Datasets:
File size: 3,725 Bytes
45f7f3d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 | """Build static geography features for the 210 challenge cities.
Inputs (raw/): cities_elev.csv (ID, Name, Lat, Lon, elevation_m) -- Open-Meteo elevation API (static DEM, no weather)
coast50.geojson -- Natural Earth 50m coastline (public domain)
Output: city_features.csv
All inputs are static geography, so they are allowed by the challenge rules.
The current axes are HAND-DRAWN APPROXIMATIONS (see below), not an official dataset.
"""
import json
import numpy as np
import pandas as pd
R = 6371.0
def hav(lat1, lon1, lat2, lon2):
p1, p2 = np.radians(lat1), np.radians(lat2)
a = np.sin((p2 - p1) / 2) ** 2 + np.cos(p1) * np.cos(p2) * np.sin(np.radians(lon2 - lon1) / 2) ** 2
return 2 * R * np.arcsin(np.sqrt(a))
def densify(pts, step_deg=0.05):
out = []
for (x0, y0), (x1, y1) in zip(pts[:-1], pts[1:]):
n = max(2, int(max(abs(x1 - x0), abs(y1 - y0)) / step_deg))
out += list(zip(np.linspace(x0, x1, n), np.linspace(y0, y1, n)))
return np.array(out) # columns: lon, lat
# Approximate axes, (lon, lat). Gulf Stream: Florida Straits -> Hatteras -> off New England.
GULF_STREAM = [(-80.0, 25.0), (-79.9, 27.0), (-79.5, 29.0), (-78.5, 31.0), (-76.5, 33.5),
(-75.3, 35.2), (-72.0, 37.5), (-69.0, 39.5), (-65.0, 40.8)]
# California Current: cool southward flow ~100-300 km offshore, Washington -> Baja.
CALIFORNIA = [(-127.0, 48.0), (-127.0, 45.0), (-126.0, 42.0), (-125.0, 39.0),
(-123.5, 36.5), (-121.5, 34.0), (-119.5, 32.0), (-117.5, 30.0)]
# Labrador Current (cold, southward) approximate shelf-edge path, useful for New England / Mid-Atlantic.
LABRADOR = [(-55.0, 50.0), (-52.0, 46.0), (-56.0, 43.0), (-62.0, 41.5), (-68.0, 40.0), (-72.0, 38.5)]
def min_dist(lat, lon, line):
return float(hav(lat, lon, line[:, 1], line[:, 0]).min())
def main():
cities = pd.read_csv("raw/cities_elev.csv")
gj = json.load(open("raw/coast50.geojson"))
segs = []
for f in gj["features"]:
g = f["geometry"]
lines = [g["coordinates"]] if g["type"] == "LineString" else g["coordinates"]
for ln in lines:
a = np.array(ln)
if len(a) > 1 and a[:, 0].max() > -140 and a[:, 0].min() < -50 and a[:, 1].max() > 10 and a[:, 1].min() < 60:
segs.append(densify([tuple(p) for p in a]))
coast = np.vstack(segs)
coast = coast[(coast[:, 0] > -140) & (coast[:, 0] < -50) & (coast[:, 1] > 10) & (coast[:, 1] < 60)]
gs, ca, lb = densify(GULF_STREAM), densify(CALIFORNIA), densify(LABRADOR)
rows = []
for _, c in cities.iterrows():
d_gs = min_dist(c.Lat, c.Lon, gs)
d_ca = min_dist(c.Lat, c.Lon, ca)
d_lb = min_dist(c.Lat, c.Lon, lb)
rows.append(dict(
ID=c.ID, Name=c.Name, Lat=c.Lat, Lon=c.Lon,
elevation_m=max(c.elevation_m, 0),
dist_coast_km=min_dist(c.Lat, c.Lon, coast),
dist_gulf_stream_km=d_gs,
dist_california_current_km=d_ca,
dist_labrador_km=d_lb,
))
f = pd.DataFrame(rows)
# Smooth 0-1 "ocean influence" weights (exponential decay, e-folding 150 km coast / 300 km currents).
f["w_coast"] = np.exp(-f.dist_coast_km / 150)
f["w_gulf_stream"] = np.exp(-f.dist_gulf_stream_km / 300) * (f.Lon > -90) # Atlantic side only
f["w_calif_current"] = np.exp(-f.dist_california_current_km / 300) * (f.Lon < -110) # Pacific side only
f.to_csv("city_features.csv", index=False)
print(f.describe().T[["min", "50%", "max"]])
print(f.sort_values("dist_coast_km").head(3)[["Name", "dist_coast_km"]])
print(f.sort_values("dist_coast_km").tail(3)[["Name", "dist_coast_km"]])
if __name__ == "__main__":
main()
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