spa-l2-cairo-shop-knn
Map (reference ← swipe → agent)
0:00
I'm putting together a retail-density readout for a downtown Cairo brief. `cairo_retail` has two layers, `shops` and `anchors`. For each anchor I need the 5 nearest shops with distance in metres and a within-1 km flag, plus a 5x3 distance matrix from those 5 shops to the anchor's 3 closest sibling anchors. The chain names in the source data have inconsistent spellings, so the output should use consistent names: the same `shop_id` always carries the same `normalised_name`, and all shops belonging to one chain share a single canonical spelling. Tidy the anchor names too.
Please write `market_neighbourhoods.json` as a top-level JSON array with one record per anchor. Each record carries the keys `anchor_id`, `anchor_name_normalised` (non-empty string), `knn` (exactly 5 entries sorted by ascending `distance_m`), and `full_distance_matrix_m` (5 rows by 3 numeric columns; rows follow the knn order, columns correspond to the sibling anchors in distance-ascending order). Each `knn` entry has `shop_id`, `normalised_name`, `distance_m` (finite numeric, in metres), and `within_1km` (boolean, true when `distance_m` is at most 1000).
0:00
System info
{
"adapter": "openrouter",
"model": "deepseek/deepseek-v4-flash",
"base_url": "https://openrouter.ai/api/v1"
}
0:08
Let me start by exploring the data to understand its structure.
0:08
Bash
cd /work && ls -la
total 1228 drwxr-xr-x 1 agent agent 34 Jun 16 04:28 . drwxr-xr-x 1 root root 14 Jun 16 04:28 .. -rw-r--r-- 1 agent agent 1257472 Jun 16 04:28 cairo_retail.gpkg
0:08
Bash
cd /work && find . -type f -name "*.gpkg" -o -name "*.geojson" -o -name "*.json"...
./cairo_retail.gpkg
0:13
Let me first explore the dataset to understand its structure.
0:13
Bash
cd /work && python3 -c "
cd /work && python3 -c "
import geopandas as gpd
import sqlite3
# List layers
conn = sqlite3.connect('cairo_retail.gpkg')
cur = conn.cursor()
cur.execute(\"SELECT name FROM sqlite_master WHERE type='table'\")
print('Tables:', cur.fetchall())
conn.close()
# Read layers
shops = gpd.read_file('cairo_retail.gpkg', layer='shops')
anchors = gpd.read_file('cairo_retail.gpkg', layer='anchors')
print('Shops columns:', shops.columns.tolist())
print('Anchors columns:', anchors.columns.tolist())
print()
print('Shops sample:')
print(shops.head(10).to_string())
print()
print('Anchors sample:')
print(anchors.head(10).to_string())
print()
print('Shops CRS:', shops.crs)
print('Anchors CRS:', anchors.crs)
"
Tables: [('gpkg_spatial_ref_sys',), ('gpkg_contents',), ('gpkg_ogr_contents',), ('gpkg_geometry_columns',), ('gpkg_tile_matrix_set',), ('gpkg_tile_matrix',), ('shops',), ('sqlite_sequence',), ('gpkg_extensions',), ('rtree_shops_geom',), ('rtree_shops_geom_rowid',), ('rtree_shops_geom_node',), ('rtree_shops_geom_parent',), ('anchors',), ('rtree_anchors_geom',), ('rtree_anchors_geom_rowid',), ('rtree_anchors_geom_node',), ('rtree_anchors_geom_parent',)]
Shops columns: ['shop_id', 'raw_name', 'geometry']
Anchors columns: ['anchor_id', 'anchor_name', 'geometry']
Shops sample:
shop_id raw_name geometry
0 S00001 OSCAR POINT (637478.058 814957.398)
1 S00002 Local Shop 00002 POINT (651773.775 816149.979)
2 S00003 Local Shop 00003 POINT (640341.689 815148.396)
3 S00004 Local Shop 00004 POINT (640225.465 805311.132)
4 S00005 Local Shop 00005 POINT (647199.272 818195.959)
5 S00006 مترو POINT (647363.601 817078.397)
6 S00007 Local Shop 00007 POINT (637452.604 813452.721)
7 S00008 Carrefour POINT (642884.749 820068.564)
8 S00009 Local Shop 00009 POINT (638401.854 815382.513)
9 S00010 Carrefour POINT (645066.08 808334.791)
Anchors sample:
anchor_id anchor_name geometry
0 M001 TAHRIR SQUARE PLAZA POINT (635109.343 805565.909)
1 M002 Zamalek Riverside POINT (637039.843 805569.597)
2 M003 Maadi Corniche POINT (638970.345 805573.62)
3 M004 HELIOPOLIS SQUARE POINT (640900.848 805577.981)
4 M005 New Cairo Tagamoa POINT (642831.353 805582.677)
5 M006 Nasr City Hub POINT (644761.86 805587.711)
6 M007 MOHANDESSIN CENTRE POINT (646692.368 805593.081)
7 M008 Garden City Walk POINT (648622.879 805598.787)
8 M009 Dokki Market POINT (650553.391 805604.83)
9 M010 GIZA PYRAMIDS GATE POINT (652483.906 805611.21)
Shops CRS: EPSG:22992
Anchors CRS: EPSG:22992
0:32
Let me understand the data: 1. **Shops layer**: has `shop_id`, `raw_name`, and geometry (point) 2. **Anchors layer**: has `anchor_id`, `anchor_name`, and geometry (point) CRS is EPSG:22992 (which is in metres - Egypt Red Belt). I need to: 1. Normalise shop names (inconsistent spellings of chain names) - same `shop_id` always has same `normalised_name`, all shops in one chain share one canonical spelling. 2. Normalise anchor names too. 3. For each anchor, find the 5 nearest shops with distance in metres. 4. For each of those 5 shops, compute distance to the anchor's 3 closest sibling anchors. Let me look at all unique raw names to understand the inconsistencies.
0:32
Bash
cd /work && python3 -c "
cd /work && python3 -c "
import pandas as pd
import geopandas as gpd
shops = gpd.read_file('cairo_retail.gpkg', layer='shops')
anchors = gpd.read_file('cairo_retail.gpkg', layer='anchors')
print('Unique shop raw_names:')
for n in sorted(shops['raw_name'].unique()):
cnt = (shops['raw_name'] == n).sum()
print(f' {repr(n)}: {cnt}')
print()
print('Unique anchor names:')
for n in sorted(anchors['anchor_name'].unique()):
cnt = (anchors['anchor_name'] == n).sum()
print(f' {repr(n)}: {cnt}')
"
Unique shop raw_names: 'Abu Zekry': 136 'Abu Zikri': 137 'Carrefour': 143 'Carrefour Egypt': 169 'Hyper One': 158 'HyperOne': 152 'Khair Zaman': 171 'Kheir Zaman': 158 'Local Shop 00002': 1 'Local Shop 00003': 1 'Local Shop 00004': 1 'Local Shop 00005': 1 'Local Shop 00007': 1 'Local Shop 00009': 1 'Local Shop 00011': 1 'Local Shop 00013': 1 'Local Shop 00014': 1 'Local Shop 00015': 1 'Local Shop 00017': 1 'Local Shop 00018': 1 'Local Shop 00019': 1 'Local Shop 00020': 1 'Local Shop 00023': 1 'Local Shop 00024': 1 'Local Shop 00025': 1 'Local Shop 00027': 1 'Local Shop 00030': 1 'Local Shop 00031': 1 'Local Shop 00033': 1 'Local Shop 00034': 1 'Local Shop 00035': 1 'Local Shop 00036': 1 'Local Shop 00037': 1 'Local Shop 00038': 1 'Local Shop 00040': 1 'Local Shop 00041': 1 'Local Shop 00048': 1 'Local Shop 00049': 1 'Local Shop 00050': 1 'Local Shop 00052': 1 'Local Shop 00053': 1 'Local Shop 00055': 1 'Local Shop 00056': 1 'Local Shop 00059': 1 'Local Shop 00063': 1 'Local Shop 00067': 1 'Local Shop 00069': 1 'Local Shop 00071': 1 'Local Shop 00072': 1 'Local Shop 00075': 1 'Local Shop 00076': 1 'Local Shop 00077': 1 'Local Shop 00079': 1 'Local Shop 00080': 1 'Local Shop 00081': 1 'Local Shop 00082': 1 'Local Shop 00084': 1 'Local Shop 00085': 1 'Local Shop 00086': 1 'Local Shop 00088': 1 'Local Shop 00090': 1 'Local Shop 00091': 1 'Local Shop 00092': 1 'Local Shop 00093': 1 'Local Shop 00095': 1 'Local Shop 00096': 1 'Local Shop 00103': 1 'Local Shop 00104': 1 'Local Shop 00105': 1 'Local Shop 00106': 1 'Local Shop 00110': 1 'Local Shop 00111': 1 'Local Shop 00113': 1 'Local Shop 00119': 1 'Local Shop 00120': 1 'Local Shop 00121': 1 'Local Shop 00125': 1 'Local Shop 00127': 1 'Local Shop 00135': 1 'Local Shop 00136': 1 'Local Shop 00137': 1 'Local Shop 00138': 1 'Local Shop 00139': 1 'Local Shop 00142': 1 'Local Shop 00143': 1 'Local Shop 00144': 1 'Local Shop 00146': 1 'Local Shop 00150': 1 'Local Shop 00151': 1 'Local Shop 00152': 1 'Local Shop 00153': 1 'Local Shop 00157': 1 'Local Shop 00158': 1 'Local Shop 00159': 1 'Local Shop 00161': 1 'Local Shop 00162': 1 'Local Shop 00164': 1 'Local Shop 00165': 1 'Local Shop 00166': 1 'Local Shop 00167': 1 'Local Shop 00169': 1 'Local Shop 00170': 1 'Local Shop 00171': 1 'Local Shop 00172': 1 'Local Shop 00176': 1 'Local Shop 00181': 1 'Local Shop 00182': 1 'Local Shop 00184': 1 'Local Shop 00185': 1 'Local Shop 00187': 1 'Local Shop 00188': 1 'Local Shop 00190': 1 'Local Shop 00191': 1 'Local Shop 00192': 1 'Local Shop 00194': 1 'Local Shop 00195': 1 'Local Shop 00197': 1 'Local Shop 00198': 1 'Local Shop 00200': 1 'Local Shop 00201': 1 'Local Shop 00202': 1 'Local Shop 00203': 1 'Local Shop 00205': 1 'Local Shop 00206': 1 'Local Shop 00209': 1 'Local Shop 00210': 1 'Local Shop 00211': 1 'Local Shop 00213': 1 'Local Shop 00214': 1 'Local Shop 00216': 1 'Local Shop 00217': 1 'Local Shop 00222': 1 'Local Shop 00225': 1 'Local Shop 00227': 1 'Local Shop 00228': 1 'Local Shop 00229': 1 'Local Shop 00230': 1 'Local Shop 00231': 1 'Local Shop 00233': 1 'Local Shop 00239': 1 'Local Shop 00241': 1 'Local Shop 00242': 1 'Local Shop 00244': 1 'Local Shop 00245': 1 'Local Shop 00246': 1 'Local Shop 00250': 1 'Local Shop 00251': 1 'Local Shop 00258': 1 'Local Shop 00259': 1 'Local Shop 00260': 1 'Local Shop 00262': 1 'Local Shop 00264': 1 'Local Shop 00266': 1 'Local Shop 00268': 1 'Local Shop 00269': 1 'Local Shop 00271': 1 'Local Shop 00275': 1 'Local Shop 00276': 1 'Local Shop 00278': 1 'Local Shop 00279': 1 'Local Shop 00280': 1 'Local Shop 00282': 1 'Local Shop 00283': 1 'Local Shop 00286': 1 'Local Shop 00289': 1 'Local Shop 00295': 1 'Local Shop 00298': 1 'Local Shop 00300': 1 'Local Shop 00301': 1 'Local Shop 00303': 1 'Local Shop 00305': 1 'Local Shop 00306': 1 'Local Shop 00307': 1 'Local Shop 00309': 1 'Local Shop 00311': 1 'Local Shop 00312': 1 'Local Shop 00313': 1 'Local Shop 00314': 1 'Local Shop 00315': 1 'Local Shop 00319': 1 'Local Shop 00320': 1 'Local Shop 00322': 1 'Local Shop 00323': 1 'Local Shop 00325': 1 'Local Shop 00328': 1 'Local Shop 00334': 1 'Local Shop 00335': 1 'Local Shop 00337': 1 'Local Shop 00338': 1 'Local Shop 00342': 1 'Local Shop 00346': 1 'Local Shop 00347': 1 'Local Shop 00350': 1 'Local Shop 00351': 1 'Local Shop 00353': 1 'Local Shop 00354': 1 'Local Shop 00357': 1 'Local Shop 00361': 1 'Local Shop 00362': 1 'Local Shop 00363': 1 'Local Shop 00365': 1 'Local Shop 00366': 1 'Local Shop 00367': 1 'Local Shop 00372': 1 'Local Shop 00375': 1 'Local Shop 00376': 1 'Local Shop 00378': 1 'Local Shop 00379': 1 'Local Shop 00380': 1 'Local Shop 00385': 1 'Local Shop 00387': 1 'Local Shop 00392': 1 'Local Shop 00394': 1 'Local Shop 00395': 1 'Local Shop 00397': 1 'Local Shop 00400': 1 'Local Shop 00403': 1 'Local Shop 00408': 1 'Local Shop 00409': 1 'Local Shop 00410': 1 'Local Shop 00413': 1 'Local Shop 00417': 1 'Local Shop 00418': 1 'Local Shop 00420': 1 'Local Shop 00423': 1 'Local Shop 00424': 1 'Local Shop 00427': 1 'Local Shop 00429': 1 'Local Shop 00432': 1 'Local Shop 00433': 1 'Local Shop 00434': 1 'Local Shop 00435': 1 'Local Shop 00437': 1 'Local Shop 00439': 1 'Local Shop 00441': 1 'Local Shop 00442': 1 'Local Shop 00443': 1 'Local Shop 00447': 1 'Local Shop 00448': 1 'Local Shop 00450': 1 'Local Shop 00452': 1 'Local Shop 00455': 1 'Local Shop 00456': 1 'Local Shop 00457': 1 'Local Shop 00459': 1 'Local Shop 00460': 1 'Local Shop 00462': 1 'Local Shop 00463': 1 'Local Shop 00465': 1 'Local Shop 00466': 1 'Local Shop 00468': 1 'Local Shop 00470': 1 'Local Shop 00471': 1 'Local Shop 00472': 1 'Local Shop 00473': 1 'Local Shop 00474': 1 'Local Shop 00477': 1 'Local Shop 00478': 1 'Local Shop 00479': 1 'Local Shop 00480': 1 'Local Shop 00481': 1 'Local Shop 00483': 1 'Local Shop 00484': 1 'Local Shop 00486': 1 'Local Shop 00487': 1 'Local Shop 00488': 1 'Local Shop 00489': 1 'Local Shop 00491': 1 'Local Shop 00493': 1 'Local Shop 00494': 1 'Local Shop 00496': 1 'Local Shop 00498': 1 'Local Shop 00499': 1 'Local Shop 00502': 1 'Local Shop 00503': 1 'Local Shop 00505': 1 'Local Shop 00506': 1 'Local Shop 00507': 1 'Local Shop 00509': 1 'Local Shop 00511': 1 'Local Shop 00513': 1 'Local Shop 00514': 1 'Local Shop 00515': 1 'Local Shop 00516': 1 'Local Shop 00517': 1 'Local Shop 00518': 1 'Local Shop 00520': 1 'Local Shop 00521': 1 'Local Shop 00524': 1 'Local Shop 00525': 1 'Local Shop 00527': 1 'Local Shop 00528': 1 'Local Shop 00529': 1 'Local Shop 00530': 1 'Local Shop 00531': 1 'Local Shop 00532': 1 'Local Shop 00536': 1 'Local Shop 00538': 1 'Local Shop 00541': 1 'Local Shop 00545': 1 'Local Shop 00547': 1 'Local Shop 00548': 1 'Local Shop 00549': 1 'Local Shop 00550': 1 'Local Shop 00551': 1 'Local Shop 00552': 1 'Local Shop 00556': 1 'Local Shop 00557': 1 'Local Shop 00560': 1 'Local Shop 00561': 1 'Local Shop 00562': 1 'Local Shop 00563': 1 'Local Shop 00566': 1 'Local Shop 00567': 1 'Local Shop 00568': 1 'Local Shop 00570': 1 'Local Shop 00571': 1 'Local Shop 00573': 1 'Local Shop 00574': 1 'Local Shop 00577': 1 'Local Shop 00578': 1 'Local Shop 00579': 1 'Local Shop 00581': 1 'Local Shop 00583': 1 'Local Shop 00585': 1 'Local Shop 00586': 1 'Local Shop 00588': 1 'Local Shop 00589': 1 'Local Shop 00590': 1 'Local Shop 00591': 1 'Local Shop 00593': 1 'Local Shop 00594': 1 'Local Shop 00596': 1 'Local Shop 00597': 1 'Local Shop 00601': 1 'Local Shop 00602': 1 'Local Shop 00603': 1 'Local Shop 00604': 1 'Local Shop 00608': 1 'Local Shop 00610': 1 'Local Shop 00611': 1 'Local Shop 00612': 1 'Local Shop 00613': 1 'Local Shop 00615': 1 'Local Shop 00616': 1 'Local Shop 00617': 1 'Local Shop 00618': 1 'Local Shop 00619': 1 'Local Shop 00623': 1 'Local Shop 00624': 1 'Local Shop 00637': 1 'Local Shop 00640': 1 'Local Shop 00641': 1 'Local Shop 00642': 1 'Local Shop 00643': 1 'Local Shop 00648': 1 'Local Shop 00650': 1 'Local Shop 00653': 1 'Local Shop 00658': 1 'Local Shop 00659': 1 'Local Shop 00660': 1 'Local Shop 00661': 1 'Local Shop 00662': 1 'Local Shop 00663': 1 'Local Shop 00664': 1 'Local Shop 00665': 1 'Local Shop 00666': 1 'Local Shop 00667': 1 'Local Shop 00669': 1 'Local Shop 00673': 1 'Local Shop 00674': 1 'Local Shop 00675': 1 'Local Shop 00676': 1 'Local Shop 00678': 1 'Local Shop 00680': 1 'Local Shop 00681': 1 'Local Shop 00683': 1 'Local Shop 00687': 1 'Local Shop 00688': 1 'Local Shop 00690': 1 'Local Shop 00693': 1 'Local Shop 00695': 1 'Local Shop 00697': 1 'Local Shop 00699': 1 'Local Shop 00707': 1 'Local Shop 00708': 1 'Local Shop 00710': 1 'Local Shop 00711': 1 'Local Shop 00712': 1 'Local Shop 00714': 1 'Local Shop 00716': 1 'Local Shop 00718': 1 'Local Shop 00719': 1 'Local Shop 00720': 1 'Local Shop 00721': 1 'Local Shop 00722': 1 'Local Shop 00731': 1 'Local Shop 00734': 1 'Local Shop 00735': 1 'Local Shop 00736': 1 'Local Shop 00737': 1 'Local Shop 00739': 1 'Local Shop 00745': 1 'Local Shop 00746': 1 'Local Shop 00749': 1 'Local Shop 00751': 1 'Local Shop 00755': 1 'Local Shop 00757': 1 'Local Shop 00762': 1 'Local Shop 00765': 1 'Local Shop 00773': 1 'Local Shop 00774': 1 'Local Shop 00776': 1 'Local Shop 00778': 1 'Local Shop 00779': 1 'Local Shop 00782': 1 'Local Shop 00786': 1 'Local Shop 00787': 1 'Local Shop 00789': 1 'Local Shop 00790': 1 'Local Shop 00791': 1 'Local Shop 00792': 1 'Local Shop 00794': 1 'Local Shop 00795': 1 'Local Shop 00797': 1 'Local Shop 00800': 1 'Local Shop 00801': 1 'Local Shop 00804': 1 'Local Shop 00808': 1 'Local Shop 00810': 1 'Local Shop 00812': 1 'Local Shop 00813': 1 'Local Shop 00814': 1 'Local Shop 00815': 1 'Local Shop 00816': 1 'Local Shop 00819': 1 'Local Shop 00821': 1 'Local Shop 00824': 1 'Local Shop 00826': 1 'Local Shop 00827': 1 'Local Shop 00829': 1 'Local Shop 00835': 1 'Local Shop 00836': 1 'Local Shop 00838': 1 'Local Shop 00841': 1 'Local Shop 00849': 1 'Local Shop 00850': 1 'Local Shop 00851': 1 'Local Shop 00853': 1 'Local Shop 00855': 1 'Local Shop 00857': 1 'Local Shop 00859': 1 'Local Shop 00863': 1 'Local Shop 00864': 1 'Local Shop 00866': 1 'Local Shop 00870': 1 'Local Shop 00871': 1 'Local Shop 00875': 1 'Local Shop 00876': 1 'Local Shop 00879': 1 'Local Shop 00880': 1 'Local Shop 00881': 1 'Local Shop 00882': 1 'Local Shop 00888': 1 'Local Shop 00890': 1 'Local Shop 00891': 1 'Local Shop 00893': 1 'Local Shop 00898': 1 'Local Shop 00900': 1 'Local Shop 00901': 1 'Local Shop 00902': 1 'Local Shop 00903': 1 'Local Shop 00910': 1 'Local Shop 00913': 1 'Local Shop 00914': 1 'Local Shop 00917': 1 'Local Shop 00918': 1 'Local Shop 00919': 1 'Local Shop 00921': 1 'Local Shop 00922': 1 'Local Shop 00932': 1 'Local Shop 00938': 1 'Local Shop 00940': 1 'Local Shop 00943': 1 'Local Shop 00945': 1 'Local Shop 00949': 1 'Local Shop 00950': 1 'Local Shop 00951': 1 'Local Shop 00952': 1 'Local Shop 00953': 1 'Local Shop 00954': 1 'Local Shop 00955': 1 'Local Shop 00958': 1 'Local Shop 00961': 1 'Local Shop 00962': 1 'Local Shop 00966': 1 'Local Shop 00969': 1 'Local Shop 00971': 1 'Local Shop 00973': 1 'Local Shop 00976': 1 'Local Shop 00977': 1 'Local Shop 00978': 1 'Local Shop 00979': 1 'Local Shop 00980': 1 'Local Shop 00981': 1 'Local Shop 00983': 1 'Local Shop 00984': 1 'Local Shop 00992': 1 'Local Shop 00999': 1 'Local Shop 01000': 1 'Local Shop 01006': 1 'Local Shop 01007': 1 'Local Shop 01008': 1 'Local Shop 01011': 1 'Local Shop 01012': 1 'Local Shop 01015': 1 'Local Shop 01018': 1 'Local Shop 01025': 1 'Local Shop 01026': 1 'Local Shop 01027': 1 'Local Shop 01030': 1 'Local Shop 01031': 1 'Local Shop 01032': 1 'Local Shop 01033': 1 'Local Shop 01034': 1 'Local Shop 01036': 1 'Local Shop 01038': 1 'Local Shop 01039': 1 'Local Shop 01041': 1 'Local Shop 01042': 1 'Local Shop 01044': 1 'Local Shop 01047': 1 'Local Shop 01049': 1 'Local Shop 01051': 1 'Local Shop 01053': 1 'Local Shop 01054': 1 'Local Shop 01057': 1 'Local Shop 01058': 1 'Local Shop 01059': 1 'Local Shop 01066': 1 'Local Shop 01069': 1 'Local Shop 01070': 1 'Local Shop 01074': 1 'Local Shop 01076': 1 'Local Shop 01079': 1 'Local Shop 01082': 1 'Local Shop 01083': 1 'Local Shop 01085': 1 'Local Shop 01088': 1 'Local Shop 01090': 1 'Local Shop 01091': 1 'Local Shop 01093': 1 'Local Shop 01095': 1 'Local Shop 01096': 1 'Local Shop 01099': 1 'Local Shop 01102': 1 'Local Shop 01103': 1 'Local Shop 01105': 1 'Local Shop 01106': 1 'Local Shop 01107': 1 'Local Shop 01108': 1 'Local Shop 01109': 1 'Local Shop 01111': 1 'Local Shop 01113': 1 'Local Shop 01115': 1 'Local Shop 01119': 1 'Local Shop 01123': 1 'Local Shop 01124': 1 'Local Shop 01126': 1 'Local Shop 01127': 1 'Local Shop 01128': 1 'Local Shop 01130': 1 'Local Shop 01131': 1 'Local Shop 01132': 1 'Local Shop 01134': 1 'Local Shop 01136': 1 'Local Shop 01137': 1 'Local Shop 01138': 1 'Local Shop 01139': 1 'Local Shop 01140': 1 'Local Shop 01142': 1 'Local Shop 01145': 1 'Local Shop 01147': 1 'Local Shop 01149': 1 'Local Shop 01151': 1 'Local Shop 01154': 1 'Local Shop 01158': 1 'Local Shop 01159': 1 'Local Shop 01160': 1 'Local Shop 01162': 1 'Local Shop 01163': 1 'Local Shop 01166': 1 'Local Shop 01167': 1 'Local Shop 01168': 1 'Local Shop 01169': 1 'Local Shop 01172': 1 'Local Shop 01173': 1 'Local Shop 01175': 1 'Local Shop 01176': 1 'Local Shop 01180': 1 'Local Shop 01181': 1 'Local Shop 01182': 1 'Local Shop 01183': 1 'Local Shop 01186': 1 'Local Shop 01188': 1 'Local Shop 01189': 1 'Local Shop 01190': 1 'Local Shop 01191': 1 'Local Shop 01196': 1 'Local Shop 01201': 1 'Local Shop 01202': 1 'Local Shop 01204': 1 'Local Shop 01206': 1 'Local Shop 01207': 1 'Local Shop 01209': 1 'Local Shop 01211': 1 'Local Shop 01212': 1 'Local Shop 01213': 1 'Local Shop 01215': 1 'Local Shop 01216': 1 'Local Shop 01221': 1 'Local Shop 01222': 1 'Local Shop 01225': 1 'Local Shop 01231': 1 'Local Shop 01232': 1 'Local [... truncated ...]ocal Shop 09001': 1 'Local Shop 09003': 1 'Local Shop 09005': 1 'Local Shop 09007': 1 'Local Shop 09009': 1 'Local Shop 09010': 1 'Local Shop 09012': 1 'Local Shop 09017': 1 'Local Shop 09020': 1 'Local Shop 09021': 1 'Local Shop 09022': 1 'Local Shop 09023': 1 'Local Shop 09024': 1 'Local Shop 09025': 1 'Local Shop 09026': 1 'Local Shop 09028': 1 'Local Shop 09030': 1 'Local Shop 09032': 1 'Local Shop 09034': 1 'Local Shop 09036': 1 'Local Shop 09038': 1 'Local Shop 09040': 1 'Local Shop 09044': 1 'Local Shop 09046': 1 'Local Shop 09047': 1 'Local Shop 09050': 1 'Local Shop 09051': 1 'Local Shop 09052': 1 'Local Shop 09053': 1 'Local Shop 09055': 1 'Local Shop 09056': 1 'Local Shop 09062': 1 'Local Shop 09066': 1 'Local Shop 09067': 1 'Local Shop 09068': 1 'Local Shop 09070': 1 'Local Shop 09072': 1 'Local Shop 09075': 1 'Local Shop 09077': 1 'Local Shop 09078': 1 'Local Shop 09080': 1 'Local Shop 09082': 1 'Local Shop 09084': 1 'Local Shop 09085': 1 'Local Shop 09089': 1 'Local Shop 09090': 1 'Local Shop 09093': 1 'Local Shop 09094': 1 'Local Shop 09095': 1 'Local Shop 09097': 1 'Local Shop 09098': 1 'Local Shop 09099': 1 'Local Shop 09104': 1 'Local Shop 09108': 1 'Local Shop 09110': 1 'Local Shop 09112': 1 'Local Shop 09113': 1 'Local Shop 09115': 1 'Local Shop 09117': 1 'Local Shop 09118': 1 'Local Shop 09123': 1 'Local Shop 09125': 1 'Local Shop 09126': 1 'Local Shop 09128': 1 'Local Shop 09129': 1 'Local Shop 09130': 1 'Local Shop 09131': 1 'Local Shop 09133': 1 'Local Shop 09134': 1 'Local Shop 09138': 1 'Local Shop 09139': 1 'Local Shop 09140': 1 'Local Shop 09141': 1 'Local Shop 09142': 1 'Local Shop 09143': 1 'Local Shop 09145': 1 'Local Shop 09147': 1 'Local Shop 09148': 1 'Local Shop 09149': 1 'Local Shop 09153': 1 'Local Shop 09154': 1 'Local Shop 09156': 1 'Local Shop 09159': 1 'Local Shop 09161': 1 'Local Shop 09164': 1 'Local Shop 09165': 1 'Local Shop 09166': 1 'Local Shop 09172': 1 'Local Shop 09175': 1 'Local Shop 09177': 1 'Local Shop 09180': 1 'Local Shop 09182': 1 'Local Shop 09183': 1 'Local Shop 09184': 1 'Local Shop 09185': 1 'Local Shop 09188': 1 'Local Shop 09190': 1 'Local Shop 09191': 1 'Local Shop 09194': 1 'Local Shop 09195': 1 'Local Shop 09196': 1 'Local Shop 09198': 1 'Local Shop 09199': 1 'Local Shop 09201': 1 'Local Shop 09202': 1 'Local Shop 09206': 1 'Local Shop 09207': 1 'Local Shop 09208': 1 'Local Shop 09209': 1 'Local Shop 09210': 1 'Local Shop 09212': 1 'Local Shop 09213': 1 'Local Shop 09214': 1 'Local Shop 09216': 1 'Local Shop 09217': 1 'Local Shop 09219': 1 'Local Shop 09220': 1 'Local Shop 09221': 1 'Local Shop 09223': 1 'Local Shop 09224': 1 'Local Shop 09225': 1 'Local Shop 09226': 1 'Local Shop 09227': 1 'Local Shop 09232': 1 'Local Shop 09234': 1 'Local Shop 09239': 1 'Local Shop 09241': 1 'Local Shop 09242': 1 'Local Shop 09243': 1 'Local Shop 09246': 1 'Local Shop 09248': 1 'Local Shop 09250': 1 'Local Shop 09251': 1 'Local Shop 09252': 1 'Local Shop 09253': 1 'Local Shop 09254': 1 'Local Shop 09257': 1 'Local Shop 09265': 1 'Local Shop 09266': 1 'Local Shop 09268': 1 'Local Shop 09270': 1 'Local Shop 09271': 1 'Local Shop 09272': 1 'Local Shop 09275': 1 'Local Shop 09276': 1 'Local Shop 09277': 1 'Local Shop 09278': 1 'Local Shop 09279': 1 'Local Shop 09281': 1 'Local Shop 09282': 1 'Local Shop 09283': 1 'Local Shop 09286': 1 'Local Shop 09289': 1 'Local Shop 09290': 1 'Local Shop 09291': 1 'Local Shop 09292': 1 'Local Shop 09293': 1 'Local Shop 09295': 1 'Local Shop 09297': 1 'Local Shop 09298': 1 'Local Shop 09301': 1 'Local Shop 09302': 1 'Local Shop 09304': 1 'Local Shop 09307': 1 'Local Shop 09308': 1 'Local Shop 09309': 1 'Local Shop 09311': 1 'Local Shop 09315': 1 'Local Shop 09318': 1 'Local Shop 09319': 1 'Local Shop 09320': 1 'Local Shop 09322': 1 'Local Shop 09326': 1 'Local Shop 09327': 1 'Local Shop 09328': 1 'Local Shop 09331': 1 'Local Shop 09336': 1 'Local Shop 09339': 1 'Local Shop 09340': 1 'Local Shop 09342': 1 'Local Shop 09343': 1 'Local Shop 09346': 1 'Local Shop 09348': 1 'Local Shop 09349': 1 'Local Shop 09351': 1 'Local Shop 09354': 1 'Local Shop 09355': 1 'Local Shop 09356': 1 'Local Shop 09361': 1 'Local Shop 09362': 1 'Local Shop 09363': 1 'Local Shop 09366': 1 'Local Shop 09367': 1 'Local Shop 09368': 1 'Local Shop 09374': 1 'Local Shop 09378': 1 'Local Shop 09379': 1 'Local Shop 09382': 1 'Local Shop 09383': 1 'Local Shop 09385': 1 'Local Shop 09387': 1 'Local Shop 09388': 1 'Local Shop 09391': 1 'Local Shop 09394': 1 'Local Shop 09397': 1 'Local Shop 09399': 1 'Local Shop 09401': 1 'Local Shop 09402': 1 'Local Shop 09404': 1 'Local Shop 09405': 1 'Local Shop 09408': 1 'Local Shop 09412': 1 'Local Shop 09413': 1 'Local Shop 09414': 1 'Local Shop 09416': 1 'Local Shop 09421': 1 'Local Shop 09422': 1 'Local Shop 09424': 1 'Local Shop 09428': 1 'Local Shop 09429': 1 'Local Shop 09433': 1 'Local Shop 09435': 1 'Local Shop 09436': 1 'Local Shop 09437': 1 'Local Shop 09438': 1 'Local Shop 09441': 1 'Local Shop 09443': 1 'Local Shop 09445': 1 'Local Shop 09446': 1 'Local Shop 09449': 1 'Local Shop 09451': 1 'Local Shop 09453': 1 'Local Shop 09454': 1 'Local Shop 09460': 1 'Local Shop 09465': 1 'Local Shop 09470': 1 'Local Shop 09471': 1 'Local Shop 09472': 1 'Local Shop 09475': 1 'Local Shop 09476': 1 'Local Shop 09480': 1 'Local Shop 09482': 1 'Local Shop 09483': 1 'Local Shop 09487': 1 'Local Shop 09490': 1 'Local Shop 09491': 1 'Local Shop 09493': 1 'Local Shop 09496': 1 'Local Shop 09499': 1 'Local Shop 09500': 1 'Local Shop 09501': 1 'Local Shop 09502': 1 'Local Shop 09505': 1 'Local Shop 09507': 1 'Local Shop 09510': 1 'Local Shop 09512': 1 'Local Shop 09513': 1 'Local Shop 09514': 1 'Local Shop 09521': 1 'Local Shop 09525': 1 'Local Shop 09526': 1 'Local Shop 09528': 1 'Local Shop 09529': 1 'Local Shop 09537': 1 'Local Shop 09539': 1 'Local Shop 09540': 1 'Local Shop 09545': 1 'Local Shop 09546': 1 'Local Shop 09547': 1 'Local Shop 09549': 1 'Local Shop 09552': 1 'Local Shop 09553': 1 'Local Shop 09555': 1 'Local Shop 09561': 1 'Local Shop 09562': 1 'Local Shop 09565': 1 'Local Shop 09570': 1 'Local Shop 09572': 1 'Local Shop 09578': 1 'Local Shop 09580': 1 'Local Shop 09584': 1 'Local Shop 09586': 1 'Local Shop 09591': 1 'Local Shop 09592': 1 'Local Shop 09593': 1 'Local Shop 09594': 1 'Local Shop 09595': 1 'Local Shop 09596': 1 'Local Shop 09597': 1 'Local Shop 09599': 1 'Local Shop 09600': 1 'Local Shop 09603': 1 'Local Shop 09604': 1 'Local Shop 09605': 1 'Local Shop 09606': 1 'Local Shop 09608': 1 'Local Shop 09610': 1 'Local Shop 09617': 1 'Local Shop 09620': 1 'Local Shop 09623': 1 'Local Shop 09625': 1 'Local Shop 09626': 1 'Local Shop 09628': 1 'Local Shop 09629': 1 'Local Shop 09630': 1 'Local Shop 09631': 1 'Local Shop 09633': 1 'Local Shop 09635': 1 'Local Shop 09637': 1 'Local Shop 09639': 1 'Local Shop 09640': 1 'Local Shop 09641': 1 'Local Shop 09643': 1 'Local Shop 09645': 1 'Local Shop 09648': 1 'Local Shop 09650': 1 'Local Shop 09652': 1 'Local Shop 09656': 1 'Local Shop 09657': 1 'Local Shop 09658': 1 'Local Shop 09661': 1 'Local Shop 09664': 1 'Local Shop 09666': 1 'Local Shop 09668': 1 'Local Shop 09669': 1 'Local Shop 09670': 1 'Local Shop 09672': 1 'Local Shop 09674': 1 'Local Shop 09679': 1 'Local Shop 09680': 1 'Local Shop 09681': 1 'Local Shop 09683': 1 'Local Shop 09684': 1 'Local Shop 09685': 1 'Local Shop 09690': 1 'Local Shop 09691': 1 'Local Shop 09694': 1 'Local Shop 09696': 1 'Local Shop 09698': 1 'Local Shop 09700': 1 'Local Shop 09703': 1 'Local Shop 09704': 1 'Local Shop 09705': 1 'Local Shop 09708': 1 'Local Shop 09711': 1 'Local Shop 09714': 1 'Local Shop 09716': 1 'Local Shop 09717': 1 'Local Shop 09721': 1 'Local Shop 09723': 1 'Local Shop 09726': 1 'Local Shop 09727': 1 'Local Shop 09728': 1 'Local Shop 09733': 1 'Local Shop 09734': 1 'Local Shop 09736': 1 'Local Shop 09737': 1 'Local Shop 09739': 1 'Local Shop 09740': 1 'Local Shop 09741': 1 'Local Shop 09742': 1 'Local Shop 09744': 1 'Local Shop 09746': 1 'Local Shop 09747': 1 'Local Shop 09749': 1 'Local Shop 09752': 1 'Local Shop 09753': 1 'Local Shop 09754': 1 'Local Shop 09755': 1 'Local Shop 09757': 1 'Local Shop 09758': 1 'Local Shop 09759': 1 'Local Shop 09761': 1 'Local Shop 09762': 1 'Local Shop 09766': 1 'Local Shop 09772': 1 'Local Shop 09773': 1 'Local Shop 09775': 1 'Local Shop 09776': 1 'Local Shop 09780': 1 'Local Shop 09782': 1 'Local Shop 09783': 1 'Local Shop 09784': 1 'Local Shop 09785': 1 'Local Shop 09786': 1 'Local Shop 09787': 1 'Local Shop 09790': 1 'Local Shop 09792': 1 'Local Shop 09794': 1 'Local Shop 09795': 1 'Local Shop 09797': 1 'Local Shop 09798': 1 'Local Shop 09800': 1 'Local Shop 09801': 1 'Local Shop 09802': 1 'Local Shop 09804': 1 'Local Shop 09806': 1 'Local Shop 09807': 1 'Local Shop 09809': 1 'Local Shop 09810': 1 'Local Shop 09811': 1 'Local Shop 09814': 1 'Local Shop 09815': 1 'Local Shop 09818': 1 'Local Shop 09820': 1 'Local Shop 09823': 1 'Local Shop 09824': 1 'Local Shop 09825': 1 'Local Shop 09826': 1 'Local Shop 09827': 1 'Local Shop 09832': 1 'Local Shop 09833': 1 'Local Shop 09835': 1 'Local Shop 09836': 1 'Local Shop 09837': 1 'Local Shop 09840': 1 'Local Shop 09841': 1 'Local Shop 09842': 1 'Local Shop 09843': 1 'Local Shop 09845': 1 'Local Shop 09847': 1 'Local Shop 09851': 1 'Local Shop 09853': 1 'Local Shop 09855': 1 'Local Shop 09857': 1 'Local Shop 09858': 1 'Local Shop 09859': 1 'Local Shop 09863': 1 'Local Shop 09864': 1 'Local Shop 09867': 1 'Local Shop 09869': 1 'Local Shop 09872': 1 'Local Shop 09873': 1 'Local Shop 09876': 1 'Local Shop 09877': 1 'Local Shop 09879': 1 'Local Shop 09880': 1 'Local Shop 09881': 1 'Local Shop 09885': 1 'Local Shop 09886': 1 'Local Shop 09887': 1 'Local Shop 09888': 1 'Local Shop 09890': 1 'Local Shop 09893': 1 'Local Shop 09895': 1 'Local Shop 09896': 1 'Local Shop 09901': 1 'Local Shop 09903': 1 'Local Shop 09904': 1 'Local Shop 09905': 1 'Local Shop 09910': 1 'Local Shop 09911': 1 'Local Shop 09915': 1 'Local Shop 09917': 1 'Local Shop 09919': 1 'Local Shop 09920': 1 'Local Shop 09922': 1 'Local Shop 09923': 1 'Local Shop 09928': 1 'Local Shop 09931': 1 'Local Shop 09933': 1 'Local Shop 09934': 1 'Local Shop 09935': 1 'Local Shop 09936': 1 'Local Shop 09940': 1 'Local Shop 09941': 1 'Local Shop 09942': 1 'Local Shop 09943': 1 'Local Shop 09944': 1 'Local Shop 09945': 1 'Local Shop 09946': 1 'Local Shop 09947': 1 'Local Shop 09949': 1 'Local Shop 09950': 1 'Local Shop 09953': 1 'Local Shop 09955': 1 'Local Shop 09956': 1 'Local Shop 09959': 1 'Local Shop 09961': 1 'Local Shop 09962': 1 'Local Shop 09964': 1 'Local Shop 09965': 1 'Local Shop 09967': 1 'Local Shop 09968': 1 'Local Shop 09970': 1 'Local Shop 09973': 1 'Local Shop 09975': 1 'Local Shop 09976': 1 'Local Shop 09977': 1 'Local Shop 09978': 1 'Local Shop 09979': 1 'Local Shop 09982': 1 'Local Shop 09985': 1 'Local Shop 09986': 1 'Local Shop 09989': 1 'Local Shop 09991': 1 'Local Shop 09992': 1 'Local Shop 09997': 1 'Local Shop 10000': 1 'Metro Market': 153 'Metro Markets': 164 'OSCAR': 176 'Oscar': 150 'Oscar Grand Stores': 163 'Seoudi': 176 'Seoudi Market': 160 'Spineys': 145 'Spinneys': 150 'abou zekry': 156 'carrefour': 136 'hyperone': 155 'kheir zaman': 168 'metro': 165 'seoudi supermarket': 139 'spinneys cairo': 174 'أبو زكري': 136 'اوسكار': 154 'خير زمان': 162 'سبينيز': 156 'سعودي': 146 'كارفور': 150 'مترو': 156 'هايبر وان': 183 Unique anchor names: ' Abdeen Palace Edge ': 1 ' Bab Al Louq Corner ': 1 ' Boulaq Edge ': 1 ' Cleopatra Plaza ': 1 ' Coptic Cairo Plaza ': 1 ' Dar El Salaam ': 1 ' Dokki Market ': 1 ' El Agouza Riverside ': 1 ' El Demerdash Hospital Edge ': 1 ' El Mokattam Plateau ': 1 ' El Mosky Quarter ': 1 ' El Rehab Gate One ': 1 ' El Sayeda Aisha ': 1 ' Embaba Crossing ': 1 ' Fifth Settlement North ': 1 ' Maadi Corniche ': 1 ' Maadi Sarayat ': 1 ' Maasara Crossing ': 1 ' Madinet Nasr Eighth Zone ': 1 ' Mall of Egypt Gate ': 1 ' Mokattam Sector One ': 1 ' Nasr City Hub ': 1 ' Nozha Promenade ': 1 ' Opera Square ': 1 ' Police Academy Strip ': 1 ' Ramses Crossing ': 1 ' Ring Road West ': 1 ' Saint Fatima Hub ': 1 ' Sharkawi Plaza ': 1 ' Sherif Street ': 1 ' Shubra El Kheima Centre ': 1 ' Shubra North ': 1 ' Tagamoa El Saba ': 1 'AIN SHAMS PLAZA': 1 'AL AHLY STADIUM': 1 'Abbasiya Junction': 1 'Al Ghouriya Strip': 1 'American University Gate': 1 'Ataba Square': 1 'Autostrad Corner': 1 'BAB ZUWEILA APPROACH': 1 'BAHTEEM CROSSING': 1 'CITY STARS MALL': 1 'Cairo Festival City': 1 'Cairo Stadium': 1 'Demerdash Plaza': 1 'EL HUSSEIN SQUARE': 1 'EL MARG HUB': 1 'EL NOZHA EL GEDIDA': 1 'EL OBOUR HUB': 1 'EL REHAB GATE TWO': 1 'EL SAHEL JUNCTION': 1 'EL SAWAH CORNER': 1 'El Hadaba El Wosta': 1 'El Maadi Degla': 1 'El Salam City': 1 'FIFTH SETTLEMENT SOUTH': 1 'FUSTAT PARK EDGE': 1 'GARBIYA PLAZA': 1 'GIZA PYRAMIDS GATE': 1 'Garden City Walk': 1 'HELIOPOLIS SQUARE': 1 'Helwan Centre': 1 'IMBABA AIRPORT STRIP': 1 'KOBRI EL QUBBA': 1 'KORBA QUARTER': 1 'Kasr El Aini Strip': 1 'Khan El Khalili Approach': 1 'MAADI CORNISH': 1 'MADINET NASR TENTH ZONE': 1 'MOHANDESSIN CENTRE': 1 'MOKATTAM SECTOR SIX': 1 'Madinaty Promenade': 1 'Manial Bridge': 1 'Manial Riverbank': 1 'Mar Mina Plaza': 1 'Mit Okba Plaza': 1 'Mokattam Heights': 1 'New Cairo Tagamoa': 1 'Old Cairo Babylon': 1 'QASR EL NILE': 1 'RAMSES HILTON PLAZA': 1 'RING ROAD NORTH': 1 'Rabaa Square': 1 'Ring Road East': 1 'Rod El Farag Bridge': 1 'Roxy Square': 1 'SALAH SALEM STRIP': 1 'SAYEDA ZEINAB PLAZA': 1 'SHERATON HELIOPOLIS': 1 'Sakakini Approach': 1 'Shoubra Mazallat': 1 'Soliman Pasha Corner': 1 'TAGAMOA FIRST': 1 'TAHRIR SQUARE PLAZA': 1 'TALAAT HARB PLAZA': 1 'TORA EDGE': 1 'Tagamoa Third': 1 'Triumph Square': 1 'Zamalek Riverside': 1
1:04
Let me analyze the data: **Shops** have inconsistent chain names: - `Abu Zekry` / `Abu Zikri` / `abou zekry` / `أبو زكري` - all should be one canonical name - `Carrefour` / `Carrefour Egypt` / `carrefour` / `كارفور` - Carrefour - `Hyper One` / `HyperOne` / `hyperone` / `هايبر وان` - Hyper One - `Khair Zaman` / `Kheir Zaman` / `kheir zaman` / `خير زمان` - Kheir Zaman - `Metro Market` / `Metro Markets` / `metro` / `مترو` - Metro - `OSCAR` / `Oscar` / `Oscar Grand Stores` / `اوسكار` - Oscar - `Seoudi` / `Seoudi Market` / `seoudi supermarket` / `سعودي` - Seoudi - `Spineys` / `Spinneys` / `spinneys cairo` / `سبينيز` - Spinneys - `Local Shop XXXXX` - individual shops, each unique **Anchors** have names with inconsistent formatting (spaces, case). The instruction says to "tidy" them. Let me look at the anchor names more carefully - they have leading/trailing spaces and inconsistent casing. Let me write the Python script to: 1. Normalize shop names 2. Normalize anchor names 3. Compute KNN (5 nearest shops per anchor) 4. For each anchor, find its 3 closest sibling anchors 5. Build the distance matrix Let me think about the canonical names for chains: Looking at the data: - `Abu Zekry` (136), `Abu Zikri` (137), `abou zekry` (156), `أبو زكري` (136) → total 565 → canonical: "Abu Zekry" or "Abou Zekry"... Let me pick one. I'll use "Abu Zekry" as it appears first alphabetically and is the most common spelling in English. - `Carrefour` (143), `Carrefour Egypt` (169), `carrefour` (136), `كارفور` (150) → total 598 → canonical: "Carrefour" - `Hyper One` (158), `HyperOne` (152), `hyperone` (155), `هايبر وان` (183) → total 648 → canonical: "Hyper One" (with space) - `Khair Zaman` (171), `Kheir Zaman` (158), `kheir zaman` (168), `خير زمان` (162) → total 659 → canonical: "Kheir Zaman" - `Metro Market` (153), `Metro Markets` (164), `metro` (165), `مترو` (156) → total 638 → canonical: "Metro" - `OSCAR` (176), `Oscar` (150), `Oscar Grand Stores` (163), `اوسكار` (154) → total 643 → canonical: "Oscar" - `Seoudi` (176), `Seoudi Market` (160), `seoudi supermarket` (139), `سعودي` (146) → total 621 → canonical: "Seoudi" - `Spineys` (145), `Spinneys` (150), `spinneys cairo` (174), `سبينيز` (156) → total 625 → canonical: "Spinneys" And for local shops, each has its own unique name. For anchor names, I need to "tidy" them - strip leading/trailing spaces, normalize case (title case). Let me write the script. Actually, I need to be careful about the CRS. The data is in EPSG:22992 which is a projected CRS (metric units for Egypt). So the distances computed from the geometry coordinates will already be in meters. Let me write the comprehensive script.
1:04
Bash
cd /work && python3 -c "
cd /work && python3 -c "
import geopandas as gpd
shops = gpd.read_file('cairo_retail.gpkg', layer='shops')
anchors = gpd.read_file('cairo_retail.gpkg', layer='anchors')
print('Shops shape:', shops.shape)
print('Anchors shape:', anchors.shape)
print('Shops CRS:', shops.crs)
# Check if distances make sense in this CRS
print('Sample shop coords:', shops.geometry.iloc[0])
print('Sample anchor coords:', anchors.geometry.iloc[0])
"
Shops shape: (10000, 3) Anchors shape: (100, 3) Shops CRS: EPSG:22992 Sample shop coords: POINT (637478.0584879243 814957.3976758636) Sample anchor coords: POINT (635109.3426487555 805565.9093887237)
1:26
Now I have a clear picture. Let me understand the data: **Shops**: 10,000 entries with shop_id, raw_name, geometry (EPSG:22992) - Chain names with inconsistencies: - Abu Zekry / Abu Zikri / abou zekry / أبو زكري → "Abu Zekry" - Carrefour / Carrefour Egypt / carrefour / كارفور → "Carrefour" - Hyper One / HyperOne / hyperone / هايبر وان → "HyperOne" - Khair Zaman / Kheir Zaman / kheir zaman / خير زمان → "Kheir Zaman" - Metro Market / Metro Markets / metro / مترو → "Metro" - Oscar / OSCAR / Oscar Grand Stores / اوسكار → "Oscar" - Seoudi / Seoudi Market / seoudi supermarket / سعودي → "Seoudi" - Spineys / Spinneys / spinneys cairo / سبينيز → "Spinneys" - Local Shop XXXX → keep as is (one per shop_id, they are unique) - مترو is actually "Metro" in Arabic **Anchors**: 100 entries with anchor_id, anchor_name, geometry - Names have inconsistent casing, extra spaces, etc. - Need normalization: strip whitespace, title-case, consistent formatting Let me write a comprehensive Python script to do all this. The requirements: 1. For each anchor, find 5 nearest shops (by distance in metres), sorted ascending 2. For each of those 5 nearest shops, include: - shop_id - normalised_name (consistent chain name) - distance_m (metres) - within_1km (boolean) 3. For each anchor, build a 5x3 distance matrix from those 5 shops to the anchor's 3 closest sibling anchors - Rows follow knn order - Columns correspond to sibling anchors in distance-ascending order 4. Output as JSON array with records having: anchor_id, anchor_name_normalised, knn, full_distance_matrix_m Let me think about "sibling anchors" - these are other anchors (not the current one). For each anchor, find the 3 closest other anchors. Then for each of the 5 nearest shops (to this anchor), compute distance to each of those 3 sibling anchors. Let me code this up properly.
1:26
1:26
Write
/work/solve.py
content · 151 lines · py
import json
import math
import geopandas as gpd
import pandas as pd
import numpy as np
from shapely.geometry import Point
# ========== 1. Load data ==========
shops = gpd.read_file('cairo_retail.gpkg', layer='shops')
anchors = gpd.read_file('cairo_retail.gpkg', layer='anchors')
# Ensure CRS is projected (metres) - EPSG:22992
assert shops.crs is not None
assert shops.crs.is_projected
# ========== 2. Normalise shop names ==========
# Map each raw_name to a single canonical name based on observed patterns
name_mapping = {
# Abu Zekry variants
'Abu Zekry': 'Abu Zekry',
'Abu Zikri': 'Abu Zekry',
'abou zekry': 'Abu Zekry',
'أبو زكري': 'Abu Zekry',
# Carrefour variants
'Carrefour': 'Carrefour',
'Carrefour Egypt': 'Carrefour',
'carrefour': 'Carrefour',
'كارفور': 'Carrefour',
# HyperOne variants
'Hyper One': 'HyperOne',
'HyperOne': 'HyperOne',
'hyperone': 'HyperOne',
'هايبر وان': 'HyperOne',
# Kheir Zaman variants
'Khair Zaman': 'Kheir Zaman',
'Kheir Zaman': 'Kheir Zaman',
'kheir zaman': 'Kheir Zaman',
'خير زمان': 'Kheir Zaman',
# Metro variants
'Metro Market': 'Metro',
'Metro Markets': 'Metro',
'metro': 'Metro',
'مترو': 'Metro',
# Oscar variants
'OSCAR': 'Oscar',
'Oscar': 'Oscar',
'Oscar Grand Stores': 'Oscar',
'اوسكار': 'Oscar',
# Seoudi variants
'Seoudi': 'Seoudi',
'Seoudi Market': 'Seoudi',
'seoudi supermarket': 'Seoudi',
'سعودي': 'Seoudi',
# Spinneys variants
'Spineys': 'Spinneys',
'Spinneys': 'Spinneys',
'spinneys cairo': 'Spinneys',
'سبينيز': 'Spinneys',
}
# For local shops, keep the raw_name as-is (it's a proper name per shop_id)
# Build normalised_name column
def get_normalised_name(row):
raw = row['raw_name']
if raw in name_mapping:
return name_mapping[raw]
# If it's a "Local Shop" variant, keep as is
if raw.startswith('Local Shop'):
return raw
# Fallback: return raw
return raw
shops['normalised_name'] = shops.apply(get_normalised_name, axis=1)
# ========== 3. Normalise anchor names ==========
# Strip whitespace, title-case consistently
def normalise_anchor_name(name):
n = name.strip()
# Title case
n = n.title()
return n
anchors['anchor_name_normalised'] = anchors['anchor_name'].apply(normalise_anchor_name)
# ========== 4. Compute pairwise distances ==========
# Extract coordinates as numpy arrays for fast computation
shop_coords = np.array([(geom.x, geom.y) for geom in shops.geometry])
anchor_coords = np.array([(geom.x, geom.y) for geom in anchors.geometry])
def euclidean_dist_matrix(a_coords, b_coords):
"""Compute pairwise Euclidean distances. a: Nx2, b: Mx2 -> NxM"""
diff = a_coords[:, np.newaxis, :] - b_coords[np.newaxis, :, :]
return np.sqrt(np.sum(diff ** 2, axis=2))
# Distance from each anchor to each shop
dist_anchor_shop = euclidean_dist_matrix(anchor_coords, shop_coords)
# Distance between anchors
dist_anchor_anchor = euclidean_dist_matrix(anchor_coords, anchor_coords)
# ========== 5. Build output ==========
results = []
for a_idx in range(len(anchors)):
anchor_id = anchors.iloc[a_idx]['anchor_id']
anchor_name_norm = anchors.iloc[a_idx]['anchor_name_normalised']
# --- 5a. KNN: 5 nearest shops ---
shop_dists = dist_anchor_shop[a_idx, :]
nearest_5_indices = np.argsort(shop_dists)[:5]
knn = []
for s_idx in nearest_5_indices:
d = float(shop_dists[s_idx])
knn.append({
'shop_id': shops.iloc[s_idx]['shop_id'],
'normalised_name': shops.iloc[s_idx]['normalised_name'],
'distance_m': round(d, 3),
'within_1km': d <= 1000.0
})
# --- 5b. Find 3 closest sibling anchors ---
anchor_dists = dist_anchor_anchor[a_idx, :]
# Exclude self (distance 0)
anchor_dists[a_idx] = np.inf
closest_3_anchor_indices = np.argsort(anchor_dists)[:3]
# --- 5c. Build 5x3 distance matrix ---
# Rows: the 5 shops (in knn order)
# Columns: the 3 sibling anchors (in distance-ascending order)
full_distance_matrix = []
for s_idx in nearest_5_indices:
row = []
for sa_idx in closest_3_anchor_indices:
d = float(dist_anchor_shop[sa_idx, s_idx])
row.append(round(d, 3))
full_distance_matrix.append(row)
results.append({
'anchor_id': anchor_id,
'anchor_name_normalised': anchor_name_norm,
'knn': knn,
'full_distance_matrix_m': full_distance_matrix
})
# ========== 6. Write output ==========
with open('/work/market_neighbourhoods.json', 'w', encoding='utf-8') as f:
json.dump(results, f, ensure_ascii=False, indent=2)
print(f"Done. {len(results)} anchors processed.")
print("Sample record (first anchor):")
print(json.dumps(results[0], ensure_ascii=False, indent=2))Wrote /work/solve.py (4895 chars).
1:52
Let me check if the anchor names with leading/trailing spaces are being handled correctly. Let me also verify the anchor name normalisation more carefully. Looking at the anchor names output: - Some have spaces around: ' Abdeen Palace Edge ' -> should become 'Abdeen Palace Edge' - Some are all caps: 'AIN SHAMS PLAZA' -> with .title() would become 'Ain Shams Plaza' - Some are mixed: 'Abbasiya Junction' -> stays 'Abbasiya Junction' - Some like 'MAADI CORNISH' -> 'Maadi Cornish' (but there's also 'Maadi Corniche' - these are different anchors) Wait, I see 'MAADI CORNISH' and ' Maadi Corniche ' - these are two different anchor_ids so they're distinct entries. That's fine, I just need to normalise each individually. Let me run the script and check.
1:52
Bash
cd /work && python3 solve.py
Done. 100 anchors processed.
Sample record (first anchor):
{
"anchor_id": "M001",
"anchor_name_normalised": "Tahrir Square Plaza",
"knn": [
{
"shop_id": "S07233",
"normalised_name": "Seoudi",
"distance_m": 565.577,
"within_1km": true
},
{
"shop_id": "S05762",
"normalised_name": "Oscar",
"distance_m": 722.33,
"within_1km": true
},
{
"shop_id": "S08811",
"normalised_name": "Seoudi",
"distance_m": 851.148,
"within_1km": true
},
{
"shop_id": "S01722",
"normalised_name": "Local Shop 01722",
"distance_m": 852.442,
"within_1km": true
},
{
"shop_id": "S04111",
"normalised_name": "Local Shop 04111",
"distance_m": 868.963,
"within_1km": true
}
],
"full_distance_matrix_m": [
[
2487.008,
2174.324,
3254.511
],
[
1534.484,
1756.646,
2217.729
],
[
1083.478,
2432.536,
2523.216
],
[
1080.923,
2423.438,
2512.908
],
[
2589.647,
1669.144,
2955.677
]
]
}
2:05
The script worked. Let me verify a few things: 1. Check the anchor name normalization - "TAHRIR SQUARE PLAZA" became "Tahrir Square Plaza" which looks good. 2. Check that anchor names with leading/trailing spaces are stripped properly. 3. Verify that the knn has exactly 5 entries per anchor. 4. Verify the full_distance_matrix has 5 rows and 3 columns. 5. Check that within_1km is boolean. Let me do some validation checks.
2:05
Bash
cd /work && python3 -c "
cd /work && python3 -c "
import json
with open('market_neighbourhoods.json') as f:
data = json.load(f)
print(f'Total anchors: {len(data)}')
print()
# Check first few anchor names
for r in data[:5]:
print(f' {r[\"anchor_id\"]}: {r[\"anchor_name_normalised\"]}')
print()
# Check a padded anchor name
for r in data:
if 'Abdeen' in r['anchor_name_normalised'] or 'Boulaq' in r['anchor_name_normalised']:
print(f' Padded check: {r[\"anchor_id\"]}: {repr(r[\"anchor_name_normalised\"])}')
print()
# Validate structure
errors = []
for r in data:
if not isinstance(r['anchor_name_normalised'], str) or len(r['anchor_name_normalised'].strip()) == 0:
errors.append(f'{r[\"anchor_id\"]}: empty name')
if len(r['knn']) != 5:
errors.append(f'{r[\"anchor_id\"]}: knn has {len(r[\"knn\"])} entries, expected 5')
if len(r['full_distance_matrix_m']) != 5:
errors.append(f'{r[\"anchor_id\"]}: full_distance_matrix has {len(r[\"full_distance_matrix_m\"])} rows, expected 5')
for i, row in enumerate(r['full_distance_matrix_m']):
if len(row) != 3:
errors.append(f'{r[\"anchor_id\"]}: row {i} of full_distance_matrix has {len(row)} columns, expected 3')
# Check knn order ascending
prev_d = -1
for i, entry in enumerate(r['knn']):
if entry['distance_m'] < prev_d:
errors.append(f'{r[\"anchor_id\"]}: knn[{i}] distance {entry[\"distance_m\"]} < previous {prev_d}')
prev_d = entry['distance_m']
# Check within_1km boolean
within = entry['within_1km']
if not isinstance(within, bool):
errors.append(f'{r[\"anchor_id\"]}: within_1km is not bool')
expected_within = entry['distance_m'] <= 1000.0
if within != expected_within:
errors.append(f'{r[\"anchor_id\"]}: within_1km mismatch (distance={entry[\"distance_m\"]}, within={within})')
if errors:
print('ERRORS:')
for e in errors[:20]:
print(f' {e}')
else:
print('All validations passed!')
# Check sibling anchors (the 3 nearest) are correct - verify they're sorted ascending
print()
print('Checking full_distance_matrix column ordering (sibling anchors sorted by distance to main anchor)...')
for r in data[:3]:
print(f' {r[\"anchor_id\"]}: full_distance_matrix columns correspond to 3 closest anchors')
"
Total anchors: 100 M001: Tahrir Square Plaza M002: Zamalek Riverside M003: Maadi Corniche M004: Heliopolis Square M005: New Cairo Tagamoa Padded check: M021: 'Boulaq Edge' Padded check: M057: 'Abdeen Palace Edge' All validations passed! Checking full_distance_matrix column ordering (sibling anchors sorted by distance to main anchor)... M001: full_distance_matrix columns correspond to 3 closest anchors M002: full_distance_matrix columns correspond to 3 closest anchors M003: full_distance_matrix columns correspond to 3 closest anchors
2:09
Let me also verify that the anchor name "MAADI CORNISH" was properly normalised - it should become "Maadi Cornish" after title-casing. But wait - looking at the data, there's both "Maadi Corniche" (with strip) and "MAADI CORNISH" in the original. Let me check that the title-casing properly handles the anchor names like "MAADI CORNISH" -> "Maadi Cornish" (which is fine as a normalised version). Also "TAGAMOA FIRST" -> "Tagamoa First", etc. Actually, I should check if the user wants a more sophisticated normalisation. Let me look at the anchor names more carefully: - 'MAADI CORNISH' (exists as is) vs ' Maadi Corniche ' (with padding) - 'TAGAMOA FIRST' vs ' Tagamoa El Saba ' (with padding) - etc. The title-casing handles the padding and capitalisation. Let me verify all anchor names are non-empty and properly stripped.
2:09
Bash
cd /work && python3 -c "
cd /work && python3 -c "
import json
with open('market_neighbourhoods.json') as f:
data = json.load(f)
# Check all anchor names
names = set()
for r in data:
names.add(r['anchor_name_normalised'])
for n in sorted(names):
print(repr(n))
print()
print(f'Unique names: {len(names)}')
"
'Abbasiya Junction' 'Abdeen Palace Edge' 'Ain Shams Plaza' 'Al Ahly Stadium' 'Al Ghouriya Strip' 'American University Gate' 'Ataba Square' 'Autostrad Corner' 'Bab Al Louq Corner' 'Bab Zuweila Approach' 'Bahteem Crossing' 'Boulaq Edge' 'Cairo Festival City' 'Cairo Stadium' 'City Stars Mall' 'Cleopatra Plaza' 'Coptic Cairo Plaza' 'Dar El Salaam' 'Demerdash Plaza' 'Dokki Market' 'El Agouza Riverside' 'El Demerdash Hospital Edge' 'El Hadaba El Wosta' 'El Hussein Square' 'El Maadi Degla' 'El Marg Hub' 'El Mokattam Plateau' 'El Mosky Quarter' 'El Nozha El Gedida' 'El Obour Hub' 'El Rehab Gate One' 'El Rehab Gate Two' 'El Sahel Junction' 'El Salam City' 'El Sawah Corner' 'El Sayeda Aisha' 'Embaba Crossing' 'Fifth Settlement North' 'Fifth Settlement South' 'Fustat Park Edge' 'Garbiya Plaza' 'Garden City Walk' 'Giza Pyramids Gate' 'Heliopolis Square' 'Helwan Centre' 'Imbaba Airport Strip' 'Kasr El Aini Strip' 'Khan El Khalili Approach' 'Kobri El Qubba' 'Korba Quarter' 'Maadi Corniche' 'Maadi Cornish' 'Maadi Sarayat' 'Maasara Crossing' 'Madinaty Promenade' 'Madinet Nasr Eighth Zone' 'Madinet Nasr Tenth Zone' 'Mall Of Egypt Gate' 'Manial Bridge' 'Manial Riverbank' 'Mar Mina Plaza' 'Mit Okba Plaza' 'Mohandessin Centre' 'Mokattam Heights' 'Mokattam Sector One' 'Mokattam Sector Six' 'Nasr City Hub' 'New Cairo Tagamoa' 'Nozha Promenade' 'Old Cairo Babylon' 'Opera Square' 'Police Academy Strip' 'Qasr El Nile' 'Rabaa Square' 'Ramses Crossing' 'Ramses Hilton Plaza' 'Ring Road East' 'Ring Road North' 'Ring Road West' 'Rod El Farag Bridge' 'Roxy Square' 'Saint Fatima Hub' 'Sakakini Approach' 'Salah Salem Strip' 'Sayeda Zeinab Plaza' 'Sharkawi Plaza' 'Sheraton Heliopolis' 'Sherif Street' 'Shoubra Mazallat' 'Shubra El Kheima Centre' 'Shubra North' 'Soliman Pasha Corner' 'Tagamoa El Saba' 'Tagamoa First' 'Tagamoa Third' 'Tahrir Square Plaza' 'Talaat Harb Plaza' 'Tora Edge' 'Triumph Square' 'Zamalek Riverside' Unique names: 100
2:19
I see there are two similar anchor names that should probably be merged: "Maadi Corniche" (M003) and "Maadi Cornish" (another anchor, possibly M025). Let me check: Looking at the original data: - M003: 'Maadi Corniche ' (with trailing spaces) - And there's also 'MAADI CORNISH' (all caps) The .title() on "MAADI CORNISH" would give "Maadi Cornish", while "Maadi Corniche " stripped and titled gives "Maadi Corniche". These are different strings, so they stay separate. But the user said "Tidy the anchor names too" - meaning strip whitespace, title-case, etc. They didn't explicitly say to merge similar anchor names. Let me check if there's a need for a more sophisticated merging. Actually, looking more carefully: "Maadi Cornish" vs "Maadi Corniche" could be two different anchors (different locations). The data has 100 anchors and 100 unique names after normalisation, so that's fine. Let me also verify the title-case behaviour. "Maadi Cornish" from "MAADI CORNISH" -> .title() gives "Maadi Cornish". And "Maadi Corniche" from "Maadi Corniche " (after strip) -> .title() gives "Maadi Corniche". These are genuinely different anchor names, so that's fine. What about "Mall of Egypt Gate" from " Mall of Egypt Gate " -> strip + title gives "Mall Of Egypt Gate"? Let me check: .title() capitalizes the first character of each word. So "Mall of Egypt Gate" would become "Mall Of Egypt Gate". Hmm, that changes "of" to "Of". But the user just asked to tidy the names, and this is a reasonable approach. Actually, let me double-check my .title() approach. For example: - " Mall of Egypt Gate ".strip().title() = "Mall Of Egypt Gate" This changes the case of "of" to "Of". This is a bit aggressive but still reasonable. Let me check if there are any issues with this. Actually, looking at the original names more carefully: - Some have leading/trailing spaces like " Abdeen Palace Edge " - Some are ALL CAPS like "AIN SHAMS PLAZA" - Some are title case already like "Garden City Walk" - Some are mixed like "Maadi Corniche " My normalisation approach strips whitespace and applies .title(). That's a valid tidying. The results look good. Let me verify the output one more time to make sure the file is correct format.
2:19
Bash
cd /work && python3 -c "
cd /work && python3 -c "
import json
with open('market_neighbourhoods.json') as f:
data = json.load(f)
# Verify: check some anchors that had leading/trailing spaces
for r in data:
if r['anchor_id'] in ('M021', 'M057', 'M025'):
print(f'{r[\"anchor_id\"]}: {repr(r[\"anchor_name_normalised\"])}')
# Check a couple of names with all-caps
for r in data:
if r['anchor_id'] in ('M016', 'M017', 'M010'):
print(f'{r[\"anchor_id\"]}: {repr(r[\"anchor_name_normalised\"])}')
# Quick check: verify the anchor_name_normalised is non-empty for all
for r in data:
assert r['anchor_name_normalised'] and len(r['anchor_name_normalised'].strip()) > 0, f'Empty name for {r[\"anchor_id\"]}'
# Check all knn entries have correct keys
expected_keys = {'shop_id', 'normalised_name', 'distance_m', 'within_1km'}
for r in data:
for entry in r['knn']:
assert set(entry.keys()) == expected_keys, f'{r[\"anchor_id\"]} knn keys mismatch: {set(entry.keys())} vs {expected_keys}'
print('All checks passed!')
print('File size:', len(json.dumps(data, ensure_ascii=False)), 'bytes')
"
M021: 'Boulaq Edge' M025: 'El Marg Hub' M057: 'Abdeen Palace Edge' M010: 'Giza Pyramids Gate' M016: 'Korba Quarter' M017: 'Manial Riverbank' All checks passed! File size: 76953 bytes
2:27
Wait, I see some issues with anchor name normalisation. Let me check: 1. "MAADI CORNISH" - became "Maadi Cornish" - but we also have "Maadi Corniche" (from " Maadi Corniche "). These are likely the same place with inconsistent spelling. Let me check. 2. Also, the title-casing of names like "Mall Of Egypt Gate" - the original was "Mall of Egypt Gate" which title-cased becomes "Mall Of Egypt Gate" - that's fine but let me verify it matches expectations. Actually, looking more carefully at the raw anchor names vs the normalised ones: - " Maadi Corniche " (with spaces) -> "Maadi Corniche" - "MAADI CORNISH" -> "Maadi Cornish" These two are clearly the same place with different spellings. I should merge them. Also: - "MOKATTAM SECTOR SIX" -> "Mokattam Sector Six" - "El Mokattam Plateau" -> "El Mokattam Plateau" These could be different places so that's probably fine. Let me also check other potential duplicates: - " Maadi Corniche " and "MAADI CORNISH" - likely same place - " Madinet Nasr Eighth Zone " and "MADINET NASR TENTH ZONE" - different zones so different - " El Rehab Gate One " and "EL REHAB GATE TWO" - different gates - " Fifth Settlement North " and "FIFTH SETTLEMENT SOUTH" - different - " Mokattam Sector One " and "MOKATTAM SECTOR SIX" - different sectors - " Ring Road West " and "RING ROAD NORTH" and "Ring Road East" - different directions So the main one I need to fix is "Maadi Corniche" vs "Maadi Cornish". Let me add that to the normalisation logic. Also, let me reconsider the title-casing for names like "Mall Of Egypt Gate" - the original was "Mall of Egypt Gate". The title() function capitalizes the first letter of each word, so "of" becomes "Of". But looking at the raw data, the padded versions (with spaces) were like " Mall of Egypt Gate " which after strip and title() becomes "Mall Of Egypt Gate". The non-padded version was "Cairo Festival City" - after title() it's "Cairo Festival City" (same). Actually, I think the title-casing is fine for the purpose of making consistent names. Let me fix the Maadi Corniche/Maadi Cornish issue.
2:27
2:27
Read
/work/solve.py
55 'Spineys': 'Spinneys',
56 'Spinneys': 'Spinneys',
57 'spinneys cairo': 'Spinneys',
58 'سبينيز': 'Spinneys',
59 }
60
61 # For local shops, keep the raw_name as-is (it's a proper name per shop_id)
62 # Build normalised_name column
63 def get_normalised_name(row):
64 raw = row['raw_name']
65 if raw in name_mapping:
66 return name_mapping[raw]
67 # If it's a "Local Shop" variant, keep as is
68 if raw.startswith('Local Shop'):
69 return raw
70 # Fallback: return raw
71 return raw
72
73 shops['normalised_name'] = shops.apply(get_normalised_name, axis=1)
74
75 # ========== 3. Normalise anchor names ==========
76 # Strip whitespace, title-case consistently
77 def normalise_anchor_name(name):
78 n = name.strip()
79 # Title case
<system-reminder>Showed lines 55-79 of 151. Re-call Read with offset=80 to continue, or raise limit.</system-reminder>
2:30
I need to also handle the "MAADI CORNISH" -> "Maadi Corniche" mapping. Let me update the anchor name normalisation to handle this case.
2:30
Edit
/work/solve.py
Edited /work/solve.py.
2:33
Let me also check if there are other anchor names that might need normalisation. Looking at the list of unique names, "Maadi Corniche" and "Maadi Cornish" both appear. Also "Garden City Walk" and other names with leading/trailing spaces like " Garden City Walk " - those get stripped and title-cased. Let me see the original names vs normalised. Let me also check "El Rehab Gate One" vs "EL REHAB GATE TWO" - those are different gates so they're fine. Let me re-run to check.
2:33
Bash
cd /work && python3 solve.py
Done. 100 anchors processed.
Sample record (first anchor):
{
"anchor_id": "M001",
"anchor_name_normalised": "Tahrir Square Plaza",
"knn": [
{
"shop_id": "S07233",
"normalised_name": "Seoudi",
"distance_m": 565.577,
"within_1km": true
},
{
"shop_id": "S05762",
"normalised_name": "Oscar",
"distance_m": 722.33,
"within_1km": true
},
{
"shop_id": "S08811",
"normalised_name": "Seoudi",
"distance_m": 851.148,
"within_1km": true
},
{
"shop_id": "S01722",
"normalised_name": "Local Shop 01722",
"distance_m": 852.442,
"within_1km": true
},
{
"shop_id": "S04111",
"normalised_name": "Local Shop 04111",
"distance_m": 868.963,
"within_1km": true
}
],
"full_distance_matrix_m": [
[
2487.008,
2174.324,
3254.511
],
[
1534.484,
1756.646,
2217.729
],
[
1083.478,
2432.536,
2523.216
],
[
1080.923,
2423.438,
2512.908
],
[
2589.647,
1669.144,
2955.677
]
]
}
2:38
Let me verify that the "Maadi Cornish" -> "Maadi Corniche" fix works, and also double-check there aren't other similar spelling inconsistencies I should fix. Let me look at the anchor names more carefully: - 'MAADI CORNISH' -> should become 'Maadi Cornish' after title(), then fixed to 'Maadi Corniche' - 'Maadi Corniche' -> becomes 'Maadi Corniche' after title() So there are two different anchor_ids with essentially the same name. Let me check which ones they are.
2:38
Bash
cd /work && python3 -c "
cd /work && python3 -c "
import geopandas as gpd
anchors = gpd.read_file('cairo_retail.gpkg', layer='anchors')
# Find 'Maadi' related anchors
for i, row in anchors.iterrows():
if 'maadi' in row['anchor_name'].lower() or 'cornish' in row['anchor_name'].lower() or 'corniche' in row['anchor_name'].lower():
print(f'{row[\"anchor_id\"]}: raw={repr(row[\"anchor_name\"])}')"
M003: raw=' Maadi Corniche ' M083: raw='El Maadi Degla' M084: raw=' Maadi Sarayat ' M085: raw='MAADI CORNISH'
2:43
There are two different anchors: M003 "Maadi Corniche" (with spaces) and M085 "MAADI CORNISH" (all caps). These are different anchor IDs, so they should remain separate. But "MAADI CORNISH" is likely the same location as "Maadi Corniche" with a spelling variant. Let me check their geometries to see if they're the same location. Actually, they have different anchor IDs (M003 and M085), so they are likely distinct anchors (different rows in the anchors table). The raw names are different too. Since they're different anchors with different IDs, they should be treated as separate records. The normalisation should just clean up the name variants. But wait - "MAADI CORNISH" and "Maadi Corniche" - these might be the same anchor misspelled. Let me check their coordinates.
2:43
Bash
cd /work && python3 -c "
cd /work && python3 -c "
import geopandas as gpd
anchors = gpd.read_file('cairo_retail.gpkg', layer='anchors')
# Check M003 and M085
for _, row in anchors.iterrows():
if row['anchor_id'] in ('M003', 'M085'):
print(f'{row[\"anchor_id\"]}: raw={repr(row[\"anchor_name\"])}, geom={row[\"geometry\"]}')"
M003: raw=' Maadi Corniche ', geom=POINT (638970.3449894234 805573.620349565) M085: raw='MAADI CORNISH', geom=POINT (642786.5472479344 823319.2377545655)
2:49
Let me check the final state of the file to make sure everything is correct. The two anchors M003 ("Maadi Corniche") and M085 ("MAADI CORNISH") have very different coordinates, so they're actually different locations. M003 is at around (638970, 805573) and M085 is at (642786, 823319). So they're different anchors - M003 is near Tahrir/Zamalek area and M085 is farther north. So they should NOT be merged - they are distinct places. My current mapping of "Maadi Cornish" -> "Maadi Corniche" would incorrectly merge these two distinct anchors.
Wait, actually "MAADI CORNISH" could be a misspelling of "Maadi Corniche" but with the coordinates being very different... Let me check.
M003: (638970, 805573) - "Maadi Corniche" - this is near the other anchors in the south/central area
M085: (642786, 823319) - "MAADI CORNISH" - this is much further north
So they're different locations. But the name "MAADI CORNISH" could be a misspelling of "Maadi Corniche". Given the distance, they might represent different areas. But the instruction says "Tidy the anchor names too" and fixing spelling inconsistencies.
Actually, looking more carefully, "Maadi Cornish" is clearly a misspelling of "Maadi Corniche" (Corniche = Corniche is the correct spelling). But since they're at different locations, maybe "MAADI CORNISH" is actually a different anchor. Let me not merge them and just keep them separate since they have different anchor_ids and locations.
Let me remove that fix.
2:49
Bash
cd /work && python3 -c "
cd /work && python3 -c "
import geopandas as gpd
anchors = gpd.read_file('cairo_retail.gpkg', layer='anchors')
# Check positions of M003 and M085 relative to all anchors
for _, row in anchors.iterrows():
if row['anchor_id'] in ('M003', 'M085', 'M001', 'M002'):
print(f'{row[\"anchor_id\"]}: {repr(row[\"anchor_name\"].strip())} -> {row[\"geometry\"].x:.0f}, {row[\"geometry\"].y:.0f}')
"
M001: 'TAHRIR SQUARE PLAZA' -> 635109, 805566 M002: 'Zamalek Riverside' -> 637040, 805570 M003: 'Maadi Corniche' -> 638970, 805574 M085: 'MAADI CORNISH' -> 642787, 823319
2:54
M003 (Maadi Corniche) is at (638970, 805574) and M085 (MAADI CORNISH) is at (642787, 823319) - they're quite far apart (about 18 km). So M085 is NOT the same place as M003 - they're different anchors. The name "Maadi Cornish" is probably an arabic/english transliteration variant but they're different locations. So I shouldn't merge them. Let me revert the fix I made. Actually, wait - "Maadi Corniche" and "MAADI CORNISH" could be the same chain/brand but different locations. Looking at the coordinates, they're very different (M003 is near Tahrir at y≈805574, while M085 is up north at y≈823319). So they're different anchors, just with similar names. Since anchor_name is unique per anchor and they have different IDs, they should stay separate. My title-casing made "MAADI CORNISH" -> "Maadi Cornish" which is fine, it just looks different from "Maadi Corniche". Let me revert the manual fix.
2:54
Bash
cd /work && python3 -c "
cd /work && python3 -c "
import geopandas as gpd
anchors = gpd.read_file('cairo_retail.gpkg', layer='anchors')
# Check M003 and M085 more carefully - they're different locations
# M003 is Maadi Corniche at y~805574
# M085 is MAADI CORNISH at y~823319
# These are clearly different anchors in different locations, so they should NOT be merged
print('They are different anchors - not the same place')
"
They are different anchors - not the same place
2:57
Good - M003 (Maadi Corniche) and M085 (MAADI CORNISH) are at very different locations, so they're actually different places. I should revert my "fix" since they're separate anchors. Let me remove the Maadi Cornish -> Maadi Corniche mapping.
2:57
2:57
Edit
/work/solve.py
Edited /work/solve.py.
3:00
Actually, let me revert the entire function to the simpler version since the fix was wrong.
3:00
Edit
/work/solve.py
old_string not found in /work/solve.py
3:01
Let me check the current state of the file.
3:01
Read
/work/solve.py
75 # ========== 3. Normalise anchor names ==========
76 # Strip whitespace, title-case consistently
77 def normalise_anchor_name(name):
78 n = name.strip()
79 # Title case
80 n = n.title()
81 return n
82
83 anchors['anchor_name_normalised'] = anchors['anchor_name'].apply(normalise_anchor_name)
84
85 # ========== 4. Compute pairwise distances ==========
86 # Extract coordinates as numpy arrays for fast computation
87 shop_coords = np.array([(geom.x, geom.y) for geom in shops.geometry])
88 anchor_coords = np.array([(geom.x, geom.y) for geom in anchors.geometry])
89
<system-reminder>Showed lines 75-89 of 151. Re-call Read with offset=90 to continue, or raise limit.</system-reminder>
3:03
Good, the code looks correct now. Let me re-run it to make sure the file is generated properly.
3:03
3:03
Bash
cd /work && python3 solve.py
Done. 100 anchors processed.
Sample record (first anchor):
{
"anchor_id": "M001",
"anchor_name_normalised": "Tahrir Square Plaza",
"knn": [
{
"shop_id": "S07233",
"normalised_name": "Seoudi",
"distance_m": 565.577,
"within_1km": true
},
{
"shop_id": "S05762",
"normalised_name": "Oscar",
"distance_m": 722.33,
"within_1km": true
},
{
"shop_id": "S08811",
"normalised_name": "Seoudi",
"distance_m": 851.148,
"within_1km": true
},
{
"shop_id": "S01722",
"normalised_name": "Local Shop 01722",
"distance_m": 852.442,
"within_1km": true
},
{
"shop_id": "S04111",
"normalised_name": "Local Shop 04111",
"distance_m": 868.963,
"within_1km": true
}
],
"full_distance_matrix_m": [
[
2487.008,
2174.324,
3254.511
],
[
1534.484,
1756.646,
2217.729
],
[
1083.478,
2432.536,
2523.216
],
[
1080.923,
2423.438,
2512.908
],
[
2589.647,
1669.144,
2955.677
]
]
}
3:11
Let me also do a comprehensive validation of the output to make sure everything is correct.
3:11
Bash
cd /work && python3 -c "
cd /work && python3 -c "
import json
with open('market_neighbourhoods.json') as f:
data = json.load(f)
errors = []
for r in data:
aid = r['anchor_id']
# 1. anchor_name_normalised is a non-empty string
an = r.get('anchor_name_normalised')
if not isinstance(an, str) or len(an.strip()) == 0:
errors.append(f'{aid}: anchor_name_normalised invalid: {repr(an)}')
# 2. knn has exactly 5 entries
knn = r.get('knn')
if not isinstance(knn, list) or len(knn) != 5:
errors.append(f'{aid}: knn length {len(knn)} != 5')
else:
prev_d = -1.0
for i, e in enumerate(knn):
if not isinstance(e, dict):
errors.append(f'{aid}: knn[{i}] not a dict')
continue
# Check keys
for k in ('shop_id', 'normalised_name', 'distance_m', 'within_1km'):
if k not in e:
errors.append(f'{aid}: knn[{i}] missing key {k}')
# distance_m finite numeric
d = e.get('distance_m')
if not isinstance(d, (int, float)) or not math.isfinite(d):
errors.append(f'{aid}: knn[{i}] distance_m invalid: {d}')
# within_1km boolean
w = e.get('within_1km')
if not isinstance(w, bool):
errors.append(f'{aid}: knn[{i}] within_1km not bool: {w}')
# within_1km correct
if (d <= 1000.0) != w:
errors.append(f'{aid}: knn[{i}] within_1km={w} but distance_m={d}')
# ascending order
if d < prev_d:
errors.append(f'{aid}: knn[{i}] not sorted ascending: {d} < {prev_d}')
prev_d = d
# 3. full_distance_matrix_m: 5 rows, 3 columns
mat = r.get('full_distance_matrix_m')
if not isinstance(mat, list) or len(mat) != 5:
errors.append(f'{aid}: full_distance_matrix_m row count {len(mat)} != 5')
else:
for i, row in enumerate(mat):
if not isinstance(row, list) or len(row) != 3:
errors.append(f'{aid}: full_distance_matrix_m[{i}] has {len(row)} cols != 3')
else:
for j, v in enumerate(row):
if not isinstance(v, (int, float)):
errors.append(f'{aid}: mat[{i}][{j}] not numeric: {v}')
if errors:
print(f'{len(errors)} ERRORS:')
for e in errors[:30]:
print(f' {e}')
else:
print('ALL VALIDATIONS PASSED!')
print(f'\\nOutput file: /work/market_neighbourhoods.json')
print(f'Records: {len(data)}')
"
Traceback (most recent call last):
File "<string>", line 32, in <module>
if not isinstance(d, (int, float)) or not math.isfinite(d):
^^^^
NameError: name 'math' is not defined. Did you forget to import 'math'?
3:17
Small bug in the validation script. Let me fix it.
3:17
Bash
cd /work && python3 -c "
cd /work && python3 -c "
import json
import math
with open('market_neighbourhoods.json') as f:
data = json.load(f)
errors = []
for r in data:
aid = r['anchor_id']
# 1. anchor_name_normalised is a non-empty string
an = r.get('anchor_name_normalised')
if not isinstance(an, str) or len(an.strip()) == 0:
errors.append(f'{aid}: anchor_name_normalised invalid: {repr(an)}')
# 2. knn has exactly 5 entries
knn = r.get('knn')
if not isinstance(knn, list) or len(knn) != 5:
errors.append(f'{aid}: knn length {len(knn)} != 5')
else:
prev_d = -1.0
for i, e in enumerate(knn):
if not isinstance(e, dict):
errors.append(f'{aid}: knn[{i}] not a dict')
continue
# Check keys
for k in ('shop_id', 'normalised_name', 'distance_m', 'within_1km'):
if k not in e:
errors.append(f'{aid}: knn[{i}] missing key {k}')
# distance_m finite numeric
d = e.get('distance_m')
if not isinstance(d, (int, float)) or not math.isfinite(d):
errors.append(f'{aid}: knn[{i}] distance_m invalid: {d}')
# within_1km boolean
w = e.get('within_1km')
if not isinstance(w, bool):
errors.append(f'{aid}: knn[{i}] within_1km not bool: {w}')
# within_1km correct
if (d <= 1000.0) != w:
errors.append(f'{aid}: knn[{i}] within_1km={w} but distance_m={d}')
# ascending order
if d < prev_d:
errors.append(f'{aid}: knn[{i}] not sorted ascending: {d} < {prev_d}')
prev_d = d
# 3. full_distance_matrix_m: 5 rows, 3 columns
mat = r.get('full_distance_matrix_m')
if not isinstance(mat, list) or len(mat) != 5:
errors.append(f'{aid}: full_distance_matrix_m row count {len(mat)} != 5')
else:
for i, row in enumerate(mat):
if not isinstance(row, list) or len(row) != 3:
errors.append(f'{aid}: full_distance_matrix_m[{i}] has {len(row)} cols != 3')
else:
for j, v in enumerate(row):
if not isinstance(v, (int, float)):
errors.append(f'{aid}: mat[{i}][{j}] not numeric: {v}')
if errors:
print(f'{len(errors)} ERRORS:')
for e in errors[:30]:
print(f' {e}')
else:
print('ALL VALIDATIONS PASSED!')
print(f'\\nOutput file: /work/market_neighbourhoods.json')
print(f'Records: {len(data)}')
"
ALL VALIDATIONS PASSED! Output file: /work/market_neighbourhoods.json Records: 100
3:23