Contents

Geographical distribution and coverage of signature types

import xarray
import rioxarray
import numpy
import matplotlib.pyplot as plt

signatures = rioxarray.open_rasterio("../../urbangrammar_samba/spatial_signatures/signatures/signatures_raster.tif")

signatures

<xarray.DataArray (band: 1, y: 12150, x: 6505)>
array([[[nan, nan, ..., nan, nan],
        [nan, nan, ..., nan, nan],
        ...,
        [nan, nan, ..., nan, nan],
        [nan, nan, ..., nan, nan]]])
Coordinates:
  * band         (band) int64 1
  * y            (y) float64 1.22e+06 1.22e+06 1.22e+06 ... 5.45e+03 5.35e+03
  * x            (x) float64 5.563e+03 5.663e+03 ... 6.559e+05 6.56e+05
    spatial_ref  int64 0
Attributes:
    _FillValue:    nan
    scale_factor:  1.0
    add_offset:    0.0
    long_name:     signature_type
    grid_mapping:  spatial_ref
unique = numpy.unique(signatures[0])
unique = unique[~numpy.isnan(unique)]

unique

array([ 0., 10., 20., 21., 22., 30., 40., 50., 60., 70., 80., 90., 91.,
       92., 93., 94., 95., 96., 97., 98.])

outliers = [93, 96, 97, 98]
unique = unique[~numpy.isin(unique, outliers)]

total_by_row = signatures[0].count("x")
total_by_col = signatures[0].count("y")

types = {
    0: "Countryside agriculture",
    10: "Accessible suburbia",
    30: "Open sprawl",
    40: "Wild countryside",
    50: "Warehouse/Park land",
    60: "Gridded residential quarters",
    70: "Urban buffer",
    80: "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",
}

proportion_by_lat = {}
proportion_by_lon = {}

for label in unique:
    proportion_by_lat[types[label]] = ((signatures[0] == label).sum("x") / total_by_row)
    proportion_by_lon[types[label]] = ((signatures[0] == label).sum("y") / total_by_col)

fig, axs = plt.subplots(4, 4, figsize=(20, 12))

for label, ax in zip(proportion_by_lat, axs.flatten()):
    proportion_by_lat[label].plot(ax=ax)
    ax.set_title(label)
plt.tight_layout()
../_images/geog_distribution_10_0.png 
fig, axs = plt.subplots(4, 4, figsize=(20, 12))

for label, ax in zip(proportion_by_lon, axs.flatten()):
    proportion_by_lon[label].plot(ax=ax)
    ax.set_title(label)
plt.tight_layout()
../_images/geog_distribution_11_0.png

Coverage

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

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/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",
}

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

signatures = signatures.dropna()

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()}

signatures["area"] = signatures.area
types_sum = signatures[["area", "signature_type"]].groupby("signature_type").sum()

types_sum
area
signature_type
Accessible suburbia 2.244586e+09
Concentrated urbanity 7.883216e+06
Connected residential neighbourhoods 5.654034e+08
Countryside agriculture 9.385615e+10
Dense residential neighbourhoods 9.572622e+08
Dense urban neighbourhoods 5.706291e+08
Disconnected suburbia 7.089617e+08
Gridded residential quarters 2.612541e+08
Hyper concentrated urbanity 2.293596e+06
Local urbanity 2.311573e+08
Metropolitan urbanity 1.658261e+07
Open sprawl 5.081598e+09
Regional urbanity 7.643967e+07
Urban buffer 3.158887e+10
Warehouse/Park land 2.462472e+09
Wild countryside 9.130631e+10 
fig, ax = plt.subplots(figsize=(12, 12))
squarify.plot(sizes=types_sum.area, pad=False, ax=ax, color=list(symbology.values()))
ax.axis('off')

(0.0, 100.0, 0.0, 100.0)
../_images/geog_distribution_20_1.png 
types_sum["color"] = types_sum.index.map(symbology)

types_sum
area color
signature_type
Accessible suburbia 2.244586e+09 (0.5625, 0.640625, 0.4921875)
Concentrated urbanity 7.883216e+06 (0.19921875, 0.203125, 0.1953125)
Connected residential neighbourhoods 5.654034e+08 (0.58203125, 0.3984375, 0.4296875)
Countryside agriculture 9.385615e+10 (0.9475259828670731, 0.9021947232500418, 0.782...
Dense residential neighbourhoods 9.572622e+08 (0.405436326872467, 0.5568241504426759, 0.6493...
Dense urban neighbourhoods 5.706291e+08 (0.9375, 0.78125, 0.34375)
Disconnected suburbia 7.089617e+08 (0.9408069995844273, 0.8211427621191237, 0.488...
Gridded residential quarters 2.612541e+08 (0.8956450438496885, 0.7949476416458632, 0.782...
Hyper concentrated urbanity 2.293596e+06 (0.6550082934095629, 0.716277287243688, 0.6001...
Local urbanity 2.311573e+08 (0.23046875, 0.4296875, 0.55078125)
Metropolitan urbanity 1.658261e+07 (0.73828125, 0.35546875, 0.30859375)
Open sprawl 5.081598e+09 (0.8429158144969133, 0.6476876988954169, 0.623...
Regional urbanity 7.643967e+07 (0.6718003015294096, 0.5329645862558573, 0.556...
Urban buffer 3.158887e+10 (0.7609260068673207, 0.8151335354690646, 0.849...
Warehouse/Park land 2.462472e+09 (0.7629942586386511, 0.6696270230872043, 0.685...
Wild countryside 9.130631e+10 (0.8429616514480401, 0.8699835216435621, 0.819... 
types_sum = types_sum.sort_values("area", ascending=False)
ax = types_sum.area.plot.bar(color=types_sum.color)
ax.set_yscale("log")
../_images/geog_distribution_23_0.png 
fig, ax = plt.subplots(figsize=(12, 12))
types_sum.area.plot.pie(colors=types_sum.color, labels=types_sum.index, ax=ax, normalize=True)
ax.axis("off")
ax.add_artist(plt.Circle((0,0), .7, color="w"))
../_images/geog_distribution_24_0.png