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"
}
0:02
Let me start by exploring the data source to understand its structure.
0:02
Bash
cd /work && ls -la cairo_retail*
-rw-r--r-- 1 agent agent 1257472 Jun 19 01:30 cairo_retail.gpkg
0:06
Let me start by exploring the GeoPackage to understand its structure.
0:06
Bash
cd /work && python3 -c "
cd /work && python3 -c "
import geopandas as gpd
# List layers
layers = gpd.list_layers('cairo_retail.gpkg')
print('Layers:', layers)
print()
for layer in layers['name']:
gdf = gpd.read_file('cairo_retail.gpkg', layer=layer)
print(f'=== {layer} ===')
print(gdf.head())
print('Columns:', list(gdf.columns))
print('Shape:', gdf.shape)
print('CRS:', gdf.crs)
print()
"
Layers: name geometry_type 0 shops Point 1 anchors Point === shops === 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) Columns: ['shop_id', 'raw_name', 'geometry'] Shape: (10000, 3) CRS: EPSG:22992 === anchors === 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) Columns: ['anchor_id', 'anchor_name', 'geometry'] Shape: (100, 3) CRS: EPSG:22992
0:12
Let me understand the data: 1. **shops** layer: 10000 shops with `shop_id`, `raw_name`, `geometry` (Point, EPSG:22992) 2. **anchors** layer: 100 anchors with `anchor_id`, `anchor_name`, `geometry` (Point, EPSG:22992) Let me explore the raw names to understand the inconsistencies.
0:12
Bash
cd /work && python3 -c "
cd /work && python3 -c "
import geopandas as gpd
shops = gpd.read_file('cairo_retail.gpkg', layer='shops')
print('Unique raw_name count:', shops['raw_name'].nunique())
print()
# See all unique names
for n in sorted(shops['raw_name'].unique()):
print(repr(n))
"
Unique raw_name count: 5035 '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 ...] 08473' 'Local Shop 08475' 'Local Shop 08476' 'Local Shop 08477' 'Local Shop 08484' 'Local Shop 08485' 'Local Shop 08487' 'Local Shop 08488' 'Local Shop 08489' 'Local Shop 08490' 'Local Shop 08493' 'Local Shop 08494' 'Local Shop 08496' 'Local Shop 08498' 'Local Shop 08499' 'Local Shop 08502' 'Local Shop 08504' 'Local Shop 08506' 'Local Shop 08513' 'Local Shop 08514' 'Local Shop 08516' 'Local Shop 08517' 'Local Shop 08524' 'Local Shop 08529' 'Local Shop 08533' 'Local Shop 08534' 'Local Shop 08535' 'Local Shop 08536' 'Local Shop 08537' 'Local Shop 08538' 'Local Shop 08539' 'Local Shop 08541' 'Local Shop 08543' 'Local Shop 08549' 'Local Shop 08550' 'Local Shop 08552' 'Local Shop 08554' 'Local Shop 08557' 'Local Shop 08558' 'Local Shop 08560' 'Local Shop 08562' 'Local Shop 08564' 'Local Shop 08565' 'Local Shop 08566' 'Local Shop 08567' 'Local Shop 08571' 'Local Shop 08572' 'Local Shop 08573' 'Local Shop 08574' 'Local Shop 08577' 'Local Shop 08578' 'Local Shop 08581' 'Local Shop 08589' 'Local Shop 08590' 'Local Shop 08591' 'Local Shop 08592' 'Local Shop 08594' 'Local Shop 08595' 'Local Shop 08597' 'Local Shop 08598' 'Local Shop 08599' 'Local Shop 08601' 'Local Shop 08603' 'Local Shop 08604' 'Local Shop 08605' 'Local Shop 08609' 'Local Shop 08610' 'Local Shop 08612' 'Local Shop 08615' 'Local Shop 08616' 'Local Shop 08622' 'Local Shop 08623' 'Local Shop 08624' 'Local Shop 08625' 'Local Shop 08629' 'Local Shop 08631' 'Local Shop 08634' 'Local Shop 08635' 'Local Shop 08637' 'Local Shop 08638' 'Local Shop 08640' 'Local Shop 08643' 'Local Shop 08644' 'Local Shop 08647' 'Local Shop 08648' 'Local Shop 08649' 'Local Shop 08650' 'Local Shop 08651' 'Local Shop 08655' 'Local Shop 08658' 'Local Shop 08659' 'Local Shop 08660' 'Local Shop 08661' 'Local Shop 08662' 'Local Shop 08666' 'Local Shop 08667' 'Local Shop 08671' 'Local Shop 08672' 'Local Shop 08674' 'Local Shop 08675' 'Local Shop 08676' 'Local Shop 08677' 'Local Shop 08678' 'Local Shop 08679' 'Local Shop 08680' 'Local Shop 08681' 'Local Shop 08687' 'Local Shop 08688' 'Local Shop 08689' 'Local Shop 08690' 'Local Shop 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'
0:20
Let me understand the data better. I need to: 1. Normalize shop names (chain names with inconsistent spellings) 2. Normalize anchor names 3. For each anchor, find the 5 nearest shops with distances 4. For each anchor, find its 3 closest sibling anchors 5. Build a 5x3 distance matrix from those 5 shops to the 3 sibling anchors Let me look at the anchor names and raw names more carefully to figure out the name normalization. The shop raw names have a lot of "Local Shop XXXXX" entries - these seem to be individual shops. Then there are chain names that have inconsistencies: - 'OSCAR', 'Oscar', 'Oscar Grand Stores', 'اوسكار' (Arabic) - all likely "Oscar" - 'Carrefour', 'Carrefour Egypt', 'carrefour', 'كارفور' - all likely "Carrefour" - 'Hyper One', 'HyperOne', 'hyperone', 'هايبر وان' - all likely "HyperOne" - 'Khair Zaman', 'Kheir Zaman', 'kheir zaman', 'خير زمان' - all likely "Kheir Zaman" or "Khair Zaman" - 'Abu Zekry', 'Abu Zikri', 'abou zekry', 'أبو زكري' - all likely "Abu Zekry" - 'Metro Market', 'Metro Markets', 'metro', 'مترو' - all likely "Metro" - 'Seoudi', 'Seoudi Market', 'seoudi supermarket', 'سعودي' - all likely "Seoudi" - 'Spineys', 'Spinneys', 'spinneys cairo', 'سبينيز' - all likely "Spinneys" Let me also check the anchor names.
0:20
0:20
Bash
cd /work && python3 -c "
cd /work && python3 -c "
import geopandas as gpd
anchors = gpd.read_file('cairo_retail.gpkg', layer='anchors')
print('Anchors:')
for _, row in anchors.iterrows():
print(f'{row[\"anchor_id\"]}: {repr(row[\"anchor_name\"])}')
print()
print('Total:', len(anchors))
"
Anchors: 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' Total: 100
0:29
Let me understand the data: 1. **Shops** - 10,000 records with `shop_id`, `raw_name`, geometry (points, EPSG:22992) 2. **Anchors** - 100 records with `anchor_id`, `anchor_name`, geometry (points, EPSG:22992) The CRS is EPSG:22992 which is a projected coordinate system (Egypt Red Belt), so distances in metres can be computed directly from the coordinates. I need to: 1. **Normalise shop names**: Each `shop_id` always carries the same `normalised_name`. All shops belonging to one chain share a single canonical spelling. Let me figure out the chain groupings: Looking at the raw names: - 'Abu Zekry', 'Abu Zikri', 'abou zekry', 'أبو زكري' → same chain - 'Carrefour', 'Carrefour Egypt', 'carrefour', 'كارفور' → same chain - 'Hyper One', 'HyperOne', 'هايبر وان' → same chain - 'Khair Zaman', 'Kheir Zaman', 'kheir zaman', 'خير زمان' → same chain - 'Metro Market', 'Metro Markets', 'metro', 'مترو' → same chain - 'OSCAR', 'Oscar', 'Oscar Grand Stores', 'اوسكار' → same chain - 'Seoudi', 'Seoudi Market', 'seoudi supermarket', 'سعودي' → same chain - 'Spineys', 'Spinneys', 'spinneys cairo', 'سبينيز' → same chain And all "Local Shop XXXXX" entries are individual shops (each unique). Let me also check: which shop_ids map to which raw_names to understand the mapping. 2. **Normalise anchor names**: strip whitespace, title-case them, consistent formatting. 3. **For each anchor**: - Find the 5 nearest shops (by Euclidean distance since CRS is projected) - Compute distance in metres - Determine within_1km flag - For each of those 5 shops, compute distance to the 3 closest sibling anchors (other anchors, sorted by distance ascending) Let me write a comprehensive Python script to do all of this. First, let me understand the shop name normalisation better.
0:29
Bash
cd /work && python3 -c "
cd /work && python3 -c "
import geopandas as gpd
shops = gpd.read_file('cairo_retail.gpkg', layer='shops')
# Check which shop_ids map to which raw_names for the chain brands
brands = ['Abu Zekry', 'Abu Zikri', 'abou zekry', 'أبو زكري',
'Carrefour', 'Carrefour Egypt', 'carrefour', 'كارفور',
'Hyper One', 'HyperOne', 'هايبر وان',
'Khair Zaman', 'Kheir Zaman', 'kheir zaman', 'خير زمان',
'Metro Market', 'Metro Markets', 'metro', 'مترو',
'OSCAR', 'Oscar', 'Oscar Grand Stores', 'اوسكار',
'Seoudi', 'Seoudi Market', 'seoudi supermarket', 'سعودي',
'Spineys', 'Spinneys', 'spinneys cairo', 'سبينيز']
for b in brands:
subset = shops[shops['raw_name'] == b]
print(f'{repr(b)}: {len(subset)} shops, ids: {list(subset[\"shop_id\"].values)[:5]}')
"
'Abu Zekry': 136 shops, ids: ['S00083', 'S00178', 'S00215', 'S00235', 'S00340'] 'Abu Zikri': 137 shops, ids: ['S00117', 'S00124', 'S00160', 'S00163', 'S00177'] 'abou zekry': 156 shops, ids: ['S00089', 'S00099', 'S00308', 'S00411', 'S00476'] 'أبو زكري': 136 shops, ids: ['S00029', 'S00179', 'S00193', 'S00287', 'S00304'] 'Carrefour': 143 shops, ids: ['S00008', 'S00010', 'S00073', 'S00098', 'S00101'] 'Carrefour Egypt': 169 shops, ids: ['S00021', 'S00057', 'S00070', 'S00175', 'S00364'] 'carrefour': 136 shops, ids: ['S00022', 'S00199', 'S00636', 'S00747', 'S00761'] 'كارفور': 150 shops, ids: ['S00044', 'S00183', 'S00196', 'S00355', 'S00440'] 'Hyper One': 158 shops, ids: ['S00028', 'S00254', 'S00356', 'S00377', 'S00402'] 'HyperOne': 152 shops, ids: ['S00174', 'S00467', 'S00540', 'S00609', 'S00682'] 'هايبر وان': 183 shops, ids: ['S00114', 'S00186', 'S00219', 'S00252', 'S00255'] 'Khair Zaman': 171 shops, ids: ['S00149', 'S00224', 'S00243', 'S00422', 'S00469'] 'Kheir Zaman': 158 shops, ids: ['S00046', 'S00238', 'S00257', 'S00267', 'S00317'] 'kheir zaman': 168 shops, ids: ['S00065', 'S00294', 'S00414', 'S00421', 'S00436'] 'خير زمان': 162 shops, ids: ['S00061', 'S00131', 'S00155', 'S00189', 'S00247'] 'Metro Market': 153 shops, ids: ['S00068', 'S00074', 'S00087', 'S00147', 'S00180'] 'Metro Markets': 164 shops, ids: ['S00039', 'S00066', 'S00130', 'S00237', 'S00285'] 'metro': 165 shops, ids: ['S00097', 'S00108', 'S00129', 'S00156', 'S00291'] 'مترو': 156 shops, ids: ['S00006', 'S00047', 'S00112', 'S00218', 'S00274'] 'OSCAR': 176 shops, ids: ['S00001', 'S00115', 'S00128', 'S00145', 'S00273'] 'Oscar': 150 shops, ids: ['S00140', 'S00154', 'S00263', 'S00345', 'S00404'] 'Oscar Grand Stores': 163 shops, ids: ['S00094', 'S00100', 'S00122', 'S00148', 'S00249'] 'اوسكار': 154 shops, ids: ['S00054', 'S00060', 'S00221', 'S00248', 'S00270'] 'Seoudi': 176 shops, ids: ['S00062', 'S00078', 'S00102', 'S00109', 'S00290'] 'Seoudi Market': 160 shops, ids: ['S00026', 'S00042', 'S00045', 'S00134', 'S00141'] 'seoudi supermarket': 139 shops, ids: ['S00043', 'S00118', 'S00173', 'S00212', 'S00445'] 'سعودي': 146 shops, ids: ['S00012', 'S00223', 'S00265', 'S00374', 'S00620'] 'Spineys': 145 shops, ids: ['S00058', 'S00116', 'S00126', 'S00133', 'S00168'] 'Spinneys': 150 shops, ids: ['S00016', 'S00051', 'S00064', 'S00220', 'S00288'] 'spinneys cairo': 174 shops, ids: ['S00123', 'S00293', 'S00500', 'S00614', 'S00644'] 'سبينيز': 156 shops, ids: ['S00032', 'S00204', 'S00360', 'S00606', 'S00607']
0:54
Now I need to understand the task fully. Let me figure out the normalisation mapping. For shops, the chain names have inconsistent spellings. I need to define canonical names: - 'Abu Zekry', 'Abu Zikri', 'abou zekry', 'أبو زكري' → 'Abu Zikri' (or some canonical) - 'Carrefour', 'Carrefour Egypt', 'carrefour', 'كارفور' → 'Carrefour' - 'Hyper One', 'HyperOne', 'هايبر وان' → 'Hyper One' - 'Khair Zaman', 'Kheir Zaman', 'kheir zaman', 'خير زمان' → 'Kheir Zaman' - 'Metro Market', 'Metro Markets', 'metro', 'مترو' → 'Metro' - 'OSCAR', 'Oscar', 'Oscar Grand Stores', 'اوسكار' → 'Oscar' - 'Seoudi', 'Seoudi Market', 'seoudi supermarket', 'سعودي' → 'Seoudi' - 'Spineys', 'Spinneys', 'spinneys cairo', 'سبينيز' → 'Spinneys' For the "Local Shop XXXXX" entries - these are unique per shop_id (each has a different number), so each is its own individual shop, not part of a chain. Their normalised_name would be the same as raw_name. For anchors: - Strip leading/trailing whitespace - Title case? Actually, looking at the data, some are all caps, some are mixed case. I'll normalise by stripping whitespace and title-casing consistently. But some like "TAHRIR SQUARE PLAZA" should become "Tahrir Square Plaza". And " Maadi Corniche " should become "Maadi Corniche". Let me now think about the algorithm: 1. Build a normalised_name for each shop_id 2. For each anchor: a. Compute distances to all shops, find 5 nearest b. For each of those 5 shops, set within_1km flag c. Find the anchor's 3 closest sibling anchors (other anchors) d. Compute distance matrix: for each of the 5 shops, compute distance to each of those 3 sibling anchors Wait, I need to re-read: "plus a 5x3 distance matrix from those 5 shops to the anchor's 3 closest sibling anchors." So for a given anchor A: - Find the 3 closest OTHER anchors (siblings) to A - For each of the 5 nearest shops (in knn order), compute distance to each of those 3 sibling anchors - The matrix has 5 rows (following knn order) and 3 columns (corresponding to sibling anchors in distance-ascending order) Let me write a comprehensive script.
0:54
0:54
Write
/work/solve.py
content · 148 lines · py
#!/usr/bin/env python3
"""
Build market_neighbourhoods.json from cairo_retail.gpkg
For each anchor:
- knn: 5 nearest shops with distance_m, within_1km flag, normalised name.
- full_distance_matrix_m: 5x3 matrix (5 shops in knn order × 3 closest sibling anchors
in distance-ascending order).
"""
import geopandas as gpd
import pandas as pd
import numpy as np
import json
# ── 1. 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) – already is.
# ── 2. Build normalised names ────────────────────────────────────────────────
# Chain-name mapping (lowercased key → canonical name)
CHAIN_MAP = {
"abu zekry": "Abu Zikri",
"abu zikri": "Abu Zikri",
"abou zekry": "Abu Zikri",
"أبو زكري": "Abu Zikri",
"carrefour": "Carrefour",
"carrefour egypt": "Carrefour",
"كارفور": "Carrefour",
"hyper one": "Hyper One",
"hyperone": "Hyper One",
"هايبر وان": "Hyper One",
"khair zaman": "Kheir Zaman",
"kheir zaman": "Kheir Zaman",
"kheir zaman": "Kheir Zaman",
"خير زمان": "Kheir Zaman",
"metro market": "Metro",
"metro markets": "Metro",
"metro": "Metro",
"مترو": "Metro",
"oscar": "Oscar",
"oscar grand stores": "Oscar",
"اوسكار": "Oscar",
"seoudi": "Seoudi",
"seoudi market": "Seoudi",
"seoudi supermarket": "Seoudi",
"سعودي": "Seoudi",
"spineys": "Spinneys",
"spinneys": "Spinneys",
"spinneys cairo": "Spinneys",
"سبينيز": "Spinneys",
}
def normalise_shop_name(raw):
key = raw.strip().lower()
if key in CHAIN_MAP:
return CHAIN_MAP[key]
# Local Shop XXXXX – keep as-is
return raw.strip()
# Apply normalisation: each shop_id always gets the same normalised name
shop_name_map = dict(zip(shops["shop_id"], shops["raw_name"].map(normalise_shop_name)))
shops["normalised_name"] = shops["raw_name"].map(normalise_shop_name)
def normalise_anchor_name(raw):
return raw.strip()
anchors["anchor_name_normalised"] = anchors["anchor_name"].map(normalise_anchor_name)
# ── 3. Prepare coordinates ───────────────────────────────────────────────────
# Extract coordinates for fast computation
shop_coords = np.column_stack([shops.geometry.x, shops.geometry.y])
shop_ids = shops["shop_id"].values
shop_names = shops["normalised_name"].values
anchor_coords = np.column_stack([anchors.geometry.x, anchors.geometry.y])
anchor_ids = anchors["anchor_id"].values
anchor_names_norm = anchors["anchor_name_normalised"].values
# ── 4. Helper: Euclidean distance from projected CRS ─────────────────────────
# EPSG:22992 uses metres, so Euclidean distance is in metres.
def dist_matrix(a_coords, b_coords):
"""Return (n_a, n_b) matrix of Euclidean distances in metres."""
diff = a_coords[:, np.newaxis, :] - b_coords[np.newaxis, :, :]
return np.sqrt((diff ** 2).sum(axis=-1))
# ── 5. For each anchor, compute everything ───────────────────────────────────
records = []
for ai, anchor_id in enumerate(anchor_ids):
# ── 5a. KNN shops ────────────────────────────────────────────────────────
# Distances from this anchor to all shops
d_all = np.sqrt(((shop_coords - anchor_coords[ai]) ** 2).sum(axis=1))
# Sort indices
sorted_idx = np.argsort(d_all)
top5_idx = sorted_idx[:5]
top5_dist = d_all[top5_idx]
knn_list = []
for si in range(5):
sidx = top5_idx[si]
knn_list.append({
"shop_id": shop_ids[sidx],
"normalised_name": shop_names[sidx],
"distance_m": round(float(top5_dist[si]), 2),
"within_1km": bool(top5_dist[si] <= 1000.0),
})
# ── 5b. 3 closest sibling anchors (other anchors) ───────────────────────
# Distances from this anchor to all OTHER anchors
d_anch = np.sqrt(((anchor_coords - anchor_coords[ai]) ** 2).sum(axis=1))
# Set self-distance to infinity so we don't pick self
d_anch[ai] = np.inf
sibling_idx = np.argsort(d_anch)[:3] # 3 closest siblings
# Sort siblings by distance ascending (already are)
sibling_idx = sibling_idx[np.argsort(d_anch[sibling_idx])]
# ── 5c. 5×3 distance matrix ─────────────────────────────────────────────
# For each of the 5 shops (in knn order), compute distance to each of the 3 siblings
matrix = np.zeros((5, 3), dtype=float)
for si in range(5):
sidx = top5_idx[si]
sx, sy = shop_coords[sidx]
for sj in range(3):
aidx = sibling_idx[sj]
ax, ay = anchor_coords[aidx]
dist = np.sqrt((sx - ax) ** 2 + (sy - ay) ** 2)
matrix[si, sj] = round(float(dist), 2)
records.append({
"anchor_id": anchor_id,
"anchor_name_normalised": anchor_names_norm[ai],
"knn": knn_list,
"full_distance_matrix_m": matrix.tolist(),
})
# ── 6. Write JSON ────────────────────────────────────────────────────────────
with open("/work/market_neighbourhoods.json", "w", encoding="utf-8") as f:
json.dump(records, f, ensure_ascii=False, indent=2)
print("Done. Wrote %d anchor records to /work/market_neighbourhoods.json" % len(records))Wrote /work/solve.py (5262 chars).
0:59
Let me run the script and check the output.
0:59
Bash
cd /work && python solve.py
Done. Wrote 100 anchor records to /work/market_neighbourhoods.json
1:12
Let me verify the output looks correct by reading some of it.
1:12
1:12
Bash
cd /work && python3 -c "
cd /work && python3 -c "
import json
with open('market_neighbourhoods.json') as f:
data = json.load(f)
print(f'Number of records: {len(data)}')
print()
# Show first record
rec = data[0]
print('First record:')
print(json.dumps(rec, ensure_ascii=False, indent=2))
print()
# Check some invariants
for rec in data:
# Check anchor_name_normalised is non-empty
assert isinstance(rec['anchor_name_normalised'], str) and len(rec['anchor_name_normalised']) > 0, f'Bad name for {rec[\"anchor_id\"]}'
# Check knn length
assert len(rec['knn']) == 5, f'knn length not 5 for {rec[\"anchor_id\"]}'
# Check knn sorted ascending by distance_m
dists = [k['distance_m'] for k in rec['knn']]
assert all(dists[i] <= dists[i+1] for i in range(4)), f'knn not sorted for {rec[\"anchor_id\"]}'
# Check within_1km consistency
for k in rec['knn']:
assert k['within_1km'] == (k['distance_m'] <= 1000), f'within_1km mismatch for {rec[\"anchor_id\"]} shop {k[\"shop_id\"]}'
# Check matrix shape
mat = rec['full_distance_matrix_m']
assert len(mat) == 5, f'matrix rows not 5 for {rec[\"anchor_id\"]}'
for row in mat:
assert len(row) == 3, f'matrix cols not 3 for {rec[\"anchor_id\"]}'
# Check shop_id → normalised_name consistency across all records
print('All invariants passed!')
print()
# Check name normalisation consistency
# Build a map from shop_id to normalised_name across all knn entries
shop_name_map = {}
for rec in data:
for knn in rec['knn']:
sid = knn['shop_id']
nm = knn['normalised_name']
if sid in shop_name_map:
assert shop_name_map[sid] == nm, f'Inconsistent name for {sid}: {shop_name_map[sid]} vs {nm}'
else:
shop_name_map[sid] = nm
print(f'Unique shop_ids referenced: {len(shop_name_map)}')
print('Name consistency check passed!')
# Check that all distance_m values are finite numeric
for rec in data:
for k in rec['knn']:
assert isinstance(k['distance_m'], (int, float)) and np.isfinite(k['distance_m'])
print('All distances are finite numbers')
"
Number of records: 100
First record:
{
"anchor_id": "M001",
"anchor_name_normalised": "TAHRIR SQUARE PLAZA",
"knn": [
{
"shop_id": "S07233",
"normalised_name": "Seoudi",
"distance_m": 565.58,
"within_1km": true
},
{
"shop_id": "S05762",
"normalised_name": "Oscar",
"distance_m": 722.33,
"within_1km": true
},
{
"shop_id": "S08811",
"normalised_name": "Seoudi",
"distance_m": 851.15,
"within_1km": true
},
{
"shop_id": "S01722",
"normalised_name": "Local Shop 01722",
"distance_m": 852.44,
"within_1km": true
},
{
"shop_id": "S04111",
"normalised_name": "Local Shop 04111",
"distance_m": 868.96,
"within_1km": true
}
],
"full_distance_matrix_m": [
[
2487.01,
2174.32,
3254.51
],
[
1534.48,
1756.65,
2217.73
],
[
1083.48,
2432.54,
2523.22
],
[
1080.92,
2423.44,
2512.91
],
[
2589.65,
1669.14,
2955.68
]
]
}
All invariants passed!
Unique shop_ids referenced: 480
Name consistency check passed!
Traceback (most recent call last):
File "<string>", line 51, in <module>
assert isinstance(k['distance_m'], (int, float)) and np.isfinite(k['distance_m'])
^^
NameError: name 'np' is not defined. Did you mean: 'nm'?
1:19
The error is just in my validation script (I forgot to import numpy). Let me fix the validation and re-run.
1:19
1:19
Bash
cd /work && python3 -c "
cd /work && python3 -c "
import json, numpy as np
with open('market_neighbourhoods.json') as f:
data = json.load(f)
# Check all distance_m values are finite numeric
for rec in data:
for k in rec['knn']:
assert isinstance(k['distance_m'], (int, float)) and np.isfinite(k['distance_m'])
for row in rec['full_distance_matrix_m']:
for v in row:
assert isinstance(v, (int, float)) and np.isfinite(v)
print('All distances are finite numbers')
# Check a couple more records
for rec in data[:3]:
print(f'{rec[\"anchor_id\"]} ({rec[\"anchor_name_normalised\"]}): '
f'dists={[k[\"distance_m\"] for k in rec[\"knn\"]]}, '
f'within={[k[\"within_1km\"] for k in rec[\"knn\"]]}')
# Check sibling anchor columns are sorted ascending
for rec in data:
mat = rec['full_distance_matrix_m']
for row in mat:
# columns should be ascending (closest sibling first, etc.)
assert row[0] <= row[1] <= row[2] or abs(row[0]-row[1])<1e-6, f'Matrix columns not sorted for {rec[\"anchor_id\"]}'
print('All matrix columns sorted ascending (closest sibling first)')
# Check output structure once more
print()
print('Sample last record:')
print(json.dumps(data[-1], ensure_ascii=False, indent=2))
"
All distances are finite numbers M001 (TAHRIR SQUARE PLAZA): dists=[565.58, 722.33, 851.15, 852.44, 868.96], within=[True, True, True, True, True] M002 (Zamalek Riverside): dists=[269.65, 312.58, 376.43, 398.44, 553.53], within=[True, True, True, True, True] M003 (Maadi Corniche): dists=[72.95, 88.73, 98.21, 99.58, 116.76], within=[True, True, True, True, True]
Traceback (most recent call last):
File "<string>", line 27, in <module>
assert row[0] <= row[1] <= row[2] or abs(row[0]-row[1])<1e-6, f'Matrix columns not sorted for {rec["anchor_id"]}'
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
AssertionError: Matrix columns not sorted for M001
1:33
The issue is that the matrix columns correspond to the sibling anchors in distance-ascending order (which I did sort), but the matrix values for a given shop row may not themselves be sorted ascending across columns. Wait, let me re-read the spec: "columns correspond to the sibling anchors in distance-ascending order" So the 3 columns correspond to the 3 sibling anchors, ordered by their distance from THIS anchor (ascending). That's what I did - I sorted sibling_idx by distance from this anchor. But then the assertion `row[0] <= row[1] <= row[2]` is not necessarily true - because the distance from a particular shop to sibling A (the closest sibling to this anchor) might be larger than to sibling B (the 2nd closest sibling to the anchor). That's fine - the columns are labeled by sibling anchor identity, not by value magnitude. So my output is correct and the assertion was wrong. Let me remove that bad assertion and just verify the structure is right.
1:33
1:33
Bash
cd /work && python3 -c "
cd /work && python3 -c "
import json
with open('market_neighbourhoods.json') as f:
data = json.load(f)
# Verify sibling columns correspond to closest siblings
import geopandas as gpd
import numpy as np
anchors = gpd.read_file('cairo_retail.gpkg', layer='anchors')
anchors['name_norm'] = anchors['anchor_name'].str.strip()
anchor_coords = np.column_stack([anchors.geometry.x, anchors.geometry.y])
for rec in data[:5]:
ai = list(anchors['anchor_id']).index(rec['anchor_id'])
# Compute 3 closest sibling anchors
d = np.sqrt(((anchor_coords - anchor_coords[ai]) ** 2).sum(axis=1))
d[ai] = np.inf
sib_idx = np.argsort(d)[:3]
sib_dists = d[sib_idx]
print(f'{rec[\"anchor_id\"]} ({rec[\"anchor_name_normalised\"]}):')
print(f' Siblings: {[anchors.iloc[i][\"anchor_id\"] + \" (\" + anchors.iloc[i][\"name_norm\"] + \" dist=\" + str(round(d[i],2)) + \")\" for i in sib_idx]}')
print(f' Matrix rows (5 shops x 3 siblings):')
for ri, row in enumerate(rec['full_distance_matrix_m']):
print(f' Shop {rec[\"knn\"][ri][\"shop_id\"]} (dist={rec[\"knn\"][ri][\"distance_m\"]}): {row}')
print()
# Check the M001 case that had the assertion failure earlier
print('M001 matrix row 0:')
rec = data[0]
print(rec['full_distance_matrix_m'][0])
# Expected: first sibling = the closest one to M001
# Let's see what those siblings are
ai = 0
d = np.sqrt(((anchor_coords - anchor_coords[ai]) ** 2).sum(axis=1))
d[ai] = np.inf
sib_idx = np.argsort(d)[:3]
print(f'M001 closest siblings:')
for i in sib_idx:
print(f' {anchors.iloc[i][\"anchor_id\"]}: {anchors.iloc[i][\"name_norm\"]} dist={d[i]:.2f}')
"
M001 (TAHRIR SQUARE PLAZA):
Siblings: ['M002 (Zamalek Riverside dist=1930.5)', 'M011 (Roxy Square dist=2217.04)', 'M012 (Ramses Crossing dist=2939.62)']
Matrix rows (5 shops x 3 siblings):
Shop S07233 (dist=565.58): [2487.01, 2174.32, 3254.51]
Shop S05762 (dist=722.33): [1534.48, 1756.65, 2217.73]
Shop S08811 (dist=851.15): [1083.48, 2432.54, 2523.22]
Shop S01722 (dist=852.44): [1080.92, 2423.44, 2512.91]
Shop S04111 (dist=868.96): [2589.65, 1669.14, 2955.68]
M002 (Zamalek Riverside):
Siblings: ['M001 (TAHRIR SQUARE PLAZA dist=1930.5)', 'M003 (Maadi Corniche dist=1930.51)', 'M012 (Ramses Crossing dist=2217.05)']
Matrix rows (5 shops x 3 siblings):
Shop S07867 (dist=269.65): [1944.49, 1953.94, 1947.44]
Shop S08880 (dist=312.58): [1618.27, 2242.84, 2252.19]
Shop S01618 (dist=376.43): [1563.29, 2300.71, 2321.03]
Shop S08973 (dist=398.44): [1556.2, 2312.91, 2370.06]
Shop S03901 (dist=553.53): [1378.43, 2483.23, 2317.79]
M003 (Maadi Corniche):
Siblings: ['M002 (Zamalek Riverside dist=1930.51)', 'M004 (HELIOPOLIS SQUARE dist=1930.51)', 'M013 (SAYEDA ZEINAB PLAZA dist=2217.05)']
Matrix rows (5 shops x 3 siblings):
Shop S04460 (dist=72.95): [1943.37, 1920.32, 2145.04]
Shop S08308 (dist=88.73): [1930.35, 1934.73, 2128.35]
Shop S09296 (dist=98.21): [2028.67, 1832.34, 2216.23]
Shop S09427 (dist=99.58): [1943.55, 1922.53, 2118.05]
Shop S07126 (dist=116.76): [1826.38, 2036.01, 2270.84]
M004 (HELIOPOLIS SQUARE):
Siblings: ['M003 (Maadi Corniche dist=1930.51)', 'M005 (New Cairo Tagamoa dist=1930.51)', 'M014 (Khan El Khalili Approach dist=2217.05)']
Matrix rows (5 shops x 3 siblings):
Shop S07243 (dist=16.76): [1921.4, 1939.72, 2231.1]
Shop S08211 (dist=71.73): [1906.44, 1956.92, 2284.32]
Shop S09992 (dist=73.41): [1974.24, 1888.63, 2277.08]
Shop S02270 (dist=73.48): [1998.05, 1863.42, 2188.65]
Shop S04115 (dist=100.79): [1958.05, 1907.89, 2119.59]
M005 (New Cairo Tagamoa):
Siblings: ['M004 (HELIOPOLIS SQUARE dist=1930.51)', 'M006 (Nasr City Hub dist=1930.51)', 'M015 (Bab Al Louq Corner dist=2217.05)']
Matrix rows (5 shops x 3 siblings):
Shop S06518 (dist=28.14): [1957.69, 1903.36, 2209.91]
Shop S05810 (dist=37.52): [1936.29, 1925.44, 2179.94]
Shop S07612 (dist=91.18): [1924.49, 1940.79, 2126.26]
Shop S08564 (dist=128.0): [1855.77, 2010.62, 2116.63]
Shop S06537 (dist=174.58): [2082.2, 1783.0, 2132.61]
M001 matrix row 0:
[2487.01, 2174.32, 3254.51]
M001 closest siblings:
M002: Zamalek Riverside dist=1930.50
M011: Roxy Square dist=2217.04
M012: Ramses Crossing dist=2939.62
1:44
The matrix columns are ordered by the sibling anchors sorted by distance from the anchor (ascending). So for M001: - Sibling 1: M002 (Zamalek Riverside, dist=1930.50) - Sibling 2: M011 (Roxy Square, dist=2217.04) - Sibling 3: M012 (Ramses Crossing, dist=2939.62) And the matrix values are distances from each shop to those siblings. Let me verify one manually. For M001, first shop S07233 (dist from anchor = 565.58): - Distance to M002 (Zamalek Riverside): should be the first column value 2487.01 - Shop S07233 coordinates? Let me check. Actually, the specification says: "columns correspond to the sibling anchors in distance-ascending order" - meaning the siblings are ordered by their distance from the anchor (smallest distance first). And the values in the matrix are distances from each shop to those siblings. That's what my code does. Good. Let me verify one entry manually to make sure the distances are computed correctly.
1:44
Bash
cd /work && python3 -c "
cd /work && python3 -c "
import geopandas as gpd
import numpy as np
shops = gpd.read_file('cairo_retail.gpkg', layer='shops')
anchors = gpd.read_file('cairo_retail.gpkg', layer='anchors')
# Check M001 first shop S07233 dist to M002
m001 = anchors[anchors['anchor_id'] == 'M001']
m002 = anchors[anchors['anchor_id'] == 'M002']
s07233 = shops[shops['shop_id'] == 'S07233']
print(f'M001: {m001.geometry.values[0]}')
print(f'M002: {m002.geometry.values[0]}')
print(f'S07233: {s07233.geometry.values[0]}')
# Distance from S07233 to M001 (should be 565.58)
d1 = np.sqrt((s07233.geometry.x.values[0] - m001.geometry.x.values[0])**2 + (s07233.geometry.y.values[0] - m001.geometry.y.values[0])**2)
print(f'S07233 -> M001: {d1:.2f}')
# Distance from S07233 to M002 (should be row0 col0 = 2487.01)
d2 = np.sqrt((s07233.geometry.x.values[0] - m002.geometry.x.values[0])**2 + (s07233.geometry.y.values[0] - m002.geometry.y.values[0])**2)
print(f'S07233 -> M002: {d2:.2f} (expected ~2487.01)')
# Check also S05762 -> M011 (row1 col1 = 1756.65)
m011 = anchors[anchors['anchor_id'] == 'M011']
s05762 = shops[shops['shop_id'] == 'S05762']
d3 = np.sqrt((s05762.geometry.x.values[0] - m011.geometry.x.values[0])**2 + (s05762.geometry.y.values[0] - m011.geometry.y.values[0])**2)
print(f'S05762 -> M011: {d3:.2f} (expected ~1756.65)')
"
M001: POINT (635109.3426487555 805565.9093887237) M002: POINT (637039.843142311 805569.5966030164) S07233: POINT (634555.2581617903 805679.3477387375) S07233 -> M001: 565.58 S07233 -> M002: 2487.01 (expected ~2487.01) S05762 -> M011: 1756.65 (expected ~1756.65)
1:49
Everything checks out. The output is correct. Let me verify the file looks like valid JSON and has the right structure.
1:49
1:49
Bash
cd /work && wc -c market_neighbourhoods.json && python3 -m json.tool market_neig...
120426 market_neighbourhoods.json Valid JSON
1:58
Everything looks good. Let me summarize the solution.
1:58