fio-l2-cairo-mixedgeom-split
Map (reference ← swipe → agent)
0:00
Heritage analyst here. I've put together `heritage_sites.geojson` from my own field work, and every site in it is several features sharing a `site_id`. I need to load the data into our desktop tool, which only ingests typed GPKG layers, no mixed-geometry files, and every feature has to be single-part. Can you give me back `heritage.gpkg` with three layers named `points`, `lines`, and `polygons`, keeping the `site_id` on every feature so I don't lose the cross-layer link? Use Egypt's national grid.
0:00
System info
{
"adapter": "openrouter",
"model": "deepseek/deepseek-v4-flash",
"base_url": "https://openrouter.ai/api/v1"
}
0:03
Let me start by reading the input file to understand its structure.
0:03
Read
/work/heritage_sites.geojson
1 {
2 "type": "FeatureCollection",
3 "name": "cairo_heritage_sites",
4 "crs": {
5 "type": "name",
6 "properties": {
7 "name": "urn:ogc:def:crs:OGC:1.3:CRS84"
8 }
9 },
10 "features": [
11 {
12 "type": "Feature",
13 "geometry": {
14 "type": "Polygon",
15 "coordinates": [
16 [
17 [
18 31.256,
19 30.0316
20 ],
21 [
22 31.2572,
23 30.0316
24 ],
25 [
26 31.2572,
27 30.0328
28 ],
29 [
30 31.256,
31 30.0328
32 ],
33 [
34 31.256,
35 30.0316
36 ]
37 ]
38 ]
39 },
40 "properties": {
41 "site_id": "EG-CAI-001",
42 "feature_kind": "enclosure",
43 "name_en": "Sultan Hassan Mosque-Madrasa",
44 "name_ar": "مسجد ومدرسة السلطان حسن"
45 }
46 },
47 {
48 "type": "Feature",
49 "geometry": {
50 "type": "LineString",
51 "coordinates": [
52 [
53 31.2556,
54 30.0321
55 ],
56 [
57 31.2566,
58 30.0322
59 ],
60 [
61 31.2576,
62 30.0323
63 ]
64 ]
65 },
66 "properties": {
67 "site_id": "EG-CAI-001",
68 "feature_kind": "axial_line_1",
69 "name_en": "Sultan Hassan Mosque-Madrasa",
70 "name_ar": "مسجد ومدرسة السلطان حسن"
71 }
72 },
73 {
74 "type": "Feature",
75 "geometry": {
76 "type": "LineString",
77 "coordinates": [
78 [
79 31.2567,
80 30.0312
81 ],
82 [
83 31.2566,
84 30.0322
85 ],
86 [
87 31.2565,
88 30.0332
89 ]
90 ]
91 },
92 "properties": {
93 "site_id": "EG-CAI-001",
94 "feature_kind": "axial_line_2",
95 "name_en": "Sultan Hassan Mosque-Madrasa",
96 "name_ar": "مسجد ومدرسة السلطان حسن"
97 }
98 },
99 {
100 "type": "Feature",
101 "geometry": {
102 "type": "Point",
103 "coordinates": [
104 31.2566,
105 30.0326
106 ]
107 },
108 "properties": {
109 "site_id": "EG-CAI-001",
110 "feature_kind": "marker_gateway",
111 "name_en": "Sultan Hassan Mosque-Madrasa",
112 "name_ar": "مسجد ومدرسة السلطان حسن"
113 }
114 },
115 {
116 "type": "Feature",
117 "geometry": {
118 "type": "Point",
119 "coordinates": [
120 31.257,
121 30.0322
122 ]
123 },
124 "properties": {
125 "site_id": "EG-CAI-001",
126 "feature_kind": "marker_mihrab",
127 "name_en": "Sultan Hassan Mosque-Madrasa",
128 "name_ar": "مسجد ومدرسة السلطان حسن"
129 }
130 },
131 {
132 "type": "Feature",
133 "geometry": {
134 "type": "Point",
135 "coordinates": [
136 31.2566,
137 30.0318
138 ]
139 },
140 "properties": {
141 "site_id": "EG-CAI-001",
142 "feature_kind": "marker_minaret",
143 "name_en": "Sultan Hassan Mosque-Madrasa",
144 "name_ar": "مسجد ومدرسة السلطان حسن"
145 }
146 },
147 {
148 "type": "Feature",
149 "geometry": {
150 "type": "MultiPolygon",
151 "coordinates": [
152 [
153 [
154 [
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157 ],
158 [
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161 ],
162 [
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170 [
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173 ]
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193 ],
194 [
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197 ]
198 ]
199 ]
200 ]
201 },
202 "properties": {
203 "site_id": "EG-CAI-002",
204 "feature_kind": "enclosure",
205 "name_en": "Al-Rifai Mosque",
206 "name_ar": "مسجد الرفاعي"
207 }
208 },
209 {
210 "type": "Feature",
211 "geometry": {
212 "type": "LineString",
213 "coordinates": [
214 [
215 31.2566,
216 30.0329
217 ],
218 [
219 31.2576,
220 30.033
221 ],
222 [
223 31.2586,
224 30.0331
225 ]
226 ]
227 },
228 "properties": {
229 "site_id": "EG-CAI-002",
230 "feature_kind": "axial_line_1",
231 "name_en": "Al-Rifai Mosque",
232 "name_ar": "مسجد الرفاعي"
233 }
234 },
235 {
236 "type": "Feature",
237 "geometry": {
238 "type": "Point",
239 "coordinates": [
240 31.2576,
241 30.0334
242 ]
243 },
244 "properties": {
245 "site_id": "EG-CAI-002",
246 "feature_kind": "marker_gateway",
247 "name_en": "Al-Rifai Mosque",
248 "name_ar": "مسجد الرفاعي"
249 }
250 },
251 {
252 "type": "Feature",
253 "geometry": {
254 "type": "Point",
255 "coordinates": [
256 31.258,
257 30.033
258 ]
259 },
260 "properties": {
261 "site_id": "EG-CAI-002",
262 "feature_kind": "marker_mihrab",
263 "name_en": "Al-Rifai Mosque",
264 "name_ar": "مسجد الرفاعي"
265 }
266 },
267 {
268 "type": "Feature",
269 "geometry": {
270 "type": "MultiPolygon",
271 "coordinates": [
272 [
273 [
274 [
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276 30.0281
277 ],
278 [
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281 ],
282 [
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285 ],
286 [
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290 [
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293 ]
294 ]
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317 ]
318 ]
319 ]
320 ]
321 },
322 "properties": {
323 "site_id": "EG-CAI-003",
324 "feature_kind": "enclosure",
325 "name_en": "Ibn Tulun Mosque",
326 "name_ar": "مسجد ابن طولون"
327 }
328 },
329 {
330 "type": "Feature",
331 "geometry": {
332 "type": "LineString",
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337 ],
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341 ],
342 [
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344 30.0288
345 ]
346 ]
347 },
348 "properties": {
349 "site_id": "EG-CAI-003",
350 "feature_kind": "axial_line_1",
351 "name_en": "Ibn Tulun Mosque",
352 "name_ar": "مسجد ابن طولون"
353 }
354 },
355 {
356 "type": "Feature",
357 "geometry": {
358 "type": "LineString",
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362 30.0277
363 ],
364 [
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366 30.0287
367 ],
368 [
369 31.2496,
370 30.0297
371 ]
372 ]
373 },
374 "properties": {
375 "site_id": "EG-CAI-003",
376 "feature_kind": "axial_line_2",
377 "name_en": "Ibn Tulun Mosque",
378 "name_ar": "مسجد ابن طولون"
379 }
380 },
381 {
382 "type": "Feature",
383 "geometry": {
384 "type": "Point",
385 "coordinates": [
386 31.2497,
387 30.0291
388 ]
389 },
390 "properties": {
391 "site_id": "EG-CAI-003",
392 "feature_kind": "marker_gateway",
393 "name_en": "Ibn Tulun Mosque",
394 "name_ar": "مسجد ابن طولون"
395 }
396 },
397 {
398 "type": "Feature",
399 "geometry": {
400 "type": "Point",
401 "coordinates": [
402 31.2501,
403 30.0287
404 ]
405 },
406 "properties": {
407 "site_id": "EG-CAI-003",
408 "feature_kind": "marker_mihrab",
409 "name_en": "Ibn Tulun Mosque",
410 "name_ar": "مسجد ابن طولون"
411 }
412 },
413 {
414 "type": "Feature",
415 "geometry": {
416 "type": "Point",
417 "coordinates": [
418 31.2497,
419 30.0283
420 ]
421 },
422 "properties": {
423 "site_id": "EG-CAI-003",
424 "feature_kind": "marker_minaret",
425 "name_en": "Ibn Tulun Mosque",
426 "name_ar": "مسجد ابن طولون"
427 }
428 },
429 {
430 "type": "Feature",
431 "geometry": {
432 "type": "Polygon",
433 "coordinates": [
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437 30.0453
438 ],
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450 ],
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453 30.0453
454 ]
455 ]
456 ]
457 },
458 "properties": {
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460 "feature_kind": "enclosure",
461 "name_en": "Al-Azhar Mosque",
462 "name_ar": "الجامع الأزهر"
463 }
464 },
465 {
466 "type": "Feature",
467 "geometry": {
468 "type": "LineString",
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473 ],
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477 ],
478 [
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480 30.046
481 ]
482 ]
483 },
484 "properties": {
485 "site_id": "EG-CAI-004",
486 "feature_kind": "axial_line_1",
487 "name_en": "Al-Azhar Mosque",
488 "name_ar": "الجامع الأزهر"
489 }
490 },
491 {
492 "type": "Feature",
493 "geometry": {
494 "type": "LineString",
495 "coordinates": [
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498 30.0449
499 ],
500 [
501 31.2624,
502 30.0459
503 ],
504 [
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506 30.0469
507 ]
508 ]
509 },
510 "properties": {
511 "site_id": "EG-CAI-004",
512 "feature_kind": "axial_line_2",
513 "name_en": "Al-Azhar Mosque",
514 "name_ar": "الجامع الأزهر"
515 }
516 },
517 {
518 "type": "Feature",
519 "geometry": {
520 "type": "Point",
521 "coordinates": [
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523 30.0463
524 ]
525 },
526 "properties": {
527 "site_id": "EG-CAI-004",
528 "feature_kind": "marker_gateway",
529 "name_en": "Al-Azhar Mosque",
530 "name_ar": "الجامع الأزهر"
531 }
532 },
533 {
534 "type": "Feature",
535 "geometry": {
536 "type": "Point",
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539 30.0459
540 ]
541 },
542 "properties": {
543 "site_id": "EG-CAI-004",
544 "feature_kind": "marker_mihrab",
545 "name_en": "Al-Azhar Mosque",
546 "name_ar": "الجامع الأزهر"
547 }
548 },
549 {
550 "type": "Feature",
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555 30.0455
556 ]
557 },
558 "properties": {
559 "site_id": "EG-CAI-004",
560 "feature_kind": "marker_minaret",
561 "name_en": "Al-Azhar Mosque",
562 "name_ar": "الجامع الأزهر"
563 }
564 },
565 {
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568 "type": "Polygon",
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590 ]
591 ]
592 ]
593 },
594 "properties": {
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596 "feature_kind": "enclosure",
597 "name_en": "Al-Hussein Mosque",
598 "name_ar": "مسجد الحسين"
599 }
600 },
601 {
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619 },
620 "properties": {
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622 "feature_kind": "axial_line_1",
623 "name_en": "Al-Hussein Mosque",
624 "name_ar": "مسجد الحسين"
625 }
626 },
627 {
628 "type": "Feature",
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630 "type": "Point",
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633 30.0483
634 ]
635 },
636 "properties": {
637 "site_id": "EG-CAI-005",
638 "feature_kind": "marker_gateway",
639 "name_en": "Al-Hussein Mosque",
640 "name_ar": "مسجد الحسين"
641 }
642 },
643 {
644 "type": "Feature",
645 "geometry": {
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650 ]
651 },
652 "properties": {
653 "site_id": "EG-CAI-005",
654 "feature_kind": "marker_mihrab",
655 "name_en": "Al-Hussein Mosque",
656 "name_ar": "مسجد الحسين"
657 }
658 },
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718 "name_ar": "بيت السحيمي"
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744 "name_ar": "بيت السحيمي"
745 }
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759 "name_en": "Bayt Al-Suhaymi",
760 "name_ar": "بيت السحيمي"
761 }
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775 "name_en": "Bayt Al-Suhaymi",
776 "name_ar": "بيت السحيمي"
777 }
778 },
779 {
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788 "properties": {
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790 "feature_kind": "marker_minaret",
791 "name_en": "Bayt Al-Suhaymi",
792 "name_ar": "بيت السحيمي"
793 }
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854 "name_ar": "باب زويلة"
855 }
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878 "feature_kind": "axial_line_2",
879 "name_en": "Bab Zuwayla",
880 "name_ar": "باب زويلة"
881 }
882 },
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1029 {
1030 "type": "Feature",
1031 "geometry": {
1032 "type": "Point",
1033 "coordinates": [
1034 31.2615,
1035 30.0477
1036 ]
1037 },
1038 "properties": {
1039 "site_id": "EG-CAI-008",
1040 "feature_kind": "marker_gateway",
1041 "name_en": "Khan el-Khalili",
1042 "name_ar": "خان الخليلي"
1043 }
1044 },
1045 {
1046 "type": "Feature",
1047 "geometry": {
1048 "type": "Point",
1049 "coordinates": [
1050 31.2619,
1051 30.0473
1052 ]
1053 },
1054 "properties": {
1055 "site_id": "EG-CAI-008",
1056 "feature_kind": "marker_mihrab",
1057 "name_en": "Khan el-Khalili",
1058 "name_ar": "خان الخليلي"
1059 }
1060 },
1061 {
1062 "type": "Feature",
1063 "geometry": {
1064 "type": "Point",
1065 "coordinates": [
1066 31.2615,
1067 30.0469
1068 ]
1069 },
1070 "properties": {
1071 "site_id": "EG-CAI-008",
1072 "feature_kind": "marker_minaret",
1073 "name_en": "Khan el-Khalili",
1074 "name_ar": "خان الخليلي"
1075 }
1076 },
1077 {
1078 "type": "Feature",
1079 "geometry": {
1080 "type": "Polygon",
1081 "coordinates": [
1082 [
1083 [
1084 31.2599,
1085 30.0504
1086 ],
1087 [
1088 31.2611,
1089 30.0504
1090 ],
1091 [
1092 31.2611,
1093 30.0516
1094 ],
1095 [
1096 31.2599,
1097 30.0516
1098 ],
1099 [
1100 31.2599,
1101 30.0504
1102 ]
1103 ]
1104 ]
1105 },
1106 "properties": {
1107 "site_id": "EG-CAI-009",
1108 "feature_kind": "enclosure",
1109 "name_en": "Sabil-Kuttab of Katkhuda",
1110 "name_ar": "سبيل وكتاب عبد الرحمن كتخدا"
1111 }
1112 },
1113 {
1114 "type": "Feature",
1115 "geometry": {
1116 "type": "LineString",
1117 "coordinates": [
1118 [
1119 31.2595,
1120 30.0509
1121 ],
1122 [
1123 31.2605,
1124 30.051
1125 ],
1126 [
1127 31.2615,
1128 30.0511
1129 ]
1130 ]
1131 },
1132 "properties": {
1133 "site_id": "EG-CAI-009",
1134 "feature_kind": "axial_line_1",
1135 "name_en": "Sabil-Kuttab of Katkhuda",
1136 "name_ar": "سبيل وكتاب عبد الرحمن كتخدا"
1137 }
1138 },
1139 {
1140 "type": "Feature",
1141 "geometry": {
1142 "type": "Point",
1143 "coordinates": [
1144 31.2605,
1145 30.0514
1146 ]
1147 },
1148 "properties": {
1149 "site_id": "EG-CAI-009",
1150 "feature_kind": "marker_gateway",
1151 "name_en": "Sabil-Kuttab of Katkhuda",
1152 "name_ar": "سبيل وكتاب عبد الرحمن كتخدا"
1153 }
1154 },
1155 {
1156 "type": "Feature",
1157 "geometry": {
1158 "type": "Point",
1159 "coordinates": [
1160 31.2609,
1161 30.051
1162 ]
1163 },
1164 "properties": {
1165 "site_id": "EG-CAI-009",
1166 "feature_kind": "marker_mihrab",
1167 "name_en": "Sabil-Kuttab of Katkhuda",
1168 "name_ar": "سبيل وكتاب عبد الرحمن كتخدا"
1169 }
1170 },
1171 {
1172 "type": "Feature",
1173 "geometry": {
1174 "type": "MultiPolygon",
1175 "coordinates": [
1176 [
1177 [
1178 [
1179 31.2624,
1180 30.0549
1181 ],
1182 [
1183 31.2636,
1184 30.0549
1185 ],
1186 [
1187 31.2636,
1188 30.0561
1189 ],
1190 [
1191 31.2624,
1192 30.0561
1193 ],
1194 [
1195 31.2624,
1196 30.0549
1197 ]
1198 ]
1199 ],
1200 [
1201 [
1202 [
1203 31.26405,
1204 30.05599
1205 ],
1206 [
1207 31.26475,
1208 30.05599
1209 ],
1210 [
1211 31.26475,
1212 30.05669
1213 ],
1214 [
1215 31.26405,
1216 30.05669
1217 ],
1218 [
1219 31.26405,
1220 30.05599
1221 ]
1222 ]
1223 ]
1224 ]
1225 },
1226 "properties": {
1227 "site_id": "EG-CAI-010",
1228 "feature_kind": "enclosure",
1229 "name_en": "Mosque of Al-Hakim",
1230 "name_ar": "مسجد الحاكم بأمر الله"
1231 }
1232 },
1233 {
1234 "type": "Feature",
1235 "geometry": {
1236 "type": "LineString",
1237 "coordinates": [
1238 [
1239 31.262,
1240 30.0554
1241 ],
1242 [
1243 31.263,
1244 30.0555
1245 ],
1246 [
1247 31.264,
1248 30.0556
1249 ]
1250 ]
1251 },
1252 "properties": {
1253 "site_id": "EG-CAI-010",
1254 "feature_kind": "axial_line_1",
1255 "name_en": "Mosque of Al-Hakim",
1256 "name_ar": "مسجد الحاكم بأمر الله"
1257 }
1258 },
1259 {
1260 "type": "Feature",
1261 "geometry": {
1262 "type": "Point",
1263 "coordinates": [
1264 31.263,
1265 30.0559
1266 ]
1267 },
1268 "properties": {
1269 "site_id": "EG-CAI-010",
1270 "feature_kind": "marker_gateway",
1271 "name_en": "Mosque of Al-Hakim",
1272 "name_ar": "مسجد الحاكم بأمر الله"
1273 }
1274 },
1275 {
1276 "type": "Feature",
1277 "geometry": {
1278 "type": "Point",
1279 "coordinates": [
1280 31.2634,
1281 30.0555
1282 ]
1283 },
1284 "properties": {
1285 "site_id": "EG-CAI-010",
1286 "feature_kind": "marker_mihrab",
1287 "name_en": "Mosque of Al-Hakim",
1288 "name_ar": "مسجد الحاكم بأمر الله"
1289 }
1290 }
1291 ]
1292 }
0:12
Let me analyze the data: 1. It's a GeoJSON FeatureCollection with CRS84 (WGS84 lat/lon) 2. Contains mixed geometry types: Polygon, LineString, Point, MultiPolygon 3. Each feature has `site_id`, `feature_kind`, `name_en`, `name_ar` properties 4. I need to: - Separate into 3 layers: points, lines, polygons - Explode Multi* geometries into single-part - Reproject to Egypt's national grid (EPSG:32636 - UTM zone 36N? Or maybe EPSG:22994? Let me think... Egypt's national grid is typically "Egypt Red Belt" or "Egypt Purple Belt". The official Egypt national grid is "Egypt 1907 / Belt" systems or "EGD 1983 / UTM zones". Actually, the most commonly used "Egypt national grid" reference is the "Egypt 1907 / Blue Belt" (EPSG:22992), "Egypt 1907 / Red Belt" (EPSG:22993), "Egypt 1907 / Purple Belt" (EPSG:22994). But these are older. More modern: "EGD 1983 / UTM zone 36N" (EPSG:23836) or "EGD 1983 / UTM zone 35N" (EPSG:23835). Or simply "WGS 84 / UTM zone 36N" (EPSG:32636) since Cairo is around 31.25°E which is in UTM zone 36N (30°E to 36°E). Actually, let me go with EPSG:32636 (WGS 84 / UTM zone 36N) which covers Cairo well. But the user said "Egypt's national grid" which might refer to the official Egyptian national projection. Let me think... The official Egyptian national grid system uses "Egypt 1907 / Red Belt" (EPSG:22993) for areas around Cairo and the Delta. Cairo is roughly at 31.25°E, which falls in the Red Belt (30°E-33°E). So EPSG:22993 would be appropriate. Actually, I'll use EPSG:22993 (Egypt 1907 / Red Belt) which is the traditional national grid for the Cairo region. The Red Belt covers longitudes 30° to 33° East, which is where Cairo is located. Let me write the Python script.
0:12
0:12
Bash
ls -la /work/heritage_sites.geojson
-rw-r--r-- 1 agent agent 26832 Jun 18 00:58 /work/heritage_sites.geojson
0:29
Let me analyze the data: 1. **Mixed geometry types**: The GeoJSON has Points, LineStrings, Polygons, and MultiPolygons. 2. **MultiPolygons need to be exploded to single-part Polygons** (the desktop tool requires single-part features). 3. **Need to split into 3 GPKG layers**: `points`, `lines`, `polygons`. 4. **Keep `site_id`** on every feature. 5. **Use Egypt's national grid** — EPSG:22992 (Egypt Gulf of Suez /... hmm) or more commonly EPSG:32635 (UTM zone 35N) or EPSG:32636 (UTM zone 36N)? Actually Egypt's national grid is typically "Egypt Red Belt" / "Egypt Purple Belt". Let me think about Egypt's national grid properly. Egypt uses several CRS: - **EPSG:22992** — Egypt Gulf of Suez / Survey of Egypt... actually no. - **EPSG:32635** — WGS 84 / UTM zone 35N (covers most of Egypt) - **EPSG:32636** — WGS 84 / UTM zone 36N (covers eastern Egypt) - **EPSG:22993** — Egypt Red Belt (old Egyptian national grid) Actually, the commonly referred "Egypt's national grid" is often EPSG:22993 (Egypt Red Belt) or EPSG:22994 (Egypt Purple Belt). Let me look up what's standard. The Egyptian national grid systems: - "Red Belt" - EPSG:22993, used for most of Egypt - "Purple Belt" - EPSG:22994 But actually those are older. More modern Egypt uses: - EPSG:32635 (UTM 35N) for western parts - EPSG:32636 (UTM 36N) for eastern parts However, I think the most common answer for "Egypt's national grid" is EPSG:22993 (Egypt - Red Belt) or possibly the newer EPSG:7275 (ETRS89-like, no that's Europe). Let me check - actually the "Egypt Red Belt" is EPSG:22993. But also there's the "Egypt 1907 / Red Belt" which is EPSG:22992. Wait no: - EPSG:22992 = Egypt 1907 / Red Belt (this is a projected CRS based on the Helmert 1907 datum) - EPSG:22993 = Egypt 1907 / Purple Belt... no. Let me just check what's the standard Egyptian national grid. According to EPSG registry: - EPSG:22992 — Egypt 1907 / Red Belt - EPSG:22993 — Egypt 1907 / Purple Belt Actually, I think the most commonly used "Egypt national grid" for mapping purposes is the "Egypt Red Belt" which is EPSG:22992. Let me verify by checking if pyproj has it. Actually, the user said "Egypt's national grid" which most commonly refers to the "Egypt Red Belt" projection, EPSG:22992. But let me also consider UTM zone 36N (EPSG:32636) since the data is around Cairo (31.25-31.26°E, ~30°N), which is in UTM zone 36N. Cairo is at ~31.25°E longitude. UTM zone 36N covers 30°E to 36°E, so Cairo is in UTM zone 36N (EPSG:32636). However, when people say "Egypt's national grid", they typically mean the old Egyptian national grid using the "Egypt Red Belt" which is EPSG:22992. Actually, I need to be more careful. Let me just use a commonly accepted one. For Egypt, the traditional national grid is: - **Egypt Red Belt** (EPSG:22992) — This is the standard survey grid. But actually I think the most common answer to "Egypt's national grid" depends on context. For modern GIS work, UTM zones are often used, but for a national grid, the "Egypt Red Belt" (EPSG:22992) is the classic answer. Let me also check: the data coordinates are around (31.25, 30.03). With EPSG:22992 (Egypt Red Belt, using meters), the coordinates would be in the 6-digit range. Alternatively, I could use EPSG:32636 (UTM zone 36N) which is also commonly used for Egypt around Cairo. I'll go with EPSG:22992 for "Egypt's national grid" since that's literally "Egypt Red Belt" which is the Egyptian national grid system. Actually, let me reconsider - the most well-known "Egypt national grid" might be "Egypt 1907 / Red Belt" EPSG:22992. Hmm, but actually I just realized there's also EPSG:22994 which is "Egypt 1907 / Purple Belt". The naming is confusing. Let me just go with one that works. I'll use EPSG:22992 (Egypt 1907 / Red Belt) as "Egypt's national grid". Wait, I should also consider that "Egypt's national grid" might refer to the "Egypt Lambert Conformal Conic" system. But that's not a standard EPSG code I can easily recall. Let me just go with EPSG:22992 for "Egypt Red Belt" which is the classic Egyptian national grid. If the user needs something different, they'll let me know. Now let me write the Python script: 1. Read the GeoJSON with geopandas 2. Split by geometry type 3. For MultiPolygons, explode to single Polygons 4. MultiLineStrings? The data only has LineString (single) and MultiPolygon, so I need to handle MultiLineString too just in case. 5. Reproject to Egypt's national grid (EPSG:22992) 6. Write each layer to GPKG Let me check: does the data have any MultiLineString? Looking at the data... only Point, LineString, Polygon, MultiPolygon. So I need to handle: - Points → `points` layer - LineStrings (and potentially MultiLineStrings) → `lines` layer - Polygons and MultiPolygons → `polygons` layer (MultiPolygons exploded to single-part) For the `explode` operation in geopandas, I can use `.explode(index_parts=False)` which will turn MultiPolygons into multiple Polygon rows. Let me write the script. Actually, let me also verify EPSG:22992 is valid by checking with pyproj.
0:29
0:29
Bash
python3 -c "import pyproj; pyproj.CRS.from_epsg(22992)"
(no output)
0:48
Good, EPSG:22992 (Egypt Red Belt) exists. Let me check if it's the right one for Egypt's national grid. There are several Egyptian grid systems: - EPSG:22992 - Egypt Red Belt - EPSG:22993 - Egypt Purple Belt - EPSG:22994 - Egypt Extended Purple Belt The most commonly used "national grid" for Egypt is the "Egypt Red Belt" (EPSG:22992). But let me also check: Actually, there's also "Egypt TM" / "Egypt Gulf of Suez" etc. Let me look closer. EPSG:22992 uses the "Egypt 1907" datum with a Lambert Conic Conformal projection (Red Belt). This is a standard national grid for Egypt. Let me just write the script. I'll use geopandas to: 1. Read the GeoJSON 2. Split by geometry type: Point, LineString, Polygon (+ MultiLineString, MultiPolygon handled with explode) 3. Explode multi-part geometries to single-part via `.explode()` 4. Filter to keep only Points, Lines, Polygons (ignore mixed or other types) 5. Reproject to EPSG:22992 (Egypt Red Belt) 6. Write each layer to the GPKG using the `layer` parameter Let me write the script.
0:48
Write
/work/solve.py
content · 110 lines · py
"""
Load heritage_sites.geojson → heritage.gpkg with three single-part, single-geometry-type
layers (points, lines, polygons) in Egypt Red Belt (EPSG:22992).
All features keep the 'site_id' property to maintain cross-layer linkage.
"""
import geopandas as gpd
from pathlib import Path
from shapely.geometry import Point, LineString, Polygon, MultiPoint, MultiLineString, MultiPolygon
SRC = "/work/heritage_sites.geojson"
DST = "/work/heritage.gpkg"
TARGET_CRS = "EPSG:22992" # Egypt Red Belt
# ---------------------------------------------------------------------------
# 1. Read source data
# ---------------------------------------------------------------------------
gdf = gpd.read_file(SRC)
# Ensure consistent CRS
if gdf.crs is None:
gdf.set_crs("EPSG:4326", inplace=True)
else:
gdf = gdf.to_crs("EPSG:4326")
print(f"Read {len(gdf)} features from source")
# ---------------------------------------------------------------------------
# 2. Separate by base geometry type (before explode)
# ---------------------------------------------------------------------------
def geom_type_label(geom):
"""Return a canonical label for a geometry."""
if geom is None:
return None
gtype = geom.geom_type
if gtype in ("Point", "MultiPoint"):
return "Point"
elif gtype in ("LineString", "MultiLineString"):
return "LineString"
elif gtype in ("Polygon", "MultiPolygon"):
return "Polygon"
return None
gdf["_geom_label"] = gdf.geometry.apply(geom_type_label)
points_gdf = gdf[gdf["_geom_label"] == "Point"].copy()
lines_gdf = gdf[gdf["_geom_label"] == "LineString"].copy()
polygons_gdf = gdf[gdf["_geom_label"] == "Polygon"].copy()
print(f" Points: {len(points_gdf)}")
print(f" Lines: {len(lines_gdf)}")
print(f" Polygons: {len(polygons_gdf)}")
# ---------------------------------------------------------------------------
# 3. Explode multi-part geometries into single-part
# ---------------------------------------------------------------------------
def explode_to_single(gdf_geom):
"""Explode multi-geometries to single-part, reset index, drop the label column."""
if gdf_geom.empty:
return gdf_geom
exploded = gdf_geom.explode(index_parts=True)
exploded = exploded.reset_index(drop=True)
# Drop the helper column
exploded = exploded.drop(columns=["_geom_label"], errors="ignore")
return exploded
points_gdf = explode_to_single(points_gdf)
lines_gdf = explode_to_single(lines_gdf)
polygons_gdf = explode_to_single(polygons_gdf)
# Keep only actual geometry types in each layer
points_gdf = points_gdf[points_gdf.geometry.geom_type == "Point"].copy()
lines_gdf = lines_gdf[lines_gdf.geometry.geom_type == "LineString"].copy()
polygons_gdf = polygons_gdf[polygons_gdf.geometry.geom_type == "Polygon"].copy()
print(f" After explode — Points: {len(points_gdf)}, Lines: {len(lines_gdf)}, Polygons: {len(polygons_gdf)}")
# ---------------------------------------------------------------------------
# 4. Reproject to Egypt Red Belt
# ---------------------------------------------------------------------------
if not points_gdf.empty:
points_gdf = points_gdf.to_crs(TARGET_CRS)
if not lines_gdf.empty:
lines_gdf = lines_gdf.to_crs(TARGET_CRS)
if not polygons_gdf.empty:
polygons_gdf = polygons_gdf.to_crs(TARGET_CRS)
# ---------------------------------------------------------------------------
# 5. Write to GPKG — one layer per geometry type
# ---------------------------------------------------------------------------
# Remove output if it exists already
Path(DST).unlink(missing_ok=True)
if not points_gdf.empty:
points_gdf.to_file(DST, layer="points", driver="GPKG")
print(f"Wrote {len(points_gdf)} features to layer 'points'")
else:
print("No point features — skipping layer 'points'")
if not lines_gdf.empty:
lines_gdf.to_file(DST, layer="lines", driver="GPKG")
print(f"Wrote {len(lines_gdf)} features to layer 'lines'")
else:
print("No line features — skipping layer 'lines'")
if not polygons_gdf.empty:
polygons_gdf.to_file(DST, layer="polygons", driver="GPKG")
print(f"Wrote {len(polygons_gdf)} features to layer 'polygons'")
else:
print("No polygon features — skipping layer 'polygons'")
print("Done.")Wrote /work/solve.py (4277 chars).
0:56
Let me run this script.
0:56
Bash
cd /work && python solve.py
Read 50 features from source Points: 25 Lines: 15 Polygons: 10 After explode — Points: 25, Lines: 15, Polygons: 15 Wrote 25 features to layer 'points' Wrote 15 features to layer 'lines' Wrote 15 features to layer 'polygons' Done.
1:04
Good - the MultiPolygons (there were several) got exploded to individual Polygon features, going from 10 polygon-group features to 15 single-part polygons. Let me verify the output.
1:04
1:04
Bash
cd /work && python3 -c "
cd /work && python3 -c "
import geopandas as gpd
from pathlib import Path
f = '/work/heritage.gpkg'
print('File size:', Path(f).stat().st_size, 'bytes')
print()
for layer in ['points', 'lines', 'polygons']:
gdf = gpd.read_file(f, layer=layer)
print(f'=== Layer: {layer} ===')
print(f' Features: {len(gdf)}')
print(f' CRS: {gdf.crs}')
print(f' Columns: {list(gdf.columns)}')
print(f' Geom type: {gdf.geometry.geom_type.unique().tolist()}')
print(f' site_ids: {sorted(gdf[\"site_id\"].unique())}')
# Check coordinate magnitude
bounds = gdf.total_bounds
print(f' Bounds: {bounds}')
print()
"
File size: 139264 bytes === Layer: points === Features: 25 CRS: EPSG:22992 Columns: ['site_id', 'feature_kind', 'name_en', 'name_ar', 'geometry'] Geom type: ['Point'] site_ids: ['EG-CAI-001', 'EG-CAI-002', 'EG-CAI-003', 'EG-CAI-004', 'EG-CAI-005', 'EG-CAI-006', 'EG-CAI-007', 'EG-CAI-008', 'EG-CAI-009', 'EG-CAI-010'] Bounds: [638924.75695448 813144.78642022 640239.52261457 816207.20324399] === Layer: lines === Features: 15 CRS: EPSG:22992 Columns: ['site_id', 'feature_kind', 'name_en', 'name_ar', 'geometry'] Geom type: ['LineString'] site_ids: ['EG-CAI-001', 'EG-CAI-002', 'EG-CAI-003', 'EG-CAI-004', 'EG-CAI-005', 'EG-CAI-006', 'EG-CAI-007', 'EG-CAI-008', 'EG-CAI-009', 'EG-CAI-010'] Bounds: [638828.41864191 813078.29551802 640297.3567806 816174.16853523] === Layer: polygons === Features: 15 CRS: EPSG:22992 Columns: ['site_id', 'feature_kind', 'name_en', 'name_ar', 'geometry'] Geom type: ['Polygon'] site_ids: ['EG-CAI-001', 'EG-CAI-002', 'EG-CAI-003', 'EG-CAI-004', 'EG-CAI-005', 'EG-CAI-006', 'EG-CAI-007', 'EG-CAI-008', 'EG-CAI-009', 'EG-CAI-010'] Bounds: [638866.8339112 813122.49016682 640369.58156503 816295.16561077]
1:16
Everything checks out: 1. **Three layers**: `points`, `lines`, `polygons` ✅ 2. **Single-geometry-type per layer**: Points layer has only Point, Lines layer has only LineString, Polygons layer has only Polygon ✅ 3. **Single-part features**: MultiPolygon enclosures (EG-CAI-002, EG-CAI-003, EG-CAI-006, EG-CAI-008, EG-CAI-010) were exploded → each produced 2 polygons, so 10 original polygon features became 15 after explode (5 multi-polygon × 2 = 10 plus 5 single polygons = 15). ✅ 4. **CRS**: EPSG:22992 (Egypt Red Belt) ✅ — coordinate magnitudes are ~638K-640K easting, ~813K-816K northing, which is correct for Egypt Red Belt (metric) 5. **site_id preserved**: All features in all layers have the `site_id` column ✅ 6. **All properties preserved**: site_id, feature_kind, name_en, name_ar ✅ 7. **File**: heritage.gpkg exists and is 139KB ✅
1:16