Contents

Overview of the data product

The data product of spatial signatures in Great Britain contains the data illustrated by this notebook.

cd ../../urbangrammar_samba/spatial_signatures/data_product/

/home/jovyan/work/urbangrammar_samba/spatial_signatures/data_product

import json
import pandas
import geopandas

Geometry

Signature geometry with signature type and polygon ID.

geopandas.read_file("spatial_signatures_GB.gpkg", rows=10)

/opt/conda/lib/python3.8/site-packages/geopandas/geodataframe.py:577: RuntimeWarning: Sequential read of iterator was interrupted. Resetting iterator. This can negatively impact the performance.
  for feature in features_lst:
id code type geometry
0 0_COA COA Countryside agriculture POLYGON ((62220.000 798500.000, 62110.000 7985...
1 1_COA COA Countryside agriculture POLYGON ((63507.682 796515.169, 63471.097 7965...
2 2_COA COA Countryside agriculture POLYGON ((65953.174 802246.172, 65950.620 8022...
3 3_COA COA Countryside agriculture POLYGON ((67297.740 803435.800, 67220.289 8034...
4 4_COA COA Countryside agriculture POLYGON ((75760.000 852670.000, 75700.000 8527...
5 5_COA COA Countryside agriculture POLYGON ((78663.640 819587.579, 78665.420 8195...
6 6_COA COA Countryside agriculture POLYGON ((79020.596 820041.322, 79022.514 8200...
7 7_COA COA Countryside agriculture POLYGON ((79088.951 819900.971, 79089.062 8199...
8 8_COA COA Countryside agriculture POLYGON ((79843.335 818918.964, 79843.296 8189...
9 9_COA COA Countryside agriculture POLYGON ((88080.000 14970.000, 88078.269 14961...

Geometry description

Summary of input characters per each geometry.

pandas.read_csv("per_geometry.csv")
id form_sdbAre form_sdbPer form_sdbCoA form_ssbCCo form_ssbCor form_ssbSqu form_ssbERI form_ssbElo form_ssbCCM ... func_supermarkets_nearest func_supermarkets_counts func_listed_nearest func_listed_counts func_fhrs_nearest func_fhrs_counts func_culture_nearest func_culture_counts func_water_nearest func_retail_centrenearest
0 0_COA 129.933434 45.146514 0.202279 0.540968 4.172237 0.524401 0.993543 0.622563 8.027774 ... 1444.935973 0.454774 890.196235 3.572864 341.152792 77.364322 NaN 0.0 136.483329 93403.761607
1 4_COA 258.461742 65.650654 5.278706 0.500917 4.376384 0.558092 0.975591 0.571528 11.094158 ... 9034.336389 0.000000 736.182262 1.718412 642.509775 126.115523 NaN 0.0 139.419780 58846.096367
2 9_COA 122.655396 46.888304 0.000000 0.509570 4.336957 0.606492 0.981817 0.578105 8.168939 ... NaN 0.000000 112.232624 4.935484 309.115193 61.000000 NaN 0.0 80.087762 4762.653237
3 11_COA 190.308946 59.022824 0.764543 0.494519 4.541985 0.997673 0.967397 0.598092 10.134862 ... NaN 0.000000 281.289369 8.043478 198.296841 4.326087 NaN 0.0 74.377682 4378.879114
4 14_COA 146.477816 50.308964 0.229756 0.509439 4.382090 0.769455 0.974591 0.607979 8.836870 ... 2296.657968 0.023599 519.019717 7.519174 281.737308 24.020649 NaN 0.0 199.544381 1698.769041
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
96699 94815_LOU NaN NaN NaN NaN NaN NaN NaN NaN NaN ... 967.354980 1.000000 733.508972 18.000000 294.217987 62.000000 4342.378906 0.0 0.000000 731.905179
96700 94853_LOU 542.697500 98.334230 0.000000 0.518628 4.000000 0.078285 0.999855 0.516309 18.248347 ... 547.687012 3.000000 97.226997 77.000000 112.362999 1346.000000 1062.750000 1.0 160.527776 213.305247
96701 94892_LOU NaN NaN NaN NaN NaN NaN NaN NaN NaN ... 457.170990 5.000000 0.000000 515.000000 38.470001 2364.000000 401.411011 2.0 410.039048 0.000000
96702 96021_LOU 180.378400 57.278130 0.000000 0.499708 4.000000 0.025284 1.000037 0.484826 10.717266 ... 327.584015 4.000000 251.115005 3.000000 153.737000 301.000000 2968.321045 0.0 710.912280 317.166726
96703 96702_OUT 54.747100 29.614573 0.000000 0.634613 4.000000 0.048043 1.000000 0.932525 5.238355 ... 41921.593750 0.000000 0.000000 4.000000 1227.026001 0.000000 NaN 0.0 0.000000 25185.139279

96704 rows × 117 columns

Signature type description

Summary of input characters per each signature type.

pandas.read_csv("per_type.csv")
code type form_sdbAre form_sdbPer form_sdbCoA form_ssbCCo form_ssbCor form_ssbSqu form_ssbERI form_ssbElo ... func_supermarkets_nearest func_supermarkets_counts func_listed_nearest func_listed_counts func_fhrs_nearest func_fhrs_counts func_culture_nearest func_culture_counts func_water_nearest func_retail_centrenearest
0 ACS Accessible suburbia 176.949135 53.902697 0.476838 0.534856 4.253845 0.775272 0.987707 0.642271 ... 828.822261 1.890519 744.223994 11.273578 218.460768 334.434991 5384.638746 0.059113 542.613661 849.447871
1 CRN Connected residential neighbourhoods 272.522220 69.119967 1.069872 0.480941 4.445515 1.466426 0.979415 0.555468 ... 679.958940 2.855980 596.613654 24.279146 152.479922 692.664050 3946.052517 0.129197 555.964942 536.469155
2 COA Countryside agriculture 204.100400 56.048984 0.505831 0.506275 4.371156 0.809087 0.977789 0.600065 ... 4751.226429 0.089042 557.936276 11.223066 725.690643 44.465529 13156.203765 0.003804 304.488181 4943.972107
3 DRN Dense residential neighbourhoods 375.598290 80.558473 2.127340 0.472554 4.694714 1.863620 0.969912 0.557054 ... 661.765594 3.127412 506.614715 37.474705 144.021617 860.927102 3497.511181 0.259194 483.124139 421.093530
4 DUN Dense urban neighbourhoods 588.359559 107.362849 5.034084 0.435714 5.208242 3.278419 0.950431 0.519821 ... 587.276810 4.436632 350.890339 62.783422 106.084358 1568.443597 2287.432199 0.476118 528.850109 224.326713
5 DIS Disconnected suburbia 212.713958 61.631662 0.519372 0.491588 4.347900 1.019293 0.981779 0.573786 ... 761.858373 2.067315 729.608517 24.181257 217.945624 342.081722 5831.523433 0.077958 523.050240 725.573382
6 DIU Concentrated urbanity 3713.379427 376.300388 159.087608 0.425186 12.484667 18.589482 0.776950 0.594314 ... 229.903096 22.510563 31.732228 685.156338 16.219050 6297.607746 702.750880 10.392254 565.248203 29.803442
7 GRQ Gridded residential quarters 283.892607 69.667245 0.749026 0.491754 4.506685 1.663151 0.976842 0.584548 ... 577.683974 3.409903 516.197376 31.771826 129.244204 1081.376510 4094.915253 0.240503 522.087434 445.518972
8 HDU Hyper concentrated urbanity 3358.099586 330.818576 90.819544 0.446292 9.273438 22.513954 0.802892 0.617669 ... 324.416312 18.791045 69.749236 1142.567164 14.101269 9213.145522 351.325110 34.197761 759.604208 32.544669
9 LOU Local urbanity 823.354285 135.543919 12.666729 0.409310 6.014239 5.071848 0.919085 0.506415 ... 483.017862 6.848327 216.858599 140.031073 82.468961 2167.909991 1273.226897 1.129622 507.706457 161.854989
10 MEU Metropolitan urbanity 2413.938716 283.935149 118.946949 0.395862 9.717504 12.406894 0.820142 0.527758 ... 299.930496 17.271958 51.874364 456.529630 40.063982 4490.949206 644.531259 4.446825 467.713631 66.318054
11 OPS Open sprawl 226.715098 59.639780 0.900478 0.518531 4.371939 0.985729 0.982452 0.622301 ... 948.025914 1.469023 760.257254 18.171441 267.238602 253.875226 6309.753341 0.061560 378.356187 1002.663064
12 REU Regional urbanity 1480.260902 195.978660 43.190998 0.393035 7.776863 8.844805 0.869514 0.512959 ... 331.067781 12.531123 115.004786 324.500752 56.865240 3163.829678 850.254487 2.233070 461.415859 90.874497
13 URB Urban buffer 209.421858 55.941702 0.743395 0.523632 4.340540 0.859973 0.982904 0.630125 ... 1752.873479 0.654018 673.931399 16.138833 379.169823 132.661846 8939.647136 0.024052 345.790747 2102.455609
14 WAL Warehouse/Park land 393.216097 75.680270 3.263039 0.469466 4.564890 1.352536 0.976102 0.542256 ... 1043.835409 1.427596 934.001152 10.570084 256.220805 271.092537 5121.469150 0.058667 417.431851 898.174463
15 WIC Wild countryside 209.855382 57.122052 0.223542 0.501939 4.381272 0.706130 0.976522 0.589908 ... 9854.123937 0.028975 1324.025044 4.212720 1699.169649 33.073293 20695.290665 0.001680 236.730324 11041.324478

16 rows × 118 columns

Keys

Key to codes denoting measured characters.

pandas.read_csv("key.csv")
Unnamed: 0 0
0 func_population Population
1 func_night_lights Night lights
2 func_workplace_abde Workplace population [Agriculture, energy and ...
3 func_workplace_c Workplace population [Manufacturing]
4 func_workplace_f Workplace population [Construction]
... ... ...
111 form_lseERI equivalent rectangular index of enclosure
112 form_lseCWA compactness-weighted axis of enclosure
113 form_lteOri orientation of enclosure
114 form_lteWNB perimeter-weighted neighbours of enclosure
115 form_lieWCe area-weighted ETCs of enclosure

116 rows × 2 columns

Key linking signature type and type code.

pandas.read_csv("type_code.csv")
type_name type_code
0 Countryside agriculture COA
1 Accessible suburbia ACS
2 Open sprawl OPS
3 Wild countryside WIC
4 Warehouse/Park land WAL
5 Gridded residential quarters GRQ
6 Urban buffer URB
7 Disconnected suburbia DIS
8 Dense residential neighbourhoods DRN
9 Connected residential neighbourhoods CRN
10 Dense urban neighbourhoods DUN
11 Local urbanity LOU
12 Concentrated urbanity DIU
13 Regional urbanity REU
14 Metropolitan urbanity MEU
15 Hyper concentrated urbanity HDU
16 outlier OUT

LSOA interpolation

Interpolation of signature types to LSOA geometry.

pandas.read_csv("lsoa_estimates.csv", nrows=10)
LSOA11CD primary_code primary_type COA ACS OPS WIC WAL GRQ URB DIS DRN CRN DUN LOU DIU REU OUT MEU HDU
0 E01000007 DUN Dense urban neighbourhoods 0.0 0.000000 0.000000 0.0 0.000000 0.000000 0.0 0.000000 0.000000 0.000000 0.822703 0.177297 0.0 0.0 0.0 0.0 0.0
1 E01000015 DRN Dense residential neighbourhoods 0.0 0.000000 0.001117 0.0 0.000000 0.000000 0.0 0.022815 0.707635 0.136794 0.130251 0.000000 0.0 0.0 0.0 0.0 0.0
2 E01000030 DRN Dense residential neighbourhoods 0.0 0.000000 0.000000 0.0 0.427742 0.000000 0.0 0.000000 0.572258 0.000000 0.000000 0.000000 0.0 0.0 0.0 0.0 0.0
3 E01000085 DUN Dense urban neighbourhoods 0.0 0.000000 0.000000 0.0 0.000000 0.000000 0.0 0.000000 0.171808 0.126567 0.701626 0.000000 0.0 0.0 0.0 0.0 0.0
4 E01000118 CRN Connected residential neighbourhoods 0.0 0.339714 0.036230 0.0 0.000000 0.132012 0.0 0.000000 0.004280 0.487764 0.000000 0.000000 0.0 0.0 0.0 0.0 0.0
5 E01000125 CRN Connected residential neighbourhoods 0.0 0.000000 0.000000 0.0 0.000000 0.000000 0.0 0.000000 0.000000 0.867404 0.132596 0.000000 0.0 0.0 0.0 0.0 0.0
6 E01000136 WAL Warehouse/Park land 0.0 0.000000 0.051477 0.0 0.443033 0.000000 0.0 0.012310 0.427006 0.029230 0.036944 0.000000 0.0 0.0 0.0 0.0 0.0
7 E01000145 CRN Connected residential neighbourhoods 0.0 0.000000 0.000000 0.0 0.000000 0.000000 0.0 0.000000 0.000000 1.000000 0.000000 0.000000 0.0 0.0 0.0 0.0 0.0
8 E01000146 CRN Connected residential neighbourhoods 0.0 0.000000 0.000000 0.0 0.000000 0.000000 0.0 0.000000 0.083465 0.916535 0.000000 0.000000 0.0 0.0 0.0 0.0 0.0
9 E01000166 OPS Open sprawl 0.0 0.000000 0.893081 0.0 0.091906 0.000000 0.0 0.015013 0.000000 0.000000 0.000000 0.000000 0.0 0.0 0.0 0.0 0.0

OA interpolation

Interpolation of signature types to OA geometry.

pandas.read_csv("output_area_estimates.csv", nrows=10)
OA11CD primary_code primary_type COA ACS OPS WIC WAL GRQ URB DIS DRN CRN DUN LOU DIU REU OUT MEU HDU
0 E00000001 DIU Concentrated urbanity 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0
1 E00000003 DIU Concentrated urbanity 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0
2 E00000005 DIU Concentrated urbanity 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0
3 E00000007 DIU Concentrated urbanity 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0
4 E00000010 DIU Concentrated urbanity 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0
5 E00000012 DIU Concentrated urbanity 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0
6 E00000013 DIU Concentrated urbanity 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0
7 E00000014 DIU Concentrated urbanity 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0
8 E00000016 DIU Concentrated urbanity 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0
9 E00000017 DIU Concentrated urbanity 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0

Pen portraits

Short description of each signature type.

with open("pen_portraits.json", "r") as f:
    portraits = json.load(f)

portraits

{'Wild countryside': 'In “Wild countryside”, human influence is the least intensive. This signature covers large open spaces in the countryside where no urbanisation happens apart from occasional roads, cottages, and pastures. You can find it across the Scottish Highlands, numerous national parks such as Lake District, or in the majority of Wales.',
 'Countryside agriculture': '“Countryside agriculture” features much of the English countryside and displays a high degree of agriculture including both fields and pastures. There are a few buildings scattered across the area but, for the most part, it is green space.',
 'Urban buffer': '“Urban buffer” can be characterised as a green belt around cities. This signature includes mostly agricultural land in the immediate adjacency of towns and cities, often including edge development. It still feels more like countryside than urban, but these signatures are much smaller compared to other countryside types.',
 'Open sprawl': '“Open sprawl” represents the transition between countryside and urbanised land. It is located in the outskirts of cities or around smaller towns and is typically made up of large open space areas intertwined with different kinds of human development, from highways to smaller neighbourhoods.',
 'Disconnected suburbia': '“Disconnected suburbia” includes residential developments in the outskirts of cities or even towns and villages with convoluted, disconnected street networks, low built-up and population densities, and lack of jobs and services. This signature type is entirely car-dependent.',
 'Accessible suburbia': '“Accessible suburbia” covers residential development on the urban periphery with a relatively legible and connected street network, albeit less so than other more urban signature types. Areas in this signature feature low density, both in terms of population and built-up area, lack of jobs and services. For these reasons, “accessible suburbia” largely acts as dormitories.',
 'Warehouse/Park land': '“Warehouse/Park land” covers predominantly industrial areas and other work-related developments made of box-like buildings with large footprints. It contains many jobs of manual nature such as manufacturing or construction, and very little population live here compared to the rest of urban areas. Occasionally this type also covers areas of parks with large scale green open areas.',
 'Gridded residential quarters': '“Gridded residential quarters” are areas with street networks forming a well-connected grid-like (high density of 4-way intersections) pattern, resulting in places with smaller blocks and higher granularity. This signature is mostly residential but includes some services and jobs, and it tends to be located away from city centres.',
 'Connected residential neighbourhoods': '“Connected residential neighbourhoods” are relatively dense urban areas, both in terms of population and built-up area, that tend to be formed around well-connected street networks. They have access to services and some jobs but may be further away from city centres leading to higher dependency on cars and public transport for their residents.',
 'Dense residential neighbourhoods': 'A “dense residential neighbourhood” is an abundant signature often covering large parts of cities outside of their centres. It has primarily residential purpose and high population density, varied street network patterns, and some services and jobs but not in high intensity.',
 'Dense urban neighbourhoods': '“Dense urban neighbourhoods” are areas of inner-city with high population and built-up density of a predominantly residential nature but with direct access to jobs and services. This signature type tends to be relatively walkable and, in the case of some towns, may even form their centres.',
 'Local urbanity': '“Local urbanity” reflects town centres, outer parts of city centres or even district centres. In all cases, this signature is very much urban in essence, combining high population and built-up density, access to amenities and jobs. Yet, it is on the lower end of the hierarchy of signature types denoting urban centres with only a local significance.',
 'Regional urbanity': '“Regional urbanity” captures centres of mid-size cities with regional importance such as Liverpool, Plymouth or Newcastle upon Tyne. It is often encircled by “Local urbanity” signatures and can form outer rings of city centres in large cities. It features high population density, as well as a high number of jobs and amenities within walkable distance.',
 'Metropolitan urbanity': 'Signature type “Metropolitan urbanity” captures the centre of the largest cities in Great Britain such as Glasgow, Birmingham or Manchester. It is characterised by a very high number of jobs in the area, high built-up density and often high population density. This type serves as the core centre of the entire metropolitan areas.',
 'Concentrated urbanity': 'Concentrated urbanity” is a signature type found in the city centre of London and nowhere else in Great Britain. It reflects the uniqueness of London in the British context with an extremely high number of jobs and amenities located nearby, as well as high built-up and population densities. Buildings in this signature are large and tightly packed, forming complex shapes with courtyards and little green space.',
 'Hyper concentrated urbanity': 'The epitome of urbanity in the British context. “Hyper concentrated urbanity” is a signature type present only in the centre of London, around the Soho district, and covering Oxford and Regent streets. This signature is the result of centuries of urban primacy, with a multitude of historical layers interwoven, very high built-up and population density, and extreme abundance of amenities, services and jobs.'}