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"
}
0:03
Let me start by understanding the data structure. I need to explore the `cairo_retail` file to understand what format it's in and what layers/columns exist.
0:03
Bash
cd /work && ls -la
total 1228 drwxr-xr-x 1 agent agent 34 Jun 18 07:18 . drwxr-xr-x 1 root root 14 Jun 18 07:18 .. -rw-r--r-- 1 agent agent 1257472 Jun 18 07:18 cairo_retail.gpkg
0:05
Let's explore the GeoPackage to understand the layers and schema.
0:05
Bash
cd /work && python3 -c "
cd /work && python3 -c "
import geopandas as gpd
# List layers
import fiona
print(fiona.list_layers('cairo_retail.gpkg'))
"
Traceback (most recent call last):
File "<string>", line 5, in <module>
print(fiona.list_layers('cairo_retail.gpkg'))
^^^^^^^^^^^^^^^^^
AttributeError: module 'fiona' has no attribute 'list_layers'. Did you mean: 'listlayers'?
0:11
Let me try the correct function name.
0:11
Bash
cd /work && python3 -c "
cd /work && python3 -c "
import fiona
print(fiona.listlayers('cairo_retail.gpkg'))
"
['shops', 'anchors']
0:15
Let me explore the data first.
0:15
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 ===')
print(shops.columns.tolist())
print(shops.head(20).to_string())
print('...')
print(shops.dtypes)
print('Shape:', shops.shape)
print()
print('=== ANCHORS ===')
print(anchors.columns.tolist())
print(anchors.head(20).to_string())
print('...')
print(anchors.dtypes)
print('Shape:', 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) ... shop_id str raw_name str geometry geometry dtype: object Shape: (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) ... anchor_id str anchor_name str geometry geometry dtype: object Shape: (100, 3)
0:22
Let me see all the unique raw names in both tables to understand the name inconsistencies.
0:22
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('=== UNIQUE SHOP raw_name ===')
for n in sorted(shops['raw_name'].unique()):
print(repr(n))
print()
print('=== UNIQUE ANCHOR anchor_name ===')
for n in sorted(anchors['anchor_name'].unique()):
print(repr(n))
"
=== UNIQUE SHOP raw_name === 'Abu Zekry' 'Abu Zikri' 'Carrefour' 'Carrefour Egypt' 'Hyper One' 'HyperOne' 'Khair Zaman' 'Kheir Zaman' 'Local Shop 00002' 'Local Shop 00003' 'Local Shop 00004' 'Local Shop 00005' 'Local Shop 00007' 'Local Shop 00009' 'Local Shop 00011' 'Local Shop 00013' 'Local Shop 00014' 'Local Shop 00015' 'Local Shop 00017' 'Local Shop 00018' 'Local Shop 00019' 'Local Shop 00020' 'Local Shop 00023' 'Local Shop 00024' 'Local Shop 00025' 'Local Shop 00027' 'Local Shop 00030' 'Local Shop 00031' 'Local Shop 00033' 'Local Shop 00034' 'Local Shop 00035' 'Local Shop 00036' 'Local Shop 00037' 'Local Shop 00038' 'Local Shop 00040' 'Local Shop 00041' 'Local Shop 00048' 'Local Shop 00049' 'Local Shop 00050' 'Local Shop 00052' 'Local Shop 00053' 'Local Shop 00055' 'Local Shop 00056' 'Local Shop 00059' 'Local Shop 00063' 'Local Shop 00067' 'Local Shop 00069' 'Local Shop 00071' 'Local Shop 00072' 'Local Shop 00075' 'Local Shop 00076' 'Local Shop 00077' 'Local Shop 00079' 'Local Shop 00080' 'Local Shop 00081' 'Local Shop 00082' 'Local Shop 00084' 'Local Shop 00085' 'Local Shop 00086' 'Local Shop 00088' 'Local Shop 00090' 'Local Shop 00091' 'Local Shop 00092' 'Local Shop 00093' 'Local Shop 00095' 'Local Shop 00096' 'Local Shop 00103' 'Local Shop 00104' 'Local Shop 00105' 'Local Shop 00106' 'Local Shop 00110' 'Local Shop 00111' 'Local Shop 00113' 'Local Shop 00119' 'Local Shop 00120' 'Local Shop 00121' 'Local Shop 00125' 'Local Shop 00127' 'Local Shop 00135' 'Local Shop 00136' 'Local Shop 00137' 'Local Shop 00138' 'Local Shop 00139' 'Local Shop 00142' 'Local Shop 00143' 'Local Shop 00144' 'Local Shop 00146' 'Local Shop 00150' 'Local Shop 00151' 'Local Shop 00152' 'Local Shop 00153' 'Local Shop 00157' 'Local Shop 00158' 'Local Shop 00159' 'Local Shop 00161' 'Local Shop 00162' 'Local Shop 00164' 'Local Shop 00165' 'Local Shop 00166' 'Local Shop 00167' 'Local Shop 00169' 'Local Shop 00170' 'Local Shop 00171' 'Local Shop 00172' 'Local Shop 00176' 'Local Shop 00181' 'Local Shop 00182' 'Local Shop 00184' 'Local Shop 00185' 'Local Shop 00187' 'Local Shop 00188' 'Local Shop 00190' 'Local Shop 00191' 'Local Shop 00192' 'Local Shop 00194' 'Local Shop 00195' 'Local Shop 00197' 'Local Shop 00198' 'Local Shop 00200' 'Local Shop 00201' 'Local Shop 00202' 'Local Shop 00203' 'Local Shop 00205' 'Local Shop 00206' 'Local Shop 00209' 'Local Shop 00210' 'Local Shop 00211' 'Local Shop 00213' 'Local Shop 00214' 'Local Shop 00216' 'Local Shop 00217' 'Local Shop 00222' 'Local Shop 00225' 'Local Shop 00227' 'Local Shop 00228' 'Local Shop 00229' 'Local Shop 00230' 'Local Shop 00231' 'Local Shop 00233' 'Local Shop 00239' 'Local Shop 00241' 'Local Shop 00242' 'Local Shop 00244' 'Local Shop 00245' 'Local Shop 00246' 'Local Shop 00250' 'Local Shop 00251' 'Local Shop 00258' 'Local Shop 00259' 'Local Shop 00260' 'Local Shop 00262' 'Local Shop 00264' 'Local Shop 00266' 'Local Shop 00268' 'Local Shop 00269' 'Local Shop 00271' 'Local Shop 00275' 'Local Shop 00276' 'Local Shop 00278' 'Local Shop 00279' 'Local Shop 00280' 'Local Shop 00282' 'Local Shop 00283' 'Local Shop 00286' 'Local Shop 00289' 'Local Shop 00295' 'Local Shop 00298' 'Local Shop 00300' 'Local Shop 00301' 'Local Shop 00303' 'Local Shop 00305' 'Local Shop 00306' 'Local Shop 00307' 'Local Shop 00309' 'Local Shop 00311' 'Local Shop 00312' 'Local Shop 00313' 'Local Shop 00314' 'Local Shop 00315' 'Local Shop 00319' 'Local Shop 00320' 'Local Shop 00322' 'Local Shop 00323' 'Local Shop 00325' 'Local Shop 00328' 'Local Shop 00334' 'Local Shop 00335' 'Local Shop 00337' 'Local Shop 00338' 'Local Shop 00342' 'Local Shop 00346' 'Local Shop 00347' 'Local Shop 00350' 'Local Shop 00351' 'Local Shop 00353' 'Local Shop 00354' 'Local Shop 00357' 'Local Shop 00361' 'Local Shop 00362' 'Local Shop 00363' 'Local Shop 00365' 'Local Shop 00366' 'Local Shop 00367' 'Local Shop 00372' 'Local Shop 00375' 'Local Shop 00376' 'Local Shop 00378' 'Local Shop 00379' 'Local Shop 00380' 'Local Shop 00385' 'Local Shop 00387' 'Local Shop 00392' 'Local Shop 00394' 'Local Shop 00395' 'Local Shop 00397' 'Local Shop 00400' 'Local Shop 00403' 'Local Shop 00408' 'Local Shop 00409' 'Local Shop 00410' 'Local Shop 00413' 'Local Shop 00417' 'Local Shop 00418' 'Local Shop 00420' 'Local Shop 00423' 'Local Shop 00424' 'Local Shop 00427' 'Local Shop 00429' 'Local Shop 00432' 'Local Shop 00433' 'Local Shop 00434' 'Local Shop 00435' 'Local Shop 00437' 'Local Shop 00439' 'Local Shop 00441' 'Local Shop 00442' 'Local Shop 00443' 'Local Shop 00447' 'Local Shop 00448' 'Local Shop 00450' 'Local Shop 00452' 'Local Shop 00455' 'Local Shop 00456' 'Local Shop 00457' 'Local Shop 00459' 'Local Shop 00460' 'Local Shop 00462' 'Local Shop 00463' 'Local Shop 00465' 'Local Shop 00466' 'Local Shop 00468' 'Local Shop 00470' 'Local Shop 00471' 'Local Shop 00472' 'Local Shop 00473' 'Local Shop 00474' 'Local Shop 00477' 'Local Shop 00478' 'Local Shop 00479' 'Local Shop 00480' 'Local Shop 00481' 'Local Shop 00483' 'Local Shop 00484' 'Local Shop 00486' 'Local Shop 00487' 'Local Shop 00488' 'Local Shop 00489' 'Local Shop 00491' 'Local Shop 00493' 'Local Shop 00494' 'Local Shop 00496' 'Local Shop 00498' 'Local Shop 00499' 'Local Shop 00502' 'Local Shop 00503' 'Local Shop 00505' 'Local Shop 00506' 'Local Shop 00507' 'Local Shop 00509' 'Local Shop 00511' 'Local Shop 00513' 'Local Shop 00514' 'Local Shop 00515' 'Local Shop 00516' 'Local Shop 00517' 'Local Shop 00518' 'Local Shop 00520' 'Local Shop 00521' 'Local Shop 00524' 'Local Shop 00525' 'Local Shop 00527' 'Local Shop 00528' 'Local Shop 00529' 'Local Shop 00530' 'Local Shop 00531' 'Local Shop 00532' 'Local Shop 00536' 'Local Shop 00538' 'Local Shop 00541' 'Local Shop 00545' 'Local Shop 00547' 'Local Shop 00548' 'Local Shop 00549' 'Local Shop 00550' 'Local Shop 00551' 'Local Shop 00552' 'Local Shop 00556' 'Local Shop 00557' 'Local Shop 00560' 'Local Shop 00561' 'Local Shop 00562' 'Local Shop 00563' 'Local Shop 00566' 'Local Shop 00567' 'Local Shop 00568' 'Local Shop 00570' 'Local Shop 00571' 'Local Shop 00573' 'Local Shop 00574' 'Local Shop 00577' 'Local Shop 00578' 'Local Shop 00579' 'Local Shop 00581' 'Local Shop 00583' 'Local Shop 00585' 'Local Shop 00586' 'Local Shop 00588' 'Local Shop 00589' 'Local Shop 00590' 'Local Shop 00591' 'Local Shop 00593' 'Local Shop 00594' 'Local Shop 00596' 'Local Shop 00597' 'Local Shop 00601' 'Local Shop 00602' 'Local Shop 00603' 'Local Shop 00604' 'Local Shop 00608' 'Local Shop 00610' 'Local Shop 00611' 'Local Shop 00612' 'Local Shop 00613' 'Local Shop 00615' 'Local Shop 00616' 'Local Shop 00617' 'Local Shop 00618' 'Local Shop 00619' 'Local Shop 00623' 'Local Shop 00624' 'Local Shop 00637' 'Local Shop 00640' 'Local Shop 00641' 'Local Shop 00642' 'Local Shop 00643' 'Local Shop 00648' 'Local Shop 00650' 'Local Shop 00653' 'Local Shop 00658' 'Local Shop 00659' 'Local Shop 00660' 'Local Shop 00661' 'Local Shop 00662' 'Local Shop 00663' 'Local Shop 00664' 'Local Shop 00665' 'Local Shop 00666' 'Local Shop 00667' 'Local Shop 00669' 'Local Shop 00673' 'Local Shop 00674' 'Local Shop 00675' 'Local Shop 00676' 'Local Shop 00678' 'Local Shop 00680' 'Local Shop 00681' 'Local Shop 00683' 'Local Shop 00687' 'Local Shop 00688' 'Local Shop 00690' 'Local Shop 00693' 'Local Shop 00695' 'Local Shop 00697' 'Local Shop 00699' 'Local Shop 00707' 'Local Shop 00708' 'Local Shop 00710' 'Local Shop 00711' 'Local Shop 00712' 'Local Shop 00714' 'Local Shop 00716' 'Local Shop 00718' 'Local Shop 00719' 'Local Shop 00720' 'Local Shop 00721' 'Local Shop 00722' 'Local Shop 00731' 'Local Shop 00734' 'Local Shop 00735' 'Local Shop 00736' 'Local Shop 00737' 'Local Shop 00739' 'Local Shop 00745' 'Local Shop 00746' 'Local Shop 00749' 'Local Shop 00751' 'Local Shop 00755' 'Local Shop 00757' 'Local Shop 00762' 'Local Shop 00765' 'Local Shop 00773' 'Local Shop 00774' 'Local Shop 00776' 'Local Shop 00778' 'Local Shop 00779' 'Local Shop 00782' 'Local Shop 00786' 'Local Shop 00787' 'Local Shop 00789' 'Local Shop 00790' 'Local Shop 00791' 'Local Shop 00792' 'Local Shop 00794' 'Local Shop 00795' 'Local Shop 00797' 'Local Shop 00800' 'Local Shop 00801' 'Local Shop 00804' 'Local Shop 00808' 'Local Shop 00810' 'Local Shop 00812' 'Local Shop 00813' 'Local Shop 00814' 'Local Shop 00815' 'Local Shop 00816' 'Local Shop 00819' 'Local Shop 00821' 'Local Shop 00824' 'Local Shop 00826' 'Local Shop 00827' 'Local Shop 00829' 'Local Shop 00835' 'Local Shop 00836' 'Local Shop 00838' 'Local Shop 00841' 'Local Shop 00849' 'Local Shop 00850' 'Local Shop 00851' 'Local Shop 00853' 'Local Shop 00855' 'Local Shop 00857' 'Local Shop 00859' 'Local Shop 00863' 'Local Shop 00864' 'Local Shop 00866' 'Local Shop 00870' 'Local Shop 00871' 'Local Shop 00875' 'Local Shop 00876' 'Local Shop 00879' 'Local Shop 00880' 'Local Shop 00881' 'Local Shop 00882' 'Local Shop 00888' 'Local Shop 00890' 'Local Shop 00891' 'Local Shop 00893' 'Local Shop 00898' 'Local Shop 00900' 'Local Shop 00901' 'Local Shop 00902' 'Local Shop 00903' 'Local Shop 00910' 'Local Shop 00913' 'Local Shop 00914' 'Local Shop 00917' 'Local Shop 00918' 'Local Shop 00919' 'Local Shop 00921' 'Local Shop 00922' 'Local Shop 00932' 'Local Shop 00938' 'Local Shop 00940' 'Local Shop 00943' 'Local Shop 00945' 'Local Shop 00949' 'Local Shop 00950' 'Local Shop 00951' 'Local Shop 00952' 'Local Shop 00953' 'Local Shop 00954' 'Local Shop 00955' 'Local Shop 00958' 'Local Shop 00961' 'Local Shop 00962' 'Local Shop 00966' 'Local Shop 00969' 'Local Shop 00971' 'Local Shop 00973' 'Local Shop 00976' 'Local Shop 00977' 'Local Shop 00978' 'Local Shop 00979' 'Local Shop 00980' 'Local Shop 00981' 'Local Shop 00983' 'Local Shop 00984' 'Local Shop 00992' 'Local Shop 00999' 'Local Shop 01000' 'Local Shop 01006' 'Local Shop 01007' 'Local Shop 01008' 'Local Shop 01011' 'Local Shop 01012' 'Local Shop 01015' 'Local Shop 01018' 'Local Shop 01025' 'Local Shop 01026' 'Local Shop 01027' 'Local Shop 01030' 'Local Shop 01031' 'Local Shop 01032' 'Local Shop 01033' 'Local Shop 01034' 'Local Shop 01036' 'Local Shop 01038' 'Local Shop 01039' 'Local Shop 01041' 'Local Shop 01042' 'Local Shop 01044' 'Local Shop 01047' 'Local Shop 01049' 'Local Shop 01051' 'Local Shop 01053' 'Local Shop 01054' 'Local Shop 01057' 'Local Shop 01058' 'Local Shop 01059' 'Local Shop 01066' 'Local Shop 01069' 'Local Shop 01070' 'Local Shop 01074' 'Local Shop 01076' 'Local Shop 01079' 'Local Shop 01082' 'Local Shop 01083' 'Local Shop 01085' 'Local Shop 01088' 'Local Shop 01090' 'Local Shop 01091' 'Local Shop 01093' 'Local Shop 01095' 'Local Shop 01096' 'Local Shop 01099' 'Local Shop 01102' 'Local Shop 01103' 'Local Shop 01105' 'Local Shop 01106' 'Local Shop 01107' 'Local Shop 01108' 'Local Shop 01109' 'Local Shop 01111' 'Local Shop 01113' 'Local Shop 01115' 'Local Shop 01119' 'Local Shop 01123' 'Local Shop 01124' 'Local Shop 01126' 'Local Shop 01127' 'Local Shop 01128' 'Local Shop 01130' 'Local Shop 01131' 'Local Shop 01132' 'Local Shop 01134' 'Local Shop 01136' 'Local Shop 01137' 'Local Shop 01138' 'Local Shop 01139' 'Local Shop 01140' 'Local Shop 01142' 'Local Shop 01145' 'Local Shop 01147' 'Local Shop 01149' 'Local Shop 01151' 'Local Shop 01154' 'Local Shop 01158' 'Local Shop 01159' 'Local Shop 01160' 'Local Shop 01162' 'Local Shop 01163' 'Local Shop 01166' 'Local Shop 01167' 'Local Shop 01168' 'Local Shop 01169' 'Local Shop 01172' 'Local Shop 01173' 'Local Shop 01175' 'Local Shop 01176' 'Local Shop 01180' 'Local Shop 01181' 'Local Shop 01182' 'Local Shop 01183' 'Local Shop 01186' 'Local Shop 01188' 'Local Shop 01189' 'Local Shop 01190' 'Local Shop 01191' 'Local Shop 01196' 'Local Shop 01201' 'Local Shop 01202' 'Local Shop 01204' 'Local Shop 01206' 'Local Shop 01207' 'Local Shop 01209' 'Local Shop 01211' 'Local Shop 01212' 'Local Shop 01213' 'Local Shop 01215' 'Local Shop 01216' 'Local Shop 01221' 'Local Shop 01222' 'Local Shop 01225' 'Local Shop 01231' 'Local Shop 01232' 'Local Shop 01234' 'Local Shop 01240' 'Local Shop 01241' 'Local Shop 01242' 'Local Shop 01243' 'Local Shop 01244' 'Local Shop 01245' 'Local Shop 01246' 'Local Shop 01249' 'Local Shop 01251' 'Local Shop 01254' 'Local Shop 01255' 'Local Shop 01258' 'Local Shop 01259' 'Local Shop 01261' 'Local Shop 01262' 'Local Shop 01263' 'Local Shop 01266' 'Local Shop 01269' 'Local Shop 01271' 'Local Shop 01272' 'Local Shop 01273' 'Local Shop 01276' 'Local Shop 01277' 'Local Shop 01278' 'Local Shop 01282' 'Local Shop 01283' 'Local Shop 01287' 'Local Shop 01289' 'Local Shop 01290' 'Local Shop 01293' 'Local Shop 01294' 'Local Shop 01297' 'Local Shop 01300' 'Local Shop 01301' 'Local Shop 01302' 'Local Shop 01303' 'Local Shop 01306' 'Local Shop 01314' 'Local Shop 01315' 'Local Shop 01316' 'Local Shop 01318' 'Local Shop 01319' 'Local Shop 01320' 'Local Shop 01325' 'Local Shop 01328' 'Local Shop 01329' 'Local Shop 01330' 'Local Shop 01333' 'Local Shop 01334' 'Local Shop 01335' 'Local Shop 01336' 'Local Shop 01337' 'Local Shop 01341' 'Local Shop 01342' 'Local Shop 01344' 'Local Shop 01346' 'Local Shop 01348' 'Local Shop 01353' 'Local Shop 01355' 'Local Shop 01356' 'Local Shop 01357' 'Local Shop 01358' 'Local Shop 01360' 'Local Shop 01364' 'Local Shop 01367' 'Local Shop 01370' 'Local Shop 01372' 'Local Shop 01375' 'Local Shop 01377' 'Local Shop 01379' 'Local Shop 01380' 'Local Shop 01384' 'Local Shop 01386' 'Local Shop 01387' 'Local Shop 01388' 'Local Shop 01389' 'Local Shop 01391' 'Local Shop 01393' 'Local Shop 01394' 'Local Shop 01395' 'Local Shop 01398' 'Local Shop 01399' 'Local Shop 01403' 'Local Shop 01404' 'Local Shop 01415' 'Local Shop 01416' 'Local Shop 01417' 'Local Shop 01419' 'Local Shop 01421' 'Local Shop 01422' 'Local Shop 01423' 'Local Shop 01426' 'Local Shop 01427' 'Local Shop 01428' 'Local Shop 01429' 'Local Shop 01430' 'Local Shop 01431' 'Local Shop 01433' 'Local Shop 01437' 'Local Shop 01438' 'Local Shop 01441' 'Local Shop 01445' 'Local Shop 01446' 'Local Shop 01450' 'Local Shop 01455' 'Local Shop 01456' 'Local Shop 01457' 'Local Shop 01460' 'Local Shop 01461' 'Local Shop 01462' 'Local Shop 01463' 'Local Shop 01464' 'Local Shop 01466' 'Local Shop 01468' 'Local Shop 01476' 'Local Shop 01481' 'Local Shop 01482' 'Local Shop 01484' 'Local Shop 01485' 'Local Shop 01486' 'Local Shop 01487' 'Local Shop 01488' 'Local Shop 01489' 'Local Shop 01490' 'Local Shop 01493' 'Local Shop 01494' 'Local Shop 01495' 'Local Shop 01496' 'Local Shop 01499' 'Local Shop 01500' 'Local Shop 01501' 'Local Shop 01503' 'Local Shop 01505' 'Local Shop 01506' 'Local Shop 01507' 'Local Shop 01509' 'Local Shop 01510' 'Local Shop 01513' 'Local Shop 01514' 'Local Shop 01518' 'Local Shop 01521' 'Local Shop 01522' 'Local Shop 01523' 'Local Shop 01524' 'Local Shop 01526' 'Local Shop 01527' 'Local Shop 01529' 'Local Shop 01531' 'Local Shop 01532' 'Local Shop 01533' 'Local Shop 01535' 'Local Shop 01545' 'Local Shop 01547' 'Local Shop 01548' 'Local Shop 01552' 'Local Shop 01553' 'Local Shop 01557' 'Local Shop 01559' 'Local Shop 01560' 'Local Shop 01561' 'Local Shop 01562' 'Local Shop 01563' 'Local Shop 01564' 'Local Shop 01565' 'Local S [... truncated ...]hop 08692' 'Local Shop 08694' 'Local Shop 08695' 'Local Shop 08699' 'Local Shop 08700' 'Local Shop 08701' 'Local Shop 08702' 'Local Shop 08705' 'Local Shop 08707' 'Local Shop 08708' 'Local Shop 08709' 'Local Shop 08710' 'Local Shop 08714' 'Local Shop 08715' 'Local Shop 08717' 'Local Shop 08718' 'Local Shop 08719' 'Local Shop 08720' 'Local Shop 08723' 'Local Shop 08724' 'Local Shop 08725' 'Local Shop 08727' 'Local Shop 08728' 'Local Shop 08730' 'Local Shop 08734' 'Local Shop 08736' 'Local Shop 08737' 'Local Shop 08738' 'Local Shop 08739' 'Local Shop 08740' 'Local Shop 08741' 'Local Shop 08742' 'Local Shop 08745' 'Local Shop 08746' 'Local Shop 08748' 'Local Shop 08750' 'Local Shop 08752' 'Local Shop 08755' 'Local Shop 08756' 'Local Shop 08758' 'Local Shop 08759' 'Local Shop 08760' 'Local Shop 08766' 'Local Shop 08768' 'Local Shop 08771' 'Local Shop 08773' 'Local Shop 08774' 'Local Shop 08775' 'Local Shop 08779' 'Local Shop 08780' 'Local Shop 08783' 'Local Shop 08784' 'Local Shop 08785' 'Local Shop 08788' 'Local Shop 08789' 'Local Shop 08790' 'Local Shop 08791' 'Local Shop 08792' 'Local Shop 08793' 'Local Shop 08795' 'Local Shop 08796' 'Local Shop 08798' 'Local Shop 08799' 'Local Shop 08801' 'Local Shop 08802' 'Local Shop 08804' 'Local Shop 08805' 'Local Shop 08807' 'Local Shop 08810' 'Local Shop 08814' 'Local Shop 08815' 'Local Shop 08818' 'Local Shop 08822' 'Local Shop 08827' 'Local Shop 08828' 'Local Shop 08830' 'Local Shop 08834' 'Local Shop 08837' 'Local Shop 08840' 'Local Shop 08841' 'Local Shop 08843' 'Local Shop 08844' 'Local Shop 08846' 'Local Shop 08847' 'Local Shop 08852' 'Local Shop 08853' 'Local Shop 08854' 'Local Shop 08855' 'Local Shop 08856' 'Local Shop 08857' 'Local Shop 08861' 'Local Shop 08863' 'Local Shop 08864' 'Local Shop 08865' 'Local Shop 08868' 'Local Shop 08870' 'Local Shop 08871' 'Local Shop 08872' 'Local Shop 08873' 'Local Shop 08875' 'Local Shop 08879' 'Local Shop 08880' 'Local Shop 08881' 'Local Shop 08883' 'Local Shop 08886' 'Local Shop 08888' 'Local Shop 08890' 'Local Shop 08895' 'Local Shop 08897' 'Local Shop 08898' 'Local Shop 08899' 'Local Shop 08900' 'Local Shop 08901' 'Local Shop 08906' 'Local Shop 08907' 'Local Shop 08910' 'Local Shop 08911' 'Local Shop 08912' 'Local Shop 08916' 'Local Shop 08921' 'Local Shop 08925' 'Local Shop 08927' 'Local Shop 08928' 'Local Shop 08930' 'Local Shop 08931' 'Local Shop 08932' 'Local Shop 08933' 'Local Shop 08934' 'Local Shop 08938' 'Local Shop 08940' 'Local Shop 08941' 'Local Shop 08943' 'Local Shop 08944' 'Local Shop 08945' 'Local Shop 08946' 'Local Shop 08947' 'Local Shop 08949' 'Local Shop 08950' 'Local Shop 08951' 'Local Shop 08955' 'Local Shop 08956' 'Local Shop 08957' 'Local Shop 08958' 'Local Shop 08959' 'Local Shop 08961' 'Local Shop 08962' 'Local Shop 08964' 'Local Shop 08965' 'Local Shop 08966' 'Local Shop 08967' 'Local Shop 08968' 'Local Shop 08969' 'Local Shop 08970' 'Local Shop 08971' 'Local Shop 08974' 'Local Shop 08975' 'Local Shop 08976' 'Local Shop 08982' 'Local Shop 08984' 'Local Shop 08985' 'Local Shop 08989' 'Local Shop 08990' 'Local Shop 08994' 'Local Shop 08996' 'Local Shop 08997' 'Local Shop 08998' 'Local Shop 09001' 'Local Shop 09003' 'Local Shop 09005' 'Local Shop 09007' 'Local Shop 09009' 'Local Shop 09010' 'Local Shop 09012' 'Local Shop 09017' 'Local Shop 09020' 'Local Shop 09021' 'Local Shop 09022' 'Local Shop 09023' 'Local Shop 09024' 'Local Shop 09025' 'Local Shop 09026' 'Local Shop 09028' 'Local Shop 09030' 'Local Shop 09032' 'Local Shop 09034' 'Local Shop 09036' 'Local Shop 09038' 'Local Shop 09040' 'Local Shop 09044' 'Local Shop 09046' 'Local Shop 09047' 'Local Shop 09050' 'Local Shop 09051' 'Local Shop 09052' 'Local Shop 09053' 'Local Shop 09055' 'Local Shop 09056' 'Local Shop 09062' 'Local Shop 09066' 'Local Shop 09067' 'Local Shop 09068' 'Local Shop 09070' 'Local Shop 09072' 'Local Shop 09075' 'Local Shop 09077' 'Local Shop 09078' 'Local Shop 09080' 'Local Shop 09082' 'Local Shop 09084' 'Local Shop 09085' 'Local Shop 09089' 'Local Shop 09090' 'Local Shop 09093' 'Local Shop 09094' 'Local Shop 09095' 'Local Shop 09097' 'Local Shop 09098' 'Local Shop 09099' 'Local Shop 09104' 'Local Shop 09108' 'Local Shop 09110' 'Local Shop 09112' 'Local Shop 09113' 'Local Shop 09115' 'Local Shop 09117' 'Local Shop 09118' 'Local Shop 09123' 'Local Shop 09125' 'Local Shop 09126' 'Local Shop 09128' 'Local Shop 09129' 'Local Shop 09130' 'Local Shop 09131' 'Local Shop 09133' 'Local Shop 09134' 'Local Shop 09138' 'Local Shop 09139' 'Local Shop 09140' 'Local Shop 09141' 'Local Shop 09142' 'Local Shop 09143' 'Local Shop 09145' 'Local Shop 09147' 'Local Shop 09148' 'Local Shop 09149' 'Local Shop 09153' 'Local Shop 09154' 'Local Shop 09156' 'Local Shop 09159' 'Local Shop 09161' 'Local Shop 09164' 'Local Shop 09165' 'Local Shop 09166' 'Local Shop 09172' 'Local Shop 09175' 'Local Shop 09177' 'Local Shop 09180' 'Local Shop 09182' 'Local Shop 09183' 'Local Shop 09184' 'Local Shop 09185' 'Local Shop 09188' 'Local Shop 09190' 'Local Shop 09191' 'Local Shop 09194' 'Local Shop 09195' 'Local Shop 09196' 'Local Shop 09198' 'Local Shop 09199' 'Local Shop 09201' 'Local Shop 09202' 'Local Shop 09206' 'Local Shop 09207' 'Local Shop 09208' 'Local Shop 09209' 'Local Shop 09210' 'Local Shop 09212' 'Local Shop 09213' 'Local Shop 09214' 'Local Shop 09216' 'Local Shop 09217' 'Local Shop 09219' 'Local Shop 09220' 'Local Shop 09221' 'Local Shop 09223' 'Local Shop 09224' 'Local Shop 09225' 'Local Shop 09226' 'Local Shop 09227' 'Local Shop 09232' 'Local Shop 09234' 'Local Shop 09239' 'Local Shop 09241' 'Local Shop 09242' 'Local Shop 09243' 'Local Shop 09246' 'Local Shop 09248' 'Local Shop 09250' 'Local Shop 09251' 'Local Shop 09252' 'Local Shop 09253' 'Local Shop 09254' 'Local Shop 09257' 'Local Shop 09265' 'Local Shop 09266' 'Local Shop 09268' 'Local Shop 09270' 'Local Shop 09271' 'Local Shop 09272' 'Local Shop 09275' 'Local Shop 09276' 'Local Shop 09277' 'Local Shop 09278' 'Local Shop 09279' 'Local Shop 09281' 'Local Shop 09282' 'Local Shop 09283' 'Local Shop 09286' 'Local Shop 09289' 'Local Shop 09290' 'Local Shop 09291' 'Local Shop 09292' 'Local Shop 09293' 'Local Shop 09295' 'Local Shop 09297' 'Local Shop 09298' 'Local Shop 09301' 'Local Shop 09302' 'Local Shop 09304' 'Local Shop 09307' 'Local Shop 09308' 'Local Shop 09309' 'Local Shop 09311' 'Local Shop 09315' 'Local Shop 09318' 'Local Shop 09319' 'Local Shop 09320' 'Local Shop 09322' 'Local Shop 09326' 'Local Shop 09327' 'Local Shop 09328' 'Local Shop 09331' 'Local Shop 09336' 'Local Shop 09339' 'Local Shop 09340' 'Local Shop 09342' 'Local Shop 09343' 'Local Shop 09346' 'Local Shop 09348' 'Local Shop 09349' 'Local Shop 09351' 'Local Shop 09354' 'Local Shop 09355' 'Local Shop 09356' 'Local Shop 09361' 'Local Shop 09362' 'Local Shop 09363' 'Local Shop 09366' 'Local Shop 09367' 'Local Shop 09368' 'Local Shop 09374' 'Local Shop 09378' 'Local Shop 09379' 'Local Shop 09382' 'Local Shop 09383' 'Local Shop 09385' 'Local Shop 09387' 'Local Shop 09388' 'Local Shop 09391' 'Local Shop 09394' 'Local Shop 09397' 'Local Shop 09399' 'Local Shop 09401' 'Local Shop 09402' 'Local Shop 09404' 'Local Shop 09405' 'Local Shop 09408' 'Local Shop 09412' 'Local Shop 09413' 'Local Shop 09414' 'Local Shop 09416' 'Local Shop 09421' 'Local Shop 09422' 'Local Shop 09424' 'Local Shop 09428' 'Local Shop 09429' 'Local Shop 09433' 'Local Shop 09435' 'Local Shop 09436' 'Local Shop 09437' 'Local Shop 09438' 'Local Shop 09441' 'Local Shop 09443' 'Local Shop 09445' 'Local Shop 09446' 'Local Shop 09449' 'Local Shop 09451' 'Local Shop 09453' 'Local Shop 09454' 'Local Shop 09460' 'Local Shop 09465' 'Local Shop 09470' 'Local Shop 09471' 'Local Shop 09472' 'Local Shop 09475' 'Local Shop 09476' 'Local Shop 09480' 'Local Shop 09482' 'Local Shop 09483' 'Local Shop 09487' 'Local Shop 09490' 'Local Shop 09491' 'Local Shop 09493' 'Local Shop 09496' 'Local Shop 09499' 'Local Shop 09500' 'Local Shop 09501' 'Local Shop 09502' 'Local Shop 09505' 'Local Shop 09507' 'Local Shop 09510' 'Local Shop 09512' 'Local Shop 09513' 'Local Shop 09514' 'Local Shop 09521' 'Local Shop 09525' 'Local Shop 09526' 'Local Shop 09528' 'Local Shop 09529' 'Local Shop 09537' 'Local Shop 09539' 'Local Shop 09540' 'Local Shop 09545' 'Local Shop 09546' 'Local Shop 09547' 'Local Shop 09549' 'Local Shop 09552' 'Local Shop 09553' 'Local Shop 09555' 'Local Shop 09561' 'Local Shop 09562' 'Local Shop 09565' 'Local Shop 09570' 'Local Shop 09572' 'Local Shop 09578' 'Local Shop 09580' 'Local Shop 09584' 'Local Shop 09586' 'Local Shop 09591' 'Local Shop 09592' 'Local Shop 09593' 'Local Shop 09594' 'Local Shop 09595' 'Local Shop 09596' 'Local Shop 09597' 'Local Shop 09599' 'Local Shop 09600' 'Local Shop 09603' 'Local Shop 09604' 'Local Shop 09605' 'Local Shop 09606' 'Local Shop 09608' 'Local Shop 09610' 'Local Shop 09617' 'Local Shop 09620' 'Local Shop 09623' 'Local Shop 09625' 'Local Shop 09626' 'Local Shop 09628' 'Local Shop 09629' 'Local Shop 09630' 'Local Shop 09631' 'Local Shop 09633' 'Local Shop 09635' 'Local Shop 09637' 'Local Shop 09639' 'Local Shop 09640' 'Local Shop 09641' 'Local Shop 09643' 'Local Shop 09645' 'Local Shop 09648' 'Local Shop 09650' 'Local Shop 09652' 'Local Shop 09656' 'Local Shop 09657' 'Local Shop 09658' 'Local Shop 09661' 'Local Shop 09664' 'Local Shop 09666' 'Local Shop 09668' 'Local Shop 09669' 'Local Shop 09670' 'Local Shop 09672' 'Local Shop 09674' 'Local Shop 09679' 'Local Shop 09680' 'Local Shop 09681' 'Local Shop 09683' 'Local Shop 09684' 'Local Shop 09685' 'Local Shop 09690' 'Local Shop 09691' 'Local Shop 09694' 'Local Shop 09696' 'Local Shop 09698' 'Local Shop 09700' 'Local Shop 09703' 'Local Shop 09704' 'Local Shop 09705' 'Local Shop 09708' 'Local Shop 09711' 'Local Shop 09714' 'Local Shop 09716' 'Local Shop 09717' 'Local Shop 09721' 'Local Shop 09723' 'Local Shop 09726' 'Local Shop 09727' 'Local Shop 09728' 'Local Shop 09733' 'Local Shop 09734' 'Local Shop 09736' 'Local Shop 09737' 'Local Shop 09739' 'Local Shop 09740' 'Local Shop 09741' 'Local Shop 09742' 'Local Shop 09744' 'Local Shop 09746' 'Local Shop 09747' 'Local Shop 09749' 'Local Shop 09752' 'Local Shop 09753' 'Local Shop 09754' 'Local Shop 09755' 'Local Shop 09757' 'Local Shop 09758' 'Local Shop 09759' 'Local Shop 09761' 'Local Shop 09762' 'Local Shop 09766' 'Local Shop 09772' 'Local Shop 09773' 'Local Shop 09775' 'Local Shop 09776' 'Local Shop 09780' 'Local Shop 09782' 'Local Shop 09783' 'Local Shop 09784' 'Local Shop 09785' 'Local Shop 09786' 'Local Shop 09787' 'Local Shop 09790' 'Local Shop 09792' 'Local Shop 09794' 'Local Shop 09795' 'Local Shop 09797' 'Local Shop 09798' 'Local Shop 09800' 'Local Shop 09801' 'Local Shop 09802' 'Local Shop 09804' 'Local Shop 09806' 'Local Shop 09807' 'Local Shop 09809' 'Local Shop 09810' 'Local Shop 09811' 'Local Shop 09814' 'Local Shop 09815' 'Local Shop 09818' 'Local Shop 09820' 'Local Shop 09823' 'Local Shop 09824' 'Local Shop 09825' 'Local Shop 09826' 'Local Shop 09827' 'Local Shop 09832' 'Local Shop 09833' 'Local Shop 09835' 'Local Shop 09836' 'Local Shop 09837' 'Local Shop 09840' 'Local Shop 09841' 'Local Shop 09842' 'Local Shop 09843' 'Local Shop 09845' 'Local Shop 09847' 'Local Shop 09851' 'Local Shop 09853' 'Local Shop 09855' 'Local Shop 09857' 'Local Shop 09858' 'Local Shop 09859' 'Local Shop 09863' 'Local Shop 09864' 'Local Shop 09867' 'Local Shop 09869' 'Local Shop 09872' 'Local Shop 09873' 'Local Shop 09876' 'Local Shop 09877' 'Local Shop 09879' 'Local Shop 09880' 'Local Shop 09881' 'Local Shop 09885' 'Local Shop 09886' 'Local Shop 09887' 'Local Shop 09888' 'Local Shop 09890' 'Local Shop 09893' 'Local Shop 09895' 'Local Shop 09896' 'Local Shop 09901' 'Local Shop 09903' 'Local Shop 09904' 'Local Shop 09905' 'Local Shop 09910' 'Local Shop 09911' 'Local Shop 09915' 'Local Shop 09917' 'Local Shop 09919' 'Local Shop 09920' 'Local Shop 09922' 'Local Shop 09923' 'Local Shop 09928' 'Local Shop 09931' 'Local Shop 09933' 'Local Shop 09934' 'Local Shop 09935' 'Local Shop 09936' 'Local Shop 09940' 'Local Shop 09941' 'Local Shop 09942' 'Local Shop 09943' 'Local Shop 09944' 'Local Shop 09945' 'Local Shop 09946' 'Local Shop 09947' 'Local Shop 09949' 'Local Shop 09950' 'Local Shop 09953' 'Local Shop 09955' 'Local Shop 09956' 'Local Shop 09959' 'Local Shop 09961' 'Local Shop 09962' 'Local Shop 09964' 'Local Shop 09965' 'Local Shop 09967' 'Local Shop 09968' 'Local Shop 09970' 'Local Shop 09973' 'Local Shop 09975' 'Local Shop 09976' 'Local Shop 09977' 'Local Shop 09978' 'Local Shop 09979' 'Local Shop 09982' 'Local Shop 09985' 'Local Shop 09986' 'Local Shop 09989' 'Local Shop 09991' 'Local Shop 09992' 'Local Shop 09997' 'Local Shop 10000' 'Metro Market' 'Metro Markets' 'OSCAR' 'Oscar' 'Oscar Grand Stores' 'Seoudi' 'Seoudi Market' 'Spineys' 'Spinneys' 'abou zekry' 'carrefour' 'hyperone' 'kheir zaman' 'metro' 'seoudi supermarket' 'spinneys cairo' 'أبو زكري' 'اوسكار' 'خير زمان' 'سبينيز' 'سعودي' 'كارفور' 'مترو' 'هايبر وان' === UNIQUE ANCHOR anchor_name === ' Abdeen Palace Edge ' ' Bab Al Louq Corner ' ' Boulaq Edge ' ' Cleopatra Plaza ' ' Coptic Cairo Plaza ' ' Dar El Salaam ' ' Dokki Market ' ' El Agouza Riverside ' ' El Demerdash Hospital Edge ' ' El Mokattam Plateau ' ' El Mosky Quarter ' ' El Rehab Gate One ' ' El Sayeda Aisha ' ' Embaba Crossing ' ' Fifth Settlement North ' ' Maadi Corniche ' ' Maadi Sarayat ' ' Maasara Crossing ' ' Madinet Nasr Eighth Zone ' ' Mall of Egypt Gate ' ' Mokattam Sector One ' ' Nasr City Hub ' ' Nozha Promenade ' ' Opera Square ' ' Police Academy Strip ' ' Ramses Crossing ' ' Ring Road West ' ' Saint Fatima Hub ' ' Sharkawi Plaza ' ' Sherif Street ' ' Shubra El Kheima Centre ' ' Shubra North ' ' Tagamoa El Saba ' 'AIN SHAMS PLAZA' 'AL AHLY STADIUM' 'Abbasiya Junction' 'Al Ghouriya Strip' 'American University Gate' 'Ataba Square' 'Autostrad Corner' 'BAB ZUWEILA APPROACH' 'BAHTEEM CROSSING' 'CITY STARS MALL' 'Cairo Festival City' 'Cairo Stadium' 'Demerdash Plaza' 'EL HUSSEIN SQUARE' 'EL MARG HUB' 'EL NOZHA EL GEDIDA' 'EL OBOUR HUB' 'EL REHAB GATE TWO' 'EL SAHEL JUNCTION' 'EL SAWAH CORNER' 'El Hadaba El Wosta' 'El Maadi Degla' 'El Salam City' 'FIFTH SETTLEMENT SOUTH' 'FUSTAT PARK EDGE' 'GARBIYA PLAZA' 'GIZA PYRAMIDS GATE' 'Garden City Walk' 'HELIOPOLIS SQUARE' 'Helwan Centre' 'IMBABA AIRPORT STRIP' 'KOBRI EL QUBBA' 'KORBA QUARTER' 'Kasr El Aini Strip' 'Khan El Khalili Approach' 'MAADI CORNISH' 'MADINET NASR TENTH ZONE' 'MOHANDESSIN CENTRE' 'MOKATTAM SECTOR SIX' 'Madinaty Promenade' 'Manial Bridge' 'Manial Riverbank' 'Mar Mina Plaza' 'Mit Okba Plaza' 'Mokattam Heights' 'New Cairo Tagamoa' 'Old Cairo Babylon' 'QASR EL NILE' 'RAMSES HILTON PLAZA' 'RING ROAD NORTH' 'Rabaa Square' 'Ring Road East' 'Rod El Farag Bridge' 'Roxy Square' 'SALAH SALEM STRIP' 'SAYEDA ZEINAB PLAZA' 'SHERATON HELIOPOLIS' 'Sakakini Approach' 'Shoubra Mazallat' 'Soliman Pasha Corner' 'TAGAMOA FIRST' 'TAHRIR SQUARE PLAZA' 'TALAAT HARB PLAZA' 'TORA EDGE' 'Tagamoa Third' 'Triumph Square' 'Zamalek Riverside'
0:38
Let me analyze the data: **Shops** - The `raw_name` column has inconsistent spellings. I need to normalize: - 'OSCAR', 'Oscar', 'Oscar Grand Stores', 'اوسكار' → 'Oscar' - 'Carrefour', 'Carrefour Egypt', 'carrefour', 'كارفور' → 'Carrefour' - 'Hyper One', 'HyperOne', 'hyperone', 'هايبر وان' → 'Hyper One' - 'Khair Zaman', 'Kheir Zaman', 'kheir zaman', 'خير زمان' → 'Khair Zaman' - 'Metro Market', 'Metro Markets', 'metro', 'مترو' → 'Metro' - 'Abu Zekry', 'Abu Zikri', 'abou zekry', 'أبو زكري' → 'Abu Zekry' - 'Seoudi', 'Seoudi Market', 'seoudi supermarket', 'سعودي' → 'Seoudi' - 'Spinneys', 'Spineys', 'spinneys cairo', 'سبينيز' → 'Spinneys' - 'Local Shop XXXXX' → Keep as is (individual shops) **Anchors** - Need to normalize names. I can see some have leading/trailing whitespace, inconsistent casing. The task says "tidy the anchor names too." Let me think about what a good normalization would be: For anchors: - Strip whitespace - Title case consistently - 'MAADI CORNISH' → 'Maadi Corniche' (maybe? or keep as corrected spelling?) Actually, let me think about this more carefully. The anchor names include: 1. Some are all uppercase: 'AIN SHAMS PLAZA', 'AL AHLY STADIUM', etc. 2. Some have leading/trailing spaces: ' Abdeen Palace Edge ', ' Bab Al Louq Corner ', etc. 3. Some are mixed case: 'Zamalek Riverside', 'Roxy Square', etc. 4. 'MAADI CORNISH' vs 'Maadi Corniche' I think tidying means: - Strip whitespace - Normalize to proper case (title case) - Fix obvious misspellings? 'MAADI CORNISH' should be 'Maadi Corniche' Actually, let me be more careful. The task says "Tidy the anchor names too." I'll strip whitespace and use a consistent format. Let me now write the solution. The approach: 1. Read both layers 2. Build a normalization mapping for shop names 3. Build a normalization mapping for anchor names 4. Use a projected CRS to compute distances (the data seems to be in a projected CRS already - the coordinates are ~630000-650000, which looks like a UTM zone for Cairo - EPSG:32636) 5. For each anchor, find the 5 nearest shops using KD-tree or similar 6. For each anchor, find the 3 closest sibling anchors 7. Compute the 5x3 distance matrix 8. Write the JSON output Let me check the CRS first.
0:38
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 CRS:', shops.crs)
print('Anchors CRS:', anchors.crs)
print('Shops geometry type:', shops.geometry.iloc[0])
print('Anchors geometry type:', anchors.geometry.iloc[0])
"
Shops CRS: EPSG:22992 Anchors CRS: EPSG:22992 Shops geometry type: POINT (637478.0584879243 814957.3976758636) Anchors geometry type: POINT (635109.3426487555 805565.9093887237)
1:04
Let me analyze the data: 1. CRS is EPSG:22992 (Egyptian Red Belt / Transverse Mercator) 2. Shops have shop_id, raw_name, geometry 3. Anchors have anchor_id, anchor_name, geometry I need to: 1. Normalize shop names (chain names with inconsistent spellings) 2. Normalize anchor names 3. For each anchor, find 5 nearest shops 4. For each anchor, find the 3 closest sibling anchors (other anchors) 5. Compute a 5x3 distance matrix Let me figure out the name normalization: **Shop names (chain names):** - 'OSCAR', 'Oscar', 'Oscar Grand Stores', 'اوسكار' → "Oscar" - 'Carrefour', 'Carrefour Egypt', 'carrefour', 'كارفور' → "Carrefour" - 'Hyper One', 'HyperOne', 'hyperone', 'هايبر وان' → "Hyper One" - 'Metro Market', 'Metro Markets', 'metro', 'مترو' → "Metro" - 'Seoudi', 'Seoudi Market', 'seoudi supermarket', 'سعودي' → "Seoudi" - 'Spinneys', 'Spineys', 'spinneys cairo', 'سبينيز' → "Spinneys" - 'Khair Zaman', 'Kheir Zaman', 'kheir zaman', 'خير زمان' → "Khair Zaman" - 'Abu Zekry', 'Abu Zikri', 'abou zekry', 'أبو زكري' → "Abu Zekry" - Local Shop XXXXX → keep as is - 'سعودي' → "Seoudi" - 'اوسكار' → "Oscar" - 'كارفور' → "Carrefour" - 'مترو' → "Metro" - 'هايبر وان' → "Hyper One" - 'سبينيز' → "Spinneys" - 'خير زمان' → "Khair Zaman" - 'أبو زكري' → "Abu Zekry" **Anchor names:** They have inconsistent whitespace (some with leading/trailing spaces, some all caps, etc.) - ' Abdeen Palace Edge ' → "Abdeen Palace Edge" - 'TAHRIR SQUARE PLAZA' → "Tahrir Square Plaza" - 'Zamalek Riverside' → "Zamalek Riverside" - 'HELIOPOLIS SQUARE' → "Heliopolis Square" - etc. Let me write a comprehensive script.
1:04
Write
/work/solve.py
content · 186 lines · py
import geopandas as gpd
import numpy as np
from shapely.geometry import Point
import json
import re
# Load data
shops = gpd.read_file('/work/cairo_retail.gpkg', layer='shops')
anchors = gpd.read_file('/work/cairo_retail.gpkg', layer='anchors')
# Ensure CRS is projected (EPSG:22992) for distance calculations
if shops.crs is None:
shops = shops.set_crs('EPSG:22992')
if anchors.crs is None:
anchors = anchors.set_crs('EPSG:22992')
# ---------- Name normalisation ----------
# Build a mapping from raw_name -> normalised_name for shops
# Chain-name normalisation rules (case-insensitive, Arabic translations)
shop_name_map = {}
def normalise_shop(raw):
raw_stripped = raw.strip()
raw_lower = raw_stripped.lower()
# Chain rules (check these first)
chain_rules = [
(['oscar', 'اوسكار'], 'Oscar'),
(['carrefour', 'كارفور'], 'Carrefour'),
(['hyper one', 'hyperone', 'هايبر وان'], 'Hyper One'),
(['metro', 'مترو'], 'Metro'),
(['seoudi', 'سعودي'], 'Seoudi'),
(['spinneys', 'spineys', 'سبينيز'], 'Spinneys'),
(['khair zaman', 'kheir zaman', 'خير زمان'], 'Khair Zaman'),
(['abu zekry', 'abu zikri', 'abou zekry', 'أبو زكري'], 'Abu Zekry'),
]
for aliases, canonical in chain_rules:
if raw_lower in aliases:
return canonical
# Also check if raw_stripped matches any alias directly (preserving original casing for local shops)
if raw_stripped.lower() in aliases:
return canonical
# Check if it contains 'Oscar Grand Stores'
if 'oscar grand' in raw_lower:
return 'Oscar'
if 'seoudi supermarket' in raw_lower:
return 'Seoudi'
if 'metro market' in raw_lower or 'metro markets' in raw_lower:
return 'Metro'
if 'spinneys cairo' in raw_lower:
return 'Spinneys'
# Local Shop NNNNN — keep as-is (just strip)
return raw_stripped
# Build per-shop_id mapping
shop_id_to_normalised = {}
for _, row in shops.iterrows():
sid = row['shop_id']
raw = row['raw_name']
shop_id_to_normalised[sid] = normalise_shop(raw)
# Also create a mapping from raw_name -> canonical chain name for reference
# (but the requirement is that same shop_id always carries the same normalised_name)
# Normalise anchor names: strip whitespace, title-case
def normalise_anchor(raw):
s = raw.strip()
# If all uppercase or mixed, convert to title case
# But preserve some proper casing patterns
words = s.split()
result_words = []
for w in words:
# Keep words that already have mixed case mostly as-is, just first-letter cap
if w.isupper() and len(w) > 1:
result_words.append(w.capitalize())
elif w.islower():
result_words.append(w.capitalize())
else:
# Already has some caps - keep but ensure first letter is upper
result_words.append(w[0].upper() + w[1:] if len(w) > 1 else w.upper())
return ' '.join(result_words)
anchor_name_map = {}
for _, row in anchors.iterrows():
aid = row['anchor_id']
raw = row['anchor_name']
anchor_name_map[aid] = normalise_anchor(raw)
# ---------- Distance computation ----------
# Get coordinates
shop_coords = shops['geometry'].apply(lambda g: (g.x, g.y))
anchor_coords = anchors['geometry'].apply(lambda g: (g.x, g.y))
shop_ids = shops['shop_id'].values
anchor_ids = anchors['anchor_id'].values
# Convert to numpy arrays for fast computation
shop_xy = np.array([(g.x, g.y) for g in shops.geometry])
anchor_xy = np.array([(g.x, g.y) for g in anchors.geometry])
def haversine(p1, p2):
"""Approximate, but for projected CRS (EPSG:22992) we can use Euclidean.
Actually EPSG:22992 is a projected CRS in metres, so Euclidean distance = metres."""
return np.sqrt((p1[0] - p2[0])**2 + (p1[1] - p2[1])**2)
def compute_dist_matrix(anchor_xy, shop_xy):
"""Returns matrix of shape (n_anchors, n_shops) with distances in metres."""
dx = anchor_xy[:, np.newaxis, 0] - shop_xy[np.newaxis, :, 0]
dy = anchor_xy[:, np.newaxis, 1] - shop_xy[np.newaxis, :, 1]
return np.sqrt(dx**2 + dy**2)
dist_a_s = compute_dist_matrix(anchor_xy, shop_xy) # (100, 10000)
# For each anchor, get 5 nearest shops indices
n_nearest = 5
top5_indices = np.argsort(dist_a_s, axis=1)[:, :n_nearest] # (100, 5)
top5_distances = np.take_along_axis(dist_a_s, top5_indices, axis=1) # (100, 5)
# ---------- Build output ----------
# Compute anchor-to-anchor distances
dist_a_a = compute_dist_matrix(anchor_xy, anchor_xy) # (100, 100)
output_records = []
for i, aid in enumerate(anchor_ids):
anchor_name_norm = anchor_name_map[aid]
# knn entries
knn_list = []
for rank in range(n_nearest):
shop_idx = top5_indices[i, rank]
sid = shop_ids[shop_idx]
d = float(top5_distances[i, rank])
knn_list.append({
'shop_id': sid,
'normalised_name': shop_id_to_normalised[sid],
'distance_m': d,
'within_1km': d <= 1000.0
})
# Find 3 closest sibling anchors (excluding self)
self_dist = dist_a_a[i, :]
# Set self distance to infinity so we skip it
self_dist[i] = np.inf
sibling_indices = np.argsort(self_dist)[:3] # 3 closest
sibling_distances = self_dist[sibling_indices]
# Sort siblings by distance ascending (they already are)
# Create columns in that order
sibling_col_dists = []
for si in sibling_indices:
# Get distances from each of the 5 nearest shops to this sibling anchor
col = []
for rank in range(n_nearest):
shop_idx = top5_indices[i, rank]
# Distance from shop_idx to sibling anchor si
d = float(dist_a_s[si, shop_idx])
col.append(d)
sibling_col_dists.append(col)
# Transpose to get 5 rows x 3 columns
full_matrix = [[sibling_col_dists[j][i] for j in range(3)] for i in range(5)]
record = {
'anchor_id': aid,
'anchor_name_normalised': anchor_name_norm,
'knn': knn_list,
'full_distance_matrix_m': full_matrix
}
output_records.append(record)
# Write output
with open('/work/market_neighbourhoods.json', 'w', encoding='utf-8') as f:
json.dump(output_records, f, ensure_ascii=False, indent=2)
print(f"Written {len(output_records)} records to market_neighbourhoods.json")
# Sanity check: print first record
print("\n=== First record ===")
print(json.dumps(output_records[0], ensure_ascii=False, indent=2)[:2000])Wrote /work/solve.py (6555 chars).
1:09
Let me run it and see how it goes.
1:09
Bash
cd /work && python solve.py
Written 100 records to market_neighbourhoods.json
=== First record ===
{
"anchor_id": "M001",
"anchor_name_normalised": "Tahrir Square Plaza",
"knn": [
{
"shop_id": "S07233",
"normalised_name": "Seoudi Market",
"distance_m": 565.5774729861073,
"within_1km": true
},
{
"shop_id": "S05762",
"normalised_name": "Oscar",
"distance_m": 722.3301477157331,
"within_1km": true
},
{
"shop_id": "S08811",
"normalised_name": "Seoudi",
"distance_m": 851.1481358488758,
"within_1km": true
},
{
"shop_id": "S01722",
"normalised_name": "Local Shop 01722",
"distance_m": 852.4416831354031,
"within_1km": true
},
{
"shop_id": "S04111",
"normalised_name": "Local Shop 04111",
"distance_m": 868.96252213481,
"within_1km": true
}
],
"full_distance_matrix_m": [
[
2487.0078080337594,
2174.324012747061,
3254.5114413442143
],
[
1534.4843964513216,
1756.6458865635916,
2217.728977070344
],
[
1083.478376599746,
2432.536076922434,
2523.2160443438365
],
[
1080.9227579664303,
2423.437856092515,
2512.907664013948
],
[
2589.6466467244945,
1669.1438268232814,
2955.6767888178206
]
]
}
1:17
Let me verify the output more carefully. Let me check a few things: 1. The shop names - I see "Seoudi Market" and "Seoudi" as separate normalised names. Let me check what happened there. 2. "Oscar Grand Stores" - I need to check if that's matching correctly. Let me look at the data more carefully.
1:17
Bash
cd /work && python3 -c "
cd /work && python3 -c "
import geopandas as gpd
shops = gpd.read_file('cairo_retail.gpkg', layer='shops')
# Check raw names that contain 'Seoudi' or 'seoudi'
for _, row in shops.iterrows():
if 'seoudi' in str(row['raw_name']).lower():
print(f\"{row['shop_id']}: {repr(row['raw_name'])}\")
"
S00026: 'Seoudi Market' S00042: 'Seoudi Market' S00043: 'seoudi supermarket' S00045: 'Seoudi Market' S00062: 'Seoudi' S00078: 'Seoudi' S00102: 'Seoudi' S00109: 'Seoudi' S00118: 'seoudi supermarket' S00134: 'Seoudi Market' S00141: 'Seoudi Market' S00173: 'seoudi supermarket' S00212: 'seoudi supermarket' S00226: 'Seoudi Market' S00234: 'Seoudi Market' S00272: 'Seoudi Market' S00290: 'Seoudi' S00296: 'Seoudi Market' S00383: 'Seoudi' S00388: 'Seoudi' S00445: 'seoudi supermarket' S00449: 'Seoudi' S00461: 'seoudi supermarket' S00475: 'Seoudi' S00490: 'Seoudi' S00544: 'Seoudi' S00553: 'Seoudi' S00633: 'Seoudi' S00638: 'Seoudi' S00639: 'seoudi supermarket' S00656: 'Seoudi' S00696: 'seoudi supermarket' S00741: 'Seoudi Market' S00744: 'seoudi supermarket' S00753: 'Seoudi Market' S00769: 'Seoudi' S00793: 'Seoudi Market' S00833: 'Seoudi Market' S00862: 'Seoudi Market' S00877: 'seoudi supermarket' S00897: 'seoudi supermarket' S00911: 'Seoudi Market' S00930: 'Seoudi Market' S00935: 'Seoudi Market' S00959: 'Seoudi Market' S00965: 'Seoudi Market' S00988: 'Seoudi Market' S00998: 'seoudi supermarket' S01009: 'seoudi supermarket' S01016: 'Seoudi Market' S01020: 'Seoudi' S01029: 'seoudi supermarket' S01050: 'Seoudi Market' S01098: 'Seoudi' S01101: 'Seoudi Market' S01120: 'seoudi supermarket' S01122: 'Seoudi' S01153: 'seoudi supermarket' S01174: 'Seoudi Market' S01184: 'Seoudi Market' S01260: 'Seoudi' S01279: 'Seoudi' S01296: 'Seoudi' S01327: 'seoudi supermarket' S01345: 'seoudi supermarket' S01374: 'Seoudi' S01412: 'Seoudi' S01443: 'seoudi supermarket' S01447: 'seoudi supermarket' S01451: 'Seoudi' S01470: 'Seoudi Market' S01473: 'Seoudi' S01541: 'Seoudi Market' S01543: 'seoudi supermarket' S01589: 'seoudi supermarket' S01594: 'seoudi supermarket' S01598: 'Seoudi Market' S01639: 'Seoudi' S01653: 'Seoudi' S01738: 'seoudi supermarket' S01767: 'Seoudi Market' S01835: 'Seoudi' S01875: 'seoudi supermarket' S01881: 'seoudi supermarket' S01894: 'Seoudi Market' S01902: 'Seoudi' S01918: 'Seoudi Market' S01927: 'seoudi supermarket' S01932: 'seoudi supermarket' S01959: 'Seoudi' S01974: 'Seoudi' S01977: 'Seoudi Market' S01988: 'Seoudi' S02043: 'seoudi supermarket' S02048: 'seoudi supermarket' S02097: 'Seoudi Market' S02112: 'Seoudi' S02154: 'seoudi supermarket' S02176: 'seoudi supermarket' S02197: 'seoudi supermarket' S02210: 'Seoudi Market' S02242: 'seoudi supermarket' S02286: 'Seoudi Market' S02294: 'seoudi supermarket' S02316: 'Seoudi' S02340: 'Seoudi Market' S02342: 'Seoudi Market' S02407: 'Seoudi Market' S02456: 'Seoudi Market' S02467: 'Seoudi' S02502: 'seoudi supermarket' S02507: 'seoudi supermarket' S02538: 'seoudi supermarket' S02543: 'Seoudi' S02544: 'Seoudi' S02587: 'Seoudi' S02593: 'Seoudi Market' S02605: 'Seoudi' S02620: 'Seoudi Market' S02624: 'seoudi supermarket' S02634: 'Seoudi' S02656: 'Seoudi Market' S02674: 'Seoudi' S02735: 'Seoudi' S02751: 'seoudi supermarket' S02783: 'Seoudi Market' S02793: 'Seoudi Market' S02800: 'seoudi supermarket' S02802: 'seoudi supermarket' S02808: 'seoudi supermarket' S02814: 'Seoudi Market' S02836: 'Seoudi Market' S02851: 'Seoudi Market' S02856: 'Seoudi' S02886: 'Seoudi Market' S02898: 'Seoudi' S02925: 'Seoudi Market' S02926: 'Seoudi' S02933: 'Seoudi Market' S02938: 'seoudi supermarket' S02988: 'Seoudi Market' S03033: 'Seoudi Market' S03072: 'Seoudi Market' S03090: 'Seoudi' S03147: 'Seoudi Market' S03205: 'seoudi supermarket' S03237: 'seoudi supermarket' S03292: 'Seoudi' S03307: 'Seoudi Market' S03394: 'Seoudi' S03424: 'Seoudi Market' S03429: 'seoudi supermarket' S03468: 'seoudi supermarket' S03513: 'Seoudi Market' S03514: 'Seoudi Market' S03516: 'Seoudi' S03531: 'Seoudi' S03544: 'Seoudi Market' S03551: 'Seoudi Market' S03556: 'Seoudi' S03567: 'seoudi supermarket' S03570: 'seoudi supermarket' S03592: 'Seoudi' S03603: 'Seoudi Market' S03604: 'Seoudi Market' S03616: 'Seoudi' S03661: 'Seoudi Market' S03669: 'Seoudi' S03676: 'Seoudi' S03687: 'seoudi supermarket' S03699: 'seoudi supermarket' S03718: 'Seoudi Market' S03737: 'Seoudi' S03766: 'Seoudi' S03791: 'Seoudi' S03793: 'seoudi supermarket' S03847: 'seoudi supermarket' S03850: 'seoudi supermarket' S03858: 'Seoudi' S03864: 'Seoudi Market' S03889: 'Seoudi Market' S03902: 'Seoudi' S03913: 'Seoudi' S03918: 'Seoudi' S03957: 'Seoudi Market' S03974: 'seoudi supermarket' S03994: 'Seoudi Market' S04070: 'Seoudi' S04103: 'Seoudi' S04106: 'Seoudi' S04109: 'Seoudi' S04149: 'Seoudi' S04207: 'Seoudi Market' S04220: 'Seoudi' S04223: 'Seoudi Market' S04311: 'Seoudi' S04339: 'seoudi supermarket' S04362: 'Seoudi Market' S04436: 'Seoudi' S04445: 'seoudi supermarket' S04452: 'Seoudi Market' S04523: 'Seoudi Market' S04540: 'Seoudi Market' S04542: 'Seoudi Market' S04556: 'seoudi supermarket' S04564: 'Seoudi' S04566: 'Seoudi Market' S04573: 'Seoudi Market' S04607: 'Seoudi Market' S04694: 'seoudi supermarket' S04710: 'Seoudi' S04714: 'Seoudi Market' S04719: 'Seoudi' S04741: 'Seoudi Market' S04776: 'Seoudi' S04839: 'Seoudi Market' S04840: 'Seoudi Market' S04845: 'Seoudi Market' S04857: 'seoudi supermarket' S04865: 'seoudi supermarket' S04870: 'Seoudi' S04885: 'Seoudi' S04927: 'Seoudi' S04930: 'seoudi supermarket' S04956: 'Seoudi' S04957: 'seoudi supermarket' S05019: 'Seoudi Market' S05026: 'Seoudi' S05042: 'Seoudi' S05044: 'Seoudi' S05065: 'seoudi supermarket' S05076: 'Seoudi Market' S05094: 'Seoudi Market' S05100: 'seoudi supermarket' S05127: 'seoudi supermarket' S05131: 'seoudi supermarket' S05137: 'seoudi supermarket' S05177: 'Seoudi' S05179: 'seoudi supermarket' S05206: 'Seoudi Market' S05223: 'seoudi supermarket' S05229: 'seoudi supermarket' S05230: 'Seoudi Market' S05234: 'Seoudi Market' S05317: 'seoudi supermarket' S05327: 'Seoudi Market' S05378: 'Seoudi' S05392: 'Seoudi' S05404: 'Seoudi Market' S05452: 'Seoudi' S05486: 'Seoudi' S05503: 'Seoudi' S05524: 'Seoudi Market' S05550: 'Seoudi Market' S05570: 'Seoudi' S05581: 'Seoudi' S05582: 'Seoudi' S05607: 'Seoudi' S05620: 'Seoudi' S05621: 'Seoudi' S05628: 'seoudi supermarket' S05654: 'Seoudi' S05655: 'Seoudi' S05663: 'Seoudi Market' S05668: 'Seoudi Market' S05683: 'Seoudi Market' S05685: 'Seoudi Market' S05733: 'Seoudi' S05789: 'Seoudi Market' S05806: 'seoudi supermarket' S05819: 'seoudi supermarket' S05839: 'Seoudi' S05849: 'seoudi supermarket' S05865: 'Seoudi Market' S05873: 'Seoudi' S05874: 'seoudi supermarket' S05881: 'seoudi supermarket' S05904: 'Seoudi Market' S05922: 'Seoudi Market' S05949: 'Seoudi' S05966: 'Seoudi' S05988: 'Seoudi' S06010: 'seoudi supermarket' S06016: 'seoudi supermarket' S06038: 'Seoudi Market' S06084: 'Seoudi Market' S06085: 'seoudi supermarket' S06129: 'Seoudi Market' S06130: 'Seoudi' S06144: 'seoudi supermarket' S06179: 'Seoudi' S06218: 'Seoudi' S06236: 'Seoudi' S06256: 'seoudi supermarket' S06259: 'Seoudi' S06273: 'seoudi supermarket' S06307: 'seoudi supermarket' S06340: 'seoudi supermarket' S06353: 'Seoudi Market' S06354: 'Seoudi' S06393: 'Seoudi' S06423: 'Seoudi Market' S06429: 'seoudi supermarket' S06447: 'Seoudi' S06476: 'seoudi supermarket' S06479: 'Seoudi Market' S06486: 'Seoudi' S06493: 'Seoudi' S06500: 'Seoudi' S06554: 'Seoudi Market' S06556: 'seoudi supermarket' S06562: 'seoudi supermarket' S06582: 'seoudi supermarket' S06591: 'Seoudi' S06598: 'Seoudi' S06604: 'Seoudi' S06616: 'Seoudi' S06635: 'seoudi supermarket' S06670: 'seoudi supermarket' S06735: 'seoudi supermarket' S06750: 'Seoudi' S06762: 'seoudi supermarket' S06768: 'Seoudi Market' S06799: 'Seoudi' S06872: 'seoudi supermarket' S06877: 'seoudi supermarket' S06885: 'Seoudi Market' S06914: 'seoudi supermarket' S06921: 'Seoudi' S06969: 'Seoudi' S06975: 'Seoudi Market' S07018: 'Seoudi' S07020: 'Seoudi Market' S07076: 'Seoudi' S07110: 'Seoudi' S07124: 'Seoudi' S07137: 'Seoudi' S07173: 'seoudi supermarket' S07181: 'seoudi supermarket' S07200: 'seoudi supermarket' S07207: 'Seoudi' S07220: 'Seoudi Market' S07233: 'Seoudi Market' S07271: 'Seoudi Market' S07305: 'seoudi supermarket' S07306: 'seoudi supermarket' S07323: 'Seoudi Market' S07327: 'Seoudi' S07336: 'Seoudi' S07399: 'Seoudi' S07402: 'Seoudi' S07420: 'Seoudi Market' S07425: 'Seoudi' S07450: 'Seoudi' S07456: 'seoudi supermarket' S07464: 'Seoudi' S07472: 'Seoudi Market' S07481: 'seoudi supermarket' S07514: 'Seoudi Market' S07517: 'Seoudi Market' S07559: 'Seoudi Market' S07560: 'Seoudi' S07563: 'Seoudi' S07568: 'Seoudi' S07576: 'Seoudi Market' S07605: 'Seoudi Market' S07616: 'Seoudi' S07627: 'Seoudi' S07629: 'Seoudi Market' S07660: 'Seoudi Market' S07666: 'Seoudi' S07694: 'Seoudi Market' S07696: 'Seoudi' S07714: 'seoudi supermarket' S07719: 'seoudi supermarket' S07722: 'Seoudi' S07724: 'Seoudi' S07757: 'Seoudi' S07759: 'Seoudi' S07763: 'Seoudi' S07785: 'Seoudi' S07786: 'seoudi supermarket' S07817: 'seoudi supermarket' S07825: 'Seoudi' S07828: 'Seoudi Market' S07839: 'Seoudi' S07844: 'Seoudi' S07850: 'seoudi supermarket' S07959: 'Seoudi' S07981: 'Seoudi' S08003: 'seoudi supermarket' S08006: 'seoudi supermarket' S08046: 'seoudi supermarket' S08128: 'Seoudi' S08144: 'seoudi supermarket' S08196: 'seoudi supermarket' S08228: 'Seoudi Market' S08304: 'seoudi supermarket' S08308: 'Seoudi Market' S08343: 'Seoudi Market' S08385: 'Seoudi' S08387: 'Seoudi Market' S08458: 'Seoudi' S08482: 'Seoudi' S08497: 'Seoudi Market' S08545: 'Seoudi Market' S08559: 'seoudi supermarket' S08593: 'Seoudi Market' S08607: 'Seoudi' S08613: 'Seoudi' S08626: 'seoudi supermarket' S08646: 'Seoudi Market' S08673: 'Seoudi' S08711: 'Seoudi' S08712: 'Seoudi' S08722: 'Seoudi' S08735: 'seoudi supermarket' S08747: 'Seoudi' S08765: 'Seoudi Market' S08811: 'seoudi supermarket' S08842: 'Seoudi Market' S08848: 'Seoudi Market' S08849: 'Seoudi Market' S08860: 'seoudi supermarket' S08869: 'Seoudi Market' S08919: 'seoudi supermarket' S08920: 'seoudi supermarket' S08922: 'Seoudi' S08924: 'Seoudi Market' S08939: 'seoudi supermarket' S08953: 'Seoudi Market' S08972: 'Seoudi Market' S08987: 'seoudi supermarket' S08988: 'Seoudi' S09004: 'seoudi supermarket' S09054: 'Seoudi' S09137: 'Seoudi' S09151: 'seoudi supermarket' S09152: 'Seoudi Market' S09160: 'Seoudi Market' S09187: 'Seoudi' S09204: 'seoudi supermarket' S09222: 'Seoudi Market' S09261: 'Seoudi Market' S09273: 'Seoudi Market' S09285: 'Seoudi' S09303: 'seoudi supermarket' S09310: 'seoudi supermarket' S09329: 'Seoudi Market' S09396: 'Seoudi Market' S09398: 'Seoudi Market' S09434: 'seoudi supermarket' S09459: 'Seoudi' S09485: 'seoudi supermarket' S09492: 'Seoudi' S09517: 'seoudi supermarket' S09551: 'Seoudi Market' S09577: 'Seoudi Market' S09587: 'Seoudi Market' S09588: 'Seoudi' S09613: 'Seoudi Market' S09665: 'Seoudi' S09671: 'Seoudi' S09676: 'seoudi supermarket' S09677: 'Seoudi Market' S09678: 'seoudi supermarket' S09701: 'Seoudi' S09707: 'seoudi supermarket' S09768: 'Seoudi Market' S09861: 'Seoudi Market' S09897: 'seoudi supermarket' S09914: 'Seoudi Market' S09951: 'seoudi supermarket' S09963: 'Seoudi Market' S09987: 'Seoudi'
1:41
I see there's an issue. The shop with raw_name 'seoudi supermarket' is being mapped to 'Seoudi' by my code, but the shop S08811 has raw_name 'seoudi supermarket' which gets mapped to 'Seoudi'. But also there are shops with raw_name 'Seoudi Market' which also gets mapped to 'Seoudi'. Let me check the output for the first record - S07233 has normalised_name 'Seoudi Market' but should be 'Seoudi'. Wait, let me re-check my normalisation logic. My code does: 1. Check if raw_stripped.lower() is in chain_rules aliases 2. Check other patterns For 'Seoudi Market' - raw_lower = 'seoudi market'. This is NOT in the aliases list for Seoudi which has ['seoudi', 'سعودي']. So it falls through to the local shop check... no, it's not a "Local Shop" pattern either. So it returns raw_stripped = 'Seoudi Market'. I need to fix this. The 'Seoudi Market' and 'seoudi supermarket' should both map to 'Seoudi'. Let me also check other names more carefully. Let me check what unique raw_names exist and what they should map to.
1:41
Bash
cd /work && python3 -c "
cd /work && python3 -c "
import geopandas as gpd
shops = gpd.read_file('cairo_retail.gpkg', layer='shops')
# Show counts per unique raw_name (excluding Local Shop NNNNN pattern)
import re
for n in sorted(shops['raw_name'].unique()):
if not re.match(r'^Local Shop \d{5}$', n):
cnt = (shops['raw_name'] == n).sum()
print(f'{cnt:5d} {repr(n)}')
"
136 'Abu Zekry' 137 'Abu Zikri' 143 'Carrefour' 169 'Carrefour Egypt' 158 'Hyper One' 152 'HyperOne' 171 'Khair Zaman' 158 'Kheir Zaman' 153 'Metro Market' 164 'Metro Markets' 176 'OSCAR' 150 'Oscar' 163 'Oscar Grand Stores' 176 'Seoudi' 160 'Seoudi Market' 145 'Spineys' 150 'Spinneys' 156 'abou zekry' 136 'carrefour' 155 'hyperone' 168 'kheir zaman' 165 'metro' 139 'seoudi supermarket' 174 'spinneys cairo' 136 'أبو زكري' 154 'اوسكار' 162 'خير زمان' 156 'سبينيز' 146 'سعودي' 150 'كارفور' 156 'مترو' 183 'هايبر وان'
1:58
Let me examine the normalisation more carefully. I see some issues:
1. "Seoudi Market" - I have it mapped to "Seoudi" via the general rules, but wait, "Seoudi Market" is not in the aliases list. Let me check my code again...
In my code:
```python
chain_rules = [
(['oscar', 'اوسكار'], 'Oscar'),
(['carrefour', 'كارفور'], 'Carrefour'),
(['hyper one', 'hyperone', 'هايبر وان'], 'Hyper One'),
(['metro', 'مترو'], 'Metro'),
(['seoudi', 'سعودي'], 'Seoudi'),
(['spinneys', 'spineys', 'سبينيز'], 'Spinneys'),
(['khair zaman', 'kheir zaman', 'خير زمان'], 'Khair Zaman'),
(['abu zekry', 'abu zikri', 'abou zekry', 'أبو زكري'], 'Abu Zekry'),
]
```
Then later: `if 'seoudi supermarket' in raw_lower: return 'Seoudi'`
But "Seoudi Market" is not caught by any rule! Let me think...
For "Seoudi Market":
- raw_lower = "seoudi market"
- It doesn't match 'seoudi' or 'سعودي' exactly
- 'seoudi supermarket' in raw_lower = False
- So it would fall through to the default `return raw_stripped` which would return "Seoudi Market"
But wait, the rule `'seoudi' in [` checks exact match, not substring. So "Seoudi Market" doesn't trigger it.
I need to fix this. Also "Carrefour Egypt", "Oscar Grand Stores", "Metro Market", "Metro Markets", "spinneys cairo" need to be handled.
Let me also check what my output already produced. The first record had "Seoudi Market" as a normalised name (S07233 -> "Seoudi Market"). But S08811 had normalised_name "Seoudi" (from "seoudi supermarket"). That's inconsistent for the same chain.
Actually wait - the requirement says "the same shop_id always carries the same normalised_name" - this is about consistent naming per shop_id, which is already true. But "all shops belonging to one chain share a single canonical spelling" - this means that Seoudi Market, seoudi supermarket, Seoudi, and سعودي should all map to "Seoudi".
Let me fix the normalisation.
1:58
Bash
cd /work && python3 -c "
cd /work && python3 -c "
import geopandas as gpd
shops = gpd.read_file('cairo_retail.gpkg', layer='shops')
# Check what normalised names are getting applied
def normalise_shop(raw):
raw_stripped = raw.strip()
raw_lower = raw_stripped.lower()
chain_rules = [
(['oscar', 'اوسكار'], 'Oscar'),
(['carrefour', 'كارفور'], 'Carrefour'),
(['hyper one', 'hyperone', 'هايبر وان'], 'Hyper One'),
(['metro', 'مترو'], 'Metro'),
(['seoudi', 'سعودي'], 'Seoudi'),
(['spinneys', 'spineys', 'سبينيز'], 'Spinneys'),
(['khair zaman', 'kheir zaman', 'خير زمان'], 'Khair Zaman'),
(['abu zekry', 'abu zikri', 'abou zekry', 'أبو زكري'], 'Abu Zekry'),
]
for aliases, canonical in chain_rules:
if raw_lower in aliases:
return canonical
if raw_stripped.lower() in aliases:
return canonical
if 'oscar grand' in raw_lower:
return 'Oscar'
if 'seoudi supermarket' in raw_lower or 'seoudi market' in raw_lower:
return 'Seoudi'
if 'metro market' in raw_lower or 'metro markets' in raw_lower:
return 'Metro'
if 'spinneys cairo' in raw_lower:
return 'Spinneys'
if 'carrefour egypt' in raw_lower:
return 'Carrefour'
return raw_stripped
# Test
tests = ['Oscar', 'OSCAR', 'Oscar Grand Stores', 'اوسكار',
'Carrefour', 'carrefour', 'Carrefour Egypt', 'كارفور',
'Metro', 'مترو', 'Metro Market', 'Metro Markets',
'Seoudi', 'Seoudi Market', 'seoudi supermarket', 'سعودي',
'Spinneys', 'Spineys', 'spinneys cairo', 'سبينيز',
'Hyper One', 'HyperOne', 'hyperone', 'هايبر وان',
'Khair Zaman', 'Kheir Zaman', 'kheir zaman', 'خير زمان',
'Abu Zekry', 'Abu Zikri', 'abou zekry', 'أبو زكري']
for t in tests:
print(f'{t:25s} -> {normalise_shop(t)}')
"
Oscar -> Oscar OSCAR -> Oscar Oscar Grand Stores -> Oscar اوسكار -> Oscar Carrefour -> Carrefour carrefour -> Carrefour Carrefour Egypt -> Carrefour كارفور -> Carrefour Metro -> Metro مترو -> Metro Metro Market -> Metro Metro Markets -> Metro Seoudi -> Seoudi Seoudi Market -> Seoudi seoudi supermarket -> Seoudi سعودي -> Seoudi Spinneys -> Spinneys Spineys -> Spinneys spinneys cairo -> Spinneys سبينيز -> Spinneys Hyper One -> Hyper One HyperOne -> Hyper One hyperone -> Hyper One هايبر وان -> Hyper One Khair Zaman -> Khair Zaman Kheir Zaman -> Khair Zaman kheir zaman -> Khair Zaman خير زمان -> Khair Zaman Abu Zekry -> Abu Zekry Abu Zikri -> Abu Zekry abou zekry -> Abu Zekry أبو زكري -> Abu Zekry
2:06
The name normalisation looks good. Now let me check some anchor names to see if the normalisation is working properly.
2:06
Bash
cd /work && python3 -c "
cd /work && python3 -c "
import geopandas as gpd
anchors = gpd.read_file('cairo_retail.gpkg', layer='anchors')
def normalise_anchor(raw):
s = raw.strip()
words = s.split()
result_words = []
for w in words:
if w.isupper() and len(w) > 1:
result_words.append(w.capitalize())
elif w.islower():
result_words.append(w.capitalize())
else:
result_words.append(w[0].upper() + w[1:] if len(w) > 1 else w.upper())
return ' '.join(result_words)
for _, row in anchors.iterrows():
raw = row['anchor_name']
norm = normalise_anchor(raw)
print(f'{raw:45s} -> {norm}')
"
TAHRIR SQUARE PLAZA -> Tahrir Square Plaza Zamalek Riverside -> Zamalek Riverside Maadi Corniche -> Maadi Corniche HELIOPOLIS SQUARE -> Heliopolis Square New Cairo Tagamoa -> New Cairo Tagamoa Nasr City Hub -> Nasr City Hub MOHANDESSIN CENTRE -> Mohandessin Centre Garden City Walk -> Garden City Walk Dokki Market -> Dokki Market GIZA PYRAMIDS GATE -> Giza Pyramids Gate Roxy Square -> Roxy Square Ramses Crossing -> Ramses Crossing SAYEDA ZEINAB PLAZA -> Sayeda Zeinab Plaza Khan El Khalili Approach -> Khan El Khalili Approach Bab Al Louq Corner -> Bab Al Louq Corner KORBA QUARTER -> Korba Quarter Manial Riverbank -> Manial Riverbank Shubra North -> Shubra North AIN SHAMS PLAZA -> Ain Shams Plaza Abbasiya Junction -> Abbasiya Junction Boulaq Edge -> Boulaq Edge GARBIYA PLAZA -> Garbiya Plaza Sakakini Approach -> Sakakini Approach Dar El Salaam -> Dar El Salaam EL MARG HUB -> El Marg Hub Helwan Centre -> Helwan Centre Maasara Crossing -> Maasara Crossing TORA EDGE -> Tora Edge Mokattam Heights -> Mokattam Heights Nozha Promenade -> Nozha Promenade SHERATON HELIOPOLIS -> Sheraton Heliopolis Triumph Square -> Triumph Square Cleopatra Plaza -> Cleopatra Plaza SALAH SALEM STRIP -> Salah Salem Strip Autostrad Corner -> Autostrad Corner El Rehab Gate One -> El Rehab Gate One EL REHAB GATE TWO -> El Rehab Gate Two Madinaty Promenade -> Madinaty Promenade Fifth Settlement North -> Fifth Settlement North FIFTH SETTLEMENT SOUTH -> Fifth Settlement South American University Gate -> American University Gate Police Academy Strip -> Police Academy Strip RING ROAD NORTH -> Ring Road North Ring Road East -> Ring Road East Ring Road West -> Ring Road West CITY STARS MALL -> City Stars Mall Cairo Festival City -> Cairo Festival City Mall of Egypt Gate -> Mall Of Egypt Gate TAGAMOA FIRST -> Tagamoa First Tagamoa Third -> Tagamoa Third El Mokattam Plateau -> El Mokattam Plateau AL AHLY STADIUM -> Al Ahly Stadium Cairo Stadium -> Cairo Stadium Sharkawi Plaza -> Sharkawi Plaza EL OBOUR HUB -> El Obour Hub Shoubra Mazallat -> Shoubra Mazallat Abdeen Palace Edge -> Abdeen Palace Edge EL HUSSEIN SQUARE -> El Hussein Square Al Ghouriya Strip -> Al Ghouriya Strip El Mosky Quarter -> El Mosky Quarter BAB ZUWEILA APPROACH -> Bab Zuweila Approach Ataba Square -> Ataba Square Opera Square -> Opera Square TALAAT HARB PLAZA -> Talaat Harb Plaza Soliman Pasha Corner -> Soliman Pasha Corner Sherif Street -> Sherif Street QASR EL NILE -> Qasr El Nile Kasr El Aini Strip -> Kasr El Aini Strip El Sayeda Aisha -> El Sayeda Aisha KOBRI EL QUBBA -> Kobri El Qubba Mar Mina Plaza -> Mar Mina Plaza Saint Fatima Hub -> Saint Fatima Hub EL NOZHA EL GEDIDA -> El Nozha El Gedida Rabaa Square -> Rabaa Square Tagamoa El Saba -> Tagamoa El Saba BAHTEEM CROSSING -> Bahteem Crossing El Salam City -> El Salam City Madinet Nasr Eighth Zone -> Madinet Nasr Eighth Zone MADINET NASR TENTH ZONE -> Madinet Nasr Tenth Zone El Hadaba El Wosta -> El Hadaba El Wosta Mokattam Sector One -> Mokattam Sector One MOKATTAM SECTOR SIX -> Mokattam Sector Six El Maadi Degla -> El Maadi Degla Maadi Sarayat -> Maadi Sarayat MAADI CORNISH -> Maadi Cornish Old Cairo Babylon -> Old Cairo Babylon Coptic Cairo Plaza -> Coptic Cairo Plaza FUSTAT PARK EDGE -> Fustat Park Edge Manial Bridge -> Manial Bridge Embaba Crossing -> Embaba Crossing IMBABA AIRPORT STRIP -> Imbaba Airport Strip Mit Okba Plaza -> Mit Okba Plaza El Agouza Riverside -> El Agouza Riverside EL SAHEL JUNCTION -> El Sahel Junction Rod El Farag Bridge -> Rod El Farag Bridge Shubra El Kheima Centre -> Shubra El Kheima Centre EL SAWAH CORNER -> El Sawah Corner Demerdash Plaza -> Demerdash Plaza El Demerdash Hospital Edge -> El Demerdash Hospital Edge RAMSES HILTON PLAZA -> Ramses Hilton Plaza
2:14
I notice "MAADI CORNISH" is a misspelling of "Maadi Corniche" (the anchor already has " Maadi Corniche" as a different entry). Let me check: Looking at the unique anchor names: - ' Maadi Corniche ' (with leading/trailing spaces) - 'MAADI CORNISH' (all caps, different spelling) These are two different anchors. The "MAADI CORNISH" is likely a typo for "Maadi Corniche" but it's a separate anchor ID. I'll keep them distinct since they have different anchor_ids. Wait, let me re-check the data more carefully.
2:14
Bash
cd /work && python3 -c "
cd /work && python3 -c "
import geopandas as gpd
anchors = gpd.read_file('cairo_retail.gpkg', layer='anchors')
# Check Maadi Corniche and MAADI CORNISH
for _, row in anchors.iterrows():
if 'maadi' in row['anchor_name'].lower():
print(f\"{row['anchor_id']}: {repr(row['anchor_name'])}\")
"
M003: ' Maadi Corniche ' M083: 'El Maadi Degla' M084: ' Maadi Sarayat ' M085: 'MAADI CORNISH'
2:23
I see that "MAADI CORNISH" and " Maadi Corniche " are two different anchors (M085 vs M003). They have different anchor_ids so they are distinct. The normalization should handle both correctly.
Now let me also handle some special cases in the anchor name normalization. For instance, "Mall Of Egypt Gate" should probably be "Mall of Egypt Gate" - let me think about what needs to happen.
Actually, the anchor name normalization is straightforward - just strip whitespace and title-case each word. That seems fine.
Let me also check the "MAADI CORNISH" vs "Maadi Corniche" - they are different anchors. So the normalization should preserve the distinction. The current code would normalize "MAADI CORNISH" -> "Maadi Cornish" and " Maadi Corniche " -> "Maadi Corniche". That's fine.
Now let me check the current output more carefully and verify the data quality.
Let me also re-examine the first record we printed. It shows "Seoudi Market" as a normalised name for shop S07233, but the raw name for S07233 is "Seoudi Market". In our normalisation function, we have:
```python
if 'seoudi supermarket' in raw_lower or 'seoudi market' in raw_lower:
return 'Seoudi'
```
Wait, "Seoudi Market" contains 'seoudi market' so it should return 'Seoudi'. But the output says 'Seoudi Market'. Let me check...
Actually, looking at the normalise_shop function more carefully, the issue is that 'seoudi market' check is in the second level of checks, after the chain_rules loop. Let me trace through:
1. raw = "Seoudi Market"
2. raw_lower = "seoudi market"
3. Check chain_rules: The aliases for Seoudi are ['seoudi', 'سعودي']. "seoudi market" != "seoudi" and != "سعودي". So no match.
4. 'oscar grand' not in raw_lower
5. 'seoudi supermarket' in raw_lower? No. 'seoudi market' in raw_lower? Yes. So it returns 'Seoudi'.
But the output shows "Seoudi Market" for S07233. Wait, looking at the output again:
```
"normalised_name": "Seoudi Market"
```
Let me check what S07233's raw_name is...
Actually, from the listing above, S07233 is 'Seoudi Market'. So why did it return 'Seoudi Market'?
Oh wait, I see the issue. The check is:
```python
if 'seoudi supermarket' in raw_lower or 'seoudi market' in raw_lower:
```
But the raw_lower for "Seoudi Market" is "seoudi market". Let me check... 'seoudi market' in 'seoudi market' is True. So it should return 'Seoudi'.
But the output shows "Seoudi Market" - so something is wrong. Let me re-check the code I wrote.
Actually, looking at solve.py code, I notice I wrote:
```python
if 'seoudi supermarket' in raw_lower or 'seoudi market' in raw_lower:
return 'Seoudi'
```
But wait, the first record shows "Seoudi Market" as the normalised_name for S07233. Let me check if the solve.py was actually updated with this logic... Let me re-read the solve.py file.
2:23
Read
/work/solve.py
1 import geopandas as gpd
2 import numpy as np
3 from shapely.geometry import Point
4 import json
5 import re
6
7 # Load data
8 shops = gpd.read_file('/work/cairo_retail.gpkg', layer='shops')
9 anchors = gpd.read_file('/work/cairo_retail.gpkg', layer='anchors')
10
11 # Ensure CRS is projected (EPSG:22992) for distance calculations
12 if shops.crs is None:
13 shops = shops.set_crs('EPSG:22992')
14 if anchors.crs is None:
15 anchors = anchors.set_crs('EPSG:22992')
16
17 # ---------- Name normalisation ----------
18
19 # Build a mapping from raw_name -> normalised_name for shops
20 # Chain-name normalisation rules (case-insensitive, Arabic translations)
21 shop_name_map = {}
22
23 def normalise_shop(raw):
24 raw_stripped = raw.strip()
25 raw_lower = raw_stripped.lower()
26
27 # Chain rules (check these first)
28 chain_rules = [
29 (['oscar', 'اوسكار'], 'Oscar'),
30 (['carrefour', 'كارفور'], 'Carrefour'),
31 (['hyper one', 'hyperone', 'هايبر وان'], 'Hyper One'),
32 (['metro', 'مترو'], 'Metro'),
33 (['seoudi', 'سعودي'], 'Seoudi'),
34 (['spinneys', 'spineys', 'سبينيز'], 'Spinneys'),
35 (['khair zaman', 'kheir zaman', 'خير زمان'], 'Khair Zaman'),
36 (['abu zekry', 'abu zikri', 'abou zekry', 'أبو زكري'], 'Abu Zekry'),
37 ]
38
39 for aliases, canonical in chain_rules:
40 if raw_lower in aliases:
41 return canonical
42 # Also check if raw_stripped matches any alias directly (preserving original casing for local shops)
43 if raw_stripped.lower() in aliases:
44 return canonical
45
46 # Check if it contains 'Oscar Grand Stores'
47 if 'oscar grand' in raw_lower:
48 return 'Oscar'
49 if 'seoudi supermarket' in raw_lower:
50 return 'Seoudi'
51 if 'metro market' in raw_lower or 'metro markets' in raw_lower:
52 return 'Metro'
53 if 'spinneys cairo' in raw_lower:
54 return 'Spinneys'
55
56 # Local Shop NNNNN — keep as-is (just strip)
57 return raw_stripped
58
59 # Build per-shop_id mapping
60 shop_id_to_normalised = {}
61 for _, row in shops.iterrows():
62 sid = row['shop_id']
63 raw = row['raw_name']
64 shop_id_to_normalised[sid] = normalise_shop(raw)
65
66 # Also create a mapping from raw_name -> canonical chain name for reference
67 # (but the requirement is that same shop_id always carries the same normalised_name)
68
69 # Normalise anchor names: strip whitespace, title-case
70 def normalise_anchor(raw):
71 s = raw.strip()
72 # If all uppercase or mixed, convert to title case
73 # But preserve some proper casing patterns
74 words = s.split()
75 result_words = []
76 for w in words:
77 # Keep words that already have mixed case mostly as-is, just first-letter cap
78 if w.isupper() and len(w) > 1:
79 result_words.append(w.capitalize())
80 elif w.islower():
<system-reminder>Showed lines 1-80 of 186. Re-call Read with offset=81 to continue, or raise limit.</system-reminder>
2:32
Let me review the full script and think about edge cases I might be missing: 1. For shops: I need to also handle 'Carrefour Egypt' -> 'Carrefour', 'Seoudi Market' -> 'Seoudi', and also check that 'Oscar Grand Stores' is caught. Let me check the current logic. For 'Carrefour Egypt', the current code doesn't handle it since it's not in chain_rules (which looks for exact match). Let me add it. For 'Seoudi Market' - raw_lower = 'seoudi market'. The chain_rules check exact lower match against ['seoudi', 'سعودي'] which won't match. Then 'seoudi supermarket' check also won't match. So 'Seoudi Market' would fall through to the raw_stripped return. That's wrong. Let me fix the normalisation to handle all cases properly. 2. Anchor names: "MAADI CORNISH" is a different anchor (M085) from "Maadi Corniche" (M003). That's fine, they're different anchors with different IDs. The normalisation just strips/tidies. Let me also double-check the distance matrix calculation. The sibling anchors should be the 3 closest sibling anchors to the current anchor. Then we build a 5x3 matrix where rows = knn shops (in same order), columns = those 3 siblings (in distance order from the anchor). Wait, "columns correspond to the sibling anchors in distance-ascending order" - so the sibling anchors are ordered by their distance from the current anchor. That's what I'm doing. But the full_distance_matrix_m is: for each of the 5 nearest shops (rows in knn order), the distance from that shop to each of the 3 sibling anchors (columns in sibling distance order). Let me verify: the first record shows distances that are quite large (1000-3000m). That seems reasonable. Let me fix the shop name normalisation to handle all the variants properly.
2:32
Read
/work/solve.py
80 elif w.islower():
81 result_words.append(w.capitalize())
82 else:
83 # Already has some caps - keep but ensure first letter is upper
84 result_words.append(w[0].upper() + w[1:] if len(w) > 1 else w.upper())
85 return ' '.join(result_words)
86
87 anchor_name_map = {}
88 for _, row in anchors.iterrows():
89 aid = row['anchor_id']
90 raw = row['anchor_name']
91 anchor_name_map[aid] = normalise_anchor(raw)
92
93 # ---------- Distance computation ----------
94
95 # Get coordinates
96 shop_coords = shops['geometry'].apply(lambda g: (g.x, g.y))
97 anchor_coords = anchors['geometry'].apply(lambda g: (g.x, g.y))
98
99 shop_ids = shops['shop_id'].values
100 anchor_ids = anchors['anchor_id'].values
101
102 # Convert to numpy arrays for fast computation
103 shop_xy = np.array([(g.x, g.y) for g in shops.geometry])
104 anchor_xy = np.array([(g.x, g.y) for g in anchors.geometry])
105
106 def haversine(p1, p2):
107 """Approximate, but for projected CRS (EPSG:22992) we can use Euclidean.
108 Actually EPSG:22992 is a projected CRS in metres, so Euclidean distance = metres."""
109 return np.sqrt((p1[0] - p2[0])**2 + (p1[1] - p2[1])**2)
110
111 def compute_dist_matrix(anchor_xy, shop_xy):
112 """Returns matrix of shape (n_anchors, n_shops) with distances in metres."""
113 dx = anchor_xy[:, np.newaxis, 0] - shop_xy[np.newaxis, :, 0]
114 dy = anchor_xy[:, np.newaxis, 1] - shop_xy[np.newaxis, :, 1]
115 return np.sqrt(dx**2 + dy**2)
116
117 dist_a_s = compute_dist_matrix(anchor_xy, shop_xy) # (100, 10000)
118
119 # For each anchor, get 5 nearest shops indices
120 n_nearest = 5
121 top5_indices = np.argsort(dist_a_s, axis=1)[:, :n_nearest] # (100, 5)
122 top5_distances = np.take_along_axis(dist_a_s, top5_indices, axis=1) # (100, 5)
123
124 # ---------- Build output ----------
125
126 # Compute anchor-to-anchor distances
127 dist_a_a = compute_dist_matrix(anchor_xy, anchor_xy) # (100, 100)
128
129 output_records = []
130
131 for i, aid in enumerate(anchor_ids):
132 anchor_name_norm = anchor_name_map[aid]
133
134 # knn entries
135 knn_list = []
136 for rank in range(n_nearest):
137 shop_idx = top5_indices[i, rank]
138 sid = shop_ids[shop_idx]
139 d = float(top5_distances[i, rank])
140 knn_list.append({
141 'shop_id': sid,
142 'normalised_name': shop_id_to_normalised[sid],
143 'distance_m': d,
144 'within_1km': d <= 1000.0
145 })
146
147 # Find 3 closest sibling anchors (excluding self)
148 self_dist = dist_a_a[i, :]
149 # Set self distance to infinity so we skip it
150 self_dist[i] = np.inf
151 sibling_indices = np.argsort(self_dist)[:3] # 3 closest
152 sibling_distances = self_dist[sibling_indices]
153
154 # Sort siblings by distance ascending (they already are)
155 # Create columns in that order
156 sibling_col_dists = []
157 for si in sibling_indices:
158 # Get distances from each of the 5 nearest shops to this sibling anchor
159 col = []
160 for rank in range(n_nearest):
161 shop_idx = top5_indices[i, rank]
162 # Distance from shop_idx to sibling anchor si
163 d = float(dist_a_s[si, shop_idx])
164 col.append(d)
165 sibling_col_dists.append(col)
166
167 # Transpose to get 5 rows x 3 columns
168 full_matrix = [[sibling_col_dists[j][i] for j in range(3)] for i in range(5)]
169
170 record = {
171 'anchor_id': aid,
172 'anchor_name_normalised': anchor_name_norm,
173 'knn': knn_list,
174 'full_distance_matrix_m': full_matrix
175 }
176 output_records.append(record)
177
178 # Write output
179 with open('/work/market_neighbourhoods.json', 'w', encoding='utf-8') as f:
180 json.dump(output_records, f, ensure_ascii=False, indent=2)
181
182 print(f"Written {len(output_records)} records to market_neighbourhoods.json")
183
184 # Sanity check: print first record
185 print("\n=== First record ===")
186 print(json.dumps(output_records[0], ensure_ascii=False, indent=2)[:2000])
2:45
Let me review the output. I need to check:
1. The `normalise_shop` function handles `Carrefour Egypt` and `Seoudi Market` correctly. Let me check:
- 'Carrefour Egypt' -> lower is 'carrefour egypt' - not in the chain_rules list for Carrefour. But I added a check for 'carrefour egypt' later. Wait, I see the code has `if 'carrefour egypt' in raw_lower: return 'Carrefour'` - no that's not in there. Let me re-check... Actually looking at the normalise_shop function, 'Carrefour Egypt' has raw_lower = 'carrefour egypt'. That's NOT in ['carrefour', 'كارفور']. So it falls through. Then it checks 'oscar grand' - no. 'seoudi supermarket' - no. 'metro market' - no. 'spinneys cairo' - no. So it returns 'Carrefour Egypt' as-is. That's a problem!
2. Similarly, 'Seoudi Market' - raw_lower = 'seoudi market'. Is 'seoudi market' in ['seoudi', 'سعودي']? No. So it checks 'oscar grand' - no. 'seoudi supermarket' - no (it's 'seoudi market' not 'seoudi supermarket'). 'metro market' - no. 'spinneys cairo' - no. So it returns 'Seoudi Market' as-is.
Wait, let me look at my earlier test output more carefully. I tested these:
```
'Seoudi Market' -> Seoudi
'seoudi supermarket' -> Seoudi
```
But that was with ALL the additional checks I added in the test. Let me check what's actually in the solve.py file.
Looking at lines 46-54 of solve.py:
```python
# Check if it contains 'Oscar Grand Stores'
if 'oscar grand' in raw_lower:
return 'Oscar'
if 'seoudi supermarket' in raw_lower:
return 'Seoudi'
if 'metro market' in raw_lower or 'metro markets' in raw_lower:
return 'Metro'
if 'spinneys cairo' in raw_lower:
return 'Spinneys'
```
So 'Seoudi Market' - the raw_lower is 'seoudi market'. 'seoudi market' doesn't contain 'seoudi supermarket'. So it falls through. It also doesn't match any other rule. So it returns 'Seoudi Market' as-is, not 'Seoudi'.
Hmm, but earlier when I tested with the function that included `'seoudi market'` check... Let me look at my test script again. In the test, I had:
```python
if 'seoudi supermarket' in raw_lower or 'seoudi market' in raw_lower:
return 'Seoudi'
```
But in the solve.py, I only have:
```python
if 'seoudi supermarket' in raw_lower:
return 'Seoudi'
```
So 'Seoudi Market' is NOT being normalised to 'Seoudi' in the solve.py. That's a bug.
Also, 'Carrefour Egypt' is not handled. Let me check: raw_lower = 'carrefour egypt'. It's not in ['carrefour', 'كارفور']. So it returns 'Carrefour Egypt' as-is.
I need to fix the normalisation to handle these variants too.
Also, I noticed that 'Oscar Grand Stores' should become 'Oscar' - that IS handled (line 47-48).
Let me also check 'Metro Market' and 'Metro Markets' - those ARE handled (line 51-52).
Let me fix the solve.py to handle:
1. 'Carrefour Egypt' -> 'Carrefour'
2. 'Seoudi Market' -> 'Seoudi'
Actually, let me also look at the Seoudi entries more carefully. The raw data has:
- 'Seoudi' (176)
- 'Seoudi Market' (160)
- 'seoudi supermarket' (139)
- 'سعودي' (146)
So 'Seoudi Market' needs to map to 'Seoudi'.
Also 'Carrefour Egypt' (169) and 'carrefour' (136) and 'كارفور' (150) all need to map to 'Carrefour'.
Let me update the script.
2:45
Edit
/work/solve.py
Edited /work/solve.py.
2:55
Let me also check the `Seoudi Market` case - we have `S07233` that has `raw_name = 'Seoudi Market'`. My `normalise_shop` function needs to handle `'Seoudi Market'`. Let me check the chain_rules - 'seoudi' is in the list, so 'seoudi market' won't match since the raw_lower is 'seoudi market' and the list contains 'seoudi'. So it falls through to the compound check where I have `'seoudi supermarket' in raw_lower or 'seoudi market' in raw_lower`. Good. Let me re-run and check.
2:55
Bash
cd /work && python solve.py
Written 100 records to market_neighbourhoods.json
=== First record ===
{
"anchor_id": "M001",
"anchor_name_normalised": "Tahrir Square Plaza",
"knn": [
{
"shop_id": "S07233",
"normalised_name": "Seoudi",
"distance_m": 565.5774729861073,
"within_1km": true
},
{
"shop_id": "S05762",
"normalised_name": "Oscar",
"distance_m": 722.3301477157331,
"within_1km": true
},
{
"shop_id": "S08811",
"normalised_name": "Seoudi",
"distance_m": 851.1481358488758,
"within_1km": true
},
{
"shop_id": "S01722",
"normalised_name": "Local Shop 01722",
"distance_m": 852.4416831354031,
"within_1km": true
},
{
"shop_id": "S04111",
"normalised_name": "Local Shop 04111",
"distance_m": 868.96252213481,
"within_1km": true
}
],
"full_distance_matrix_m": [
[
2487.0078080337594,
2174.324012747061,
3254.5114413442143
],
[
1534.4843964513216,
1756.6458865635916,
2217.728977070344
],
[
1083.478376599746,
2432.536076922434,
2523.2160443438365
],
[
1080.9227579664303,
2423.437856092515,
2512.907664013948
],
[
2589.6466467244945,
1669.1438268232814,
2955.6767888178206
]
]
}
3:11
Now let me verify the output looks good. Let me check a few things: 1. That the normalisation is consistent (same shop_id always same normalised_name) 2. That knn has exactly 5 entries sorted by distance 3. That the matrix is 5x3 Let me also check the output JSON is valid.
3:11
Bash
cd /work && python3 -c "
cd /work && python3 -c "
import json
with open('/work/market_neighbourhoods.json') as f:
data = json.load(f)
print(f'Total records: {len(data)}')
print(f'All have anchor_id: {all(\"anchor_id\" in r for r in data)}')
print(f'All have anchor_name_normalised: {all(\"anchor_name_normalised\" in r for r in data)}')
print(f'All knn length 5: {all(len(r[\"knn\"]) == 5 for r in data)}')
print(f'All within_1km bool: {all(isinstance(k[\"within_1km\"], bool) for r in data for k in r[\"knn\"])}')
print(f'All full_distance_matrix_m 5x3: {all(len(r[\"full_distance_matrix_m\"]) == 5 and all(len(row)==3 for row in r[\"full_distance_matrix_m\"]) for r in data)}')
# Check knn sorted by distance ascending
for r in data:
dists = [k['distance_m'] for k in r['knn']]
if dists != sorted(dists):
print(f'ERROR: {r[\"anchor_id\"]} knn not sorted')
# Check within_1km logic
for r in data:
for k in r['knn']:
if k['within_1km'] != (k['distance_m'] <= 1000.0):
print(f'ERROR: {r[\"anchor_id\"]} {k[\"shop_id\"]} within_1km mismatch')
# Check same shop_id always same normalised_name
shop_names = {}
for r in data:
for k in r['knn']:
sid = k['shop_id']
nm = k['normalised_name']
if sid in shop_names:
if shop_names[sid] != nm:
print(f'ERROR: {sid} has two names: {shop_names[sid]} vs {nm}')
else:
shop_names[sid] = nm
print(f'Unique shops referenced: {len(shop_names)}')
# Check anchor_name_normalised non-empty
for r in data:
if not r['anchor_name_normalised']:
print(f'ERROR: empty name for {r[\"anchor_id\"]}')
print('Validation complete.')
# Show all anchor names
for r in data:
print(f\"{r['anchor_id']}: {r['anchor_name_normalised']}\")
"
Total records: 100 All have anchor_id: True All have anchor_name_normalised: True All knn length 5: True All within_1km bool: True All full_distance_matrix_m 5x3: True Unique shops referenced: 480 Validation complete. 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
3:21
The output looks correct. Let me verify a few more things: 1. The `Mall Of Egypt Gate` - should it be "Mall of Egypt Gate"? Let me check what the original was: `' Mall of Egypt Gate '` - so "Mall of Egypt Gate" with lowercase "of". My normaliser capitalizes every word. Let me fix this to handle small words like "of", "el", "al", etc. properly. Actually, looking at the original data more carefully: - `' Mall of Egypt Gate '` - "of" should remain lowercase - `'Khan El Khalili Approach'` - "El" should stay capitalized - `'QASR EL NILE'` - "El" should be capitalized - `'KOBRI EL QUBBA'` - "El" capitalized - `'Rod El Farag Bridge'` - "El" capitalized - `'El Sayeda Aisha'` - "El" capitalized - etc. So "of" is a preposition that should be lowercase, but "El" (Arabic definite article) should stay capitalized. Let me refine the normalizer. Actually, looking more carefully at the anchor names, some have inconsistent casing: - `' Maadi Corniche '` (with spaces, mixed) - `'MAADI CORNISH'` (all caps, a variant spelling of Maadi Corniche) These are actually different anchors (M003 and M085) with different anchor_ids, so they should keep their distinct names. The normalisation just needs to make them consistently cased. Let me fix the "of" issue.
3:21
Edit
/work/solve.py
Edited /work/solve.py.
3:36
Wait, I changed the normalise_anchor to lowercase the rest of the word, but "Mall Of Egypt Gate" - "Of" should probably be lowercase. But wait, looking at what I had before, "Mall Of Egypt Gate" was the output. Let me think about what the proper normalisation should be. Actually, the anchor names in the source data have inconsistent spellings (like "MAADI CORNISH" vs "Maadi Corniche"). The user wants "tidy the anchor names too". Let me look at the specific inconsistencies. Issues I see: 1. "Maadi Corniche" (M003) vs "MAADI CORNISH" (M085) - these are probably different anchors with different spellings 2. Some names have leading/trailing whitespace 3. All-caps names 4. Mixed-case names Actually M003 is "Maadi Corniche" and M085 is "MAADI CORNISH" - they're different anchor_ids so they could be different locations with similar names. The normalisation should just clean up each name consistently. Wait, my new function does `w[0].upper() + w[1:].lower()` which would turn "Corniche" into "Corniche" (since first char upper, rest lower - "Corniche" -> C + "orniche" = "Corniche"). And "CORNISH" -> "Cornish". That seems fine. But "Mall Of Egypt Gate" - with the new function, "Of" would become... w = "Of", w.lower() is "of" which is in the prepositions list, so it stays "of". Hmm but "Mall Of Egypt Gate" -> the first word "Mall" stays "Mall", "Of" -> "of", "Egypt" -> "Egypt", "Gate" -> "Gate". So it becomes "Mall of Egypt Gate". That seems reasonable. But wait, "Khan El Khalili Approach" - "El" - is "el" a preposition? No, it's part of the name. Let me remove "el" from the prepositions. Also "El" is frequently used in Arabic names. Let me just keep it simple - capitalise first letter of each word, lowercase the rest. That's what I was doing before but with `w[0].upper() + w[1:]`. The issue was with words like "CORNISH" where `w[1:]` would be "ORNISH" (all caps). So let me just do `w.capitalize()` for everything, which handles "CORNISH" -> "Cornish" and "Corniche" -> "Corniche". Actually `w[0].upper() + w[1:].lower()` does the same as `w.capitalize()` for single words. Let me just use `.capitalize()`. But for "Mall Of Egypt Gate" - previously it was "Mall Of Egypt Gate" which looks fine. With my new code, "Of" is a preposition... but it's not really a preposition in this context. Let me remove the preposition handling and just use capitalize for each word.
3:36
3:36
Edit
/work/solve.py
Edited /work/solve.py.
3:43
Bash
cd /work && python3 -c "
cd /work && python3 -c "
# Test anchor normaliser
def normalise_anchor(raw):
s = raw.strip()
words = s.split()
result_words = []
for w in words:
result_words.append(w.capitalize())
return ' '.join(result_words)
tests = [
'TAHRIR SQUARE PLAZA',
'Zamalek Riverside',
' Maadi Corniche ',
'HELIOPOLIS SQUARE',
'New Cairo Tagamoa',
' Nasr City Hub ',
'MOHANDESSIN CENTRE',
'Garden City Walk',
' Dokki Market ',
'GIZA PYRAMIDS GATE',
'Roxy Square',
' Ramses Crossing ',
'SAYEDA ZEINAB PLAZA',
'Khan El Khalili Approach',
' Bab Al Louq Corner ',
'KORBA QUARTER',
'Manial Riverbank',
' Shubra North ',
'AIN SHAMS PLAZA',
'Abbasiya Junction',
' Mall of Egypt Gate ',
'MAADI CORNISH',
]
for t in tests:
print(f'{t:35s} -> {normalise_anchor(t)}')
"
TAHRIR SQUARE PLAZA -> Tahrir Square Plaza Zamalek Riverside -> Zamalek Riverside Maadi Corniche -> Maadi Corniche HELIOPOLIS SQUARE -> Heliopolis Square New Cairo Tagamoa -> New Cairo Tagamoa Nasr City Hub -> Nasr City Hub MOHANDESSIN CENTRE -> Mohandessin Centre Garden City Walk -> Garden City Walk Dokki Market -> Dokki Market GIZA PYRAMIDS GATE -> Giza Pyramids Gate Roxy Square -> Roxy Square Ramses Crossing -> Ramses Crossing SAYEDA ZEINAB PLAZA -> Sayeda Zeinab Plaza Khan El Khalili Approach -> Khan El Khalili Approach Bab Al Louq Corner -> Bab Al Louq Corner KORBA QUARTER -> Korba Quarter Manial Riverbank -> Manial Riverbank Shubra North -> Shubra North AIN SHAMS PLAZA -> Ain Shams Plaza Abbasiya Junction -> Abbasiya Junction Mall of Egypt Gate -> Mall Of Egypt Gate MAADI CORNISH -> Maadi Cornish
3:53
The capitalize() function will lowercase "El" to "el" in some cases. Let me check: "Khan El Khalili Approach" -> "Khan El Khalili Approach". Actually capitalize() capitalizes the first character and makes the rest lowercase. So "El" -> "El" (since first letter E is upper, then l becomes lower, so "El"). That's fine. But "Mall of Egypt Gate" -> "Mall Of Egypt Gate" with capitalize() since "Of" would become "Of" (capital O, lower f). Actually "of" -> "Of" with capitalize(). Hmm but that's what we get with simple capitalize(). Let me check if the requirement mentions specific formatting. The user says "the output should use consistent names" and "Tidy the anchor names too." I think just stripping and capitalizing each word is fine. Let me also check that the "Mall Of Egypt Gate" looks reasonable. Actually, "Mall of Egypt Gate" might be more natural but "Mall Of Egypt Gate" is fine for normalised data. Let me run the full script again and verify the output once more.
3:53
Bash
cd /work && python solve.py
Written 100 records to market_neighbourhoods.json
=== First record ===
{
"anchor_id": "M001",
"anchor_name_normalised": "Tahrir Square Plaza",
"knn": [
{
"shop_id": "S07233",
"normalised_name": "Seoudi",
"distance_m": 565.5774729861073,
"within_1km": true
},
{
"shop_id": "S05762",
"normalised_name": "Oscar",
"distance_m": 722.3301477157331,
"within_1km": true
},
{
"shop_id": "S08811",
"normalised_name": "Seoudi",
"distance_m": 851.1481358488758,
"within_1km": true
},
{
"shop_id": "S01722",
"normalised_name": "Local Shop 01722",
"distance_m": 852.4416831354031,
"within_1km": true
},
{
"shop_id": "S04111",
"normalised_name": "Local Shop 04111",
"distance_m": 868.96252213481,
"within_1km": true
}
],
"full_distance_matrix_m": [
[
2487.0078080337594,
2174.324012747061,
3254.5114413442143
],
[
1534.4843964513216,
1756.6458865635916,
2217.728977070344
],
[
1083.478376599746,
2432.536076922434,
2523.2160443438365
],
[
1080.9227579664303,
2423.437856092515,
2512.907664013948
],
[
2589.6466467244945,
1669.1438268232814,
2955.6767888178206
]
]
}
4:05
The output looks correct. Let me do a final validation to ensure everything is right, particularly that the `within_1km` flag is correct (true when distance_m <= 1000).
4:05
Bash
cd /work && python3 -c "
cd /work && python3 -c "
import json
with open('/work/market_neighbourhoods.json') as f:
data = json.load(f)
# Detailed checks
ok = True
# 1. All records have required keys
for r in data:
for k in ['anchor_id', 'anchor_name_normalised', 'knn', 'full_distance_matrix_m']:
if k not in r:
print(f'Missing key {k} in record')
ok = False
# 2. knn has exactly 5 entries sorted ascending by distance_m
for r in data:
knn = r['knn']
if len(knn) != 5:
print(f'{r[\"anchor_id\"]}: knn length {len(knn)}')
ok = False
dists = [k['distance_m'] for k in knn]
if dists != sorted(dists):
print(f'{r[\"anchor_id\"]}: knn not sorted by distance')
ok = False
for k in knn:
if not isinstance(k['distance_m'], (int, float)):
print(f'{r[\"anchor_id\"]}: distance_m not numeric')
ok = False
if k['within_1km'] != (k['distance_m'] <= 1000.0):
print(f'{r[\"anchor_id\"]}: within_1km mismatch for {k[\"shop_id\"]}')
ok = False
if not k['normalised_name']:
print(f'{r[\"anchor_id\"]}: empty normalised_name for {k[\"shop_id\"]}')
ok = False
# 3. full_distance_matrix_m is 5x3 numeric
for r in data:
mat = r['full_distance_matrix_m']
if len(mat) != 5:
print(f'{r[\"anchor_id\"]}: matrix rows {len(mat)}')
ok = False
for row in mat:
if len(row) != 3:
print(f'{r[\"anchor_id\"]}: matrix cols {len(row)}')
ok = False
for v in row:
if not isinstance(v, (int, float)):
print(f'{r[\"anchor_id\"]}: matrix value not numeric')
ok = False
# 4. anchor_name_normalised non-empty string
for r in data:
if not isinstance(r['anchor_name_normalised'], str) or not r['anchor_name_normalised']:
print(f'{r[\"anchor_id\"]}: bad anchor_name_normalised')
ok = False
# 5. Same shop_id same normalised_name
shop_map = {}
for r in data:
for k in r['knn']:
sid = k['shop_id']
nm = k['normalised_name']
if sid in shop_map:
if shop_map[sid] != nm:
print(f'{sid}: inconsistent names: {shop_map[sid]} vs {nm}')
ok = False
else:
shop_map[sid] = nm
if ok:
print('All validations passed.')
else:
print('Some validations FAILED.')
"
All validations passed.
4:17
The solution is working correctly. Let me verify a few more things about the output to ensure quality: 1. Each anchor has exactly 5 knn entries sorted by distance ascending ✅ 2. within_1km is boolean ✅ 3. distance_m is numeric ✅ 4. full_distance_matrix_m is 5x3 numeric ✅ 5. Same shop_id always has same normalised_name ✅ 6. anchor_name_normalised is non-empty string ✅ Let me also verify the output file is properly formatted.
4:17
Bash
cd /work && python3 -c "
cd /work && python3 -c "
import json
with open('/work/market_neighbourhoods.json') as f:
data = json.load(f)
# Check that the JSON is valid and can be read back
print(f'Valid JSON: {len(data)} records')
# Show a middle record for spot check
print()
print('=== Record M050 ===')
print(json.dumps(data[49], ensure_ascii=False, indent=2))
"
Valid JSON: 100 records
=== Record M050 ===
{
"anchor_id": "M050",
"anchor_name_normalised": "Tagamoa Third",
"knn": [
{
"shop_id": "S08198",
"normalised_name": "Spinneys",
"distance_m": 192.36742287622147,
"within_1km": true
},
{
"shop_id": "S06874",
"normalised_name": "Spinneys",
"distance_m": 526.392779820765,
"within_1km": true
},
{
"shop_id": "S00883",
"normalised_name": "Hyper One",
"distance_m": 631.8184951531942,
"within_1km": true
},
{
"shop_id": "S03165",
"normalised_name": "Local Shop 03165",
"distance_m": 722.2952125672768,
"within_1km": true
},
{
"shop_id": "S03197",
"normalised_name": "Carrefour",
"distance_m": 786.021905834417,
"within_1km": true
}
],
"full_distance_matrix_m": [
[
1766.3565820840124,
2125.253147124172,
2321.2763149430584
],
[
2441.148674178562,
2141.5875382861877,
2408.0702621611886
],
[
2298.9962836714817,
2788.4263216380186,
1689.401530930335
],
[
2650.674173014068,
2364.25784251828,
2298.853211210525
],
[
1477.928120850934,
2825.989531742687,
1755.1199136354458
]
]
}
4:28
4:28
Bash
cd /work && wc -c market_neighbourhoods.json && python3 -c "
cd /work && wc -c market_neighbourhoods.json && python3 -c "
import json
with open('/work/market_neighbourhoods.json') as f:
data = json.load(f)
print(f'Records: {len(data)}')
print(f'Total knn entries: {sum(len(r[\"knn\"]) for r in data)}')
print(f'Total matrix cells: {sum(len(r[\"full_distance_matrix_m\"])*3 for r in data)}')
"
142251 market_neighbourhoods.json Records: 100 Total knn entries: 500 Total matrix cells: 1500
4:39
The file is ready. Let me do a final review of what I've done: 1. Loaded the `shops` (10,000 points) and `anchors` (100 points) from the GeoPackage. 2. Normalised shop names: - Mapped inconsistent spellings to canonical forms (Oscar, Carrefour, Hyper One, Metro, Seoudi, Spinneys, Khair Zaman, Abu Zekry) - Used `raw_name.strip()` for Local Shop entries - Each `shop_id` maps to a single `normalised_name` 3. Normalised anchor names: stripped whitespace and capitalised each word 4. Computed Euclidean distances (CRS is EPSG:22992, projected in metres) 5. For each anchor: - Found 5 nearest shops, sorted by distance ascending - Created knn entries with `shop_id`, `normalised_name`, `distance_m`, `within_1km` - Found 3 closest sibling anchors (excluding self) - Built 5x3 matrix: rows follow knn order, columns follow sibling anchor order (ascending distance) 6. Wrote JSON array to `market_neighbourhoods.json` All validations passed.
4:39