Lots of improvements
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33 changed files with 2587 additions and 1866 deletions
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@ -50,7 +50,6 @@ _AREA_COLUMNS = [
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"Number of restaurants within 2km",
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"Number of grocery shops and supermarkets within 2km",
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"Number of parks within 2km",
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"Train or tube stations within 1km",
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"Distance to nearest train or tube station (km)",
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"Distance to nearest park (km)",
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# Environment
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@ -325,7 +324,6 @@ def _build(
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"restaurants_2km": "Number of restaurants within 2km",
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"groceries_2km": "Number of grocery shops and supermarkets within 2km",
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"parks_2km": "Number of parks within 2km",
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"train_tube_1km": "Train or tube stations within 1km",
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"train_tube_nearest_km": "Distance to nearest train or tube station (km)",
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"parks_nearest_km": "Distance to nearest park (km)",
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"latest_price": "Last known price",
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@ -15,11 +15,6 @@ POI_GROUPS_2KM = {
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"groceries": ["Greengrocer", "Supermarket", "Convenience Store"],
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}
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# Train/tube stations counted at 1km radius
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TRAIN_TUBE_GROUP = {
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"train_tube": ["Metro or Tram stop", "Rail station"],
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}
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# Groups for which to compute distance to nearest POI (from filtered POIs)
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DISTANCE_GROUPS = {
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"train_tube": ["Metro or Tram stop", "Rail station"],
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@ -67,11 +62,6 @@ def main():
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postcodes, pois, groups=POI_GROUPS_2KM, radius_km=2
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)
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# Count train/tube stations within 1km
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counts_1km = count_pois_per_postcode(
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postcodes, pois, groups=TRAIN_TUBE_GROUP, radius_km=1
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)
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# Distance to nearest train/tube station (from filtered POIs)
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distances = min_distance_per_postcode(postcodes, pois, groups=DISTANCE_GROUPS)
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@ -86,8 +76,7 @@ def main():
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# Join all results on postcode
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result = (
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counts_2km.join(counts_1km, on="postcode")
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.join(distances, on="postcode")
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counts_2km.join(distances, on="postcode")
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.join(park_counts_2km, on="postcode")
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.join(park_distances, on="postcode")
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)
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