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29 changed files with 250 additions and 126 deletions
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@ -7,7 +7,7 @@ site id and the site's polygon centroid. Sites without access points fall
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back to polygon centroids.
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Using access points rather than polygon centroids gives much more accurate
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distance calculations — a property next to Hyde Park won't show 400m just
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distance calculations: a property next to Hyde Park won't show 400m just
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because the centroid is in the middle of the park. The site id / centroid
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columns let downstream consumers (poi_proximity) collapse the frame back to
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one row per SITE for counting, so a park with 30 gates counts as one park.
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@ -190,7 +190,7 @@ def download_greenspace(output: Path) -> None:
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print(f"Reading {site_shps[0].name} for function types...")
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site_funcs = _read_site_functions(site_shps[0])
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# Step 2: Read access points (primary — park entrances)
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# Step 2: Read access points (primary: park entrances)
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print(f"Reading {access_shps[0].name}...")
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ap_lats, ap_lngs, ap_cats, ap_site_ids = _read_access_points(
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access_shps[0], site_funcs
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