"don't think this is a post office"
OSM snapshot date: 2026-04-05T19:23:11Z
POI name: Doddle
POI types: building:part amenity-post_office
#CoMaps android 2026.04.07-8

"I'm standing here now and there is no bus stop it's further down the road"
The place has gone or never existed. A user of Organic Maps application has reported that the POI was visible on the map (see snapshot date below), but was not found on the ground.
OSM snapshot date: 2026-04-04T03:36:32Z
POI name: Clapham Junction Station
POI types: public_transport-platform highway-bus_stop
#organicmaps android 2026.04.07-8-Google

March 7, 1945, a German V2 hit this area, destroying a row of homes and killing 53 people.

Unable to answer "Do these steps have a ramp? What kind?" for way/302578480 via StreetComplete 53.0:

Inaccessible due to construction

Attached photo(s):
https://westnordost.de/p/150428.jpg

lift entrance on opposite side

via StreetComplete 63.0

Unable to answer "What are the opening hours here?" – Grab & Go (Convenience Store) – way/432267488 via StreetComplete 63.0:

Not sure if this is still a convenience store

Attached photo(s):
https://streetcomplete.app/p/339000.jpg

Charles Bell House

In context of overlay "Places" – Hagen (Cafe) – node/12628306251 via StreetComplete 60.3:

Duplicates in wrong place

70 Upper Street Islington is Bar Silver, a cocktail bar and nightclub
www.barsilver.uk
@barsilvern1
https://share.google/PYkwmDXoSHLir0yLG

import pandas as pd
import geopandas as gpd
from shapely.geometry import Point
import overpy
from geopy.distance import geodesic

# 1. Get pubs from OpenStreetMap
api = overpy.Overpass()
result = api.query("""
area["name"="London"]->.searchArea;
(
node["amenity"="pub"](area.searchArea);
way["amenity"="pub"](area.searchArea);
relation["amenity"="pub"](area.searchArea);
);
out center;
""")

# 2. Build dataset
pubs = []
for node in result.nodes:
pubs.append({
"name": node.tags.get("name", "Unknown"),
"lat": float(node.lat),
"lon": float(node.lon)
})

df = pd.DataFrame(pubs)

# 3. Origin point (SE15 2LP approx centroid)
origin = (51.4696, -0.0699)

# 4. Compute exact distances
df["distance_m"] = df.apply(
lambda r: geodesic(origin, (r["lat"], r["lon"])).meters,
axis=1
)

# 5. Sort and take top 500
nearest_500 = df.sort_values("distance_m").head(500)

print(nearest_500)