Population by commune

[1]:

from pynsee.geodata import get_geodata_list, get_geodata, GeoFrDataFrame

import math
import geopandas as gpd
import pandas as pd
from pandas.api.types import CategoricalDtype
import matplotlib.cm as cm
import matplotlib.pyplot as plt
import descartes

[2]:

import logging
import sys
logging.basicConfig(stream=sys.stdout,
                    level=logging.INFO,
                    format="%(message)s")

[3]:

# get geographical data list
geodata_list = get_geodata_list()
# get departments geographical limits
com = get_geodata('ADMINEXPRESS-COG-CARTO.LATEST:commune')

Previously saved data has been used:
/home/onyxia/.cache/pynsee/pynsee/2ac583be54e866b2b1b30fb0113c1fd0
Set update=True to get the most up-to-date data
Previously saved data has been used:
/home/onyxia/.cache/pynsee/pynsee/a4f589755961ab3aa6bdff99605334d1
Set update=True to get the most up-to-date data

[4]:

mapcom = gpd.GeoDataFrame(com).set_crs("EPSG:3857")

mapcom = mapcom.to_crs(epsg=3035)
mapcom["area"] = mapcom['geometry'].area / 10**6
mapcom = mapcom.to_crs(epsg=3857)

mapcom['REF_AREA'] = 'D' + mapcom['insee_dep']
mapcom['density'] = mapcom['population'] / mapcom['area']

[5]:

mapcom = GeoFrDataFrame(mapcom)
mapcom = mapcom.translate(departement = ['971', '972', '974', '973', '976'],
                          factor = [1.5, 1.5, 1.5, 0.35, 1.5])

[6]:

mapcom = mapcom.zoom(departement = ["75","92", "93", "91", "77", "78", "95", "94"],
                     factor=1.5, startAngle = math.pi * (1 - 3 * 1/9))
mapcom

[6]:
id nom nom_m insee_com statut population insee_can insee_arr insee_dep insee_reg siren_epci geometry bbox crsCoord area REF_AREA density
0 COMMUNE_0000001169858680 Basse-Terre BASSE-TERRE 97105 Préfecture de région 9892 06 1 971 01 249710070 MULTIPOLYGON (((-750724.8169219699 6268681.730... [-6873564.10795631, 1803062.14507434, -6869802... EPSG:3857 4.472072 D971 2211.950239
1 COMMUNE_0000001169858685 Saint-Claude SAINT-CLAUDE 97124 Commune simple 10506 06 1 971 01 249710070 MULTIPOLYGON (((-747693.5502122547 6266066.185... [-6871849.64839863, 1804329.75663203, -6863926... EPSG:3857 34.403608 D971 305.374943
2 COMMUNE_0000001169858684 Capesterre-Belle-Eau CAPESTERRE-BELLE-EAU 97107 Commune simple 17628 07 1 971 01 249710070 MULTIPOLYGON (((-742954.5199814234 6282205.684... [-6866643.13408756, 1803123.47210352, -6852304... EPSG:3857 103.037919 D971 171.082648
3 COMMUNE_0000001169858692 Capesterre-de-Marie-Galante CAPESTERRE-DE-MARIE-GALANTE 97108 Commune simple 3247 10 2 971 01 249710047 MULTIPOLYGON (((-666227.3979513757 6260492.339... [-6819904.02215333, 1789762.25363456, -6812187... EPSG:3857 46.124250 D971 70.396809
4 COMMUNE_0000001169858666 Saint-Louis SAINT-LOUIS 97126 Commune simple 2462 10 2 971 01 249710047 MULTIPOLYGON (((-666227.3979513757 6260492.339... [-6826466.0858777, 1795861.91154973, -6815342.... EPSG:3857 57.347426 D971 42.931308
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
36208 COMMUNE_0000000009734470 Dhuisy DHUISY 77157 Commune simple 317 06 1 77 11 247700065 MULTIPOLYGON (((-351625.76655622484 6618194.16... [349321.12599156, 6276714.73065152, 355190.946... EPSG:3857 8.149347 D77 38.898824
36209 COMMUNE_0000000009734981 Mary-sur-Marne MARY-SUR-MARNE 77280 Commune simple 1095 06 1 77 11 247700065 MULTIPOLYGON (((-373298.8244794502 6618451.969... [335409.61945441, 6276030.38893351, 339858.746... EPSG:3857 2.233051 D77 490.360507
36210 COMMUNE_0000000009734468 Vendrest VENDREST 77490 Commune simple 689 06 1 77 11 247700065 MULTIPOLYGON (((-361891.457866755 6622609.1719... [341330.98775089, 6278825.84895588, 350904.847... EPSG:3857 17.692283 D77 38.943532
36211 COMMUNE_0000000009734980 Ocquerre OCQUERRE 77343 Commune simple 386 06 1 77 11 247700065 MULTIPOLYGON (((-364995.7685882246 6618752.634... [337038.06572493, 6277160.42905055, 343734.463... EPSG:3857 10.165914 D77 37.970024
36212 COMMUNE_0000000009734978 Cocherel COCHEREL 77120 Commune simple 631 06 1 77 11 247700065 MULTIPOLYGON (((-352471.63668425917 6618618.26... [340986.98316224, 6275520.45955761, 350843.596... EPSG:3857 8.296800 D77 76.053415

36213 rows × 17 columns

[7]:

mapplot = gpd.GeoDataFrame(mapcom)
mapplot.loc[mapplot.density < 40, 'range'] = "< 40"
mapplot.loc[mapplot.density >= 20000, 'range'] = "> 20 000"

density_ranges = [40, 80, 100, 120, 150, 200, 250, 400, 600, 1000, 2000, 5000, 10000, 20000]
list_ranges = []
list_ranges.append( "< 40")

for i in range(len(density_ranges)-1):
    min_range = density_ranges[i]
    max_range = density_ranges[i+1]
    range_string = "[{}, {}[".format(min_range, max_range)
    mapplot.loc[(mapplot.density >= min_range) & (mapplot.density < max_range), 'range'] = range_string
    list_ranges.append(range_string)

list_ranges.append("> 20 000")

mapplot['range'] = mapplot['range'].astype(CategoricalDtype(categories=list_ranges, ordered=True))

[8]:

fig, ax = plt.subplots(1,1,figsize=[15,15])
mapplot.plot(column='range', cmap=cm.viridis,
    legend=True, ax=ax,
    legend_kwds={'bbox_to_anchor': (1.1, 0.8),
                 'title':'density per km2'})
ax.set_axis_off()
ax.set(title='Distribution of population in France')
plt.show()

fig.savefig('pop_france.svg',
            format='svg', dpi=1200,
            bbox_inches = 'tight',
            pad_inches = 0)
../_images/examples_example_population_map_com_8_0.png