What Are The Ranges From British Airfields?

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A while ago I published a blog post about how the BOT (British Overseas Territories) cover such a large extent that there is always sunlight falling on them. This led me to think about whether this means that the UK still has the potential to have a global reach. I decided that the easiest way of quantifying that was with the presence of an airfield. Airfields allow huge amounts of resource to be deployed extremely quickly - far quicker than by sea.

A lot of the islands that make up the BOT are so small and/or rugged that they cannot have runways built on them. I thought that it would be quite interesting to see what the distances are from the airfields that are on British territories as the coverage certainly cannot be 100%. I wrote the following Python script to investigate this issue: it simply obtains a list of airfields and selects the ones that are on British, or BOT, land. Once it has compiled this list of points it buffers them to create a map of ranges.

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#!/usr/bin/env python3

# Use the proper idiom in the main module ...
# NOTE: See https://docs.python.org/3.11/library/multiprocessing.html#the-spawn-and-forkserver-start-methods
if __name__ == "__main__":
    # Import standard modules ...
    import os

    # Import special modules ...
    try:
        import cartopy
        cartopy.config.update(
            {
                "cache_dir" : os.path.expanduser("~/.local/share/cartopy_cache"),
            }
        )
    except:
        raise Exception("\"cartopy\" is not installed; run \"pip install --user Cartopy\"") from None
    try:
        import matplotlib
        matplotlib.rcParams.update(
            {
                       "backend" : "Agg",                                       # NOTE: See https://matplotlib.org/stable/gallery/user_interfaces/canvasagg.html
                    "figure.dpi" : 300,
                "figure.figsize" : (9.6, 7.2),                                  # NOTE: See https://github.com/Guymer/misc/blob/main/README.md#matplotlib-figure-sizes
                     "font.size" : 8,
            }
        )
        import matplotlib.pyplot
    except:
        raise Exception("\"matplotlib\" is not installed; run \"pip install --user matplotlib\"") from None
    try:
        import shapely
        import shapely.geometry
        import shapely.ops
    except:
        raise Exception("\"shapely\" is not installed; run \"pip install --user Shapely\"") from None

    # Import my modules ...
    try:
        import fmc
    except:
        raise Exception("\"fmc\" is not installed; you need to have the Python module from https://github.com/Guymer/fmc located somewhere in your $PYTHONPATH") from None
    try:
        import pyguymer3
        import pyguymer3.geo
        import pyguymer3.image
    except:
        raise Exception("\"pyguymer3\" is not installed; you need to have the Python module from https://github.com/Guymer/PyGuymer3 located somewhere in your $PYTHONPATH") from None

    # **************************************************************************

    # Create short-hand for the colour map and define the nautical mile, number
    # of angles and resolution ...
    cmap = matplotlib.pyplot.get_cmap("jet")
    nm = 1852.0                                                                 # [m/NM]
    fill = 0.1                                                                  # [°]
    nang = 361                                                                  # [#]
    res = "10m"
    simp = 0.01                                                                 # [°]
    tol = 1.0e-10                                                               # [°]

    # Find file containing all the country shapes ...
    sfile = cartopy.io.shapereader.natural_earth(
          category = "cultural",
              name = "admin_0_countries",
        resolution = res,
    )

    # Create a list of tuples of territories ...
    # NOTE: The first entry is the territory name and the second is the list of
    #       Natural Earth country names that make up the territory. The BAT has
    #       been ignored as it has restrictions on its use.
    territories = [
        ("United Kingdom"                            , ["United Kingdom"]       ),
        ("Akrotiri & Dhekelia"                       , ["Akrotiri", "Dhekelia"] ),
        ("Anguilla"                                  , ["Anguilla"]             ),
        ("Bermuda"                                   , ["Bermuda"]              ),
        ("Cayman Islands"                            , ["Cayman Is."]           ),
        ("Falkland Islands"                          , ["Falkland Is."]         ),
        ("Gibraltar"                                 , ["Gibraltar"]            ),
        ("South Georgia & the South Sandwich Islands", ["S. Geo. and the Is."]  ),
        ("British Indian Ocean Territory"            , ["Br. Indian Ocean Ter."]),
        ("Montserrat"                                , ["Montserrat"]           ),
        ("Pitcairn Islands"                          , ["Pitcairn Is."]         ),
        ("Saint Helena, Ascension & Tristan da Cunha", ["Saint Helena"]         ),
        ("Turks and Caicos Islands"                  , ["Turks and Caicos Is."] ),
        ("British Virgin Islands"                    , ["British Virgin Is."]   ),
    ]

    # Create list of British polygons ...
    polys = []

    # Loop over records ...
    for record in cartopy.io.shapereader.Reader(sfile).records():
        # Create short-hand ...
        neName = pyguymer3.geo.getRecordAttribute(record, "NAME")

        # Skip this record if it is not for a territory in the list ...
        skip = True
        for territory, countries in territories:
            if neName in countries:
                skip = False
                break
        if skip:
            continue

        # Loop over Polygons and add them to the list ...
        for poly in pyguymer3.geo.extract_polys(record.geometry):
            # HACK: The Natural Earth polygons are sometimes so coarse that they
            #       do not cover the runway (e.g. Diego Garcia) so they are
            #       buffered by 5km.
            polys += pyguymer3.geo.extract_polys(
                pyguymer3.geo.buffer(
                    poly,
                    5000.0,
                    debug = False,
                     fill = fill,
                     nang = nang,
                     simp = simp,
                      tol = tol,
                )
            )

    print(f"There are {len(polys):d} British polygons.")

    # Create list of British airfields ...
    # HACK: Manually add HLE as it is missing from the OpenFlights GitHub
    #       repository.
    airfields = []                                                              # [°], [°]
    airfields.append(shapely.geometry.Point(-5.645833, -15.959167))             # [°], [°]

    # Loop over airports ...
    for airport in fmc.load_airport_list():
        # Create point and check if it is within any of the polygons ...
        airfield = shapely.geometry.Point(airport["Longitude"], airport["Latitude"])# [°], [°]
        for poly in polys:
            if poly.contains(airfield):
                airfields.append(airfield)                                      # [°], [°]
                break

    print(f"There are {len(airfields):d} British airfields.")

    # Create figure ...
    fg = matplotlib.pyplot.figure(figsize = (12.8, 7.2))

    # Create axis ...
    ax = pyguymer3.geo.add_axis(fg)

    # Configure axis ...
    pyguymer3.geo.add_map_background(ax, resolution = "large8192px")

    # Add country outlines ...
    for record in cartopy.io.shapereader.Reader(sfile).records():
        ax.add_geometries(
            pyguymer3.geo.extract_polys(record.geometry),
            cartopy.crs.PlateCarree(),
            edgecolor = "black",
            facecolor = "none",
            linewidth = 0.5,
        )

    # Initialize distance, labels and lines ...
    dist2 = 0.0                                                                 # [NM]
    labels = []
    lines = []

    # Loop over distances ...
    for i in range(8):
        # Set distance ...
        dist1 = 500.0                                                           # [NM]
        dist2 += dist1                                                          # [NM]

        # Decide what needs doing ...
        if i == 0:
            print(f"Calculating for {dist2:6,.0f} NM (using {len(airfields):,d} airfields) ...")

            # Buffer airfields ...
            # NOTE: "airfields" is the Point-list to be buffered.
            geoms = []
            for airfield in airfields:
                geoms += pyguymer3.geo.extract_polys(
                    pyguymer3.geo.buffer(
                        airfield,
                        dist1 * nm,
                        debug = False,
                         fill = fill,
                         nang = nang,
                         simp = simp,
                          tol = tol,
                    )
                )
            geoms = shapely.ops.unary_union(geoms).simplify(tol)
            pyguymer3.geo.check(geoms)
            # NOTE: "geoms" is now the [Multi]Polygon buffer of "airfields".
        else:
            # Find out how complicated the [Multi]Polygon is ...
            npoints = 0                                                         # [#]
            npolys = 0                                                          # [#]
            for poly in pyguymer3.geo.extract_polys(geoms):
                npoints += len(poly.exterior.coords)                            # [#]
                npolys += 1                                                     # [#]
                for interior in poly.interiors:
                    npoints += len(interior.coords)                             # [#]

            print(f"Calculating for {dist2:6,.0f} NM (using {npoints:,d} points in {npolys:,d} Polygons) ...")

            # Buffer area ...
            # NOTE: "geoms" is the [Multi]Polygon to be buffered.
            geoms = pyguymer3.geo.buffer(
                geoms,
                dist1 * nm,
                debug = False,
                 fill = fill,
                 nang = nang,
                 simp = simp,
                  tol = tol,
            )
            # NOTE: "geoms" is now the [Multi]Polygon buffer of "geoms".

        # Add to plot ...
        ax.add_geometries(
            pyguymer3.geo.extract_polys(geoms),
            cartopy.crs.PlateCarree(),
            edgecolor = cmap(float(i) / 7.0),
            facecolor = "none",
            linewidth = 1.0,
        )
        labels.append(f"{dist2:6,.0f} NM")
        lines.append(matplotlib.lines.Line2D([], [], color = cmap(float(i) / 7.0)))

    print("Plotting ...")

    # Configure axis ...
    ax.legend(
        lines,
        labels,
         loc = "upper center",
        ncol = 4,
    )

    # Configure figure ...
    fg.tight_layout()

    # Save figure ...
    fg.savefig("find_airfields.png")
    matplotlib.pyplot.close(fg)

    # Optimize PNG ...
    pyguymer3.image.optimize_image(
        "find_airfields.png",
        strip = True,
    )

              
You may also download “find_airfields.py” directly or view “find_airfields.py” on GitHub Gist (you may need to manually checkout the “main” branch).

The database of airfields is taken from OpenFlights.org and only needed one manual addition (as the Saint Helena Airport only opened a couple of years ago and is currently missing from the airport database on GitHub). This map that this script creates is shown below.

Download:
  1. 512 px × 288 px (0.1 Mpx; 231.9 KiB)
  2. 1,024 px × 576 px (0.6 Mpx; 871.4 KiB)
  3. 2,048 px × 1,152 px (2.4 Mpx; 3.0 MiB)
  4. 3,840 px × 2,160 px (8.3 Mpx; 7.2 MiB)

As is clear, the available airfields have a decent coverage between the longitudes of the Cayman Islands in the West and the British Indian Ocean Territory (BIOT) in the East. There is no coverage of the Pacific Ocean even though that is where the Pitcairn Islands are.