spa-l2-cairo-shop-knn

Status: done
Score: 90
Duration: 2:29
Cost: 2.82¢
Model: google/gemma-4-26b-a4b-it

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": "google/gemma-4-26b-a4b-it",
  "base_url": "https://openrouter.ai/api/v1"
}
0:01
Bash ls -F 
cairo_retail.gpkg
0:02
Bash ogrinfo -al /work/cairo_retail.gpkg 
INFO: Open of `/work/cairo_retail.gpkg'
      using driver `GPKG' successful.

Layer name: shops
Geometry: Point
Feature Count: 10000
Extent: (634124.820679, 804501.897982) - (653413.229591, 826664.941539)
Layer SRS WKT:
PROJCRS["Egypt 1907 / Red Belt",
    BASEGEOGCRS["Egypt 1907",
        DATUM["Egypt 1907",
            ELLIPSOID["Helmert 1906",6378200,298.3,
                LENGTHUNIT["metre",1]]],
        PRIMEM["Greenwich",0,
            ANGLEUNIT["degree",0.0174532925199433]],
        ID["EPSG",4229]],
    CONVERSION["Egypt Red Belt",
        METHOD["Transverse Mercator",
            ID["EPSG",9807]],
        PARAMETER["Latitude of natural origin",30,
            ANGLEUNIT["degree",0.0174532925199433],
            ID["EPSG",8801]],
        PARAMETER["Longitude of natural origin",31,
            ANGLEUNIT["degree",0.0174532925199433],
            ID["EPSG",8802]],
        PARAMETER["Scale factor at natural origin",1,
            SCALEUNIT["unity",1],
            ID["EPSG",8805]],
        PARAMETER["False easting",615000,
            LENGTHUNIT["metre",1],
            ID["EPSG",8806]],
        PARAMETER["False northing",810000,
            LENGTHUNIT["metre",1],
            ID["EPSG",8807]]],
    CS[Cartesian,2],
        AXIS["(E)",east,
            ORDER[1],
            LENGTHUNIT["metre",1]],
        AXIS["(N)",north,
            ORDER[2],
            LENGTHUNIT["metre",1]],
    USAGE[
        SCOPE["Engineering survey, topographic mapping."],
        AREA["Egypt - onshore between 29°E and 33°E, offshore Mediterranean east of 29°E and offshore Gulf of Suez."],
        BBOX[21.99,29,33.82,34.27]],
    ID["EPSG",22992]]
Data axis to CRS axis mapping: 1,2
FID Column = fid
Geometry Column = geom
shop_id: String (0.0)
raw_name: String (0.0)
OGRFeature(shops):1
  shop_id (String) = S00001
  raw_name (String) = OSCAR
  POINT (637478.058487924 814957.397675864)

OGRFeature(shops):2
  shop_id (String) = S00002
  raw_name (String) = Local Shop 00002
  POINT (651773.775467847 816149.978750284)

OGRFeature(shops):3
  shop_id (String) = S00003
  raw_name (String) = Local Shop 00003
  POINT (640341.689388647 815148.396308898)

OGRFeature(shops):4
  shop_id (String) = S00004
  raw_name (String) = Local Shop 00004
  POINT (640225.465313055 805311.132268022)

OGRFeature(shops):5
  shop_id (String) = S00005
  raw_name (String) = Local Shop 00005
  POINT (647199.272147092 818195.958649989)

OGRFeature(shops):6
  shop_id (String) = S00006
  raw_name (String) = مترو
  POINT (647363.601317037 817078.396547119)

OGRFeature(shops):7
  shop_id (String) = S00007
  raw_name (String) = Local Shop 00007
  POINT (637452.603531365 813452.72091361)

OGRFeature(shops):8
  shop_id (String) = S00008
  raw_name (String) = Carrefour
  POINT (642884.748634319 820068.563638251)

OGRFeature(shops):9
  shop_id (String) = S00009
  raw_name (String) = Local Shop 00009
  POINT (638401.854261998 815382.513480527)

OGRFeature(shops):10
  shop_id (String) = S00010
  raw_name (String) = Carrefour
  POINT (645066.080274557 808334.791083099)

OGRFeature(shops):11
  shop_id (String) = S00011
  raw_name (String) = Local Shop 00011
  POINT (643741.745157574 821785.240227738)

OGRFeature(shops):12
  shop_id (String) = S00012
  raw_name (String) = سعودي
  POINT (644818.535108658 821149.698078065)

OGRFeature(shops):13
  shop_id (String) = S00013
  raw_name (String) = Local Shop 00013
  POINT (650857.192266837 824183.112555852)

OGRFeature(shops):14
  shop_id (String) = S00014
  raw_name (String) = Local Shop 00014
  POINT (638852.939982444 816104.428331507)

OGRFeature(shops):15
  shop_id (String) = S00015
  raw_name (String) = Local Shop 00015
  POINT (647652.575844648 814307.8050582)

OGRFeature(shops):16
  shop_id (String) = S00016
  raw_name (String) = Spinneys
  POINT (634460.575376554 810550.892087218)

OGRFeature(shops):17
  shop_id (String) = S00017
  raw_name (String) = Local Shop 00017
  POINT (635587.163773858 814404.568685004)

OGRFeature(shops):18
  shop_id (String) = S00018
  raw_name (String) = Local Shop 00018
  POINT (639796.624798416 816106.1176905)

OGRFeature(shops):19
  shop_id (String) = S00019
  raw_name (String) = Local Shop 00019
  POINT (650413.267683925 823729.980828671)

OGRFeature(shops):20
  shop_id (String) = S00020
  raw_name (String) = Local Shop 00020
  POINT (636848.935390639 814863.407903552)

OGRFeature(shops):21
  shop_id (String) = S00021
  raw_name (String) = Carrefour Egypt
  POINT (645992.624943993 820312.549167156)

OGRFeature(shops):22
  shop_id (String) = S00022
  raw_name (String) = carrefour
  POINT (639307.514299775 814383.71812242)

OGRFeature(shops):23
  shop_id (String) = S00023
  raw_name (String) = Local Shop 00023
  POINT (642137.39677221 818942.648728208)

OGRFeature(shops):24
  shop_id (String) = S00024
  raw_name (String) = Local Shop 00024
  POINT (647939.256790264 820996.284100536)

OGRFeature(shops):25
  shop_id (String) = S00025
  raw_name (String) = Local Shop 00025
  POINT (636845.403996236 817961.517214108)

OGRFeature(shops):26
  shop_id (String) = S00026
  raw_name (String) = Seoudi Market
  POINT (640745.308517588 817734.951406899)

OGRFeature(shops):27
  shop_id (String) = S00027
  raw_name (String) = Local Shop 00027
  POINT (636914.1650506 811626.618509781)

OGRFeature(shops):28
  shop_id (String) = S00028
  raw_name (String) = Hyper One
  POINT (645400.803003731 822380.568640866)

OGRFeature(shops):29
  shop_id (String) = S00029
  raw_name (String) = أبو زكري
  POINT (651120.617150177 821806.250641437)

OGRFeature(shops):30
  shop_id (String) = S00030
  raw_name (String) = Local Shop 00030
  POINT (649654.934704055 825045.327781002)

OGRFeature(shops):31
  shop_id (String) = S00031
  raw_name (String) = Local Shop 00031
  POINT (645234.472923498 821521.316476857)

OGRFeature(shops):32
  shop_id (String) = S00032
  raw_name (String) = سبينيز
  POINT (653201.717802904 826046.011867956)

OGRFeature(shops):33
  shop_id (String) = S00033
  raw_name (String) = Local Shop 00033
  POINT (640143.021333039 806893.158943572)

OGRFeature(shops):34
  shop_id (String) = S00034
  raw_name (String) = Local Shop 00034
  POINT (634220.24606244 814230.919050761)

OGRFeature(shops):35
  shop_id (String) = S00035
  raw_name (String) = Local Shop 00035
  POINT (641185.051175902 818686.505275732)

OGRFeature(shops):36
  shop_id (String) = S00036
  raw_name (String) = Local Shop 00036
  POINT (647445.148557739 815390.030194781)

OGRFeature(shops):37
  shop_id (String) = S00037
  raw_name (String) = Local Shop 00037
  POINT (642353.25356077 811539.845918871)

OGRFeature(shops):38
  shop_id (String) = S00038
  raw_name (String) = Local Shop 00038
  POINT (639024.246853763 806973.805950547)

OGRFeature(shops):39
  shop_id (String) = S00039
  raw_name (String) = Metro Markets
  POINT (634296.071778366 815940.224664069)

OGRFeature(shops):40
  shop_id (String) = S00040
  raw_name (String) = Local Shop 00040
  POINT (647839.797174084 819881.662878391)

OGRFeature(shops):41
  shop_id (String) = S00041
  raw_name (String) = Local Shop 00041
  POINT (642159.910221419 822242.371250848)

OGRFeature(shops):42
  shop_id (String) = S00042
  raw_name (String) = Seoudi Market
  POINT (648212.720482228 814081.216722253)

OGRFeature(shops):43
  shop_id (String) = S00043
  raw_name (String) = seoudi supermarket
  POINT (639554.942020051 815744.568750557)

OGRFeature(shops):44
  shop_id (String) = S00044
  raw_name (String) = كارفور
  POINT (640126.116557115 805053.313967132)

OGRFeature(shops):45
  shop_id (String) = S00045
  raw_name (String) = Seoudi Market
  POINT (638735.343553551 819982.238237748)

OGRFeature(shops):46
  shop_id (String) = S00046
  raw_name (String) = Kheir Zaman
  POINT (639213.525535796 821591.67361862)

OGRFeature(shops):47
  shop_id (String) = S00047
  raw_name (String) = مترو
  POINT (637736.840266277 815347.148521926)

OGRFeature(shops):48
  shop_id (String) = S00048
  raw_name (String) = Local Shop 00048
  POINT (646506.495534073 815898.228078493)

OGRFeature(shops):49
  shop_id (String) = S00049
  raw_name (String) = Local Shop 00049
  POINT (645294.460107304 807162.265522615)

OGRFeature(shops):50
  shop_id (String) = S00050
  raw_name (String) = Local Shop 00050
  POINT (642609.624706283 805053.575899187)

OGRFeature(shops):51
  shop_id (String) = S00051
  raw_name (String) = Spinneys
  POINT (635260.828189161 809274.201978989)

OGRFeature(shops):52
  shop_id (String) = S00052
  raw_name (String) = Local Shop 00052
  POINT (637559.978772543 813924.078892127)

OGRFeature(shops):53
  shop_id (String) = S00053
  raw_name (String) = Local Shop 00053
  POINT (641666.880297001 817560.974840412)

OGRFeature(shops):54
  shop_id (String) = S00054
  raw_name (String) = اوسكار
  POINT (643893.093046164 819843.010013626)

OGRFeature(shops):55
  shop_id (String) = S00055
  raw_name (String) = Local Shop 00055
  POINT (640849.07951945 804547.430194517)

OGRFeature(shops):56
  shop_id (String) = S00056
  raw_name (String) = Local Shop 00056
  POINT (650635.472219923 814276.972137346)

OGRFeature(shops):57
  shop_id (String) = S00057
  raw_name (String) = Carrefour Egypt
  POINT (651539.838906531 816504.475099993)

OGRFeature(shops):58
  shop_id (String) = S00058
  raw_name (String) = Spineys
  POINT (644121.283363534 820085.70171023)

OGRFeature(shops):59
  shop_id (String) = S00059
  raw_name (String) = Local Shop 00059
  POINT (636550.691587081 811882.16719895)

OGRFeature(shops):60
  shop_id (String) = S00060
  raw_name (String) = اوسكار
  POINT (647677.314110989 814338.137693293)

OGRFeature(shops):61
  shop_id (String) = S00061
  raw_name (String) = خير زمان
  POINT (647846.933009284 822407.582068612)

OGRFeature(shops):62
  shop_id (String) = S00062
  raw_name (String) = Seoudi
  POINT (647137.91190756 816135.872012627)

OGRFeature(shops):63
  shop_id (String) = S00063
  raw_name (String) = Local Shop 00063
  POINT (639398.018282206 805607.597990786)

OGRFeature(shops):64
  shop_id (String) = S00064
  raw_name (String) = Spinneys
  POINT (642113.564066383 821776.742038064)

OGRFeature(shops):65
  shop_id (String) = S00065
  raw_name (String) = kheir zaman
  POINT (638080.248996985 815616.584832917)

OGRFeature(shops):66
  shop_id (String) = S00066
  raw_name (String) = Metro Markets
  POINT (635495.067490723 817073.324157865)

OGRFeature(shops):67
  shop_id (String) = S00067
  raw_name (String) = Local Shop 00067
  POINT (647792.570615126 823473.840426822)

OGRFeature(shops):68
  shop_id (String) = S00068
  raw_name (String) = Metro Market
  POINT (642394.680454836 812067.142725599)

OGRFeature(shops):69
  shop_id (String) = S00069
  raw_name (String) = Local Shop 00069
  POINT (634414.285380876 817275.398639876)

OGRFeature(shops):70
  shop_id (String) = S00070
  raw_name (String) = Carrefour Egypt
  POINT (635901.646970307 813443.53309074)

OGRFeature(shops):71
  shop_id (String) = S00071
  raw_name (String) = Local Shop 00071
  POINT (647536.593510533 825036.026203852)

OGRFeature(shops):72
  shop_id (String) = S00072
  raw_name (String) = Local Shop 00072
  POINT (635614.830714245 818759.084433544)

OGRFeature(shops):73
  shop_id (String) = S00073
  raw_name (String) = Carrefour
  POINT (644993.652256943 819891.894431124)

OGRFeature(shops):74
  shop_id (String) = S00074
  raw_name (String) = Metro Market
  POINT (635591.864231081 809012.205494479)

OGRFeature(shops):75
  shop_id (String) = S00075
  raw_name (String) = Local Shop 00075
  POINT (645632.943132826 823889.456276904)

OGRFeature(shops):76
  shop_id (String) = S00076
  raw_name (String) = Local Shop 00076
  POINT (638726.785918316 822885.809224467)

OGRFeature(shops):77
  shop_id (String) = S00077
  raw_name (String) = Local Shop 00077
  POINT (639203.849629931 806972.466961293)

OGRFeature(shops):78
  shop_id (String) = S00078
  raw_name (String) = Seoudi
  POINT (635049.143874879 816024.314888034)

OGRFeature(shops):79
  shop_id (String) = S00079
  raw_name (String) = Local Shop 00079
  POINT (645727.5822966 824173.504482637)

OGRFeature(shops):80
  shop_id (String) = S00080
  raw_name (String) = Local Shop 00080
  POINT (635745.421464002 814347.998185277)

OGRFeature(shops):81
  shop_id (String) = S00081
  raw_name (String) = Local Shop 00081
  POINT (635965.716554733 817635.196727522)

OGRFeature(shops):82
  shop_id (String) = S00082
  raw_name (String) = Local Shop 00082
  POINT (637364.614006502 813842.908207791)

OGRFeature(shops):83
  shop_id (String) = S00083
  raw_name (String) = Abu Zekry
  POINT (639951.183564329 823097.206411547)

OGRFeature(shops):84
  shop_id (String) = S00084
  raw_name (String) = Local Shop 00084
  POINT (646880.380156415 820564.31034136)

OGRFeature(shops):85
  shop_id (String) = S00085
  raw_name (String) = Local Shop 00085
  POINT (640468.159893446 823189.55523023)

OGRFeature(shops):86
  shop_id (String) = S00086
  raw_name (String) = Local Shop 00086
  POINT (650856.888250377 818827.462567867)

OGRFeature(shops):87
  shop_id (String) = S00087
  raw_name (String) = Metro Market
  POINT (643259.70699192 819700.63474687)

OGRFeature(shops):88
  shop_id (String) = S00088
  raw_name (String) = Local Shop 00088
  POINT (646884.583792702 816767.236782552)

OGRFeature(shops):89
  shop_id (String) = S00089
  raw_name (String) = abou zekry
  POINT (641246.345782533 816997.192330094)

OGRFeature(shops):90
  shop_id (String) = S00090
  raw_name (String) = Local Shop 00090
  POINT (639437.536153606 806437.046899634)

OGRFeature(shops):91
  shop_id (String) = S00091
  raw_name (String) = Local Shop 00091
  POINT (646236.069973403 820103.136384075)

OGRFeature(shops):92
  shop_id (String) = S00092
  raw_name (String) = Local Shop 00092
  POINT (647574.17153671 818773.379679601)

OGRFeature(shops):93
  shop_id (String) = S00093
  raw_name (String) = Local Shop 00093
  POINT (642238.457050183 821122.432278458)

OGRFeature(shops):94
  shop_id (String) = S00094
  raw_name (String) = Oscar Grand Stores
  POINT (636738.611442621 811775.42134796)

OGRFeature(shops):95
  shop_id (String) = S00095
  raw_name (String) = Local Shop 00095
  POINT (639230.502329634 815712.056518643)

OGRFeature(shops):96
  shop_id (String) = S00096
  raw_name (String) = Local Shop 00096
  POINT (651081.784028535 821053.183619872)

OGRFeature(shops):97
  shop_id (String) = S00097
  raw_name (String) = metro
  POINT (648008.514442601 821846.01246577)

OGRFeature(shops):98
  shop_id (String) = S00098
  raw_name (String) = Carrefour
  POINT (646335.786242196 821288.867309655)

OGRFeature(shops):99
  shop_id (String) = S00099
  raw_name (String) = abou zekry
  POINT (637912.954125579 815062.619568849)

OGRFeature(shops):100
  shop_id (String) = S00100
  raw_name (String) = Oscar Grand Stores
  POINT (646984.425046821 823005.860642439)

OGRFeature(shops):101
  shop_id (String) = S00101
  raw_name (String) = Carrefour
  POINT (638464.536455515 816188.790674229)

OGRFeature(shops):102
  shop_id (String) = S00102
  raw_name (String) = Seoudi
  POINT (638164.3
[... truncated ...],0.0174532925199433],
            ID["EPSG",8801]],
        PARAMETER["Longitude of natural origin",31,
            ANGLEUNIT["degree",0.0174532925199433],
            ID["EPSG",8802]],
        PARAMETER["Scale factor at natural origin",1,
            SCALEUNIT["unity",1],
            ID["EPSG",8805]],
        PARAMETER["False easting",615000,
            LENGTHUNIT["metre",1],
            ID["EPSG",8806]],
        PARAMETER["False northing",810000,
            LENGTHUNIT["metre",1],
            ID["EPSG",8807]]],
    CS[Cartesian,2],
        AXIS["(E)",east,
            ORDER[1],
            LENGTHUNIT["metre",1]],
        AXIS["(N)",north,
            ORDER[2],
            LENGTHUNIT["metre",1]],
    USAGE[
        SCOPE["Engineering survey, topographic mapping."],
        AREA["Egypt - onshore between 29°E and 33°E, offshore Mediterranean east of 29°E and offshore Gulf of Suez."],
        BBOX[21.99,29,33.82,34.27]],
    ID["EPSG",22992]]
Data axis to CRS axis mapping: 1,2
FID Column = fid
Geometry Column = geom
anchor_id: String (0.0)
anchor_name: String (0.0)
OGRFeature(anchors):1
  anchor_id (String) = M001
  anchor_name (String) = TAHRIR SQUARE PLAZA
  POINT (635109.342648756 805565.909388724)

OGRFeature(anchors):2
  anchor_id (String) = M002
  anchor_name (String) = Zamalek Riverside
  POINT (637039.843142311 805569.596603016)

OGRFeature(anchors):3
  anchor_id (String) = M003
  anchor_name (String) =   Maadi Corniche  
  POINT (638970.344989423 805573.620349565)

OGRFeature(anchors):4
  anchor_id (String) = M004
  anchor_name (String) = HELIOPOLIS SQUARE
  POINT (640900.848308882 805577.980630096)

OGRFeature(anchors):5
  anchor_id (String) = M005
  anchor_name (String) = New Cairo Tagamoa
  POINT (642831.353219476 805582.677446487)

OGRFeature(anchors):6
  anchor_id (String) = M006
  anchor_name (String) =   Nasr City Hub  
  POINT (644761.859839994 805587.71080076)

OGRFeature(anchors):7
  anchor_id (String) = M007
  anchor_name (String) = MOHANDESSIN CENTRE
  POINT (646692.368289223 805593.08069508)

OGRFeature(anchors):8
  anchor_id (String) = M008
  anchor_name (String) = Garden City Walk
  POINT (648622.878685952 805598.787131757)

OGRFeature(anchors):9
  anchor_id (String) = M009
  anchor_name (String) =   Dokki Market  
  POINT (650553.391148969 805604.830113248)

OGRFeature(anchors):10
  anchor_id (String) = M010
  anchor_name (String) = GIZA PYRAMIDS GATE
  POINT (652483.90579706 805611.209642154)

OGRFeature(anchors):11
  anchor_id (String) = M011
  anchor_name (String) = Roxy Square
  POINT (635105.300171254 807782.94910312)

OGRFeature(anchors):12
  anchor_id (String) = M012
  anchor_name (String) =   Ramses Crossing  
  POINT (637035.414065736 807786.637802769)

OGRFeature(anchors):13
  anchor_id (String) = M013
  anchor_name (String) = SAYEDA ZEINAB PLAZA
  POINT (638965.529311871 807790.663171011)

OGRFeature(anchors):14
  anchor_id (String) = M014
  anchor_name (String) = Khan El Khalili Approach
  POINT (640895.646028282 807795.025209573)

OGRFeature(anchors):15
  anchor_id (String) = M015
  anchor_name (String) =   Bab Al Louq Corner  
  POINT (642825.764333592 807799.723920335)

OGRFeature(anchors):16
  anchor_id (String) = M016
  anchor_name (String) = KORBA QUARTER
  POINT (644755.884346419 807804.759305316)

OGRFeature(anchors):17
  anchor_id (String) = M017
  anchor_name (String) = Manial Riverbank
  POINT (646686.006185388 807810.131366678)

OGRFeature(anchors):18
  anchor_id (String) = M018
  anchor_name (String) =   Shubra North  
  POINT (648616.129969119 807815.840106739)

OGRFeature(anchors):19
  anchor_id (String) = M019
  anchor_name (String) = AIN SHAMS PLAZA
  POINT (650546.255816234 807821.885527948)

OGRFeature(anchors):20
  anchor_id (String) = M020
  anchor_name (String) = Abbasiya Junction
  POINT (652476.383845353 807828.26763291)

OGRFeature(anchors):21
  anchor_id (String) = M021
  anchor_name (String) =   Boulaq Edge  
  POINT (635101.255244391 809999.995552928)

OGRFeature(anchors):22
  anchor_id (String) = M022
  anchor_name (String) = GARBIYA PLAZA
  POINT (637030.982304631 810003.685736143)

OGRFeature(anchors):23
  anchor_id (String) = M023
  anchor_name (String) = Sakakini Approach
  POINT (638960.71071462 810007.712724123)

OGRFeature(anchors):24
  anchor_id (String) = M024
  anchor_name (String) =   Dar El Salaam  
  POINT (640890.440592813 810012.076518601)

OGRFeature(anchors):25
  anchor_id (String) = M025
  anchor_name (String) = EL MARG HUB
  POINT (642820.172057666 810016.77712145)

OGRFeature(anchors):26
  anchor_id (String) = M026
  anchor_name (String) = Helwan Centre
  POINT (644749.905227635 810021.814534693)

OGRFeature(anchors):27
  anchor_id (String) = M027
  anchor_name (String) =   Maasara Crossing  
  POINT (646679.640221173 810027.188760494)

OGRFeature(anchors):28
  anchor_id (String) = M028
  anchor_name (String) = TORA EDGE
  POINT (648609.377156735 810032.899801163)

OGRFeature(anchors):29
  anchor_id (String) = M029
  anchor_name (String) = Mokattam Heights
  POINT (650539.116152777 810038.947659156)

OGRFeature(anchors):30
  anchor_id (String) = M030
  anchor_name (String) =   Nozha Promenade  
  POINT (652468.857327752 810045.332337074)

OGRFeature(anchors):31
  anchor_id (String) = M031
  anchor_name (String) = SHERATON HELIOPOLIS
  POINT (635097.207868641 812217.0487409)

OGRFeature(anchors):32
  anchor_id (String) = M032
  anchor_name (String) = Triumph Square
  POINT (637026.547859514 812220.740405891)

OGRFeature(anchors):33
  anchor_id (String) = M033
  anchor_name (String) =   Cleopatra Plaza  
  POINT (638955.889198232 812224.769011655)

OGRFeature(anchors):34
  anchor_id (String) = M034
  anchor_name (String) = SALAH SALEM STRIP
  POINT (640885.232003083 812229.13455993)

OGRFeature(anchors):35
  anchor_id (String) = M035
  anchor_name (String) = Autostrad Corner
  POINT (642814.576392355 812233.837052586)

OGRFeature(anchors):36
  anchor_id (String) = M036
  anchor_name (String) =   El Rehab Gate One  
  POINT (644743.922484339 812238.876491647)

OGRFeature(anchors):37
  anchor_id (String) = M037
  anchor_name (String) = EL REHAB GATE TWO
  POINT (646673.270397321 812244.252879275)

OGRFeature(anchors):38
  anchor_id (String) = M038
  anchor_name (String) = Madinaty Promenade
  POINT (648602.62024959 812249.966217781)

OGRFeature(anchors):39
  anchor_id (String) = M039
  anchor_name (String) =   Fifth Settlement North  
  POINT (650531.972159432 812256.01650962)

OGRFeature(anchors):40
  anchor_id (String) = M040
  anchor_name (String) = FIFTH SETTLEMENT SOUTH
  POINT (652461.326245137 812262.403757393)

OGRFeature(anchors):41
  anchor_id (String) = M041
  anchor_name (String) = American University Gate
  POINT (635093.158044477 814434.108669789)

OGRFeature(anchors):42
  anchor_id (String) = M042
  anchor_name (String) =   Police Academy Strip  
  POINT (637022.110730905 814437.801814759)

OGRFeature(anchors):43
  anchor_id (String) = M043
  anchor_name (String) = RING ROAD NORTH
  POINT (638951.064763271 814441.832036356)

OGRFeature(anchors):44
  anchor_id (String) = M044
  anchor_name (String) = Ring Road East
  POINT (640880.020259701 814446.199336309)

OGRFeature(anchors):45
  anchor_id (String) = M045
  anchor_name (String) =   Ring Road West  
  POINT (642808.977338314 814450.903716488)

OGRFeature(anchors):46
  anchor_id (String) = M046
  anchor_name (String) = CITY STARS MALL
  POINT (644737.936117233 814455.945178918)

OGRFeature(anchors):47
  anchor_id (String) = M047
  anchor_name (String) = Cairo Festival City
  POINT (646666.896714578 814461.323725762)

OGRFeature(anchors):48
  anchor_id (String) = M048
  anchor_name (String) =   Mall of Egypt Gate  
  POINT (648595.859248473 814467.039359331)

OGRFeature(anchors):49
  anchor_id (String) = M049
  anchor_name (String) = TAGAMOA FIRST
  POINT (650524.823837037 814473.092082079)

OGRFeature(anchors):50
  anchor_id (String) = M050
  anchor_name (String) = Tagamoa Third
  POINT (652453.790598391 814479.481896607)

OGRFeature(anchors):51
  anchor_id (String) = M051
  anchor_name (String) =   El Mokattam Plateau  
  POINT (635089.105772372 816651.175342334)

OGRFeature(anchors):52
  anchor_id (String) = M052
  anchor_name (String) = AL AHLY STADIUM
  POINT (637017.67091932 816654.869965498)

OGRFeature(anchors):53
  anchor_id (String) = M053
  anchor_name (String) = Cairo Stadium
  POINT (638946.237410303 816658.901800969)

OGRFeature(anchors):54
  anchor_id (String) = M054
  anchor_name (String) =   Sharkawi Plaza  
  POINT (640874.805363277 816663.270850477)

OGRFeature(anchors):55
  anchor_id (String) = M055
  anchor_name (String) = EL OBOUR HUB
  POINT (642803.374896195 816667.977115899)

OGRFeature(anchors):56
  anchor_id (String) = M056
  anchor_name (String) = Shoubra Mazallat
  POINT (644731.946127015 816673.020599251)

OGRFeature(anchors):57
  anchor_id (String) = M057
  anchor_name (String) =   Abdeen Palace Edge  
  POINT (646660.51917369 816678.401302701)

OGRFeature(anchors):58
  anchor_id (String) = M058
  anchor_name (String) = EL HUSSEIN SQUARE
  POINT (648589.094154174 816684.119228557)

OGRFeature(anchors):59
  anchor_id (String) = M059
  anchor_name (String) = Al Ghouriya Strip
  POINT (650517.671186424 816690.174379273)

OGRFeature(anchors):60
  anchor_id (String) = M060
  anchor_name (String) =   El Mosky Quarter  
  POINT (652446.250388393 816696.566757451)

OGRFeature(anchors):61
  anchor_id (String) = M061
  anchor_name (String) = BAB ZUWEILA APPROACH
  POINT (635085.0510528 818868.248761288)

OGRFeature(anchors):62
  anchor_id (String) = M062
  anchor_name (String) = Ataba Square
  POINT (637013.228425281 818871.944860848)

OGRFeature(anchors):63
  anchor_id (String) = M063
  anchor_name (String) =   Opera Square  
  POINT (638941.407139891 818875.978308235)

OGRFeature(anchors):64
  anchor_id (String) = M064
  anchor_name (String) = TALAAT HARB PLAZA
  POINT (640869.587314421 818880.349105182)

OGRFeature(anchors):65
  anchor_id (String) = M065
  anchor_name (String) = Soliman Pasha Corner
  POINT (642797.769066654 818885.057253557)

OGRFeature(anchors):66
  anchor_id (String) = M066
  anchor_name (String) =   Sherif Street  
  POINT (644725.952514387 818890.102755384)

OGRFeature(anchors):67
  anchor_id (String) = M067
  anchor_name (String) = QASR EL NILE
  POINT (646654.137775401 818895.485612828)

OGRFeature(anchors):68
  anchor_id (String) = M068
  anchor_name (String) = Kasr El Aini Strip
  POINT (648582.324967487 818901.205828196)

OGRFeature(anchors):69
  anchor_id (String) = M069
  anchor_name (String) =   El Sayeda Aisha  
  POINT (650510.514208432 818907.263403939)

OGRFeature(anchors):70
  anchor_id (String) = M070
  anchor_name (String) = KOBRI EL QUBBA
  POINT (652438.705616025 818913.658342663)

OGRFeature(anchors):71
  anchor_id (String) = M071
  anchor_name (String) = Mar Mina Plaza
  POINT (635080.993886236 821085.328929381)

OGRFeature(anchors):72
  anchor_id (String) = M072
  anchor_name (String) =   Saint Fatima Hub  
  POINT (637008.783249307 821089.026503546)

OGRFeature(anchors):73
  anchor_id (String) = M073
  anchor_name (String) = EL NOZHA EL GEDIDA
  POINT (638936.5739526 821093.061560896)

OGRFeature(anchors):74
  anchor_id (String) = M074
  anchor_name (String) = Rabaa Square
  POINT (640864.366113741 821097.434103156)

OGRFeature(anchors):75
  anchor_id (String) = M075
  anchor_name (String) =   Tagamoa El Saba  
  POINT (642792.159850349 821102.144132202)

OGRFeature(anchors):76
  anchor_id (String) = M076
  anchor_name (String) = BAHTEEM CROSSING
  POINT (644719.955280048 821107.191650056)

OGRFeature(anchors):77
  anchor_id (String) = M077
  anchor_name (String) = El Salam City
  POINT (646647.752520459 821112.576658876)

OGRFeature(anchors):78
  anchor_id (String) = M078
  anchor_name (String) =   Madinet Nasr Eighth Zone  
  POINT (648575.551689202 821118.299160976)

OGRFeature(anchors):79
  anchor_id (String) = M079
  anchor_name (String) = MADINET NASR TENTH ZONE
  POINT (650503.352903899 821124.359158809)

OGRFeature(anchors):80
  anchor_id (String) = M080
  anchor_name (String) = El Hadaba El Wosta
  POINT (652431.15628217 821130.756654973)

OGRFeature(anchors):81
  anchor_id (String) = M081
  anchor_name (String) =   Mokattam Sector One  
  POINT (635076.934273154 823302.415849354)

OGRFeature(anchors):82
  anchor_id (String) = M082
  anchor_name (String) = MOKATTAM SECTOR SIX
  POINT (637004.335391916 823306.114896331)

OGRFeature(anchors):83
  anchor_id (String) = M083
  anchor_name (String) = El Maadi Degla
  POINT (638931.737848996 823310.151561682)

OGRFeature(anchors):84
  anchor_id (String) = M084
  anchor_name (String) =   Maadi Sarayat  
  POINT (640859.141761851 823314.525847135)

OGRFeature(anchors):85
  anchor_id (String) = M085
  anchor_name (String) = MAADI CORNISH
  POINT (642786.547247934 823319.237754566)

OGRFeature(anchors):86
  anchor_id (String) = M086
  anchor_name (String) = Old Cairo Babylon
  POINT (644713.954424702 823324.287285992)

OGRFeature(anchors):87
  anchor_id (String) = M087
  anchor_name (String) =   Coptic Cairo Plaza  
  POINT (646641.36340961 823329.674443578)

OGRFeature(anchors):88
  anchor_id (String) = M088
  anchor_name (String) = FUSTAT PARK EDGE
  POINT (648568.774320111 823335.399229632)

OGRFeature(anchors):89
  anchor_id (String) = M089
  anchor_name (String) = Manial Bridge
  POINT (650496.187273661 823341.461646609)

OGRFeature(anchors):90
  anchor_id (String) = M090
  anchor_name (String) =   Embaba Crossing  
  POINT (652423.602387713 823347.861697106)

OGRFeature(anchors):91
  anchor_id (String) = M091
  anchor_name (String) = IMBABA AIRPORT STRIP
  POINT (635072.87221403 825519.509523944)

OGRFeature(anchors):92
  anchor_id (String) = M092
  anchor_name (String) = Mit Okba Plaza
  POINT (636999.88485363 825523.210041937)

OGRFeature(anchors):93
  anchor_id (String) = M093
  anchor_name (String) =   El Agouza Riverside  
  POINT (638926.898829644 825527.248313331)

OGRFeature(anchors):94
  anchor_id (String) = M094
  anchor_name (String) = EL SAHEL JUNCTION
  POINT (640853.91425936 825531.624339853)

OGRFeature(anchors):95
  anchor_id (String) = M095
  anchor_name (String) = Rod El Farag Bridge
  POINT (642780.931260066 825536.33812338)

OGRFeature(anchors):96
  anchor_id (String) = M096
  anchor_name (String) =   Shubra El Kheima Centre  
  POINT (644707.94994905 825541.389665928)

OGRFeature(anchors):97
  anchor_id (String) = M097
  anchor_name (String) = EL SAWAH CORNER
  POINT (646634.970443601 825546.778969663)

OGRFeature(anchors):98
  anchor_id (String) = M098
  anchor_name (String) = Demerdash Plaza
  POINT (648561.992861007 825552.506036892)

OGRFeature(anchors):99
  anchor_id (String) = M099
  anchor_name (String) =   El Demerdash Hospital Edge  
  POINT (650489.017318556 825558.570870069)

OGRFeature(anchors):100
  anchor_id (String) = M100
  anchor_name (String) = RAMSES HILTON PLAZA
  POINT (652416.043933533 825564.973471791)
0:29
Write /work/solve.py
content · 187 lines · py 
import geopandas as gpd
import pandas as pd
import json
from shapely.ops import nearest_points
from scipy.spatial import cKDTree
import numpy as np

def normalize_name(name):
    if not name or pd.isna(name):
        return ""
    name = str(name).strip().upper()
    
    # Mapping common variations to a canonical name
    # Based on the ogrinfo output, we see some patterns.
    # Since we don't have the full list, we'll use a more robust approach.
    # We'll group by some kind of "semantic" similarity or just a mapping.
    # For this task, let's define a mapping for the obvious ones.
    
    mapping = {
        "CARREFOUR": "Carrefour",
        "CARREFOUR EGYPT": "Carrefour",
        "كارفور": "Carrefour",
        "OSCAR": "Oscar",
        "اوسكار": "Oscar",
        "OSCAR GRAND STORES": "Oscar",
        "SEOUDI MARKET": "Seoudi",
        "SEOUDI": "Seoudi",
        "سعودي": "Seoudi",
        "SEOUDI SUPERMARKET": "Seoudi",
        "SPINNEYS": "Spinneys",
        "SPINEYS": "Spinneys",
        "سبينيز": "Spinneys",
        "METRO MARKETS": "Metro Markets",
        "METRO MARKET": "Metro Markets",
        "مترو": "Metro Markets",
        "KHEIR ZAMAN": "Kheir Zaman",
        "خير زمان": "Kheir Zaman"
    }
    
    # Check if the name is in our mapping (exact match after stripping/uppercasing)
    if name in mapping:
        return mapping[name]
    
    # For names like "Local Shop 00002", let's just normalize to "Local Shop" or something?
    # Actually, the instruction says: "the same shop_id always carries the same normalised_name, 
    # and all shops belonging to one chain share a single canonical spelling."
    # And "Tidy the anchor names too."
    
    # If it looks like "Local Shop XXXX", let's treat it as "Local Shop"
    import re
    if re.match(r"LOCAL SHOP \d+", name):
        return "Local Shop"
        
    # For Abu Zekry / Abou Zekry / أبو زكري
    if "ABU ZE KRY" in name or "ABOU ZE KRY" in name or "أبو زكري" in name:
        return "Abu Zekry"

    return name

def solve():
    # Load data
    gdf_shops = gpd.read_file("/work/cairo_retail.gpkg", layer="shops")
    gdf_anchors = gpd.read_file("/work/cairo_retail.gpkg", layer="anchors")

    # 1. Normalize Shop Names
    # We need to ensure that all shops in a chain share a canonical spelling.
    # A good way is to create a dictionary: raw_name -> normalized_name.
    # But we must also ensure that the same normalized_name is used for all shops.
    
    # First, create a list of all raw names and try to group them.
    # Given the prompt's specific mention of inconsistent spellings, we'll 
    # use a manual mapping for known chains and a fallback.
    
    def get_canonical_name(raw_name):
        name = str(raw_name).strip().upper()
        if any(x in name for x in ["CARREFOUR", "كارفور"]): return "Carrefour"
        if any(x in name for x in ["OSCAR", "اوسكار", "GRAND STORES"]): return "Oscar"
        if any(x in name for x in ["SEOUDI", "سعودي"]): return "Seoudi"
        if any(x in name for x in ["SPINNEY", "SPINEY", "سبينيز"]): return "Spinneys"
        if any(x in name for x in ["METRO", "مترو"]): return "Metro Markets"
        if any(x in name for x in ["KHEIR ZAMAN", "خير زمان"]): return "Kheir Zaman"
        if any(x in name for x in ["ABU ZE KRY", "ABOU ZE KRY", "أبو زكري"]): return "Abu Zekry"
        if "LOCAL SHOP" in name: return "Local Shop"
        return name.title()

    gdf_shops['normalised_name'] = gdf_shops['raw_name'].apply(get_canonical_name)
    
    # 2. Normalize Anchor Names
    gdf_anchors['anchor_name_normalised'] = gdf_anchors['anchor_name'].str.strip()

    # Prepare for KNN
    # Coordinates are in Egypt 1907 / Red Belt (EPSG:22992), which is metric.
    shop_coords = np.array(list(zip(gdf_shops.geometry.x, gdf_shops.geometry.y)))
    shop_tree = cKDTree(shop_coords)
    
    anchor_coords = np.array(list(zip(gdf_anchors.geometry.x, gdf_anchors.geometry.y)))
    anchor_tree = cKDTree(anchor_coords)

    results = []

    for i, anchor in gdf_anchors.iterrows():
        a_id = str(anchor['anchor_id'])
        a_name = str(anchor['anchor_name_normalised'])
        a_geom = anchor.geometry
        a_coord = (anchor.geometry.x, anchor.geometry.y)

        # Find 5 nearest shops
        # k=5 because we want exactly 5.
        distances, indices = shop_tree.query(a_coord, k=5)
        
        # If there are fewer than 5 shops total (unlikely here), handle it.
        # But the prompt implies 5.
        
        knn_list = []
        # distances and indices are arrays. If k=1, they might be scalars.
        if isinstance(distances, float):
            distances = [distances]
            indices = [indices]
        
        # Re-order by distance if not already (cKDTree query usually returns sorted)
        # We'll sort just in case.
        sort_idx = np.argsort(distances)
        distances = distances[sort_idx]
        indices = indices[sort_idx]

        for d, idx in zip(distances, indices):
            shop = gdf_shops.iloc[idx]
            knn_list.append({
                "shop_id": str(shop['shop_id']),
                "normalised_name": str(shop['normalised_name']),
                "distance_m": float(d),
                "within_1km": bool(d <= 1000.0)
            })

        # Find 3 nearest sibling anchors
        # A "sibling" anchor is any other anchor.
        # The instruction says "closest sibling anchors". Usually this excludes self.
        # cKDTree.query with k=4 to skip self (the closest is the anchor itself).
        d_anc, i_anc = anchor_tree.query(a_coord, k=4)
        
        # i_anc[0] is the anchor itself.
        # We want the next 3.
        if isinstance(i_anc, (int, float)): # should not happen for k=4
             sibling_indices = []
        else:
            sibling_indices = i_anc[1:4]
            sibling_distances = d_anc[1:4]

        # We need a 5x3 distance matrix from these 5 shops to the 3 sibling anchors.
        # rows: 5 shops (knn order), columns: 3 sibling anchors.
        # matrix[i][j] = distance from shop i to sibling anchor j.
        
        matrix = []
        for shop_entry in knn_list:
            row = []
            s_geom = gdf_shops.iloc[indices[knn_list.index(shop_entry)]].geometry # This is wrong. 
            # Let's fix the indexing.
            pass
            
        # Let's redo the matrix part properly.
        matrix = []
        for idx_in_knn in range(5):
            actual_shop_idx = indices[idx_in_knn]
            s_coord = shop_coords[actual_shop_idx]
            
            row = []
            for s_anc_idx in sibling_indices:
                # Distance between shop and sibling anchor
                # We can use the coordinates directly.
                anc_coord = anchor_coords[s_anc_idx]
                dist_s_a = np.sqrt(np.sum((s_coord - anc_coord)**2))
                row.append(float(dist_s_a))
            matrix.append(row)

        results.append({
            "anchor_id": a_id,
            "anchor_name_normalised": a_name,
            "knn": knn_list,
            "full_distance_matrix_m": matrix
        })

    with open("/work/market_neighbourhoods.json", "w") as f:
        json.dump(results, f, indent=2)

if __name__ == "__main__":
    solve()

Wrote /work/solve.py (7273 chars).
0:47
Edit /work/solve.py 
old_string not found in /work/solve.py
0:50
Read /work/solve.py 
     1	import geopandas as gpd
     2	import pandas as pd
     3	import json
     4	from shapely.ops import nearest_points
     5	from scipy.spatial import cKDTree
     6	import numpy as np
     7	
     8	def normalize_name(name):
     9	    if not name or pd.isna(name):
    10	        return ""
    11	    name = str(name).strip().upper()
    12	    
    13	    # Mapping common variations to a canonical name
    14	    # Based on the ogrinfo output, we see some patterns.
    15	    # Since we don't have the full list, we'll use a more robust approach.
    16	    # We'll group by some kind of "semantic" similarity or just a mapping.
    17	    # For this task, let's define a mapping for the obvious ones.
    18	    
    19	    mapping = {
    20	        "CARREFOUR": "Carrefour",
    21	        "CARREFOUR EGYPT": "Carrefour",
    22	        "كارفور": "Carrefour",
    23	        "OSCAR": "Oscar",
    24	        "اوسكار": "Oscar",
    25	        "OSCAR GRAND STORES": "Oscar",
    26	        "SEOUDI MARKET": "Seoudi",
    27	        "SEOUDI": "Seoudi",
    28	        "سعودي": "Seoudi",
    29	        "SEOUDI SUPERMARKET": "Seoudi",
    30	        "SPINNEYS": "Spinneys",
    31	        "SPINEYS": "Spinneys",
    32	        "سبينيز": "Spinneys",
    33	        "METRO MARKETS": "Metro Markets",
    34	        "METRO MARKET": "Metro Markets",
    35	        "مترو": "Metro Markets",
    36	        "KHEIR ZAMAN": "Kheir Zaman",
    37	        "خير زمان": "Kheir Zaman"
    38	    }
    39	    
    40	    # Check if the name is in our mapping (exact match after stripping/uppercasing)
    41	    if name in mapping:
    42	        return mapping[name]
    43	    
    44	    # For names like "Local Shop 00002", let's just normalize to "Local Shop" or something?
    45	    # Actually, the instruction says: "the same shop_id always carries the same normalised_name, 
    46	    # and all shops belonging to one chain share a single canonical spelling."
    47	    # And "Tidy the anchor names too."
    48	    
    49	    # If it looks like "Local Shop XXXX", let's treat it as "Local Shop"
    50	    import re
    51	    if re.match(r"LOCAL SHOP \d+", name):
    52	        return "Local Shop"
    53	        
    54	    # For Abu Zekry / Abou Zekry / أبو زكري
    55	    if "ABU ZE KRY" in name or "ABOU ZE KRY" in name or "أبو زكري" in name:
    56	        return "Abu Zekry"
    57	
    58	    return name
    59	
    60	def solve():
    61	    # Load data
    62	    gdf_shops = gpd.read_file("/work/cairo_retail.gpkg", layer="shops")
    63	    gdf_anchors = gpd.read_file("/work/cairo_retail.gpkg", layer="anchors")
    64	
    65	    # 1. Normalize Shop Names
    66	    # We need to ensure that all shops in a chain share a canonical spelling.
    67	    # A good way is to create a dictionary: raw_name -> normalized_name.
    68	    # But we must also ensure that the same normalized_name is used for all shops.
    69	    
    70	    # First, create a list of all raw names and try to group them.
    71	    # Given the prompt's specific mention of inconsistent spellings, we'll 
    72	    # use a manual mapping for known chains and a fallback.
    73	    
    74	    def get_canonical_name(raw_name):
    75	        name = str(raw_name).strip().upper()
    76	        if any(x in name for x in ["CARREFOUR", "كارفور"]): return "Carrefour"
    77	        if any(x in name for x in ["OSCAR", "اوسكار", "GRAND STORES"]): return "Oscar"
    78	        if any(x in name for x in ["SEOUDI", "سعودي"]): return "Seoudi"
    79	        if any(x in name for x in ["SPINNEY", "SPINEY", "سبينيز"]): return "Spinneys"
    80	        if any(x in name for x in ["METRO", "مترو"]): return "Metro Markets"
    81	        if any(x in name for x in ["KHEIR ZAMAN", "خير زمان"]): return "Kheir Zaman"
    82	        if any(x in name for x in ["ABU ZE KRY", "ABOU ZE KRY", "أبو زكري"]): return "Abu Zekry"
    83	        if "LOCAL SHOP" in name: return "Local Shop"
    84	        return name.title()
    85	
    86	    gdf_shops['normalised_name'] = gdf_shops['raw_name'].apply(get_canonical_name)
    87	    
    88	    # 2. Normalize Anchor Names
    89	    gdf_anchors['anchor_name_normalised'] = gdf_anchors['anchor_name'].str.strip()
    90	
    91	    # Prepare for KNN
    92	    # Coordinates are in Egypt 1907 / Red Belt (EPSG:22992), which is metric.
    93	    shop_coords = np.array(list(zip(gdf_shops.geometry.x, gdf_shops.geometry.y)))
    94	    shop_tree = cKDTree(shop_coords)
    95	    
    96	    anchor_coords = np.array(list(zip(gdf_anchors.geometry.x, gdf_anchors.geometry.y)))
    97	    anchor_tree = cKDTree(anchor_coords)
    98	
    99	    results = []
   100	
   101	    for i, anchor in gdf_anchors.iterrows():
   102	        a_id = str(anchor['anchor_id'])
   103	        a_name = str(anchor['anchor_name_normalised'])
   104	        a_geom = anchor.geometry
   105	        a_coord = (anchor.geometry.x, anchor.geometry.y)
   106	
   107	        # Find 5 nearest shops
   108	        # k=5 because we want exactly 5.
   109	        distances, indices = shop_tree.query(a_coord, k=5)
   110	        
   111	        # If there are fewer than 5 shops total (unlikely here), handle it.
   112	        # But the prompt implies 5.
   113	        
   114	        knn_list = []
   115	        # distances and indices are arrays. If k=1, they might be scalars.
   116	        if isinstance(distances, float):
   117	            distances = [distances]
   118	            indices = [indices]
   119	        
   120	        # Re-order by distance if not already (cKDTree query usually returns sorted)
   121	        # We'll sort just in case.
   122	        sort_idx = np.argsort(distances)
   123	        distances = distances[sort_idx]
   124	        indices = indices[sort_idx]
   125	
   126	        for d, idx in zip(distances, indices):
   127	            shop = gdf_shops.iloc[idx]
   128	            knn_list.append({
   129	                "shop_id": str(shop['shop_id']),
   130	                "normalised_name": str(shop['normalised_name']),
   131	                "distance_m": float(d),
   132	                "within_1km": bool(d <= 1000.0)
   133	            })
   134	
   135	        # Find 3 nearest sibling anchors
   136	        # A "sibling" anchor is any other anchor.
   137	        # The instruction says "closest sibling anchors". Usually this excludes self.
   138	        # cKDTree.query with k=4 to skip self (the closest is the anchor itself).
   139	        d_anc, i_anc = anchor_tree.query(a_coord, k=4)
   140	        
   141	        # i_anc[0] is the anchor itself.
   142	        # We want the next 3.
   143	        if isinstance(i_anc, (int, float)): # should not happen for k=4
   144	             sibling_indices = []
   145	        else:
   146	            sibling_indices = i_anc[1:4]
   147	            sibling_distances = d_anc[1:4]
   148	
   149	        # We need a 5x3 distance matrix from these 5 shops to the 3 sibling anchors.
   150	        # rows: 5 shops (knn order), columns: 3 sibling anchors.
   151	        # matrix[i][j] = distance from shop i to sibling anchor j.
   152	        
   153	        matrix = []
   154	        for shop_entry in knn_list:
   155	            row = []
   156	            s_geom = gdf_shops.iloc[indices[knn_list.index(shop_entry)]].geometry # This is wrong. 
   157	            # Let's fix the indexing.
   158	            pass
   159	            
   160	        # Let's redo the matrix part properly.
   161	        matrix = []
   162	        for idx_in_knn in range(5):
   163	            actual_shop_idx = indices[idx_in_knn]
   164	            s_coord = shop_coords[actual_shop_idx]
   165	            
   166	            row = []
   167	            for s_anc_idx in sibling_indices:
   168	                # Distance between shop and sibling anchor
   169	                # We can use the coordinates directly.
   170	                anc_coord = anchor_coords[s_anc_idx]
   171	                dist_s_a = np.sqrt(np.sum((s_coord - anc_coord)**2))
   172	                row.append(float(dist_s_a))
   173	            matrix.append(row)
   174	
   175	        results.append({
   176	            "anchor_id": a_id,
   177	            "anchor_name_normalised": a_name,
   178	            "knn": knn_list,
   179	            "full_distance_matrix_m": matrix
   180	        })
   181	
   182	    with open("/work/market_neighbourhoods.json", "w") as f:
   183	        json.dump(results, f, indent=2)
   184	
   185	if __name__ == "__main__":
   186	    solve()
1:08
Write /work/solve.py
content · 92 lines · py 
import geopandas as gpd
import pandas as pd
import json
from scipy.spatial import cKDTree
import numpy as np

def get_canonical_name(raw_name):
    name = str(raw_name).strip().upper()
    if any(x in name for x in ["CARREFOUR", "كارفور"]): return "Carrefour"
    if any(x in name for x in ["OSCAR", "اوسكار", "GRAND STORES"]): return "Oscar"
    if any(x in name for x in ["SEOUDI", "سعودي"]): return "Seoudi"
    if any(x in name for x in ["SPINNEY", "SPINEY", "سبينيز"]): return "Spinneys"
    if any(x in name for x in ["METRO", "مترو"]): return "Metro Markets"
    if any(x in name for x in ["KHEIR ZAMAN", "خير زمان"]): return "Kheir Zaman"
    if any(x in name for x in ["ABU ZE KRY", "ABOU ZE KRY", "أبو زكري"]): return "Abu Zekry"
    if "LOCAL SHOP" in name: return "Local Shop"
    return name.title()

def solve():
    # Load data
    gdf_shops = gpd.read_file("/work/cairo_retail.gpkg", layer="shops")
    gdf_anchors = gpd.read_file("/work/cairo_retail.gpkg", layer="anchors")

    # 1. Normalize Shop Names
    gdf_shops['normalised_name'] = gdf_shops['raw_name'].apply(get_canonical_name)
    
    # 2. Normalize Anchor Names
    gdf_anchors['anchor_name_normalised'] = gdf_anchors['anchor_name'].str.strip()

    # Prepare for KNN
    # Coordinates are in Egypt 1907 / Red Belt (EPSG:22992), which is metric.
    shop_coords = np.array(list(zip(gdf_shops.geometry.x, gdf_shops.geometry.y)))
    shop_tree = cKDTree(shop_coords)
    
    anchor_coords = np.array(list(zip(gdf_anchors.geometry.x, gdf_anchors.geometry.y)))
    anchor_tree = cKDTree(anchor_coords)

    results = []

    for i, anchor in gdf_anchors.iterrows():
        a_id = str(anchor['anchor_id'])
        a_name = str(anchor['anchor_name_normalised'])
        a_coord = (anchor.geometry.x, anchor.geometry.y)

        # Find 5 nearest shops
        distances, indices = shop_tree.query(a_coord, k=5)
        
        # cKDTree.query with k=5 returns sorted distances
        knn_list = []
        for d, idx in zip(distances, indices):
            shop = gdf_shops.iloc[idx]
            knn_list.append({
                "shop_id": str(shop['shop_id']),
                "normalised_name": str(shop['normalised_name']),
                "distance_m": float(d),
                "within_1km": bool(d <= 1000.0)
            })

        # Find 3 nearest sibling anchors (excluding self)
        # We need k=4 to skip the first one (itself)
        d_anc, i_anc = anchor_tree.query(a_coord, k=4)
        
        # The closest one is index i, skip it.
        # Use i_anc[1:4] for the 3 nearest siblings.
        sibling_indices = i_anc[1:4]

        # 5x3 distance matrix: rows=5 shops, cols=3 sibling anchors
        matrix = []
        for idx_in_knn in range(5):
            actual_shop_idx = indices[idx_in_knn]
            s_coord = shop_coords[actual_shop_idx]
            
            row = []
            for s_anc_idx in sibling_indices:
                anc_coord = anchor_coords[s_anc_idx]
                dist_s_a = np.sqrt(np.sum((s_coord - anc_coord)**2))
                row.append(float(dist_s_a))
            matrix.append(row)

        results.append({
            "anchor_id": a_id,
            "anchor_name_normalised": a_name,
            "knn": knn_list,
            "full_distance_matrix_m": matrix
        })

    with open("/work/market_neighbourhoods.json", "w") as f:
        json.dump(results, f, indent=2)

if __name__ == "__main__":
    solve()

Wrote /work/solve.py (3454 chars).
1:11
Bash python solve.py 
Traceback (most recent call last):
  File "/work/solve.py", line 4, in <module>
    from scipy.spatial import cKDTree
ModuleNotFoundError: No module named 'scipy'
1:16
Bash pip install --user scipy 
Collecting scipy
  Downloading scipy-1.17.1-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl.metadata (62 kB)
Requirement already satisfied: numpy<2.7,>=1.26.4 in /usr/local/lib/python3.14/site-packages (from scipy) (2.4.6)
Downloading scipy-1.17.1-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl (35.2 MB)
   ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 35.2/35.2 MB 59.8 MB/s  0:00:00
Installing collected packages: scipy
Successfully installed scipy-1.17.1
1:26
Bash python solve.py
(no output)
1:32
Bash ls -F /work/market_neighbourhoods.json 
/work/market_neighbourhoods.json
1:45
Bash head -n 50 /work/market_neighbourhoods.json 
[
  {
    "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",
        "distance_m": 852.4416831354031,
        "within_1km": true
      },
      {
        "shop_id": "S04111",
        "normalised_name": "Local Shop",
        "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,
1:48
Bash # Checking the size of the JSON to ensure it's not empty and has reasonable cont... 
# Checking the size of the JSON to ensure it's not empty and has reasonable content.
wc -l /work/market_neighbourhoods.json

6301 /work/market_neighbourhoods.json
2:01
Bash python -c "import json; data = json.load(open('/work/market_neighbourhoods.json'... 
  File "<string>", line 1
    import json; data = json.load(open('/work/market_neighbourhoods.json')); print(f'Anchors: {len(data)}'); print(f'First anchor knn length: {len(data[0["knn"]])}'); print(f'First anchor matrix shape: {len(data[0["full_distance_matrix_m"])}, {len(data[0["full_distance_matrix_m"][0]])}')
                                                                                                                                                                                                                                               ^
SyntaxError: closing parenthesis ')' does not match opening parenthesis '['
2:09
Bash python -c "import json; data = json.load(open('/work/market_neighbourhoods.json'... 
Anchors: 100
First anchor knn length: 5
First anchor matrix shape: 5, 3
2:28