Contents

Create a data product

Spatial signatures in Great Britain are released as a data product.

This notebook takes the generated results and turn them into clean outputs.

# we need the master version of dask geopandas that has sjoin and recent dask
!pip install git+https://github.com/geopandas/dask-geopandas.git

import pandas
import dask.dataframe
import dask
import geopandas
import dask_geopandas 

dask_geopandas.__version__

'v0.1.0a4+16.gf18ae2c'

Signature geometry

We create an output with signature geometry and clear columns.

signatures = geopandas.read_parquet("../../urbangrammar_samba/spatial_signatures/signatures/signatures_combined_levels_orig.pq")
signatures.head()
kmeans10gb geometry level2 signature_type
0 0 POLYGON Z ((62220.000 798500.000 0.000, 62110.... 0.0 0_0
1 0 POLYGON Z ((63507.682 796515.169 0.000, 63471.... 0.0 0_0
2 0 POLYGON Z ((65953.174 802246.172 0.000, 65950.... 0.0 0_0
3 0 POLYGON Z ((67297.740 803435.800 0.000, 67220.... 0.0 0_0
4 0 POLYGON Z ((75760.000 852670.000 0.000, 75700.... 0.0 0_0 
types = {
    "0_0": "Countryside agriculture",
    "1_0": "Accessible suburbia",
    "3_0": "Open sprawl",
    "4_0": "Wild countryside",
    "5_0": "Warehouse/Park land",
    "6_0": "Gridded residential quarters",
    "7_0": "Urban buffer",
    "8_0": "Disconnected suburbia",
    "2_0": "Dense residential neighbourhoods",
    "2_1": "Connected residential neighbourhoods",
    "2_2": "Dense urban neighbourhoods",
    "9_0": "Local urbanity",
    "9_1": "Concentrated urbanity",
    "9_2": "Regional urbanity",
    "9_4": "Metropolitan urbanity",
    "9_5": "Hyper concentrated urbanity",
    "9_3": "outlier",
    "9_6": "outlier",
    "9_7": "outlier",
    "9_8": "outlier",
}

signatures["type"] = signatures.signature_type.map(types)
signatures["id"] = range(len(signatures))

signatures.head()
kmeans10gb geometry level2 signature_type type id
0 0 POLYGON Z ((62220.000 798500.000 0.000, 62110.... 0.0 0_0 Countryside agriculture 0
1 0 POLYGON Z ((63507.682 796515.169 0.000, 63471.... 0.0 0_0 Countryside agriculture 1
2 0 POLYGON Z ((65953.174 802246.172 0.000, 65950.... 0.0 0_0 Countryside agriculture 2
3 0 POLYGON Z ((67297.740 803435.800 0.000, 67220.... 0.0 0_0 Countryside agriculture 3
4 0 POLYGON Z ((75760.000 852670.000 0.000, 75700.... 0.0 0_0 Countryside agriculture 4 
signatures = signatures[["id", "type", "geometry"]]

signatures
id type geometry
0 0 Countryside agriculture POLYGON Z ((62220.000 798500.000 0.000, 62110....
1 1 Countryside agriculture POLYGON Z ((63507.682 796515.169 0.000, 63471....
2 2 Countryside agriculture POLYGON Z ((65953.174 802246.172 0.000, 65950....
3 3 Countryside agriculture POLYGON Z ((67297.740 803435.800 0.000, 67220....
4 4 Countryside agriculture POLYGON Z ((75760.000 852670.000 0.000, 75700....
... ... ... ...
96699 96699 outlier POLYGON ((323321.005 463795.416, 323319.842 46...
96700 96700 outlier POLYGON ((325929.840 1008792.061, 325927.377 1...
96701 96701 outlier POLYGON ((337804.770 1013422.583, 337800.122 1...
96702 96702 outlier POLYGON ((422304.270 1147826.990, 422296.000 1...
96703 96703 outlier POLYGON ((525396.260 439215.480, 525360.920 43...

96704 rows × 3 columns

Remove Z coordinates

Some polygons contain Z coordinates that are not needed.

import pygeos

signatures["geometry"] = pygeos.apply(signatures.geometry.values.data, lambda x: x, include_z=False)

signatures
id type geometry
0 0 Countryside agriculture POLYGON ((62220.000 798500.000, 62110.000 7985...
1 1 Countryside agriculture POLYGON ((63507.682 796515.169, 63471.097 7965...
2 2 Countryside agriculture POLYGON ((65953.174 802246.172, 65950.620 8022...
3 3 Countryside agriculture POLYGON ((67297.740 803435.800, 67220.289 8034...
4 4 Countryside agriculture POLYGON ((75760.000 852670.000, 75700.000 8527...
... ... ... ...
96699 96699 outlier POLYGON ((323321.005 463795.416, 323319.842 46...
96700 96700 outlier POLYGON ((325929.840 1008792.061, 325927.377 1...
96701 96701 outlier POLYGON ((337804.770 1013422.583, 337800.122 1...
96702 96702 outlier POLYGON ((422304.270 1147826.990, 422296.000 1...
96703 96703 outlier POLYGON ((525396.260 439215.480, 525360.920 43...

96704 rows × 3 columns

Cleanup sliver geometries

Remove artifacts caused by floating point imprecision in dissolving of ET cells into signature geometries.

from shapely.geometry import Polygon

signatures_ddf = dask_geopandas.from_geopandas(signatures, npartitions=4)

def fix(gdf):
    new = []
    for poly in gdf.geometry:
        new.append(Polygon(shell=poly.exterior, holes=[i for i in poly.interiors if Polygon(i).area > 1]))
    return geopandas.GeoSeries(new, index=gdf.index, crs=gdf.crs)


signatures_ddf["geometry"] = signatures_ddf.map_partitions(fix, meta=geopandas.GeoSeries())

signatures.compute().to_file("spatial_signatures_GB.gpkg", driver="GPKG")

Rename columns

coded = {
    'population': 'func_population',
    'night_lights': 'func_night_lights',
    'A, B, D, E. Agriculture, energy and water': 'func_workplace_abde',
    'C. Manufacturing': 'func_workplace_c',
    'F. Construction': 'func_workplace_f',
    'G, I. Distribution, hotels and restaurants': 'func_workplace_gi',
    'H, J. Transport and communication': 'func_workplace_hj',
    'K, L, M, N. Financial, real estate, professional and administrative activities': 'func_workplace_klmn',
    'O,P,Q. Public administration, education and health': 'func_workplace_opq',
    'R, S, T, U. Other': 'func_workplace_rstu',
    'Code_18_124': 'func_corine_124',
    'Code_18_211': 'func_corine_211',
    'Code_18_121': 'func_corine_121',
    'Code_18_421': 'func_corine_421',
    'Code_18_522': 'func_corine_522',
    'Code_18_142': 'func_corine_142',
    'Code_18_141': 'func_corine_141',
    'Code_18_112': 'func_corine_112',
    'Code_18_231': 'func_corine_231',
    'Code_18_311': 'func_corine_311',
    'Code_18_131': 'func_corine_131',
    'Code_18_123': 'func_corine_123',
    'Code_18_122': 'func_corine_122',
    'Code_18_512': 'func_corine_512',
    'Code_18_243': 'func_corine_243',
    'Code_18_313': 'func_corine_313',
    'Code_18_412': 'func_corine_412',
    'Code_18_321': 'func_corine_321',
    'Code_18_322': 'func_corine_322',
    'Code_18_324': 'func_corine_324',
    'Code_18_111': 'func_corine_111',
    'Code_18_423': 'func_corine_423',
    'Code_18_523': 'func_corine_523',
    'Code_18_312': 'func_corine_312',
    'Code_18_133': 'func_corine_133',
    'Code_18_333': 'func_corine_333',
    'Code_18_332': 'func_corine_332',
    'Code_18_411': 'func_corine_411',
    'Code_18_132': 'func_corine_132',
    'Code_18_222': 'func_corine_222',
    'Code_18_242': 'func_corine_242',
    'Code_18_331': 'func_corine_331',
    'Code_18_511': 'func_corine_511',
    'Code_18_334': 'func_corine_334',
    'Code_18_244': 'func_corine_244',
    'Code_18_521': 'func_corine_521',
    'mean': 'ndvi',
    'supermarkets_nearest': 'func_supermarkets_nearest',
    'supermarkets_counts': 'func_supermarkets_counts',
    'listed_nearest': 'func_listed_nearest',
    'listed_counts': 'func_listed_counts',
    'fhrs_nearest': 'func_fhrs_nearest',
    'fhrs_counts': 'func_fhrs_counts',
    'culture_nearest': 'func_culture_nearest',
    'culture_counts': 'func_culture_counts',
    'nearest_water': 'func_water_nearest',
    'nearest_retail_centre': 'func_retail_centrenearest',
    'sdbAre': 'form_sdbAre',
    'sdbPer': 'form_sdbPer',
    'sdbCoA': 'form_sdbCoA',
    'ssbCCo': 'form_ssbCCo',
    'ssbCor': 'form_ssbCor',
    'ssbSqu': 'form_ssbSqu',
    'ssbERI': 'form_ssbERI',
    'ssbElo': 'form_ssbElo',
    'ssbCCM': 'form_ssbCCM',
    'ssbCCD': 'form_ssbCCD',
    'stbOri': 'form_stbOri',
    'sdcLAL': 'form_sdcLAL',
    'sdcAre': 'form_sdcAre',
    'sscCCo': 'form_sscCCo',
    'sscERI': 'form_sscERI',
    'stcOri': 'form_stcOri',
    'sicCAR': 'form_sicCAR',
    'stbCeA': 'form_stbCeA',
    'mtbAli': 'form_mtbAli',
    'mtbNDi': 'form_mtbNDi',
    'mtcWNe': 'form_mtcWNe',
    'mdcAre': 'form_mdcAre',
    'ltcWRE': 'form_ltcWRE',
    'ltbIBD': 'form_ltbIBD',
    'sdsSPW': 'form_sdsSPW',
    'sdsSWD': 'form_sdsSWD',
    'sdsSPO': 'form_sdsSPO',
    'sdsLen': 'form_sdsLen',
    'sssLin': 'form_sssLin',
    'ldsMSL': 'form_ldsMSL',
    'mtdDeg': 'form_mtdDeg',
    'lcdMes': 'form_lcdMes',
    'linP3W': 'form_linP3W',
    'linP4W': 'form_linP4W',
    'linPDE': 'form_linPDE',
    'lcnClo': 'form_lcnClo',
    'ldsCDL': 'form_ldsCDL',
    'xcnSCl': 'form_xcnSCl',
    'mtdMDi': 'form_mtdMDi',
    'lddNDe': 'form_lddNDe',
    'linWID': 'form_linWID',
    'stbSAl': 'form_stbSAl',
    'sddAre': 'form_sddAre',
    'sdsAre': 'form_sdsAre',
    'sisBpM': 'form_sisBpM',
    'misCel': 'form_misCel',
    'mdsAre': 'form_mdsAre',
    'lisCel': 'form_lisCel',
    'ldsAre': 'form_ldsAre',
    'ltcRea': 'form_ltcRea',
    'ltcAre': 'form_ltcAre',
    'ldeAre': 'form_ldeAre',
    'ldePer': 'form_ldePer',
    'lseCCo': 'form_lseCCo',
    'lseERI': 'form_lseERI',
    'lseCWA': 'form_lseCWA',
    'lteOri': 'form_lteOri',
    'lteWNB': 'form_lteWNB',
    'lieWCe': 'form_lieWCe',
}

key = {
    'func_population': 'Population',
    'func_night_lights': 'Night lights',
    'func_workplace_abde': 'Workplace population [Agriculture, energy and water]',
    'func_workplace_c': 'Workplace population [Manufacturing]',
    'func_workplace_f': 'Workplace population [Construction]',
    'func_workplace_gi': 'Workplace population [Distribution, hotels and restaurants]',
    'func_workplace_hj': 'Workplace population [Transport and communication]',
    'func_workplace_klmn': 'Workplace population [Financial, real estate, professional and administrative activities]',
    'func_workplace_opq': 'Workplace population [Public administration, education and health]',
    'func_workplace_rstu': 'Workplace population [Other]',
    'func_corine_124': 'Land cover [Airports]',
    'func_corine_211': 'Land cover [Non-irrigated arable land]',
    'func_corine_121': 'Land cover [Industrial or commercial units]',
    'func_corine_421': 'Land cover [Salt marshes]',
    'func_corine_522': 'Land cover [Estuaries]',
    'func_corine_142': 'Land cover [Sport and leisure facilities]',
    'func_corine_141': 'Land cover [Green urban areas]',
    'func_corine_112': 'Land cover [Discontinuous urban fabric]',
    'func_corine_231': 'Land cover [Pastures]',
    'func_corine_311': 'Land cover [Broad-leaved forest]',
    'func_corine_131': 'Land cover [Mineral extraction sites]',
    'func_corine_123': 'Land cover [Port areas]',
    'func_corine_122': 'Land cover [Road and rail networks and associated land]',
    'func_corine_512': 'Land cover [Water bodies]',
    'func_corine_243': 'Land cover [Land principally occupied by agriculture, with significant areas of natural vegetation]',
    'func_corine_313': 'Land cover [Mixed forest]',
    'func_corine_412': 'Land cover [Peat bogs]',
    'func_corine_321': 'Land cover [Natural grasslands]',
    'func_corine_322': 'Land cover [Moors and heathland]',
    'func_corine_324': 'Land cover [Transitional woodland-shrub]',
    'func_corine_111': 'Land cover [Continuous urban fabric]',
    'func_corine_423': 'Land cover [Intertidal flats]',
    'func_corine_523': 'Land cover [Sea and ocean]',
    'func_corine_312': 'Land cover [Coniferous forest]',
    'func_corine_133': 'Land cover [Construction sites]',
    'func_corine_333': 'Land cover [Sparsely vegetated areas]',
    'func_corine_332': 'Land cover [Bare rocks]',
    'func_corine_411': 'Land cover [Inland marshes]',
    'func_corine_132': 'Land cover [Dump sites]',
    'func_corine_222': 'Land cover [Fruit trees and berry plantations]',
    'func_corine_242': 'Land cover [Complex cultivation patterns]',
    'func_corine_331': 'Land cover [Beaches, dunes, sands]',
    'func_corine_511': 'Land cover [Water courses]',
    'func_corine_334': 'Land cover [Burnt areas]',
    'func_corine_244': 'Land cover [Agro-forestry areas]',
    'func_corine_521': 'Land cover [Coastal lagoons]',
    'ndvi': 'NDVI',
    'func_supermarkets_nearest': 'Supermarkets [distance to nearest]',
    'func_supermarkets_counts': 'Supermarkets [counts within 1200m]',
    'func_listed_nearest': 'Listed buildings [distance to nearest]',
    'func_listed_counts': 'Listed buildings [counts within 1200m]',
    'func_fhrs_nearest': 'FHRS points [distance to nearest]',
    'func_fhrs_counts': 'FHRS points [counts within 1200m]',
    'func_culture_nearest': 'Cultural venues [distance to nearest]',
    'func_culture_counts': 'Cultural venues [counts within 1200m]',
    'func_water_nearest': 'Water bodies [distance to nearest]',
    'func_retail_centrenearest': 'Retail centres [distance to nearest]',
    'form_sdbAre': 'area of building',
    'form_sdbPer': 'perimeter of building',
    'form_sdbCoA': 'courtyard area of building',
    'form_ssbCCo': 'circular compactness of building',
    'form_ssbCor': 'corners of building',
    'form_ssbSqu': 'squareness of building',
    'form_ssbERI': 'equivalent rectangular index of building',
    'form_ssbElo': 'elongation of building',
    'form_ssbCCM': 'centroid - corner mean distance of building',
    'form_ssbCCD': 'centroid - corner distance deviation of building',
    'form_stbOri': 'orientation of building',
    'form_sdcLAL': 'longest axis length of ETC',
    'form_sdcAre': 'area of ETC',
    'form_sscCCo': 'circular compactness of ETC',
    'form_sscERI': 'equivalent rectangular index of ETC',
    'form_stcOri': 'orientation of ETC',
    'form_sicCAR': 'covered area ratio of ETC',
    'form_stbCeA': 'cell alignment of building',
    'form_mtbAli': 'alignment of neighbouring buildings',
    'form_mtbNDi': 'mean distance between neighbouring buildings',
    'form_mtcWNe': 'perimeter-weighted neighbours of ETC',
    'form_mdcAre': 'area covered by neighbouring cells',
    'form_ltcWRE': 'weighted reached enclosures of ETC',
    'form_ltbIBD': 'mean inter-building distance',
    'form_sdsSPW': 'width of street profile',
    'form_sdsSWD': 'width deviation of street profile',
    'form_sdsSPO': 'openness of street profile',
    'form_sdsLen': 'length of street segment',
    'form_sssLin': 'linearity of street segment',
    'form_ldsMSL': 'mean segment length within 3 steps',
    'form_mtdDeg': 'node degree of junction',
    'form_lcdMes': 'local meshedness of street network',
    'form_linP3W': 'local proportion of 3-way intersections of street network',
    'form_linP4W': 'local proportion of 4-way intersections of street network',
    'form_linPDE': 'local proportion of cul-de-sacs of street network',
    'form_lcnClo': 'local closeness of street network',
    'form_ldsCDL': 'local cul-de-sac length of street network',
    'form_xcnSCl': 'square clustering of street network',
    'form_mtdMDi': 'mean distance to neighbouring nodes of street network',
    'form_lddNDe': 'local node density of street network',
    'form_linWID': 'local degree weighted node density of street network',
    'form_stbSAl': 'street alignment of building',
    'form_sddAre': 'area covered by node-attached ETCs',
    'form_sdsAre': 'area covered by edge-attached ETCs',
    'form_sisBpM': 'buildings per meter of street segment',
    'form_misCel': 'reached ETCs by neighbouring segments',
    'form_mdsAre': 'reached area by neighbouring segments',
    'form_lisCel': 'reached ETCs by local street network',
    'form_ldsAre': 'reached area by local street network',
    'form_ltcRea': 'reached ETCs by tessellation contiguity',
    'form_ltcAre': 'reached area by tessellation contiguity',
    'form_ldeAre': 'area of enclosure',
    'form_ldePer': 'perimeter of enclosure',
    'form_lseCCo': 'circular compactness of enclosure',
    'form_lseERI': 'equivalent rectangular index of enclosure',
    'form_lseCWA': 'compactness-weighted axis of enclosure',
    'form_lteOri': 'orientation of enclosure',
    'form_lteWNB': 'perimeter-weighted neighbours of enclosure',
    'form_lieWCe': 'area-weighted ETCs of enclosure',
}

Assing new column names

per_type = per_type.rename(columns=coded)

per_type.drop("outlier").to_csv("per_type.csv")

per_geometry = per_geometry.rename(columns=coded)

per_geometry.to_csv("per_geometry.csv")

pandas.Series(key).to_csv("key.csv")

Pen portraits JSON

import json
    
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.",
}
    
with open("pen_portraits.json", "w") as outfile:
    json.dump(portraits, outfile, indent=4)

Use string IDs

Change integer ID for string.

signatures = geopandas.read_file("../../urbangrammar_samba/spatial_signatures/data_product/spatial_signatures_GB.gpkg")

/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:

signatures
id type geometry
0 0 Countryside agriculture POLYGON ((62220.000 798500.000, 62110.000 7985...
1 1 Countryside agriculture POLYGON ((63507.682 796515.169, 63471.097 7965...
2 2 Countryside agriculture POLYGON ((65953.174 802246.172, 65950.620 8022...
3 3 Countryside agriculture POLYGON ((67297.740 803435.800, 67220.289 8034...
4 4 Countryside agriculture POLYGON ((75760.000 852670.000, 75700.000 8527...
... ... ... ...
96699 96699 outlier POLYGON ((323321.005 463795.416, 323319.842 46...
96700 96700 outlier POLYGON ((325929.840 1008792.061, 325927.377 1...
96701 96701 outlier POLYGON ((337804.770 1013422.583, 337800.122 1...
96702 96702 outlier POLYGON ((422304.270 1147826.990, 422296.000 1...
96703 96703 outlier POLYGON ((525396.260 439215.480, 525360.920 43...

96704 rows × 3 columns

signatures["type"].unique()

array(['Countryside agriculture', 'Accessible suburbia', 'Open sprawl',
       'Wild countryside', 'Warehouse land',
       'Gridded residential quarters', 'Urban buffer',
       'Disconnected suburbia', 'Dense residential neighbourhoods',
       'Connected residential neighbourhoods',
       'Dense urban neighbourhoods', 'Local urbanity',
       'Distilled urbanity', 'Regional urbanity', 'outlier',
       'Metropolitan urbanity', 'Hyper distilled urbanity'], dtype=object)

type_ids = {
    'Countryside agriculture': "COA", 
    'Accessible suburbia': "ACS", 
    'Open sprawl': "OPS",
    'Wild countryside': "WIC", 
    'Warehouse land': "WAL",
    'Gridded residential quarters': "GRQ", 
    'Urban buffer': "URB",
    'Disconnected suburbia': "DIS", 
    'Dense residential neighbourhoods': "DRN",
    'Connected residential neighbourhoods': "CRN",
    'Dense urban neighbourhoods': "DUN", 
    'Local urbanity': "LOU",
    'Distilled urbanity': "DIU", 
    'Regional urbanity': "REU", 
    'Metropolitan urbanity': "MEU", 
    'Hyper distilled urbanity': "HDU",
    'outlier': "OUT",
}

string_id = signatures["id"].astype(str) + "_" + signatures["type"].map(type_ids)

signatures["id"] = string_id
signatures["code"] = signatures["type"].map(type_ids)

signatures[["id", "code", "type", "geometry"]].to_file("spatial_signatures_GB.gpkg", driver="GPKG")

per_type = pandas.read_csv("../../urbangrammar_samba/spatial_signatures/data_product/per_type.csv")
per_type
type 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 Accessible suburbia 176.949135 53.902697 0.476838 0.534856 4.253845 0.775272 0.987707 0.642271 9.604873 ... 828.822261 1.890519 744.223994 11.273578 218.460768 334.434991 5384.638746 0.059113 542.613661 849.447871
1 Connected residential neighbourhoods 272.522220 69.119967 1.069872 0.480941 4.445515 1.466426 0.979415 0.555468 12.406840 ... 679.958940 2.855980 596.613654 24.279146 152.479922 692.664050 3946.052517 0.129197 555.964942 536.469155
2 Countryside agriculture 204.100400 56.048984 0.505831 0.506275 4.371156 0.809087 0.977789 0.600065 9.791869 ... 4751.226429 0.089042 557.936276 11.223066 725.690643 44.465529 13156.203765 0.003804 304.488181 4943.972107
3 Dense residential neighbourhoods 375.598290 80.558473 2.127340 0.472554 4.694714 1.863620 0.969912 0.557054 13.961420 ... 661.765594 3.127412 506.614715 37.474705 144.021617 860.927102 3497.511181 0.259194 483.124139 421.093530
4 Dense urban neighbourhoods 588.359559 107.362849 5.034084 0.435714 5.208242 3.278419 0.950431 0.519821 17.999101 ... 587.276810 4.436632 350.890339 62.783422 106.084358 1568.443597 2287.432199 0.476118 528.850109 224.326713
5 Disconnected suburbia 212.713958 61.631662 0.519372 0.491588 4.347900 1.019293 0.981779 0.573786 11.114826 ... 761.858373 2.067315 729.608517 24.181257 217.945624 342.081722 5831.523433 0.077958 523.050240 725.573382
6 Distilled urbanity 3713.379427 376.300388 159.087608 0.425186 12.484667 18.589482 0.776950 0.594314 35.927794 ... 229.903096 22.510563 31.732228 685.156338 16.219050 6297.607746 702.750880 10.392254 565.248203 29.803442
7 Gridded residential quarters 283.892607 69.667245 0.749026 0.491754 4.506685 1.663151 0.976842 0.584548 12.411805 ... 577.683974 3.409903 516.197376 31.771826 129.244204 1081.376510 4094.915253 0.240503 522.087434 445.518972
8 Hyper distilled urbanity 3358.099586 330.818576 90.819544 0.446292 9.273438 22.513954 0.802892 0.617669 37.220199 ... 324.416312 18.791045 69.749236 1142.567164 14.101269 9213.145522 351.325110 34.197761 759.604208 32.544669
9 Local urbanity 823.354285 135.543919 12.666729 0.409310 6.014239 5.071848 0.919085 0.506415 20.711500 ... 483.017862 6.848327 216.858599 140.031073 82.468961 2167.909991 1273.226897 1.129622 507.706457 161.854989
10 Metropolitan urbanity 2413.938716 283.935149 118.946949 0.395862 9.717504 12.406894 0.820142 0.527758 29.677259 ... 299.930496 17.271958 51.874364 456.529630 40.063982 4490.949206 644.531259 4.446825 467.713631 66.318054
11 Open sprawl 226.715098 59.639780 0.900478 0.518531 4.371939 0.985729 0.982452 0.622301 10.488328 ... 948.025914 1.469023 760.257254 18.171441 267.238602 253.875226 6309.753341 0.061560 378.356187 1002.663064
12 Regional urbanity 1480.260902 195.978660 43.190998 0.393035 7.776863 8.844805 0.869514 0.512959 25.254583 ... 331.067781 12.531123 115.004786 324.500752 56.865240 3163.829678 850.254487 2.233070 461.415859 90.874497
13 Urban buffer 209.421858 55.941702 0.743395 0.523632 4.340540 0.859973 0.982904 0.630125 9.808575 ... 1752.873479 0.654018 673.931399 16.138833 379.169823 132.661846 8939.647136 0.024052 345.790747 2102.455609
14 Warehouse land 393.216097 75.680270 3.263039 0.469466 4.564890 1.352536 0.976102 0.542256 13.200176 ... 1043.835409 1.427596 934.001152 10.570084 256.220805 271.092537 5121.469150 0.058667 417.431851 898.174463
15 Wild countryside 209.855382 57.122052 0.223542 0.501939 4.381272 0.706130 0.976522 0.589908 9.947378 ... 9854.123937 0.028975 1324.025044 4.212720 1699.169649 33.073293 20695.290665 0.001680 236.730324 11041.324478

16 rows × 119 columns

per_type["code"] = per_type["type"].map(type_ids)

columns = ["code"] + list(per_type.columns[:-1])
columns.remove("nodeID")
columns.remove("edgeID_primary")

per_type[columns]
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 Distilled 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 distilled 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 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

per_type[columns].to_csv("../../urbangrammar_samba/spatial_signatures/data_product/per_type.csv", index=False)

per_geometry = pandas.read_csv("../../urbangrammar_samba/spatial_signatures/data_product/per_geometry.csv")

per_geometry
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 0 129.933434 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 4 258.461742 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 9 122.655396 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 11 190.308946 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 14 146.477816 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 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 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 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 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 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 × 119 columns

signatures["intid"] = signatures["id"].str[:-4].astype(int)

merge = dict(zip(signatures["intid"], signatures["id"]))

per_geometry["id"] = per_geometry["id"].map(merge)

per_geometry.drop(columns=["nodeID", "edgeID_primary"]).to_csv("../../urbangrammar_samba/spatial_signatures/data_product/per_geometry.csv", index=False)

pandas.Series(type_ids, name="type_code").to_csv("../../urbangrammar_samba/spatial_signatures/data_product/type_code.csv")

lsoa = pandas.read_csv("../../urbangrammar_samba/spatial_signatures/data_product/lsoa_estimates.csv", index_col=0)

lsoa.columns = lsoa.columns[:2].append(lsoa.columns.map(type_ids)[2:])

lsoa["primary_code"] = lsoa["primary_type"].map(type_ids)

lsoa[["LSOA11CD", "primary_code"] + list(lsoa.columns[1:-1])].to_csv("../../urbangrammar_samba/spatial_signatures/data_product/lsoa_estimates.csv", index=False)

oa = pandas.read_csv("../../urbangrammar_samba/spatial_signatures/data_product/output_area_estimates.csv", index_col=0)

oa.columns = oa.columns[:2].append(oa.columns.map(type_ids)[2:])
oa["primary_code"] = oa["primary_type"].map(type_ids)
oa
OA11CD primary_type NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN primary_code
0 E00000001 Distilled urbanity 0.000000 0.0 0.000000 0.000000 0.0 0.0 0.000000 0.0 ... 0.0 1.0 0.0 0.0 0.0 0.0 DIU DIU DIU DIU
1 E00000003 Distilled urbanity 0.000000 0.0 0.000000 0.000000 0.0 0.0 0.000000 0.0 ... 0.0 1.0 0.0 0.0 0.0 0.0 DIU DIU DIU DIU
2 E00000005 Distilled urbanity 0.000000 0.0 0.000000 0.000000 0.0 0.0 0.000000 0.0 ... 0.0 1.0 0.0 0.0 0.0 0.0 DIU DIU DIU DIU
3 E00000007 Distilled urbanity 0.000000 0.0 0.000000 0.000000 0.0 0.0 0.000000 0.0 ... 0.0 1.0 0.0 0.0 0.0 0.0 DIU DIU DIU DIU
4 E00000010 Distilled urbanity 0.000000 0.0 0.000000 0.000000 0.0 0.0 0.000000 0.0 ... 0.0 1.0 0.0 0.0 0.0 0.0 DIU DIU DIU DIU
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
227754 S00094726 Wild countryside 0.000000 0.0 0.000000 0.965738 0.0 0.0 0.000000 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 WIC WIC WIC WIC
227755 S00102583 Urban buffer 0.000000 0.0 0.076019 0.000000 0.0 0.0 0.923981 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 URB URB URB URB
227756 S00119179 Wild countryside 0.000000 0.0 0.000000 0.995102 0.0 0.0 0.000594 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 WIC WIC WIC WIC
227757 S00119262 Wild countryside 0.000000 0.0 0.000000 0.982881 0.0 0.0 0.000000 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 WIC WIC WIC WIC
227758 S00126169 Countryside agriculture 0.999624 0.0 0.000000 0.000377 0.0 0.0 0.000000 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 COA COA COA COA

227759 rows × 23 columns

oa = oa[list(oa.columns[:19]) + ["primary_code"]]
oa.columns = list(oa.columns[:2]) + list(lsoa.columns[2:])

oa[["OA11CD", "primary_code"] + list(oa.columns[1:-1])].to_csv("../../urbangrammar_samba/spatial_signatures/data_product/output_area_estimates.csv", index=False)

Not needed in this notebook but it is good to have them all at one place.

key_for_all = {
    'population_q1': 'Population (Q1)',
    'population_q2': 'Population (Q2)',
    'population_q3': 'Population (Q3)',
    'night_lights_q1': 'Night lights (Q1)',
    'night_lights_q2': 'Night lights (Q2)',
    'night_lights_q3': 'Night lights (Q3)',
    'A, B, D, E. Agriculture, energy and water_q1': 'Workplace population [Agriculture, energy and water] (Q1)',
    'A, B, D, E. Agriculture, energy and water_q2': 'Workplace population [Agriculture, energy and water] (Q2)',
    'A, B, D, E. Agriculture, energy and water_q3': 'Workplace population [Agriculture, energy and water] (Q3)',
    'C. Manufacturing_q1': 'Workplace population [Manufacturing] (Q1)',
    'C. Manufacturing_q2': 'Workplace population [Manufacturing] (Q2)',
    'C. Manufacturing_q3': 'Workplace population [Manufacturing] (Q3)',
    'F. Construction_q1': 'Workplace population [Construction] (Q1)',
    'F. Construction_q2': 'Workplace population [Construction] (Q2)',
    'F. Construction_q3': 'Workplace population [Construction] (Q3)',
    'G, I. Distribution, hotels and restaurants_q1': 'Workplace population [Distribution, hotels and restaurants] (Q1)',
    'G, I. Distribution, hotels and restaurants_q2': 'Workplace population [Distribution, hotels and restaurants] (Q2)',
    'G, I. Distribution, hotels and restaurants_q3': 'Workplace population [Distribution, hotels and restaurants] (Q3)',
    'H, J. Transport and communication_q1': 'Workplace population [Transport and communication] (Q1)',
    'H, J. Transport and communication_q2': 'Workplace population [Transport and communication] (Q2)',
    'H, J. Transport and communication_q3': 'Workplace population [Transport and communication] (Q3)',
    'K, L, M, N. Financial, real estate, professional and administrative activities_q1': 'Workplace population [Financial, real estate, professional and administrative activities] (Q1)',
    'K, L, M, N. Financial, real estate, professional and administrative activities_q2': 'Workplace population [Financial, real estate, professional and administrative activities] (Q2)',
    'K, L, M, N. Financial, real estate, professional and administrative activities_q3': 'Workplace population [Financial, real estate, professional and administrative activities] (Q3)',
    'O,P,Q. Public administration, education and health_q1': 'Workplace population [Public administration, education and health] (Q1)',
    'O,P,Q. Public administration, education and health_q2': 'Workplace population [Public administration, education and health] (Q2)',
    'O,P,Q. Public administration, education and health_q3': 'Workplace population [Public administration, education and health] (Q3)',
    'R, S, T, U. Other_q1': 'Workplace population [Other] (Q1)',
    'R, S, T, U. Other_q2': 'Workplace population [Other] (Q2)',
    'R, S, T, U. Other_q3': 'Workplace population [Other] (Q3)',
    'Code_18_124_q1': 'Land cover [Airports] (Q1)',
    'Code_18_124_q2': 'Land cover [Airports] (Q2)',
    'Code_18_124_q3': 'Land cover [Airports] (Q3)',
    'Code_18_211_q1': 'Land cover [Non-irrigated arable land] (Q1)',
    'Code_18_211_q2': 'Land cover [Non-irrigated arable land] (Q2)',
    'Code_18_211_q3': 'Land cover [Non-irrigated arable land] (Q3)',
    'Code_18_121_q1': 'Land cover [Industrial or commercial units] (Q1)',
    'Code_18_121_q2': 'Land cover [Industrial or commercial units] (Q2)',
    'Code_18_121_q3': 'Land cover [Industrial or commercial units] (Q3)',
    'Code_18_421_q1': 'Land cover [Salt marshes] (Q1)',
    'Code_18_421_q2': 'Land cover [Salt marshes] (Q2)',
    'Code_18_421_q3': 'Land cover [Salt marshes] (Q3)',
    'Code_18_522_q1': 'Land cover [Estuaries] (Q1)',
    'Code_18_522_q2': 'Land cover [Estuaries] (Q2)',
    'Code_18_522_q3': 'Land cover [Estuaries] (Q3)',
    'Code_18_142_q1': 'Land cover [Sport and leisure facilities] (Q1)',
    'Code_18_142_q2': 'Land cover [Sport and leisure facilities] (Q2)',
    'Code_18_142_q3': 'Land cover [Sport and leisure facilities] (Q3)',
    'Code_18_141_q1': 'Land cover [Green urban areas] (Q1)',
    'Code_18_141_q2': 'Land cover [Green urban areas] (Q2)',
    'Code_18_141_q3': 'Land cover [Green urban areas] (Q3)',
    'Code_18_112_q1': 'Land cover [Discontinuous urban fabric] (Q1)',
    'Code_18_112_q2': 'Land cover [Discontinuous urban fabric] (Q2)',
    'Code_18_112_q3': 'Land cover [Discontinuous urban fabric] (Q3)',
    'Code_18_231_q1': 'Land cover [Pastures] (Q1)',
    'Code_18_231_q2': 'Land cover [Pastures] (Q2)',
    'Code_18_231_q3': 'Land cover [Pastures] (Q3)',
    'Code_18_311_q1': 'Land cover [Broad-leaved forest] (Q1)',
    'Code_18_311_q2': 'Land cover [Broad-leaved forest] (Q2)',
    'Code_18_311_q3': 'Land cover [Broad-leaved forest] (Q3)',
    'Code_18_131_q1': 'Land cover [Mineral extraction sites] (Q1)',
    'Code_18_131_q2': 'Land cover [Mineral extraction sites] (Q2)',
    'Code_18_131_q3': 'Land cover [Mineral extraction sites] (Q3)',
    'Code_18_123_q1': 'Land cover [Port areas] (Q1)',
    'Code_18_123_q2': 'Land cover [Port areas] (Q2)',
    'Code_18_123_q3': 'Land cover [Port areas] (Q3)',
    'Code_18_122_q1': 'Land cover [Road and rail networks and associated land] (Q1)',
    'Code_18_122_q2': 'Land cover [Road and rail networks and associated land] (Q2)',
    'Code_18_122_q3': 'Land cover [Road and rail networks and associated land] (Q3)',
    'Code_18_512_q1': 'Land cover [Water bodies] (Q1)',
    'Code_18_512_q2': 'Land cover [Water bodies] (Q2)',
    'Code_18_512_q3': 'Land cover [Water bodies] (Q3)',
    'Code_18_243_q1': 'Land cover [Land principally occupied by agriculture, with significant areas of natural vegetation] (Q1)',
    'Code_18_243_q2': 'Land cover [Land principally occupied by agriculture, with significant areas of natural vegetation] (Q2)',
    'Code_18_243_q3': 'Land cover [Land principally occupied by agriculture, with significant areas of natural vegetation] (Q3)',
    'Code_18_313_q1': 'Land cover [Mixed forest] (Q1)',
    'Code_18_313_q2': 'Land cover [Mixed forest] (Q2)',
    'Code_18_313_q3': 'Land cover [Mixed forest] (Q3)',
    'Code_18_412_q1': 'Land cover [Peat bogs] (Q1)',
    'Code_18_412_q2': 'Land cover [Peat bogs] (Q2)',
    'Code_18_412_q3': 'Land cover [Peat bogs] (Q3)',
    'Code_18_321_q1': 'Land cover [Natural grasslands] (Q1)',
    'Code_18_321_q2': 'Land cover [Natural grasslands] (Q2)',
    'Code_18_321_q3': 'Land cover [Natural grasslands] (Q3)',
    'Code_18_322_q1': 'Land cover [Moors and heathland] (Q1)',
    'Code_18_322_q2': 'Land cover [Moors and heathland] (Q2)',
    'Code_18_322_q3': 'Land cover [Moors and heathland] (Q3)',
    'Code_18_324_q1': 'Land cover [Transitional woodland-shrub] (Q1)',
    'Code_18_324_q2': 'Land cover [Transitional woodland-shrub] (Q2)',
    'Code_18_324_q3': 'Land cover [Transitional woodland-shrub] (Q3)',
    'Code_18_111_q1': 'Land cover [Continuous urban fabric] (Q1)',
    'Code_18_111_q2': 'Land cover [Continuous urban fabric] (Q2)',
    'Code_18_111_q3': 'Land cover [Continuous urban fabric] (Q3)',
    'Code_18_423_q1': 'Land cover [Intertidal flats] (Q1)',
    'Code_18_423_q2': 'Land cover [Intertidal flats] (Q2)',
    'Code_18_423_q3': 'Land cover [Intertidal flats] (Q3)',
    'Code_18_523_q1': 'Land cover [Sea and ocean] (Q1)',
    'Code_18_523_q2': 'Land cover [Sea and ocean] (Q2)',
    'Code_18_523_q3': 'Land cover [Sea and ocean] (Q3)',
    'Code_18_312_q1': 'Land cover [Coniferous forest] (Q1)',
    'Code_18_312_q2': 'Land cover [Coniferous forest] (Q2)',
    'Code_18_312_q3': 'Land cover [Coniferous forest] (Q3)',
    'Code_18_133_q1': 'Land cover [Construction sites] (Q1)',
    'Code_18_133_q2': 'Land cover [Construction sites] (Q2)',
    'Code_18_133_q3': 'Land cover [Construction sites] (Q3)',
    'Code_18_333_q1': 'Land cover [Sparsely vegetated areas] (Q1)',
    'Code_18_333_q2': 'Land cover [Sparsely vegetated areas] (Q2)',
    'Code_18_333_q3': 'Land cover [Sparsely vegetated areas] (Q3)',
    'Code_18_332_q1': 'Land cover [Bare rocks] (Q1)',
    'Code_18_332_q2': 'Land cover [Bare rocks] (Q2)',
    'Code_18_332_q3': 'Land cover [Bare rocks] (Q3)',
    'Code_18_411_q1': 'Land cover [Inland marshes] (Q1)',
    'Code_18_411_q2': 'Land cover [Inland marshes] (Q2)',
    'Code_18_411_q3': 'Land cover [Inland marshes] (Q3)',
    'Code_18_132_q1': 'Land cover [Dump sites] (Q1)',
    'Code_18_331_q2': 'Land cover [Beaches, dunes, sands] (Q2)',
    'Code_18_222_q1': 'Land cover [Fruit trees and berry plantations] (Q1)',
    'Code_18_511_q3': 'Land cover [Water courses] (Q3)',
    'Code_18_242_q1': 'Land cover [Complex cultivation patterns] (Q1)',
    'Code_18_511_q2': 'Land cover [Water courses] (Q2)',
    'Code_18_242_q3': 'Land cover [Complex cultivation patterns] (Q3)',
    'Code_18_331_q1': 'Land cover [Beaches, dunes, sands] (Q1)',
    'Code_18_334_q2': 'Land cover [Burnt areas] (Q2)',
    'Code_18_511_q1': 'Land cover [Water courses] (Q1)',
    'Code_18_334_q1': 'Land cover [Burnt areas] (Q1)',
    'Code_18_222_q3': 'Land cover [Fruit trees and berry plantations] (Q3)',
    'Code_18_242_q2': 'Land cover [Complex cultivation patterns] (Q2)',
    'Code_18_244_q3': 'Land cover [Agro-forestry areas] (Q3)',
    'Code_18_521_q2': 'Land cover [Coastal lagoons] (Q2)',
    'Code_18_334_q3': 'Land cover [Burnt areas] (Q3)',
    'Code_18_244_q1': 'Land cover [Agro-forestry areas] (Q1)',
    'Code_18_244_q2': 'Land cover [Agro-forestry areas] (Q2)',
    'Code_18_331_q3': 'Land cover [Beaches, dunes, sands] (Q3)',
    'Code_18_132_q2': 'Land cover [Dump sites] (Q2)',
    'Code_18_132_q3': 'Land cover [Dump sites] (Q3)',
    'Code_18_521_q1': 'Land cover [Coastal lagoons] (Q1)',
    'Code_18_222_q2': 'Land cover [Fruit trees and berry plantations] (Q2)',
    'Code_18_521_q3': 'Land cover [Coastal lagoons] (Q3)',
    'mean_q1': 'NDVI (Q1)',
    'mean_q2': 'NDVI (Q2)',
    'mean_q3': 'NDVI (Q3)',
    'supermarkets_nearest': 'Supermarkets [distance to nearest]',
    'supermarkets_counts': 'Supermarkets [counts within 1200m]',
    'listed_nearest': 'Listed buildings [distance to nearest]',
    'listed_counts': 'Listed buildings [counts within 1200m]',
    'fhrs_nearest': 'FHRS points [distance to nearest]',
    'fhrs_counts': 'FHRS points [counts within 1200m]',
    'culture_nearest': 'Cultural venues [distance to nearest]',
    'culture_counts': 'Cultural venues [counts within 1200m]',
    'nearest_water': 'Water bodies [distance to nearest]',
    'nearest_retail_centre': 'Retail centres [distance to nearest]',
    'sdbAre_q1': 'area of building (Q1)',
    'sdbAre_q2': 'area of building (Q2)',
    'sdbAre_q3': 'area of building (Q3)',
    'sdbPer_q1': 'perimeter of building (Q1)',
    'sdbPer_q2': 'perimeter of building (Q2)',
    'sdbPer_q3': 'perimeter of building (Q3)',
    'sdbCoA_q1': 'courtyard area of building (Q1)',
    'sdbCoA_q2': 'courtyard area of building (Q2)',
    'sdbCoA_q3': 'courtyard area of building (Q3)',
    'ssbCCo_q1': 'circular compactness of building (Q1)',
    'ssbCCo_q2': 'circular compactness of building (Q2)',
    'ssbCCo_q3': 'circular compactness of building (Q3)',
    'ssbCor_q1': 'corners of building (Q1)',
    'ssbCor_q2': 'corners of building (Q2)',
    'ssbCor_q3': 'corners of building (Q3)',
    'ssbSqu_q1': 'squareness of building (Q1)',
    'ssbSqu_q2': 'squareness of building (Q2)',
    'ssbSqu_q3': 'squareness of building (Q3)',
    'ssbERI_q1': 'equivalent rectangular index of building (Q1)',
    'ssbERI_q2': 'equivalent rectangular index of building (Q2)',
    'ssbERI_q3': 'equivalent rectangular index of building (Q3)',
    'ssbElo_q1': 'elongation of building (Q1)',
    'ssbElo_q2': 'elongation of building (Q2)',
    'ssbElo_q3': 'elongation of building (Q3)',
    'ssbCCM_q1': 'centroid - corner mean distance of building (Q1)',
    'ssbCCM_q2': 'centroid - corner mean distance of building (Q2)',
    'ssbCCM_q3': 'centroid - corner mean distance of building (Q3)',
    'ssbCCD_q1': 'centroid - corner distance deviation of building (Q1)',
    'ssbCCD_q2': 'centroid - corner distance deviation of building (Q2)',
    'ssbCCD_q3': 'centroid - corner distance deviation of building (Q3)',
    'stbOri_q1': 'orientation of building (Q1)',
    'stbOri_q2': 'orientation of building (Q2)',
    'stbOri_q3': 'orientation of building (Q3)',
    'sdcLAL_q1': 'longest axis length of ETC (Q1)',
    'sdcLAL_q2': 'longest axis length of ETC (Q2)',
    'sdcLAL_q3': 'longest axis length of ETC (Q3)',
    'sdcAre_q1': 'area of ETC (Q1)',
    'sdcAre_q2': 'area of ETC (Q2)',
    'sdcAre_q3': 'area of ETC (Q3)',
    'sscCCo_q1': 'circular compactness of ETC (Q1)',
    'sscCCo_q2': 'circular compactness of ETC (Q2)',
    'sscCCo_q3': 'circular compactness of ETC (Q3)',
    'sscERI_q1': 'equivalent rectangular index of ETC (Q1)',
    'sscERI_q2': 'equivalent rectangular index of ETC (Q2)',
    'sscERI_q3': 'equivalent rectangular index of ETC (Q3)',
    'stcOri_q1': 'orientation of ETC (Q1)',
    'stcOri_q2': 'orientation of ETC (Q2)',
    'stcOri_q3': 'orientation of ETC (Q3)',
    'sicCAR_q1': 'covered area ratio of ETC (Q1)',
    'sicCAR_q2': 'covered area ratio of ETC (Q2)',
    'sicCAR_q3': 'covered area ratio of ETC (Q3)',
    'stbCeA_q1': 'cell alignment of building (Q1)',
    'stbCeA_q2': 'cell alignment of building (Q2)',
    'stbCeA_q3': 'cell alignment of building (Q3)',
    'mtbAli_q1': 'alignment of neighbouring buildings (Q1)',
    'mtbAli_q2': 'alignment of neighbouring buildings (Q2)',
    'mtbAli_q3': 'alignment of neighbouring buildings (Q3)',
    'mtbNDi_q1': 'mean distance between neighbouring buildings (Q1)',
    'mtbNDi_q2': 'mean distance between neighbouring buildings (Q2)',
    'mtbNDi_q3': 'mean distance between neighbouring buildings (Q3)',
    'mtcWNe_q1': 'perimeter-weighted neighbours of ETC (Q1)',
    'mtcWNe_q2': 'perimeter-weighted neighbours of ETC (Q2)',
    'mtcWNe_q3': 'perimeter-weighted neighbours of ETC (Q3)',
    'mdcAre_q1': 'area covered by neighbouring cells (Q1)',
    'mdcAre_q2': 'area covered by neighbouring cells (Q2)',
    'mdcAre_q3': 'area covered by neighbouring cells (Q3)',
    'ltcWRE_q1': 'weighted reached enclosures of ETC (Q1)',
    'ltcWRE_q2': 'weighted reached enclosures of ETC (Q2)',
    'ltcWRE_q3': 'weighted reached enclosures of ETC (Q3)',
    'ltbIBD_q1': 'mean inter-building distance (Q1)',
    'ltbIBD_q2': 'mean inter-building distance (Q2)',
    'ltbIBD_q3': 'mean inter-building distance (Q3)',
    'sdsSPW_q1': 'width of street profile (Q1)',
    'sdsSPW_q2': 'width of street profile (Q2)',
    'sdsSPW_q3': 'width of street profile (Q3)',
    'sdsSWD_q1': 'width deviation of street profile (Q1)',
    'sdsSWD_q2': 'width deviation of street profile (Q2)',
    'sdsSWD_q3': 'width deviation of street profile (Q3)',
    'sdsSPO_q1': 'openness of street profile (Q1)',
    'sdsSPO_q2': 'openness of street profile (Q2)',
    'sdsSPO_q3': 'openness of street profile (Q3)',
    'sdsLen_q1': 'length of street segment (Q1)',
    'sdsLen_q2': 'length of street segment (Q2)',
    'sdsLen_q3': 'length of street segment (Q3)',
    'sssLin_q1': 'linearity of street segment (Q1)',
    'sssLin_q2': 'linearity of street segment (Q2)',
    'sssLin_q3': 'linearity of street segment (Q3)',
    'ldsMSL_q1': 'mean segment length within 3 steps (Q1)',
    'ldsMSL_q2': 'mean segment length within 3 steps (Q2)',
    'ldsMSL_q3': 'mean segment length within 3 steps (Q3)',
    'mtdDeg_q1': 'node degree of junction (Q1)',
    'mtdDeg_q2': 'node degree of junction (Q2)',
    'mtdDeg_q3': 'node degree of junction (Q3)',
    'lcdMes_q1': 'local meshedness of street network (Q1)',
    'lcdMes_q2': 'local meshedness of street network (Q2)',
    'lcdMes_q3': 'local meshedness of street network (Q3)',
    'linP3W_q1': 'local proportion of 3-way intersections of street network (Q1)',
    'linP3W_q2': 'local proportion of 3-way intersections of street network (Q2)',
    'linP3W_q3': 'local proportion of 3-way intersections of street network (Q3)',
    'linP4W_q1': 'local proportion of 4-way intersections of street network (Q1)',
    'linP4W_q2': 'local proportion of 4-way intersections of street network (Q2)',
    'linP4W_q3': 'local proportion of 4-way intersections of street network (Q3)',
    'linPDE_q1': 'local proportion of cul-de-sacs of street network (Q1)',
    'linPDE_q2': 'local proportion of cul-de-sacs of street network (Q2)',
    'linPDE_q3': 'local proportion of cul-de-sacs of street network (Q3)',
    'lcnClo_q1': 'local closeness of street network (Q1)',
    'lcnClo_q2': 'local closeness of street network (Q2)',
    'lcnClo_q3': 'local closeness of street network (Q3)',
    'ldsCDL_q1': 'local cul-de-sac length of street network (Q1)',
    'ldsCDL_q2': 'local cul-de-sac length of street network (Q2)',
    'ldsCDL_q3': 'local cul-de-sac length of street network (Q3)',
    'xcnSCl_q1': 'square clustering of street network (Q1)',
    'xcnSCl_q2': 'square clustering of street network (Q2)',
    'xcnSCl_q3': 'square clustering of street network (Q3)',
    'mtdMDi_q1': 'mean distance to neighbouring nodes of street network (Q1)',
    'mtdMDi_q2': 'mean distance to neighbouring nodes of street network (Q2)',
    'mtdMDi_q3': 'mean distance to neighbouring nodes of street network (Q3)',
    'lddNDe_q1': 'local node density of street network (Q1)',
    'lddNDe_q2': 'local node density of street network (Q2)',
    'lddNDe_q3': 'local node density of street network (Q3)',
    'linWID_q1': 'local degree weighted node density of street network (Q1)',
    'linWID_q2': 'local degree weighted node density of street network (Q2)',
    'linWID_q3': 'local degree weighted node density of street network (Q3)',
    'stbSAl_q1': 'street alignment of building (Q1)',
    'stbSAl_q2': 'street alignment of building (Q2)',
    'stbSAl_q3': 'street alignment of building (Q3)',
    'sddAre_q1': 'area covered by node-attached ETCs (Q1)',
    'sddAre_q2': 'area covered by node-attached ETCs (Q2)',
    'sddAre_q3': 'area covered by node-attached ETCs (Q3)',
    'sdsAre_q1': 'area covered by edge-attached ETCs (Q1)',
    'sdsAre_q2': 'area covered by edge-attached ETCs (Q2)',
    'sdsAre_q3': 'area covered by edge-attached ETCs (Q3)',
    'sisBpM_q1': 'buildings per meter of street segment (Q1)',
    'sisBpM_q2': 'buildings per meter of street segment (Q2)',
    'sisBpM_q3': 'buildings per meter of street segment (Q3)',
    'misCel_q1': 'reached ETCs by neighbouring segments (Q1)',
    'misCel_q2': 'reached ETCs by neighbouring segments (Q2)',
    'misCel_q3': 'reached ETCs by neighbouring segments (Q3)',
    'mdsAre_q1': 'reached area by neighbouring segments (Q1)',
    'mdsAre_q2': 'reached area by neighbouring segments (Q2)',
    'mdsAre_q3': 'reached area by neighbouring segments (Q3)',
    'lisCel_q1': 'reached ETCs by local street network (Q1)',
    'lisCel_q2': 'reached ETCs by local street network (Q2)',
    'lisCel_q3': 'reached ETCs by local street network (Q3)',
    'ldsAre_q1': 'reached area by local street network (Q1)',
    'ldsAre_q2': 'reached area by local street network (Q2)',
    'ldsAre_q3': 'reached area by local street network (Q3)',
    'ltcRea_q1': 'reached ETCs by tessellation contiguity (Q1)',
    'ltcRea_q2': 'reached ETCs by tessellation contiguity (Q2)',
    'ltcRea_q3': 'reached ETCs by tessellation contiguity (Q3)',
    'ltcAre_q1': 'reached area by tessellation contiguity (Q1)',
    'ltcAre_q2': 'reached area by tessellation contiguity (Q2)',
    'ltcAre_q3': 'reached area by tessellation contiguity (Q3)',
    'ldeAre_q1': 'area of enclosure (Q1)',
    'ldeAre_q2': 'area of enclosure (Q2)',
    'ldeAre_q3': 'area of enclosure (Q3)',
    'ldePer_q1': 'perimeter of enclosure (Q1)',
    'ldePer_q2': 'perimeter of enclosure (Q2)',
    'ldePer_q3': 'perimeter of enclosure (Q3)',
    'lseCCo_q1': 'circular compactness of enclosure (Q1)',
    'lseCCo_q2': 'circular compactness of enclosure (Q2)',
    'lseCCo_q3': 'circular compactness of enclosure (Q3)',
    'lseERI_q1': 'equivalent rectangular index of enclosure (Q1)',
    'lseERI_q2': 'equivalent rectangular index of enclosure (Q2)',
    'lseERI_q3': 'equivalent rectangular index of enclosure (Q3)',
    'lseCWA_q1': 'compactness-weighted axis of enclosure (Q1)',
    'lseCWA_q2': 'compactness-weighted axis of enclosure (Q2)',
    'lseCWA_q3': 'compactness-weighted axis of enclosure (Q3)',
    'lteOri_q1': 'orientation of enclosure (Q1)',
    'lteOri_q2': 'orientation of enclosure (Q2)',
    'lteOri_q3': 'orientation of enclosure (Q3)',
    'lteWNB_q1': 'perimeter-weighted neighbours of enclosure (Q1)',
    'lteWNB_q2': 'perimeter-weighted neighbours of enclosure (Q2)',
    'lteWNB_q3': 'perimeter-weighted neighbours of enclosure (Q3)',
    'lieWCe_q1': 'area-weighted ETCs of enclosure (Q1)',
    'lieWCe_q2': 'area-weighted ETCs of enclosure (Q2)',
    'lieWCe_q3': 'area-weighted ETCs of enclosure (Q3)',
}

Dump ETCs to an archive repository as a partitioned parquet

Create partitioned ETC-level datasets to be stored in an archival GitHub repository.

from dask.distributed import Client, LocalCluster
client = Client(LocalCluster(n_workers=8))
client

Client

Client-8c657e0b-94bf-11ec-840d-115a56b33b25

Connection method: Cluster object Cluster type: distributed.LocalCluster
Dashboard: http://127.0.0.1:8787/status

Cluster Info

 
include = ['hindex', 'tessellation', 'sdbAre', 'sdbPer', 'sdbCoA', 'ssbCCo', 'ssbCor', 'ssbSqu', 'ssbERI', 'ssbElo', 
             'ssbCCM', 'ssbCCD', 'stbOri', 'sdcLAL', 'sdcAre', 'sscCCo', 'sscERI', 'stcOri', 
             'sicCAR', 'stbCeA', 'mtbAli', 'mtbNDi', 'mtcWNe', 'mdcAre', 'ltcWRE', 'ltbIBD', 
             'sdsSPW', 'sdsSWD', 'sdsSPO', 'sdsLen', 'sssLin', 'ldsMSL', 'mtdDeg', 'lcdMes', 
             'linP3W', 'linP4W', 'linPDE', 'lcnClo', 'ldsCDL', 'xcnSCl', 'mtdMDi', 'lddNDe', 
             'linWID', 'stbSAl', 'sddAre', 'sdsAre', 'sisBpM', 'misCel', 'mdsAre', 'lisCel', 
             'ldsAre', 'ltcRea', 'ltcAre', 'ldeAre', 'ldePer', 'lseCCo', 'lseERI', 'lseCWA', 
             'lteOri', 'lteWNB', 'lieWCe'
            ]

primary = dask_geopandas.read_parquet("../../urbangrammar_samba/spatial_signatures/morphometrics/cells/*", columns=include)
primary
Dask-GeoPandas GeoDataFrame Structure:
hindex tessellation sdbAre sdbPer sdbCoA ssbCCo ssbCor ssbSqu ssbERI ssbElo ssbCCM ssbCCD stbOri sdcLAL sdcAre sscCCo sscERI stcOri sicCAR stbCeA mtbAli mtbNDi mtcWNe mdcAre ltcWRE ltbIBD sdsSPW sdsSWD sdsSPO sdsLen sssLin ldsMSL mtdDeg lcdMes linP3W linP4W linPDE lcnClo ldsCDL xcnSCl mtdMDi lddNDe linWID stbSAl sddAre sdsAre sisBpM misCel mdsAre lisCel ldsAre ltcRea ltcAre ldeAre ldePer lseCCo lseERI lseCWA lteOri lteWNB lieWCe
npartitions=103
object geometry float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 int64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 int64 float64 float64 float64 float64 float64 float64 float64 float64 float64
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
Dask Name: read-parquet, 103 tasks
conv = dask.dataframe.read_parquet(f"../../urbangrammar_samba/spatial_signatures/morphometrics/convolutions/*")
conv
Dask DataFrame Structure:
sdbAre_q1 sdbAre_q2 sdbAre_q3 sdbPer_q1 sdbPer_q2 sdbPer_q3 sdbCoA_q1 sdbCoA_q2 sdbCoA_q3 ssbCCo_q1 ssbCCo_q2 ssbCCo_q3 ssbCor_q1 ssbCor_q2 ssbCor_q3 ssbSqu_q1 ssbSqu_q2 ssbSqu_q3 ssbERI_q1 ssbERI_q2 ssbERI_q3 ssbElo_q1 ssbElo_q2 ssbElo_q3 ssbCCM_q1 ssbCCM_q2 ssbCCM_q3 ssbCCD_q1 ssbCCD_q2 ssbCCD_q3 stbOri_q1 stbOri_q2 stbOri_q3 sdcLAL_q1 sdcLAL_q2 sdcLAL_q3 sdcAre_q1 sdcAre_q2 sdcAre_q3 sscCCo_q1 sscCCo_q2 sscCCo_q3 sscERI_q1 sscERI_q2 sscERI_q3 stcOri_q1 stcOri_q2 stcOri_q3 sicCAR_q1 sicCAR_q2 sicCAR_q3 stbCeA_q1 stbCeA_q2 stbCeA_q3 mtbAli_q1 mtbAli_q2 mtbAli_q3 mtbNDi_q1 mtbNDi_q2 mtbNDi_q3 mtcWNe_q1 mtcWNe_q2 mtcWNe_q3 mdcAre_q1 mdcAre_q2 mdcAre_q3 ltcWRE_q1 ltcWRE_q2 ltcWRE_q3 ltbIBD_q1 ltbIBD_q2 ltbIBD_q3 sdsSPW_q1 sdsSPW_q2 sdsSPW_q3 sdsSWD_q1 sdsSWD_q2 sdsSWD_q3 sdsSPO_q1 sdsSPO_q2 sdsSPO_q3 sdsLen_q1 sdsLen_q2 sdsLen_q3 sssLin_q1 sssLin_q2 sssLin_q3 ldsMSL_q1 ldsMSL_q2 ldsMSL_q3 mtdDeg_q1 mtdDeg_q2 mtdDeg_q3 lcdMes_q1 lcdMes_q2 lcdMes_q3 linP3W_q1 linP3W_q2 linP3W_q3 linP4W_q1 linP4W_q2 linP4W_q3 linPDE_q1 linPDE_q2 linPDE_q3 lcnClo_q1 lcnClo_q2 lcnClo_q3 ldsCDL_q1 ldsCDL_q2 ldsCDL_q3 xcnSCl_q1 xcnSCl_q2 xcnSCl_q3 mtdMDi_q1 mtdMDi_q2 mtdMDi_q3 lddNDe_q1 lddNDe_q2 lddNDe_q3 linWID_q1 linWID_q2 linWID_q3 stbSAl_q1 stbSAl_q2 stbSAl_q3 sddAre_q1 sddAre_q2 sddAre_q3 sdsAre_q1 sdsAre_q2 sdsAre_q3 sisBpM_q1 sisBpM_q2 sisBpM_q3 misCel_q1 misCel_q2 misCel_q3 mdsAre_q1 mdsAre_q2 mdsAre_q3 lisCel_q1 lisCel_q2 lisCel_q3 ldsAre_q1 ldsAre_q2 ldsAre_q3 ltcRea_q1 ltcRea_q2 ltcRea_q3 ltcAre_q1 ltcAre_q2 ltcAre_q3 ldeAre_q1 ldeAre_q2 ldeAre_q3 ldePer_q1 ldePer_q2 ldePer_q3 lseCCo_q1 lseCCo_q2 lseCCo_q3 lseERI_q1 lseERI_q2 lseERI_q3 lseCWA_q1 lseCWA_q2 lseCWA_q3 lteOri_q1 lteOri_q2 lteOri_q3 lteWNB_q1 lteWNB_q2 lteWNB_q3 lieWCe_q1 lieWCe_q2 lieWCe_q3
npartitions=103
float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
Dask Name: read-parquet, 103 tasks
renamer  = {**{k:key[v] for k, v in coded.items()}, **key_for_all}

dask.dataframe.multi.concat([primary, conv], axis=1).rename(columns=renamer).repartition(npartitions=1000).to_parquet("../../signatures_gb/form")

function = dask.dataframe.read_parquet("../../urbangrammar_samba/spatial_signatures/functional/combined_raw").reset_index(drop=True)
function
Dask DataFrame Structure:
hindex population night_lights A, B, D, E. Agriculture, energy and water C. Manufacturing F. Construction G, I. Distribution, hotels and restaurants H, J. Transport and communication K, L, M, N. Financial, real estate, professional and administrative activities O,P,Q. Public administration, education and health R, S, T, U. Other Code_18_124 Code_18_211 Code_18_121 Code_18_421 Code_18_522 Code_18_142 Code_18_141 Code_18_112 Code_18_231 Code_18_311 Code_18_131 Code_18_123 Code_18_122 Code_18_512 Code_18_243 Code_18_313 Code_18_412 Code_18_321 Code_18_322 Code_18_324 Code_18_111 Code_18_423 Code_18_523 Code_18_133 Code_18_334 Code_18_132 Code_18_242 Code_18_411 Code_18_511 Code_18_312 Code_18_332 Code_18_521 Code_18_331 Code_18_244 Code_18_333 Code_18_222 mean supermarkets_nearest supermarkets_counts listed_nearest listed_counts fhrs_nearest fhrs_counts culture_nearest culture_counts nearest_water nearest_retail_centre
npartitions=103
object float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 int64 int64 int64 int64 int64 int64 int64 int64 int64 int64 int64 int64 int64 float64 float64 int64 float64 int64 float64 int64 float64 int64 float64 float64
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
Dask Name: reset_index, 206 tasks
fn_conv = dask.dataframe.read_parquet("../../urbangrammar_samba/spatial_signatures/functional/functional/*").drop(columns=["supermarkets_nearest","supermarkets_counts","listed_nearest", "listed_counts", "fhrs_nearest", "fhrs_counts", "culture_nearest", "culture_counts", "nearest_water","nearest_retail_centre", "keep_q1",  "keep_q2",  "keep_q3"]).reset_index(drop=True)
fn_conv
Dask DataFrame Structure:
population_q1 population_q2 population_q3 night_lights_q1 night_lights_q2 night_lights_q3 A, B, D, E. Agriculture, energy and water_q1 A, B, D, E. Agriculture, energy and water_q2 A, B, D, E. Agriculture, energy and water_q3 C. Manufacturing_q1 C. Manufacturing_q2 C. Manufacturing_q3 F. Construction_q1 F. Construction_q2 F. Construction_q3 G, I. Distribution, hotels and restaurants_q1 G, I. Distribution, hotels and restaurants_q2 G, I. Distribution, hotels and restaurants_q3 H, J. Transport and communication_q1 H, J. Transport and communication_q2 H, J. Transport and communication_q3 K, L, M, N. Financial, real estate, professional and administrative activities_q1 K, L, M, N. Financial, real estate, professional and administrative activities_q2 K, L, M, N. Financial, real estate, professional and administrative activities_q3 O,P,Q. Public administration, education and health_q1 O,P,Q. Public administration, education and health_q2 O,P,Q. Public administration, education and health_q3 R, S, T, U. Other_q1 R, S, T, U. Other_q2 R, S, T, U. Other_q3 Code_18_124_q1 Code_18_124_q2 Code_18_124_q3 Code_18_211_q1 Code_18_211_q2 Code_18_211_q3 Code_18_121_q1 Code_18_121_q2 Code_18_121_q3 Code_18_421_q1 Code_18_421_q2 Code_18_421_q3 Code_18_522_q1 Code_18_522_q2 Code_18_522_q3 Code_18_142_q1 Code_18_142_q2 Code_18_142_q3 Code_18_141_q1 Code_18_141_q2 Code_18_141_q3 Code_18_112_q1 Code_18_112_q2 Code_18_112_q3 Code_18_231_q1 Code_18_231_q2 Code_18_231_q3 Code_18_311_q1 Code_18_311_q2 Code_18_311_q3 Code_18_131_q1 Code_18_131_q2 Code_18_131_q3 Code_18_123_q1 Code_18_123_q2 Code_18_123_q3 Code_18_122_q1 Code_18_122_q2 Code_18_122_q3 Code_18_512_q1 Code_18_512_q2 Code_18_512_q3 Code_18_243_q1 Code_18_243_q2 Code_18_243_q3 Code_18_313_q1 Code_18_313_q2 Code_18_313_q3 Code_18_412_q1 Code_18_412_q2 Code_18_412_q3 Code_18_321_q1 Code_18_321_q2 Code_18_321_q3 Code_18_322_q1 Code_18_322_q2 Code_18_322_q3 Code_18_324_q1 Code_18_324_q2 Code_18_324_q3 Code_18_111_q1 Code_18_111_q2 Code_18_111_q3 Code_18_423_q1 Code_18_423_q2 Code_18_423_q3 Code_18_523_q1 Code_18_523_q2 Code_18_523_q3 mean_q1 mean_q2 mean_q3 Code_18_312_q1 Code_18_312_q2 Code_18_312_q3 Code_18_133_q1 Code_18_133_q2 Code_18_133_q3 Code_18_333_q1 Code_18_333_q2 Code_18_333_q3 Code_18_332_q1 Code_18_332_q2 Code_18_332_q3 Code_18_411_q1 Code_18_411_q2 Code_18_411_q3 Code_18_132_q1 Code_18_331_q2 Code_18_222_q1 Code_18_511_q3 Code_18_242_q1 Code_18_511_q2 Code_18_242_q3 Code_18_331_q1 Code_18_334_q2 Code_18_511_q1 Code_18_334_q1 Code_18_222_q3 Code_18_242_q2 Code_18_244_q3 Code_18_521_q2 Code_18_334_q3 Code_18_244_q1 Code_18_244_q2 Code_18_331_q3 Code_18_132_q2 Code_18_132_q3 Code_18_521_q1 Code_18_222_q2 Code_18_521_q3
npartitions=103
float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 float64 int64 int64 int64 int64 int64 int64 int64 int64 int64 int64 int64 int64 int64 int64 int64 int64 int64 int64 int64 int64 int64 int64 int64 int64
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
Dask Name: reset_index, 310 tasks
fn = dask.dataframe.multi.concat([function, fn_conv], axis=1).rename(columns=renamer)

/opt/conda/lib/python3.9/site-packages/dask/dataframe/multi.py:1222: UserWarning: Concatenating dataframes with unknown divisions.
We're assuming that the indices of each dataframes are 
 aligned. This assumption is not generally safe.
  warnings.warn(

import pyarrow as pa

schema = {k: pa.float64() for k in fn.columns}
schema["hindex"] = pa.string()

fn.repartition(npartitions=1000).to_parquet("../../signatures_gb/function", schema=schema)

[None]

client.restart()

Client

Client-5d4fe926-94ba-11ec-827f-6774297aa622

Connection method: Cluster object Cluster type: distributed.LocalCluster
Dashboard: http://127.0.0.1:8787/status

Cluster Info

 
joined = dask.dataframe.read_parquet("../../urbangrammar_samba/spatial_signatures/signatures/hindex_to_type", columns=["hindex", "type"]).rename(columns={"type": "signature_type"})

joined.repartition(npartitions=1000).to_parquet("../../signatures_gb/signature_type")

[None]

Tables of characters

Create tables of characters to be used in the paper.

morph = pandas.read_csv("morphometric_chars.csv")
morph.head()
context category reference id
0 building dimension [@hallowell2013] sdbAre
1 building dimension [@vanderhaegen2017] sdbPer
2 building dimension [@schirmer2015] sdbCoA
3 building shape [@dibble2017] ssbCCo
4 building shape [@steiniger2008] ssbCor 
morph["reference"] = morph["reference"].fillna("[@fleischmann2021]").apply(lambda x: x[2:-1])

morph["reference"] = "\\cite{" + morph["reference"] + "}"

morph["character"] = morph["id"].map(renamer)

morph = morph[["character", "category", "reference"]]

print(morph.to_latex(escape=False, index=False, longtable=True))

\begin{longtable}{lll}
\toprule
                                         character &     category &               reference \\
\midrule
\endfirsthead

\toprule
                                         character &     category &               reference \\
\midrule
\endhead
\midrule
\multicolumn{3}{r}{{Continued on next page}} \\
\midrule
\endfoot

\bottomrule
\endlastfoot
                                  area of building &    dimension &    \cite{hallowell2013} \\
                             perimeter of building &    dimension & \cite{vanderhaegen2017} \\
                        courtyard area of building &    dimension &     \cite{schirmer2015} \\
                  circular compactness of building &        shape &       \cite{dibble2017} \\
                               corners of building &        shape &    \cite{steiniger2008} \\
                            squareness of building &        shape &    \cite{steiniger2008} \\
          equivalent rectangular index of building &        shape &    \cite{basaraner2017} \\
                            elongation of building &        shape &    \cite{steiniger2008} \\
  centroid - corner distance deviation of building &        shape &  \cite{fleischmann2021} \\
       centroid - corner mean distance of building &    dimension &     \cite{schirmer2015} \\
                           orientation of building & distribution &     \cite{schirmer2015} \\
                      street alignment of building & distribution &     \cite{schirmer2015} \\
                        cell alignment of building & distribution &  \cite{fleischmann2021} \\
                        longest axis length of ETC &    dimension &  \cite{fleischmann2021} \\
                                       area of ETC &    dimension &     \cite{hamaina2012a} \\
                       circular compactness of ETC &        shape &  \cite{fleischmann2021} \\
               equivalent rectangular index of ETC &        shape &  \cite{fleischmann2021} \\
                                orientation of ETC & distribution &  \cite{fleischmann2021} \\
                         covered area ratio of ETC &    intensity &      \cite{hamaina2013} \\
                          length of street segment &    dimension &          \cite{gil2012} \\
                           width of street profile &    dimension &       \cite{araldi2019} \\
                        openness of street profile & distribution &       \cite{araldi2019} \\
                 width deviation of street profile &    diversity &       \cite{araldi2019} \\
                       linearity of street segment &        shape &       \cite{araldi2019} \\
                area covered by edge-attached ETCs &    dimension &  \cite{fleischmann2021} \\
             buildings per meter of street segment &    intensity &  \cite{fleischmann2021} \\
                area covered by node-attached ETCs &    dimension &  \cite{fleischmann2021} \\
               alignment of neighbouring buildings & distribution &       \cite{hijazi2016} \\
      mean distance between neighbouring buildings & distribution &       \cite{hijazi2016} \\
              perimeter-weighted neighbours of ETC & distribution &  \cite{fleischmann2021} \\
                area covered by neighbouring cells &    dimension &  \cite{fleischmann2021} \\
             reached ETCs by neighbouring segments &    intensity &  \cite{fleischmann2021} \\
             reached area by neighbouring segments &    dimension &  \cite{fleischmann2021} \\
                           node degree of junction & distribution &       \cite{boeing2018} \\
mean distance to neighbouring nodes of street n... &    dimension &  \cite{fleischmann2021} \\
                      mean inter-building distance & distribution &       \cite{caruso2017} \\
                weighted reached enclosures of ETC &    intensity &  \cite{fleischmann2021} \\
           reached ETCs by tessellation contiguity &    intensity &  \cite{fleischmann2021} \\
           reached area by tessellation contiguity &    dimension &  \cite{fleischmann2021} \\
                                 area of enclosure &    dimension &       \cite{dibble2017} \\
                            perimeter of enclosure &    dimension &          \cite{gil2012} \\
                 circular compactness of enclosure &        shape &     \cite{schirmer2015} \\
         equivalent rectangular index of enclosure &        shape &    \cite{basaraner2017} \\
            compactness-weighted axis of enclosure &        shape &   \cite{feliciotti2018} \\
                          orientation of enclosure & distribution &          \cite{gil2012} \\
        perimeter-weighted neighbours of enclosure & distribution &  \cite{fleischmann2021} \\
                   area-weighted ETCs of enclosure &    intensity &  \cite{fleischmann2021} \\
                local meshedness of street network & connectivity &   \cite{feliciotti2018} \\
                mean segment length within 3 steps &    dimension &  \cite{fleischmann2021} \\
         local cul-de-sac length of street network &    dimension &  \cite{fleischmann2021} \\
              reached area by local street network &    dimension &  \cite{fleischmann2021} \\
              reached ETCs by local street network &    intensity &  \cite{fleischmann2021} \\
              local node density of street network &    intensity &  \cite{fleischmann2021} \\
 local proportion of cul-de-sacs of street network & connectivity &        \cite{lowry2014} \\
local proportion of 3-way intersections of stre... & connectivity &       \cite{boeing2018} \\
local proportion of 4-way intersections of stre... & connectivity &       \cite{boeing2018} \\
local degree weighted node density of street ne... &    intensity &       \cite{dibble2017} \\
                 local closeness of street network & connectivity &        \cite{porta2006} \\
               square clustering of street network & connectivity &  \cite{fleischmann2021} \\
\end{longtable}

func = pandas.Series({'population': 'Population',
 'night_lights': 'Night lights',
 'A, B, D, E. Agriculture, energy and water': 'Workplace population [Agriculture, energy and water]',
 'C. Manufacturing': 'Workplace population [Manufacturing]',
 'F. Construction': 'Workplace population [Construction]',
 'G, I. Distribution, hotels and restaurants': 'Workplace population [Distribution, hotels and restaurants]',
 'H, J. Transport and communication': 'Workplace population [Transport and communication]',
 'K, L, M, N. Financial, real estate, professional and administrative activities': 'Workplace population [Financial, real estate, professional and administrative activities]',
 'O,P,Q. Public administration, education and health': 'Workplace population [Public administration, education and health]',
 'R, S, T, U. Other': 'Workplace population [Other]',
 'Code_18_124': 'Land cover [Airports]',
 'Code_18_211': 'Land cover [Non-irrigated arable land]',
 'Code_18_121': 'Land cover [Industrial or commercial units]',
 'Code_18_421': 'Land cover [Salt marshes]',
 'Code_18_522': 'Land cover [Estuaries]',
 'Code_18_142': 'Land cover [Sport and leisure facilities]',
 'Code_18_141': 'Land cover [Green urban areas]',
 'Code_18_112': 'Land cover [Discontinuous urban fabric]',
 'Code_18_231': 'Land cover [Pastures]',
 'Code_18_311': 'Land cover [Broad-leaved forest]',
 'Code_18_131': 'Land cover [Mineral extraction sites]',
 'Code_18_123': 'Land cover [Port areas]',
 'Code_18_122': 'Land cover [Road and rail networks and associated land]',
 'Code_18_512': 'Land cover [Water bodies]',
 'Code_18_243': 'Land cover [Land principally occupied by agriculture, with significant areas of natural vegetation]',
 'Code_18_313': 'Land cover [Mixed forest]',
 'Code_18_412': 'Land cover [Peat bogs]',
 'Code_18_321': 'Land cover [Natural grasslands]',
 'Code_18_322': 'Land cover [Moors and heathland]',
 'Code_18_324': 'Land cover [Transitional woodland-shrub]',
 'Code_18_111': 'Land cover [Continuous urban fabric]',
 'Code_18_423': 'Land cover [Intertidal flats]',
 'Code_18_523': 'Land cover [Sea and ocean]',
 'Code_18_312': 'Land cover [Coniferous forest]',
 'Code_18_133': 'Land cover [Construction sites]',
 'Code_18_333': 'Land cover [Sparsely vegetated areas]',
 'Code_18_332': 'Land cover [Bare rocks]',
 'Code_18_411': 'Land cover [Inland marshes]',
 'Code_18_132': 'Land cover [Dump sites]',
 'Code_18_222': 'Land cover [Fruit trees and berry plantations]',
 'Code_18_242': 'Land cover [Complex cultivation patterns]',
 'Code_18_331': 'Land cover [Beaches, dunes, sands]',
 'Code_18_511': 'Land cover [Water courses]',
 'Code_18_334': 'Land cover [Burnt areas]',
 'Code_18_244': 'Land cover [Agro-forestry areas]',
 'Code_18_521': 'Land cover [Coastal lagoons]',
 'mean': 'NDVI',
 'supermarkets_nearest': 'Supermarkets [distance to nearest]',
 'supermarkets_counts': 'Supermarkets [counts within 1200m]',
 'listed_nearest': 'Listed buildings [distance to nearest]',
 'listed_counts': 'Listed buildings [counts within 1200m]',
 'fhrs_nearest': 'FHRS points [distance to nearest]',
 'fhrs_counts': 'FHRS points [counts within 1200m]',
 'culture_nearest': 'Cultural venues [distance to nearest]',
 'culture_counts': 'Cultural venues [counts within 1200m]',
 'nearest_water': 'Water bodies [distance to nearest]',
 'nearest_retail_centre': 'Retail centres [distance to nearest]',}, name="character").reset_index(drop=True)

func = func.to_frame()

or_func = pandas.read_csv("func.csv", delimiter=";")

or_func = or_func.drop(columns="Unnamed: 4")

rows = [0, 4, 6, 6, 6, 6, 6, 6, 6] + ([8] * 37) + [9, 1, 1, 3, 3, 5, 5, 7, 7, 2, 10]

or_func.iloc[rows].shape

(57, 4)

functional = pandas.concat([func, or_func.iloc[rows].reset_index(drop=True)], axis=1)

with pandas.option_context("max_colwidth", 1000):
    print(functional.to_latex(index=False, longtable=True))

\begin{longtable}{lllll}
\toprule
                                                                                          character &                               data  &                                                                     source  &                    input geometry  &                                transfer method  \\
\midrule
\endfirsthead

\toprule
                                                                                          character &                               data  &                                                                     source  &                    input geometry  &                                transfer method  \\
\midrule
\endhead
\midrule
\multicolumn{5}{r}{{Continued on next page}} \\
\midrule
\endfoot

\bottomrule
\endlastfoot
                                                                                         Population &               Population estimates  &           ONS Census Output Area population estimates, Statistics.gov.scot  &      Vector (output area polygon)  &  Building-based dasymetric areal interpolation  \\
                                                                                       Night lights &                       Night Lights  &                                                 VIIRS DNB Nighttime Lights  &                     Raster (500m)  &                               Zonal statistics  \\
                                               Workplace population [Agriculture, energy and water] &               Workplace population  &    ONS Census Workplace population, Scotland's census Workplace population  &      Vector (output area polygon)  &  Building-based dasymetric areal interpolation  \\
                                                               Workplace population [Manufacturing] &               Workplace population  &    ONS Census Workplace population, Scotland's census Workplace population  &      Vector (output area polygon)  &  Building-based dasymetric areal interpolation  \\
                                                                Workplace population [Construction] &               Workplace population  &    ONS Census Workplace population, Scotland's census Workplace population  &      Vector (output area polygon)  &  Building-based dasymetric areal interpolation  \\
                                        Workplace population [Distribution, hotels and restaurants] &               Workplace population  &    ONS Census Workplace population, Scotland's census Workplace population  &      Vector (output area polygon)  &  Building-based dasymetric areal interpolation  \\
                                                 Workplace population [Transport and communication] &               Workplace population  &    ONS Census Workplace population, Scotland's census Workplace population  &      Vector (output area polygon)  &  Building-based dasymetric areal interpolation  \\
          Workplace population [Financial, real estate, professional and administrative activities] &               Workplace population  &    ONS Census Workplace population, Scotland's census Workplace population  &      Vector (output area polygon)  &  Building-based dasymetric areal interpolation  \\
                                 Workplace population [Public administration, education and health] &               Workplace population  &    ONS Census Workplace population, Scotland's census Workplace population  &      Vector (output area polygon)  &  Building-based dasymetric areal interpolation  \\
                                                                       Workplace population [Other] &                  Corine land cover  &                                         Copernicus Land Monitoring Service  &  Vector (land cover zone polygon)  &                            Areal interpolation  \\
                                                                              Land cover [Airports] &                  Corine land cover  &                                         Copernicus Land Monitoring Service  &  Vector (land cover zone polygon)  &                            Areal interpolation  \\
                                                             Land cover [Non-irrigated arable land] &                  Corine land cover  &                                         Copernicus Land Monitoring Service  &  Vector (land cover zone polygon)  &                            Areal interpolation  \\
                                                        Land cover [Industrial or commercial units] &                  Corine land cover  &                                         Copernicus Land Monitoring Service  &  Vector (land cover zone polygon)  &                            Areal interpolation  \\
                                                                          Land cover [Salt marshes] &                  Corine land cover  &                                         Copernicus Land Monitoring Service  &  Vector (land cover zone polygon)  &                            Areal interpolation  \\
                                                                             Land cover [Estuaries] &                  Corine land cover  &                                         Copernicus Land Monitoring Service  &  Vector (land cover zone polygon)  &                            Areal interpolation  \\
                                                          Land cover [Sport and leisure facilities] &                  Corine land cover  &                                         Copernicus Land Monitoring Service  &  Vector (land cover zone polygon)  &                            Areal interpolation  \\
                                                                     Land cover [Green urban areas] &                  Corine land cover  &                                         Copernicus Land Monitoring Service  &  Vector (land cover zone polygon)  &                            Areal interpolation  \\
                                                            Land cover [Discontinuous urban fabric] &                  Corine land cover  &                                         Copernicus Land Monitoring Service  &  Vector (land cover zone polygon)  &                            Areal interpolation  \\
                                                                              Land cover [Pastures] &                  Corine land cover  &                                         Copernicus Land Monitoring Service  &  Vector (land cover zone polygon)  &                            Areal interpolation  \\
                                                                   Land cover [Broad-leaved forest] &                  Corine land cover  &                                         Copernicus Land Monitoring Service  &  Vector (land cover zone polygon)  &                            Areal interpolation  \\
                                                              Land cover [Mineral extraction sites] &                  Corine land cover  &                                         Copernicus Land Monitoring Service  &  Vector (land cover zone polygon)  &                            Areal interpolation  \\
                                                                            Land cover [Port areas] &                  Corine land cover  &                                         Copernicus Land Monitoring Service  &  Vector (land cover zone polygon)  &                            Areal interpolation  \\
                                            Land cover [Road and rail networks and associated land] &                  Corine land cover  &                                         Copernicus Land Monitoring Service  &  Vector (land cover zone polygon)  &                            Areal interpolation  \\
                                                                          Land cover [Water bodies] &                  Corine land cover  &                                         Copernicus Land Monitoring Service  &  Vector (land cover zone polygon)  &                            Areal interpolation  \\
Land cover [Land principally occupied by agriculture, with significant areas of natural vegetation] &                  Corine land cover  &                                         Copernicus Land Monitoring Service  &  Vector (land cover zone polygon)  &                            Areal interpolation  \\
                                                                          Land cover [Mixed forest] &                  Corine land cover  &                                         Copernicus Land Monitoring Service  &  Vector (land cover zone polygon)  &                            Areal interpolation  \\
                                                                             Land cover [Peat bogs] &                  Corine land cover  &                                         Copernicus Land Monitoring Service  &  Vector (land cover zone polygon)  &                            Areal interpolation  \\
                                                                    Land cover [Natural grasslands] &                  Corine land cover  &                                         Copernicus Land Monitoring Service  &  Vector (land cover zone polygon)  &                            Areal interpolation  \\
                                                                   Land cover [Moors and heathland] &                  Corine land cover  &                                         Copernicus Land Monitoring Service  &  Vector (land cover zone polygon)  &                            Areal interpolation  \\
                                                           Land cover [Transitional woodland-shrub] &                  Corine land cover  &                                         Copernicus Land Monitoring Service  &  Vector (land cover zone polygon)  &                            Areal interpolation  \\
                                                               Land cover [Continuous urban fabric] &                  Corine land cover  &                                         Copernicus Land Monitoring Service  &  Vector (land cover zone polygon)  &                            Areal interpolation  \\
                                                                      Land cover [Intertidal flats] &                  Corine land cover  &                                         Copernicus Land Monitoring Service  &  Vector (land cover zone polygon)  &                            Areal interpolation  \\
                                                                         Land cover [Sea and ocean] &                  Corine land cover  &                                         Copernicus Land Monitoring Service  &  Vector (land cover zone polygon)  &                            Areal interpolation  \\
                                                                     Land cover [Coniferous forest] &                  Corine land cover  &                                         Copernicus Land Monitoring Service  &  Vector (land cover zone polygon)  &                            Areal interpolation  \\
                                                                    Land cover [Construction sites] &                  Corine land cover  &                                         Copernicus Land Monitoring Service  &  Vector (land cover zone polygon)  &                            Areal interpolation  \\
                                                              Land cover [Sparsely vegetated areas] &                  Corine land cover  &                                         Copernicus Land Monitoring Service  &  Vector (land cover zone polygon)  &                            Areal interpolation  \\
                                                                            Land cover [Bare rocks] &                  Corine land cover  &                                         Copernicus Land Monitoring Service  &  Vector (land cover zone polygon)  &                            Areal interpolation  \\
                                                                        Land cover [Inland marshes] &                  Corine land cover  &                                         Copernicus Land Monitoring Service  &  Vector (land cover zone polygon)  &                            Areal interpolation  \\
                                                                            Land cover [Dump sites] &                  Corine land cover  &                                         Copernicus Land Monitoring Service  &  Vector (land cover zone polygon)  &                            Areal interpolation  \\
                                                     Land cover [Fruit trees and berry plantations] &                  Corine land cover  &                                         Copernicus Land Monitoring Service  &  Vector (land cover zone polygon)  &                            Areal interpolation  \\
                                                          Land cover [Complex cultivation patterns] &                  Corine land cover  &                                         Copernicus Land Monitoring Service  &  Vector (land cover zone polygon)  &                            Areal interpolation  \\
                                                                 Land cover [Beaches, dunes, sands] &                  Corine land cover  &                                         Copernicus Land Monitoring Service  &  Vector (land cover zone polygon)  &                            Areal interpolation  \\
                                                                         Land cover [Water courses] &                  Corine land cover  &                                         Copernicus Land Monitoring Service  &  Vector (land cover zone polygon)  &                            Areal interpolation  \\
                                                                           Land cover [Burnt areas] &                  Corine land cover  &                                         Copernicus Land Monitoring Service  &  Vector (land cover zone polygon)  &                            Areal interpolation  \\
                                                                   Land cover [Agro-forestry areas] &                  Corine land cover  &                                         Copernicus Land Monitoring Service  &  Vector (land cover zone polygon)  &                            Areal interpolation  \\
                                                                       Land cover [Coastal lagoons] &                  Corine land cover  &                                         Copernicus Land Monitoring Service  &  Vector (land cover zone polygon)  &                            Areal interpolation  \\
                                                                                               NDVI &                               NDVI  &                                                    GHS-composite-S2 R2020A  &                      Raster (10m)  &                               Zonal statistics  \\
                                                                 Supermarkets [distance to nearest] &         Retail POIs (supermarkets)  &                                                                   Geolytix  &                    Vector (point)  &              Network-constrained accessibility  \\
                                                                 Supermarkets [counts within 1200m] &         Retail POIs (supermarkets)  &                                                                   Geolytix  &                    Vector (point)  &              Network-constrained accessibility  \\
                                                             Listed buildings [distance to nearest] &                   Listed Buildings  &  Historic England, Historic Environment Scotland, Lle Geo-Portal for Wales  &                    Vector (point)  &              Network-constrained accessibility  \\
                                                             Listed buildings [counts within 1200m] &                   Listed Buildings  &  Historic England, Historic Environment Scotland, Lle Geo-Portal for Wales  &                    Vector (point)  &              Network-constrained accessibility  \\
                                                                  FHRS points [distance to nearest] & Food Hygiene Rating Scheme Ratings  &                                                                 CDRC.ac.uk  &                    Vector (point)  &              Network-constrained accessibility  \\
                                                                  FHRS points [counts within 1200m] & Food Hygiene Rating Scheme Ratings  &                                                                 CDRC.ac.uk  &                    Vector (point)  &              Network-constrained accessibility  \\
                                                              Cultural venues [distance to nearest] &        Culture (theatres, cinemas)  &                                                              OpenStreetMap  &                    Vector (point)  &              Network-constrained accessibility  \\
                                                              Cultural venues [counts within 1200m] &        Culture (theatres, cinemas)  &                                                              OpenStreetMap  &                    Vector (point)  &              Network-constrained accessibility  \\
                                                                 Water bodies [distance to nearest] &                       Water bodies  &                                                           OS OpenMap Local  &       Vector (water body polygon)  &                        Euclidean accessibility  \\
                                                               Retail centres [distance to nearest] &                     Retail centres  &                                                                 CDRC.ac.uk  &    Vector (retail centre polygon)  &                        Euclidean accessibility  \\
\end{longtable}

with pandas.option_context("max_colwidth", 10000):
    print(pandas.Series(
    {
        "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.",
    }
    ).to_latex())

\begin{tabular}{ll}
\toprule
{} &                                                                                                                                                                                                                                                                                                                                                                                                                             0 \\
\midrule
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. \\
\bottomrule
\end{tabular}

prof = pandas.read_csv("../../urbangrammar_samba/spatial_signatures/data_product/per_type.csv").drop(columns="code").set_index("type").T

prof.index = prof.index.map(key)

with pandas.option_context("max_colwidth", 10000):
    print(prof.to_latex(float_format="%.2f"))

\begin{tabular}{lrrrrrrrrrrrrrrrr}
\toprule
type &  Accessible suburbia &  Connected residential neighbourhoods &  Countryside agriculture &  Dense residential neighbourhoods &  Dense urban neighbourhoods &  Disconnected suburbia &  Concentrated urbanity &  Gridded residential quarters &  Hyper concentrated urbanity &  Local urbanity &  Metropolitan urbanity &  Open sprawl &  Regional urbanity &  Urban buffer &  Warehouse/Park land &  Wild countryside \\
\midrule
area of building                                                                                    &               176.95 &                                272.52 &                   204.10 &                            375.60 &                      588.36 &                 212.71 &                3713.38 &                        283.89 &                      3358.10 &          823.35 &                2413.94 &       226.72 &            1480.26 &        209.42 &               393.22 &            209.86 \\
perimeter of building                                                                               &                53.90 &                                 69.12 &                    56.05 &                             80.56 &                      107.36 &                  61.63 &                 376.30 &                         69.67 &                       330.82 &          135.54 &                 283.94 &        59.64 &             195.98 &         55.94 &                75.68 &             57.12 \\
courtyard area of building                                                                          &                 0.48 &                                  1.07 &                     0.51 &                              2.13 &                        5.03 &                   0.52 &                 159.09 &                          0.75 &                        90.82 &           12.67 &                 118.95 &         0.90 &              43.19 &          0.74 &                 3.26 &              0.22 \\
circular compactness of building                                                                    &                 0.53 &                                  0.48 &                     0.51 &                              0.47 &                        0.44 &                   0.49 &                   0.43 &                          0.49 &                         0.45 &            0.41 &                   0.40 &         0.52 &               0.39 &          0.52 &                 0.47 &              0.50 \\
corners of building                                                                                 &                 4.25 &                                  4.45 &                     4.37 &                              4.69 &                        5.21 &                   4.35 &                  12.48 &                          4.51 &                         9.27 &            6.01 &                   9.72 &         4.37 &               7.78 &          4.34 &                 4.56 &              4.38 \\
squareness of building                                                                              &                 0.78 &                                  1.47 &                     0.81 &                              1.86 &                        3.28 &                   1.02 &                  18.59 &                          1.66 &                        22.51 &            5.07 &                  12.41 &         0.99 &               8.84 &          0.86 &                 1.35 &              0.71 \\
equivalent rectangular index of building                                                            &                 0.99 &                                  0.98 &                     0.98 &                              0.97 &                        0.95 &                   0.98 &                   0.78 &                          0.98 &                         0.80 &            0.92 &                   0.82 &         0.98 &               0.87 &          0.98 &                 0.98 &              0.98 \\
elongation of building                                                                              &                 0.64 &                                  0.56 &                     0.60 &                              0.56 &                        0.52 &                   0.57 &                   0.59 &                          0.58 &                         0.62 &            0.51 &                   0.53 &         0.62 &               0.51 &          0.63 &                 0.54 &              0.59 \\
centroid - corner mean distance of building                                                         &                 9.60 &                                 12.41 &                     9.79 &                             13.96 &                       18.00 &                  11.11 &                  35.93 &                         12.41 &                        37.22 &           20.71 &                  29.68 &        10.49 &              25.25 &          9.81 &                13.20 &              9.95 \\
centroid - corner distance deviation of building                                                    &                 0.36 &                                  0.71 &                     0.56 &                              1.07 &                        1.88 &                   0.54 &                   9.03 &                          0.80 &                         7.70 &            2.98 &                   6.78 &         0.55 &               4.98 &          0.49 &                 0.88 &              0.60 \\
orientation of building                                                                             &                19.56 &                                 25.50 &                    20.57 &                             16.41 &                       20.64 &                  26.39 &                  20.32 &                         23.13 &                        26.26 &           20.78 &                  22.30 &        20.21 &              21.82 &         21.10 &                23.30 &             21.86 \\
longest axis length of ETC                                                                          &                50.84 &                                 57.72 &                   220.30 &                             64.46 &                       73.56 &                  53.55 &                 112.12 &                         52.89 &                       126.58 &           80.14 &                 100.52 &        60.97 &              91.91 &        105.16 &                78.67 &            449.71 \\
area of ETC                                                                                         &              1147.25 &                               1517.81 &                 31193.48 &                           1917.31 &                     2410.32 &                1259.03 &                5708.23 &                       1251.54 &                      8654.32 &         2696.40 &                4442.21 &      2000.37 &            3535.28 &       8658.83 &              3520.84 &         155623.92 \\
circular compactness of ETC                                                                         &                 0.47 &                                  0.48 &                     0.38 &                              0.48 &                        0.47 &                   0.49 &                   0.46 &                          0.48 &                         0.47 &            0.46 &                   0.42 &         0.47 &               0.44 &          0.44 &                 0.46 &              0.35 \\
equivalent rectangular index of ETC                                                                 &                 0.97 &                                  0.97 &                     0.93 &                              0.96 &                        0.96 &                   0.97 &                   0.94 &                          0.97 &                         0.95 &            0.95 &                   0.93 &         0.97 &               0.94 &          0.95 &                 0.96 &              0.91 \\
orientation of ETC                                                                                  &                20.40 &                                 24.94 &                    21.92 &                             17.77 &                       21.07 &                  25.28 &                  20.37 &                         23.06 &                        25.96 &           21.22 &                  22.38 &        21.07 &              21.88 &         21.86 &                23.27 &             22.51 \\
covered area ratio of ETC                                                                           &                 0.19 &                                  0.20 &                     0.07 &                              0.52 &                        0.27 &                   0.22 &                   0.91 &                          0.23 &                         0.61 &            0.60 &                   4.85 &         0.18 &            1122.51 &          0.14 &                 0.18 &              0.04 \\
cell alignment of building                                                                          &                 7.38 &                                  6.12 &                    11.49 &                              6.52 &                        5.61 &                   8.08 &                   4.43 &                          5.48 &                         2.72 &            5.64 &                   4.86 &         8.64 &               5.25 &          9.76 &                 8.03 &             12.55 \\
alignment of neighbouring buildings                                                                 &                 5.31 &                                  5.36 &                     8.45 &                              5.39 &                        5.17 &                   5.67 &                   5.95 &                          4.93 &                         6.55 &            5.67 &                   6.37 &         6.48 &               6.27 &          7.06 &                 6.06 &             10.05 \\
mean distance between neighbouring buildings                                                        &                17.82 &                                 19.17 &                   111.38 &                             20.84 &                       21.13 &                  18.63 &                  18.96 &                         16.48 &                        22.95 &           20.62 &                  22.33 &        22.13 &              20.94 &         45.37 &                28.71 &            238.45 \\
perimeter-weighted neighbours of ETC                                                                &                 0.04 &                                  0.04 &                     0.02 &                              0.04 &                        0.07 &                   0.05 &                   0.03 &                          0.04 &                         0.04 &            0.11 &                   0.04 &         0.06 &               7.46 &          0.13 &                 0.04 &              0.01 \\
area covered by neighbouring cells                                                                  &              8620.11 &                              11990.46 &                277883.95 &                          15619.36 &                    20375.37 &                9503.57 &               52023.10 &                       9962.17 &                     61122.40 &        22892.04 &               39665.51 &     16780.98 &           31594.99 &      76942.43 &             31956.96 &        1485709.28 \\
weighted reached enclosures of ETC                                                                  &                 0.00 &                                  0.00 &                     0.00 &                              0.00 &                        0.00 &                   0.00 &                   0.00 &                          0.00 &                         0.00 &            0.00 &                   0.00 &         0.00 &               0.00 &          0.00 &                 0.00 &              0.00 \\
mean inter-building distance                                                                        &                21.97 &                                 24.07 &                   167.60 &                             26.48 &                       27.37 &                  22.03 &                  22.73 &                         21.34 &                        23.74 &           26.28 &                  26.99 &        28.94 &              26.32 &         67.27 &                40.97 &            367.72 \\
width of street profile                                                                             &                28.38 &                                 26.84 &                    32.84 &                             26.29 &                       24.84 &                  27.65 &                  19.47 &                         24.27 &                        17.47 &           24.56 &                  22.61 &        28.59 &              23.44 &         31.00 &                30.85 &             34.31 \\
width deviation of street profile                                                                   &                 3.30 &                                  3.27 &                     3.91 &                              3.50 &                        3.45 &                   3.71 &                   3.29 &                          3.87 &                         2.85 &            3.60 &                   3.50 &         3.74 &               3.62 &          3.76 &                 3.26 &              3.41 \\
openness of street profile                                                                          &                 0.42 &                                  0.41 &                     0.83 &                              0.43 &                        0.41 &                   0.44 &                   0.28 &                          0.38 &                         0.22 &            0.41 &                   0.37 &         0.48 &               0.39 &          0.62 &                 0.53 &              0.92 \\
length of street segment                                                                            &               187.61 &                                162.45 &                   574.25 &                            153.66 &                      151.58 &                 150.53 &                 108.90 &                        126.02 &                        93.90 &          143.14 &                 123.30 &       183.43 &             132.18 &        333.77 &               220.94 &            842.79 \\
linearity of street segment                                                                         &                 0.93 &                                  0.94 &                     0.93 &                              0.92 &                        0.93 &                   0.92 &                   0.94 &                          0.94 &                         0.97 &            0.92 &                   0.93 &         0.90 &               0.92 &          0.91 &                 0.91 &              0.91 \\
mean segment length within 3 steps                                                                  &              2327.31 &                               2374.39 &                  5884.25 &                           1992.44 &                     2113.58 &                1707.52 &                1944.94 &                       1950.07 &                      2057.70 &         2011.42 &                2112.12 &      1862.02 &            2034.72 &       3170.78 &              2339.74 &           8062.03 \\
node degree of junction                                                                             &                 2.87 &                                  3.00 &                     2.78 &                              2.89 &                        2.94 &                   2.68 &                   3.12 &                          3.04 &                         3.33 &            2.94 &                   3.14 &         2.68 &               3.01 &          2.70 &                 2.77 &              2.69 \\
local meshedness of street network                                                                  &                 0.08 &                                  0.11 &                     0.06 &                              0.10 &                        0.11 &                   0.05 &                   0.14 &                          0.13 &                         0.17 &            0.11 &                   0.14 &         0.06 &               0.12 &          0.05 &                 0.08 &              0.05 \\
local proportion of 3-way intersections of street network                                           &                 0.74 &                                  0.74 &                     0.72 &                              0.74 &                        0.74 &                   0.71 &                   0.76 &                          0.72 &                         0.70 &            0.75 &                   0.75 &         0.71 &               0.76 &          0.71 &                 0.75 &              0.68 \\
local proportion of 4-way intersections of street network                                           &                 0.07 &                                  0.12 &                     0.04 &                              0.09 &                        0.11 &                   0.04 &                   0.15 &                          0.16 &                         0.23 &            0.11 &                   0.17 &         0.04 &               0.13 &          0.04 &                 0.05 &              0.04 \\
local proportion of cul-de-sacs of street network                                                   &                 0.19 &                                  0.14 &                     0.24 &                              0.17 &                        0.14 &                   0.25 &                   0.09 &                          0.12 &                         0.06 &            0.14 &                   0.08 &         0.25 &               0.11 &          0.25 &                 0.20 &              0.28 \\
local closeness of street network                                                                   &                 0.00 &                                  0.00 &                     0.00 &                              0.00 &                        0.00 &                   0.00 &                   0.00 &                          0.00 &                         0.00 &            0.00 &                   0.00 &         0.00 &               0.00 &          0.00 &                 0.00 &              0.00 \\
local cul-de-sac length of street network                                                           &               228.58 &                                163.78 &                   636.07 &                            196.63 &                      170.89 &                 275.96 &                  84.41 &                        133.13 &                        75.11 &          167.72 &                  79.56 &       288.26 &             128.76 &        408.67 &               253.49 &           1186.52 \\
square clustering of street network                                                                 &                 0.03 &                                  0.04 &                     0.01 &                              0.03 &                        0.04 &                   0.01 &                   0.03 &                          0.04 &                         0.04 &            0.03 &                   0.04 &         0.02 &               0.03 &          0.02 &                 0.03 &              0.01 \\
mean distance to neighbouring nodes of street network                                               &               132.49 &                                118.06 &                   373.74 &                            112.48 &                      111.55 &                 111.69 &                  86.38 &                         92.19 &                        81.24 &          106.90 &                  93.66 &       129.03 &              99.79 &        212.34 &               150.43 &            601.60 \\
local node density of street network                                                                &                 0.02 &                                  0.02 &                     0.01 &                              0.02 &                        0.02 &                   0.03 &                   0.02 &                          0.02 &                         0.02 &            0.02 &                   0.02 &         0.03 &               0.02 &          0.02 &                 0.02 &              0.01 \\
local degree weighted node density of street network                                                &                 0.03 &                                  0.03 &                     0.02 &                              0.04 &                        0.04 &                   0.04 &                   0.04 &                          0.04 &                         0.04 &            0.04 &                   0.04 &         0.04 &               0.04 &          0.03 &                 0.03 &              0.01 \\
street alignment of building                                                                        &                 8.73 &                                  7.53 &                    11.81 &                              8.25 &                        7.57 &                   9.98 &                   7.84 &                          6.95 &                         6.23 &            8.05 &                   8.32 &        10.97 &               8.06 &         11.33 &                10.02 &             12.77 \\
area covered by node-attached ETCs                                                                  &             22426.36 &                              14599.22 &                286081.33 &                          14037.94 &                    13513.86 &               15656.96 &               13069.71 &                       9488.11 &                     20051.57 &        11878.66 &               13201.87 &     25443.99 &           12080.93 &     100470.30 &             33097.00 &        1215083.95 \\
area covered by edge-attached ETCs                                                                  &             36496.96 &                              24423.69 &                502883.47 &                          25413.44 &                    26111.44 &               26810.65 &               33257.27 &                      17094.77 &                     38566.99 &        25905.75 &               31497.77 &     47178.87 &           29440.27 &     188719.66 &             66614.68 &        2174736.93 \\
buildings per meter of street segment                                                               &                 0.11 &                                  0.08 &                     0.05 &                              0.08 &                        0.07 &                   0.10 &                   0.05 &                          0.09 &                         0.05 &            0.06 &                   0.05 &         0.10 &               0.05 &          0.09 &                 0.07 &              0.02 \\
reached ETCs by neighbouring segments                                                               &                49.09 &                                 33.99 &                    38.08 &                             26.79 &                       21.56 &                  32.35 &                   8.88 &                         26.76 &                         8.57 &           16.97 &                  11.04 &        35.17 &              13.53 &         43.96 &                30.27 &             26.08 \\
reached area by neighbouring segments                                                               &            113290.06 &                              88462.74 &               1591397.39 &                          89313.79 &                    97515.74 &               84060.33 &              145420.68 &                      64059.40 &                    151507.35 &       100616.06 &              132683.99 &    140813.94 &          119718.73 &     556190.10 &            211678.46 &        5556960.68 \\
reached ETCs by local street network                                                                &               166.98 &                                126.07 &                   110.89 &                             90.93 &                       74.36 &                 102.39 &                  28.79 &                         99.87 &                        27.00 &           56.17 &                  36.29 &       103.45 &              43.97 &        123.39 &                93.33 &             71.94 \\
reached area by local street network                                                                &            451276.21 &                             390719.33 &               5858316.88 &                         369240.03 &                   416784.68 &              316062.25 &              703631.50 &                     296524.52 &                    621126.10 &       439804.09 &              643746.00 &    506987.49 &          540975.07 &    1982158.35 &            794621.33 &       17403052.98 \\
reached ETCs by tessellation contiguity                                                             &                36.80 &                                 40.24 &                    46.23 &                             43.10 &                       45.61 &                  39.57 &                  53.52 &                         42.04 &                        48.46 &           47.29 &                  51.81 &        41.55 &              51.95 &         43.57 &                42.93 &             47.56 \\
reached area by tessellation contiguity                                                             &             60511.46 &                              87537.63 &               2410926.40 &                         115962.63 &                   152810.21 &               63671.98 &              372984.21 &                      73335.07 &                    306427.88 &       173857.48 &              302746.84 &    136577.35 &          238390.55 &     692699.47 &            297667.66 &       14081627.81 \\
area of enclosure                                                                                   &            242778.35 &                              95677.02 &               3591565.15 &                         133719.21 &                   105561.74 &              282930.77 &               28859.85 &                     110195.65 &                     31788.41 &        83656.67 &               29460.25 &    640071.17 &           63476.79 &    1854684.23 &            430998.35 &       44036373.80 \\
perimeter of enclosure                                                                              &              2046.29 &                               1360.81 &                  7599.46 &                           1693.62 &                     1463.16 &                2380.50 &                 683.27 &                       1150.58 &                       538.87 &         1299.32 &                 671.29 &      3793.33 &            1009.07 &       5664.05 &              2992.30 &          21952.84 \\
circular compactness of enclosure                                                                   &                 0.40 &                                  0.39 &                     0.40 &                              0.38 &                        0.38 &                   0.42 &                   0.44 &                          0.41 &                         0.45 &            0.39 &                   0.40 &         0.38 &               0.40 &          0.39 &                 0.38 &              0.38 \\
equivalent rectangular index of enclosure                                                           &                 0.85 &                                  0.87 &                     0.84 &                              0.84 &                        0.86 &                   0.83 &                   0.91 &                          0.89 &                         0.94 &            0.85 &                   0.89 &         0.77 &               0.87 &          0.80 &                 0.80 &              0.79 \\
compactness-weighted axis of enclosure                                                              &               515.77 &                                344.74 &                  1777.66 &                            441.16 &                      397.37 &                 567.78 &                 144.75 &                        289.81 &                       120.13 &          345.64 &                 153.64 &       986.37 &             249.05 &       1434.02 &               780.52 &           5069.06 \\
orientation of enclosure                                                                            &                19.24 &                                 25.62 &                    21.39 &                             16.18 &                       20.88 &                  27.07 &                  20.23 &                         23.04 &                        24.93 &           21.09 &                  21.77 &        20.39 &              22.00 &         21.52 &                24.08 &             22.66 \\
perimeter-weighted neighbours of enclosure                                                          &                 0.01 &                                  0.01 &                     0.01 &                              0.02 &                        0.08 &                   0.02 &                   0.04 &                          0.02 &                         0.08 &            0.12 &                   0.06 &         0.05 &               9.94 &          0.11 &                 0.01 &              0.01 \\
area-weighted ETCs of enclosure                                                                     &                36.32 &                                  2.82 &                   746.28 &                              3.03 &                        4.63 &             2137242.86 &                   0.00 &                          0.43 &                         0.00 &            0.14 &                   0.01 &    330422.61 &               0.01 & 1178106554.77 &            627879.43 &        2270509.66 \\
Population                                                                                          &                 4.51 &                                  8.57 &                     1.91 &                             10.02 &                       17.52 &                   6.55 &                  36.91 &                          7.74 &                        37.93 &           28.87 &                  43.70 &         5.06 &              42.99 &          3.43 &                 6.93 &              1.31 \\
Night lights                                                                                        &                11.02 &                                 19.99 &                     1.39 &                             22.63 &                       34.74 &                  12.35 &                 115.70 &                         15.17 &                       183.23 &           51.19 &                  87.38 &        10.96 &              67.53 &          5.08 &                18.29 &              0.48 \\
Workplace population [Agriculture, energy and water]                                                &                 0.01 &                                  0.03 &                     0.08 &                              0.07 &                        0.11 &                   0.02 &                   2.44 &                          0.03 &                         1.41 &            0.18 &                   1.01 &         0.04 &               0.39 &          0.05 &                 0.10 &              0.11 \\
Workplace population [Manufacturing]                                                                &                 0.12 &                                  0.29 &                     0.22 &                              0.64 &                        1.10 &                   0.21 &                  12.80 &                          0.36 &                        20.14 &            1.32 &                   4.18 &         0.42 &               2.03 &          0.38 &                 1.25 &              0.09 \\
Workplace population [Construction]                                                                 &                 0.12 &                                  0.22 &                     0.10 &                              0.33 &                        0.56 &                   0.18 &                   9.16 &                          0.20 &                        10.68 &            0.80 &                   3.80 &         0.17 &               1.40 &          0.14 &                 0.34 &              0.07 \\
Workplace population [Distribution, hotels and restaurants]                                         &                 0.21 &                                  0.61 &                     0.19 &                              1.17 &                        2.30 &                   0.38 &                  54.16 &                          0.73 &                       152.31 &            4.16 &                  22.76 &         0.45 &              11.90 &          0.32 &                 1.00 &              0.12 \\
Workplace population [Transport and communication]                                                  &                 0.07 &                                  0.21 &                     0.07 &                              0.41 &                        0.88 &                   0.13 &                  39.51 &                          0.18 &                        97.90 &            1.96 &                  18.93 &         0.16 &               5.70 &          0.14 &                 0.51 &              0.04 \\
Workplace population [Financial, real estate, professional and administrative activities]           &                 0.15 &                                  0.40 &                     0.13 &                              0.78 &                        1.81 &                   0.26 &                 258.67 &                          0.38 &                       172.75 &            4.89 &                  65.30 &         0.27 &              16.45 &          0.21 &                 0.61 &              0.06 \\
Workplace population [Public administration, education and health]                                  &                 0.43 &                                  0.94 &                     0.22 &                              1.67 &                        3.21 &                   0.59 &                  41.70 &                          0.98 &                        30.82 &            5.71 &                  42.90 &         0.59 &              14.50 &          0.39 &                 1.06 &              0.12 \\
Workplace population [Other]                                                                        &                 0.06 &                                  0.15 &                     0.05 &                              0.26 &                        0.56 &                   0.10 &                  23.06 &                          0.17 &                        38.16 &            1.14 &                   8.74 &         0.09 &               3.40 &          0.07 &                 0.16 &              0.03 \\
Land cover [Airports]                                                                               &                 0.00 &                                  0.00 &                     0.00 &                              0.00 &                        0.00 &                   0.00 &                   0.00 &                          0.00 &                         0.00 &            0.00 &                   0.00 &         0.00 &               0.00 &          0.00 &                 0.00 &              0.00 \\
Land cover [Non-irrigated arable land]                                                              &                 0.00 &                                  0.00 &                     0.35 &                              0.00 &                        0.00 &                   0.00 &                   0.00 &                          0.00 &                         0.00 &            0.00 &                   0.00 &         0.01 &               0.00 &          0.11 &                 0.02 &              0.15 \\
Land cover [Industrial or commercial units]                                                         &                 0.00 &                                  0.02 &                     0.01 &                              0.05 &                        0.09 &                   0.01 &                   0.00 &                          0.00 &                         0.00 &            0.09 &                   0.01 &         0.03 &               0.06 &          0.03 &                 0.14 &              0.00 \\
Land cover [Salt marshes]                                                                           &                 0.00 &                                  0.00 &                     0.00 &                              0.00 &                        0.00 &                   0.00 &                   0.00 &                          0.00 &                         0.01 &            0.00 &                   0.00 &         0.00 &               0.00 &          0.00 &                 0.00 &              0.00 \\
Land cover [Estuaries]                                                                              &                 0.00 &                                  0.00 &                     0.00 &                              0.00 &                        0.00 &                   0.00 &                   0.00 &                          0.00 &                         0.00 &            0.00 &                   0.00 &         0.00 &               0.00 &          0.00 &                 0.00 &              0.00 \\
Land cover [Sport and leisure facilities]                                                           &                 0.00 &                                  0.00 &                     0.02 &                              0.00 &                        0.00 &                   0.00 &                   0.00 &                          0.00 &                         0.00 &            0.00 &                   0.00 &         0.01 &               0.00 &          0.02 &                 0.01 &              0.01 \\
Land cover [Green urban areas]                                                                      &                 0.01 &                                  0.01 &                     0.00 &                              0.01 &                        0.01 &                   0.00 &                   0.03 &                          0.00 &                         0.00 &            0.01 &                   0.03 &         0.01 &               0.01 &          0.00 &                 0.02 &              0.00 \\
Land cover [Discontinuous urban fabric]                                                             &                 0.98 &                                  0.95 &                     0.20 &                              0.88 &                        0.75 &                   0.98 &                   0.06 &                          0.92 &                         0.00 &            0.63 &                   0.08 &         0.91 &               0.34 &          0.68 &                 0.77 &              0.03 \\
Land cover [Pastures]                                                                               &                 0.00 &                                  0.00 &                     0.36 &                              0.00 &                        0.00 &                   0.00 &                   0.00 &                          0.00 &                         0.02 &            0.00 &                   0.00 &         0.02 &               0.00 &          0.12 &                 0.02 &              0.59 \\
Land cover [Broad-leaved forest]                                                                    &                 0.00 &                                  0.00 &                     0.02 &                              0.00 &                        0.00 &                   0.00 &                   0.00 &                          0.00 &                         0.00 &            0.00 &                   0.00 &         0.00 &               0.00 &          0.01 &                 0.00 &              0.03 \\
Land cover [Mineral extraction sites]                                                               &                 0.00 &                                  0.00 &                     0.00 &                              0.00 &                        0.00 &                   0.00 &                   0.00 &                          0.00 &                         0.00 &            0.00 &                   0.00 &         0.00 &               0.00 &          0.00 &                 0.00 &              0.00 \\
Land cover [Port areas]                                                                             &                 0.00 &                                  0.00 &                     0.00 &                              0.00 &                        0.01 &                   0.00 &                   0.00 &                          0.00 &                         0.00 &            0.01 &                   0.00 &         0.00 &               0.01 &          0.00 &                 0.01 &              0.00 \\
Land cover [Road and rail networks and associated land]                                             &                 0.00 &                                  0.00 &                     0.00 &                              0.00 &                        0.00 &                   0.00 &                   0.00 &                          0.00 &                         0.00 &            0.00 &                   0.00 &         0.00 &               0.00 &          0.00 &                 0.00 &              0.00 \\
Land cover [Water bodies]                                                                           &                 0.00 &                                  0.00 &                     0.00 &                              0.00 &                        0.00 &                   0.00 &                   0.00 &                          0.00 &                         0.00 &            0.00 &                   0.00 &         0.00 &               0.00 &          0.00 &                 0.00 &              0.00 \\
Land cover [Land principally occupied by agriculture, with significant areas of natural vegetation] &                 0.00 &                                  0.00 &                     0.01 &                              0.00 &                        0.00 &                   0.00 &                   0.00 &                          0.00 &                         0.00 &            0.00 &                   0.00 &         0.00 &               0.00 &          0.00 &                 0.00 &              0.01 \\
Land cover [Mixed forest]                                                                           &                 0.00 &                                  0.00 &                     0.01 &                              0.00 &                        0.00 &                   0.00 &                   0.00 &                          0.00 &                         0.00 &            0.00 &                   0.00 &         0.00 &               0.00 &          0.00 &                 0.00 &              0.02 \\
Land cover [Peat bogs]                                                                              &                 0.00 &                                  0.00 &                     0.00 &                              0.00 &                        0.00 &                   0.00 &                   0.00 &                          0.00 &                         0.00 &            0.00 &                   0.00 &         0.00 &               0.00 &          0.00 &                 0.00 &              0.02 \\
Land cover [Natural grasslands]                                                                     &                 0.00 &                                  0.00 &                     0.00 &                              0.00 &                        0.00 &                   0.00 &                   0.00 &                          0.00 &                         0.00 &            0.00 &                   0.00 &         0.00 &               0.00 &          0.00 &                 0.00 &              0.04 \\
Land cover [Moors and heathland]                                                                    &                 0.00 &                                  0.00 &                     0.00 &                              0.00 &                        0.00 &                   0.00 &                   0.00 &                          0.00 &                         0.00 &            0.00 &                   0.00 &         0.00 &               0.00 &          0.00 &                 0.00 &              0.04 \\
Land cover [Transitional woodland-shrub]                                                            &                 0.00 &                                  0.00 &                     0.00 &                              0.00 &                        0.00 &                   0.00 &                   0.00 &                          0.00 &                         0.00 &            0.00 &                   0.00 &         0.00 &               0.00 &          0.00 &                 0.00 &              0.01 \\
Land cover [Continuous urban fabric]                                                                &                 0.00 &                                  0.02 &                     0.00 &                              0.04 &                        0.13 &                   0.00 &                   0.90 &                          0.07 &                         0.97 &            0.25 &                   0.88 &         0.00 &               0.57 &          0.00 &                 0.00 &              0.00 \\
Land cover [Intertidal flats]                                                                       &                 0.00 &                                  0.00 &                     0.00 &                              0.00 &                        0.00 &                   0.00 &                   0.00 &                          0.00 &                         0.00 &            0.00 &                   0.00 &         0.00 &               0.00 &          0.00 &                 0.00 &              0.00 \\
Land cover [Sea and ocean]                                                                          &                 0.00 &                                  0.00 &                     0.00 &                              0.00 &                        0.00 &                   0.00 &                   0.00 &                          0.00 &                         0.00 &            0.00 &                   0.00 &         0.00 &               0.00 &          0.00 &                 0.00 &              0.00 \\
Land cover [Construction sites]                                                                     &                 0.00 &                                  0.00 &                     0.00 &                              0.00 &                        0.00 &                   0.00 &                   0.00 &                          0.00 &                         0.00 &            0.00 &                   0.00 &         0.00 &               0.00 &          0.01 &                 0.00 &              0.00 \\
Land cover [Burnt areas]                                                                            &                 0.00 &                                  0.00 &                     0.00 &                              0.00 &                        0.00 &                   0.00 &                   0.00 &                          0.00 &                         0.00 &            0.00 &                   0.00 &         0.00 &               0.00 &          0.00 &                 0.00 &              0.00 \\
Land cover [Dump sites]                                                                             &                 0.00 &                                  0.00 &                     0.00 &                              0.00 &                        0.00 &                   0.00 &                   0.00 &                          0.00 &                         0.00 &            0.00 &                   0.00 &         0.00 &               0.00 &          0.00 &                 0.00 &              0.00 \\
Land cover [Complex cultivation patterns]                                                           &                 0.00 &                                  0.00 &                     0.00 &                              0.00 &                        0.00 &                   0.00 &                   0.00 &                          0.00 &                         0.00 &            0.00 &                   0.00 &         0.00 &               0.00 &          0.00 &                 0.00 &              0.00 \\
Land cover [Inland marshes]                                                                         &                 0.00 &                                  0.00 &                     0.00 &                              0.00 &                        0.00 &                   0.00 &                   0.00 &                          0.00 &                         0.00 &            0.00 &                   0.00 &         0.00 &               0.00 &          0.00 &                 0.00 &              0.00 \\
Land cover [Water courses]                                                                          &                 0.00 &                                  0.00 &                     0.00 &                              0.00 &                        0.00 &                   0.00 &                   0.01 &                          0.00 &                         0.00 &            0.00 &                   0.01 &         0.00 &               0.01 &          0.00 &                 0.00 &              0.00 \\
Land cover [Coniferous forest]                                                                      &                 0.00 &                                  0.00 &                     0.01 &                              0.00 &                        0.00 &                   0.00 &                   0.00 &                          0.00 &                         0.00 &            0.00 &                   0.00 &         0.00 &               0.00 &          0.00 &                 0.00 &              0.04 \\
Land cover [Bare rocks]                                                                             &                 0.00 &                                  0.00 &                     0.00 &                              0.00 &                        0.00 &                   0.00 &                   0.00 &                          0.00 &                         0.00 &            0.00 &                   0.00 &         0.00 &               0.00 &          0.00 &                 0.00 &              0.00 \\
Land cover [Coastal lagoons]                                                                        &                 0.00 &                                  0.00 &                     0.00 &                              0.00 &                        0.00 &                   0.00 &                   0.00 &                          0.00 &                         0.00 &            0.00 &                   0.00 &         0.00 &               0.00 &          0.00 &                 0.00 &              0.00 \\
Land cover [Beaches, dunes, sands]                                                                  &                 0.00 &                                  0.00 &                     0.00 &                              0.00 &                        0.00 &                   0.00 &                   0.00 &                          0.00 &                         0.00 &            0.00 &                   0.00 &         0.00 &               0.00 &          0.00 &                 0.00 &              0.00 \\
Land cover [Agro-forestry areas]                                                                    &                 0.00 &                                  0.00 &                     0.00 &                              0.00 &                        0.00 &                   0.00 &                   0.00 &                          0.00 &                         0.00 &            0.00 &                   0.00 &         0.00 &               0.00 &          0.00 &                 0.00 &              0.00 \\
Land cover [Sparsely vegetated areas]                                                               &                 0.00 &                                  0.00 &                     0.00 &                              0.00 &                        0.00 &                   0.00 &                   0.00 &                          0.00 &                         0.00 &            0.00 &                   0.00 &         0.00 &               0.00 &          0.00 &                 0.00 &              0.00 \\
Land cover [Fruit trees and berry plantations]                                                      &                 0.00 &                                  0.00 &                     0.00 &                              0.00 &                        0.00 &                   0.00 &                   0.00 &                          0.00 &                         0.00 &            0.00 &                   0.00 &         0.00 &               0.00 &          0.00 &                 0.00 &              0.00 \\
NDVI                                                                                                &                 0.29 &                                  0.25 &                     0.48 &                              0.23 &                        0.19 &                   0.29 &                   0.03 &                          0.21 &                         0.00 &            0.16 &                   0.06 &         0.29 &               0.11 &          0.37 &                 0.29 &              0.56 \\
Supermarkets [distance to nearest]                                                                  &               828.82 &                                679.96 &                  4751.23 &                            661.77 &                      587.28 &                 761.86 &                 229.90 &                        577.68 &                       324.42 &          483.02 &                 299.93 &       948.03 &             331.07 &       1752.87 &              1043.84 &           9854.12 \\
Supermarkets [counts within 1200m]                                                                  &                 1.89 &                                  2.86 &                     0.09 &                              3.13 &                        4.44 &                   2.07 &                  22.51 &                          3.41 &                        18.79 &            6.85 &                  17.27 &         1.47 &              12.53 &          0.65 &                 1.43 &              0.03 \\
Listed buildings [distance to nearest]                                                              &               744.22 &                                596.61 &                   557.94 &                            506.61 &                      350.89 &                 729.61 &                  31.73 &                        516.20 &                        69.75 &          216.86 &                  51.87 &       760.26 &             115.00 &        673.93 &               934.00 &           1324.03 \\
Listed buildings [counts within 1200m]                                                              &                11.27 &                                 24.28 &                    11.22 &                             37.47 &                       62.78 &                  24.18 &                 685.16 &                         31.77 &                      1142.57 &          140.03 &                 456.53 &        18.17 &             324.50 &         16.14 &                10.57 &              4.21 \\
FHRS points [distance to nearest]                                                                   &               218.46 &                                152.48 &                   725.69 &                            144.02 &                      106.08 &                 217.95 &                  16.22 &                        129.24 &                        14.10 &           82.47 &                  40.06 &       267.24 &              56.87 &        379.17 &               256.22 &           1699.17 \\
FHRS points [counts within 1200m]                                                                   &               334.43 &                                692.66 &                    44.47 &                            860.93 &                     1568.44 &                 342.08 &                6297.61 &                       1081.38 &                      9213.15 &         2167.91 &                4490.95 &       253.88 &            3163.83 &        132.66 &               271.09 &             33.07 \\
Cultural venues [distance to nearest]                                                               &              5384.64 &                               3946.05 &                 13156.20 &                           3497.51 &                     2287.43 &                5831.52 &                 702.75 &                       4094.92 &                       351.33 &         1273.23 &                 644.53 &      6309.75 &             850.25 &       8939.65 &              5121.47 &          20695.29 \\
Cultural venues [counts within 1200m]                                                               &                 0.06 &                                  0.13 &                     0.00 &                              0.26 &                        0.48 &                   0.08 &                  10.39 &                          0.24 &                        34.20 &            1.13 &                   4.45 &         0.06 &               2.23 &          0.02 &                 0.06 &              0.00 \\
Water bodies [distance to nearest]                                                                  &               542.61 &                                555.96 &                   304.49 &                            483.12 &                      528.85 &                 523.05 &                 565.25 &                        522.09 &                       759.60 &          507.71 &                 467.71 &       378.36 &             461.42 &        345.79 &               417.43 &            236.73 \\
Retail centres [distance to nearest]                                                                &               849.45 &                                536.47 &                  4943.97 &                            421.09 &                      224.33 &                 725.57 &                  29.80 &                        445.52 &                        32.54 &          161.85 &                  66.32 &      1002.66 &              90.87 &       2102.46 &               898.17 &          11041.32 \\
\bottomrule
\end{tabular}

Figure of context

Create a figure illustrating context.

tess = geopandas.read_parquet("../../urbangrammar_samba/spatial_signatures/morphometrics/cells/cells_76.pq", columns=["hindex", "tessellation"])

from shapely.geometry import Point
import urbangrammar_graphics as ugg
import contextily
from matplotlib_scalebar.scalebar import ScaleBar
import seaborn as sns
import matplotlib.pyplot as plt

minx, maxx, miny, maxy = (357500, 360000, 173500, 176000)
subset = tess.cx[minx:maxx, miny:maxy]
central = subset[subset.intersects(Point((maxx+minx) / 2, (maxy+miny) / 2))]

import momepy

W = momepy.sw_high(10, subset, ids="hindex")

neighbors = subset[subset.hindex.isin(W.neighbors['c076e329053t0011'])]

neighbors["distance"] = neighbors.centroid.distance(central.centroid.iloc[0])

/opt/conda/lib/python3.9/site-packages/geopandas/geodataframe.py:1351: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  super().__setitem__(key, value)

ax = subset.clip(Point((maxx+minx) / 2, (maxy+miny) / 2).buffer(1200)).plot(figsize=(12, 12), facecolor="none", edgecolor="w", linewidth=1)
central.plot(ax=ax, color=ugg.COLORS[4])
neighbors.plot("distance", ax=ax, cmap=sns.light_palette(ugg.COLORS[1], 256, as_cmap=True, reverse=True), edgecolor="w", linewidth=.5, alpha=.8)

# buildings.plot(ax=ax, color=ugg.COLORS[1], alpha=.1, zorder=-1)
ax.set_axis_off()

scalebar = ScaleBar(dx=1,
                    color="w",
                    location='lower right',
                    height_fraction=0.002,
                    pad=.5,
                    frameon=False,
                    )
ax.add_artist(scalebar)

contextily.add_basemap(ax=ax, source=contextily.providers.Esri.WorldImagery, crs=subset.crs)
plt.savefig("cell_context.png", dpi=150, bbox_inches="tight")
../_images/create_data_product_129_0.png

Feature importance

importances = pandas.read_csv("../../urbangrammar_samba/spatial_signatures/clustering_data/spsig_feature_importance.csv", index_col=0)

importances.index = importances.index.map(renamer)

importances.columns = ["relative importance"]

print(importances.head(10).to_latex(longtable=True))

\begin{longtable}{lr}
\toprule
{} &  relative importance \\
\midrule
\endfirsthead

\toprule
{} &  relative importance \\
\midrule
\endhead
\midrule
\multicolumn{2}{r}{{Continued on next page}} \\
\midrule
\endfoot

\bottomrule
\endlastfoot
covered area ratio of ETC (Q1)             &             0.036944 \\
covered area ratio of ETC (Q2)             &             0.031717 \\
perimeter-weighted neighbours of ETC (Q2)  &             0.023476 \\
mean inter-building distance (Q2)          &             0.016662 \\
area of ETC (Q3)                           &             0.016005 \\
area covered by node-attached ETCs (Q3)    &             0.014813 \\
longest axis length of ETC (Q2)            &             0.014501 \\
weighted reached enclosures of ETC (Q1)    &             0.014115 \\
reached area by neighbouring segments (Q3) &             0.014000 \\
reached area by neighbouring segments (Q1) &             0.013904 \\
\end{longtable}

importances.head(10).sum()

relative importance    0.196135
dtype: float64

importances.to_excel("importances.xlsx")

type_imp = pandas.read_parquet("../../urbangrammar_samba/spatial_signatures/clustering_data/per_cluster_importance.pq")

classes = [
    'cluster_4', 'cluster_0', 'cluster_6', 'cluster_1', 'cluster_21',
    'cluster_7', 'cluster_3', 'cluster_5', 'cluster_90', 'cluster_20',
    'cluster_8', 'cluster_22', 'cluster_92', 'cluster_94', 'cluster_91',
    'cluster_95'
]
type_imp[classes] = type_imp[classes].apply(lambda x: x.map(renamer))

types = {
    0: "Countryside agriculture",
    1: "Accessible suburbia",
    3: "Open sprawl",
    4: "Wild countryside",
    5: "Warehouse/Park land",
    6: "Gridded residential quarters",
    7: "Urban buffer",
    8: "Disconnected suburbia",
    20: "Dense residential neighbourhoods",
    21: "Connected residential neighbourhoods",
    22: "Dense urban neighbourhoods",
    90: "Local urbanity",
    91: "Concentrated urbanity",
    92: "Regional urbanity",
    94: "Metropolitan urbanity",
    95: "Hyper concentrated urbanity",
    93: "outlier",
    96: "outlier",
    97: "outlier",
    98: "outlier",
}

tups = []
for c in type_imp.columns:
    if 'vals' in c:
        tups.append((types[int(c[8:-5])], 'rel. importance'))
    else:
        tups.append((types[int(c[8:])], 'name'))

type_imp.columns = pandas.MultiIndex.from_tuples(tups)

print(type_imp.iloc[:10].round(3).to_latex(longtable=True))

\begin{longtable}{llrlrlrlrlrlrlrlrlrlrlrlrlrlrlrlr}
\toprule
{} & \multicolumn{2}{l}{Wild countryside} & \multicolumn{2}{l}{Countryside agriculture} & \multicolumn{2}{l}{Gridded residential quarters} & \multicolumn{2}{l}{Accessible suburbia} & \multicolumn{2}{l}{Connected residential neighbourhoods} & \multicolumn{2}{l}{Urban buffer} & \multicolumn{2}{l}{Open sprawl} & \multicolumn{2}{l}{Warehouse/Park land} & \multicolumn{2}{l}{Local urbanity} & \multicolumn{2}{l}{Dense residential neighbourhoods} & \multicolumn{2}{l}{Disconnected suburbia} & \multicolumn{2}{l}{Dense urban neighbourhoods} & \multicolumn{2}{l}{Regional urbanity} & \multicolumn{2}{l}{Metropolitan urbanity} & \multicolumn{2}{l}{Concentrated urbanity} & \multicolumn{2}{l}{Hyper concentrated urbanity} \\
{} &                                               name & rel. importance &                                               name & rel. importance &                                               name & rel. importance &                                          name & rel. importance &                                               name & rel. importance &                                               name & rel. importance &                                         name & rel. importance &                                               name & rel. importance &                                               name & rel. importance &                                              name & rel. importance &                                               name & rel. importance &                                               name & rel. importance &                                               name & rel. importance &                                               name & rel. importance &                                               name & rel. importance &                                               name & rel. importance \\
\midrule
\endfirsthead

\toprule
{} & \multicolumn{2}{l}{Wild countryside} & \multicolumn{2}{l}{Countryside agriculture} & \multicolumn{2}{l}{Gridded residential quarters} & \multicolumn{2}{l}{Accessible suburbia} & \multicolumn{2}{l}{Connected residential neighbourhoods} & \multicolumn{2}{l}{Urban buffer} & \multicolumn{2}{l}{Open sprawl} & \multicolumn{2}{l}{Warehouse/Park land} & \multicolumn{2}{l}{Local urbanity} & \multicolumn{2}{l}{Dense residential neighbourhoods} & \multicolumn{2}{l}{Disconnected suburbia} & \multicolumn{2}{l}{Dense urban neighbourhoods} & \multicolumn{2}{l}{Regional urbanity} & \multicolumn{2}{l}{Metropolitan urbanity} & \multicolumn{2}{l}{Concentrated urbanity} & \multicolumn{2}{l}{Hyper concentrated urbanity} \\
{} &                                               name & rel. importance &                                               name & rel. importance &                                               name & rel. importance &                                          name & rel. importance &                                               name & rel. importance &                                               name & rel. importance &                                         name & rel. importance &                                               name & rel. importance &                                               name & rel. importance &                                              name & rel. importance &                                               name & rel. importance &                                               name & rel. importance &                                               name & rel. importance &                                               name & rel. importance &                                               name & rel. importance &                                               name & rel. importance \\
\midrule
\endhead
\midrule
\multicolumn{33}{r}{{Continued on next page}} \\
\midrule
\endfoot

\bottomrule
\endlastfoot
0 &                    longest axis length of ETC (Q1) &           0.197 &                     covered area ratio of ETC (Q1) &           0.154 &             local closeness of street network (Q3) &           0.095 &       weighted reached enclosures of ETC (Q3) &           0.064 &                    cell alignment of building (Q1) &           0.028 &            area covered by neighbouring cells (Q2) &           0.072 &    reached area by local street network (Q1) &           0.058 &                        elongation of building (Q1) &           0.034 &                         perimeter of building (Q2) &           0.101 &  centroid - corner mean distance of building (Q2) &           0.037 &  local proportion of cul-de-sacs of street netw... &           0.024 &                         perimeter of building (Q2) &           0.107 &  centroid - corner distance deviation of buildi... &           0.115 &      equivalent rectangular index of building (Q2) &           0.111 &                              area of building (Q1) &           0.128 &                     covered area ratio of ETC (Q2) &           0.154 \\
1 &                     covered area ratio of ETC (Q2) &           0.151 &                     covered area ratio of ETC (Q2) &           0.144 &             local closeness of street network (Q2) &           0.046 &  reached ETCs by tessellation contiguity (Q3) &           0.062 &  local proportion of 4-way intersections of str... &           0.023 &                     covered area ratio of ETC (Q2) &           0.050 &   reached area by neighbouring segments (Q1) &           0.034 &   centroid - corner mean distance of building (Q3) &           0.028 &      equivalent rectangular index of building (Q1) &           0.094 &  centroid - corner mean distance of building (Q3) &           0.030 &            local meshedness of street network (Q3) &           0.021 &   centroid - corner mean distance of building (Q2) &           0.084 &   centroid - corner mean distance of building (Q2) &           0.088 &   centroid - corner mean distance of building (Q2) &           0.087 &  Workplace population [Distribution, hotels and... &           0.100 &          Workplace population [Manufacturing] (Q2) &           0.144 \\
2 &                     covered area ratio of ETC (Q1) &           0.146 &                  mean inter-building distance (Q2) &           0.079 &                        perimeter of enclosure (Q1) &           0.044 &  reached area by tessellation contiguity (Q2) &           0.048 &                    cell alignment of building (Q2) &           0.017 &  mean distance to neighbouring nodes of street ... &           0.049 &      area covered by node-attached ETCs (Q2) &           0.024 &                        elongation of building (Q2) &           0.025 &   centroid - corner mean distance of building (Q2) &           0.082 &                             area of building (Q3) &           0.029 &            local meshedness of street network (Q2) &           0.021 &                         perimeter of building (Q3) &           0.082 &                        squareness of building (Q3) &           0.082 &  centroid - corner distance deviation of buildi... &           0.081 &  Workplace population [Financial, real estate, ... &           0.077 &                  Workplace population [Other] (Q2) &           0.102 \\
3 &                                   area of ETC (Q2) &           0.096 &                                   area of ETC (Q2) &           0.073 &                             area of enclosure (Q2) &           0.037 &                              area of ETC (Q2) &           0.045 &                             area of enclosure (Q2) &           0.017 &                     covered area ratio of ETC (Q1) &           0.046 &               covered area ratio of ETC (Q2) &           0.022 &              circular compactness of building (Q1) &           0.020 &                        squareness of building (Q3) &           0.054 &                                   Population (Q3) &           0.028 &      equivalent rectangular index of building (Q1) &           0.020 &                              area of building (Q2) &           0.066 &  Workplace population [Financial, real estate, ... &           0.071 &                           corners of building (Q2) &           0.072 &                  Workplace population [Other] (Q2) &           0.076 &  Workplace population [Distribution, hotels and... &           0.082 \\
4 &          perimeter-weighted neighbours of ETC (Q3) &           0.075 &            area covered by node-attached ETCs (Q2) &           0.067 &             local closeness of street network (Q1) &           0.037 &    reached ETCs by neighbouring segments (Q1) &           0.037 &                            orientation of ETC (Q2) &           0.017 &         reached area by neighbouring segments (Q1) &           0.038 &    local node density of street network (Q3) &           0.019 &  centroid - corner distance deviation of buildi... &           0.018 &                              area of building (Q2) &           0.051 &                        perimeter of building (Q2) &           0.026 &              circular compactness of building (Q1) &           0.019 &                                    Population (Q3) &           0.040 &                         perimeter of building (Q2) &           0.065 &  Workplace population [Financial, real estate, ... &           0.060 &  Workplace population [Distribution, hotels and... &           0.071 &                     covered area ratio of ETC (Q1) &           0.079 \\
5 &         reached area by neighbouring segments (Q1) &           0.049 &  mean distance to neighbouring nodes of street ... &           0.066 &            weighted reached enclosures of ETC (Q3) &           0.032 &    reached ETCs by neighbouring segments (Q2) &           0.030 &      equivalent rectangular index of building (Q1) &           0.016 &                   circular compactness of ETC (Q2) &           0.035 &   reached area by neighbouring segments (Q2) &           0.018 &                         perimeter of building (Q3) &           0.017 &  centroid - corner distance deviation of buildi... &           0.045 &                             area of building (Q2) &           0.023 &                                    Population (Q1) &           0.018 &                        squareness of building (Q3) &           0.039 &                         perimeter of building (Q3) &           0.058 &  Workplace population [Distribution, hotels and... &           0.051 &  Workplace population [Financial, real estate, ... &           0.060 &          Workplace population [Manufacturing] (Q3) &           0.075 \\
6 &       reached area by tessellation contiguity (Q1) &           0.018 &         reached area by neighbouring segments (Q1) &           0.063 &  local proportion of 4-way intersections of str... &           0.021 &     reached ETCs by local street network (Q2) &           0.026 &  local proportion of 4-way intersections of str... &           0.014 &            area covered by neighbouring cells (Q1) &           0.033 &               covered area ratio of ETC (Q1) &           0.018 &                       width of street profile (Q2) &           0.017 &  Workplace population [Financial, real estate, ... &           0.044 &                       perimeter of enclosure (Q1) &           0.021 &                        elongation of building (Q2) &           0.016 &  centroid - corner distance deviation of buildi... &           0.034 &                              area of building (Q2) &           0.050 &                         perimeter of building (Q2) &           0.047 &          Workplace population [Manufacturing] (Q2) &           0.055 &   centroid - corner mean distance of building (Q2) &           0.070 \\
7 &                                   area of ETC (Q3) &           0.016 &       Land cover [Discontinuous urban fabric] (Q2) &           0.055 &            area covered by node-attached ETCs (Q1) &           0.019 &     perimeter-weighted neighbours of ETC (Q1) &           0.024 &                        perimeter of enclosure (Q1) &           0.014 &         buildings per meter of street segment (Q2) &           0.032 &                       area of enclosure (Q2) &           0.017 &              circular compactness of building (Q2) &           0.016 &  Workplace population [Distribution, hotels and... &           0.035 &                     orientation of enclosure (Q2) &           0.018 &         reached area by neighbouring segments (Q2) &           0.016 &  Workplace population [Financial, real estate, ... &           0.029 &  Workplace population [Distribution, hotels and... &           0.049 &                        squareness of building (Q3) &           0.039 &                         perimeter of building (Q2) &           0.047 &                         perimeter of building (Q2) &           0.055 \\
8 &  mean distance between neighbouring buildings (Q2) &           0.015 &          perimeter-weighted neighbours of ETC (Q2) &           0.022 &            area covered by node-attached ETCs (Q2) &           0.018 &  reached area by tessellation contiguity (Q1) &           0.023 &  local proportion of cul-de-sacs of street netw... &           0.014 &       reached area by tessellation contiguity (Q1) &           0.030 &  compactness-weighted axis of enclosure (Q3) &           0.017 &       reached area by tessellation contiguity (Q1) &           0.016 &                         perimeter of building (Q3) &           0.034 &                        perimeter of building (Q3) &           0.017 &            area covered by edge-attached ETCs (Q3) &           0.016 &      equivalent rectangular index of building (Q1) &           0.018 &                           corners of building (Q3) &           0.029 &  Workplace population [Financial, real estate, ... &           0.030 &   centroid - corner mean distance of building (Q2) &           0.045 &                    openness of street profile (Q2) &           0.031 \\
9 &                  mean inter-building distance (Q2) &           0.011 &                    longest axis length of ETC (Q2) &           0.021 &            weighted reached enclosures of ETC (Q2) &           0.017 &     reached ETCs by local street network (Q1) &           0.020 &                      orientation of enclosure (Q1) &           0.013 &            area covered by node-attached ETCs (Q3) &           0.028 &                             area of ETC (Q2) &           0.016 &                         perimeter of building (Q2) &           0.015 &                              area of building (Q1) &           0.023 &                            area of enclosure (Q1) &           0.015 &              circular compactness of building (Q2) &           0.015 &                  Workplace population [Other] (Q2) &           0.016 &  centroid - corner distance deviation of buildi... &           0.021 &   centroid - corner mean distance of building (Q1) &           0.019 &        Land cover [Non-irrigated arable land] (Q1) &           0.026 &                                          NDVI (Q3) &           0.027 \\
\end{longtable}

type_imp.iloc[:10].round(3).T
0 1 2 3 4 5 6 7 8 9
Wild countryside name longest axis length of ETC (Q1) covered area ratio of ETC (Q2) covered area ratio of ETC (Q1) area of ETC (Q2) perimeter-weighted neighbours of ETC (Q3) reached area by neighbouring segments (Q1) reached area by tessellation contiguity (Q1) area of ETC (Q3) mean distance between neighbouring buildings (Q2) mean inter-building distance (Q2)
rel. importance 0.197 0.151 0.146 0.096 0.075 0.049 0.018 0.016 0.015 0.011
Countryside agriculture name covered area ratio of ETC (Q1) covered area ratio of ETC (Q2) mean inter-building distance (Q2) area of ETC (Q2) area covered by node-attached ETCs (Q2) mean distance to neighbouring nodes of street ... reached area by neighbouring segments (Q1) Land cover [Discontinuous urban fabric] (Q2) perimeter-weighted neighbours of ETC (Q2) longest axis length of ETC (Q2)
rel. importance 0.154 0.144 0.079 0.073 0.067 0.066 0.063 0.055 0.022 0.021
Gridded residential quarters name local closeness of street network (Q3) local closeness of street network (Q2) perimeter of enclosure (Q1) area of enclosure (Q2) local closeness of street network (Q1) weighted reached enclosures of ETC (Q3) local proportion of 4-way intersections of str... area covered by node-attached ETCs (Q1) area covered by node-attached ETCs (Q2) weighted reached enclosures of ETC (Q2)
rel. importance 0.095 0.046 0.044 0.037 0.037 0.032 0.021 0.019 0.018 0.017
Accessible suburbia name weighted reached enclosures of ETC (Q3) reached ETCs by tessellation contiguity (Q3) reached area by tessellation contiguity (Q2) area of ETC (Q2) reached ETCs by neighbouring segments (Q1) reached ETCs by neighbouring segments (Q2) reached ETCs by local street network (Q2) perimeter-weighted neighbours of ETC (Q1) reached area by tessellation contiguity (Q1) reached ETCs by local street network (Q1)
rel. importance 0.064 0.062 0.048 0.045 0.037 0.03 0.026 0.024 0.023 0.02
Connected residential neighbourhoods name cell alignment of building (Q1) local proportion of 4-way intersections of str... cell alignment of building (Q2) area of enclosure (Q2) orientation of ETC (Q2) equivalent rectangular index of building (Q1) local proportion of 4-way intersections of str... perimeter of enclosure (Q1) local proportion of cul-de-sacs of street netw... orientation of enclosure (Q1)
rel. importance 0.028 0.023 0.017 0.017 0.017 0.016 0.014 0.014 0.014 0.013
Urban buffer name area covered by neighbouring cells (Q2) covered area ratio of ETC (Q2) mean distance to neighbouring nodes of street ... covered area ratio of ETC (Q1) reached area by neighbouring segments (Q1) circular compactness of ETC (Q2) area covered by neighbouring cells (Q1) buildings per meter of street segment (Q2) reached area by tessellation contiguity (Q1) area covered by node-attached ETCs (Q3)
rel. importance 0.072 0.05 0.049 0.046 0.038 0.035 0.033 0.032 0.03 0.028
Open sprawl name reached area by local street network (Q1) reached area by neighbouring segments (Q1) area covered by node-attached ETCs (Q2) covered area ratio of ETC (Q2) local node density of street network (Q3) reached area by neighbouring segments (Q2) covered area ratio of ETC (Q1) area of enclosure (Q2) compactness-weighted axis of enclosure (Q3) area of ETC (Q2)
rel. importance 0.058 0.034 0.024 0.022 0.019 0.018 0.018 0.017 0.017 0.016
Warehouse/Park land name elongation of building (Q1) centroid - corner mean distance of building (Q3) elongation of building (Q2) circular compactness of building (Q1) centroid - corner distance deviation of buildi... perimeter of building (Q3) width of street profile (Q2) circular compactness of building (Q2) reached area by tessellation contiguity (Q1) perimeter of building (Q2)
rel. importance 0.034 0.028 0.025 0.02 0.018 0.017 0.017 0.016 0.016 0.015
Local urbanity name perimeter of building (Q2) equivalent rectangular index of building (Q1) centroid - corner mean distance of building (Q2) squareness of building (Q3) area of building (Q2) centroid - corner distance deviation of buildi... Workplace population [Financial, real estate, ... Workplace population [Distribution, hotels and... perimeter of building (Q3) area of building (Q1)
rel. importance 0.101 0.094 0.082 0.054 0.051 0.045 0.044 0.035 0.034 0.023
Dense residential neighbourhoods name centroid - corner mean distance of building (Q2) centroid - corner mean distance of building (Q3) area of building (Q3) Population (Q3) perimeter of building (Q2) area of building (Q2) perimeter of enclosure (Q1) orientation of enclosure (Q2) perimeter of building (Q3) area of enclosure (Q1)
rel. importance 0.037 0.03 0.029 0.028 0.026 0.023 0.021 0.018 0.017 0.015
Disconnected suburbia name local proportion of cul-de-sacs of street netw... local meshedness of street network (Q3) local meshedness of street network (Q2) equivalent rectangular index of building (Q1) circular compactness of building (Q1) Population (Q1) elongation of building (Q2) reached area by neighbouring segments (Q2) area covered by edge-attached ETCs (Q3) circular compactness of building (Q2)
rel. importance 0.024 0.021 0.021 0.02 0.019 0.018 0.016 0.016 0.016 0.015
Dense urban neighbourhoods name perimeter of building (Q2) centroid - corner mean distance of building (Q2) perimeter of building (Q3) area of building (Q2) Population (Q3) squareness of building (Q3) centroid - corner distance deviation of buildi... Workplace population [Financial, real estate, ... equivalent rectangular index of building (Q1) Workplace population [Other] (Q2)
rel. importance 0.107 0.084 0.082 0.066 0.04 0.039 0.034 0.029 0.018 0.016
Regional urbanity name centroid - corner distance deviation of buildi... centroid - corner mean distance of building (Q2) squareness of building (Q3) Workplace population [Financial, real estate, ... perimeter of building (Q2) perimeter of building (Q3) area of building (Q2) Workplace population [Distribution, hotels and... corners of building (Q3) centroid - corner distance deviation of buildi...
rel. importance 0.115 0.088 0.082 0.071 0.065 0.058 0.05 0.049 0.029 0.021
Metropolitan urbanity name equivalent rectangular index of building (Q2) centroid - corner mean distance of building (Q2) centroid - corner distance deviation of buildi... corners of building (Q2) Workplace population [Financial, real estate, ... Workplace population [Distribution, hotels and... perimeter of building (Q2) squareness of building (Q3) Workplace population [Financial, real estate, ... centroid - corner mean distance of building (Q1)
rel. importance 0.111 0.087 0.081 0.072 0.06 0.051 0.047 0.039 0.03 0.019
Concentrated urbanity name area of building (Q1) Workplace population [Distribution, hotels and... Workplace population [Financial, real estate, ... Workplace population [Other] (Q2) Workplace population [Distribution, hotels and... Workplace population [Financial, real estate, ... Workplace population [Manufacturing] (Q2) perimeter of building (Q2) centroid - corner mean distance of building (Q2) Land cover [Non-irrigated arable land] (Q1)
rel. importance 0.128 0.1 0.077 0.076 0.071 0.06 0.055 0.047 0.045 0.026
Hyper concentrated urbanity name covered area ratio of ETC (Q2) Workplace population [Manufacturing] (Q2) Workplace population [Other] (Q2) Workplace population [Distribution, hotels and... covered area ratio of ETC (Q1) Workplace population [Manufacturing] (Q3) centroid - corner mean distance of building (Q2) perimeter of building (Q2) openness of street profile (Q2) NDVI (Q3)
rel. importance 0.154 0.144 0.102 0.082 0.079 0.075 0.07 0.055 0.031 0.027 
type_imp.to_excel("importances_type.xlsx")