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"
}
0:07
Let me start by exploring the input file to understand its structure.
0:07
Bash
ls -la /work/lagos_zoning_legacy.gpkg
-rw-r--r-- 1 agent agent 2527232 Jun 16 22:32 /work/lagos_zoning_legacy.gpkg
0:07
Bash
python3 -c "
python3 -c "
import geopandas as gpd
gdf = gpd.read_file('/work/lagos_zoning_legacy.gpkg')
print('Shape:', gdf.shape)
print('Columns:', list(gdf.columns))
print('CRS:', gdf.crs)
print('Dtypes:')
print(gdf.dtypes)
if 'geometry' in gdf.columns:
print('Geometry types:', gdf.geometry.type.value_counts().to_dict())
print('Total area (CRS units):', gdf.geometry.area.sum())
"
Shape: (10080, 5)
Columns: ['parcel_id', 'lga_source', 'zoning_class', 'area_m2', 'geometry']
CRS: EPSG:26331
Dtypes:
parcel_id int64
lga_source str
zoning_class str
area_m2 float64
geometry geometry
dtype: object
Geometry types: {'Polygon': 10080}
Total area (CRS units): 1005000.000350127
0:14
Let me explore the data more to understand the zoning_class values and the issues.
0:14
Bash
python3 -c "
python3 -c "
import geopandas as gpd
gdf = gpd.read_file('/work/lagos_zoning_legacy.gpkg')
print('Unique zoning_class values:')
for v in sorted(gdf['zoning_class'].unique()):
cnt = (gdf['zoning_class'] == v).sum()
print(f' [{repr(v)}] count={cnt}')
print()
print('Null/blank counts:')
print(' None:', gdf['zoning_class'].isna().sum())
print(' empty string:', (gdf['zoning_class'] == '').sum())
print(' whitespace:', gdf['zoning_class'].str.strip().eq('').sum())
"
Unique zoning_class values:
Traceback (most recent call last):
File "<string>", line 5, in <module>
for v in sorted(gdf['zoning_class'].unique()):
~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
TypeError: '<' not supported between instances of 'float' and 'str'
0:22
There are some NaN values mixed with strings. Let me handle that.
0:22
Bash
python3 -c "
python3 -c "
import geopandas as gpd
import numpy as np
gdf = gpd.read_file('/work/lagos_zoning_legacy.gpkg')
# Get unique values sorted as strings
vals = gdf['zoning_class'].unique()
str_vals = [str(v) for v in vals]
for sv in sorted(set(str_vals)):
cnt = sum(1 for v in vals if str(v) == sv)
print(f' [{repr(sv)}] count={cnt}')
print()
print('NaN count:', gdf['zoning_class'].isna().sum())
print('empty string:', (gdf['zoning_class'] == '').sum())
"
[''] count=1 ['\t'] count=1 [' '] count=1 ['AGRI.'] count=1 ['AGRICULTURAL'] count=1 ['Agri.'] count=1 ['Agricultural'] count=1 ['COMM.'] count=1 ['COMMERCIAL'] count=1 ['Comm.'] count=1 ['Commercial'] count=1 ['INDUS.'] count=1 ['INDUSTRIAL'] count=1 ['Indus.'] count=1 ['Industrial'] count=1 ['RESI.'] count=1 ['RESIDENTIAL'] count=1 ['Resi.'] count=1 ['Residential'] count=1 ['agri.'] count=1 ['agricultural'] count=1 ['comm.'] count=1 ['commercial'] count=1 ['indus.'] count=1 ['industrial'] count=1 ['nan'] count=1 ['resi.'] count=1 ['residential'] count=1 NaN count: 12 empty string: 13
0:30
Let me get a better view of the data. Let's see what's actually going on with the unique values more precisely.
0:30
Bash
python3 -c "
python3 -c "
import geopandas as gpd
gdf = gpd.read_file('/work/lagos_zoning_legacy.gpkg')
# Count all rows
print('Total rows:', len(gdf))
# Show unique zoning_class values with their counts, handling NaN
for v in gdf['zoning_class'].unique():
if pd.isna(v):
cnt = gdf['zoning_class'].isna().sum()
print(f' NaN count={cnt}')
else:
cnt = (gdf['zoning_class'] == v).sum()
print(f' [{repr(v)}] count={cnt}')
" 2>&1 || python3 -c "
import pandas as pd
import geopandas as gpd
gdf = gpd.read_file('/work/lagos_zoning_legacy.gpkg')
for v in gdf['zoning_class'].unique():
if pd.isna(v):
cnt = gdf['zoning_class'].isna().sum()
print(f' NaN count={cnt}')
else:
cnt = (gdf['zoning_class'] == v).sum()
print(f' [{repr(v)}] count={cnt}')
"
Total rows: 10080
Traceback (most recent call last):
File "<string>", line 10, in <module>
if pd.isna(v):
^^
NameError: name 'pd' is not defined. Did you mean: 'gpd'?
['RESIDENTIAL'] count=417
['residential'] count=419
['Resi.'] count=416
['resi.'] count=419
['RESI.'] count=417
['Residential'] count=420
['Comm.'] count=419
['comm.'] count=417
['COMM.'] count=419
['Commercial'] count=416
['COMMERCIAL'] count=420
['commercial'] count=417
['Indus.'] count=417
['indus.'] count=419
['INDUS.'] count=416
['Industrial'] count=418
['INDUSTRIAL'] count=417
['industrial'] count=420
['AGRI.'] count=419
['Agricultural'] count=417
['AGRICULTURAL'] count=418
['agricultural'] count=416
['Agri.'] count=420
['agri.'] count=417
[''] count=13
[' '] count=13
NaN count=12
['\t'] count=12
0:39
Let me understand the data: 1. **zoning_class values** - There are 4 main classes: - Residential (variants: RESIDENTIAL, residential, Resi., resi., RESI., Residential) - Commercial (variants: COMMERCIAL, commercial, Comm., comm., COMM., Commercial) - Industrial (variants: INDUSTRIAL, industrial, Indus., indus., INDUS., Industrial) - Agricultural (variants: AGRI., Agricultural, AGRICULTURAL, agricultural, Agri., agri.) 2. **Blank/whitespace entries**: - '' (empty string): 13 - ' ' (spaces): 13 - NaN: 12 - '\t' (tab): 12 - Total: 50 rows to drop 3. **Goal**: - Clean to canonical TitleCase: Residential, Commercial, Industrial, Agricultural - Drop blank/whitespace rows - Fix vertex precision issues (snap to a grid or use buffer(0) technique) - Merge geometries per class (dissolve) - Output: zoning_class + area_m2 in zoning_aggregated.gpkg Let me write a comprehensive script.
0:39
Bash
python3 -c "
python3 -c "
import geopandas as gpd
gdf = gpd.read_file('/work/lagos_zoning_legacy.gpkg')
print('Sample geometries:')
for i in range(5):
geom = gdf.geometry.iloc[i]
print(f' {i}: type={geom.geom_type}, coords={list(geom.exterior.coords)[:5]}...')
"
Sample geometries: 0: type=Polygon, coords=[(540000.0, 720000.0), (540010.00002, 720000.000005), (540010.000005, 720010.000015), (540000.000025, 720010.00002), (540000.0, 720000.0)]... 1: type=Polygon, coords=[(540010.00001, 720000.000025), (540020.0, 720000.000005), (540020.00002, 720010.00001), (540010.000005, 720010.00002), (540010.00001, 720000.000025)]... 2: type=Polygon, coords=[(540020.000025, 720000.00002), (540030.000015, 720000.0), (540030.0, 720010.00001), (540020.00002, 720010.000015), (540020.000025, 720000.00002)]... 3: type=Polygon, coords=[(540030.000005, 720000.00002), (540040.000025, 720000.000025), (540040.000015, 720010.000005), (540030.0, 720010.000015), (540030.000005, 720000.00002)]... 4: type=Polygon, coords=[(540040.00002, 720000.000015), (540050.000005, 720000.000025), (540050.00003, 720010.0), (540040.000015, 720010.00001), (540040.00002, 720000.000015)]...
1:01
Now I understand the data. Let me plan the solution: 1. **Canonical zoning class mapping**: I need to normalize all the variations to proper TitleCase: - 'Agricultural', 'AGRI.', 'Agri.', 'agri.', 'agricultural', 'AGRICULTURAL' → 'Agricultural' - 'Commercial', 'COMM.', 'Comm.', 'comm.', 'commercial', 'COMMERCIAL' → 'Commercial' - 'Industrial', 'INDUS.', 'Indus.', 'indus.', 'industrial', 'INDUSTRIAL' → 'Industrial' - 'Residential', 'RESI.', 'Resi.', 'resi.', 'residential', 'RESIDENTIAL' → 'Residential' - '', ' ', '\t', NaN → drop these rows 2. **Vertex precision**: The coordinates have many decimal places like `540000.0, 720000.0`, `540010.00002`, etc. I need to snap/round coordinates to a sensible precision. Since the data seems to be in a 10m grid (values like 540000, 540010, 540020...), I'll round to 3 decimal places (millimeter precision) or maybe snap to a small grid. Actually, looking at the coordinates, they seem to be in a 10m grid pattern. The tiny variations (like 540010.00002 instead of 540010.0) cause gaps/overlaps. So I should round to a reasonable precision - maybe 2 decimal places (cm) or even 0 decimal places (whole units) since the grid is 10m. Let me think about this. The coordinates like 540000.0, 720000.0 - they look like they should be at integer boundaries. The tiny offsets like .00002, .000005, .000025 are what cause gaps. I think snapping to 0.001 (1mm) or 0.01 (1cm) precision would be fine. But actually, since the base values are in whole meters (540000, 720000, 540010, etc.), maybe rounding to 1 decimal place or even integer should work. Let me use round to 3 decimal places (mm precision) which should clean up the noise while preserving any legitimate sub-meter precision. Actually, better approach: use `shapely.set_precision()` or `buffer(0)` after rounding. The `normalize` function in shapely combined with `set_precision` can snap coordinates to a grid. 3. **Merge per zoning class**: After cleaning, dissolve by zoning_class with `dissolve(by='zoning_class', aggfunc='first')`, then compute area. Let me write the solution script.
1:01
Bash
python3 -c "
python3 -c "
import geopandas as gpd
gdf = gpd.read_file('/work/lagos_zoning_legacy.gpkg')
# Check if buffer(0) would help with topology
print('Total area before any fix:', gdf.geometry.area.sum())
print('Number of self-intersections:', (~gdf.is_valid).sum())
"
Total area before any fix: 1005000.000350127 Number of self-intersections: 30
1:17
Let me analyze the data: 1. **zoning_class values**: There are variations like: - Residential: 'RESIDENTIAL', 'residential', 'Residential', 'RESI.', 'resi.', 'Resi.' - Commercial: 'COMMERCIAL', 'commercial', 'Commercial', 'COMM.', 'comm.', 'Comm.' - Industrial: 'INDUSTRIAL', 'industrial', 'Industrial', 'INDUS.', 'indus.', 'Indus.' - Agricultural: 'AGRICULTURAL', 'agricultural', 'Agricultural', 'AGRI.', 'agri.', 'Agri.' - Blank/whitespace: '', ' ', '\t', NaN 2. **Vertex precision**: Coordinates have many decimal places like 540000.00002, 720000.000005. I need to snap to a reasonable precision (e.g., 3 decimal places or 0.001 m). 3. **Task**: Clean up class names to canonical TitleCase, drop blank rows, fix vertex precision, then merge/dissolve by class. Let me write a comprehensive Python script. For canonical mapping: - Residential: 'Residential' - Commercial: 'Commercial' - Industrial: 'Industrial' - Agricultural: 'Agricultural' For vertex precision: I'll snap coordinates to 3 decimal places (1mm) using a snap operation or simply rounding coordinates. For merging: dissolve by zoning_class after fixing precision and dropping blanks. Let me write the script.
1:17
Write
/work/solve.py
content · 93 lines · py
import geopandas as gpd
import pandas as pd
import numpy as np
from shapely.geometry import Polygon, MultiPolygon
from shapely import wkt, set_precision
import warnings
warnings.filterwarnings('ignore')
# ── 1. Read the legacy data ──────────────────────────────────────────────────
gdf = gpd.read_file('/work/lagos_zoning_legacy.gpkg')
print(f'Read {len(gdf)} rows from source.')
print(f'Original unique zoning_class values: {sorted(gdf["zoning_class"].dropna().unique())}')
# ── 2. Standardise zoning_class to canonical TitleCase ──────────────────────
# Build a mapping from any observed variant to the canonical TitleCase name.
canonical_map = {
'RESIDENTIAL': 'Residential',
'residential': 'Residential',
'Residential': 'Residential',
'RESI.': 'Residential',
'resi.': 'Residential',
'Resi.': 'Residential',
'COMMERCIAL': 'Commercial',
'commercial': 'Commercial',
'Commercial': 'Commercial',
'COMM.': 'Commercial',
'comm.': 'Commercial',
'Comm.': 'Commercial',
'INDUSTRIAL': 'Industrial',
'industrial': 'Industrial',
'Industrial': 'Industrial',
'INDUS.': 'Industrial',
'indus.': 'Industrial',
'Indus.': 'Industrial',
'AGRICULTURAL':'Agricultural',
'agricultural':'Agricultural',
'Agricultural':'Agricultural',
'AGRI.': 'Agricultural',
'agri.': 'Agricultural',
'Agri.': 'Agricultural',
}
# Drop rows with blank / whitespace-only / NaN zoning_class
mask_blank = gdf['zoning_class'].isna() | gdf['zoning_class'].astype(str).str.strip().eq('')
print(f'Rows with blank/whitespace/NaN zoning_class: {mask_blank.sum()}')
gdf = gdf[~mask_blank].copy()
print(f'Remaining rows after dropping blanks: {len(gdf)}')
# Apply canonical mapping (preserve any unmapped values as-is)
gdf['zoning_class'] = gdf['zoning_class'].map(canonical_map).fillna(gdf['zoning_class'])
print(f'Canonical classes:\n{gdf["zoning_class"].value_counts()}')
# ── 3. Fix vertex precision ──────────────────────────────────────────────────
# Snap coordinates to 3 decimal places (1 mm) to eliminate tiny gaps/overlaps.
# set_precision snaps vertices to a grid; we use a small grid size.
PRECISION = 0.001 # 1 mm
print(f'Precision grid size: {PRECISION} m')
# Before fix stats
n_invalid_before = (~gdf.is_valid).sum()
print(f'Invalid geometries before snap: {n_invalid_before}')
# Snap coordinates to the grid
gdf['geometry'] = set_precision(gdf['geometry'], grid_size=PRECISION)
# Fix any remaining invalid geometries (e.g. self-intersections from snapping)
n_invalid_after = (~gdf.is_valid).sum()
print(f'Invalid geometries after snap: {n_invalid_after}')
# Buffer(0) to fix any self-intersections / ring orientation issues
gdf['geometry'] = gdf['geometry'].buffer(0)
n_invalid_fixed = (~gdf.is_valid).sum()
print(f'Invalid geometries after buffer(0): {n_invalid_fixed}')
# ── 4. Dissolve by zoning_class ─────────────────────────────────────────────
dissolved = gdf.dissolve(by='zoning_class', aggfunc='first')
dissolved = dissolved.reset_index()
# Compute area in m² from the merged geometry
dissolved['area_m2'] = dissolved.geometry.area
# Keep only the two requested columns
result = dissolved[['zoning_class', 'area_m2']].copy()
result = result.sort_values('zoning_class').reset_index(drop=True)
print(f'\nResult ({len(result)} classes):')
print(result.to_string(index=False))
print(f'\nTotal area (aggregated): {result["area_m2"].sum():.6f} m²')
# ── 5. Write output ─────────────────────────────────────────────────────────
result.to_file('/work/zoning_aggregated.gpkg', driver='GPKG')
print('\nWritten to /work/zoning_aggregated.gpkg')Wrote /work/solve.py (3697 chars).
1:26
Let me check if `set_precision` is available in the shapely version installed. Also, let me check the version of shapely.
1:26
Bash
python3 -c "import shapely; print(shapely.__version__)"
2.1.2
1:32
Let me run the script.
1:32
Bash
cd /work && python3 solve.py
Read 10080 rows from source. Original unique zoning_class values: ['', '\t', ' ', 'AGRI.', 'AGRICULTURAL', 'Agri.', 'Agricultural', 'COMM.', 'COMMERCIAL', 'Comm.', 'Commercial', 'INDUS.', 'INDUSTRIAL', 'Indus.', 'Industrial', 'RESI.', 'RESIDENTIAL', 'Resi.', 'Residential', 'agri.', 'agricultural', 'comm.', 'commercial', 'indus.', 'industrial', 'resi.', 'residential'] Rows with blank/whitespace/NaN zoning_class: 50 Remaining rows after dropping blanks: 10030 Canonical classes: zoning_class Residential 2508 Commercial 2508 Industrial 2507 Agricultural 2507 Name: count, dtype: int64 Precision grid size: 0.001 m Invalid geometries before snap: 30 Invalid geometries after snap: 0 Invalid geometries after buffer(0): 0 Result (4 classes): zoning_class area_m2 Agricultural 250000.0 Commercial 250000.0 Industrial 250000.0 Residential 250000.0 Total area (aggregated): 1000000.000000 m²
Traceback (most recent call last):
File "/work/solve.py", line 92, in <module>
result.to_file('/work/zoning_aggregated.gpkg', driver='GPKG')
^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/pandas/core/generic.py", line 6206, in __getattr__
return object.__getattribute__(self, name)
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'to_file'. Did you mean: 'to_pickle'?
1:39
The to_file method is on GeoDataFrame, not DataFrame. I need to convert result back to a GeoDataFrame before writing.
1:39
Edit
/work/solve.py
Edited /work/solve.py.
1:42
Wait, the issue is that after dissolve, I selected only ['zoning_class', 'area_m2'] but then I'm calling to_file on a DataFrame that doesn't have a geometry column. Let me check - the dissolved variable had geometry but after selecting only two columns, it loses geometry. I need to keep the geometry. Let me fix the script properly.
1:42
Bash
cd /work && python3 solve.py 2>&1
Read 10080 rows from source.
Original unique zoning_class values: ['', '\t', ' ', 'AGRI.', 'AGRICULTURAL', 'Agri.', 'Agricultural', 'COMM.', 'COMMERCIAL', 'Comm.', 'Commercial', 'INDUS.', 'INDUSTRIAL', 'Indus.', 'Industrial', 'RESI.', 'RESIDENTIAL', 'Resi.', 'Residential', 'agri.', 'agricultural', 'comm.', 'commercial', 'indus.', 'industrial', 'resi.', 'residential']
Rows with blank/whitespace/NaN zoning_class: 50
Remaining rows after dropping blanks: 10030
Canonical classes:
zoning_class
Residential 2508
Commercial 2508
Industrial 2507
Agricultural 2507
Name: count, dtype: int64
Precision grid size: 0.001 m
Invalid geometries before snap: 30
Invalid geometries after snap: 0
Invalid geometries after buffer(0): 0
Result (4 classes):
zoning_class area_m2
Agricultural 250000.0
Commercial 250000.0
Industrial 250000.0
Residential 250000.0
Total area (aggregated): 1000000.000000 m²
Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/pandas/core/indexes/base.py", line 3641, in get_loc
return self._engine.get_loc(casted_key)
~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^
File "pandas/_libs/index.pyx", line 168, in pandas._libs.index.IndexEngine.get_loc
File "pandas/_libs/index.pyx", line 197, in pandas._libs.index.IndexEngine.get_loc
File "pandas/_libs/hashtable_class_helper.pxi", line 7668, in pandas._libs.hashtable.PyObjectHashTable.get_item
File "pandas/_libs/hashtable_class_helper.pxi", line 7676, in pandas._libs.hashtable.PyObjectHashTable.get_item
KeyError: 'geometry'
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/geopandas/geodataframe.py", line 420, in set_geometry
level = frame[col]
~~~~~^^^^^
File "/usr/local/lib/python3.14/site-packages/geopandas/geodataframe.py", line 1891, in __getitem__
result = super().__getitem__(key)
File "/usr/local/lib/python3.14/site-packages/pandas/core/frame.py", line 4378, in __getitem__
indexer = self.columns.get_loc(key)
File "/usr/local/lib/python3.14/site-packages/pandas/core/indexes/base.py", line 3648, in get_loc
raise KeyError(key) from err
KeyError: 'geometry'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/work/solve.py", line 93, in <module>
result_gdf = gpd.GeoDataFrame(result, geometry='geometry', crs=gdf.crs)
File "/usr/local/lib/python3.14/site-packages/geopandas/geodataframe.py", line 243, in __init__
self.set_geometry(geometry, inplace=True, crs=crs)
~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/geopandas/geodataframe.py", line 422, in set_geometry
raise ValueError(f"Unknown column {col}")
ValueError: Unknown column geometry
1:49
The dissolved GeoDataFrame has a geometry column, but after I select ['zoning_class', 'area_m2'], the geometry column is lost. I need to keep the geometry in the result. Let me fix this.
1:49
Read
/work/solve.py
1 import geopandas as gpd
2 import pandas as pd
3 import numpy as np
4 from shapely.geometry import Polygon, MultiPolygon
5 from shapely import wkt, set_precision
6 import warnings
7 warnings.filterwarnings('ignore')
8
9 # ── 1. Read the legacy data ──────────────────────────────────────────────────
10 gdf = gpd.read_file('/work/lagos_zoning_legacy.gpkg')
11 print(f'Read {len(gdf)} rows from source.')
12 print(f'Original unique zoning_class values: {sorted(gdf["zoning_class"].dropna().unique())}')
13
14 # ── 2. Standardise zoning_class to canonical TitleCase ──────────────────────
15 # Build a mapping from any observed variant to the canonical TitleCase name.
16 canonical_map = {
17 'RESIDENTIAL': 'Residential',
18 'residential': 'Residential',
19 'Residential': 'Residential',
20 'RESI.': 'Residential',
21 'resi.': 'Residential',
22 'Resi.': 'Residential',
23 'COMMERCIAL': 'Commercial',
24 'commercial': 'Commercial',
25 'Commercial': 'Commercial',
26 'COMM.': 'Commercial',
27 'comm.': 'Commercial',
28 'Comm.': 'Commercial',
29 'INDUSTRIAL': 'Industrial',
30 'industrial': 'Industrial',
31 'Industrial': 'Industrial',
32 'INDUS.': 'Industrial',
33 'indus.': 'Industrial',
34 'Indus.': 'Industrial',
35 'AGRICULTURAL':'Agricultural',
36 'agricultural':'Agricultural',
37 'Agricultural':'Agricultural',
38 'AGRI.': 'Agricultural',
39 'agri.': 'Agricultural',
40 'Agri.': 'Agricultural',
41 }
42
43 # Drop rows with blank / whitespace-only / NaN zoning_class
44 mask_blank = gdf['zoning_class'].isna() | gdf['zoning_class'].astype(str).str.strip().eq('')
45 print(f'Rows with blank/whitespace/NaN zoning_class: {mask_blank.sum()}')
46 gdf = gdf[~mask_blank].copy()
47 print(f'Remaining rows after dropping blanks: {len(gdf)}')
48
49 # Apply canonical mapping (preserve any unmapped values as-is)
50 gdf['zoning_class'] = gdf['zoning_class'].map(canonical_map).fillna(gdf['zoning_class'])
51 print(f'Canonical classes:\n{gdf["zoning_class"].value_counts()}')
52
53 # ── 3. Fix vertex precision ──────────────────────────────────────────────────
54 # Snap coordinates to 3 decimal places (1 mm) to eliminate tiny gaps/overlaps.
55 # set_precision snaps vertices to a grid; we use a small grid size.
56 PRECISION = 0.001 # 1 mm
57 print(f'Precision grid size: {PRECISION} m')
58
59 # Before fix stats
60 n_invalid_before = (~gdf.is_valid).sum()
61 print(f'Invalid geometries before snap: {n_invalid_before}')
62
63 # Snap coordinates to the grid
64 gdf['geometry'] = set_precision(gdf['geometry'], grid_size=PRECISION)
65
66 # Fix any remaining invalid geometries (e.g. self-intersections from snapping)
67 n_invalid_after = (~gdf.is_valid).sum()
68 print(f'Invalid geometries after snap: {n_invalid_after}')
69
70 # Buffer(0) to fix any self-intersections / ring orientation issues
71 gdf['geometry'] = gdf['geometry'].buffer(0)
72
73 n_invalid_fixed = (~gdf.is_valid).sum()
74 print(f'Invalid geometries after buffer(0): {n_invalid_fixed}')
75
76 # ── 4. Dissolve by zoning_class ─────────────────────────────────────────────
77 dissolved = gdf.dissolve(by='zoning_class', aggfunc='first')
78 dissolved = dissolved.reset_index()
79
80 # Compute area in m² from the merged geometry
81 dissolved['area_m2'] = dissolved.geometry.area
82
83 # Keep only the two requested columns
84 result = dissolved[['zoning_class', 'area_m2']].copy()
85 result = result.sort_values('zoning_class').reset_index(drop=True)
86
87 print(f'\nResult ({len(result)} classes):')
88 print(result.to_string(index=False))
89 print(f'\nTotal area (aggregated): {result["area_m2"].sum():.6f} m²')
90
91 # ── 5. Write output ─────────────────────────────────────────────────────────
92 # Convert back to GeoDataFrame for writing
93 result_gdf = gpd.GeoDataFrame(result, geometry='geometry', crs=gdf.crs)
94 result_gdf.to_file('/work/zoning_aggregated.gpkg', driver='GPKG')
95 print('\nWritten to /work/zoning_aggregated.gpkg')
1:58
The issue is that `result` doesn't have a `geometry` column because I only selected `['zoning_class', 'area_m2']`. I need to keep the geometry column from `dissolved` so I can write it to a GPKG file. Let me fix that.
1:58
Edit
/work/solve.py
Edited /work/solve.py.
2:02
Wait, dissolved already has a geometry column since it's a GeoDataFrame. So when I do `dissolved[['zoning_class', 'area_m2']]` I lose the geometry column. I need to keep the geometry column. Let me fix it to keep geometry in the selection.
2:02
Bash
cd /work && python3 solve.py 2>&1
Read 10080 rows from source. Original unique zoning_class values: ['', '\t', ' ', 'AGRI.', 'AGRICULTURAL', 'Agri.', 'Agricultural', 'COMM.', 'COMMERCIAL', 'Comm.', 'Commercial', 'INDUS.', 'INDUSTRIAL', 'Indus.', 'Industrial', 'RESI.', 'RESIDENTIAL', 'Resi.', 'Residential', 'agri.', 'agricultural', 'comm.', 'commercial', 'indus.', 'industrial', 'resi.', 'residential'] Rows with blank/whitespace/NaN zoning_class: 50 Remaining rows after dropping blanks: 10030 Canonical classes: zoning_class Residential 2508 Commercial 2508 Industrial 2507 Agricultural 2507 Name: count, dtype: int64 Precision grid size: 0.001 m Invalid geometries before snap: 30 Invalid geometries after snap: 0 Invalid geometries after buffer(0): 0 Result (4 classes): zoning_class area_m2 Agricultural 250000.0 Commercial 250000.0 Industrial 250000.0 Residential 250000.0 Total area (aggregated): 1000000.000000 m² Written to /work/zoning_aggregated.gpkg
2:10
The script ran successfully. Let me verify the output file to make sure it's correct.
2:10
Bash
python3 -c "
python3 -c "
import geopandas as gpd
gdf = gpd.read_file('/work/zoning_aggregated.gpkg')
print('Shape:', gdf.shape)
print('Columns:', list(gdf.columns))
print('CRS:', gdf.crs)
print()
print(gdf[['zoning_class', 'area_m2']].to_string(index=False))
print()
print('Total area:', gdf['area_m2'].sum())
print('Geometry types:', gdf.geometry.type.value_counts().to_dict())
print('All valid:', gdf.is_valid.all())
"
Shape: (4, 3)
Columns: ['zoning_class', 'area_m2', 'geometry']
CRS: EPSG:26331
zoning_class area_m2
Agricultural 250000.0
Commercial 250000.0
Industrial 250000.0
Residential 250000.0
Total area: 1000000.0
Geometry types: {'Polygon': 4}
All valid: True
2:20
Let me verify the output is correct. Let me also double-check that the original total area was 1,005,000.000350127 m², and after dropping 50 blank rows and dissolving, we got exactly 1,000,000 m². That makes sense - the 50 dropped rows accounted for ~5,000 m², and the snapping/buffer(0) cleaned up the tiny precision gaps (the 0.00035 difference). Let me also verify the output has the requested format - two columns: zoning_class and area_m2. Well, it has 3 columns including geometry. The task says "each row carrying two columns: zoning_class and a numeric area_m2" - but in a geospatial file, having the geometry is implicit. Let me check if they want exactly two columns or if geometry is fine. Actually, since it's a geopackage, having a geometry column is expected. The user said "two columns: zoning_class and a numeric area_m2" - I think they mean those are the attribute columns plus geometry. But to be safe, let me re-read: "produce one merged geometry per canonical zoning class" - so yes, geometry is needed. The "two columns" probably refers to the attribute columns. Let me keep it as is. Let me verify the output is correct and complete.
2:20