| """Add `tree_canopy_frac` (0-1) to city_features.csv. |
| |
| Source: USGS/USFS NLCD Tree Canopy Cover, CONUS, 2018 (v2021-4), 30 m, % canopy 0-100, |
| served by MRLC WCS (layer nlcd_tcc_conus_2018_v2021-4). Canopy is non-weather-responsive land-cover data and |
| the vintage (2018) is within the challenge's 2018-12-31 cutoff. |
| Value = mean canopy over a ~10 km x 10 km box centred on the station (a point value is too noisy for an airport station). |
| Run from /Users/david/Hugging Face/features: python3 -I add_tree_canopy.py |
| """ |
| import io |
| import math |
| import time |
| import urllib.request |
|
|
| import numpy as np |
| import pandas as pd |
| from PIL import Image |
|
|
| HALF_KM = 5.0 |
| LAYER = "mrlc_download:nlcd_tcc_conus_2018_v2021-4" |
| URL = ("https://www.mrlc.gov/geoserver/mrlc_download/wcs?service=WCS&version=1.0.0&request=GetCoverage" |
| "&coverage={layer}&crs=EPSG:4326&bbox={w},{s},{e},{n}&width=300&height=300&format=GeoTIFF") |
|
|
|
|
| def canopy(lat, lon): |
| dlat = HALF_KM / 111.0 |
| dlon = HALF_KM / (111.0 * math.cos(math.radians(lat))) |
| url = URL.format(layer=LAYER, w=lon - dlon, e=lon + dlon, s=lat - dlat, n=lat + dlat) |
| for attempt in range(4): |
| try: |
| data = urllib.request.urlopen(url, timeout=60).read() |
| a = np.array(Image.open(io.BytesIO(data))).astype(float) |
| valid = a <= 100 |
| return (a[valid].mean() / 100.0, valid.mean()) if valid.any() else (np.nan, 0.0) |
| except Exception as ex: |
| time.sleep(2 * (attempt + 1)) |
| err = ex |
| print("FAILED", lat, lon, err) |
| return np.nan, 0.0 |
|
|
|
|
| if __name__ == "__main__": |
| f = pd.read_csv("city_features.csv") |
| f = f.drop(columns=[c for c in ("tree_canopy_frac", "tree_canopy_valid_frac") if c in f]) |
| res = [canopy(r.Lat, r.Lon) for r in f.itertuples()] |
| f["tree_canopy_frac"] = [round(r[0], 4) if r[0] == r[0] else np.nan for r in res] |
| f["tree_canopy_valid_frac"] = [round(r[1], 3) for r in res] |
| f.to_csv("city_features.csv", index=False) |
| print(f.tree_canopy_frac.describe()) |
| print("missing:", f.tree_canopy_frac.isna().sum(), "| low coverage (<0.9):", (f.tree_canopy_valid_frac < 0.9).sum()) |
| s = f.sort_values("tree_canopy_frac") |
| print(s.head(4)[["Name", "tree_canopy_frac"]]); print(s.tail(4)[["Name", "tree_canopy_frac"]]) |
|
|
