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import os
from collections import defaultdict
from datetime import datetime
import numpy as np
import rasterio
from rasterio.transform import rowcol
from rasterio.warp import transform as warp_transform
INPUT_FILE = os.path.join(
os.path.dirname(__file__),
"data",
"InFORM_FireOccurrence_Public_-7825632427851538956.csv",
)
NLCD_FILE = os.path.join(
os.path.dirname(__file__),
"data",
"Annual_NLCD_LndCov_2024_CU_C1V1.tif",
)
OUTPUT_FIRES = os.path.join(os.path.dirname(__file__), "data", "fires_clean.csv")
OUTPUT_DENSITY = os.path.join(os.path.dirname(__file__), "data", "fire_density.csv")
OUTPUT_LAND_COVER = os.path.join(os.path.dirname(__file__), "data", "land_cover.csv")
BOUNDS = [
(24.5, 49.5, -130.0, -66.9), # CONUS (extended west for offshore)
(51.0, 71.5, -180.0, -129.9), # Alaska
(18.9, 22.2, -160.2, -154.8), # Hawaii
]
VALID_YEARS = range(2010, 2027)
GRID_RES = 0.1
# NLCD classes considered non-burnable
NON_BURNABLE = {
11, # Open Water
12, # Perennial Ice/Snow
21, # Developed, Open Space
22, # Developed, Low Intensity
23, # Developed, Medium Intensity
24, # Developed, High Intensity
}
OUTPUT_FIRES_COLS = [
"latitude",
"longitude",
"year",
"month",
"day_of_year",
"land_cover",
"fire_density",
]
OUTPUT_DENSITY_COLS = ["lat_cell", "lon_cell", "fire_count"]
OUTPUT_LAND_COVER_COLS = ["lat_cell", "lon_cell", "land_cover"]
def in_bounds(lat, lon):
for lat_min, lat_max, lon_min, lon_max in BOUNDS:
if lat_min <= lat <= lat_max and lon_min <= lon <= lon_max:
return True
return False
def parse_date(date_str):
for fmt in ("%m/%d/%Y %I:%M:%S %p", "%m/%d/%Y %H:%M:%S", "%Y/%m/%d %H:%M:%S+00"):
try:
dt = datetime.strptime(date_str.strip(), fmt)
return dt.year, dt.month, dt.timetuple().tm_yday
except ValueError:
continue
return None
def snap_to_grid(lat, lon, res=GRID_RES):
return (
round(round(lat / res) * res, 6),
round(round(lon / res) * res, 6),
)
def sample_land_cover(dataset, lat, lon):
try:
xs, ys = warp_transform("EPSG:4326", dataset.crs, [lon], [lat])
row, col = rowcol(dataset.transform, xs[0], ys[0])
data = dataset.read(1, window=((row, row + 1), (col, col + 1)))
return int(data[0, 0])
except Exception:
return -1
def main():
print("Pass 1: reading fire records and computing fire density...")
fire_density = defaultdict(int)
raw_records = []
kept = 0
dropped_no_coords = 0
dropped_bad_bounds = 0
dropped_bad_date = 0
dropped_bad_year = 0
dropped_bad_type = 0
total = 0
with open(INPUT_FILE, newline="", encoding="utf-8-sig") as fin:
reader = csv.DictReader(fin)
for row in reader:
total += 1
lat_str = row.get("Initial Latitude", "").strip()
lon_str = row.get("Initial Longitude", "").strip()
if not lat_str or not lon_str:
dropped_no_coords += 1
continue
try:
lat = float(lat_str)
lon = float(lon_str)
except ValueError:
dropped_no_coords += 1
continue
if not in_bounds(lat, lon):
dropped_bad_bounds += 1
continue
date_str = row.get("Fire Discovery Date Time", "").strip()
parsed = parse_date(date_str)
if parsed is None:
dropped_bad_date += 1
continue
year, month, day_of_year = parsed
if year not in VALID_YEARS:
dropped_bad_year += 1
continue
incident_type = row.get("Incident Type Category", "").strip().upper()
if incident_type != "WF":
dropped_bad_type += 1
continue
lat_c, lon_c = snap_to_grid(lat, lon)
fire_density[(lat_c, lon_c)] += 1
raw_records.append(
{
"latitude": lat_c,
"longitude": lon_c,
"year": year,
"month": month,
"day_of_year": day_of_year,
}
)
kept += 1
print(f" Read {total:,} rows, kept {kept:,} fire records")
print(f" Unique grid cells with fires: {len(fire_density):,}")
print("Pass 2: sampling land cover from NLCD...")
with (
rasterio.open(NLCD_FILE) as nlcd,
open(OUTPUT_FIRES, "w", newline="", encoding="utf-8") as fout,
):
writer = csv.DictWriter(fout, fieldnames=OUTPUT_FIRES_COLS)
writer.writeheader()
for i, rec in enumerate(raw_records):
if i % 50000 == 0:
print(f" {i:,} / {kept:,}")
lat_c = rec["latitude"]
lon_c = rec["longitude"]
lc = sample_land_cover(nlcd, lat_c, lon_c)
density = fire_density[(lat_c, lon_c)]
writer.writerow(
{
"latitude": lat_c,
"longitude": lon_c,
"year": rec["year"],
"month": rec["month"],
"day_of_year": rec["day_of_year"],
"land_cover": lc,
"fire_density": density,
}
)
print(f"\nFire records written to: {OUTPUT_FIRES}")
print("Writing fire density table...")
with open(OUTPUT_DENSITY, "w", newline="", encoding="utf-8") as f:
writer = csv.DictWriter(f, fieldnames=OUTPUT_DENSITY_COLS)
writer.writeheader()
for (lat_c, lon_c), count in sorted(fire_density.items()):
writer.writerow({"lat_cell": lat_c, "lon_cell": lon_c, "fire_count": count})
print(f"Fire density table written to: {OUTPUT_DENSITY}")
print("Pass 3: precomputing land cover for entire US grid...")
lat_min, lat_max, lon_min, lon_max = 24.5, 49.5, -130.0, -66.9
lats = np.arange(lat_min, lat_max, GRID_RES)
lons = np.arange(lon_min, lon_max, GRID_RES)
total_cells = len(lats) * len(lons)
print(f" Total US grid cells: {total_cells:,}")
with (
rasterio.open(NLCD_FILE) as nlcd,
open(OUTPUT_LAND_COVER, "w", newline="", encoding="utf-8") as f,
):
writer = csv.DictWriter(f, fieldnames=OUTPUT_LAND_COVER_COLS)
writer.writeheader()
done = 0
# batch reproject all points at once per lat row for speed
for lat in lats:
lat_col = [round(float(lat), 6)] * len(lons)
lon_col = [round(float(lon), 6) for lon in lons]
try:
xs, ys = warp_transform("EPSG:4326", nlcd.crs, lon_col, lat_col)
rows, cols = rowcol(nlcd.transform, xs, ys)
for i, (r, c) in enumerate(zip(rows, cols)):
try:
val = int(nlcd.read(1, window=((r, r + 1), (c, c + 1)))[0, 0])
except Exception:
val = -1
writer.writerow(
{
"lat_cell": lat_col[i],
"lon_cell": lon_col[i],
"land_cover": val,
}
)
except Exception:
for lon in lon_col:
writer.writerow(
{
"lat_cell": round(float(lat), 6),
"lon_cell": lon,
"land_cover": -1,
}
)
done += len(lons)
if done % 50000 < len(lons):
print(f" {done:,} / {total_cells:,}")
print(f"Land cover table written to: {OUTPUT_LAND_COVER}")
print(f"\nTotal rows read : {total:>10,}")
print(f"Kept : {kept:>10,} ({100 * kept / total:.1f}%)")
print(f"Dropped - no coords : {dropped_no_coords:>10,}")
print(f"Dropped - out of bounds: {dropped_bad_bounds:>10,}")
print(f"Dropped - bad date : {dropped_bad_date:>10,}")
print(f"Dropped - bad year : {dropped_bad_year:>10,}")
print(f"Dropped - bad type : {dropped_bad_type:>10,}")
if __name__ == "__main__":
main()
|