crs-l1-london-laea-areas
Map (reference ← swipe → agent)
0:00
I need to create a land-area comparison for the Horizon report. Can you get me the area in km² for every administrative unit in `london_admin.geojson` (both the borough-level `county` features and the surrounding `locality` features)? Please write the result to `borough_areas.csv`, with one row per feature containing its id, name, and area_km2. Use the `id` field to identify each feature.
0:00
System info
{
"adapter": "openrouter",
"model": "deepseek/deepseek-v4-flash",
"base_url": "https://openrouter.ai/api/v1"
}
0:02
Let me start by reading the GeoJSON file to understand its structure.
0:02
Bash
head -100 /work/london_admin.geojson
{
"type": "FeatureCollection",
"name": "london_admin",
"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:OGC:1.3:CRS84" } },
"features": [
{ "type": "Feature", "properties": { "id": "255163d2-43e6-4c60-af14-b2c29b57a97e", "name": "Abbots Langley", "subtype": "locality", "country": "GB" }, "geometry": { "type": "Polygon", "coordinates": [ [ [ -0.4207805, 51.7396924 ], [ -0.4209356, 51.7398132 ], [ -0.4217659, 51.74055 ], [ -0.4221024, 51.7408305 ], [ -0.422606, 51.7406754 ], [ -0.4230639, 51.7405872 ], [ -0.4235411, 51.740483 ], [ -0.42414, 51.7403795 ], [ -0.4248118, 51.7402195 ], [ -0.4253471, 51.7401124 ], [ -0.4250515, 51.7395339 ], [ -0.4265069, 51.7392045 ], [ -0.4261163, 51.7384404 ], [ -0.4265582, 51.7383224 ], [ -0.4272069, 51.7381492 ], [ -0.4282117, 51.7378669 ], [ -0.4287224, 51.7377577 ], [ -0.4295369, 51.7375303 ], [ -0.4299798, 51.7374139 ], [ -0.4304226, 51.7372984 ], [ -0.4309032, 51.7371817 ], [ -0.4315455, 51.7370151 ], [ -0.4324858, 51.7367712 ], [ -0.4337727, 51.7362884 ], [ -0.4340441, 51.7362273 ], [ -0.4341862, 51.7361824 ], [ -0.4342582, 51.7361501 ], [ -0.4344283, 51.7360498 ], [ -0.4349337, 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0:09
Let me understand the data structure. The GeoJSON has features with properties including `id`, `name`, `subtype`, and `country`. The `subtype` can be "locality" or "county" (borough-level). I need to calculate the area in km² for each feature and write the results to `borough_areas.csv` with columns: id, name, area_km2. Let me write a Python script using geopandas to read the GeoJSON, calculate areas, and write the CSV.
0:09
0:09
Bash
cd /work && python3 -c "
cd /work && python3 -c "
import geopandas as gpd
gdf = gpd.read_file('london_admin.geojson')
print(gdf.columns.tolist())
print(gdf['subtype'].value_counts())
print(gdf[['id','name','subtype']].head(10))
print('Total features:', len(gdf))
"
['id', 'name', 'subtype', 'country', 'geometry']
subtype
locality 199
county 33
Name: count, dtype: int64
id name subtype
0 255163d2-43e6-4c60-af14-b2c29b57a97e Abbots Langley locality
1 1f71e803-826e-4313-ab53-d8cc4c346743 Addington locality
2 7f738dcf-eb8b-4c85-8141-9ef35825714d Addlestone locality
3 62053957-e9df-444e-b63d-39a6c4dc5179 Aldenham locality
4 5e1cd051-a913-4b44-8d51-9ed93670ffad Artington locality
5 322630b0-4f06-46b2-8832-fe89e705e64e Ash-cum-Ridley locality
6 a4849d19-ca87-42db-b840-e8208a64aff1 Ashley Green locality
7 24e2ecaa-1ad3-4cf5-9c96-43edf9dd7f78 Badgers Mount locality
8 b26b2bd8-f8f3-4266-ba74-6585e3293298 Banstead locality
9 bbb19ffc-24db-460c-927b-13f25f989aae Barking and Dagenham county
Total features: 232
0:22
Now I have a good understanding. I need to: 1. Read the GeoJSON file 2. For each feature, calculate its area in km² 3. Write a CSV with id, name, and area_km2 Let me write a Python script that uses geopandas with a projected CRS for accurate area calculation. The data is in WGS84 (CRS84 / EPSG:4326), so I'll need to project to a suitable metric CRS. For areas in the UK, EPSG:27700 (OSGB36 / British National Grid) which uses meters, would work well. Or I could use the Authalic equal-area projection. Let me write the script.
0:22
0:22
Write
/work/solve.py
content · 30 lines · py
import geopandas as gpd
import pandas as pd
# Read the GeoJSON file
gdf = gpd.read_file('/work/london_admin.geojson')
# Check CRS - it's likely WGS84 (EPSG:4326), need to project for accurate area
print("Original CRS:", gdf.crs)
# Reproject to a suitable projected CRS for area calculation
# For the UK, EPSG:27700 (British National Grid) uses meters, good for areas
gdf_projected = gdf.to_crs('EPSG:27700')
# Calculate area in km² (values are in m² since EPSG:27700 uses metres)
gdf_projected['area_km2'] = gdf_projected.geometry.area / 1_000_000
# Build the output dataframe
result = gdf_projected[['id', 'name', 'area_km2']].copy()
# Round to reasonable precision
result['area_km2'] = result['area_km2'].round(6)
# Sort for readability
result = result.sort_values(['name'])
# Write to CSV
result.to_csv('/work/borough_areas.csv', index=False)
print(f"Written {len(result)} rows to /work/borough_areas.csv")
print(result.head(10))Wrote /work/solve.py (941 chars).
0:26
Let me run the script now.
0:26
Bash
cd /work && python solve.py
Original CRS: EPSG:4326
Written 232 rows to /work/borough_areas.csv
id name area_km2
0 255163d2-43e6-4c60-af14-b2c29b57a97e Abbots Langley 18.537401
1 1f71e803-826e-4313-ab53-d8cc4c346743 Addington 3.345514
2 7f738dcf-eb8b-4c85-8141-9ef35825714d Addlestone 2.922214
3 62053957-e9df-444e-b63d-39a6c4dc5179 Aldenham 23.856257
4 5e1cd051-a913-4b44-8d51-9ed93670ffad Artington 4.921422
5 322630b0-4f06-46b2-8832-fe89e705e64e Ash-cum-Ridley 14.299209
6 a4849d19-ca87-42db-b840-e8208a64aff1 Ashley Green 9.240459
7 24e2ecaa-1ad3-4cf5-9c96-43edf9dd7f78 Badgers Mount 2.696026
8 b26b2bd8-f8f3-4266-ba74-6585e3293298 Banstead 5.354076
9 bbb19ffc-24db-460c-927b-13f25f989aae Barking and Dagenham 36.924214
0:31
Let me verify the output looks correct by checking a few rows of the CSV.
0:31
Read
/work/borough_areas.csv
1 id,name,area_km2
2 255163d2-43e6-4c60-af14-b2c29b57a97e,Abbots Langley,18.537401
3 1f71e803-826e-4313-ab53-d8cc4c346743,Addington,3.345514
4 7f738dcf-eb8b-4c85-8141-9ef35825714d,Addlestone,2.922214
5 62053957-e9df-444e-b63d-39a6c4dc5179,Aldenham,23.856257
6 5e1cd051-a913-4b44-8d51-9ed93670ffad,Artington,4.921422
7 322630b0-4f06-46b2-8832-fe89e705e64e,Ash-cum-Ridley,14.299209
8 a4849d19-ca87-42db-b840-e8208a64aff1,Ashley Green,9.240459
9 24e2ecaa-1ad3-4cf5-9c96-43edf9dd7f78,Badgers Mount,2.696026
10 b26b2bd8-f8f3-4266-ba74-6585e3293298,Banstead,5.354076
11 bbb19ffc-24db-460c-927b-13f25f989aae,Barking and Dagenham,36.924214
12 0b8fccb7-0983-4079-9549-dd7f806e8c9f,Barnet,86.643073
13 68551377-cb1d-4f45-b7c5-dc3152ed5327,Batchworth,14.493003
14 2488df32-0cf3-4482-80f4-0302f99748ac,Bean,3.378739
15 573e6ddb-ff4e-4dde-a3a7-cfc434dc102d,Bellenden,0.583634
16 60486af9-218d-4749-97ab-b93419012d69,Berkhamsted,12.783588
17 23954d4a-2cce-4890-b4f4-203e14346e6c,Betchworth,9.905126
18 c313c0c6-14ac-426a-81d6-179ecd1c95d9,Bexley,60.423297
19 5a61e5dd-b9ff-47a8-99b1-73f558649fcf,Birling,6.804199
20 e1ae7d32-47d2-4348-87d3-bd0a3f3c2c23,Blackmore,0.219307
21 cc911033-9a2b-4c3b-9730-453340dc83b4,"Blackmore, Hook End and Wyatts Green",10.412117
22 ab0ed7cb-9811-46fa-aa9e-fea961981067,Bobbingworth,10.045979
23 a8ed179d-3a7a-4515-b0f1-1da4acdd5f08,Borough Green,2.169239
24 9fcbfbe4-5e84-47f4-adad-2512b7b3ef15,Bovingdon,15.427754
25 d5cb1816-8c24-4399-92aa-db1c8415af76,Brasted,14.515432
26 aa4ec19e-6df9-4d7a-bab7-b1ce95e661d9,Brent,44.16827
27 22d8caed-ab8f-4022-9002-8acce534a526,Britwell,0.401724
28 41157c8e-eceb-43c8-b80c-dfffa08ebe97,Brockham,6.900526
29 44517541-95cd-4c96-ac07-181b631d8d38,Bromley,150.198156
30 06c9dc74-c773-4334-b8a2-de3354d37178,Buckhurst Hill,3.851309
31 fb101d94-a797-4316-82fc-939a50994a26,Buckland,5.506271
32 04a7b3c7-09c5-47cc-a812-50c8559d6b72,Camden,21.274131
33 1df5e294-8b9c-421e-b03e-ccdfe4cf8843,Caterham Valley,5.882726
34 d7f45bd4-42be-4e05-bee5-40a77dd60f29,Caterham-on-the-Hill,3.520973
35 c8cb1323-bc59-444b-9261-efd3a00f6a05,Chaldon,4.720176
36 c411e5eb-f444-4d28-8844-538065f880f1,Chalfont St Giles,13.297363
37 d21d9cc1-94e4-47f8-a1db-5ba025296bbb,Chalfont St Peter,16.101844
38 e19ecda5-5265-4aac-8693-32c17cc0bbdf,Chelsham and Farleigh,14.912637
39 71ec6b4f-29a3-478c-9f7e-fbb937314b4d,Chenies,6.695274
40 a92965bf-8d2a-48d2-bfb7-3cff44bb2b02,Chertsey,3.734219
41 1a0607ce-aabe-4bf0-86cb-ab31cffd4e38,Chesham,14.174855
42 b9606022-db9f-4aa6-831f-fee7bbc9183b,Chesham Bois,2.661132
43 1331c07d-21b5-4fc6-a60d-726cdf3d613a,Chevening,14.873231
44 42ed1159-88df-4c87-be9d-0b1be715053c,Chigwell,15.677266
45 7a1d1081-75d3-4c72-8efb-97984b99ab0f,Chipperfield,5.577471
46 e7db0767-b3ba-4bf7-b416-57781cae33a8,Chobham,23.117943
47 2f356a6c-afa6-46d0-b411-e275ba13dc68,Chorleywood,9.84707
48 89c092f8-4287-4401-b72f-4a5a067eee22,City of London,2.505777
49 bed4905c-f822-4304-8da0-5e15b316da95,Claygate,4.708671
50 915c2bc0-ae1c-4321-a87d-81d5ac73b97e,Cobham,1.987912
51 03689961-9dcd-4683-aa75-92cf98563e4a,Coleshill,6.782334
52 545e7ff3-b92b-4228-9103-cc87de666809,Colnbrook with Poyle,5.634109
53 1204e7c9-99eb-4a19-a575-dc542f536e2f,Colney Heath,8.843505
54 88312be4-b2b4-48b0-9d1d-94097d362146,Compton,8.429118
55 8a50b7a3-4de8-4b35-a6d7-1a13652d5bae,Crockenhill,7.043079
56 30a609f0-e2f6-40dc-a0d8-bade19596b73,Croxley Green,5.005856
57 405fd056-348f-4880-906f-f31fcf29b2e9,Croydon,85.993777
58 11d99fc2-362e-4c66-9600-93b4930fa174,Darenth,7.900722
59 5e3432a9-87c8-4989-b885-20bec8e16ceb,Datchet,6.663089
60 e2575550-b092-4c20-bec1-5024d7d8309d,Denham,16.2497
61 f0bb8805-96c6-4bd3-9941-66f2e1c33831,Doddinghurst,6.613137
62 f0d44bb4-3e5d-44b7-9a3f-16692ece6752,Dorking,5.205778
63 b8e598fe-ed27-40c9-a63c-4ef53465792e,Dunton Green,3.903121
64 54413cc7-a7c9-4e4f-a7fe-e6ffec6ca137,Ealing,54.71223
65 40fd9abb-6bd9-43fe-962e-6ff4d7a8552e,East Clandon,5.858806
66 9f1b1d8c-739c-4b90-8784-b7164e6d44b2,East Horsley,7.475939
67 87d7ac85-4b4a-4dcf-9103-d3037b04d908,Effingham,11.908501
68 b85e9365-7929-4862-b3af-b7e6cf001ba4,Egham,1.453644
69 a9c290d4-e700-49dc-91d3-cb20bfde4192,Egham Hythe,0.404907
70 08ad1454-c1f3-41d2-8008-a66468d42ca7,Elstree and Borehamwood,13.759864
71 4384ae4c-b5a6-4b35-bffd-574b3a2ad0b5,Enfield,83.022176
72 06f3732c-3192-4bbc-b79c-ab2740ee6cb4,Epping,7.730262
73 6b6ff44f-a1e5-4abc-8d8a-0dffdcd02519,Epping Upland,17.743192
74 e15b109a-3ca4-48e0-b629-d539b36840aa,Esher,5.029826
75 a191e84c-9349-4f55-9ba1-478740cd6b5a,Eton,4.012496
76 ce99e897-b03b-4dae-9e8e-3892883f2001,Eton Wick,0.654856
77 a185eeb3-9322-4fd9-abc9-a6622fe2531d,Eynsford,15.7007
78 f73f3107-3d4c-4b2e-bbe5-60247295c9ad,Farnham Royal,4.459126
79 cf0f9908-c5ab-46de-bd8d-0238a3e431f4,Farningham,9.645156
80 6a3216f0-e202-4c44-97e6-46d002b07e10,Fawkham,6.647744
81 ec1b3b4a-0c63-493c-8875-88949130eb91,Flaunden,4.168312
82 643908fd-f9a1-4cde-8d6d-8b29ff5723ab,Fulmer,5.662409
83 e8b5c0cd-9c74-4a90-a5b4-41633050585f,Fyfield,9.904416
84 abefaa77-d1f4-41c1-8d72-3c1695d175b6,Gerrards Cross,10.655582
85 6c31d3a7-5158-42ce-a553-8e3216e0db4b,Gravesend,18.653283
86 47beb255-3718-475f-99c8-eafe548c1c12,Greenwich,47.629632
87 99b2d655-74c6-486f-b975-b7167a354aa9,Hackney,19.833986
88 98fedfe9-b6b3-4501-bb1a-785a8fd3c788,Halstead,5.884765
89 18ccc841-7df8-4fc7-8f9b-21dc70d61007,Hammersmith and Fulham,17.58723
90 af13982b-7b3a-4363-a747-180ab1f6f330,Harbour Village,0.088784
91 1fcb93df-a077-4346-9911-43813c00e50f,Haringey,28.655635
92 7013dd8f-3f1e-495d-a622-e41423194900,Harrow,51.786258
93 fe844f95-945c-4ec4-9cb9-88c59aa1d489,Hartley,5.481211
94 d5242497-8023-4bf0-b641-92f0041e3998,Havering,113.83632
95 55683ac5-e6d0-45a4-83c9-fc8401523f61,Headley,6.743385
96 045d51df-618b-4387-a57c-585bdf141200,Hedgerley,6.793739
97 5b8ab9ad-8a57-427d-b816-3a75a34a7543,Herongate and Ingrave,12.197642
98 f055e2ad-bde7-4f67-8ae9-976b63a29ce5,Hertsmere Meriden,0.332464
99 5d63a327-fda6-4c33-aeda-19b03a8054a5,Hextable,2.345477
100 8a4eede3-876b-486e-b442-a194ecdacba6,High Laver,7.32349
101 75147f2a-dc5d-432d-92bd-5320d0c68682,High Ongar,15.858295
102 76e72543-7c6d-4c04-817b-83aac40c395e,Highwood,14.100713
103 958dfe3f-8623-44f0-bd62-58cac435c995,Hillingdon,115.520674
104 cdf3470e-cdf7-408a-ba0a-c04b80f8fbcb,Hook End,0.088574
105 293e28ce-ed2b-44d9-b4ca-76348e5871f6,Horton,4.976582
106 4dc27a0f-b564-4d51-8e03-2681aa7a7d22,Horton Kirby and South Darenth,10.516833
107 8403ec1e-d226-4615-9c2a-233e5903aab4,Hounslow,55.053198
108 a8c95108-bc69-4c29-ac9c-bf61a0c999fa,Ightham,9.239454
109 3e649557-b23b-4be4-a20a-5b5ddda58023,Ingatestone and Fryerning,15.845605
110 b8ccf51b-0f33-4692-9da2-b1adc67d7333,Islington,15.152149
111 30bd488d-4824-4936-b466-8a875662dffb,Iver,20.118609
112 469d4260-9447-4866-a663-5cf6ba86f5be,Kelvedon Hatch,7.653364
113 67dc59bf-7b9b-4055-9f5b-445d63d030a6,Kemsing,8.949538
114 5c687f23-fd2a-4654-843a-4c98ee70d7de,Kensington and Chelsea,11.70151
115 72ac39b6-5664-43a2-a43c-239ab54a862e,Kings Hill,3.452823
116 4c74c37d-37c9-4011-86b4-03bf3e823e72,Kings Langley,8.323332
117 94ca71bb-532b-4e23-8f70-4cbc3838ddc2,Kingston upon Thames,38.007492
118 409b3e7f-942d-47bc-8646-ddb417ecf469,Knockholt,7.435328
119 31f05076-ca88-4d7f-be84-6f04e008698a,Lambeth,26.699841
120 37d57da8-2d2e-4777-ad8d-306dec492e78,Lambourne,10.417939
121 7c310ef2-4dff-492b-894b-9c7d8eefac98,Latimer and Ley Hill,9.01278
122 6d4226c5-5ace-4377-9c5e-6c73da733d6e,Leatherhead,5.400106
123 327bccae-8132-41f0-90db-dfe80ea3569d,Lewisham,34.283466
124 4ee10b82-6c35-4be8-be4b-c479d038b69f,Leybourne,3.014407
125 b1567160-4ce6-49c3-83aa-351d71196ffc,Little Berkhamsted,6.427349
126 f9d5d8a3-7861-4f48-9f17-d3925335aff9,Little Burstead,7.594974
127 ec53c4b7-ba92-4d4b-ba65-537bc4a498fb,Little Chalfont,5.293496
128 d500679e-3710-4924-97d2-bfb0ddb8faca,Little Heath,0.579285
129 dfc56015-86ec-42f8-b81f-3f2fa1639d7f,Little Laver,3.767185
130 92d8e117-a76c-4448-a315-290226589873,London Colney,5.302532
131 378554ca-7ab7-4a0c-85c1-f5fdbef2dbf5,Longfield and New Barn,3.57157
132 6940a3cf-3173-4c5a-855f-fa19fca0c443,Loughton,15.121774
133 71a9e366-91ad-47ed-8d4c-dfaf45bc5e2c,Luddesdown,8.090491
134 4b167d69-f8ec-4589-abdb-00462029addb,Magdalen Laver,6.064327
135 c91cd675-a9e6-4957-9966-140367dad122,Margaretting,11.263363
136 885ef3cc-fa2a-44e2-85f8-ff4aa52eb616,Meopham,20.222906
137 3f34eed0-54d3-4f53-8ff3-c10265b0ab1d,Mereworth,9.729255
138 657725e7-85b7-4c39-a335-5906f33e5e28,Merton,37.361486
139 7ff9f637-114a-474a-85e5-c84e87d32e51,Mickleham,7.310619
140 76ebcd45-3164-429f-a71f-291d74a42c62,Molesey,4.203281
141 cc15fb0c-bb38-4198-b65c-66288e5f205d,Moreton,5.960157
142 20a9c870-ac75-4012-80ef-2df5423e3ed1,Mountnessing,12.090642
143 a585d0b7-f2e3-4317-bfb9-7f1f75a1f033,Nash Mills,1.816272
144 a91f8e8a-03d7-49fa-8491-8543f86ecc25,Navestock,18.295306
145 126da99b-3447-456c-a27c-a06cf3b87556,Nazeing,16.441339
146 e5532ba5-ad16-40a3-993a-23e0dcc2051c,Nettlestead,5.275008
147 f43d31c9-0d13-418b-8a27-c5df26920e01,Newham,36.59856
148 a770478c-3573-4406-aea6-b80661a0d3ea,North Mymms,18.911195
149 f0734d6f-0a20-449a-aca1-00e18cc68876,North Weald Bassett,22.784253
150 2df46c6d-b4f1-40cf-a03d-e369a2e0c1ce,Northaw and Cuffley,15.427198
151 e777bc61-ed65-4271-9f87-ad7afa0bbfc1,Northfleet,15.9654
152 ce7e5b55-c653-4677-b415-716529ca3cf3,Ockham,12.127086
153 c2940fbe-44e9-457d-8c47-bec91cc66d7c,Offham,8.047759
154 e950e699-2193-4b1d-b49a-0db87b40605a,Ongar,9.015376
155 d4eb17e4-7478-43af-93af-8e08a95d53a4,Otford,7.018847
156 2dd8b4c0-6b79-4301-9fa1-94f28af02955,Oxted,15.143509
157 1705f8f5-0508-42b6-86f6-a135d4f77da4,Platt,6.959945
158 99884e4e-4793-4243-bc3d-aeac06e102a8,Plaxtol,9.718252
159 ae3d057e-66ef-4cea-8c84-2091219d2804,Queen's Park,0.583831
160 7f573586-a89f-4fe2-bd7a-82512a817e84,Redbridge,56.904415
161 a17422ad-c1a2-4e67-814b-a939ceabbf3f,Richmond upon Thames,59.203313
162 10004864-e485-4d86-bee0-7c7d91da3f69,Ripley,9.724497
163 abcc2386-2b05-4a58-ba43-02c1d7fa6576,Riverhead,2.856023
164 52176288-7f73-4f38-921a-98eff767733d,Riverview Park,0.004423
165 96a839ef-cef7-415b-9628-dc33bd3a318a,Roxwell,19.349146
166 04a50869-e03e-4348-a08b-4e0134da69e1,Roydon,10.943829
167 fb4dfe78-b505-4626-b7b9-e9706d9c98c8,Ryarsh,3.514347
168 cfb6dc05-f533-4bd1-a5be-1b3f21d09603,Sarratt,20.561175
169 2a8cbd7b-203c-462a-aa99-7be9528b6e93,Seal,18.617102
170 f0f6ec23-93bc-4b16-9a05-1270ebf1f6bc,Seer Green,3.491714
171 f2d7f2c0-763a-4f87-a82c-f4d187390045,Send,6.971761
172 3bc09a80-e13d-4178-a84a-9d29496214d9,Sevenoaks,15.959439
173 7ce68e84-dff3-4761-af12-4a4d1b4ea8a7,Sevenoaks Weald,9.693497
174 ec0ef800-e6ed-4ea7-b0b3-ce8dcfedfda0,Shenley,13.742675
175 8595735f-08a9-4314-92d6-88f41b4d604f,Shepperton,3.495297
176 d03194b8-1c32-4a2e-bf69-de29a667a2b2,Shipbourne,8.999601
177 248a1f95-91d4-432d-9f73-396e26019224,Shoreham,16.269507
178 f355f087-db87-49f5-9502-0a8b0df7ceb7,South Mimms and Ridge,20.877829
179 bdeab59a-4578-404c-b937-5f40ccca6ad3,Southdowns,0.074234
180 fc267066-0807-490d-a519-3079b81bcdf8,Southfleet,9.145153
181 e9a62052-f1fe-4949-afa4-a7087a27748a,Southwark,30.251139
182 d0dc61b1-16fe-4f04-b4ba-03766b750151,Springfield Village,0.32539
183 b16d8cdd-562d-4be3-83e5-364c8a15c98d,St Stephen,23.478482
184 1b71e357-ef12-4a00-88f8-efbbec5afa2c,St. Martha,3.442133
185 a096b079-02eb-4422-9595-3d5df057ddb7,Stanford Rivers,17.50075
186 cab7623d-df21-40c0-b97d-ef4fd1d6fda2,Stansted,7.981711
187 49633353-2f6d-45d3-bbd6-352e6fbb4165,Stapleford Abbotts,9.568437
188 35e8e04f-a0c9-4055-ac58-ef3d0c149075,Stapleford Tawney,6.696576
189 47478b63-caa2-4ec2-a03b-e6879934bcd2,Stoke Poges,10.09147
190 24e29ccd-4634-42b2-b50c-2bfd4fb49ea5,Stondon Green,0.122913
191 b4e2f32d-5888-4c2b-ab76-b4da45fbc81f,Stondon Massey,4.859464
192 9a37913f-bae9-4d93-a488-c511acd83a7d,Stone,6.717456
193 d8b3a9a4-12b3-40b9-a98c-640d26935235,Sunbury-on-Thames,7.198293
194 10b9590d-82dc-4152-a5e1-3831078b7901,Sundridge with Ide Hill,16.768277
195 5a35d510-23ef-4f61-9048-ce7e5b0eb61c,Sutton,44.47506
196 c0266b5d-5c75-4739-8f76-6ef153ae5e56,Sutton-at-Hone and Hawley,7.534584
197 72cc8783-8d75-4b4d-8ecb-6f27a0d90c94,Swanley,6.786813
198 2a251ac0-0bf0-48e4-a5ec-f4545462ea4c,Swanscombe and Greenhithe,5.971336
199 d68f06d7-a939-4ba4-add9-b561fa105db5,Tatsfield,5.335838
200 921ad000-ec7b-453c-8df0-d7a6c9fa3613,Theydon Bois,8.316206
201 cc06fa2a-dd98-49cd-ac9d-2f58c1e81c2d,Theydon Garnon,8.042063
202 27a8f8be-46a8-4aa8-83e1-20f250bfcc0f,Theydon Mount,6.320282
203 3f0ef154-938d-42a6-a383-56cf2ff2417f,Titsey,8.023297
204 1d4c8dbc-e4c4-4090-a102-7632b402cde7,Tower Hamlets,19.546972
205 5d96fd14-ebbd-4c20-b8e4-3eec3fd4f278,Trottiscliffe,5.533786
206 e3ee6616-8508-4662-8ecf-030cbc0b5a0a,Vigo,0.686609
207 1e457aff-8400-4181-a9d2-3dd2ce426a01,Waltham Abbey,42.404738
208 c715494b-3a8e-473c-94f2-41e48d981cdd,Waltham Forest,39.257108
209 2dddafd8-bfd0-49e5-a644-173c969ee628,Walton-on-Thames,6.202123
210 7541dfd0-1427-4d22-88fd-cd901e52d714,Wandsworth,34.625993
211 b2a408e3-b0b2-4d05-b84b-c9aa959fd375,Warlingham,6.008341
212 3bb58940-001c-4e67-84a2-f26bad2c9497,Wateringbury,5.402477
213 c0bf652f-f9e6-4e6d-857a-28d8285b6eea,Watford Rural,8.157043
214 f1956318-bc82-42ac-94c7-0fca9afa0a20,West Clandon,9.563852
215 e0aae426-3cc3-4d70-a07f-4cd40337f081,West Horndon,7.647755
216 40134e53-e768-419b-bd49-987263af8038,West Horsley,10.833448
217 08bc3f0f-dad3-4537-ad67-d0c8f74736b3,West Kingsdown,15.973711
218 31fd5873-c70f-4ed1-b4d9-a4edf54e2863,West Malling,3.826002
219 ada3b58a-51b9-4dd8-b388-d677649d3f48,West Peckham,6.344779
220 72e1a57b-1e1b-470f-98e2-398bc2d8f488,Westerham,22.887066
221 4cef7232-6c8a-40a5-b99a-4c4884ba9fa7,Westminster,22.938487
222 65ae112c-cf9a-48c3-8db5-7f6e3b5ea869,Weston Green,0.230776
223 416ee1e1-2a85-41cc-86f4-4cd05ef94a29,Wexham,11.079122
224 38f28d80-76cd-49a0-9ae1-3300f99d0834,Wexham Court,1.15863
225 413d4a17-44cd-4098-8b12-dca64428489e,Weybridge,5.24732
226 6abb4365-c25c-45ee-9353-a4e0fbca405c,Whyteleafe,2.171324
227 802a0a5d-683d-49d2-ac9b-3e76093ab4eb,Willingale,13.985637
228 fd419955-34e2-4128-9b6e-1add1861e395,Wilmington,6.77082
229 df362796-d8c7-445a-923e-7cae1350ba76,Wisley,4.027234
230 4caa4290-2c4c-44f8-9fd8-b4eace7ebe75,Woldingham,10.873029
231 0342ee1b-51ff-4529-8959-a469c97b037b,Wraysbury,6.734868
232 3c9ee3f0-6fea-4ea8-84bc-69496a04a83f,Writtle,18.060102
233 a49150e6-7c61-4474-924b-6ff2a9fdefcb,Wrotham,13.782621
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The CSV has been generated successfully with all 232 features (33 counties/boroughs and 199 localities), each with its id, name, and area_km2. The data is sorted by name for readability.
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