DEV Community: Jeremy Salsburg

Proyecto STEM: construye un monitor de aeronaves con una API y datos abiertos

Jeremy Salsburg — Sun, 23 Aug 2026 02:16:26 +0000

Proyecto STEM: construye un monitor de aeronaves con una API y datos abiertos

Â¿Buscas un proyecto que combine programaciÃ³n, sistemas en tiempo real, GIS,
ciencia de datos, radio y experimentaciÃ³n? Los datos de aeronaves ofrecen un
caso prÃ¡ctico que puede crecer desde una consulta API de diez lÃneas hasta un
laboratorio completo con Raspberry Pi y radio definida por software.

Transparencia: estoy afiliado con ADSBiq, el proyecto comunitario que ofrece
estos recursos. La API, los ejemplos y la muestra educativa pueden utilizarse
sin instalar hardware. Un receptor es completamente opcional.

Cuatro niveles posibles

1. Primera consulta a una API

Una aplicaciÃ³n puede solicitar aeronaves cercanas a una coordenada, buscar una
matrÃcula o consultar actividad aeroportuaria. Es un ejercicio apropiado para
JSON, HTTP, autenticaciÃ³n, manejo de errores y visualizaciÃ³n.

import os
import requests

respuesta = requests.get(
    "https://api.adsbiq.com/v2/nearby",
    params={"lat": 4.711, "lon": -74.072, "radius_nm": 100},
    headers={"Authorization": f"Bearer {os.environ['ADSBIQ_API_KEY']}"},
    timeout=20,
)
respuesta.raise_for_status()
datos = respuesta.json()
print(datos)

Nunca publiques una clave API en GitHub, un cuaderno o una captura de pantalla.

2. AnÃ¡lisis reproducible con Parquet

Una muestra caribeÃ±a de 100.000 observaciones estÃ¡ archivada en Zenodo con el
DOI permanente 10.5281/zenodo.22062551.
DuckDB puede consultar el archivo comprimido directamente:

SELECT
    floor(lat) AS celda_lat,
    floor(lon) AS celda_lon,
    count(*) AS observaciones,
    count(DISTINCT hex) AS aeronaves
FROM read_parquet('caribbean_sample_2026-06-29.parquet')
WHERE lat IS NOT NULL AND lon IS NOT NULL
GROUP BY ALL
ORDER BY observaciones DESC;

Las filas son cambios de estado: una instantÃ¡nea completa puede estar seguida
por filas dispersas que contienen solamente los campos que cambiaron. Es un
buen ejercicio de modelado temporal, particiones y reconstrucciÃ³n de estado.

3. AplicaciÃ³n regional

Un equipo puede crear:

un monitor para su ciudad, aeropuerto o regiÃ³n;
un mapa de actividad y observaciones por altitud;
una canalizaciÃ³n con DuckDB, Polars, pandas o PostGIS;
un tablero de conectividad aÃ©rea regional;
un experimento sobre cobertura, terreno y lÃnea de vista.

Preparamos pÃ¡ginas en espaÃ±ol con ideas y recursos para
SudamÃ©rica,
Colombia,
PerÃº,
Chile y
Argentina.

4. Laboratorio opcional de radio

Un club o laboratorio puede agregar un receptor pasivo de 1090 MHz utilizando
una Raspberry Pi, un receptor RTL-SDR y una antena adecuada. El experimento
permite medir cÃ³mo cambian las observaciones con la altura de antena, pÃ©rdidas
de cable, edificios, terreno e interferencia.

ADSBiq puede compartir datos junto con otras redes ADS-B. No es necesario
abandonar ningÃºn servicio existente.

Preguntas de investigaciÃ³n

Â¿CÃ³mo cambia la densidad de observaciones entre costa, montaÃ±a y selva?
Â¿QuÃ© relaciÃ³n existe entre altitud y distancia de recepciÃ³n?
Â¿CÃ³mo se reconstruye correctamente una trayectoria a partir de cambios?
Â¿QuÃ© arquitectura soporta instantÃ¡neas y actualizaciones en tiempo real?
Â¿DÃ³nde aportarÃa mÃ¡s un nuevo receptor universitario o comunitario?

InterpretaciÃ³n responsable

Estos son datos colaborativos e incompletos. La ausencia de observaciones no
demuestra que no hubiera una aeronave. No deben utilizarse para navegaciÃ³n,
separaciÃ³n, control, vigilancia, seguridad operacional ni decisiones crÃticas.

Recursos

SerÃan muy Ãºtiles las sugerencias de docentes, estudiantes y comunidades
tÃ©cnicas sudamericanas sobre ejercicios, traducciones y casos regionales.

Query 100,000 Caribbean Aircraft State Changes with DuckDB

Jeremy Salsburg — Sun, 23 Aug 2026 02:01:21 +0000

Query 100,000 Caribbean Aircraft State Changes with DuckDB

Real-world streaming data rarely arrives as perfectly reconstructed rows. To
save bandwidth and storage, ADS-B archives can record a complete aircraft state
followed by sparse rows containing only fields that changed.

This tutorial uses a free, bounded Caribbean aircraft-data sample to explore
that model directly with DuckDB. The file contains 100,000 observations, is
only about 1.6 MB compressed, and has a permanent DOI.

Disclosure: I am affiliated with ADSBiq, the community-powered project that
produced this dataset. The sample is free under ODbL-1.0, and no hardware is
required.

What is in the sample?

The sample covers positioned observations between 5-30 degrees north and
95-55 degrees west on June 29, 2026. Common columns include:

ts: observation timestamp
hex: ICAO 24-bit aircraft address
flight: transmitted callsign
lat, lon: position
alt_baro: barometric altitude
gs: ground speed
track: direction of travel
type: aircraft type designator
is_snapshot: whether the row contains a complete state
is_removed: whether the aircraft left coverage

Rows after a snapshot can be sparse diffs. A null value therefore often means
"unchanged," not "unknown forever."

Install DuckDB

Use the DuckDB CLI or its Python package:

python -m pip install duckdb

The data is small enough for a laptop, but DuckDB can query Parquet lazily and
push filters into the scan. There is no reason to load every column into a
large dataframe first.

Query the Parquet file directly from Zenodo

import duckdb

url = (
    "https://zenodo.org/api/records/22062551/files/"
    "caribbean_sample_2026-06-29.parquet/content"
)

con = duckdb.connect()
con.execute("INSTALL httpfs")
con.execute("LOAD httpfs")

summary = con.execute("""
    SELECT
        count(*) AS rows,
        count(DISTINCT hex) AS aircraft,
        min(ts) AS first_observation,
        max(ts) AS last_observation
    FROM read_parquet(?)
""", [url]).fetchdf()

print(summary)

Count positioned observations by one-degree grid cell

Grid aggregation gives a quick, privacy-safe view of where the sample contains
observations:

grid = con.execute("""
    SELECT
        floor(lat) AS latitude_cell,
        floor(lon) AS longitude_cell,
        count(*) AS observations,
        count(DISTINCT hex) AS aircraft
    FROM read_parquet(?)
    WHERE lat IS NOT NULL
      AND lon IS NOT NULL
    GROUP BY ALL
    ORDER BY observations DESC
    LIMIT 25
""", [url]).fetchdf()

print(grid)

This measures observations in the sample. It does not measure all aircraft
activity. Receiver locations, antenna height, terrain, interference, aircraft
altitude, traffic, and time all affect crowdsourced coverage.

Start reconstruction with snapshots

For analyses that do not need every intermediate state, begin with complete
snapshot rows:

snapshots = con.execute("""
    SELECT hex, ts, flight, lat, lon, alt_baro, gs, track, type
    FROM read_parquet(?)
    WHERE is_snapshot
      AND lat BETWEEN 17 AND 19
      AND lon BETWEEN -68.5 AND -65
    ORDER BY ts
    LIMIT 1000
""", [url]).fetchdf()

For a complete trajectory, partition by hex, order by ts, and forward-fill
nullable state fields after a snapshot. Keep removal rows so that separate
coverage sessions are not accidentally joined into one continuous flight.

Turn it into a student or club project

Useful extensions include:

Compare observation density near different Caribbean airports.
Visualize altitude against reception distance.
Build a DuckDB or Polars pipeline that reconstructs selected aircraft.
Design a live island aviation dashboard using the REST API.
Measure how receiver or antenna changes affect subsequent observations.

The Caribbean STEM project page
collects the API, dataset, coding examples, and optional receiver path. Regional
versions are also available for Jamaica, Puerto Rico, Key West, and the Cayman
Islands.

Resources

Missing observations do not prove that no aircraft were present, and this data
must not be used for navigation, separation, enforcement, or safety-of-life
decisions.