126 lines
4.1 KiB
Python
126 lines
4.1 KiB
Python
"""Count POIs within a radius of properties, optimized via postcode deduplication."""
|
|
|
|
import numpy as np
|
|
import polars as pl
|
|
|
|
from .haversine import haversine_km
|
|
|
|
|
|
def count_pois_per_postcode(
|
|
postcodes_df: pl.DataFrame,
|
|
pois: pl.DataFrame,
|
|
groups: dict[str, list[str]],
|
|
radius_km: float = 2.0,
|
|
) -> pl.DataFrame:
|
|
"""
|
|
For each unique postcode, count POIs within radius_km by category group.
|
|
Uses spatial grid with vectorized distance calculations.
|
|
"""
|
|
print(f"Counting POIs within {radius_km}km per postcode...")
|
|
|
|
n_postcodes = len(postcodes_df)
|
|
n_pois = len(pois)
|
|
print(f" {n_postcodes:,} postcodes, {n_pois:,} POIs")
|
|
|
|
# Build spatial grid for POIs (0.05 degree cells ~5.5km)
|
|
grid_size = 0.05
|
|
print(" Building POI spatial grid...")
|
|
|
|
# Convert to numpy arrays
|
|
poi_lats = pois["lat"].to_numpy()
|
|
poi_lngs = pois["lng"].to_numpy()
|
|
poi_cats = pois["category"].to_numpy()
|
|
|
|
# Compute grid coordinates for all POIs
|
|
poi_grid_lats = np.floor(poi_lats / grid_size).astype(np.int32)
|
|
poi_grid_lngs = np.floor(poi_lngs / grid_size).astype(np.int32)
|
|
|
|
# Build grid cell lookup using numpy indexing
|
|
poi_grid = {}
|
|
for i in range(n_pois):
|
|
key = (poi_grid_lats[i], poi_grid_lngs[i])
|
|
if key not in poi_grid:
|
|
poi_grid[key] = []
|
|
poi_grid[key].append(i)
|
|
|
|
# Convert grid values to numpy arrays for faster indexing
|
|
for key in poi_grid:
|
|
poi_grid[key] = np.array(poi_grid[key], dtype=np.int32)
|
|
|
|
print(f" POI grid has {len(poi_grid):,} occupied cells")
|
|
|
|
# Pre-compute category masks
|
|
category_masks = {}
|
|
for group, categories in groups.items():
|
|
mask = np.isin(poi_cats, categories)
|
|
category_masks[group] = mask
|
|
print(f" {group}: {mask.sum():,} POIs")
|
|
|
|
# Extract postcode coordinates as numpy arrays
|
|
pc_lats = postcodes_df["lat"].to_numpy()
|
|
pc_lons = postcodes_df["lon"].to_numpy()
|
|
pc_codes = postcodes_df["postcode"].to_list()
|
|
|
|
# Initialize result arrays
|
|
result_counts = {group: np.zeros(n_postcodes, dtype=np.int32) for group in groups}
|
|
|
|
# Process in batches with progress
|
|
batch_size = 50000
|
|
n_batches = (n_postcodes + batch_size - 1) // batch_size
|
|
|
|
print(f" Processing {n_postcodes:,} postcodes in {n_batches} batches...")
|
|
|
|
for batch_idx in range(n_batches):
|
|
start_idx = batch_idx * batch_size
|
|
end_idx = min(start_idx + batch_size, n_postcodes)
|
|
|
|
if batch_idx % 5 == 0:
|
|
print(
|
|
f" Batch {batch_idx + 1}/{n_batches}: postcodes {start_idx:,} - {end_idx:,}"
|
|
)
|
|
|
|
# Process batch
|
|
for i in range(start_idx, end_idx):
|
|
pc_lat = pc_lats[i]
|
|
pc_lon = pc_lons[i]
|
|
|
|
# Find grid cells to check (3x3 grid)
|
|
grid_lat = int(np.floor(pc_lat / grid_size))
|
|
grid_lng = int(np.floor(pc_lon / grid_size))
|
|
|
|
# Collect nearby POI indices
|
|
nearby_indices = []
|
|
for dlat in [-1, 0, 1]:
|
|
for dlng in [-1, 0, 1]:
|
|
cell_key = (grid_lat + dlat, grid_lng + dlng)
|
|
if cell_key in poi_grid:
|
|
nearby_indices.append(poi_grid[cell_key])
|
|
|
|
if not nearby_indices:
|
|
continue
|
|
|
|
# Concatenate all nearby POI indices
|
|
nearby = np.concatenate(nearby_indices)
|
|
|
|
# Vectorized distance calculation for all nearby POIs
|
|
distances = haversine_km(poi_lats[nearby], poi_lngs[nearby], pc_lat, pc_lon)
|
|
|
|
# Filter by radius
|
|
within_mask = distances <= radius_km
|
|
within_indices = nearby[within_mask]
|
|
|
|
if len(within_indices) == 0:
|
|
continue
|
|
|
|
# Count by category group using pre-computed masks
|
|
for group, cat_mask in category_masks.items():
|
|
result_counts[group][i] = cat_mask[within_indices].sum()
|
|
|
|
# Build result dataframe
|
|
result_data = {"postcode": pc_codes}
|
|
for group in groups:
|
|
result_data[f"{group}_{int(radius_km)}km"] = result_counts[group]
|
|
|
|
result = pl.DataFrame(result_data)
|
|
print(" Completed POI counting")
|
|
return result
|