spa-l2-lagos-hotspot-overlaps
Task error
Retrospective
assessed 2026-06-09T12:13:25Z
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"
}
0:06
Let me start by understanding the task and reading the input files to see what we're working with.
0:06
Bash
ls -la /work/
total 4244 drwxr-xr-x 1 agent agent 86 Jun 9 07:40 . drwxr-xr-x 1 root root 14 Jun 9 07:40 .. -rw-r--r-- 1 agent agent 736774 Jun 9 07:40 lagos_hex_grid.geojson -rw-r--r-- 1 agent agent 3607119 Jun 9 07:40 lagos_landuse.geojson
0:12
Let me read the input files to understand their structure.
0:12
Bash
head -c 5000 /work/lagos_landuse.geojson
{
"type": "FeatureCollection",
"name": "lagos_landuse",
"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:OGC:1.3:CRS84" } },
"features": [
{ "type": "Feature", "properties": { "id": "004ca249-d2cf-3617-906e-dea1138e762b", "class": "commercial", "pop_density": 17613.3 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 3.469915103042965, 6.44615229808863 ], [ 3.469928703042899, 6.445659098088482 ], [ 3.470089903042861, 6.445659098088504 ], [ 3.47013210304285, 6.44565849808851 ], [ 3.470202203042833, 6.445657498088515 ], [ 3.470228903042826, 6.445653898088521 ], [ 3.470262803042818, 6.445653398088522 ], [ 3.470425703042777, 6.445657098088541 ], [ 3.470436503042833, 6.446129098088679 ], [ 3.470436703042834, 6.446141598088685 ], [ 3.469915103042965, 6.44615229808863 ] ] ] } },
{ "type": "Feature", "properties": { "id": "006a7dc6-2b5e-34fa-84a2-8a679620c9de", "class": "retail", "pop_density": 1218.3 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 3.421357303053059, 6.43091109807875 ], [ 3.421180303053053, 6.430510398078611 ], [ 3.421130303053054, 6.430405798078577 ], [ 3.421242203053021, 6.430373998078581 ], [ 3.421460803052961, 6.430314198078587 ], [ 3.421614003052986, 6.430837598078758 ], [ 3.421484703053022, 6.430874098078753 ], [ 3.42146200305303, 6.430880498078752 ], [ 3.421392803053048, 6.43090069807875 ], [ 3.421357303053059, 6.43091109807875 ] ] ] } },
{ "type": "Feature", "properties": { "id": "00a334d1-7e8e-3e4c-8565-3501d4ed74df", "class": "construction", "pop_density": 41621.5 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 3.458240803044293, 6.433608798083667 ], [ 3.458720803044107, 6.433061598083558 ], [ 3.458933003044077, 6.433245398083635 ], [ 3.458453003044264, 6.433792598083746 ], [ 3.458240803044293, 6.433608798083667 ] ] ] } },
{ "type": "Feature", "properties": { "id": "00af9ff7-3138-37a7-bbb8-0a2fbe2bb8e2", "class": "pitch", "pop_density": 5778.7 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 3.348343903093713, 6.616033798125705 ], [ 3.348415103093682, 6.615931998125678 ], [ 3.348611103093651, 6.616067498125741 ], [ 3.348539803093681, 6.616169198125763 ], [ 3.348343903093713, 6.616033798125705 ] ] ] } },
{ "type": "Feature", "properties": { "id": "00d4b7d6-09b5-3d43-97c5-009fd8c97362", "class": "industrial", "pop_density": 39367.0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 3.31147450308641, 6.480733698081184 ], [ 3.310410403086549, 6.479713098080762 ], [ 3.310602503086473, 6.479485398080715 ], [ 3.310846403086385, 6.479257798080675 ], [ 3.311053303086319, 6.479147698080665 ], [ 3.311238103086266, 6.47908889808067 ], [ 3.311592803086173, 6.479044898080696 ], [ 3.311866203086107, 6.47906689808073 ], [ 3.311863003086097, 6.478980298080703 ], [ 3.311845403086094, 6.478907898080681 ], [ 3.311782103086104, 6.478869398080663 ], [ 3.31181650308608, 6.478745198080631 ], [ 3.311761803086085, 6.478682998080604 ], [ 3.312179403085844, 6.477561498080317 ], [ 3.312649503085648, 6.476908598080179 ], [ 3.314273603085247, 6.476930498080363 ], [ 3.314522403085221, 6.47722099808048 ], [ 3.314576703085243, 6.47751149808057 ], [ 3.313235903085803, 6.479357198080971 ], [ 3.312900803085903, 6.479485398080969 ], [ 3.31147450308641, 6.480733698081184 ] ] ] } },
{ "type": "Feature", "properties": { "id": "01235f09-b3b4-3e9b-916f-7968a1f30618", "class": "construction", "pop_density": 39440.1 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 3.361395303073177, 6.47376109808466 ], [ 3.362232903072924, 6.473393098084641 ], [ 3.363570603072938, 6.476199698085619 ], [ 3.363203103073047, 6.476346198085625 ], [ 3.362798103073163, 6.476468798085618 ], [ 3.361395303073177, 6.47376109808466 ] ] ] } },
{ "type": "Feature", "properties": { "id": "018dc5c4-7856-3fe9-af49-1c7999b63a11", "class": "landfill", "pop_density": 22566.6 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 3.578791003034536, 6.597089698144876 ], [ 3.580030203034205, 6.596857898144942 ], [ 3.580507603034065, 6.596658098144937 ], [ 3.581087003033921, 6.596652698144998 ], [ 3.58125330303396, 6.597318898145212 ], [ 3.581666403033879, 6.597492598145307 ], [ 3.581796503033873, 6.597715798145384 ], [ 3.581545103033997, 6.598212898145503 ], [ 3.581392803034168, 6.599338498145823 ], [ 3.581038703034289, 6.599620898145865 ], [ 3.580432503034495, 6.600100498145942 ], [ 3.580561303034496, 6.600377598146039 ], [ 3.580926103034433, 6.600606798146146 ], [ 3.581526903034322, 6.600905198146297 ], [ 3.581720003034339, 6.601438098146476 ], [ 3.5815912030344, 6.601683198146533 ], [ 3.581398103034499, 6.602109498146637 ], [ 3.57950450303492, 6.601741798146328 ], [ 3.579118203034935, 6.601070398146091 ], [ 3.578785603034944, 6.600473598145873 ], [ 3.579086103034765, 6.599588998145647 ], [ 3.578791003034536, 6.597089698144876 ] ] ] } },
{ "type": "Feature", "properties": { "id": "019c2714-5775-34ac-bc9c-b6f974f6c192", "class": "retail", "pop_density": 48168.9 }, "geometry": { "type": "Polygon", "coordina
0:12
Bash
head -c 5000 /work/lagos_hex_grid.geojson
{
"type": "FeatureCollection",
"name": "lagos_hex_grid",
"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:OGC:1.3:CRS84" } },
"features": [
{ "type": "Feature", "properties": { "hex_id": "H000-000" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 3.254907018164061, 6.400821875141842 ], [ 3.252298859306255, 6.405346193333362 ], [ 3.247078053690569, 6.405348732311686 ], [ 3.244465498825615, 6.40082694948331 ], [ 3.247073706776846, 6.396302668472707 ], [ 3.252294420543779, 6.396300133109508 ], [ 3.254907018164061, 6.400821875141842 ] ] ] } },
{ "type": "Feature", "properties": { "hex_id": "H000-001" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 3.254911506086189, 6.409867931932494 ], [ 3.252303304473299, 6.414392250361581 ], [ 3.247082406876335, 6.414394792955152 ], [ 3.244469802917716, 6.40987301350433 ], [ 3.247078053690569, 6.405348732311686 ], [ 3.252298859306255, 6.405346193333362 ], [ 3.254911506086189, 6.409867931932494 ] ] ] } },
{ "type": "Feature", "properties": { "hex_id": "H000-002" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 3.25491600047943, 6.418913985525243 ], [ 3.252307756045468, 6.423438304189768 ], [ 3.247086766334689, 6.42344085039871 ], [ 3.244474113215867, 6.418919074327702 ], [ 3.247082406876335, 6.414394792955152 ], [ 3.252303304473299, 6.414392250361581 ], [ 3.25491600047943, 6.418913985525243 ] ] ] } },
{ "type": "Feature", "properties": { "hex_id": "H000-003" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 3.254920501344345, 6.427960035915699 ], [ 3.252312214023318, 6.432484354813521 ], [ 3.247091132066174, 6.432486904637956 ], [ 3.244478429720606, 6.427965131949017 ], [ 3.247086766334689, 6.42344085039871 ], [ 3.252307756045468, 6.423438304189768 ], [ 3.254920501344345, 6.427960035915699 ] ] ] } },
{ "type": "Feature", "properties": { "hex_id": "H000-004" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 3.254925008681497, 6.437006083099464 ], [ 3.252316678407408, 6.441530402228447 ], [ 3.247095504071337, 6.441532955668494 ], [ 3.244482752432472, 6.437011186363888 ], [ 3.247091132066174, 6.432486904637956 ], [ 3.252312214023318, 6.432484354813521 ], [ 3.254925008681497, 6.437006083099464 ] ] ] } },
{ "type": "Feature", "properties": { "hex_id": "H000-005" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 3.254929522491449, 6.446052127072138 ], [ 3.252321149198293, 6.450576446430146 ], [ 3.247099882350726, 6.450579003485932 ], [ 3.244487081352006, 6.446057237567911 ], [ 3.247095504071337, 6.441532955668494 ], [ 3.252316678407408, 6.441530402228447 ], [ 3.254929522491449, 6.446052127072138 ] ] ] } },
{ "type": "Feature", "properties": { "hex_id": "H000-006" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 3.254934042774767, 6.455098167829325 ], [ 3.252325626396535, 6.459622487414224 ], [ 3.247104266904887, 6.459625048085866 ], [ 3.244491416479749, 6.455103285556696 ], [ 3.247099882350726, 6.450579003485932 ], [ 3.252321149198293, 6.450576446430146 ], [ 3.254934042774767, 6.455098167829325 ] ] ] } },
{ "type": "Feature", "properties": { "hex_id": "H000-007" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 3.254938569532016, 6.464144205366633 ], [ 3.252330110002692, 6.468668525176283 ], [ 3.247108657734369, 6.468671089463908 ], [ 3.244495757816245, 6.464149330325842 ], [ 3.247104266904887, 6.459625048085866 ], [ 3.252325626396535, 6.459622487414224 ], [ 3.254938569532016, 6.464144205366633 ] ] ] } },
{ "type": "Feature", "properties": { "hex_id": "H000-008" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 3.25494310276376, 6.473190239679657 ], [ 3.252334600017325, 6.47771455971193 ], [ 3.247113054839719, 6.477717127615657 ], [ 3.244500105362035, 6.473195371870957 ], [ 3.247108657734369, 6.468671089463908 ], [ 3.252330110002692, 6.468668525176283 ], [ 3.25494310276376, 6.473190239679657 ] ] ] } },
{ "type": "Feature", "properties": { "hex_id": "H000-009" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 3.254947642470568, 6.482236270764012 ], [ 3.252339096440998, 6.486760591016763 ], [ 3.24711745822149, 6.486763162536722 ], [ 3.244504459117662, 6.482241410187647 ], [ 3.247113054839719, 6.477717127615657 ], [ 3.252334600017325, 6.47771455971193 ], [ 3.254947642470568, 6.482236270764012 ] ] ] } },
{ "type": "Feature", "properties": { "hex_id": "H000-010" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 3.254952188653005, 6.491282298615296 ], [ 3.252343599274269, 6.495806619086395 ], [ 3.247121867880231, 6.495809194222707 ], [ 3.244508819083672, 6.49128744527151 ], [ 3.24711745822149, 6.486763162536722 ], [ 3.252339096440998, 6.486760591016763 ], [ 3.254952188653005, 6.491282298615296 ] ] ] } },
{ "type": "Feature", "properties": { "hex_id": "H000-011" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 3.254956741311645, 6.500328323229114 ], [ 3.252348108517704, 6.504852643916427 ], [ 3.247126283816493, 6.504855222669211 ], [ 3.24451318526061, 6.500333477118153 ], [ 3.2471218
0:51
Let me understand the data:
1. `lagos_landuse.geojson` - has land-use polygons with attributes: `id`, `class`, `pop_density`
2. `lagos_hex_grid.geojson` - has hex grid with attributes: `hex_id`
Both are in CRS84 (WGS84) - lat/lon.
I need to:
1. Filter out land-use polygons with area < 100 m² (these are "slivers")
2. For each hex cell, find overlapping land-use polygons
3. Compute area-weighted mean population density for each hex cell
4. Count how many polygons overlap each hex cell (`n_overlap_polygons`)
5. Count how many polygons were filtered out as slivers for each hex cell (`n_slivers_filtered`)
6. Take top 10% by area-weighted density
7. Output to two files:
- `hotspots.geoparquet` - with geometry in Nigeria's national grid (EPSG:26391? Let me think... Nigeria uses several projections. The most common is EPSG:26391 - Minna / Nigeria West Belt, or EPSG:26392 - Minna / Nigeria East Belt, or EPSG:26393 - Minna / Nigeria Mid Belt... Actually the common one for Nigeria surveys is EPSG:26391 (Nigeria West Belt) or EPSG:26392 (Nigeria East Belt). Actually, Nigeria's national grid is typically EPSG:26391 (Minna / Nigeria West Belt) for the western part (Lagos is in the west).
Let me check: Nigeria uses the "Minna" datum. The zones are:
- EPSG:26391 - Minna / Nigeria West Belt (zone 1)
- EPSG:26392 - Minna / Nigeria East Belt (zone 2)
- EPSG:26393 - Minna / Nigeria Mid Belt (zone 3)
Lagos is in western Nigeria, so EPSG:26391 (Nigeria West Belt) would be appropriate.
Actually, I should also consider EPSG:26331 (Minna / UTM zone 31N) and EPSG:26332 (Minna / UTM zone 32N) as alternatives. But the prompt says "Nigeria's national grid" which typically refers to the Nigeria West/Mid/East belt system. Since Lagos is in the west, EPSG:26391 (Minna / Nigeria West Belt) is likely the right one.
Let me write the Python script.
Plan:
1. Read both GeoJSON files using geopandas
2. Calculate area of each land-use polygon in a metric CRS (projected)
3. Filter out polygons with area < 100 m² - these are slivers. Count how many per hex later.
4. For each hex cell, find all overlapping land-use polygons (intersection)
5. For each overlapping pair, compute intersection area and weight the density
6. Compute area-weighted mean density = sum(pop_density * intersection_area) / sum(intersection_area)
7. Count n_overlap_polygons and n_slivers_filtered per hex
8. Rank by area_weighted_density descending
9. Take top 10%
10. Save to GeoParquet (projected to Nigeria national grid) and plain Parquet
Let me code this up carefully.
For area calculation, I need to project to a metric CRS. Nigeria West Belt (EPSG:26391) uses meters.
Actually, for area calculation accuracy, I'll project to EPSG:26391 to get areas in m².
Let me write the script.
```python
import geopandas as gpd
import pandas as pd
import numpy as np
# Read inputs
landuse = gpd.read_file('/work/lagos_landuse.geojson')
hex_grid = gpd.read_file('/work/lagos_hex_grid.geojson')
# Project to metric CRS for area calculations
# Nigeria West Belt (Minna / Nigeria West Belt)
crs_metric = 'EPSG:26391'
landuse_proj = landuse.to_crs(crs_metric)
hex_proj = hex_grid.to_crs(crs_metric)
# Calculate area of each land-use polygon
landuse_proj['area_m2'] = landuse_proj.area
# Identify slivers (area < 100 m²) before intersection
# We need to track which polygons are slivers per hex cell
landuse_proj['is_sliver'] = landuse_proj['area_m2'] < 100.0
landuse_non_sliver = landuse_proj[~landuse_proj['is_sliver']].copy()
# Spatial join: for each hex, find overlapping land-use polygons
# Use predicate='intersects'
joined = gpd.sjoin(hex_proj, landuse_non_sliver, predicate='intersects', how='inner')
# For each hex-polygon pair, compute intersection area
# First, get the actual intersection geometry
intersections = []
for idx, row in joined.iterrows():
hex_geom = hex_proj.loc[row['index_left'] if 'index_left' in joined.columns else ...].geometry
# Actually, let me use a different approach. Since sjoin gives us pairs, I need to compute intersection geometry.
# Better approach: use overlay operation
overlay = gpd.overlay(hex_proj, landuse_non_sliver, how='intersection')
# Compute intersection area
overlay['intersection_area_m2'] = overlay.area
# Compute weighted density contribution
overlay['weighted_density'] = overlay['pop_density'] * overlay['intersection_area_m2']
# Group by hex_id
grouped = overlay.groupby('hex_id')
result = grouped.agg(
area_weighted_density=('weighted_density', 'sum'),
total_intersection_area=('intersection_area_m2', 'sum'),
n_overlap_polygons=('id', 'count')
)
result['area_weighted_density'] = result['area_weighted_density'] / result['total_intersection_area']
# For n_slivers_filtered: count how many sliver polygons overlap each hex
# We need to figure out which slivers would have overlapped each hex
sliver_join = gpd.sjoin(hex_proj, landuse_proj[landuse_proj['is_sliver']], predicate='intersects')
n_slivers = sliver_join.groupby('hex_id').size().reset_index(name='n_slivers_filtered')
# Merge
result = result.merge(n_slivers, on='hex_id', how='left')
result['n_slivers_filtered'] = result['n_slivers_filtered'].fillna(0).astype(int)
result['n_overlap_polygons'] = result['n_overlap_polygons'].astype(int)
# Rank by area_weighted_density descending
result = result.sort_values('area_weighted_density', ascending=False)
result['rank'] = range(1, len(result) + 1)
# Take top 10%
n_top = max(1, int(np.ceil(0.1 * len(result))))
top10 = result.head(n_top).copy()
# Get geometry for the top hex cells
geometry_hex = hex_grid.set_index('hex_id').loc[top10.index, 'geometry']
top10_with_geom = top10.join(geometry_hex)
# Convert to GeoDataFrame for output - projected to Nigeria national grid
gdf_out = gpd.GeoDataFrame(
top10_with_geom[['rank', 'area_weighted_density']],
geometry=top10_with_geom['geometry'],
crs=hex_grid.crs # Keep original CRS for now, then reproject
)
gdf_out = gdf_out.to_crs(crs_metric)
gdf_out['hex_id'] = top10_with_geom.index
# Reorder columns
gdf_out = gdf_out[['hex_id', 'rank', 'area_weighted_density', 'geometry']]
# Save GeoParquet
gdf_out.to_parquet('/work/hotspots.geoparquet')
# Save plain Parquet
plain_out = top10_with_geom[['rank', 'area_weighted_density', 'n_overlap_polygons', 'n_slivers_filtered']].reset_index()
plain_out = plain_out[['hex_id', 'rank', 'area_weighted_density', 'n_overlap_polygons', 'n_slivers_filtered']]
plain_out.to_parquet('/work/hotspot_ranking.parquet', index=False)
```
Wait, there's a subtlety. The `n_slivers_filtered` - the instruction says "Tiny polygons under 100 m² are noise and should be excluded." So I need to count how many slivers (polygons < 100 m²) were filtered out from each hex cell - i.e., for each hex, count how many land-use polygons with area < 100 m² overlap that hex.
Also for `n_overlap_polygons`, it should be the count of non-sliver polygons that overlap the hex cell.
Let me think about edge cases and write it cleanly.
Also for the geometry in `hotspots.geoparquet`, the prompt says "in Nigeria's national grid" - I'll use EPSG:26391 (Minna / Nigeria West Belt) since Lagos is in western Nigeria.
Let me write the complete script now.
Actually, I realize I should also handle the case where a hex cell might have no overlapping non-sliver land-use polygons - such cells would have no data and wouldn't be in the ranking. That's fine.
Also, I need to consider what CRS the output geometry should be in. The prompt says "Nigeria's national grid". Let me check what that is exactly.
The "Nigerian national grid" typically refers to the "Nigeria Belts" system. Since Lagos is in the west, that would be EPSG:26391 (Minna / Nigeria West Belt). Let me proceed with that.
Let me write the script cleanly.
0:51
Write
/work/solve.py
content · 105 lines · py
import geopandas as gpd
import pandas as pd
import numpy as np
# ── 1. Read inputs ──────────────────────────────────────────────────────
landuse = gpd.read_file("/work/lagos_landuse.geojson")
hex_grid = gpd.read_file("/work/lagos_hex_grid.geojson")
# ── 2. Project to metric CRS (Nigeria West Belt — Lagos is in the west) ─
# This is needed for accurate area calculations.
CRS_METRIC = "EPSG:26391" # Minna / Nigeria West Belt
landuse_proj = landuse.to_crs(CRS_METRIC)
hex_proj = hex_grid.to_crs(CRS_METRIC)
# ── 3. Compute polygon areas and flag slivers (< 100 m²) ────────────────
landuse_proj["area_m2"] = landuse_proj.area
landuse_proj["is_sliver"] = landuse_proj["area_m2"] < 100.0
# Separate slivers (noise) and valid polygons
lu_valid = landuse_proj[~landuse_proj["is_sliver"]].copy()
lu_slivers = landuse_proj[landuse_proj["is_sliver"]].copy()
print(f"Total land-use polygons: {len(landuse_proj)}")
print(f" Valid (>= 100 m²): {len(lu_valid)}")
print(f" Slivers (< 100 m²): {len(lu_slivers)}")
# ── 4. Count slivers per hex cell (before they are excluded) ────────────
if len(lu_slivers) > 0:
sliver_join = gpd.sjoin(
hex_proj[["hex_id", "geometry"]],
lu_slivers[["geometry"]],
predicate="intersects",
how="inner",
)
n_slivers = (
sliver_join.groupby("hex_id").size().reset_index(name="n_slivers_filtered")
)
else:
n_slivers = pd.DataFrame({"hex_id": hex_proj["hex_id"], "n_slivers_filtered": 0})
# ── 5. Intersect valid land-use with hex grid ───────────────────────────
overlay = gpd.overlay(
hex_proj[["hex_id", "geometry"]],
lu_valid[["id", "pop_density", "geometry"]],
how="intersection",
)
# Area of each intersection piece (in metric units ~ m²)
overlay["intersection_area_m2"] = overlay.area
# Area-weighted density contribution
overlay["weighted_density"] = overlay["pop_density"] * overlay["intersection_area_m2"]
# ── 6. Aggregate to hex cell ────────────────────────────────────────────
agg = overlay.groupby("hex_id").agg(
weighted_sum=("weighted_density", "sum"),
total_area=("intersection_area_m2", "sum"),
n_overlap_polygons=("id", "count"),
).reset_index()
agg["area_weighted_density"] = agg["weighted_sum"] / agg["total_area"]
# Merge sliver counts
agg = agg.merge(n_slivers, on="hex_id", how="left")
agg["n_slivers_filtered"] = agg["n_slivers_filtered"].fillna(0).astype(int)
agg["n_overlap_polygons"] = agg["n_overlap_polygons"].astype(int)
print(f"Hex cells with overlap data: {len(agg)}")
# ── 7. Rank by descending area_weighted_density ─────────────────────────
agg = agg.sort_values("area_weighted_density", ascending=False).reset_index(drop=True)
agg["rank"] = np.arange(1, len(agg) + 1)
# ── 8. Top 10 % ─────────────────────────────────────────────────────────
n_top = max(1, int(np.ceil(0.1 * len(agg))))
top10 = agg.head(n_top).copy()
print(f"Top 10 % count: {n_top}")
# ── 9. Attach geometry (original hex geometry) ──────────────────────────
hex_idx = hex_grid.set_index("hex_id")
top10_geom = top10.join(hex_idx[["geometry"]])
# Ensure it's a GeoDataFrame in the original CRS, then project to metric
gdf_out = gpd.GeoDataFrame(
top10_geom[["hex_id", "rank", "area_weighted_density"]],
geometry=top10_geom["geometry"],
crs=hex_grid.crs,
)
gdf_out = gdf_out.to_crs(CRS_METRIC)
# ── 10. Write hotspots.geoparquet ───────────────────────────────────────
gdf_out[["hex_id", "rank", "area_weighted_density", "geometry"]].to_parquet(
"/work/hotspots.geoparquet"
)
print("Written hotspots.geoparquet")
# ── 11. Write hotspot_ranking.parquet (no geometry) ─────────────────────
plain_out = top10_geom[
["hex_id", "rank", "area_weighted_density", "n_overlap_polygons", "n_slivers_filtered"]
].reset_index(drop=True)
plain_out.to_parquet("/work/hotspot_ranking.parquet", index=False)
print("Written hotspot_ranking.parquet")
print("\nDone.")Wrote /work/solve.py (3911 chars).
0:55
Let me run the script and see if it works.
0:55
Bash
cd /work && python solve.py
Total land-use polygons: 5542 Valid (>= 100 m²): 2520 Slivers (< 100 m²): 3022 Hex cells with overlap data: 1032 Top 10 % count: 104 Written hotspots.geoparquet Written hotspot_ranking.parquet Done.
1:05
Let me verify the outputs now.
1:05
Bash
cd /work && python -c "
cd /work && python -c "
import geopandas as gpd
import pandas as pd
# Check hotspots.geoparquet
h = gpd.read_parquet('/work/hotspots.geoparquet')
print('=== hotspots.geoparquet ===')
print(f'Columns: {list(h.columns)}')
print(f'Rows: {len(h)}')
print(f'CRS: {h.crs}')
print(f'dtypes:\n{h.dtypes}')
print(f'hex_id sample: {h[\"hex_id\"].tolist()[:5]}')
print(f'rank sample: {h[\"rank\"].tolist()[:5]}')
print(f'density sample: {h[\"area_weighted_density\"].tolist()[:5]}')
print()
# Check hotspot_ranking.parquet
r = pd.read_parquet('/work/hotspot_ranking.parquet')
print('=== hotspot_ranking.parquet ===')
print(f'Columns: {list(r.columns)}')
print(f'Rows: {len(r)}')
print(f'dtypes:\n{r.dtypes}')
print(f'hex_id sample: {r[\"hex_id\"].tolist()[:5]}')
print(f'rank sample: {r[\"rank\"].tolist()[:5]}')
print(f'n_overlap_polygons sample: {r[\"n_overlap_polygons\"].tolist()[:5]}')
print(f'n_slivers_filtered sample: {r[\"n_slivers_filtered\"].tolist()[:5]}')
print()
# Verify same hex_ids
h_ids = set(h['hex_id'])
r_ids = set(r['hex_id'])
print(f'hex_id sets equal: {h_ids == r_ids}')
print(f'Overlap size: {len(h_ids & r_ids)}')
print(f'Difference h-r: {h_ids - r_ids}')
print(f'Difference r-h: {r_ids - h_ids}')
print()
# Check rank is unique 1..N
print(f'rank unique: {r[\"rank\"].is_unique}')
print(f'rank min: {r[\"rank\"].min()}, max: {r[\"rank\"].max()}, count: {r[\"rank\"].count()}')
print(f'rank == 1..N: {set(r[\"rank\"]) == set(range(1, len(r)+1))}')
print()
# Verify descending density order
densities = r['area_weighted_density'].tolist()
print(f'Density descending: {all(densities[i] >= densities[i+1] for i in range(len(densities)-1))}')
# Check geometry type
print(f'Geometry types: {h.geometry.type.value_counts()}')
"
=== hotspots.geoparquet ===
Columns: ['hex_id', 'rank', 'area_weighted_density', 'geometry']
Rows: 104
CRS: {"$schema": "https://proj.org/schemas/v0.7/projjson.schema.json", "type": "ProjectedCRS", "name": "Minna / Nigeria West 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 West 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": 4.5, "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": 230738.26, "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 - onshore west of 6\u00b030'E, onshore and offshore shelf.", "bbox": {"south_latitude": 3.57, "west_longitude": 2.69, "north_latitude": 13.9, "east_longitude": 6.5}, "id": {"authority": "EPSG", "code": 26391}}
dtypes:
hex_id str
rank int64
area_weighted_density float64
geometry geometry
dtype: object
hex_id sample: ['H011-017', 'H049-008', 'H031-006', 'H035-007', 'H039-014']
rank sample: [1, 2, 3, 4, 5]
density sample: [49816.76161708467, 49618.4, 49405.2, 49167.2, 49163.0]
=== hotspot_ranking.parquet ===
Columns: ['hex_id', 'rank', 'area_weighted_density', 'n_overlap_polygons', 'n_slivers_filtered']
Rows: 104
dtypes:
hex_id str
rank int64
area_weighted_density float64
n_overlap_polygons int64
n_slivers_filtered int64
dtype: object
hex_id sample: ['H011-017', 'H049-008', 'H031-006', 'H035-007', 'H039-014']
rank sample: [1, 2, 3, 4, 5]
n_overlap_polygons sample: [2, 1, 1, 1, 1]
n_slivers_filtered sample: [1, 2, 3, 2, 0]
hex_id sets equal: True
Overlap size: 104
Difference h-r: set()
Difference r-h: set()
rank unique: True
rank min: 1, max: 104, count: 104
rank == 1..N: True
Density descending: True
Geometry types: Series([], Name: count, dtype: int64)
1:12
The geometry types show as empty series, probably because the GeoParquet was read back and the geometry column didn't get registered properly. Let me check more carefully.
1:12
Bash
cd /work && python -c "
cd /work && python -c "
import geopandas as gpd
h = gpd.read_parquet('/work/hotspots.geoparquet')
print('Geometry type:', type(h.geometry))
print('First geom:', h.geometry.iloc[0])
print('All geom types:', h.geometry.type.unique())
"
Geometry type: <class 'geopandas.geoseries.GeoSeries'> First geom: None All geom types: [None]
1:22
The geometry is None for all rows. That means the geometry wasn't properly joined. Let me check - the issue is that `top10_geom = top10.join(hex_idx[["geometry"]])` - but `top10` is a regular DataFrame (from the groupby aggregation), not a GeoDataFrame, so the `join` with hex_idx should work since hex_idx has a geometry column.
Let me check - actually the issue might be that after the groupby, the geometry column is gone because we don't keep it. Let me trace through:
1. `overlay = gpd.overlay(hex_proj[["hex_id", "geometry"]], lu_valid[["id", "pop_density", "geometry"]], how="intersection")` — this produces a GeoDataFrame with geometry of the intersection pieces.
2. `agg = overlay.groupby("hex_id").agg(...)` — this drops geometry.
3. `top10 = agg.head(n_top).copy()` — regular DataFrame, no geometry.
4. `hex_idx = hex_grid.set_index("hex_id")` — GeoDataFrame with geometry in EPSG:4326.
5. `top10_geom = top10.join(hex_idx[["geometry"]])` — should join on hex_id. But `top10` is a DataFrame indexed by 0..N, and `hex_idx` is indexed by hex_id. So `join` on an index... let me check.
Actually `top10` has index from 0..N (the default integer index from reset_index), while hex_idx has hex_id as index. The `join` method by default joins on the index, not on a column. So it's trying to match integer indices with string hex_ids, which won't match. That's why all geometries are None.
Let me fix this by using `merge` instead.
1:22
Read
/work/solve.py
84 # Ensure it's a GeoDataFrame in the original CRS, then project to metric
85 gdf_out = gpd.GeoDataFrame(
86 top10_geom[["hex_id", "rank", "area_weighted_density"]],
87 geometry=top10_geom["geometry"],
88 crs=hex_grid.crs,
89 )
90 gdf_out = gdf_out.to_crs(CRS_METRIC)
91
92 # ── 10. Write hotspots.geoparquet ───────────────────────────────────────
93 gdf_out[["hex_id", "rank", "area_weighted_density", "geometry"]].to_parquet(
94 "/work/hotspots.geoparquet"
95 )
96 print("Written hotspots.geoparquet")
97
98 # ── 11. Write hotspot_ranking.parquet (no geometry) ─────────────────────
99 plain_out = top10_geom[
100 ["hex_id", "rank", "area_weighted_density", "n_overlap_polygons", "n_slivers_filtered"]
101 ].reset_index(drop=True)
102 plain_out.to_parquet("/work/hotspot_ranking.parquet", index=False)
103 print("Written hotspot_ranking.parquet")
104
105 print("\nDone.")
1:29
The issue is that when joining the top10 results with hex_idx geometry, the geometry is coming through as NaN/None because the join is using the index but the top10 dataframe's hex_id column isn't being used as index properly. Let me look at the issue.
The problem is in this part:
```python
hex_idx = hex_grid.set_index("hex_id")
top10_geom = top10.join(hex_idx[["geometry"]])
```
The `top10` DataFrame has `hex_id` as a regular column, and `.join()` joins on the index. So it would try to align on the integer index of top10 with the hex_id index of hex_idx, which would fail and produce NaN geometry.
I need to fix this by either:
1. Setting hex_id as index on top10 before joining, or
2. Using `merge` instead of `join`
Let me fix it.
1:29
1:29
Edit
/work/solve.py
Edited /work/solve.py.
1:34
Also, the issue is that `top10_geom` comes from `top10.join(hex_idx[["geometry"]])` which uses the index of `top10` (which is a default RangeIndex) to join with `hex_idx` (which has hex_id as index). That's wrong — it would try to match by row position not by hex_id. The merge is correct. Let me re-run.
1:34
Bash
cd /work && python solve.py
Total land-use polygons: 5542 Valid (>= 100 m²): 2520 Slivers (< 100 m²): 3022 Hex cells with overlap data: 1032 Top 10 % count: 104 Written hotspots.geoparquet Written hotspot_ranking.parquet Done.
1:44
Now let me verify the geometry issue is fixed.
1:44
Bash
cd /work && python -c "
cd /work && python -c "
import geopandas as gpd
import pandas as pd
# Check hotspots.geoparquet
h = gpd.read_parquet('/work/hotspots.geoparquet')
print('=== hotspots.geoparquet ===')
print(f'Columns: {list(h.columns)}')
print(f'Rows: {len(h)}')
print(f'CRS: {h.crs}')
print(f'All non-null geometry: {h.geometry.notna().all()}')
print(f'Geometry types: {h.geometry.type.unique()}')
print(f'First geom type: {type(h.geometry.iloc[0])}')
print(f'hex_id sample: {h[\"hex_id\"].tolist()[:5]}')
print(f'rank sample: {h[\"rank\"].tolist()[:5]}')
print()
# Check hotspot_ranking.parquet
r = pd.read_parquet('/work/hotspot_ranking.parquet')
print('=== hotspot_ranking.parquet ===')
print(f'Columns: {list(r.columns)}')
print(f'Rows: {len(r)}')
print(f'dtypes:\n{r.dtypes}')
print(f'hex_id sample: {r[\"hex_id\"].tolist()[:5]}')
print(f'rank sample: {r[\"rank\"].tolist()[:5]}')
print()
# Verify same hex_ids
h_ids = set(h['hex_id'])
r_ids = set(r['hex_id'])
print(f'hex_id sets equal: {h_ids == r_ids}')
print(f'Overlap size: {len(h_ids & r_ids)}')
# Check rank uniqueness and ordering
print(f'rank unique: {r[\"rank\"].is_unique}')
print(f'rank min: {r[\"rank\"].min()}, max: {r[\"rank\"].max()}')
print(f'rank == 1..{len(r)}: {set(r[\"rank\"]) == set(range(1, len(r)+1))}')
densities = r['area_weighted_density'].tolist()
print(f'Density descending: {all(densities[i] >= densities[i+1] for i in range(len(densities)-1))}')
print(f'n_overlap_polygons dtype: {r[\"n_overlap_polygons\"].dtype}')
print(f'n_slivers_filtered dtype: {r[\"n_slivers_filtered\"].dtype}')
print(f'Total cells: {len(h_ids)}')
"
=== hotspots.geoparquet ===
Columns: ['hex_id', 'rank', 'area_weighted_density', 'geometry']
Rows: 104
CRS: {"$schema": "https://proj.org/schemas/v0.7/projjson.schema.json", "type": "ProjectedCRS", "name": "Minna / Nigeria West 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 West 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": 4.5, "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": 230738.26, "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 - onshore west of 6\u00b030'E, onshore and offshore shelf.", "bbox": {"south_latitude": 3.57, "west_longitude": 2.69, "north_latitude": 13.9, "east_longitude": 6.5}, "id": {"authority": "EPSG", "code": 26391}}
All non-null geometry: True
Geometry types: <ArrowStringArray>
['Polygon']
Length: 1, dtype: str
First geom type: <class 'shapely.geometry.polygon.Polygon'>
hex_id sample: ['H011-017', 'H049-008', 'H031-006', 'H035-007', 'H039-014']
rank sample: [1, 2, 3, 4, 5]
=== hotspot_ranking.parquet ===
Columns: ['hex_id', 'rank', 'area_weighted_density', 'n_overlap_polygons', 'n_slivers_filtered']
Rows: 104
dtypes:
hex_id str
rank int64
area_weighted_density float64
n_overlap_polygons int64
n_slivers_filtered int64
dtype: object
hex_id sample: ['H011-017', 'H049-008', 'H031-006', 'H035-007', 'H039-014']
rank sample: [1, 2, 3, 4, 5]
hex_id sets equal: True
Overlap size: 104
rank unique: True
rank min: 1, max: 104
rank == 1..104: True
Density descending: True
n_overlap_polygons dtype: int64
n_slivers_filtered dtype: int64
Total cells: 104
1:53
Everything checks out perfectly: 1. `hotspots.geoparquet` — has `hex_id`, `rank`, `area_weighted_density`, `geometry` (Polygon), CRS is EPSG:26391 (Minna / Nigeria West Belt), all geometries non-null. 2. `hotspot_ranking.parquet` — has `hex_id`, `rank`, `area_weighted_density`, `n_overlap_polygons` (int64), `n_slivers_filtered` (int64), no geometry. 3. Both files share exactly the same 104 `hex_id` values. 4. Rank is unique 1..104, ascending corresponds to descending area_weighted_density. 5. Top 10% of 1032 = ceil(103.2) = 104 cells. 6. Slivers (< 100 m²) were filtered out before area-weighted density calculation; 3022 slivers identified. Let me verify the files exist.
1:53
Bash
ls -la /work/hotspots.geoparquet /work/hotspot_ranking.parquet
-rw-r--r-- 1 agent agent 5794 Jun 9 07:41 /work/hotspot_ranking.parquet -rw-r--r-- 1 agent agent 22214 Jun 9 07:41 /work/hotspots.geoparquet
2:01
Everything looks good. Let me summarize what was done and the verification results: 1. **hotspots.geoparquet** — 104 rows, columns: `hex_id`, `rank`, `area_weighted_density`, `geometry` (Polygon), CRS = EPSG:26391 (Minna / Nigeria West Belt) 2. **hotspot_ranking.parquet** — 104 rows, columns: `hex_id`, `rank`, `area_weighted_density`, `n_overlap_polygons` (int64), `n_slivers_filtered` (int64) Both have: - Same set of 104 hex_ids - Unique ranks 1–104 - Descending density order - Correct column names and types The processing: - 5542 land-use polygons total - 3022 slivers (< 100 m²) filtered out as noise - 2520 valid polygons used for overlap - 1032 hex cells had overlap with valid polygons - Top 10% = 104 cells
2:01