20260221
11480900
1.0
FALSE
Bell_FlightTrack_Zenabled_ExportToUpdate
002
https://www.arcgis.com/sharing/rest/content/items/1b496d1b79fd4344ab548a1f2399c5fa/info/thumbnail/thumbnail1773106380593.png
Projected
GCS_WGS_1984
Linear Unit: Meter (1.000000)
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WGS_1984_Web_Mercator_Auxiliary_Sphere
20260224
17323900
20260224
17323900
Esri ArcGIS 13.6.0.59527
Bell_FlightTrack_Zenabled_ExportToUpdate
<b>Overview</b><p>This Feature Service provides detailed historical flight track data for Bell helicopters operating over the New York region. The data captures various flight characteristics including aircraft type, flight start and end times, altitudes, as well as computed flight speeds. The information is sourced from AeroWhere by Root Geospatial, reflecting accurate and comprehensive tracking of helicopter flights within this specific geographic corridor.</p><b>Feature Layer</b><ul><li><b>Bell Flight Track Layer</b>: This layer contains the flight paths of individual Bell helicopters, including key flight attributes such as the aircraft type, start and end heights above ground level, flight duration, and approximate speeds calculated from spatial and temporal data. The layer supports analysis of helicopter movements with rich temporal and spatial context, making it valuable for aviation studies, flight safety analysis, and operational planning.</li></ul><b>Geographic Coverage</b><p>The dataset covers a focused area in and around the New York City metropolitan region, encompassing urban to suburban flight corridors. This localized coverage aids in detailed examination and planning within one of the most densely trafficked urban airspaces in the United States.</p><b>Use Cases</b><ul><li>Flight operations monitoring and management</li><li>Aviation safety and performance analysis</li><li>Urban airspace planning and management</li><li>Research involving helicopter traffic patterns and environmental impact</li></ul><b>Data Source and Citation</b><p>This data is © AeroWhere by Root Geospatial, accessed through Source Cooperative. Proper citation and licensing follow the usage guidelines provided by the data owner.</p>
Detailed historical flight tracks of Bell helicopters in the New York metropolitan area, including flight paths, altitudes, and speed estimations for aviation analysis and planning. From Jan 12, 2026 to Feb 16, 2026.
Root Geospatial LLC. 2025. "AeroWhere Flight Tracks: Historical Flight Tracks Dataset". Source Cooperative, https://source.coop/root-geospatial/flight-tracks. Accesed [Feb 20,2025]. © AeroWhere by Root Geospatial.
Root Geospatial LLC. 2025. "AeroWhere Flight Tracks: Historical Flight Tracks Dataset". Source Cooperative, https://source.coop/root-geospatial/flight-tracks. Accesed [Feb 20,2025].
© AeroWhere by Root Geospatial.
helicopter flight tracksaviation dataNew Yorkurban airspaceflight speed analysis
File Geodatabase Feature Class
dataset
EPSG
6.18.3(9.3.1.2)
0
Simple
FALSE
0
TRUE
FALSE
Bell_FlightTrack_Zenabled_ExportToUpdate
Feature Class
0
OBJECTID
OBJECTID
OID
8
0
0
Internal feature number.
Esri
Sequential unique whole numbers that are automatically generated.
Shape
Shape
Geometry
0
0
0
Feature geometry.
Esri
Coordinates defining the features.
icao
icao
String
2147483647
0
0
r
r
String
2147483647
0
0
aircraft_type
aircraft_type
String
2147483647
0
0
desc_
desc_
String
2147483647
0
0
start_height_agl_ft
start_height_agl_ft
Single
4
0
0
end_height_agl_ft
end_height_agl_ft
Single
4
0
0
start_t
start_t
Date
8
1
0
end_t
end_t
Date
8
1
0
geometry_Length
geometry_Length
Double
8
0
0
DateOfFlight
Day of flight
DateOnly
8
0
0
Shape_Length
Shape_Length
Double
8
0
0
Length of feature in internal units.
Esri
Positive real numbers that are automatically generated.
20260224
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