Composition of signatures within TTWA boundaries¶

import geopandas
import tobler

tobler.__version__

https://services1.arcgis.com/ESMARspQHYMw9BZ9/ArcGIS/rest/services/TTWA_2011_UK/FeatureServer

url = "https://services1.arcgis.com/ESMARspQHYMw9BZ9/arcgis/rest/services/TTWA_2011_UK/FeatureServer/0/query?where=1%3D1&objectIds=&time=&geometry=&geometryType=esriGeometryPolygon&inSR=&spatialRel=esriSpatialRelIntersects&resultType=none&distance=0.0&units=esriSRUnit_Meter&returnGeodetic=false&outFields=&returnGeometry=true&returnCentroid=false&featureEncoding=esriDefault&multipatchOption=xyFootprint&maxAllowableOffset=&geometryPrecision=&outSR=&datumTransformation=&applyVCSProjection=false&returnIdsOnly=false&returnUniqueIdsOnly=false&returnCountOnly=false&returnExtentOnly=false&returnQueryGeometry=false&returnDistinctValues=false&cacheHint=false&orderByFields=&groupByFieldsForStatistics=&outStatistics=&having=&resultOffset=&resultRecordCount=&returnZ=false&returnM=false&returnExceededLimitFeatures=true&quantizationParameters=&sqlFormat=none&f=pgeojson&token="

ttwa = geopandas.read_file(url)

ttwa

	TTWA11CD	geometry
0	E30000004	POLYGON ((-1.34865 53.58333, -1.32673 53.55813...
1	E30000018	POLYGON ((-1.93086 53.95027, -1.89833 53.92398...
2	E30000029	POLYGON ((-1.73679 53.70811, -1.73128 53.68037...
3	E30000039	POLYGON ((-2.00451 54.17288, -1.99254 54.15528...
4	E30000046	POLYGON ((-2.26489 50.75912, -2.24239 50.75563...
...	...	...
223	W22000030	MULTIPOLYGON (((-4.77992 52.76561, -4.80242 52...
224	W22000031	POLYGON ((-3.14509 53.26082, -3.17735 53.24382...
225	W22000032	POLYGON ((-3.47007 51.82850, -3.47633 51.80947...
226	W22000033	POLYGON ((-3.48307 52.86552, -3.50541 52.84191...
227	W22000034	POLYGON ((-2.72684 52.98327, -2.73532 52.97002...

228 rows × 2 columns

# drop NI
ttwa = ttwa[~ttwa.TTWA11CD.str.startswith("N")]

ttwa.plot()

<AxesSubplot:>

ttwa.to_parquet("../../urbangrammar_samba/spatial_signatures/esda/ttwa.pq")

<ipython-input-30-01f59cdfba9b>:1: UserWarning: this is an initial implementation of Parquet/Feather file support and associated metadata.  This is tracking version 0.1.0 of the metadata specification at https://github.com/geopandas/geo-arrow-spec

This metadata specification does not yet make stability promises.  We do not yet recommend using this in a production setting unless you are able to rewrite your Parquet/Feather files.

To further ignore this warning, you can do: 
import warnings; warnings.filterwarnings('ignore', message='.*initial implementation of Parquet.*')
  ttwa.to_parquet("../../urbangrammar_samba/spatial_signatures/esda/ttwa.pq")

signatures = geopandas.read_parquet("../../urbangrammar_samba/spatial_signatures/signatures/signatures_combined_levels_simplified.pq")

types = {
    "0_0": "Countryside agriculture",
    "1_0": "Accessible suburbia",
    "3_0": "Open sprawl",
    "4_0": "Wild countryside",
    "5_0": "Warehouse 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": "Distilled urbanity",
    "9_2": "Regional urbanity",
    "9_4": "Metropolitan urbanity",
    "9_5": "Hyper distilled urbanity",
}

signatures["signature_type"] = signatures["signature_type"].map(types)

signatures = signatures[["signature_type", "geometry"]].dropna()

signatures

	signature_type	geometry
0	Countryside agriculture	POLYGON ((62219.999 798499.999, 62109.999 7985...
1	Countryside agriculture	POLYGON ((63507.682 796515.168, 63471.096 7965...
2	Countryside agriculture	POLYGON ((65953.174 802246.171, 65523.864 8023...
3	Countryside agriculture	POLYGON ((67297.740 803435.799, 67220.290 8034...
4	Countryside agriculture	POLYGON ((75760.000 852669.999, 75699.999 8527...
...	...	...
96687	Hyper distilled urbanity	POLYGON ((528602.733 181135.451, 528572.629 18...
96688	Hyper distilled urbanity	POLYGON ((528643.907 181217.443, 528632.000 18...
96689	Hyper distilled urbanity	POLYGON ((528734.583 181316.821, 528722.252 18...
96690	Hyper distilled urbanity	POLYGON ((530336.433 181039.754, 530301.430 18...
96691	Hyper distilled urbanity	POLYGON ((581275.000 184964.001, 581431.000 18...

96689 rows × 2 columns

%%time
interpolated = tobler.area_weighted.area_interpolate(signatures, ttwa.to_crs(signatures.crs), categorical_variables=["signature_type"], n_jobs=-1)

CPU times: user 5.54 s, sys: 336 ms, total: 5.88 s
Wall time: 56.1 s

interpolated.columns = [c[15:] for c in interpolated.columns]

interpolated["TTWA11CD"] = ttwa.TTWA11CD.values

interpolated

	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	Metropolitan urbanity	Hyper distilled urbanity		TTWA11CD
0	0.218571	0.043969	0.176920	0.079282	1.235609e-03	0.000215	0.467117	0.003725	0.006016	7.620440e-04	0.002179	0.000008	0.0	0.000000	0.00000	0.0	POLYGON ((443219.518 409835.037, 444696.854 40...	E30000004
1	0.166363	0.045175	0.229543	0.030462	8.473206e-03	0.002243	0.431080	0.008993	0.032779	1.287391e-02	0.023451	0.008330	0.0	0.000230	0.00000	0.0	POLYGON ((404635.122 450465.360, 406774.289 44...	E30000018
2	0.504775	0.008234	0.078947	0.115431	7.741750e-03	0.000413	0.254977	0.004334	0.018119	1.105400e-03	0.005319	0.000605	0.0	0.000000	0.00000	0.0	POLYGON ((417471.523 423552.896, 417846.958 42...	E30000029
3	0.333012	0.000010	0.002617	0.626129	6.710558e-04	0.000000	0.036716	0.000069	0.000567	4.414055e-07	0.000182	0.000000	0.0	0.000000	0.00000	0.0	POLYGON ((399803.434 475232.566, 400584.619 47...	E30000039
4	0.802061	0.003388	0.021588	0.029028	4.503935e-03	0.000505	0.122362	0.000286	0.004367	4.048234e-04	0.000823	0.000234	0.0	0.000074	0.00004	0.0	POLYGON ((381412.611 95536.380, 382998.301 951...	E30000046
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
213	0.293667	0.000214	0.000676	0.634816	1.737027e-05	0.000003	0.031056	0.000000	0.000197	1.286061e-05	0.000000	0.000000	0.0	0.000000	0.00000	0.0	MULTIPOLYGON (((212541.096 322292.766, 210936....	W22000030
214	0.336676	0.009619	0.006018	0.554385	3.207763e-04	0.000965	0.089908	0.000154	0.000000	2.772906e-04	0.000000	0.000000	0.0	0.000000	0.00000	0.0	POLYGON ((323712.675 374369.204, 321530.068 37...	W22000031
215	0.284508	0.017178	0.035579	0.107998	1.688731e-02	0.007048	0.512841	0.001141	0.003019	1.465991e-03	0.001280	0.000994	0.0	0.000110	0.00000	0.0	POLYGON ((298789.856 215454.183, 298315.838 21...	W22000032
216	0.226124	0.000061	0.000709	0.750838	8.901256e-07	0.000000	0.016346	0.000098	0.000000	0.000000e+00	0.000000	0.000000	0.0	0.000000	0.00000	0.0	POLYGON ((300258.629 330812.406, 298700.069 32...	W22000033
217	0.679714	0.008352	0.019098	0.066124	3.936716e-03	0.000004	0.216473	0.002296	0.002831	4.258767e-04	0.000747	0.000000	0.0	0.000000	0.00000	0.0	POLYGON ((351298.707 343128.550, 350714.368 34...	W22000034

218 rows × 18 columns

import pandas

names = pandas.read_csv("https://opendata.arcgis.com/api/v3/datasets/9ac894d3086641bebcbfa9960895db39_0/downloads/data?format=csv&spatialRefId=4326")

names

	TTWA11CD	TTWA11NM	FID
0	E30000004	Barnsley	1
1	S22000048	Alness and Invergordon	2
2	E30000018	Bradford	3
3	E30000029	Halifax	4
4	E30000039	Skipton	5
...	...	...	...
223	S22000013	Western Isles	224
224	S22000032	Mull and Islay	225
225	S22000035	Orkney Islands	226
226	S22000039	Shetland Islands	227
227	S22000047	Aberdeen	228

228 rows × 3 columns

names.to_parquet("../../urbangrammar_samba/spatial_signatures/esda/ttwa_names.pq")

interpolated = interpolated.merge(names[["TTWA11CD", "TTWA11NM"]], on="TTWA11CD", how="left")

interpolated

	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	Metropolitan urbanity	Hyper distilled urbanity		TTWA11CD	TTWA11NM
0	0.218571	0.043969	0.176920	0.079282	1.235609e-03	0.000215	0.467117	0.003725	0.006016	7.620440e-04	0.002179	0.000008	0.0	0.000000	0.00000	0.0	POLYGON ((443219.518 409835.037, 444696.854 40...	E30000004	Barnsley
1	0.166363	0.045175	0.229543	0.030462	8.473206e-03	0.002243	0.431080	0.008993	0.032779	1.287391e-02	0.023451	0.008330	0.0	0.000230	0.00000	0.0	POLYGON ((404635.122 450465.360, 406774.289 44...	E30000018	Bradford
2	0.504775	0.008234	0.078947	0.115431	7.741750e-03	0.000413	0.254977	0.004334	0.018119	1.105400e-03	0.005319	0.000605	0.0	0.000000	0.00000	0.0	POLYGON ((417471.523 423552.896, 417846.958 42...	E30000029	Halifax
3	0.333012	0.000010	0.002617	0.626129	6.710558e-04	0.000000	0.036716	0.000069	0.000567	4.414055e-07	0.000182	0.000000	0.0	0.000000	0.00000	0.0	POLYGON ((399803.434 475232.566, 400584.619 47...	E30000039	Skipton
4	0.802061	0.003388	0.021588	0.029028	4.503935e-03	0.000505	0.122362	0.000286	0.004367	4.048234e-04	0.000823	0.000234	0.0	0.000074	0.00004	0.0	POLYGON ((381412.611 95536.380, 382998.301 951...	E30000046	Dorchester and Weymouth
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
213	0.293667	0.000214	0.000676	0.634816	1.737027e-05	0.000003	0.031056	0.000000	0.000197	1.286061e-05	0.000000	0.000000	0.0	0.000000	0.00000	0.0	MULTIPOLYGON (((212541.096 322292.766, 210936....	W22000030	Pwllheli and Porthmadog
214	0.336676	0.009619	0.006018	0.554385	3.207763e-04	0.000965	0.089908	0.000154	0.000000	2.772906e-04	0.000000	0.000000	0.0	0.000000	0.00000	0.0	POLYGON ((323712.675 374369.204, 321530.068 37...	W22000031	Rhyl
215	0.284508	0.017178	0.035579	0.107998	1.688731e-02	0.007048	0.512841	0.001141	0.003019	1.465991e-03	0.001280	0.000994	0.0	0.000110	0.00000	0.0	POLYGON ((298789.856 215454.183, 298315.838 21...	W22000032	Swansea
216	0.226124	0.000061	0.000709	0.750838	8.901256e-07	0.000000	0.016346	0.000098	0.000000	0.000000e+00	0.000000	0.000000	0.0	0.000000	0.00000	0.0	POLYGON ((300258.629 330812.406, 298700.069 32...	W22000033	Tywyn and Dolgellau
217	0.679714	0.008352	0.019098	0.066124	3.936716e-03	0.000004	0.216473	0.002296	0.002831	4.258767e-04	0.000747	0.000000	0.0	0.000000	0.00000	0.0	POLYGON ((351298.707 343128.550, 350714.368 34...	W22000034	Wrexham

218 rows × 19 columns

interpolated.columns = ['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',
       'Metropolitan urbanity', 'Hyper distilled urbanity', 'geometry', 'TTWA11CD',
       'TTWA11NM']

interpolated = geopandas.GeoDataFrame(interpolated, crs=27700)

interpolated.to_parquet("../../urbangrammar_samba/spatial_signatures/esda/ttwa_interpolated.pq")

<ipython-input-29-0c6f61fce238>:1: UserWarning: this is an initial implementation of Parquet/Feather file support and associated metadata.  This is tracking version 0.1.0 of the metadata specification at https://github.com/geopandas/geo-arrow-spec

This metadata specification does not yet make stability promises.  We do not yet recommend using this in a production setting unless you are able to rewrite your Parquet/Feather files.

To further ignore this warning, you can do: 
import warnings; warnings.filterwarnings('ignore', message='.*initial implementation of Parquet.*')
  geopandas.GeoDataFrame(interpolated, crs=27700).to_parquet("../../urbangrammar_samba/spatial_signatures/esda/ttwa_interpolated.pq")

pip install squarify

Collecting squarify
  Downloading squarify-0.4.3-py3-none-any.whl (4.3 kB)
Installing collected packages: squarify
Successfully installed squarify-0.4.3
Note: you may need to restart the kernel to use updated packages.

import matplotlib.pyplot as plt
# import squarify
import urbangrammar_graphics as ugg

plot_data = interpolated.set_index("TTWA11NM").T.drop(["geometry", "TTWA11CD"])
plot_data

TTWA11NM	Barnsley	Bradford	Halifax	Skipton	Dorchester and Weymouth	Falmouth	Grantham	Hastings	Hexham	Isle of Wight	...	Cardiff	Cardigan	Colwyn Bay	Llanelli	Merthyr Tydfil	Pwllheli and Porthmadog	Rhyl	Swansea	Tywyn and Dolgellau	Wrexham
Countryside agriculture	0.218571	0.166363	0.504775	0.333012	0.802061	0.814348	0.895776	0.760641	0.094955	0.597957	...	0.195072	0.52362	0.247824	0.31493	0.354032	0.293667	0.336676	0.284508	0.226124	0.679714
Accessible suburbia	0.043969	0.045175	0.008234	0.00001	0.003388	0.001462	0.003265	0.029552	0.0	0.02578	...	0.026886	0.0	0.008341	0.000882	0.001534	0.000214	0.009619	0.017178	0.000061	0.008352
Open sprawl	0.17692	0.229543	0.078947	0.002617	0.021588	0.014442	0.008393	0.015345	0.000812	0.019786	...	0.104492	0.000554	0.006211	0.004573	0.043843	0.000676	0.006018	0.035579	0.000709	0.019098
Wild countryside	0.079282	0.030462	0.115431	0.626129	0.029028	0.026181	0.00005	0.000017	0.897122	0.000098	...	0.0	0.459751	0.668945	0.613634	0.201801	0.634816	0.554385	0.107998	0.750838	0.066124
Warehouse land	0.001236	0.008473	0.007742	0.000671	0.004504	0.000552	0.000426	0.000163	0.000008	0.000908	...	0.034154	0.000037	0.002057	0.000003	0.009063	0.000017	0.000321	0.016887	0.000001	0.003937
Gridded residential quarters	0.000215	0.002243	0.000413	0.0	0.000505	0.003124	0.000075	0.0	0.0	0.006856	...	0.018411	0.0	0.0008	0.001571	0.002301	0.000003	0.000965	0.007048	0.0	0.000004
Urban buffer	0.467117	0.43108	0.254977	0.036716	0.122362	0.094889	0.09007	0.165469	0.007009	0.325959	...	0.577927	0.007025	0.05864	0.059869	0.385135	0.031056	0.089908	0.512841	0.016346	0.216473
Disconnected suburbia	0.003725	0.008993	0.004334	0.000069	0.000286	0.002359	0.000676	0.003295	0.00001	0.000196	...	0.007809	0.000003	0.000006	0.000143	0.00054	0.0	0.000154	0.001141	0.000098	0.002296
Dense residential neighbourhoods	0.006016	0.032779	0.018119	0.000567	0.004367	0.000202	0.001139	0.005219	0.000085	0.003164	...	0.008218	0.0	0.000323	0.000008	0.000775	0.000197	0.0	0.003019	0.0	0.002831
Connected residential neighbourhoods	0.000762	0.012874	0.001105	0.0	0.000405	0.001662	0.000002	0.00368	0.0	0.000155	...	0.008751	0.0	0.002724	0.000003	0.000977	0.000013	0.000277	0.001466	0.0	0.000426
Dense urban neighbourhoods	0.002179	0.023451	0.005319	0.000182	0.000823	0.0	0.000129	0.001775	0.0	0.0	...	0.011502	0.0	0.000005	0.0	0.0	0.0	0.0	0.00128	0.0	0.000747
Local urbanity	0.000008	0.00833	0.000605	0.0	0.000234	0.000201	0.0	0.000069	0.0	0.0	...	0.003291	0.0	0.0	0.0	0.0	0.0	0.0	0.000994	0.0	0.0
Distilled urbanity	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
Regional urbanity	0.0	0.00023	0.0	0.0	0.000074	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.00011	0.0	0.0
Metropolitan urbanity	0.0	0.0	0.0	0.0	0.00004	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
Hyper distilled urbanity	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0

16 rows × 218 columns

cmap = ugg.get_colormap(20, randomize=False)
cols = cmap.colors

symbology = {'0_0': cols[16],
             '1_0': cols[15],
             '3_0': cols[9],
             '4_0': cols[12],
             '5_0': cols[21],
             '6_0': cols[8],
             '7_0': cols[4],
             '8_0': cols[18],
             '2_0': cols[6],
             '2_1': cols[23],
             '2_2': cols[19],
             '9_0': cols[7],
             '9_1': cols[3],
             '9_2': cols[22],
             '9_4': cols[11],
             '9_5': cols[14],
            }
symbology = {types[k]:v for k, v in symbology.items()}

for place in plot_data.columns:
    fig, ax = plt.subplots(figsize=(12, 12))
    squarify.plot(sizes=plot_data[place] + 0.00000001, pad=True, ax=ax, color=list(symbology.values()))
    ax.axis('off')
    plt.savefig(f"../../urbangrammar_samba/spatial_signatures/esda/ttwa_treemaps/{place}.pdf")
    plt.close()

plot_data.T["Wild countryside"].sort_values(ascending=False).iloc[:50].plot.bar(figsize=(18, 7))

<AxesSubplot:xlabel='TTWA11NM'>

for sig in plot_data.index:
    
    ax = interpolated.plot(sig, figsize=(15, 15), legend=True)
    ax.axis('off')
    plt.savefig(f"../../urbangrammar_samba/spatial_signatures/esda/ttwa_maps/{sig}.png")
    plt.close()

fig, axs = plt.subplots(4, 4, figsize=(20, 30))
axs = axs.flatten()
for i, sig in enumerate(plot_data.index):
    interpolated.plot(sig, ax=axs[i], legend=True)
    axs[i].axis("off")
    axs[i].set_title(sig)

ax = plot_data.T.plot.bar(color=list(symbology.values()), stacked=True, figsize=(40, 10), legend=False)

ax = plot_data["Barnsley"].plot.pie(colors=list(symbology.values()), labels=None, normalize=True)
ax.add_artist(plt.Circle((0,0), .7, color="w"))

<matplotlib.patches.Circle at 0x7f833ee4fc10>

plot_data

TTWA11NM	Barnsley	Bradford	Halifax	Skipton	Dorchester and Weymouth	Falmouth	Grantham	Hastings	Hexham	Isle of Wight	...	Cardiff	Cardigan	Colwyn Bay	Llanelli	Merthyr Tydfil	Pwllheli and Porthmadog	Rhyl	Swansea	Tywyn and Dolgellau	Wrexham
Countryside agriculture	0.218571	0.166363	0.504775	0.333012	0.802061	0.814348	0.895776	0.760641	0.094955	0.597957	...	0.195072	0.52362	0.247824	0.31493	0.354032	0.293667	0.336676	0.284508	0.226124	0.679714
Accessible suburbia	0.043969	0.045175	0.008234	0.00001	0.003388	0.001462	0.003265	0.029552	0.0	0.02578	...	0.026886	0.0	0.008341	0.000882	0.001534	0.000214	0.009619	0.017178	0.000061	0.008352
Open sprawl	0.17692	0.229543	0.078947	0.002617	0.021588	0.014442	0.008393	0.015345	0.000812	0.019786	...	0.104492	0.000554	0.006211	0.004573	0.043843	0.000676	0.006018	0.035579	0.000709	0.019098
Wild countryside	0.079282	0.030462	0.115431	0.626129	0.029028	0.026181	0.00005	0.000017	0.897122	0.000098	...	0.0	0.459751	0.668945	0.613634	0.201801	0.634816	0.554385	0.107998	0.750838	0.066124
Warehouse land	0.001236	0.008473	0.007742	0.000671	0.004504	0.000552	0.000426	0.000163	0.000008	0.000908	...	0.034154	0.000037	0.002057	0.000003	0.009063	0.000017	0.000321	0.016887	0.000001	0.003937
Gridded residential quarters	0.000215	0.002243	0.000413	0.0	0.000505	0.003124	0.000075	0.0	0.0	0.006856	...	0.018411	0.0	0.0008	0.001571	0.002301	0.000003	0.000965	0.007048	0.0	0.000004
Urban buffer	0.467117	0.43108	0.254977	0.036716	0.122362	0.094889	0.09007	0.165469	0.007009	0.325959	...	0.577927	0.007025	0.05864	0.059869	0.385135	0.031056	0.089908	0.512841	0.016346	0.216473
Disconnected suburbia	0.003725	0.008993	0.004334	0.000069	0.000286	0.002359	0.000676	0.003295	0.00001	0.000196	...	0.007809	0.000003	0.000006	0.000143	0.00054	0.0	0.000154	0.001141	0.000098	0.002296
Dense residential neighbourhoods	0.006016	0.032779	0.018119	0.000567	0.004367	0.000202	0.001139	0.005219	0.000085	0.003164	...	0.008218	0.0	0.000323	0.000008	0.000775	0.000197	0.0	0.003019	0.0	0.002831
Connected residential neighbourhoods	0.000762	0.012874	0.001105	0.0	0.000405	0.001662	0.000002	0.00368	0.0	0.000155	...	0.008751	0.0	0.002724	0.000003	0.000977	0.000013	0.000277	0.001466	0.0	0.000426
Dense urban neighbourhoods	0.002179	0.023451	0.005319	0.000182	0.000823	0.0	0.000129	0.001775	0.0	0.0	...	0.011502	0.0	0.000005	0.0	0.0	0.0	0.0	0.00128	0.0	0.000747
Local urbanity	0.000008	0.00833	0.000605	0.0	0.000234	0.000201	0.0	0.000069	0.0	0.0	...	0.003291	0.0	0.0	0.0	0.0	0.0	0.0	0.000994	0.0	0.0
Distilled urbanity	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
Regional urbanity	0.0	0.00023	0.0	0.0	0.000074	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.00011	0.0	0.0
Metropolitan urbanity	0.0	0.0	0.0	0.0	0.00004	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
Hyper distilled urbanity	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0

16 rows × 218 columns

fig, axs = plt.subplots(15, 15, figsize=(40, 40))
axs = axs.flatten()
for i, place in enumerate(plot_data.columns):
    plot_data[place].plot.pie(colors=list(symbology.values()), labels=None, ax=axs[i], normalize=True)
    axs[i].axis("off")
    axs[i].add_artist(plt.Circle((0,0), .7, color="w"))
    axs[i].set_title(place)

interpolated = geopandas.read_parquet("../../urbangrammar_samba/spatial_signatures/esda/ttwa_interpolated.pq")

interpolated = interpolated.set_index("TTWA11NM")

plot_data = geopandas.GeoDataFrame(columns=["level", "order", "area", "urbanity", "geometry"], crs=interpolated.crs)

centres = ['Distilled urbanity', 'Metropolitan urbanity', 'Regional urbanity', 'Local urbanity', 'Dense urban neighbourhoods']

centroids = interpolated.centroid
for level, c in enumerate(centres):
    sub = interpolated[interpolated[c] > 0]
    area = sub.area
    sub["sorter"] = sub[c] * area
    sub = sub.sort_values("sorter", ascending=False)
    for i, ix in enumerate(sub.index):
        plot_data.loc[ix] = [level, i, sub["sorter"].loc[ix], c, centroids.loc[ix]]
    interpolated = interpolated.drop(sub.index)

/opt/conda/lib/python3.8/site-packages/geopandas/geodataframe.py:1322: 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(GeoDataFrame, self).__setitem__(key, value)
/opt/conda/lib/python3.8/site-packages/geopandas/geodataframe.py:1322: 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(GeoDataFrame, self).__setitem__(key, value)
/opt/conda/lib/python3.8/site-packages/geopandas/geodataframe.py:1322: 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(GeoDataFrame, self).__setitem__(key, value)
/opt/conda/lib/python3.8/site-packages/geopandas/geodataframe.py:1322: 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(GeoDataFrame, self).__setitem__(key, value)
/opt/conda/lib/python3.8/site-packages/geopandas/geodataframe.py:1322: 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(GeoDataFrame, self).__setitem__(key, value)

plot_data

	level	order	area	urbanity	geometry
London	0	0	7.883216e+06	Distilled urbanity	POINT (538859.548 181287.313)
Birmingham	1	0	1.075148e+06	Metropolitan urbanity	POINT (411486.764 285105.634)
Edinburgh	1	1	6.384964e+05	Metropolitan urbanity	POINT (336480.582 665538.228)
Glasgow	1	2	6.059172e+05	Metropolitan urbanity	POINT (254427.143 665611.252)
Manchester	1	3	5.153730e+05	Metropolitan urbanity	POINT (386884.766 394820.807)
...	...	...	...	...	...
Clacton	4	42	1.356900e+03	Dense urban neighbourhoods	POINT (616999.223 223054.267)
Yeovil	4	43	1.311523e+03	Dense urban neighbourhoods	POINT (353541.196 119693.402)
Kendal	4	44	8.299633e+02	Dense urban neighbourhoods	POINT (352816.440 492003.682)
Worksop and Retford	4	45	6.096959e+02	Dense urban neighbourhoods	POINT (468458.241 381521.949)
Bridport	4	46	1.525966e+02	Dense urban neighbourhoods	POINT (344817.841 97711.329)

152 rows × 5 columns

mapdata = interpolated.set_geometry(interpolated.centroid)

centres = ['Hyper distilled urbanity', 'Distilled urbanity', 'Metropolitan urbanity', 'Regional urbanity', 'Local urbanity', 'Dense urban neighbourhoods']
sizes = pandas.DataFrame()
for centre in centres:
    sizes[centre] = interpolated[centre] * interpolated.area

sizes = sizes.cumsum(axis=1)

fig, ax = plt.subplots(figsize=(16, 16))
scaler = 20000

for i, centre in enumerate(centres[::-1]):
    if centre == 'Dense urban neighbourhoods':
        mapdata.drop("London").plot(markersize=sizes[centre].drop("London") / scaler, ax=ax, zorder=i+10, color=symbology[centre], edgecolor="w")
        mapdata.loc[["London"]].plot(markersize=sizes[centre].loc[["London"]] / scaler, ax=ax, zorder=i, color=symbology[centre], edgecolor="w")
    else:
        mapdata.drop("London").plot(markersize=sizes[centre].drop("London") / scaler, ax=ax, zorder=i+10, color=symbology[centre])
        mapdata.loc[["London"]].plot(markersize=sizes[centre].loc[["London"]] / scaler, ax=ax, zorder=i, color=symbology[centre])

ax.set_axis_off()

fig, axs = plt.subplots(1, 6, figsize=(52, 25))
scaler = 20000
axs = axs.flatten()

for i, centre in enumerate(centres[::-1]):
    mapdata.drop("London").plot(markersize=sizes[centre].drop("London") / scaler, ax=axs[i], zorder=i+10, color=symbology[centre], edgecolor="w")
    mapdata.loc[["London"]].plot(markersize=sizes[centre].loc[["London"]] / scaler, ax=axs[i], zorder=i, color=symbology[centre], edgecolor="w")

    axs[i].set_axis_off()

plot_data

	level	order	area	urbanity	geometry
London	0	0	7.883216e+06	Distilled urbanity	POINT (538859.548 181287.313)
Birmingham	1	0	1.075148e+06	Metropolitan urbanity	POINT (411486.764 285105.634)
Edinburgh	1	1	6.384964e+05	Metropolitan urbanity	POINT (336480.582 665538.228)
Glasgow	1	2	6.059172e+05	Metropolitan urbanity	POINT (254427.143 665611.252)
Manchester	1	3	5.153730e+05	Metropolitan urbanity	POINT (386884.766 394820.807)
...	...	...	...	...	...
Clacton	4	42	1.356900e+03	Dense urban neighbourhoods	POINT (616999.223 223054.267)
Yeovil	4	43	1.311523e+03	Dense urban neighbourhoods	POINT (353541.196 119693.402)
Kendal	4	44	8.299633e+02	Dense urban neighbourhoods	POINT (352816.440 492003.682)
Worksop and Retford	4	45	6.096959e+02	Dense urban neighbourhoods	POINT (468458.241 381521.949)
Bridport	4	46	1.525966e+02	Dense urban neighbourhoods	POINT (344817.841 97711.329)

152 rows × 5 columns

mapdata[["level", "order"]] = plot_data[["level", "order"]]

mapdata.order.max()

mapdata.loc[mapdata.level == 1, "order"] = mapdata.loc[mapdata.level == 1, "order"] * 5

mapdata.loc[mapdata.level == 2, "order"] = mapdata.loc[mapdata.level == 2, "order"] * 2.5
mapdata.loc[mapdata.level == 4, "order"] = mapdata.loc[mapdata.level == 4, "order"] * 1.5

for l in mapdata.level.dropna().unique():
    mapdata.loc[mapdata.level == l, "x"] = (mapdata.loc[mapdata.level == l, "order"] - mapdata.loc[mapdata.level == l, "order"].mean()) / mapdata.loc[mapdata.level == l, "order"].std()

mapdata.loc["London", 'x'] = 0 

fig, ax = plt.subplots(figsize=(10, 10))
scaler = 30000

for i, centre in enumerate(centres[::-1]):
    ax.scatter(mapdata.x * 10, 1 - mapdata.level * 10, s=sizes[centre] / scaler, zorder=i+10, color=symbology[centre])

ax.set_axis_off()
# ax.axis('equal')
ax.set_ylim(-42, 7)

(-42.0, 7.0)

Urban Grammar AI | WP2 - Spatial Signatures

Composition of signatures within TTWA boundaries¶