spa-l2-cairo-shop-knn

Status: done
Score: 1.000
Duration: 5:03
Cost: 1.35¢
Model: deepseek/deepseek-v4-flash

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"
}
0:13 
Let me start by understanding the data structure. I'll explore the GeoPackage file to see what tables and columns are available.
0:13
Bash cd /work && python -c " 
cd /work && python -c "
import geopandas as gpd
# List layers
layers = gpd.list_layers('cairo_retail')
print('Layers:')
print(layers)
"
Traceback (most recent call last):
  File "<string>", line 4, in <module>
    layers = gpd.list_layers('cairo_retail')
  File "/usr/local/lib/python3.14/site-packages/geopandas/io/file.py", line 894, in _list_layers
    pyogrio.list_layers(filename), columns=["name", "geometry_type"]
    ~~~~~~~~~~~~~~~~~~~^^^^^^^^^^
  File "/usr/local/lib/python3.14/site-packages/pyogrio/core.py", line 129, in list_layers
    return ogr_list_layers(get_vsi_path_or_buffer(path_or_buffer))
  File "pyogrio/_io.pyx", line 2099, in pyogrio._io.ogr_list_layers
  File "pyogrio/_io.pyx", line 262, in pyogrio._io.ogr_open
pyogrio.errors.DataSourceError: cairo_retail: No such file or directory
5:54 
The file might be in a different format. Let me check what's in the /work directory.
5:54
Bash ls -la /work/ 
total 1228
drwxr-xr-x 1 agent agent      34 Jun 15 14:38 .
drwxr-xr-x 1 root  root       14 Jun 15 14:38 ..
-rw-r--r-- 1 agent agent 1257472 Jun 15 14:38 cairo_retail.gpkg
6:02 
The file is called `cairo_retail.gpkg`, not just `cairo_retail`. Let me explore it.
6:02
Bash cd /work && python -c " 
cd /work && python -c "
import geopandas as gpd
layers = gpd.list_layers('cairo_retail.gpkg')
print('Layers:')
print(layers)
"
Layers:
      name geometry_type
0    shops         Point
1  anchors         Point
6:16 
Let me explore the data.
6:16
Bash cd /work && python -c " 
cd /work && python -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 ===')
print(shops.columns.tolist())
print(shops.head(20).to_string())
print('...')
print(shops.shape)
print()
print('=== ANCHORS ===')
print(anchors.columns.tolist())
print(anchors.head(20).to_string())
print('...')
print(anchors.shape)
"
=== SHOPS ===
['shop_id', 'raw_name', 'geometry']
   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)
10  S00011  Local Shop 00011   POINT (643741.745 821785.24)
11  S00012             سعودي  POINT (644818.535 821149.698)
12  S00013  Local Shop 00013  POINT (650857.192 824183.113)
13  S00014  Local Shop 00014   POINT (638852.94 816104.428)
14  S00015  Local Shop 00015  POINT (647652.576 814307.805)
15  S00016          Spinneys  POINT (634460.575 810550.892)
16  S00017  Local Shop 00017  POINT (635587.164 814404.569)
17  S00018  Local Shop 00018  POINT (639796.625 816106.118)
18  S00019  Local Shop 00019  POINT (650413.268 823729.981)
19  S00020  Local Shop 00020  POINT (636848.935 814863.408)
...
(10000, 3)

=== ANCHORS ===
['anchor_id', 'anchor_name', 'geometry']
   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)
10      M011               Roxy Square    POINT (635105.3 807782.949)
11      M012         Ramses Crossing    POINT (637035.414 807786.638)
12      M013       SAYEDA ZEINAB PLAZA  POINT (638965.529 807790.663)
13      M014  Khan El Khalili Approach  POINT (640895.646 807795.025)
14      M015      Bab Al Louq Corner    POINT (642825.764 807799.724)
15      M016             KORBA QUARTER  POINT (644755.884 807804.759)
16      M017          Manial Riverbank  POINT (646686.006 807810.131)
17      M018            Shubra North      POINT (648616.13 807815.84)
18      M019           AIN SHAMS PLAZA  POINT (650546.256 807821.886)
19      M020         Abbasiya Junction  POINT (652476.384 807828.268)
...
(100, 3)
6:31 
Let me explore the data more to understand the patterns in the names.
6:31
Bash cd /work && python -c " 
cd /work && python -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 raw names in shops ===')
print(shops['raw_name'].value_counts().to_string())

print()
print('=== Unique anchor names ===')
print(anchors['anchor_name'].value_counts().to_string())

print()
print('=== CRS ===')
print(shops.crs)
"
=== Unique raw names in shops ===
raw_name
هايبر وان             183
OSCAR                 176
Seoudi                176
spinneys cairo        174
Khair Zaman           171
Carrefour Egypt       169
kheir zaman           168
metro                 165
Metro Markets         164
Oscar Grand Stores    163
خير زمان              162
Seoudi Market         160
Hyper One             158
Kheir Zaman           158
مترو                  156
سبينيز                156
abou zekry            156
hyperone              155
اوسكار                154
Metro Market          153
HyperOne              152
Spinneys              150
كارفور                150
Oscar                 150
سعودي                 146
Spineys               145
Carrefour             143
seoudi supermarket    139
Abu Zikri             137
carrefour             136
أبو زكري              136
Abu Zekry             136
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 S
[... truncated ...]ocal 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

=== Unique anchor names ===
anchor_name
TAHRIR SQUARE PLAZA               1
Zamalek Riverside                 1
  Maadi Corniche                  1
HELIOPOLIS SQUARE                 1
New Cairo Tagamoa                 1
  Nasr City Hub                   1
MOHANDESSIN CENTRE                1
Garden City Walk                  1
  Dokki Market                    1
GIZA PYRAMIDS GATE                1
Roxy Square                       1
  Ramses Crossing                 1
SAYEDA ZEINAB PLAZA               1
Khan El Khalili Approach          1
  Bab Al Louq Corner              1
KORBA QUARTER                     1
Manial Riverbank                  1
  Shubra North                    1
AIN SHAMS PLAZA                   1
Abbasiya Junction                 1
  Boulaq Edge                     1
GARBIYA PLAZA                     1
Sakakini Approach                 1
  Dar El Salaam                   1
EL MARG HUB                       1
Helwan Centre                     1
  Maasara Crossing                1
TORA EDGE                         1
Mokattam Heights                  1
  Nozha Promenade                 1
SHERATON HELIOPOLIS               1
Triumph Square                    1
  Cleopatra Plaza                 1
SALAH SALEM STRIP                 1
Autostrad Corner                  1
  El Rehab Gate One               1
EL REHAB GATE TWO                 1
Madinaty Promenade                1
  Fifth Settlement North          1
FIFTH SETTLEMENT SOUTH            1
American University Gate          1
  Police Academy Strip            1
RING ROAD NORTH                   1
Ring Road East                    1
  Ring Road West                  1
CITY STARS MALL                   1
Cairo Festival City               1
  Mall of Egypt Gate              1
TAGAMOA FIRST                     1
Tagamoa Third                     1
  El Mokattam Plateau             1
AL AHLY STADIUM                   1
Cairo Stadium                     1
  Sharkawi Plaza                  1
EL OBOUR HUB                      1
Shoubra Mazallat                  1
  Abdeen Palace Edge              1
EL HUSSEIN SQUARE                 1
Al Ghouriya Strip                 1
  El Mosky Quarter                1
BAB ZUWEILA APPROACH              1
Ataba Square                      1
  Opera Square                    1
TALAAT HARB PLAZA                 1
Soliman Pasha Corner              1
  Sherif Street                   1
QASR EL NILE                      1
Kasr El Aini Strip                1
  El Sayeda Aisha                 1
KOBRI EL QUBBA                    1
Mar Mina Plaza                    1
  Saint Fatima Hub                1
EL NOZHA EL GEDIDA                1
Rabaa Square                      1
  Tagamoa El Saba                 1
BAHTEEM CROSSING                  1
El Salam City                     1
  Madinet Nasr Eighth Zone        1
MADINET NASR TENTH ZONE           1
El Hadaba El Wosta                1
  Mokattam Sector One             1
MOKATTAM SECTOR SIX               1
El Maadi Degla                    1
  Maadi Sarayat                   1
MAADI CORNISH                     1
Old Cairo Babylon                 1
  Coptic Cairo Plaza              1
FUSTAT PARK EDGE                  1
Manial Bridge                     1
  Embaba Crossing                 1
IMBABA AIRPORT STRIP              1
Mit Okba Plaza                    1
  El Agouza Riverside             1
EL SAHEL JUNCTION                 1
Rod El Farag Bridge               1
  Shubra El Kheima Centre         1
EL SAWAH CORNER                   1
Demerdash Plaza                   1
  El Demerdash Hospital Edge      1
RAMSES HILTON PLAZA               1

=== CRS ===
EPSG:22992
8:28 
Let me analyze the data:

1. **Shops (10,000 records)**: Each has `shop_id`, `raw_name`, `geometry`
2. **Anchors (100 records)**: Each has `anchor_id`, `anchor_name`, `geometry`
3. **CRS**: EPSG:22992 (Egyptian Red Belt, projected in metres)

The shop names have many inconsistently spelled chain names. I need to create a normalised name mapping. Let me look at what chains exist:

Chain names observed:
- OSCAR / Oscar / Oscar Grand Stores / اوسكار → "Oscar"
- مترو / Metro Market / Metro Markets / metro → "Metro"
- Carrefour / Carrefour Egypt / كارفور / carrefour → "Carrefour"
- سعودي / Seoudi / Seoudi Market / seoudi supermarket / سعودي → "Seoudi"
- Spinneys / spinneys cairo / سبينيز / Spineys → "Spinneys"
- Khair Zaman / Kheir Zaman / خير زمان / kheir zaman / Khair Zaman → "Kheir Zaman"
- هايبر وان / Hyper One / hyperone / HyperOne / Hyper One → "Hyper One"
- اوسكار → "Oscar"
- أبو زكري / Abu Zikri / Abu Zekry / abou zekry → "Abu Zekry"
- Local Shop XXXX → "Local Shop XXXX" (keep as is, these are unique local shops)

Let me also check the anchor names for tidying. Some have leading whitespace, some are ALL CAPS, some are mixed case.

Let me create the script.
8:28
Bash cd /work && python -c " 
cd /work && python -c "
import geopandas as gpd
shops = gpd.read_file('cairo_retail.gpkg', layer='shops')
print('Total shops:', len(shops))
print('CRS:', shops.crs)
print()
# Check if CRS units are metres
from pyproj import CRS
crs = CRS('EPSG:22992')
print('CRS info:', crs.name)
print('Axis units:', crs.axis_info)
"
Total shops: 10000
CRS: EPSG:22992

CRS info: Egypt 1907 / Red Belt
Axis units: [Axis(name=Easting, abbrev=E, direction=east, unit_auth_code=EPSG, unit_code=9001, unit_name=metre), Axis(name=Northing, abbrev=N, direction=north, unit_auth_code=EPSG, unit_code=9001, unit_name=metre)]
8:59 
Now I understand the data. Let me think about the normalization of names.

For shops, the unique raw names are:
- هايبر وان (Hyper One in Arabic)
- OSCAR (and Oscar, Oscar Grand Stores, اوسكار)
- Seoudi (and Seoudi Market, seoudi supermarket, سعودي)
- spinneys cairo (and Spinneys, Spineys, سبينيز)
- Khair Zaman (and Kheir Zaman, kheir zaman, خير زمان)
- Carrefour (and Carrefour Egypt, كارفور, carrefour)
- metro (and Metro Markets, metro market, مترو)
- Hyper One (and hyperone, HyperOne, هايبر وان)
- abou zekry (and Abu Zikri, Abu Zekry, أبو زكري)
- Local Shop NNNNN (various - these are independent local shops)

I need to create a normalised_name for each shop, consistent across spellings.

For anchor names, they have inconsistent capitalization/spacing but seem uniquely identifiable. I need to tidy them.

Let me look at the shop names more carefully to build a mapping:

Chain names:
1. OSCAR / Oscar / Oscar Grand Stores / اوسكار → "Oscar"
2. Seoudi / Seoudi Market / seoudi supermarket / سعودي → "Seoudi"
3. spinneys cairo / Spinneys / Spineys / سبينيز → "Spinneys"
4. Khair Zaman / kheir zaman / Kheir Zaman / خير زمان → "Khair Zaman" (or "Kheir Zaman"? Let me pick "Khair Zaman")
5. Carrefour / Carrefour Egypt / كارفور / carrefour → "Carrefour"
6. metro / Metro Markets / Metro Market / مترو → "Metro"
7. Hyper One / hyperone / HyperOne / هايبر وان → "Hyper One" (or "HyperOne"? Let me use "Hyper One")
8. abou zekry / Abu Zikri / Abu Zekry / أبو زكري → "Abu Zekry"

And "Local Shop NNNNN" → keep as is (each is its own independent shop).

Let me check the number of unique raw names more carefully.
8:59
Bash cd /work && python -c " 
cd /work && python -c "
import geopandas as gpd
shops = gpd.read_file('cairo_retail.gpkg', layer='shops')
# Only show chain names (non-local)
chains = shops[~shops['raw_name'].str.startswith('Local Shop')]
print(chains['raw_name'].value_counts())
print()
print('Total chain shops:', len(chains))
print('Total local shops:', len(shops) - len(chains))
"
raw_name
هايبر وان             183
OSCAR                 176
Seoudi                176
spinneys cairo        174
Khair Zaman           171
Carrefour Egypt       169
kheir zaman           168
metro                 165
Metro Markets         164
Oscar Grand Stores    163
خير زمان              162
Seoudi Market         160
Hyper One             158
Kheir Zaman           158
مترو                  156
سبينيز                156
abou zekry            156
hyperone              155
اوسكار                154
Metro Market          153
HyperOne              152
Spinneys              150
كارفور                150
Oscar                 150
سعودي                 146
Spineys               145
Carrefour             143
seoudi supermarket    139
Abu Zikri             137
carrefour             136
أبو زكري              136
Abu Zekry             136
Name: count, dtype: int64

Total chain shops: 4997
Total local shops: 5003
9:13 
Now I need to figure out the mapping of raw names to normalised names. Let me analyze the chains:

1. **Carrefour**: Carrefour Egypt, Carrefour, carrefour, كارفور → "Carrefour"
2. **Metro**: metro, Metro Markets, مترو, Metro Market → "Metro"
3. **Oscar**: OSCAR, Oscar Grand Stores, اوسكار, Oscar → "Oscar"
4. **Seoudi**: Seoudi, Seoudi Market, سعودي, seoudi supermarket → "Seoudi"
5. **Spinneys**: spinneys cairo, سبينيز, Spinneys, Spineys → "Spinneys"
6. **Khair Zaman**: Khair Zaman, kheir zaman, خير زمان, Kheir Zaman → "Khair Zaman"
7. **Hyper One**: هايبر وان, Hyper One, hyperone, HyperOne → "Hyper One"
8. **Abu Zekry**: abou zekry, Abu Zikri, أبو زكري, Abu Zekry → "Abu Zekry"
9. **Local shops**: "Local Shop 00001" etc → keep as is or... The task says "normalised_name" and "all shops belonging to one chain share a single canonical spelling". Local shops are individual and unique, so each one is its own "chain" of one. So just keep the raw_name as is.

For anchor names, I need to normalise them too. Let me see the anchor names more carefully.

Looking at anchor names, some have leading spaces (e.g., "  Maadi Corniche  "), inconsistent capitalisation etc.

Let me look more closely at the anchor names to figure out normalisation.
9:13
Bash cd /work && python -c " 
cd /work && python -c "
import geopandas as gpd
anchors = gpd.read_file('cairo_retail.gpkg', layer='anchors')
for i, row in anchors.iterrows():
    print(f'{row[\"anchor_id\"]}: [{row[\"anchor_name\"]}]')
"
M001: [TAHRIR SQUARE PLAZA]
M002: [Zamalek Riverside]
M003: [  Maadi Corniche  ]
M004: [HELIOPOLIS SQUARE]
M005: [New Cairo Tagamoa]
M006: [  Nasr City Hub  ]
M007: [MOHANDESSIN CENTRE]
M008: [Garden City Walk]
M009: [  Dokki Market  ]
M010: [GIZA PYRAMIDS GATE]
M011: [Roxy Square]
M012: [  Ramses Crossing  ]
M013: [SAYEDA ZEINAB PLAZA]
M014: [Khan El Khalili Approach]
M015: [  Bab Al Louq Corner  ]
M016: [KORBA QUARTER]
M017: [Manial Riverbank]
M018: [  Shubra North  ]
M019: [AIN SHAMS PLAZA]
M020: [Abbasiya Junction]
M021: [  Boulaq Edge  ]
M022: [GARBIYA PLAZA]
M023: [Sakakini Approach]
M024: [  Dar El Salaam  ]
M025: [EL MARG HUB]
M026: [Helwan Centre]
M027: [  Maasara Crossing  ]
M028: [TORA EDGE]
M029: [Mokattam Heights]
M030: [  Nozha Promenade  ]
M031: [SHERATON HELIOPOLIS]
M032: [Triumph Square]
M033: [  Cleopatra Plaza  ]
M034: [SALAH SALEM STRIP]
M035: [Autostrad Corner]
M036: [  El Rehab Gate One  ]
M037: [EL REHAB GATE TWO]
M038: [Madinaty Promenade]
M039: [  Fifth Settlement North  ]
M040: [FIFTH SETTLEMENT SOUTH]
M041: [American University Gate]
M042: [  Police Academy Strip  ]
M043: [RING ROAD NORTH]
M044: [Ring Road East]
M045: [  Ring Road West  ]
M046: [CITY STARS MALL]
M047: [Cairo Festival City]
M048: [  Mall of Egypt Gate  ]
M049: [TAGAMOA FIRST]
M050: [Tagamoa Third]
M051: [  El Mokattam Plateau  ]
M052: [AL AHLY STADIUM]
M053: [Cairo Stadium]
M054: [  Sharkawi Plaza  ]
M055: [EL OBOUR HUB]
M056: [Shoubra Mazallat]
M057: [  Abdeen Palace Edge  ]
M058: [EL HUSSEIN SQUARE]
M059: [Al Ghouriya Strip]
M060: [  El Mosky Quarter  ]
M061: [BAB ZUWEILA APPROACH]
M062: [Ataba Square]
M063: [  Opera Square  ]
M064: [TALAAT HARB PLAZA]
M065: [Soliman Pasha Corner]
M066: [  Sherif Street  ]
M067: [QASR EL NILE]
M068: [Kasr El Aini Strip]
M069: [  El Sayeda Aisha  ]
M070: [KOBRI EL QUBBA]
M071: [Mar Mina Plaza]
M072: [  Saint Fatima Hub  ]
M073: [EL NOZHA EL GEDIDA]
M074: [Rabaa Square]
M075: [  Tagamoa El Saba  ]
M076: [BAHTEEM CROSSING]
M077: [El Salam City]
M078: [  Madinet Nasr Eighth Zone  ]
M079: [MADINET NASR TENTH ZONE]
M080: [El Hadaba El Wosta]
M081: [  Mokattam Sector One  ]
M082: [MOKATTAM SECTOR SIX]
M083: [El Maadi Degla]
M084: [  Maadi Sarayat  ]
M085: [MAADI CORNISH]
M086: [Old Cairo Babylon]
M087: [  Coptic Cairo Plaza  ]
M088: [FUSTAT PARK EDGE]
M089: [Manial Bridge]
M090: [  Embaba Crossing  ]
M091: [IMBABA AIRPORT STRIP]
M092: [Mit Okba Plaza]
M093: [  El Agouza Riverside  ]
M094: [EL SAHEL JUNCTION]
M095: [Rod El Farag Bridge]
M096: [  Shubra El Kheima Centre  ]
M097: [EL SAWAH CORNER]
M098: [Demerdash Plaza]
M099: [  El Demerdash Hospital Edge  ]
M100: [RAMSES HILTON PLAZA]
9:40 
Now I have a clear picture. Let me analyze the data:

1. **Shops** (10000 points):
   - `shop_id`: S00001 to S10000
   - `raw_name`: Various chain names with inconsistent spellings
   - `geometry`: Point in EPSG:22992 (metres)
   - Some are "Local Shop XXXXX" (individual shops) and others are chain stores

2. **Anchors** (100 points):
   - `anchor_id`: M001 to M100
   - `anchor_name`: Various names with inconsistent spacing/formatting
   - `geometry`: Point

I need to:
1. Normalize shop names - group by canonical chain name
2. Normalize anchor names - trim whitespace and standardize
3. For each anchor, find the 5 nearest shops (any shops, including locals)
4. For each anchor, find the 3 closest sibling anchors
5. Build a 5x3 distance matrix

Let me create a normalization mapping for the shop names:

Chain names and their variants:
- OSCAR, Oscar Grand Stores, Oscar, اوسكار → "Oscar"
- Carrefour Egypt, Carrefour, carrefour, كارفور → "Carrefour"
- metro, Metro Markets, Metro Market, مترو → "Metro"
- هايبر وان, Hyper One, hyperone, HyperOne → "Hyper One"
- Seoudi, Seoudi Market, سعودي, seoudi supermarket → "Seoudi"
- spinneys cairo, Spinneys, سبينيز, Spineys → "Spinneys"
- Khair Zaman, kheir zaman, Kheir Zaman, خير زمان → "Kheir Zaman"
- abou zekry, Abu Zikri, أبو زكري, Abu Zekry → "Abu Zekry"
- Local Shop XXXXX → keep as-is (each is its own entity)

For anchor names: trim whitespace, and use title case consistently? Actually the task says "tidy the anchor names too" - I'll strip leading/trailing spaces and normalize the case a bit, keeping meaningful capitalization.

Let me write the script.
9:40
Write /work/solve.py
content · 170 lines · py 
import json
import numpy as np
import pandas as pd
import geopandas as gpd

# ============================================================
# 1. Load data
# ============================================================
shops = gpd.read_file('/work/cairo_retail.gpkg', layer='shops')
anchors = gpd.read_file('/work/cairo_retail.gpkg', layer='anchors')

# CRS is EPSG:22992 (Egypt 1907 / Red Belt), units = metres
# We'll compute distances directly from the projected coordinates.

# ============================================================
# 2. Normalise shop names
# ============================================================
# Build a mapping from raw_name -> normalised_name
name_map = {
    # Oscar
    'OSCAR': 'Oscar',
    'Oscar Grand Stores': 'Oscar',
    'Oscar': 'Oscar',
    'اوسكار': 'Oscar',
    # Carrefour
    'Carrefour Egypt': 'Carrefour',
    'Carrefour': 'Carrefour',
    'carrefour': 'Carrefour',
    'كارفور': 'Carrefour',
    # Metro
    'metro': 'Metro',
    'Metro Markets': 'Metro',
    'Metro Market': 'Metro',
    'مترو': 'Metro',
    # Hyper One
    'هايبر وان': 'Hyper One',
    'Hyper One': 'Hyper One',
    'hyperone': 'Hyper One',
    'HyperOne': 'Hyper One',
    # Seoudi
    'Seoudi': 'Seoudi',
    'Seoudi Market': 'Seoudi',
    'سعودي': 'Seoudi',
    'seoudi supermarket': 'Seoudi',
    # Spinneys
    'spinneys cairo': 'Spinneys',
    'Spinneys': 'Spinneys',
    'سبينيز': 'Spinneys',
    'Spineys': 'Spinneys',
    # Kheir Zaman
    'Khair Zaman': 'Kheir Zaman',
    'kheir zaman': 'Kheir Zaman',
    'Kheir Zaman': 'Kheir Zaman',
    'خير زمان': 'Kheir Zaman',
    # Abu Zekry
    'abou zekry': 'Abu Zekry',
    'Abu Zikri': 'Abu Zekry',
    'أبو زكري': 'Abu Zekry',
    'Abu Zekry': 'Abu Zekry',
}

def normalise_shop_name(raw):
    if raw in name_map:
        return name_map[raw]
    # Local Shop XXXXX — keep as-is, each is uniquely named
    return raw

shops['normalised_name'] = shops['raw_name'].apply(normalise_shop_name)

# ============================================================
# 3. Tidy anchor names (strip whitespace, normalise spacing)
# ============================================================
def tidy_anchor_name(raw):
    return ' '.join(raw.split())  # collapses whitespace, strips

anchors['anchor_name_normalised'] = anchors['anchor_name'].apply(tidy_anchor_name)

# ============================================================
# 4. For each anchor, find the 5 nearest shops
# ============================================================
# Extract coordinates for fast computation
shop_coords = np.column_stack([shops.geometry.x.values, shops.geometry.y.values])
anchor_coords = np.column_stack([anchors.geometry.x.values, anchors.geometry.y.values])

shop_ids = shops['shop_id'].values
shop_names = shops['normalised_name'].values

anchor_ids = anchors['anchor_id'].values
anchor_names_norm = anchors['anchor_name_normalised'].values

results = []

for i in range(len(anchors)):
    ax, ay = anchor_coords[i]
    # Compute Euclidean distances (since CRS is projected with metre units)
    dx = shop_coords[:, 0] - ax
    dy = shop_coords[:, 1] - ay
    dists = np.sqrt(dx**2 + dy**2)
    
    # Get indices of 5 nearest shops
    nearest_idx = np.argsort(dists)[:5]
    
    knn = []
    for idx in nearest_idx:
        d = float(dists[idx])
        knn.append({
            'shop_id': str(shop_ids[idx]),
            'normalised_name': str(shop_names[idx]),
            'distance_m': round(d, 2),
            'within_1km': d <= 1000.0
        })
    
    # ============================================================
    # 5. For this anchor, find the 3 closest sibling anchors
    # ============================================================
    # Sibling = any other anchor
    other_indices = [j for j in range(len(anchors)) if j != i]
    other_dists = []
    for j in other_indices:
        bx, by = anchor_coords[j]
        d = np.sqrt((ax - bx)**2 + (ay - by)**2)
        other_dists.append((j, d))
    other_dists.sort(key=lambda x: x[1])
    sibling_indices = [j for j, d in other_dists[:3]]
    
    # ============================================================
    # 6. Build 5x3 distance matrix
    #    rows follow knn order, columns correspond to sibling anchors
    #    in distance-ascending order
    # ============================================================
    matrix = []
    for shop_idx in nearest_idx:
        sx, sy = shop_coords[shop_idx]
        row = []
        for sib_idx in sibling_indices:
            bx, by = anchor_coords[sib_idx]
            d = np.sqrt((sx - bx)**2 + (sy - by)**2)
            row.append(round(float(d), 2))
        matrix.append(row)
    
    results.append({
        'anchor_id': str(anchor_ids[i]),
        'anchor_name_normalised': str(anchor_names_norm[i]),
        'knn': knn,
        'full_distance_matrix_m': matrix
    })

# ============================================================
# 7. Write output
# ============================================================
with open('/work/market_neighbourhoods.json', 'w', encoding='utf-8') as f:
    json.dump(results, f, ensure_ascii=False, indent=2)

print('Done. Output written to /work/market_neighbourhoods.json')
print(f'Total anchors: {len(results)}')

# Validate
with open('/work/market_neighbourhoods.json', 'r') as f:
    data = json.load(f)
print(f'Number of records: {len(data)}')
# Check first record
r = data[0]
print(f'First anchor: {r[\"anchor_id\"]} -> {r[\"anchor_name_normalised\"]}')
print(f'  knn length: {len(r[\"knn\"])}')
print(f'  matrix shape: {len(r[\"full_distance_matrix_m\"])} x {len(r[\"full_distance_matrix_m\"][0])}')
# Verify knn sorted by distance
for ri in data[:5]:
    dists = [e['distance_m'] for e in ri['knn']]
    assert all(dists[k] <= dists[k+1] for k in range(4)), f"knn not sorted for {ri['anchor_id']}"
print('All checks passed.')
Wrote /work/solve.py (5869 chars).
9:52 
Let me run it to see if it works.
9:52
Bash cd /work && python solve.py 
  File "/work/solve.py", line 163
    print(f'First anchor: {r[\"anchor_id\"]} -> {r[\"anchor_name_normalised\"]}')
                              ^
SyntaxError: unexpected character after line continuation character
10:00 
Let me fix the string escaping issue in Python.
10:00
Read /work/solve.py 
     1	import json
     2	import numpy as np
     3	import pandas as pd
     4	import geopandas as gpd
     5	
     6	# ============================================================
     7	# 1. Load data
     8	# ============================================================
     9	shops = gpd.read_file('/work/cairo_retail.gpkg', layer='shops')
    10	anchors = gpd.read_file('/work/cairo_retail.gpkg', layer='anchors')
    11	
    12	# CRS is EPSG:22992 (Egypt 1907 / Red Belt), units = metres
    13	# We'll compute distances directly from the projected coordinates.
    14	
    15	# ============================================================
    16	# 2. Normalise shop names
    17	# ============================================================
    18	# Build a mapping from raw_name -> normalised_name
    19	name_map = {
    20	    # Oscar
    21	    'OSCAR': 'Oscar',
    22	    'Oscar Grand Stores': 'Oscar',
    23	    'Oscar': 'Oscar',
    24	    'اوسكار': 'Oscar',
    25	    # Carrefour
    26	    'Carrefour Egypt': 'Carrefour',
    27	    'Carrefour': 'Carrefour',
    28	    'carrefour': 'Carrefour',
    29	    'كارفور': 'Carrefour',
    30	    # Metro
    31	    'metro': 'Metro',
    32	    'Metro Markets': 'Metro',
    33	    'Metro Market': 'Metro',
    34	    'مترو': 'Metro',
    35	    # Hyper One
    36	    'هايبر وان': 'Hyper One',
    37	    'Hyper One': 'Hyper One',
    38	    'hyperone': 'Hyper One',
    39	    'HyperOne': 'Hyper One',
    40	    # Seoudi
    41	    'Seoudi': 'Seoudi',
    42	    'Seoudi Market': 'Seoudi',
    43	    'سعودي': 'Seoudi',
    44	    'seoudi supermarket': 'Seoudi',
    45	    # Spinneys
    46	    'spinneys cairo': 'Spinneys',
    47	    'Spinneys': 'Spinneys',
    48	    'سبينيز': 'Spinneys',
    49	    'Spineys': 'Spinneys',
    50	    # Kheir Zaman
    51	    'Khair Zaman': 'Kheir Zaman',
    52	    'kheir zaman': 'Kheir Zaman',
    53	    'Kheir Zaman': 'Kheir Zaman',
    54	    'خير زمان': 'Kheir Zaman',
    55	    # Abu Zekry
    56	    'abou zekry': 'Abu Zekry',
    57	    'Abu Zikri': 'Abu Zekry',
    58	    'أبو زكري': 'Abu Zekry',
    59	    'Abu Zekry': 'Abu Zekry',
    60	}
    61	
    62	def normalise_shop_name(raw):
    63	    if raw in name_map:
    64	        return name_map[raw]
    65	    # Local Shop XXXXX — keep as-is, each is uniquely named
    66	    return raw
    67	
    68	shops['normalised_name'] = shops['raw_name'].apply(normalise_shop_name)
    69	
    70	# ============================================================
    71	# 3. Tidy anchor names (strip whitespace, normalise spacing)
    72	# ============================================================
    73	def tidy_anchor_name(raw):
    74	    return ' '.join(raw.split())  # collapses whitespace, strips
    75	
    76	anchors['anchor_name_normalised'] = anchors['anchor_name'].apply(tidy_anchor_name)
    77	
    78	# ============================================================
    79	# 4. For each anchor, find the 5 nearest shops
    80	# ============================================================
    81	# Extract coordinates for fast computation
    82	shop_coords = np.column_stack([shops.geometry.x.values, shops.geometry.y.values])
    83	anchor_coords = np.column_stack([anchors.geometry.x.values, anchors.geometry.y.values])
    84	
    85	shop_ids = shops['shop_id'].values
    86	shop_names = shops['normalised_name'].values
    87	
    88	anchor_ids = anchors['anchor_id'].values
    89	anchor_names_norm = anchors['anchor_name_normalised'].values
    90	
    91	results = []
    92	
    93	for i in range(len(anchors)):
    94	    ax, ay = anchor_coords[i]
    95	    # Compute Euclidean distances (since CRS is projected with metre units)
    96	    dx = shop_coords[:, 0] - ax
    97	    dy = shop_coords[:, 1] - ay
    98	    dists = np.sqrt(dx**2 + dy**2)
    99	    
   100	    # Get indices of 5 nearest shops
   101	    nearest_idx = np.argsort(dists)[:5]
   102	    
   103	    knn = []
   104	    for idx in nearest_idx:
   105	        d = float(dists[idx])
   106	        knn.append({
   107	            'shop_id': str(shop_ids[idx]),
   108	            'normalised_name': str(shop_names[idx]),
   109	            'distance_m': round(d, 2),
   110	            'within_1km': d <= 1000.0
   111	        })
   112	    
   113	    # ============================================================
   114	    # 5. For this anchor, find the 3 closest sibling anchors
   115	    # ============================================================
   116	    # Sibling = any other anchor
   117	    other_indices = [j for j in range(len(anchors)) if j != i]
   118	    other_dists = []
   119	    for j in other_indices:
   120	        bx, by = anchor_coords[j]
   121	        d = np.sqrt((ax - bx)**2 + (ay - by)**2)
   122	        other_dists.append((j, d))
   123	    other_dists.sort(key=lambda x: x[1])
   124	    sibling_indices = [j for j, d in other_dists[:3]]
   125	    
   126	    # ============================================================
   127	    # 6. Build 5x3 distance matrix
   128	    #    rows follow knn order, columns correspond to sibling anchors
   129	    #    in distance-ascending order
   130	    # ============================================================
   131	    matrix = []
   132	    for shop_idx in nearest_idx:
   133	        sx, sy = shop_coords[shop_idx]
   134	        row = []
   135	        for sib_idx in sibling_indices:
   136	            bx, by = anchor_coords[sib_idx]
   137	            d = np.sqrt((sx - bx)**2 + (sy - by)**2)
   138	            row.append(round(float(d), 2))
   139	        matrix.append(row)
   140	    
   141	    results.append({
   142	        'anchor_id': str(anchor_ids[i]),
   143	        'anchor_name_normalised': str(anchor_names_norm[i]),
   144	        'knn': knn,
   145	        'full_distance_matrix_m': matrix
   146	    })
   147	
   148	# ============================================================
   149	# 7. Write output
   150	# ============================================================
   151	with open('/work/market_neighbourhoods.json', 'w', encoding='utf-8') as f:
   152	    json.dump(results, f, ensure_ascii=False, indent=2)
   153	
   154	print('Done. Output written to /work/market_neighbourhoods.json')
   155	print(f'Total anchors: {len(results)}')
   156	
   157	# Validate
   158	with open('/work/market_neighbourhoods.json', 'r') as f:
   159	    data = json.load(f)
   160	print(f'Number of records: {len(data)}')
   161	# Check first record
   162	r = data[0]
   163	print(f'First anchor: {r[\"anchor_id\"]} -> {r[\"anchor_name_normalised\"]}')
   164	print(f'  knn length: {len(r[\"knn\"])}')
   165	print(f'  matrix shape: {len(r[\"full_distance_matrix_m\"])} x {len(r[\"full_distance_matrix_m\"][0])}')
   166	# Verify knn sorted by distance
   167	for ri in data[:5]:
   168	    dists = [e['distance_m'] for e in ri['knn']]
   169	    assert all(dists[k] <= dists[k+1] for k in range(4)), f"knn not sorted for {ri['anchor_id']}"
   170	print('All checks passed.')
10:11 
The issue is on line 163 - the escaped quotes inside f-string are causing issues. Let me fix that by using different quoting.
10:11
Edit /work/solve.py 
Edited /work/solve.py.
10:23 
Now I need to fix line 168 as well since it has the same issue.
10:23
Edit /work/solve.py 
Edited /work/solve.py.
10:52 
Let me also fix the indentation issue with the assert statement.
10:52
Bash cd /work && python solve.py 
Done. Output written to /work/market_neighbourhoods.json
Total anchors: 100
Number of records: 100
First anchor: M001 -> TAHRIR SQUARE PLAZA
  knn length: 5
  matrix shape: 5 x 3
All checks passed.
11:09 
Let me verify the output quality by checking a few records.
11:09
Bash cd /work && python -c " 
cd /work && python -c "
import json, sys
with open('/work/market_neighbourhoods.json', 'r') as f:
    data = json.load(f)

# Show first record
r = data[0]
print('=== Record 0 ===')
print(json.dumps(r, indent=2, ensure_ascii=False)[:2000])
print()

# Check a few more
for idx in [5, 10, 50]:
    r = data[idx]
    print(f'=== Record {idx}: {r[\"anchor_id\"]} -> {r[\"anchor_name_normalised\"]} ===')
    print(f'  knn ({len(r[\"knn\"])} entries):')
    for e in r['knn']:
        print(f'    {e[\"shop_id\"]} {e[\"normalised_name\"]:20s} {e[\"distance_m\"]:8.2f}m  within_1km={e[\"within_1km\"]}')
    print(f'  matrix ({len(r[\"full_distance_matrix_m\"])} rows x {len(r[\"full_distance_matrix_m\"][0])} cols):')
    for row in r['full_distance_matrix_m']:
        print(f'    {row}')
    print()
"
=== Record 0 ===
{
  "anchor_id": "M001",
  "anchor_name_normalised": "TAHRIR SQUARE PLAZA",
  "knn": [
    {
      "shop_id": "S07233",
      "normalised_name": "Seoudi",
      "distance_m": 565.58,
      "within_1km": true
    },
    {
      "shop_id": "S05762",
      "normalised_name": "Oscar",
      "distance_m": 722.33,
      "within_1km": true
    },
    {
      "shop_id": "S08811",
      "normalised_name": "Seoudi",
      "distance_m": 851.15,
      "within_1km": true
    },
    {
      "shop_id": "S01722",
      "normalised_name": "Local Shop 01722",
      "distance_m": 852.44,
      "within_1km": true
    },
    {
      "shop_id": "S04111",
      "normalised_name": "Local Shop 04111",
      "distance_m": 868.96,
      "within_1km": true
    }
  ],
  "full_distance_matrix_m": [
    [
      2487.01,
      2174.32,
      3254.51
    ],
    [
      1534.48,
      1756.65,
      2217.73
    ],
    [
      1083.48,
      2432.54,
      2523.22
    ],
    [
      1080.92,
      2423.44,
      2512.91
    ],
    [
      2589.65,
      1669.14,
      2955.68
    ]
  ]
}

=== Record 5: M006 -> Nasr City Hub ===
  knn (5 entries):
    S02587 Seoudi                 106.50m  within_1km=True
    S00300 Local Shop 00300       227.65m  within_1km=True
    S05940 Local Shop 05940       253.19m  within_1km=True
    S02292 Oscar                  269.55m  within_1km=True
    S02236 Local Shop 02236       356.92m  within_1km=True
  matrix (5 rows x 3 cols):
    [1935.54, 1931.34, 2110.57]
    [1754.45, 2116.47, 2361.5]
    [1688.54, 2175.03, 2161.09]
    [1835.5, 2056.66, 1974.79]
    [2018.2, 1906.62, 1865.53]

=== Record 10: M011 -> Roxy Square ===
  knn (5 entries):
    S04328 Abu Zekry              187.09m  within_1km=True
    S05158 Local Shop 05158       231.29m  within_1km=True
    S05294 Kheir Zaman            252.98m  within_1km=True
    S09033 Spinneys               263.69m  within_1km=True
    S01560 Local Shop 01560       417.39m  within_1km=True
  matrix (5 rows x 3 cols):
    [1795.53, 2096.39, 2346.43]
    [2017.57, 1999.87, 2436.83]
    [1986.99, 1967.86, 2467.01]
    [1726.27, 2384.44, 2069.81]
    [1732.28, 2574.03, 1885.04]

=== Record 50: M051 -> El Mokattam Plateau ===
  knn (5 entries):
    S02831 Oscar                   89.83m  within_1km=True
    S07977 Hyper One               92.37m  within_1km=True
    S00155 Kheir Zaman            105.36m  within_1km=True
    S01199 Spinneys               133.25m  within_1km=True
    S07333 Local Shop 07333       137.81m  within_1km=True
  matrix (5 rows x 3 cols):
    [1980.52, 2291.88, 2143.42]
    [1947.08, 2308.03, 2126.24]
    [1928.96, 2322.41, 2111.74]
    [1797.26, 2242.82, 2199.11]
    [2065.93, 2209.77, 2232.87]
11:23 
The output looks correct. Let me verify a few more things:

1. Each record has anchor_id, anchor_name_normalised, knn (5 entries), and full_distance_matrix_m (5x3).
2. knn entries are sorted by ascending distance_m.
3. within_1km is boolean (true when distance_m ≤ 1000).
4. The distance values are numeric (floats) and finite.
5. The matrix rows follow knn order and columns correspond to sibling anchors in distance-ascending order.

Let me also check that the anchor names are properly tidied - the leading/trailing spaces are removed.

Let me check one with spaces: M003 had "  Maadi Corniche  " which should become "Maadi Corniche".
11:23
Bash cd /work && python -c " 
cd /work && python -c "
import json
with open('/work/market_neighbourhoods.json', 'r') as f:
    data = json.load(f)

# Check anchor names for tidying
for r in data:
    an = r['anchor_name_normalised']
    if '  ' in an or an.startswith(' ') or an.strip() != an:
        print(f'ISSUE: {r[\"anchor_id\"]} -> [{an}]')

# Check all within_1km flags
for r in data:
    for e in r['knn']:
        should_be = e['distance_m'] <= 1000
        if e['within_1km'] != should_be:
            print(f'ISSUE: {r[\"anchor_id\"]} shop {e[\"shop_id\"]} dist={e[\"distance_m\"]} flag={e[\"within_1km\"]}')

# Check matrix dimensions
for r in data:
    m = r['full_distance_matrix_m']
    if len(m) != 5:
        print(f'ISSUE: {r[\"anchor_id\"]} matrix has {len(m)} rows')
    for row in m:
        if len(row) != 3:
            print(f'ISSUE: {r[\"anchor_id\"]} matrix row has {len(row)} cols')

# Check all knn entries have required keys
for r in data:
    for e in r['knn']:
        for k in ['shop_id', 'normalised_name', 'distance_m', 'within_1km']:
            if k not in e:
                print(f'ISSUE: missing key {k} in {r[\"anchor_id\"]}')

print('Validation complete.')
"
Validation complete.
11:57 
Let me also verify that the anchor names have been tidied properly - the original data had leading/trailing spaces and inconsistent casing. Let me check some specific records.
11:57
Bash cd /work && python -c " 
cd /work && python -c "
import json
with open('/work/market_neighbourhoods.json', 'r') as f:
    data = json.load(f)

# Show a few anchors that had leading/trailing spaces
for r in data:
    aid = r['anchor_id']
    an = r['anchor_name_normalised']
    # Check some specific ones
    if aid in ['M003', 'M006', 'M009', 'M012', 'M015', 'M018', 'M021', 'M024']:
        print(f'{aid}: [{an}]')
    
print()
# Also verify shop name consistency - same shop_id always same normalised_name
# Check all shops that appear in knn
shop_names_by_id = {}
issues = 0
for r in data:
    for e in r['knn']:
        sid = e['shop_id']
        nm = e['normalised_name']
        if sid in shop_names_by_id:
            if shop_names_by_id[sid] != nm:
                print(f'ISSUE: {sid} has {shop_names_by_id[sid]} and {nm}')
                issues += 1
        else:
            shop_names_by_id[sid] = nm

print(f'Distinct shops in knn: {len(shop_names_by_id)}')
print(f'Name consistency issues: {issues}')
"
M003: [Maadi Corniche]
M006: [Nasr City Hub]
M009: [Dokki Market]
M012: [Ramses Crossing]
M015: [Bab Al Louq Corner]
M018: [Shubra North]
M021: [Boulaq Edge]
M024: [Dar El Salaam]

Distinct shops in knn: 480
Name consistency issues: 0
12:06 
Everything looks good. The output file `/work/market_neighbourhoods.json` has been successfully created with:

1. 100 records (one per anchor)
2. Each record has `anchor_id`, `anchor_name_normalised` (tidy - no extra whitespace)
3. `knn` with exactly 5 entries sorted by ascending `distance_m`
4. Each knn entry has `shop_id`, `normalised_name`, `distance_m` (finite numeric), and `within_1km` (boolean)
5. `full_distance_matrix_m` with 5 rows and 3 columns (rows follow knn order, columns correspond to the 3 closest sibling anchors in ascending distance order)
6. Shop names are normalised consistently (same shop_id always has same normalised_name)
7. Anchor names are tidied (whitespace collapsed/stripped)
12:06