dc-l2-lagos-snap-normalize
Map (reference ← swipe → agent)
0:00
I need to get our legacy zoning layer ready for the state portal. lagos_zoning_legacy.gpkg has inconsistent zoning_class values (mixed casing, spelling variations, and some blank entries) plus vertex precision issues that cause tiny gaps and overlaps between adjacent parcels. Can you clean up the class names to consistent canonical TitleCase values, drop any rows with blank or whitespace-only classes, fix the vertex precision problems, and produce one merged geometry per canonical zoning class? Please write the result to zoning_aggregated.gpkg, with each row carrying two columns: zoning_class and a numeric area_m2.
0:00
System info
{
"adapter": "openrouter",
"model": "deepseek/deepseek-v4-flash",
"base_url": "https://openrouter.ai/api/v1"
}
0:03
Let me start by reading the input file to understand its structure.
0:03
Bash
ls -la /work/
total 2468 drwxr-xr-x 1 agent agent 48 Jun 15 16:33 . drwxr-xr-x 1 root root 14 Jun 15 16:33 .. -rw-r--r-- 1 agent agent 2527232 Jun 15 16:33 lagos_zoning_legacy.gpkg
0:06
Let me first inspect the GPKG file to understand its structure, columns, and data.
0:06
Bash
python3 -c "
python3 -c "
import geopandas as gpd
gdf = gpd.read_file('/work/lagos_zoning_legacy.gpkg')
print('Columns:', gdf.columns.tolist())
print('CRS:', gdf.crs)
print('Shape:', gdf.shape)
print('---')
print(gdf.head(20).to_string())
print('---')
print('dtypes:', gdf.dtypes)
"
Columns: ['parcel_id', 'lga_source', 'zoning_class', 'area_m2', 'geometry']
CRS: EPSG:26331
Shape: (10080, 5)
---
parcel_id lga_source zoning_class area_m2 geometry
0 1 Surulere RESIDENTIAL 100.0 POLYGON ((540000 720000, 540010 720000, 540010 720010, 540000 720010, 540000 720000))
1 2 Yaba residential 100.0 POLYGON ((540010 720000, 540020 720000, 540020 720010, 540010 720010, 540010 720000))
2 3 Apapa Resi. 100.0 POLYGON ((540020 720000, 540030 720000, 540030 720010, 540020 720010, 540020 720000))
3 4 Eti-Osa resi. 100.0 POLYGON ((540030 720000, 540040 720000, 540040 720010, 540030 720010, 540030 720000))
4 5 Ojo RESI. 100.0 POLYGON ((540040 720000, 540050 720000, 540050 720010, 540040 720010, 540040 720000))
5 6 Ikeja Residential 100.0 POLYGON ((540050 720000, 540060 720000, 540060 720010, 540050 720010, 540050 720000))
6 7 Surulere RESIDENTIAL 100.0 POLYGON ((540060 720000, 540070 720000, 540070 720010, 540060 720010, 540060 720000))
7 8 Yaba residential 100.0 POLYGON ((540070 720000, 540080 720000, 540080 720010, 540070 720010, 540070 720000))
8 9 Apapa Resi. 100.0 POLYGON ((540080 720000, 540090 720000, 540090 720010, 540080 720010, 540080 720000))
9 10 Eti-Osa resi. 100.0 POLYGON ((540090 720000, 540100 720000, 540100 720010, 540090 720010, 540090 720000))
10 11 Ojo RESI. 100.0 POLYGON ((540100 720000, 540110 720000, 540110 720010, 540100 720010, 540100 720000))
11 12 Ikeja Residential 100.0 POLYGON ((540110 720000, 540120 720000, 540120 720010, 540110 720010, 540110 720000))
12 13 Surulere RESIDENTIAL 100.0 POLYGON ((540120 720000, 540130 720000, 540130 720010, 540120 720010, 540120 720000))
13 14 Yaba residential 100.0 POLYGON ((540130 720000, 540140 720000, 540140 720010, 540130 720010, 540130 720000))
14 15 Apapa Resi. 100.0 POLYGON ((540140 720000, 540150 720000, 540150 720010, 540140 720010, 540140 720000))
15 16 Eti-Osa resi. 100.0 POLYGON ((540150 720000, 540160 720000, 540160 720010, 540150 720010, 540150 720000))
16 17 Ojo RESI. 100.0 POLYGON ((540160 720000, 540170 720000, 540170 720010, 540160 720010, 540160 720000))
17 18 Ikeja Residential 100.0 POLYGON ((540170 720000, 540180 720000, 540180 720010, 540170 720010, 540170 720000))
18 19 Surulere RESIDENTIAL 100.0 POLYGON ((540180 720000, 540190 720000, 540190 720010, 540180 720010, 540180 720000))
19 20 Yaba residential 100.0 POLYGON ((540190 720000, 540200 720000, 540200 720010, 540190 720010, 540190 720000))
---
dtypes: parcel_id int64
lga_source str
zoning_class str
area_m2 float64
geometry geometry
dtype: object
0:17
Let me explore the data more to understand the zoning_class variations and identify blank entries.
0:17
Bash
python3 -c "
python3 -c "
import geopandas as gpd
gdf = gpd.read_file('/work/lagos_zoning_legacy.gpkg')
print('Unique zoning_class values:')
print(gdf['zoning_class'].value_counts().to_string())
print()
print('Blank/whitespace rows:')
blank = gdf['zoning_class'].isna() | (gdf['zoning_class'].str.strip() == '')
print(f'Count: {blank.sum()}')
if blank.sum() > 0:
print(gdf[blank].head(20).to_string())
print()
print('Geometry types:', gdf.geometry.type.value_counts().to_string())
print()
# Check a few multi-polygon examples
print('Sample coordinates from first few rows:')
for i in range(5):
g = gdf.geometry.iloc[i]
print(f' Row {i}: {g}')
"
Unique zoning_class values:
zoning_class
Residential 420
COMMERCIAL 420
industrial 420
Agri. 420
residential 419
resi. 419
Comm. 419
COMM. 419
indus. 419
AGRI. 419
Industrial 418
AGRICULTURAL 418
RESIDENTIAL 417
RESI. 417
comm. 417
commercial 417
Indus. 417
INDUSTRIAL 417
Agricultural 417
agri. 417
Resi. 416
Commercial 416
INDUS. 416
agricultural 416
13
13
\t 12
Blank/whitespace rows:
Count: 50
parcel_id lga_source zoning_class area_m2 geometry
10000 10001 Unassigned 100.0 POLYGON ((542000 720000, 542010 720000, 542010 720010, 542000 720010, 542000 720000))
10001 10002 Unassigned 100.0 POLYGON ((542010 720000, 542020 720000, 542020 720010, 542010 720010, 542010 720000))
10002 10003 Unassigned NaN 100.0 POLYGON ((542020 720000, 542030 720000, 542030 720010, 542020 720010, 542020 720000))
10003 10004 Unassigned \t 100.0 POLYGON ((542030 720000, 542040 720000, 542040 720010, 542030 720010, 542030 720000))
10004 10005 Unassigned 100.0 POLYGON ((542040 720000, 542050 720000, 542050 720010, 542040 720010, 542040 720000))
10005 10006 Unassigned 100.0 POLYGON ((542050 720000, 542060 720000, 542060 720010, 542050 720010, 542050 720000))
10006 10007 Unassigned NaN 100.0 POLYGON ((542060 720000, 542070 720000, 542070 720010, 542060 720010, 542060 720000))
10007 10008 Unassigned \t 100.0 POLYGON ((542070 720000, 542080 720000, 542080 720010, 542070 720010, 542070 720000))
10008 10009 Unassigned 100.0 POLYGON ((542080 720000, 542090 720000, 542090 720010, 542080 720010, 542080 720000))
10009 10010 Unassigned 100.0 POLYGON ((542090 720000, 542100 720000, 542100 720010, 542090 720010, 542090 720000))
10010 10011 Unassigned NaN 100.0 POLYGON ((542000 720010, 542010 720010, 542010 720020, 542000 720020, 542000 720010))
10011 10012 Unassigned \t 100.0 POLYGON ((542010 720010, 542020 720010, 542020 720020, 542010 720020, 542010 720010))
10012 10013 Unassigned 100.0 POLYGON ((542020 720010, 542030 720010, 542030 720020, 542020 720020, 542020 720010))
10013 10014 Unassigned 100.0 POLYGON ((542030 720010, 542040 720010, 542040 720020, 542030 720020, 542030 720010))
10014 10015 Unassigned NaN 100.0 POLYGON ((542040 720010, 542050 720010, 542050 720020, 542040 720020, 542040 720010))
10015 10016 Unassigned \t 100.0 POLYGON ((542050 720010, 542060 720010, 542060 720020, 542050 720020, 542050 720010))
10016 10017 Unassigned 100.0 POLYGON ((542060 720010, 542070 720010, 542070 720020, 542060 720020, 542060 720010))
10017 10018 Unassigned 100.0 POLYGON ((542070 720010, 542080 720010, 542080 720020, 542070 720020, 542070 720010))
10018 10019 Unassigned NaN 100.0 POLYGON ((542080 720010, 542090 720010, 542090 720020, 542080 720020, 542080 720010))
10019 10020 Unassigned \t 100.0 POLYGON ((542090 720010, 542100 720010, 542100 720020, 542090 720020, 542090 720010))
Geometry types: Polygon 10080
Sample coordinates from first few rows:
Row 0: POLYGON ((540000 720000, 540010.00002 720000.000005, 540010.000005 720010.000015, 540000.000025 720010.00002, 540000 720000))
Row 1: POLYGON ((540010.00001 720000.000025, 540020 720000.000005, 540020.00002 720010.00001, 540010.000005 720010.00002, 540010.00001 720000.000025))
Row 2: POLYGON ((540020.000025 720000.00002, 540030.000015 720000, 540030 720010.00001, 540020.00002 720010.000015, 540020.000025 720000.00002))
Row 3: POLYGON ((540030.000005 720000.00002, 540040.000025 720000.000025, 540040.000015 720010.000005, 540030 720010.000015, 540030.000005 720000.00002))
Row 4: POLYGON ((540040.00002 720000.000015, 540050.000005 720000.000025, 540050.00003 720010, 540040.000015 720010.00001, 540040.00002 720000.000015))
0:33
Now I have a good picture of the data. Let me analyze what needs to be done: 1. **Class name normalization**: Map all variations to canonical TitleCase values. Here's the mapping I can see: - residential, RESIDENTIAL, Residential → "Residential" - resi., RESI., Resi. → "Residential" (these are abbreviations) - commercial, COMMERCIAL, Commercial, Comm., COMM., comm. → "Commercial" - industrial, INDUSTRIAL, Industrial, Indus., INDUS., indus. → "Industrial" - agricultural, AGRICULTURAL, Agricultural, Agri., AGRI., agri. → "Agricultural" 2. **Drop blank/whitespace rows**: 50 rows have blank/whitespace/NaN zoning_class values. 3. **Fix vertex precision**: The coordinates have tiny sub-meter variations (e.g., 540010.00002). Since the CRS is EPSG:26331 which uses meters, we should round/snap to a reasonable precision (e.g., 0.001 or 0.01). Actually, looking at the patterns, the vertices should be aligned at integer positions like 540000, 720000, 540010, 720010, etc. The tiny offsets cause gaps/overlaps. We need to snap to a grid. 4. **Merge per canonical zoning class**: After fixing precision, dissolve/union all polygons by their canonical zoning class, producing one multi-polygon per class with area_m2 as a numeric column. Let me write a comprehensive Python script.
0:33
Write
/work/solve.py
content · 106 lines · py
import geopandas as gpd
import pandas as pd
from shapely.geometry import Polygon, MultiPolygon
from shapely import wkt
# ── 1. Read ──────────────────────────────────────────────────────────────────
gdf = gpd.read_file("/work/lagos_zoning_legacy.gpkg")
print(f"Read {len(gdf)} rows — columns: {gdf.columns.tolist()}")
print(f"Original CRS: {gdf.crs}")
# ── 2. Drop rows with blank / whitespace-only / NaN zoning_class ──────────────
mask_keep = gdf["zoning_class"].notna() & (gdf["zoning_class"].astype(str).str.strip() != "")
dropped = (~mask_keep).sum()
gdf = gdf[mask_keep].copy()
print(f"Dropped {dropped} rows with blank/whitespace zoning_class, {len(gdf)} remaining")
# ── 3. Normalize zoning_class to canonical TitleCase values ─────────────────
# Build the mapping dictionary
normalize_map = {
"residential": "Residential",
"resi.": "Residential",
"commercial": "Commercial",
"comm.": "Commercial",
"industrial": "Industrial",
"indus.": "Industrial",
"agricultural": "Agricultural",
"agri.": "Agricultural",
}
def normalize(val):
s = str(val).strip()
key = s.lower().rstrip(".")
if key in normalize_map:
return normalize_map[key]
# If it's already a proper TitleCase known value, keep it
if s in ("Residential", "Commercial", "Industrial", "Agricultural"):
return s
# Fallback: whatever it is, title-case it
return s.title()
gdf["zoning_class"] = gdf["zoning_class"].apply(normalize)
print("Normalized class distribution:")
print(gdf["zoning_class"].value_counts().to_string())
# ── 4. Fix vertex precision — snap to 0.001 m grid ──────────────────────────
# The CRS is EPSG:26331 (metres); tiny sub-mm differences cause gaps/overlaps.
# We'll snap all coordinate values to the nearest 1 mm (0.001 m).
PRECISION = 0.001 # snap grid resolution
def snap_coords(x, y, z=None):
"""Snap a coordinate pair (or triple) to the grid."""
sx = round(x / PRECISION) * PRECISION
sy = round(y / PRECISION) * PRECISION
if z is not None:
sz = round(z / PRECISION) * PRECISION
return (sx, sy, sz)
return (sx, sy)
def snap_polygon_coords(poly):
"""Return a new Polygon with coords snapped to the PRECISION grid."""
# exterior ring
ext = [snap_coords(*c) for c in poly.exterior.coords]
# interior rings
ints = []
for ring in poly.interiors:
ints.append([snap_coords(*c) for c in ring.coords])
return Polygon(ext, ints)
def snap_geometry(geom):
if geom is None:
return None
if geom.geom_type == "Polygon":
return snap_polygon_coords(geom)
elif geom.geom_type == "MultiPolygon":
return MultiPolygon([snap_polygon_coords(p) for p in geom.geoms])
else:
return geom
print("Snapping vertices to ±", PRECISION, "m grid...")
gdf["geometry"] = gdf["geometry"].apply(snap_geometry)
print("Done snapping.")
# ── 5. Remove any invalid or sliver geometries after snapping ───────────────
# Buffer by 0 to clean up any self-intersections introduced by snapping
print("Repairing geometries (buffer(0))...")
gdf["geometry"] = gdf["geometry"].buffer(0)
print("Done.")
# ── 6. Dissolve (merge) by canonical zoning_class ───────────────────────────
print("Dissolving by zoning_class...")
dissolved = gdf.dissolve(by="zoning_class", aggfunc="first")
# Keep only the geometry column; the rest of the aggfunc doesn't matter
# Compute area_m2 for each merged geometry
dissolved["area_m2"] = dissolved.geometry.area
# Reset index so zoning_class becomes a regular column
dissolved = dissolved.reset_index()[["zoning_class", "area_m2", "geometry"]]
print(f"\nResult has {len(dissolved)} rows:")
print(dissolved[["zoning_class", "area_m2"]].to_string(index=False))
# ── 7. Write output ─────────────────────────────────────────────────────────
dissolved.to_file("/work/zoning_aggregated.gpkg", layer="zoning_aggregated", driver="GPKG")
print("\n✓ Written to /work/zoning_aggregated.gpkg")Wrote /work/solve.py (3965 chars).
0:45
Let me run the script first to see if it works, then check the output.
0:45
Bash
cd /work && python solve.py
Read 10080 rows — columns: ['parcel_id', 'lga_source', 'zoning_class', 'area_m2', 'geometry']
Original CRS: EPSG:26331
Dropped 50 rows with blank/whitespace zoning_class, 10030 remaining
Normalized class distribution:
zoning_class
Residential 1256
Agri. 1256
Comm. 1255
Industrial 1255
Commercial 1253
Resi. 1252
Indus. 1252
Agricultural 1251
Snapping vertices to ± 0.001 m grid...
Done snapping.
Repairing geometries (buffer(0))...
Done.
Dissolving by zoning_class...
Result has 8 rows:
zoning_class area_m2
Agri. 125100.0
Agricultural 124900.0
Comm. 125000.0
Commercial 125000.0
Indus. 125000.0
Industrial 125000.0
Resi. 124900.0
Residential 125100.0
✓ Written to /work/zoning_aggregated.gpkg
0:52
Hmm, the normalization didn't work as expected. "Agri.", "Comm.", "Resi.", "Indus." are still there with dots. Let me check what happened with my normalization logic.
Looking at the data, the original values were:
- "RESIDENTIAL", "residential", "Residential", "RESI.", "resi.", "Resi."
- "COMMERCIAL", "commercial", "Commercial", "COMM.", "comm.", "Comm."
- "INDUSTRIAL", "industrial", "Industrial", "INDUS.", "indus.", "Indus."
- "AGRICULTURAL", "agricultural", "Agricultural", "AGRI.", "agri.", "Agri."
My normalize function does:
1. Strip whitespace
2. Lowercase and strip trailing dots → key
3. Look up key in normalize_map
But wait, "Agri." has a trailing dot. If I do `"Agri.".lower().rstrip(".")` I get `"agri"`. My map has keys "agri." with the dot... But I used `rstrip(".")` so it removes the dot. Let me check the map keys.
```python
normalize_map = {
"residential": "Residential",
"resi.": "Residential",
...etc
}
```
The map keys include dots. But the lookup key is created with `s.lower().rstrip(".")` which removes trailing dots. So the key would be `"resi"` not `"resi."`. That's the bug! The map keys have dots but I'm stripping them from the lookup.
Also I have a fallback that maps to `s.title()` which for "resi." becomes "Resi." because title() capitalizes the first letter after the dot... wait no, `"resi.".title()` returns `"Resi."` indeed. So "Resi." stays as "Resi.".
I need to fix the mapping. Both the map keys should be without dots, or I shouldn't strip the dot.
Let me fix the normalize function so the map keys don't have dots.
0:52
Read
/work/solve.py
1 import geopandas as gpd
2 import pandas as pd
3 from shapely.geometry import Polygon, MultiPolygon
4 from shapely import wkt
5
6 # ── 1. Read ──────────────────────────────────────────────────────────────────
7 gdf = gpd.read_file("/work/lagos_zoning_legacy.gpkg")
8 print(f"Read {len(gdf)} rows — columns: {gdf.columns.tolist()}")
9 print(f"Original CRS: {gdf.crs}")
10
11 # ── 2. Drop rows with blank / whitespace-only / NaN zoning_class ──────────────
12 mask_keep = gdf["zoning_class"].notna() & (gdf["zoning_class"].astype(str).str.strip() != "")
13 dropped = (~mask_keep).sum()
14 gdf = gdf[mask_keep].copy()
15 print(f"Dropped {dropped} rows with blank/whitespace zoning_class, {len(gdf)} remaining")
16
17 # ── 3. Normalize zoning_class to canonical TitleCase values ─────────────────
18 # Build the mapping dictionary
19 normalize_map = {
20 "residential": "Residential",
21 "resi.": "Residential",
22 "commercial": "Commercial",
23 "comm.": "Commercial",
24 "industrial": "Industrial",
25 "indus.": "Industrial",
26 "agricultural": "Agricultural",
27 "agri.": "Agricultural",
28 }
29
30 def normalize(val):
31 s = str(val).strip()
32 key = s.lower().rstrip(".")
33 if key in normalize_map:
34 return normalize_map[key]
35 # If it's already a proper TitleCase known value, keep it
36 if s in ("Residential", "Commercial", "Industrial", "Agricultural"):
37 return s
38 # Fallback: whatever it is, title-case it
39 return s.title()
40
41 gdf["zoning_class"] = gdf["zoning_class"].apply(normalize)
42 print("Normalized class distribution:")
43 print(gdf["zoning_class"].value_counts().to_string())
44
45 # ── 4. Fix vertex precision — snap to 0.001 m grid ──────────────────────────
46 # The CRS is EPSG:26331 (metres); tiny sub-mm differences cause gaps/overlaps.
47 # We'll snap all coordinate values to the nearest 1 mm (0.001 m).
48
49 PRECISION = 0.001 # snap grid resolution
50
51 def snap_coords(x, y, z=None):
52 """Snap a coordinate pair (or triple) to the grid."""
53 sx = round(x / PRECISION) * PRECISION
54 sy = round(y / PRECISION) * PRECISION
55 if z is not None:
56 sz = round(z / PRECISION) * PRECISION
57 return (sx, sy, sz)
58 return (sx, sy)
59
60 def snap_polygon_coords(poly):
61 """Return a new Polygon with coords snapped to the PRECISION grid."""
62 # exterior ring
63 ext = [snap_coords(*c) for c in poly.exterior.coords]
64 # interior rings
65 ints = []
66 for ring in poly.interiors:
67 ints.append([snap_coords(*c) for c in ring.coords])
68 return Polygon(ext, ints)
69
70 def snap_geometry(geom):
71 if geom is None:
72 return None
73 if geom.geom_type == "Polygon":
74 return snap_polygon_coords(geom)
75 elif geom.geom_type == "MultiPolygon":
76 return MultiPolygon([snap_polygon_coords(p) for p in geom.geoms])
77 else:
78 return geom
79
80 print("Snapping vertices to ±", PRECISION, "m grid...")
81 gdf["geometry"] = gdf["geometry"].apply(snap_geometry)
82 print("Done snapping.")
83
84 # ── 5. Remove any invalid or sliver geometries after snapping ───────────────
85 # Buffer by 0 to clean up any self-intersections introduced by snapping
86 print("Repairing geometries (buffer(0))...")
87 gdf["geometry"] = gdf["geometry"].buffer(0)
88 print("Done.")
89
90 # ── 6. Dissolve (merge) by canonical zoning_class ───────────────────────────
91 print("Dissolving by zoning_class...")
92 dissolved = gdf.dissolve(by="zoning_class", aggfunc="first")
93 # Keep only the geometry column; the rest of the aggfunc doesn't matter
94
95 # Compute area_m2 for each merged geometry
96 dissolved["area_m2"] = dissolved.geometry.area
97
98 # Reset index so zoning_class becomes a regular column
99 dissolved = dissolved.reset_index()[["zoning_class", "area_m2", "geometry"]]
100
101 print(f"\nResult has {len(dissolved)} rows:")
102 print(dissolved[["zoning_class", "area_m2"]].to_string(index=False))
103
104 # ── 7. Write output ─────────────────────────────────────────────────────────
105 dissolved.to_file("/work/zoning_aggregated.gpkg", layer="zoning_aggregated", driver="GPKG")
106 print("\n✓ Written to /work/zoning_aggregated.gpkg")
1:11
Looking at the output, I see the issue - the normalization didn't fully work as intended. The unique values I see are:
- Agri. → this was mapped to "Agricultural" but it's showing as "Agri."
- Comm. → showing as "Comm." instead of "Commercial"
- Indus. → showing as "Indus." instead of "Industrial"
- Resi. → showing as "Resi." instead of "Residential"
Wait, let me look at the normalize function more carefully.
```python
key = s.lower().rstrip(".")
```
The issue is that `.rstrip(".")` only strips the trailing period if it's at the end. So `"Agri."` → `"agri"` ... and `"agri"` IS in the normalize_map, it maps to `"Agricultural"`. But the output shows "Agri."...
Let me check more carefully. The value_counts from the original showed:
- "Agri." → 420
- "AGRI." → 419
- "agricultural" → 416
- "Agricultural" → 417
So there are two distinct families here:
1. The "Agri." / "AGRI." / "agri." family (abbreviated forms)
2. The "Agricultural" / "agricultural" / "AGRICULTURAL" family (full forms)
My normalize_map has:
- "agricultural": "Agricultural" (this handles the full-form lowercase)
- "agri.": "Agricultural" (this handles the abbreviated form with period)
But `.rstrip(".")` strips the period first, so "Agri." → lower → "agri." → rstrip(".") → "agri"
Wait no: `s.lower().rstrip(".")` - this lowercases first, then strips trailing periods.
"Agri." -> lower -> "agri." -> rstrip(".") -> "agri"
And "agri" IS in the map: `"agri.": "Agricultural"` - but the key is "agri." NOT "agri"!
The problem is the rstrip removes the period from the key too. So "Agri." becomes "agri" after rstrip, but the map key is "agri." with the period.
I need to fix this. Let me update the normalize function so the keys don't have periods.
Actually, looking at the output again:
```
zoning_class
Residential 1256
Agri. 1256
Comm. 1255
Industrial 1255
Commercial 1253
Resi. 1252
Indus. 1252
Agricultural 1251
```
So what happened:
- "RESIDENTIAL", "residential", etc. → the function gets key = "residential" (from s.lower().rstrip(".")), which IS in the map → "Residential" ✓
- "Resi.", "RESI.", "resi." → key = "resi" (after rstrip), but map has "resi." → NOT FOUND → falls to the "title" fallback → "Resi."
- "Agri.", "AGRI.", "agri." → key = "agri" but map has "agri." → NOT FOUND → "Agri."
- "Comm.", "COMM.", "comm." → key = "comm" but map has "comm." → NOT FOUND → "Comm."
- "Indus.", "INDUS.", "indus." → key = "indus" but map has "indus." → NOT FOUND → "Indus."
So the fix is to either:
1. Strip the period from the keys in the map as well, OR
2. Don't strip the period in the rstrip call
Actually, the simplest fix is to strip the period from the dictionary keys too. Let me update the function:
```python
normalize_map = {
"residential": "Residential",
"resi": "Residential",
"commercial": "Commercial",
"comm": "Commercial",
"industrial": "Industrial",
"indus": "Industrial",
"agricultural": "Agricultural",
"agri": "Agricultural",
}
```
This way both "agri" (from "Agri." → rstrip(".")) and "agricultural" (from "agricultural" → rstrip(".") → still "agricultural") map correctly.
Let me fix this.
1:11
1:11
Edit
/work/solve.py
Edited /work/solve.py.
1:26
Wait, the issue is that the normalize function does `key = s.lower().rstrip(".")` which strips the trailing period. So "RESI." becomes "resi" (after lowercasing and stripping the period). That should match "resi" in the dictionary. Let me trace through:
- Input: "RESI." → s = "RESI." (strip won't change it much), key = "resi." → .rstrip(".") → "resi". That matches "resi" → "Residential". Good.
- Input: "RESIDENTIAL" → s = "RESIDENTIAL", key = "residential". Matches "residential" → "Residential". Good.
- Input: "resi." → s = "resi.", key = "resi" → matches "resi" → "Residential". Good.
- Input: "Resi." → same as above, "resi" → "Residential". Good.
- Input: "Residential" → key = "residential" → "Residential". Good.
- Input: "COMMERCIAL" → key = "commercial" → "Commercial". Good.
- Input: "COMM." → key = "comm" → "Commercial". Good.
- Input: "Comm." → key = "comm" → "Commercial". Good.
- Input: "commercial" → key = "commercial" → "Commercial". Good.
- Input: "INDUSTRIAL" → key = "industrial" → "Industrial". Good.
- Input: "INDUS." → key = "indus" → "Industrial". Good.
- Input: "Indus." → key = "indus" → "Industrial". Good.
- Input: "industrial" → key = "industrial" → "Industrial". Good.
- Input: "AGRICULTURAL" → key = "agricultural" → "Agricultural". Good.
- Input: "AGRI." → key = "agri" → "Agricultural". Good.
- Input: "Agri." → key = "agri" → "Agricultural". Good.
- Input: "agricultural" → key = "agricultural" → "Agricultural". Good.
- Input: "Agricultural" → key = "agricultural" → "Agricultural". Good.
But what about:
- Input: "RESI." → the normalize function does `s.lower()` first, so "RESI." → "resi.", then rstrip(".") → "resi". Good.
- But wait - "RESI." has `s.lower()` → "resi." and then `.rstrip(".")` → "resi". Then key = "resi", matches normalize_map["resi"] = "Residential". Good.
But the original output showed "Resi." and "Agri." and "Comm." and "Indus." as separate classes! So the mapping didn't work for those. Let me look more carefully...
The original unique values were:
- Residential, residential, Resi., resi., RESI., RESIDENTIAL
- Commercial, commercial, Comm., comm., COMM., COMMERCIAL
- Industrial, industrial, Indus., indus., INDUS., INDUSTRIAL
- Agricultural, agricultural, Agri., agri., AGRI., AGRICULTURAL
The normalize function should convert:
- "RESI." → s.lower() = "resi.", rstrip(".") = "resi" → "Residential". ✅
- "Resi." → s.lower() = "resi.", rstrip(".") = "resi" → "Residential". ✅
- "resi." → s.lower() = "resi.", rstrip(".") = "resi" → "Residential". ✅
But the output showed 8 classes: Agri., Agricultural, Comm., Commercial, Indus., Industrial, Resi., Residential
So the mapping clearly didn't work for those! The issue might be that `.lower()` on the string and then `.rstrip(".")` — oh wait, I see! The `rstrip(".")` removes "." from the end, but what about values without a period?
"RESIDENTIAL" → s.lower() = "residential", no period → "residential" → "Residential" ✅
"residential" → "residential" → "Residential" ✅
"Residential" → "residential" → "Residential" ✅
So all the residential variations should normalize to "Residential". But "RESI." → "resi." → rstrip(".") = "resi" → "Residential".
Hmm, but 1252 "Resi." and 1256 "Residential" were still separate... That means the normalize function was being applied but not working properly for abbreviations.
Oh wait! I see the problem. The output after normalize showed "Resi." (1252), "Residential" (1256), etc. Those are the NAMES of the classes as they appear after normalization. But "Resi." would require the normalize function to return "Resi." which would only happen if it didn't match the map. Let me trace again:
"RESI." → str(val).strip() → "RESI." → s = "RESI."
s.lower() → "resi."
.rstrip(".") → "resi"
key = "resi"
normalize_map.get("resi") = "Residential"
So it should return "Residential"... unless my mapping was wrong. Let me re-check the original code.
Oh! I had `"resi.": "Residential"` with the period in the key! But I do `key = s.lower().rstrip(".")` which strips the period. So key = "resi" and the map has "resi." — they don't match!
So my fix was correct — removing the period from the dictionary keys. Let me re-run.
But wait, I already made the edit. Let me re-run.
1:26
Bash
cd /work && python solve.py
Read 10080 rows — columns: ['parcel_id', 'lga_source', 'zoning_class', 'area_m2', 'geometry'] Original CRS: EPSG:26331 Dropped 50 rows with blank/whitespace zoning_class, 10030 remaining Normalized class distribution: zoning_class Residential 2508 Commercial 2508 Industrial 2507 Agricultural 2507 Snapping vertices to ± 0.001 m grid... Done snapping. Repairing geometries (buffer(0))... Done. Dissolving by zoning_class... Result has 4 rows: zoning_class area_m2 Agricultural 250000.0 Commercial 250000.0 Industrial 250000.0 Residential 250000.0 ✓ Written to /work/zoning_aggregated.gpkg
1:34
Now we have consistent TitleCase canonical values — 4 classes: Agricultural, Commercial, Industrial, Residential. The area for each is 250,000 m², which matches (10030 original rows - 50 blank = 9980... hmm, 10030 rows before dissolve, each 100 m², gives 1,003,000 m² across all classes, but after merging and snapping, the areas add up to 1,000,000 m². That's totally reasonable — the snapping and buffer(0) cleanup might have resolved overlaps/gaps correctly.) Let me verify the output file.
1:34
Bash
python3 -c "
python3 -c "
import geopandas as gpd
gdf = gpd.read_file('/work/zoning_aggregated.gpkg')
print('Columns:', gdf.columns.tolist())
print('CRS:', gdf.crs)
print('Shape:', gdf.shape)
print()
print(gdf.to_string())
print()
print('Geometry types:', gdf.geometry.type.value_counts().to_string())
"
Columns: ['zoning_class', 'area_m2', 'geometry'] CRS: EPSG:26331 Shape: (4, 3) zoning_class area_m2 geometry 0 Agricultural 250000.0 POLYGON ((540500 720580, 540500 720590, 540500 720600, 540500 720610, 540500 720620, 540500 720630, 540500 720640, 540500 720650, 540500 720660, 540500 720670, 540500 720680, 540500 720690, 540500 720700, 540500 720710, 540500 720720, 540500 720730, 540500 720740, 540500 720750, 540500 720760, 540500 720770, 540500 720780, 540500 720790, 540500 720800, 540500 720810, 540500 720820, 540500 720830, 540500 720840, 540500 720850, 540500 720860, 540500 720870, 540500 720880, 540500 720890, 540500 720900, 540500 720910, 540500 720920, 540500 720930, 540500 720940, 540500 720950, 540500 720960, 540500 720970, 540500 720980, 540500 720990, 540500 721000, 540510 721000, 540520 721000, 540530 721000, 540540 721000, 540550 721000, 540560 721000, 540570 721000, 540580 721000, 540590 721000, 540600 721000, 540610 721000, 540620 721000, 540630 721000, 540640 721000, 540650 721000, 540660 721000, 540670 721000, 540680 721000, 540690 721000, 540700 721000, 540710 721000, 540720 721000, 540730 721000, 540740 721000, 540750 721000, 540760 721000, 540770 721000, 540780 721000, 540790 721000, 540800 721000, 540810 721000, 540820 721000, 540830 721000, 540840 721000, 540850 721000, 540860 721000, 540870 721000, 540880 721000, 540890 721000, 540900 721000, 540910 721000, 540920 721000, 540930 721000, 540940 721000, 540950 721000, 540960 721000, 540970 721000, 540980 721000, 540990 721000, 541000 721000, 541000 720990, 541000 720980, 541000 720970, 541000 720960, 541000 720950, 541000 720940, 541000 720930, 541000 720920, 541000 720910, 541000 720900, 541000 720890, 541000 720880, 541000 720870, 541000 720860, 541000 720850, 541000 720840, 541000 720830, 541000 720820, 541000 720810, 541000 720800, 541000 720790, 541000 720780, 541000 720770, 541000 720760, 541000 720750, 541000 720740, 541000 720730, 541000 720720, 541000 720710, 541000 720700, 541000 720690, 541000 720680, 541000 720670, 541000 720660, 541000 720650, 541000 720640, 541000 720630, 541000 720620, 541000 720610, 541000 720600, 541000 720590, 541000 720580, 541000 720570, 541000 720560, 541000 720550, 541000 720540, 541000 720530, 541000 720520, 541000 720510, 541000 720500, 540990 720500, 540980 720500, 540970 720500, 540960 720500, 540950 720500, 540940 720500, 540930 720500, 540920 720500, 540910 720500, 540900 720500, 540890 720500, 540880 720500, 540870 720500, 540860 720500, 540850 720500, 540840 720500, 540830 720500, 540820 720500, 540810 720500, 540800 720500, 540790 720500, 540780 720500, 540770 720500, 540760 720500, 540750 720500, 540740 720500, 540730 720500, 540720 720500, 540710 720500, 540700 720500, 540690 720500, 540680 720500, 540670 720500, 540660 720500, 540650 720500, 540640 720500, 540630 720500, 540620 720500, 540610 720500, 540600 720500, 540590 720500, 540580 720500, 540570 720500, 540560 720500, 540550 720500, 540540 720500, 540530 720500, 540520 720500, 540510 720500, 540500 720500, 540500 720510, 540500 720520, 540500 720530, 540500 720540, 540500 720550, 540500 720560, 540500 720570, 540500 720580)) 1 Commercial 250000.0 POLYGON ((540500 720080, 540500 720090, 540500 720100, 540500 720110, 540500 720120, 540500 720130, 540500 720140, 540500 720150, 540500 720160, 540500 720170, 540500 720180, 540500 720190, 540500 720200, 540500 720210, 540500 720220, 540500 720230, 540500 720240, 540500 720250, 540500 720260, 540500 720270, 540500 720280, 540500 720290, 540500 720300, 540500 720310, 540500 720320, 540500 720330, 540500 720340, 540500 720350, 540500 720360, 540500 720370, 540500 720380, 540500 720390, 540500 720400, 540500 720410, 540500 720420, 540500 720430, 540500 720440, 540500 720450, 540500 720460, 540500 720470, 540500 720480, 540500 720490, 540500 720500, 540510 720500, 540520 720500, 540530 720500, 540540 720500, 540550 720500, 540560 720500, 540570 720500, 540580 720500, 540590 720500, 540600 720500, 540610 720500, 540620 720500, 540630 720500, 540640 720500, 540650 720500, 540660 720500, 540670 720500, 540680 720500, 540690 720500, 540700 720500, 540710 720500, 540720 720500, 540730 720500, 540740 720500, 540750 720500, 540760 720500, 540770 720500, 540780 720500, 540790 720500, 540800 720500, 540810 720500, 540820 720500, 540830 720500, 540840 720500, 540850 720500, 540860 720500, 540870 720500, 540880 720500, 540890 720500, 540900 720500, 540910 720500, 540920 720500, 540930 720500, 540940 720500, 540950 720500, 540960 720500, 540970 720500, 540980 720500, 540990 720500, 541000 720500, 541000 720490, 541000 720480, 541000 720470, 541000 720460, 541000 720450, 541000 720440, 541000 720430, 541000 720420, 541000 720410, 541000 720400, 541000 720390, 541000 720380, 541000 720370, 541000 720360, 541000 720350, 541000 720340, 541000 720330, 541000 720320, 541000 720310, 541000 720300, 541000 720290, 541000 720280, 541000 720270, 541000 720260, 541000 720250, 541000 720240, 541000 720230, 541000 720220, 541000 720210, 541000 720200, 541000 720190, 541000 720180, 541000 720170, 541000 720160, 541000 720150, 541000 720140, 541000 720130, 541000 720120, 541000 720110, 541000 720100, 541000 720090, 541000 720080, 541000 720070, 541000 720060, 541000 720050, 541000 720040, 541000 720030, 541000 720020, 541000 720010, 541000 720000, 540990 720000, 540980 720000, 540970 720000, 540960 720000, 540950 720000, 540940 720000, 540930 720000, 540920 720000, 540910 720000, 540900 720000, 540890 720000, 540880 720000, 540870 720000, 540860 720000, 540850 720000, 540840 720000, 540830 720000, 540820 720000, 540810 720000, 540800 720000, 540790 720000, 540780 720000, 540770 720000, 540760 720000, 540750 720000, 540740 720000, 540730 720000, 540720 720000, 540710 720000, 540700 720000, 540690 720000, 540680 720000, 540670 720000, 540660 720000, 540650 720000, 540640 720000, 540630 720000, 540620 720000, 540610 720000, 540600 720000, 540590 720000, 540580 720000, 540570 720000, 540560 720000, 540550 720000, 540540 720000, 540530 720000, 540520 720000, 540510 720000, 540500 720000, 540500 720010, 540500 720020, 540500 720030, 540500 720040, 540500 720050, 540500 720060, 540500 720070, 540500 720080)) 2 Industrial 250000.0 POLYGON ((540000 720580, 540000 720590, 540000 720600, 540000 720610, 540000 720620, 540000 720630, 540000 720640, 540000 720650, 540000 720660, 540000 720670, 540000 720680, 540000 720690, 540000 720700, 540000 720710, 540000 720720, 540000 720730, 540000 720740, 540000 720750, 540000 720760, 540000 720770, 540000 720780, 540000 720790, 540000 720800, 540000 720810, 540000 720820, 540000 720830, 540000 720840, 540000 720850, 540000 720860, 540000 720870, 540000 720880, 540000 720890, 540000 720900, 540000 720910, 540000 720920, 540000 720930, 540000 720940, 540000 720950, 540000 720960, 540000 720970, 540000 720980, 540000 720990, 540000 721000, 540010 721000, 540020 721000, 540030 721000, 540040 721000, 540050 721000, 540060 721000, 540070 721000, 540080 721000, 540090 721000, 540100 721000, 540110 721000, 540120 721000, 540130 721000, 540140 721000, 540150 721000, 540160 721000, 540170 721000, 540180 721000, 540190 721000, 540200 721000, 540210 721000, 540220 721000, 540230 721000, 540240 721000, 540250 721000, 540260 721000, 540270 721000, 540280 721000, 540290 721000, 540300 721000, 540310 721000, 540320 721000, 540330 721000, 540340 721000, 540350 721000, 540360 721000, 540370 721000, 540380 721000, 540390 721000, 540400 721000, 540410 721000, 540420 721000, 540430 721000, 540440 721000, 540450 721000, 540460 721000, 540470 721000, 540480 721000, 540490 721000, 540500 721000, 540500 720990, 540500 720980, 540500 720970, 540500 720960, 540500 720950, 540500 720940, 540500 720930, 540500 720920, 540500 720910, 540500 720900, 540500 720890, 540500 720880, 540500 720870, 540500 720860, 540500 720850, 540500 720840, 540500 720830, 540500 720820, 540500 720810, 540500 720800, 540500 720790, 540500 720780, 540500 720770, 540500 720760, 540500 720750, 540500 720740, 540500 720730, 540500 720720, 540500 720710, 540500 720700, 540500 720690, 540500 720680, 540500 720670, 540500 720660, 540500 720650, 540500 720640, 540500 720630, 540500 720620, 540500 720610, 540500 720600, 540500 720590, 540500 720580, 540500 720570, 540500 720560, 540500 720550, 540500 720540, 540500 720530, 540500 720520, 540500 720510, 540500 720500, 540490 720500, 540480 720500, 540470 720500, 540460 720500, 540450 720500, 540440 720500, 540430 720500, 540420 720500, 540410 720500, 540400 720500, 540390 720500, 540380 720500, 540370 720500, 540360 720500, 540350 720500, 540340 720500, 540330 720500, 540320 720500, 540310 720500, 540300 720500, 540290 720500, 540280 720500, 540270 720500, 540260 720500, 540250 720500, 540240 720500, 540230 720500, 540220 720500, 540210 720500, 540200 720500, 540190 720500, 540180 720500, 540170 720500, 540160 720500, 540150 720500, 540140 720500, 540130 720500, 540120 720500, 540110 720500, 540100 720500, 540090 720500, 540080 720500, 540070 720500, 540060 720500, 540050 720500, 540040 720500, 540030 720500, 540020 720500, 540010 720500, 540000 720500, 540000 720510, 540000 720520, 540000 720530, 540000 720540, 540000 720550, 540000 720560, 540000 720570, 540000 720580)) 3 Residential 250000.0 POLYGON ((540000 720080, 540000 720090, 540000 720100, 540000 720110, 540000 720120, 540000 720130, 540000 720140, 540000 720150, 540000 720160, 540000 720170, 540000 720180, 540000 720190, 540000 720200, 540000 720210, 540000 720220, 540000 720230, 540000 720240, 540000 720250, 540000 720260, 540000 720270, 540000 720280, 540000 720290, 540000 720300, 540000 720310, 540000 720320, 540000 720330, 540000 720340, 540000 720350, 540000 720360, 540000 720370, 540000 720380, 540000 720390, 540000 720400, 540000 720410, 540000 720420, 540000 720430, 540000 720440, 540000 720450, 540000 720460, 540000 720470, 540000 720480, 540000 720490, 540000 720500, 540010 720500, 540020 720500, 540030 720500, 540040 720500, 540050 720500, 540060 720500, 540070 720500, 540080 720500, 540090 720500, 540100 720500, 540110 720500, 540120 720500, 540130 720500, 540140 720500, 540150 720500, 540160 720500, 540170 720500, 540180 720500, 540190 720500, 540200 720500, 540210 720500, 540220 720500, 540230 720500, 540240 720500, 540250 720500, 540260 720500, 540270 720500, 540280 720500, 540290 720500, 540300 720500, 540310 720500, 540320 720500, 540330 720500, 540340 720500, 540350 720500, 540360 720500, 540370 720500, 540380 720500, 540390 720500, 540400 720500, 540410 720500, 540420 720500, 540430 720500, 540440 720500, 540450 720500, 540460 720500, 540470 720500, 540480 720500, 540490 720500, 540500 720500, 540500 720490, 540500 720480, 540500 720470, 540500 720460, 540500 720450, 540500 720440, 540500 720430, 540500 720420, 540500 720410, 540500 720400, 540500 720390, 540500 720380, 540500 720370, 540500 720360, 540500 720350, 540500 720340, 540500 720330, 540500 720320, 540500 720310, 540500 720300, 540500 720290, 540500 720280, 540500 720270, 540500 720260, 540500 720250, 540500 720240, 540500 720230, 540500 720220, 540500 720210, 540500 720200, 540500 720190, 540500 720180, 540500 720170, 540500 720160, 540500 720150, 540500 720140, 540500 720130, 540500 720120, 540500 720110, 540500 720100, 540500 720090, 540500 720080, 540500 720070, 540500 720060, 540500 720050, 540500 720040, 540500 720030, 540500 720020, 540500 720010, 540500 720000, 540490 720000, 540480 720000, 540470 720000, 540460 720000, 540450 720000, 540440 720000, 540430 720000, 540420 720000, 540410 720000, 540400 720000, 540390 720000, 540380 720000, 540370 720000, 540360 720000, 540350 720000, 540340 720000, 540330 720000, 540320 720000, 540310 720000, 540300 720000, 540290 720000, 540280 720000, 540270 720000, 540260 720000, 540250 720000, 540240 720000, 540230 720000, 540220 720000, 540210 720000, 540200 720000, 540190 720000, 540180 720000, 540170 720000, 540160 720000, 540150 720000, 540140 720000, 540130 720000, 540120 720000, 540110 720000, 540100 720000, 540090 720000, 540080 720000, 540070 720000, 540060 720000, 540050 720000, 540040 720000, 540030 720000, 540020 720000, 540010 720000, 540000 720000, 540000 720010, 540000 720020, 540000 720030, 540000 720040, 540000 720050, 540000 720060, 540000 720070, 540000 720080)) Geometry types: Polygon 4
1:45
The result looks great. Let me verify the key requirements: 1. ✅ **zoning_class normalized to canonical TitleCase**: Residential, Commercial, Industrial, Agricultural (4 canonical classes) 2. ✅ **Dropped blank/whitespace rows**: 50 rows dropped 3. ✅ **Vertex precision fixed**: Coordinates snapped to 0.001 m grid 4. ✅ **Dissolved by class**: One merged geometry per class (4 rows) 5. ✅ **Two columns**: zoning_class and area_m2 6. ✅ **Written to zoning_aggregated.gpkg** 7. ✅ **CRS preserved**: EPSG:26331 8. ✅ **All geometries are valid polygons** Let me also check the area values make sense. Each original parcel was 100 m² (10x10), and we had about 2500 parcels per class, so 250,000 m² total per class. That checks out. Let me verify the file exists.
1:45
Bash
ls -la /work/zoning_aggregated.gpkg
-rw-r--r-- 1 agent agent 434176 Jun 15 16:35 /work/zoning_aggregated.gpkg
2:01
Let me verify everything checks out: 1. **Output file exists**: `zoning_aggregated.gpkg` ✓ 2. **Columns**: `zoning_class` and `area_m2` ✓ 3. **CRS**: EPSG:26331 (original CRS preserved) ✓ 4. **Blank rows dropped**: 50 rows with blank/whitespace/NaN were dropped ✓ 5. **Class names normalized** to canonical TitleCase: - "Residential", "Resi.", "resi.", "RESIDENTIAL", "RESI.", "residential" → "Residential" - "Commercial", "Comm.", "comm.", "COMMERCIAL", "COMM.", "commercial" → "Commercial" - "Industrial", "Indus.", "indus.", "INDUSTRIAL", "INDUS.", "industrial" → "Industrial" - "Agricultural", "Agri.", "agri.", "AGRICULTURAL", "AGRI.", "agricultural" → "Agricultural" ✓ 6. **One merged geometry per class**: 4 rows (Agricultural, Commercial, Industrial, Residential) ✓ 7. **Area values**: area_m2 is present and numeric ✓ 8. **Vertex precision**: coordinates are now all nice round numbers (e.g., 540000, 720000) ✓ Everything looks good!
2:01