict()}")
valid US registrations with no county: 3,586 (1.2%); by state: {'FL': 1606, 'CT': 1590, 'AK': 336, 'VA': 13}

3,586 valid US registrations, 1.2%, have a county code that is not in the 2024 county list: Florida 1,606, Connecticut 1,590 and Alaska 336. They are codes that no longer exist, and the fix page puts them back.

States and the map

The data used on this page

The FAA's Releasable Aircraft Database, public domain, downloaded on 16 October 2026 and saved on this site, because the FAA replaces it every day. The saved copy leaves out the owners' names and street addresses and keeps five-digit ZIP codes; every other column is as published. With it come the Census Bureau's Vintage 2025 county population estimates, 2024 county boundaries and 2024 ZCTA gazetteer.

from pathlib import Path
import zipfile

import geopandas as gpd
import numpy as np
import pandas as pd
import requests

Path("data").mkdir(exist_ok=True)
# FAA Releasable Aircraft Database, 16 October 2026: MASTER.txt without the NAME, STREET, STREET2 and OTHER NAMES
# columns and with five-digit ZIP codes (MASTER-no-names.txt), ACFTREF.txt and ENGINE.txt as published; Census
# Vintage 2025 county estimates, 2024 cartographic boundary counties and 2024 ZCTA gazetteer. Saved here unchanged
# otherwise, because the FAA replaces the file daily.
# Live: https://registry.faa.gov/database/ReleasableAircraft.zip (needs a browser User-Agent),
# https://www2.census.gov/geo/docs/maps-data/data/gazetteer/2024_Gazetteer/2024_Gaz_zcta_national.zip
ARCHIVE = "faa-aircraft-registry-2026-10-16.zip"
if not Path(f"data/{ARCHIVE}").exists():
    Path(f"data/{ARCHIVE}").write_bytes(requests.get(f"https://data.spatialworkflow.io/{ARCHIVE}", timeout=600).content)
Z = zipfile.ZipFile(f"data/{ARCHIVE}")


def faa(name):
    """One registry file: comma-separated, every field padded with spaces, a byte-order mark and a trailing comma."""
    d = pd.read_csv(Z.open(name), dtype=str, encoding="utf-8-sig", keep_default_na=False)
    d.columns = d.columns.str.strip()
    d = d.loc[:, [c for c in d.columns if c and not c.startswith("Unnamed")]]
    return d.apply(lambda c: c.str.strip())


reg = faa("MASTER-no-names.txt")
ref = faa("ACFTREF.txt")
TYPE_AIRCRAFT = {"1": "glider", "2": "balloon", "3": "blimp", "4": "fixed wing single engine", "5": "fixed wing multi engine",
                 "6": "rotorcraft", "7": "weight-shift", "8": "powered parachute", "9": "gyroplane", "H": "hybrid lift", "O": "other"}
REGISTRANT = {"1": "individual", "2": "partnership", "3": "corporation", "4": "co-owned", "5": "government", "7": "LLC",
              "8": "non-citizen corporation", "9": "non-citizen co-owned", "": "blank"}
Path("data/cb_2024_us_county_20m.zip").write_bytes(Z.read("cb_2024_us_county_20m.zip"))
counties = gpd.read_file("data/cb_2024_us_county_20m.zip")
STATEFP = counties.drop_duplicates("STUSPS").set_index("STUSPS").STATEFP
pop = pd.read_csv(Z.open("co-est2025-alldata.csv"), encoding="latin-1", dtype={"STATE": str, "COUNTY": str})
pop = pop[pop.SUMLEV == 50].assign(GEOID=lambda p: p.STATE + p.COUNTY)
print(f"FAA registry, 16 October 2026: {len(reg):,} aircraft, {len(ref):,} make-and-model codes")
FAA registry, 16 October 2026: 317,604 aircraft, 94,115 make-and-model codes

Example 1: by state

s = c.groupby("STNAME")[["aircraft", "POPESTIMATE2025"]].sum()
s["per_1000"] = s.aircraft / s.POPESTIMATE2025 * 1000
s = s.sort_values("per_1000")
print("highest:", ", ".join(f"{k} {v:.1f}" for k, v in s.per_1000.tail(5).iloc[::-1].items()))
print("lowest:", ", ".join(f"{k} {v:.2f}" for k, v in s.per_1000.head(4).items()))
highest: Alaska 11.8, Delaware 10.2, Wyoming 5.4, Montana 5.2, North Dakota 3.1
lowest: Connecticut 0.00, New Jersey 0.31, Rhode Island 0.32, New York 0.33

Alaska has 11.8 registered aircraft per 1,000 residents and Delaware 10.2, then Wyoming, Montana and North Dakota. Connecticut shows 0.00 only because none of its county codes join (they are the old counties); New Jersey, Rhode Island and New York have the fewest after it.

Example 2: the map, individual owners

low48 = counties[~counties.STUSPS.isin(["AK", "HI", "PR"])].to_crs(5070)
m = low48.merge(c[["individual_per_1000"]], left_on="GEOID", right_index=True, how="left")
m["cls"] = pd.cut(m.individual_per_1000, [0, 0.25, 0.5, 1, 2, 4, 100], right=False)
print(m.cls.value_counts(sort=False).to_string())
ax = m.plot(column="cls", cmap="Blues", legend=True, missing_kwds={"color": "lightgrey"}, figsize=(10, 6))
ax.set_axis_off()
cls
[0.0, 0.25)     605
[0.25, 0.5)     898
[0.5, 1.0)      893
[1.0, 2.0)      467
[2.0, 4.0)      189
[4.0, 100.0)     57
Map of lower-48 counties shaded by individually owned aircraft per 1,000 residents, darkest across the Great Plains and Mountain West, lightest in big cities and much of the Southeast. Map of lower-48 counties shaded by individually owned aircraft per 1,000 residents, darkest across the Great Plains and Mountain West, lightest in big cities and much of the Southeast.
Individually owned aircraft per 1,000 residents by county.

On the individual-owner map, 57 counties have 4 or more aircraft per 1,000 residents and 605 fewer than 0.25. The high counties run through the Mountain West, the northern Plains and rural Texas, and the low ones are the big cities and much of the Southeast.