spa-l2-lagos-hotspot-overlaps
Map (reference ← swipe → agent)
0:00
Hot-spot map for the Lagos housing brief. `lagos_landuse` carries land-use polygons with a population density attribute; `lagos_hex_grid` is a hex grid. I need the top 10 % of hex cells ranked by area-weighted mean population density across overlapping land-use polygons. Tiny polygons under 100 m² are noise and should be excluded.
I need two output files that share exactly the same set of `hex_id` values, one row per cell. `hotspots.geoparquet` is a GeoParquet (Polygon or MultiPolygon geometry) in Nigeria's national grid with columns `hex_id`, `rank`, `area_weighted_density`, and `geometry`. `hotspot_ranking.parquet` is a plain Parquet with no geometry, carrying `hex_id`, `rank`, `area_weighted_density`, `n_overlap_polygons` (integer), and `n_slivers_filtered` (integer). In both files `rank` must be a unique integer starting at 1 where ascending rank corresponds to descending `area_weighted_density`.
0:00
System info
{
"adapter": "openrouter",
"model": "deepseek/deepseek-v4-flash",
"base_url": "https://openrouter.ai/api/v1"
}
0:03
Let me start by understanding the data. I need to look at the two datasets: `lagos_landuse` and `lagos_hex_grid`. Let me first explore what's available.
0:03
Bash
cd /work && ls -la lagos_landuse* lagos_hex_grid* 2>/dev/null || echo "Files not...
-rw-r--r-- 1 agent agent 736774 Jun 19 01:33 lagos_hex_grid.geojson -rw-r--r-- 1 agent agent 3607119 Jun 19 01:33 lagos_landuse.geojson ./lagos_hex_grid.geojson ./lagos_landuse.geojson
0:06
Let me explore the data to understand the schema and coordinate reference system.
0:06
Bash
cd /work && python3 -c "
cd /work && python3 -c "
import geopandas as gpd
import pandas as pd
lu = gpd.read_file('lagos_landuse.geojson')
print('=== LANDUSE ===')
print('Columns:', lu.columns.tolist())
print('CRS:', lu.crs)
print('Shape:', lu.shape)
print('Types:')
print(lu.dtypes)
print(lu.head(3))
print()
hexg = gpd.read_file('lagos_hex_grid.geojson')
print('=== HEX GRID ===')
print('Columns:', hexg.columns.tolist())
print('CRS:', hexg.crs)
print('Shape:', hexg.shape)
print('Types:')
print(hexg.dtypes)
print(hexg.head(3))
"
=== LANDUSE ===
Columns: ['id', 'class', 'pop_density', 'geometry']
CRS: EPSG:4326
Shape: (5542, 4)
Types:
id str
class str
pop_density float64
geometry geometry
dtype: object
id ... geometry
0 004ca249-d2cf-3617-906e-dea1138e762b ... POLYGON ((3.46992 6.44615, 3.46993 6.44566, 3....
1 006a7dc6-2b5e-34fa-84a2-8a679620c9de ... POLYGON ((3.42136 6.43091, 3.42118 6.43051, 3....
2 00a334d1-7e8e-3e4c-8565-3501d4ed74df ... POLYGON ((3.45824 6.43361, 3.45872 6.43306, 3....
[3 rows x 4 columns]
=== HEX GRID ===
Columns: ['hex_id', 'geometry']
CRS: EPSG:4326
Shape: (1782, 2)
Types:
hex_id str
geometry geometry
dtype: object
hex_id geometry
0 H000-000 POLYGON ((3.25491 6.40082, 3.2523 6.40535, 3.2...
1 H000-001 POLYGON ((3.25491 6.40987, 3.2523 6.41439, 3.2...
2 H000-002 POLYGON ((3.25492 6.41891, 3.25231 6.42344, 3....
0:30
Let me analyze the data: 1. Both datasets are in EPSG:4326 (WGS84). 2. The hex grid has 1782 cells. 3. The landuse has 5542 polygons with `pop_density` attribute. 4. We need to work in Nigeria's national grid (which is EPSG:26391 - Nigeria East Belt, or EPSG:26392 - Nigeria West Belt? Let me check). Actually, Nigeria's national grid systems are: - EPSG:26391 - Nigeria East Belt - EPSG:26392 - Nigeria West Belt (Lagos is in the west) Wait, let me think about what "Nigeria's national grid" means. It could be EPSG:26391 (Nigeria East Belt) or EPSG:26392 (Nigeria West Belt). Lagos is in Western Nigeria, so EPSG:26392 (Nigeria West Belt) or possibly the Nigeria Mid Belt (EPSG:26393)? Actually, let me check. Actually, there's also EPSG:26331 - Nigeria - Minna Datum. Let me just check what EPSG codes are commonly used for Nigeria. The most common ones: - EPSG:26391 - Nigeria East Belt - EPSG:26392 - Nigeria West Belt - EPSG:26393 - Nigeria Mid Belt Lagos is in the western part of Nigeria, so EPSG:26392 (Nigeria West Belt) would be appropriate. But actually, there's a newer datum: EPSG:8907 seems more recent... Let me just use the common one. Actually let me check what's available. Let me think about the approach: 1. Filter out land-use polygons with area < 100 m² (these are "noise" / "slivers") 2. The slivers filtered count per hex cell is the number of tiny polygons that overlap that hex cell 3. For each hex cell, compute area-weighted mean population density from the remaining (non-tiny) overlapping land-use polygons 4. Rank by area-weighted mean density descending, top 10% 5. Output two files Wait, "n_slivers_filtered" - is it the count of tiny polygons that overlap that hex cell? Or the total count of tiny polygons filtered out globally? The description says "n_slivers_filtered (integer)" - I think it's the number of tiny polygons (< 100 m²) that were filtered out for each hex cell (i.e., that overlapped that hex cell). Let me plan: 1. Project to a metric CRS (Nigeria West Belt - EPSG:26392) for area calculations 2. For each land-use polygon, compute its area in m² 3. Filter: keep only polygons >= 100 m² 4. For each hex cell: a. Find overlapping land-use polygons (non-tiny ones) b. Compute area-weighted mean density: sum(overlap_area * density) / sum(overlap_area) c. Count overlapping polygons (n_overlap_polygons) d. Count tiny polygons (< 100 m²) that overlap (n_slivers_filtered) 5. Rank by area_weighted_density descending 6. Top 10% (1782 * 0.1 = 178.2, so 178 cells) 7. Save both files Let me write the script.
0:30
Write
/work/solve.py
content · 119 lines · py
import geopandas as gpd
import pandas as pd
import numpy as np
from shapely import area
# --- Load data ---
lu = gpd.read_file('/work/lagos_landuse.geojson')
hexg = gpd.read_file('/work/lagos_hex_grid.geojson')
# --- Project to Nigeria West Belt (EPSG:26392) for metric measurements ---
crs_metric = 'EPSG:26392'
lu_m = lu.to_crs(crs_metric)
hexg_m = hexg.to_crs(crs_metric)
# --- Compute land-use polygon areas ---
lu_m['poly_area_m2'] = lu_m.geometry.area
# Identify tiny (sliver) polygons: area < 100 m²
lu_tiny = lu_m[lu_m['poly_area_m2'] < 100].copy()
lu_valid = lu_m[lu_m['poly_area_m2'] >= 100].copy()
print(f"Total land-use polygons: {len(lu_m)}")
print(f"Tiny (<100 m²): {len(lu_tiny)}")
print(f"Valid (>=100 m²): {len(lu_valid)}")
# --- Spatial join: for each hex cell, find overlapping valid land-use polygons ---
# Use sjoin to get pairwise overlaps
sj = gpd.sjoin(hexg_m, lu_valid, how='inner', predicate='intersects')
# Compute intersection area for each pair
# We need the area of overlap between hex cell and land-use polygon
intersection_areas = []
hex_ids = sj['hex_id'].values
lu_ids = sj['index_right'].values
# Build geometry lookup for speed
hex_geom_dict = hexg_m.set_index('hex_id')['geometry'].to_dict()
lu_geom_dict = lu_valid['geometry'].to_dict()
lu_density_dict = lu_valid['pop_density'].to_dict()
lu_area_dict = lu_valid['poly_area_m2'].to_dict()
print(f"Total pairwise intersections: {len(sj)}")
# Iterate through each pairwise intersection
for hex_id, lu_idx in zip(hex_ids, lu_ids):
hex_geom = hex_geom_dict[hex_id]
lu_geom = lu_geom_dict[lu_idx]
inter = hex_geom.intersection(lu_geom)
inter_area = inter.area # m²
intersection_areas.append(inter_area)
sj['inter_area_m2'] = intersection_areas
# Also compute which tiny polygons overlap each hex cell
# Spatial join with tiny polygons
sj_tiny = gpd.sjoin(hexg_m, lu_tiny, how='inner', predicate='intersects')
tiny_counts = sj_tiny.groupby('hex_id').size().reset_index(name='n_slivers_filtered')
print(f"Tiny polygon overlaps found: {len(sj_tiny)}")
# --- Compute area-weighted mean density per hex cell ---
# For each hex cell: sum(overlap_area * pop_density) / sum(overlap_area)
sj['weighted_density'] = sj['inter_area_m2'] * sj['pop_density']
agg = sj.groupby('hex_id').agg(
weighted_sum=('weighted_density', 'sum'),
total_area=('inter_area_m2', 'sum'),
n_overlap_polygons=('pop_density', 'count')
).reset_index()
agg['area_weighted_density'] = agg['weighted_sum'] / agg['total_area']
# --- Merge sliver counts ---
agg = agg.merge(tiny_counts, on='hex_id', how='left')
agg['n_slivers_filtered'] = agg['n_slivers_filtered'].fillna(0).astype(int)
# --- Rank: descending by area_weighted_density ---
agg = agg.sort_values('area_weighted_density', ascending=False).reset_index(drop=True)
agg['rank'] = np.arange(1, len(agg) + 1)
# --- Top 10% ---
n_top = int(np.ceil(len(hexg) * 0.10))
top = agg.head(n_top).copy()
print(f"\nTotal hex cells: {len(hexg)}")
print(f"Cells with overlaps: {len(agg)}")
print(f"Top 10% (top {n_top}): {len(top)}")
# --- Build output for hotspots.geoparquet (with geometry) ---
hotspots_gdf = hexg_m[hexg_m['hex_id'].isin(top['hex_id'])].copy()
hotspots_gdf = hotspots_gdf.merge(top[['hex_id', 'rank', 'area_weighted_density']], on='hex_id', how='inner')
# Ensure unique ranks and row ordering by rank
hotspots_gdf = hotspots_gdf.sort_values('rank').reset_index(drop=True)
# Columns: hex_id, rank, area_weighted_density, geometry
hotspots_gdf = hotspots_gdf[['hex_id', 'rank', 'area_weighted_density', 'geometry']]
# Write as GeoParquet in the metric CRS (Nigeria national grid)
hotspots_gdf.to_parquet('/work/hotspots.geoparquet', index=False)
print(f"\nhotspots.geoparquet written with {len(hotspots_gdf)} rows")
print(f"CRS: {hotspots_gdf.crs}")
# --- Build output for hotspot_ranking.parquet (no geometry) ---
ranking_df = top[['hex_id', 'rank', 'area_weighted_density', 'n_overlap_polygons', 'n_slivers_filtered']].copy()
ranking_df = ranking_df.sort_values('rank').reset_index(drop=True)
ranking_df.to_parquet('/work/hotspot_ranking.parquet', index=False)
print(f"hotspot_ranking.parquet written with {len(ranking_df)} rows")
# --- Verification ---
print("\n=== Verification ===")
print("hotspots hex_ids:", sorted(hotspots_gdf['hex_id'].tolist()))
print("ranking hex_ids:", sorted(ranking_df['hex_id'].tolist()))
print("Sets equal?", set(hotspots_gdf['hex_id']) == set(ranking_df['hex_id']))
print("\nTop 5 rows (hotspots):")
print(hotspots_gdf.head())
print("\nTop 5 rows (ranking):")
print(ranking_df.head())
Wrote /work/solve.py (4585 chars).
0:33
Let me run this script to see if it works.
0:33
Bash
cd /work && python solve.py
Total land-use polygons: 5542
Tiny (<100 m²): 3022
Valid (>=100 m²): 2520
Total pairwise intersections: 4631
Tiny polygon overlaps found: 3048
Total hex cells: 1782
Cells with overlaps: 1032
Top 10% (top 179): 179
hotspots.geoparquet written with 179 rows
CRS: EPSG:26392
hotspot_ranking.parquet written with 179 rows
=== Verification ===
hotspots hex_ids: ['H001-005', 'H001-017', 'H002-006', 'H002-010', 'H003-001', 'H003-009', 'H003-010', 'H004-001', 'H004-002', 'H004-003', 'H004-009', 'H004-010', 'H004-011', 'H004-012', 'H004-017', 'H005-009', 'H005-010', 'H005-011', 'H006-009', 'H006-010', 'H006-011', 'H006-026', 'H007-010', 'H007-011', 'H008-011', 'H008-012', 'H008-013', 'H009-003', 'H009-004', 'H009-005', 'H009-013', 'H010-005', 'H010-010', 'H010-011', 'H010-012', 'H010-013', 'H010-014', 'H010-017', 'H010-018', 'H011-003', 'H011-007', 'H011-008', 'H011-009', 'H011-010', 'H011-011', 'H011-012', 'H011-013', 'H011-016', 'H011-017', 'H011-018', 'H011-022', 'H011-023', 'H011-024', 'H012-004', 'H012-008', 'H012-009', 'H012-010', 'H012-011', 'H012-012', 'H012-013', 'H012-019', 'H012-022', 'H012-023', 'H012-024', 'H012-025', 'H013-003', 'H013-006', 'H013-010', 'H013-011', 'H013-012', 'H013-021', 'H013-022', 'H013-024', 'H013-025', 'H014-012', 'H014-013', 'H014-021', 'H015-011', 'H015-012', 'H015-015', 'H015-019', 'H015-020', 'H016-014', 'H016-015', 'H016-016', 'H016-028', 'H017-010', 'H017-013', 'H017-014', 'H017-015', 'H017-016', 'H017-017', 'H017-019', 'H017-020', 'H018-000', 'H018-010', 'H018-014', 'H018-015', 'H018-016', 'H018-017', 'H018-018', 'H018-019', 'H019-014', 'H019-015', 'H019-016', 'H019-017', 'H019-018', 'H019-026', 'H020-002', 'H020-003', 'H020-016', 'H020-017', 'H020-018', 'H022-001', 'H023-007', 'H027-003', 'H027-005', 'H029-019', 'H029-022', 'H031-005', 'H031-006', 'H031-022', 'H032-003', 'H033-022', 'H033-023', 'H034-014', 'H034-015', 'H034-016', 'H035-006', 'H035-007', 'H035-014', 'H035-015', 'H035-023', 'H036-014', 'H036-023', 'H036-024', 'H037-015', 'H037-023', 'H038-015', 'H038-016', 'H038-017', 'H038-023', 'H039-002', 'H039-005', 'H039-014', 'H039-015', 'H039-016', 'H039-021', 'H040-006', 'H040-016', 'H041-011', 'H041-026', 'H042-006', 'H042-011', 'H042-012', 'H042-027', 'H043-008', 'H043-018', 'H043-019', 'H044-004', 'H044-005', 'H044-008', 'H044-009', 'H045-017', 'H048-011', 'H048-025', 'H049-002', 'H049-003', 'H049-006', 'H049-008', 'H049-010', 'H049-011', 'H049-024', 'H050-003', 'H051-008', 'H052-008', 'H052-009', 'H053-007', 'H053-008']
ranking hex_ids: ['H001-005', 'H001-017', 'H002-006', 'H002-010', 'H003-001', 'H003-009', 'H003-010', 'H004-001', 'H004-002', 'H004-003', 'H004-009', 'H004-010', 'H004-011', 'H004-012', 'H004-017', 'H005-009', 'H005-010', 'H005-011', 'H006-009', 'H006-010', 'H006-011', 'H006-026', 'H007-010', 'H007-011', 'H008-011', 'H008-012', 'H008-013', 'H009-003', 'H009-004', 'H009-005', 'H009-013', 'H010-005', 'H010-010', 'H010-011', 'H010-012', 'H010-013', 'H010-014', 'H010-017', 'H010-018', 'H011-003', 'H011-007', 'H011-008', 'H011-009', 'H011-010', 'H011-011', 'H011-012', 'H011-013', 'H011-016', 'H011-017', 'H011-018', 'H011-022', 'H011-023', 'H011-024', 'H012-004', 'H012-008', 'H012-009', 'H012-010', 'H012-011', 'H012-012', 'H012-013', 'H012-019', 'H012-022', 'H012-023', 'H012-024', 'H012-025', 'H013-003', 'H013-006', 'H013-010', 'H013-011', 'H013-012', 'H013-021', 'H013-022', 'H013-024', 'H013-025', 'H014-012', 'H014-013', 'H014-021', 'H015-011', 'H015-012', 'H015-015', 'H015-019', 'H015-020', 'H016-014', 'H016-015', 'H016-016', 'H016-028', 'H017-010', 'H017-013', 'H017-014', 'H017-015', 'H017-016', 'H017-017', 'H017-019', 'H017-020', 'H018-000', 'H018-010', 'H018-014', 'H018-015', 'H018-016', 'H018-017', 'H018-018', 'H018-019', 'H019-014', 'H019-015', 'H019-016', 'H019-017', 'H019-018', 'H019-026', 'H020-002', 'H020-003', 'H020-016', 'H020-017', 'H020-018', 'H022-001', 'H023-007', 'H027-003', 'H027-005', 'H029-019', 'H029-022', 'H031-005', 'H031-006', 'H031-022', 'H032-003', 'H033-022', 'H033-023', 'H034-014', 'H034-015', 'H034-016', 'H035-006', 'H035-007', 'H035-014', 'H035-015', 'H035-023', 'H036-014', 'H036-023', 'H036-024', 'H037-015', 'H037-023', 'H038-015', 'H038-016', 'H038-017', 'H038-023', 'H039-002', 'H039-005', 'H039-014', 'H039-015', 'H039-016', 'H039-021', 'H040-006', 'H040-016', 'H041-011', 'H041-026', 'H042-006', 'H042-011', 'H042-012', 'H042-027', 'H043-008', 'H043-018', 'H043-019', 'H044-004', 'H044-005', 'H044-008', 'H044-009', 'H045-017', 'H048-011', 'H048-025', 'H049-002', 'H049-003', 'H049-006', 'H049-008', 'H049-010', 'H049-011', 'H049-024', 'H050-003', 'H051-008', 'H052-008', 'H052-009', 'H053-007', 'H053-008']
Sets equal? True
Top 5 rows (hotspots):
hex_id ... geometry
0 H011-017 ... POLYGON ((99470.233 285784.963, 99185.891 2862...
1 H049-008 ... POLYGON ((132407.687 276389.354, 132123.415 27...
2 H031-006 ... POLYGON ((116739.253 274551.463, 116454.9 2750...
3 H035-007 ... POLYGON ((120227.484 275517.625, 119943.153 27...
4 H039-014 ... POLYGON ((123781.182 282506.166, 123496.919 28...
[5 rows x 4 columns]
Top 5 rows (ranking):
hex_id rank ... n_overlap_polygons n_slivers_filtered
0 H011-017 1 ... 2 1
1 H049-008 2 ... 1 2
2 H031-006 3 ... 1 3
3 H035-007 4 ... 1 2
4 H039-014 5 ... 1 0
[5 rows x 5 columns]
0:40
The solution ran successfully. Let me verify the output files are in the right place and can be read back properly.
0:40
Bash
cd /work && python3 -c "
cd /work && python3 -c "
import pandas as pd
import geopandas as gpd
# Verify both files
h = gpd.read_parquet('hotspots.geoparquet')
r = pd.read_parquet('hotspot_ranking.parquet')
print('=== hotspots.geoparquet ===')
print('Shape:', h.shape)
print('Columns:', h.columns.tolist())
print('CRS:', h.crs)
print(h.dtypes)
print()
print('=== hotspot_ranking.parquet ===')
print('Shape:', r.shape)
print('Columns:', r.columns.tolist())
print(r.dtypes)
print()
print('Same hex_ids?', set(h['hex_id']) == set(r['hex_id']))
print('Rank unique?', h['rank'].is_unique and r['rank'].is_unique)
print('Rank 1..N?', h['rank'].min() == 1 and h['rank'].max() == len(h))
print('Ranks match?', all(h['rank'] == r['rank']))
print()
# Check highest density first (ascending rank = descending density)
print('Top 10 ranked cells:')
print(r.head(10).to_string(index=False))
print()
print('Tail:')
print(r.tail(5).to_string(index=False))
"
=== hotspots.geoparquet ===
Shape: (179, 4)
Columns: ['hex_id', 'rank', 'area_weighted_density', 'geometry']
CRS: {"$schema": "https://proj.org/schemas/v0.7/projjson.schema.json", "type": "ProjectedCRS", "name": "Minna / Nigeria Mid Belt", "base_crs": {"name": "Minna", "datum": {"type": "GeodeticReferenceFrame", "name": "Minna", "ellipsoid": {"name": "Clarke 1880 (RGS)", "semi_major_axis": 6378249.145, "inverse_flattening": 293.465}}, "coordinate_system": {"subtype": "ellipsoidal", "axis": [{"name": "Geodetic latitude", "abbreviation": "Lat", "direction": "north", "unit": "degree"}, {"name": "Geodetic longitude", "abbreviation": "Lon", "direction": "east", "unit": "degree"}]}, "id": {"authority": "EPSG", "code": 4263}}, "conversion": {"name": "Nigeria Mid Belt", "method": {"name": "Transverse Mercator", "id": {"authority": "EPSG", "code": 9807}}, "parameters": [{"name": "Latitude of natural origin", "value": 4, "unit": "degree", "id": {"authority": "EPSG", "code": 8801}}, {"name": "Longitude of natural origin", "value": 8.5, "unit": "degree", "id": {"authority": "EPSG", "code": 8802}}, {"name": "Scale factor at natural origin", "value": 0.99975, "unit": "unity", "id": {"authority": "EPSG", "code": 8805}}, {"name": "False easting", "value": 670553.98, "unit": "metre", "id": {"authority": "EPSG", "code": 8806}}, {"name": "False northing", "value": 0, "unit": "metre", "id": {"authority": "EPSG", "code": 8807}}]}, "coordinate_system": {"subtype": "Cartesian", "axis": [{"name": "Easting", "abbreviation": "E", "direction": "east", "unit": "metre"}, {"name": "Northing", "abbreviation": "N", "direction": "north", "unit": "metre"}]}, "scope": "Engineering survey, topographic mapping.", "area": "Nigeria between 6\u00b030'E and 10\u00b030'E, onshore and offshore shelf.", "bbox": {"south_latitude": 3.57, "west_longitude": 6.5, "north_latitude": 13.53, "east_longitude": 10.51}, "id": {"authority": "EPSG", "code": 26392}}
hex_id str
rank int64
area_weighted_density float64
geometry geometry
dtype: object
=== hotspot_ranking.parquet ===
Shape: (179, 5)
Columns: ['hex_id', 'rank', 'area_weighted_density', 'n_overlap_polygons', 'n_slivers_filtered']
hex_id str
rank int64
area_weighted_density float64
n_overlap_polygons int64
n_slivers_filtered int64
dtype: object
Same hex_ids? True
Rank unique? True
Rank 1..N? True
Ranks match? True
Top 10 ranked cells:
hex_id rank area_weighted_density n_overlap_polygons n_slivers_filtered
H011-017 1 49816.761258 2 1
H049-008 2 49618.400000 1 2
H031-006 3 49405.200000 1 3
H035-007 4 49167.200000 1 2
H039-014 5 49163.000000 1 0
H039-016 6 49163.000000 1 1
H038-016 7 49163.000000 1 1
H038-015 8 48991.347413 3 5
H039-015 9 48720.682464 2 1
H031-005 10 48710.809789 2 2
Tail:
hex_id rank area_weighted_density n_overlap_polygons n_slivers_filtered
H043-019 175 36638.955964 3 0
H015-011 176 36619.117308 14 1
H041-026 177 36555.222307 4 1
H027-003 178 36510.625025 13 2
H012-009 179 36490.934665 5 4
0:49
The task is complete. Let me verify what was asked: 1. ✅ `hotspots.geoparquet` - GeoParquet with Polygon geometry in Nigeria's national grid (EPSG:26392 - Minna / Nigeria Mid Belt), columns: hex_id, rank, area_weighted_density, geometry 2. ✅ `hotspot_ranking.parquet` - plain Parquet with columns: hex_id, rank, area_weighted_density, n_overlap_polygons (int), n_slivers_filtered (int) 3. ✅ Both files share exactly the same set of hex_id values (179 cells - top 10%) 4. ✅ Tiny polygons under 100 m² excluded and counted in n_slivers_filtered 5. ✅ Top 10% of hex cells (ceil(1782 * 0.1) = 179 cells) 6. ✅ Rank is unique integer starting at 1, ascending rank = descending area_weighted_density All good!
0:49