Plotting territorial income distribution and other demographic indicators in the city of Barcelona with Python

In this Notebook we explain how to implement Python codes to plot choropleth maps of territorial income distribution and other demographic indicators in the city of Barcelona obtained from Barcelona open data repository using Python's Folium library.

The first dataset that we are going to analyze is the Renta Familiar Disponible (RFD) index dataset, this dataset contains the Disposable Family Income Index by Neighborhood, the most recent one is from year 2017.

import folium
import pandas as pd
import numpy as np

renta = pd.read_csv("2017_distribucio_territorial_renda_familiar.csv")
renta['NOM'] = renta['Nom_Barri'] # add new column for later ploting the choropleth map
renta['NOM'][renta['NOM']=='el Poble Sec'] = 'el Poble-sec' # make sure to be consistent with JSON file
renta

/var/folders/03/gv3k10bj7135k97m7mbkq26h0000gn/T/ipykernel_8504/3333412267.py:7: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  renta['NOM'][renta['NOM']=='el Poble Sec'] = 'el Poble-sec' # make sure to be consistent with JSON file

	Any	Codi_Districte	Nom_Districte	Codi_Barri	Nom_Barri	Població	Índex RFD Barcelona = 100	NOM
0	2017	1	Ciutat Vella	1	el Raval	47986	71.2	el Raval
1	2017	1	Ciutat Vella	2	el Barri Gòtic	16240	106.1	el Barri Gòtic
2	2017	1	Ciutat Vella	3	la Barceloneta	15101	79.6	la Barceloneta
3	2017	1	Ciutat Vella	4	Sant Pere, Santa Caterina i la Ribera	22923	99.4	Sant Pere, Santa Caterina i la Ribera
4	2017	2	Eixample	5	el Fort Pienc	32048	106.5	el Fort Pienc
...	...	...	...	...	...	...	...	...
68	2017	10	Sant Martí	69	Diagonal Mar i el Front Marítim del Poblenou	13710	150.1	Diagonal Mar i el Front Marítim del Poblenou
69	2017	10	Sant Martí	70	el Besòs i el Maresme	22893	60.4	el Besòs i el Maresme
70	2017	10	Sant Martí	71	Provençals del Poblenou	20649	102.3	Provençals del Poblenou
71	2017	10	Sant Martí	72	Sant Martí de Provençals	26187	67.4	Sant Martí de Provençals
72	2017	10	Sant Martí	73	la Verneda i la Pau	28725	57.0	la Verneda i la Pau

73 rows × 8 columns

renta.nlargest(3, 'Índex RFD Barcelona = 100')

	Any	Codi_Districte	Nom_Districte	Codi_Barri	Nom_Barri	Població	Índex RFD Barcelona = 100	NOM
20	2017	4	Les Corts	21	Pedralbes	12117	248.8	Pedralbes
23	2017	5	Sarrià-Sant Gervasi	24	les Tres Torres	16660	215.8	les Tres Torres
22	2017	5	Sarrià-Sant Gervasi	23	Sarrià	25106	193.6	Sarrià

renta.nsmallest(3, 'Índex RFD Barcelona = 100')

	Any	Codi_Districte	Nom_Districte	Codi_Barri	Nom_Barri	Població	Índex RFD Barcelona = 100	NOM
54	2017	8	Nou Barris	55	Ciutat Meridiana	10369	38.6	Ciutat Meridiana
11	2017	3	Sants-Montjuïc	12	la Marina del Prat Vermell	1151	40.0	la Marina del Prat Vermell
55	2017	8	Nou Barris	56	Vallbona	1379	40.9	Vallbona

https://opendata-ajuntament.barcelona.cat/data/en/dataset/20170706-districtes-barris provides json files with detail of administrative units of the city of Barcelona : districts, neighbourhoods, area of interest, basic statistical areas (AEB) and census areas. Today, Barcelona contains 10 different districts and 73 neighborhoods. In this article we employ neighborhood datas.

'''
import json

with open('0301100100_UNITATS_ADM_POLIGONS.json', 'r') as f:
    data_o = json.load(f) 
    
# Neighborhood datas are located from 11 to 84 therefore we only get these slice of data
data_o['features'] = data_o['features'][11:84]
for n in range(73):
    data_o['features'][n]['properties']['Índex RFD Barcelona = 100']= renta['Índex RFD Barcelona = 100'].iloc[n]

'''

According to https://opendata-ajuntament.barcelona.cat/ca/faqs , the coordinates in this json file use Spatial Reference System EPSG 25831, we need to transfom this system into EPSG 4326, which is the more standard reference system that we use nowadays. The simplest way to transform coordinates systems in Python is to use pyproj, i.e. the Python interface to PROJ.4 library.

'''
from pyproj import Transformer

def trans(coordinate):
    transformer = Transformer.from_crs("epsg:25831", "epsg:4326")
    x2,y2 = transformer.transform(coordinate[0],coordinate[1])
    return [y2, x2]

for n in range(73):
    for i in range(len(data['features'][n]['geometry']['coordinates'][0])):
        data['features'][n]['geometry']['coordinates'][0][i] = trans(data['features'][n]['geometry']['coordinates'][0][i])
    if len(data['features'][n]['geometry']['coordinates'])>1:
        for j in range(len(data['features'][n]['geometry']['coordinates'][1])):
            data['features'][n]['geometry']['coordinates'][1][j] = trans(data['features'][n]['geometry']['coordinates'][1][j])
    print(n)

# Once we transformed the coordinates we save the it as bcn_distric.json to avoid trasforming it again
data_json = json.dumps(data)
with open("bcn_neighborhood.json", "w") as outfile:
    outfile.write(data_json)
'''

import json
from copy import deepcopy

with open('bcn_neighborhood.json', 'r') as f:
    data = json.load(f)

for n in range(73):
    data['features'][n]['properties']['Índex RFD Barcelona = 100']= renta['Índex RFD Barcelona = 100'].iloc[n]

BCN_map = folium.Map(location=[41.39, 2.17], zoom_start=12) # in location we must add the city's coordinates
choropleth = folium.Choropleth(geo_data=data, # GeoJson Coordinates
                   data=renta,   # The table which contains the values we are analysing
                   columns=['NOM', 'Índex RFD Barcelona = 100'],
                   key_on='feature.properties.NOM', # We chose the key we need in the GeoJson file
                   fill_color='YlOrRd',
                   fill_opacity=0.7,
                   line_opacity=0.4,
                   legend_name='BCN income by neighborhood').add_to(BCN_map)
choropleth.geojson.add_child(folium.features.GeoJsonTooltip(['NOM', 'Índex RFD Barcelona = 100'], 
aliases=['Neiboorhood','RFD index']))
BCN_map

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BCN_map.save("choropleth maps/bcn_renta_2017.html")

To better differentiate neigborhoods with above average RFD index with neigborhoods with below average RFD, we can plot again choropleth map with 2 different color scales as illustrated in following codes.

low_neighborhoods = deepcopy(data)
high_neighborhoods = deepcopy(data)
index1=0
index2=0
while index1< len(low_neighborhoods['features']):
    if low_neighborhoods['features'][index1]['properties']['Índex RFD Barcelona = 100']>=100:
        low_neighborhoods['features'].pop(index1)
    else: index1+=1

while index2< len(high_neighborhoods['features']):
    if high_neighborhoods['features'][index2]['properties']['Índex RFD Barcelona = 100']<100:
        high_neighborhoods['features'].pop(index2)
    else: index2+=1

print('Numebr of neighborhoods with below average RFD index:', len(low_neighborhoods['features']))
print('Numebr of neighborhoods with equal or above average RFD index:', len(high_neighborhoods['features']))

Numebr of neighborhoods with below average RFD index: 49
Numebr of neighborhoods with equal or above average RFD index: 24

BCN_map = folium.Map(location=[41.39, 2.17], zoom_start=12) # in location we must add the city's coordinates
choropleth = folium.Choropleth(geo_data=low_neighborhoods, # GeoJson Coordinates
                   data= renta[renta['Índex RFD Barcelona = 100']<100],   # The table which contains the values we are analysing
                   columns=['NOM', 'Índex RFD Barcelona = 100'],
                   key_on='feature.properties.NOM', # We chose the key we need in the GeoJson file
                   fill_color='PuRd',
                   fill_opacity=0.7,
                   line_opacity=0.4,
                   legend_name='Below average RFD index').add_to(BCN_map)

choropleth.geojson.add_child(folium.features.GeoJsonTooltip(['NOM', 'Índex RFD Barcelona = 100'], 
aliases=['Neiboorhood','RFD index']))

choropleth = folium.Choropleth(geo_data=high_neighborhoods, # GeoJson Coordinates
                   data=renta[renta['Índex RFD Barcelona = 100']>=100],   # The table which contains the values we are analysing
                   columns=['NOM', 'Índex RFD Barcelona = 100'],
                   key_on='feature.properties.NOM', # We chose the key we need in the GeoJson file
                   fill_color='YlGn',
                   legend_name='Equal or above average RFD index').add_to(BCN_map)
choropleth.geojson.add_child(folium.features.GeoJsonTooltip(['NOM', 'Índex RFD Barcelona = 100'], 
aliases=['Neiboorhood','RFD index']))
BCN_map

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BCN_map.save("choropleth maps/bcn_renta_2017_2.html")

renta_07 = pd.read_csv("2007_distribucio_territorial_renda_familiar.csv")
renta_07 = renta_07.iloc[:-1] # this file contains an extra null row so here we exclude that row
renta_07['NOM'] = renta_07['Nom_Barri']
renta_07['NOM'][renta_07['NOM']=='el Poble Sec'] = 'el Poble-sec'
renta_07['Índex RFD Barcelona = 100'] = renta_07['Índex RFD Barcelona = 100'].astype('float64')
renta_07['RFD change'] = (renta['Índex RFD Barcelona = 100']-renta_07['Índex RFD Barcelona = 100'])/renta_07['Índex RFD Barcelona = 100']*100

for n in range(73):
    data['features'][n]['properties']['RFD change']= renta_07['RFD change'].iloc[n]
    data['features'][n]['properties']['Índex RFD Barcelona = 100']= renta_07['Índex RFD Barcelona = 100'].iloc[n]
renta_07

/var/folders/03/gv3k10bj7135k97m7mbkq26h0000gn/T/ipykernel_8504/4290161235.py:4: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  renta_07['NOM'][renta_07['NOM']=='el Poble Sec'] = 'el Poble-sec'

	Any	Codi_Districte	Nom_Districte	Codi_Barri	Nom_Barri	Població	Índex RFD Barcelona = 100	NOM	RFD change
0	2007	1	Ciutat Vella	1	el Raval	46595	64.7	el Raval	10.046368
1	2007	1	Ciutat Vella	2	el Barri Gòtic	27946	86.5	el Barri Gòtic	22.658960
2	2007	1	Ciutat Vella	3	la Barceloneta	15921	66.7	la Barceloneta	19.340330
3	2007	1	Ciutat Vella	4	Sant Pere, Santa Caterina i la Ribera	22572	80.2	Sant Pere, Santa Caterina i la Ribera	23.940150
4	2007	2	Eixample	5	el Fort Pienc	31521	107.9	el Fort Pienc	-1.297498
...	...	...	...	...	...	...	...	...	...
68	2007	10	Sant Martí	69	Diagonal Mar i el Front Marítim del Poblenou	9775	101.1	Diagonal Mar i el Front Marítim del Poblenou	48.466864
69	2007	10	Sant Martí	70	el Besòs i el Maresme	22652	61.7	el Besòs i el Maresme	-2.106969
70	2007	10	Sant Martí	71	Provençals del Poblenou	18731	85.7	Provençals del Poblenou	19.369895
71	2007	10	Sant Martí	72	Sant Martí de Provençals	26261	81.5	Sant Martí de Provençals	-17.300613
72	2007	10	Sant Martí	73	la Verneda i la Pau	29452	74.8	la Verneda i la Pau	-23.796791

73 rows × 9 columns

renta_07.nlargest(3, 'Índex RFD Barcelona = 100')

	Any	Codi_Districte	Nom_Districte	Codi_Barri	Nom_Barri	Població	Índex RFD Barcelona = 100	NOM	RFD change
23	2007	5	Sarrià-Sant Gervasi	24	les Tres Torres	15325	215.3	les Tres Torres	0.232234
20	2007	4	Les Corts	21	Pedralbes	11413	193.6	Pedralbes	28.512397
25	2007	5	Sarrià-Sant Gervasi	26	Sant Gervasi - Galvany	46454	187.0	Sant Gervasi - Galvany	2.727273

renta_07.nsmallest(3, 'Índex RFD Barcelona = 100')

	Any	Codi_Districte	Nom_Districte	Codi_Barri	Nom_Barri	Població	Índex RFD Barcelona = 100	NOM	RFD change
57	2007	9	Sant Andreu	58	Baró de Viver	2397	44.5	Baró de Viver	54.831461
46	2007	8	Nou Barris	47	Can Peguera	2143	49.8	Can Peguera	3.413655
55	2007	8	Nou Barris	56	Vallbona	1267	51.6	Vallbona	-20.736434

low_neighborhoods = deepcopy(data)
high_neighborhoods = deepcopy(data)
index1=0
index2=0
while index1< len(low_neighborhoods['features']):
    if low_neighborhoods['features'][index1]['properties']['Índex RFD Barcelona = 100']>=100:
        low_neighborhoods['features'].pop(index1)
    else: index1+=1

while index2< len(high_neighborhoods['features']):
    if high_neighborhoods['features'][index2]['properties']['Índex RFD Barcelona = 100']<100:
        high_neighborhoods['features'].pop(index2)
    else: index2+=1

print('Numebr of neighborhoods with below average RFD index:', len(low_neighborhoods['features']))
print('Numebr of neighborhoods with equal or above average RFD index:', len(high_neighborhoods['features']))

Numebr of neighborhoods with below average RFD index: 50
Numebr of neighborhoods with equal or above average RFD index: 23

BCN_map = folium.Map(location=[41.39, 2.17], zoom_start=12) # in location we must add the city's coordinates
choropleth = folium.Choropleth(geo_data=low_neighborhoods, # GeoJson Coordinates
                   data= renta_07[renta_07['Índex RFD Barcelona = 100']<100],   # The table which contains the values we are analysing
                   columns=['NOM', 'Índex RFD Barcelona = 100'],
                   key_on='feature.properties.NOM', # We chose the key we need in the GeoJson file
                   fill_color='PuRd',
                   fill_opacity=0.7,
                   line_opacity=0.4,
                   legend_name='Below average RFD index 2007').add_to(BCN_map)

choropleth.geojson.add_child(folium.features.GeoJsonTooltip(['NOM', 'Índex RFD Barcelona = 100'], 
aliases=['Neiboorhood','RFD index']))

choropleth = folium.Choropleth(geo_data=high_neighborhoods, # GeoJson Coordinates
                   data=renta_07[renta_07['Índex RFD Barcelona = 100']>=100],   # The table which contains the values we are analysing
                   columns=['NOM', 'Índex RFD Barcelona = 100'],
                   key_on='feature.properties.NOM', # We chose the key we need in the GeoJson file
                   fill_color='YlGn',
                   legend_name='Equal or above average RFD index 2007').add_to(BCN_map)
choropleth.geojson.add_child(folium.features.GeoJsonTooltip(['NOM', 'Índex RFD Barcelona = 100'], 
aliases=['Neiboorhood','RFD index']))
BCN_map

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BCN_map.save("choropleth maps/bcn_renta_2007.html")

Population map:

for n in range(73):
    data['features'][n]['properties']['Població']= int(renta_07['Població'].iloc[n])
    
BCN_map = folium.Map(location=[41.39, 2.17], zoom_start=12) # in location we must add the city's coordinates
choropleth = folium.Choropleth(geo_data=data, # GeoJson Coordinates
                   data=renta_07,   # The table which contains the values we are analysing
                   columns=['NOM', 'Població'],
                   key_on='feature.properties.NOM', # We chose the key we need in the GeoJson file
                   fill_color='YlOrRd',
                   fill_opacity=0.7,
                   line_opacity=0.4,
                   legend_name='BCN population').add_to(BCN_map)

choropleth.geojson.add_child(folium.features.GeoJsonTooltip(['NOM', 'Població'], 
aliases=['Neiboorhood','Population']))
BCN_map

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negative_neighborhoods = deepcopy(data) #
postive_neighborhoods = deepcopy(data) #
index1=0
index2=0
while index1< len(negative_neighborhoods['features']):
    if negative_neighborhoods['features'][index1]['properties']['RFD change']>=0:
        negative_neighborhoods['features'].pop(index1)
    else: index1+=1

while index2< len(postive_neighborhoods['features']):
    if postive_neighborhoods['features'][index2]['properties']['RFD change']<0:
        postive_neighborhoods['features'].pop(index2)
    else: index2+=1

print('Numebr of neighborhoods with negative RFD change:', len(negative_neighborhoods['features']))
print('Numebr of neighborhoods with positive RFD change:', len(postive_neighborhoods['features']))

Numebr of neighborhoods with negative RFD change: 46
Numebr of neighborhoods with positive RFD change: 27

BCN_map = folium.Map(location=[41.39, 2.17], zoom_start=12) # in location we must add the city's coordinates
choropleth = folium.Choropleth(geo_data=negative_neighborhoods, # GeoJson Coordinates
                   data=renta_07[renta_07['RFD change']<0],   # The table which contains the values we are analysing
                   columns=['NOM', 'RFD change'],
                   key_on='feature.properties.NOM', # We chose the key we need in the GeoJson file
                   fill_color='PuRd',
                   fill_opacity=0.7,
                   line_opacity=0.4,
                   legend_name='Negative RFD change %').add_to(BCN_map)
choropleth.geojson.add_child(folium.features.GeoJsonTooltip(['NOM', 'RFD change'], 
aliases=['Neiboorhood','RFD change %']))
choropleth = folium.Choropleth(geo_data=postive_neighborhoods, # GeoJson Coordinates
                   data=renta_07[renta_07['RFD change']>=0],   # The table which contains the values we are analysing
                   columns=['NOM', 'RFD change'],
                   key_on='feature.properties.NOM', # We chose the key we need in the GeoJson file
                   fill_color='YlGn',
                   legend_name='Positive RFD change %').add_to(BCN_map)
choropleth.geojson.add_child(folium.features.GeoJsonTooltip(['NOM', 'RFD change'], 
aliases=['Neiboorhood','RFD change %']))
BCN_map

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BCN_map.save("choropleth maps/bcn_renta_change.html")

Disposable household income in euros per capita in the city of Barcelona

renta_15 = pd.read_csv("2015_renda_disponible_llars.csv")
renta_15

	Any	Codi_Districte	Nom_Districte	Codi_Barri	Nom_Barri	Import_€_Any
0	2015	1	Ciutat Vella	1	el Raval	10896
1	2015	1	Ciutat Vella	2	el Barri Gòtic	14456
2	2015	1	Ciutat Vella	3	la Barceloneta	14714
3	2015	1	Ciutat Vella	4	Sant Pere, Santa Caterina i la Ribera	15154
4	2015	2	Eixample	5	el Fort Pienc	20817
...	...	...	...	...	...	...
68	2015	10	Sant Martí	69	Diagonal Mar i el Front Marítim del Poblenou	23274
69	2015	10	Sant Martí	70	el Besòs i el Maresme	12474
70	2015	10	Sant Martí	71	Provençals del Poblenou	18642
71	2015	10	Sant Martí	72	Sant Martí de Provençals	17613
72	2015	10	Sant Martí	73	la Verneda i la Pau	16022

73 rows × 6 columns

renta_15 = pd.read_csv("2015_renda_disponible_llars.csv")
renta_15['NOM'] = renta_15['Nom_Barri']
renta_15['NOM'][renta_15['NOM']=='el Poble Sec'] = 'el Poble-sec'
for n in range(73):
    data['features'][n]['properties']['Import_€_Any']= int(renta_15['Import_€_Any'].iloc[n])
renta_15

/var/folders/03/gv3k10bj7135k97m7mbkq26h0000gn/T/ipykernel_8504/579969240.py:3: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  renta_15['NOM'][renta_15['NOM']=='el Poble Sec'] = 'el Poble-sec'

	Any	Codi_Districte	Nom_Districte	Codi_Barri	Nom_Barri	Import_€_Any	NOM
0	2015	1	Ciutat Vella	1	el Raval	10896	el Raval
1	2015	1	Ciutat Vella	2	el Barri Gòtic	14456	el Barri Gòtic
2	2015	1	Ciutat Vella	3	la Barceloneta	14714	la Barceloneta
3	2015	1	Ciutat Vella	4	Sant Pere, Santa Caterina i la Ribera	15154	Sant Pere, Santa Caterina i la Ribera
4	2015	2	Eixample	5	el Fort Pienc	20817	el Fort Pienc
...	...	...	...	...	...	...	...
68	2015	10	Sant Martí	69	Diagonal Mar i el Front Marítim del Poblenou	23274	Diagonal Mar i el Front Marítim del Poblenou
69	2015	10	Sant Martí	70	el Besòs i el Maresme	12474	el Besòs i el Maresme
70	2015	10	Sant Martí	71	Provençals del Poblenou	18642	Provençals del Poblenou
71	2015	10	Sant Martí	72	Sant Martí de Provençals	17613	Sant Martí de Provençals
72	2015	10	Sant Martí	73	la Verneda i la Pau	16022	la Verneda i la Pau

73 rows × 7 columns

BCN_map = folium.Map(location=[41.39, 2.17], zoom_start=12) # in location we must add the city's coordinates
choropleth = folium.Choropleth(geo_data=data, # GeoJson Coordinates
                   data=renta_15,   # The table which contains the values we are analysing
                   columns=['NOM', 'Import_€_Any'],
                   key_on='feature.properties.NOM', # We chose the key we need in the GeoJson file
                   fill_color='YlOrRd',
                   fill_opacity=0.7,
                   line_opacity=0.4,
                   legend_name='Disposable household income per capita year 2015').add_to(BCN_map)
choropleth.geojson.add_child(folium.features.GeoJsonTooltip(['NOM', 'Import_€_Any'], 
aliases=['Neiboorhood','Euros_Any €']))
BCN_map

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BCN_map.save("choropleth maps/disposable_income_15.html")

renta_19 = pd.read_csv("2019_renda_disponible_llars.csv")
renta_19['NOM'] = renta_19['Nom_Barri']
renta_19['NOM'][renta_19['NOM']=='el Poble Sec'] = 'el Poble-sec'
for n in range(73):
    data['features'][n]['properties']['Euros_Any']= int(renta_19['Euros_Any'].iloc[n])
renta_19

/var/folders/03/gv3k10bj7135k97m7mbkq26h0000gn/T/ipykernel_8504/3458952715.py:3: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  renta_19['NOM'][renta_19['NOM']=='el Poble Sec'] = 'el Poble-sec'

	Any	Codi_Districte	Nom_Districte	Codi_Barri	Nom_Barri	Euros_Any	NOM
0	2019	1	Ciutat Vella	1	el Raval	12142	el Raval
1	2019	1	Ciutat Vella	2	el Barri Gòtic	16732	el Barri Gòtic
2	2019	1	Ciutat Vella	3	la Barceloneta	16322	la Barceloneta
3	2019	1	Ciutat Vella	4	Sant Pere, Santa Caterina i la Ribera	18075	Sant Pere, Santa Caterina i la Ribera
4	2019	2	Eixample	5	el Fort Pienc	23137	el Fort Pienc
...	...	...	...	...	...	...	...
68	2019	10	Sant Martí	69	Diagonal Mar i el Front Marítim del Poblenou	25589	Diagonal Mar i el Front Marítim del Poblenou
69	2019	10	Sant Martí	70	el Besòs i el Maresme	12787	el Besòs i el Maresme
70	2019	10	Sant Martí	71	Provençals del Poblenou	20080	Provençals del Poblenou
71	2019	10	Sant Martí	72	Sant Martí de Provençals	18637	Sant Martí de Provençals
72	2019	10	Sant Martí	73	la Verneda i la Pau	16820	la Verneda i la Pau

73 rows × 7 columns

BCN_map = folium.Map(location=[41.39, 2.17], zoom_start=12) # in location we must add the city's coordinates
choropleth = folium.Choropleth(geo_data=data, # GeoJson Coordinates
                   data=renta_19,   # The table which contains the values we are analysing
                   columns=['NOM', 'Euros_Any'],
                   key_on='feature.properties.NOM', # We chose the key we need in the GeoJson file
                   fill_color='YlOrRd',
                   fill_opacity=0.7,
                   line_opacity=0.4,
                   legend_name='Disposable household income per capita year 2019').add_to(BCN_map)
choropleth.geojson.add_child(folium.features.GeoJsonTooltip(['NOM', 'Euros_Any'], 
aliases=['Neiboorhood','Euros_Any €']))
BCN_map

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BCN_map.save("choropleth maps/disposable_income_19.html")

Demographic indicators. Immigration registration rate (‰ inhabitants) of the city of Barcelona. This rate is the relation between registrations due to immigration in one year and the population of the same year per thousand inhabitants. Column 'Nombre' represents the number of Immigration registration per thousand inhabitants. https://opendata-ajuntament.barcelona.cat/data/en/dataset/est-demo-taxa-immigracio

immigration_20 = pd.read_csv("2020_taxa_immigracio.csv")
immigration_20['NOM'] = immigration_20['Nom_Barri']
immigration_20['NOM'][immigration_20['NOM']=='el Poble Sec'] = 'el Poble-sec'
for n in range(73):
    data['features'][n]['properties']['Nombre']= immigration_20['Nombre'].iloc[n]
immigration_20

/var/folders/03/gv3k10bj7135k97m7mbkq26h0000gn/T/ipykernel_8504/3056993680.py:3: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  immigration_20['NOM'][immigration_20['NOM']=='el Poble Sec'] = 'el Poble-sec'

	Any	Codi_Districte	Nom_Districte	Codi_Barri	Nom_Barri	Taxa_mil_hab	Nombre	NOM
0	2020	1	Ciutat Vella	1	el Raval	Taxa per mil habitants	83.2	el Raval
1	2020	1	Ciutat Vella	2	el Barri Gòtic	Taxa per mil habitants	128.3	el Barri Gòtic
2	2020	1	Ciutat Vella	3	la Barceloneta	Taxa per mil habitants	87.2	la Barceloneta
3	2020	1	Ciutat Vella	4	Sant Pere, Santa Caterina i la Ribera	Taxa per mil habitants	89.1	Sant Pere, Santa Caterina i la Ribera
4	2020	2	Eixample	5	el Fort Pienc	Taxa per mil habitants	56.1	el Fort Pienc
...	...	...	...	...	...	...	...	...
68	2020	10	Sant Martí	69	Diagonal Mar i el Front Marítim del Poblenou	Taxa per mil habitants	39.1	Diagonal Mar i el Front Marítim del Poblenou
69	2020	10	Sant Martí	70	el Besòs i el Maresme	Taxa per mil habitants	68.6	el Besòs i el Maresme
70	2020	10	Sant Martí	71	Provençals del Poblenou	Taxa per mil habitants	34.4	Provençals del Poblenou
71	2020	10	Sant Martí	72	Sant Martí de Provençals	Taxa per mil habitants	32.5	Sant Martí de Provençals
72	2020	10	Sant Martí	73	la Verneda i la Pau	Taxa per mil habitants	29.1	la Verneda i la Pau

73 rows × 8 columns

BCN_map = folium.Map(location=[41.39, 2.17], zoom_start=12) # in location we must add the city's coordinates
choropleth = folium.Choropleth(geo_data=data, # GeoJson Coordinates
                   data=immigration_20,   # The table which contains the values we are analysing
                   columns=['NOM', 'Nombre'],
                   key_on='feature.properties.NOM', # We chose the key we need in the GeoJson file
                   fill_color='YlOrRd',
                   fill_opacity=0.7,
                   line_opacity=0.4,
                   legend_name='Immigration registration rate').add_to(BCN_map)
choropleth.geojson.add_child(folium.features.GeoJsonTooltip(['NOM', 'Nombre'], 
aliases=['Neiboorhood','Immigration rate']))
BCN_map

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BCN_map.save("choropleth maps/immigration_rate_20.html")

Demographic indicators. Birth rate (‰ inhabitants) of the city of Barcelona available at: https://opendata-ajuntament.barcelona.cat/data/en/dataset/est-demo-taxa-natalitat

birthrate_20 = pd.read_csv("2020_taxa_natalitat.csv")
birthrate_20['NOM'] = birthrate_20['Nom_Barri']
birthrate_20['NOM'][birthrate_20['NOM']=='el Poble Sec'] = 'el Poble-sec'
birthrate_20 = birthrate_20.rename(columns={"Nombre": "Birth_rate"})
for n in range(73):
    data['features'][n]['properties']['Birth_rate']= birthrate_20['Birth_rate'].iloc[n]
birthrate_20

/var/folders/03/gv3k10bj7135k97m7mbkq26h0000gn/T/ipykernel_8504/2588264616.py:3: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  birthrate_20['NOM'][birthrate_20['NOM']=='el Poble Sec'] = 'el Poble-sec'

	Any	Codi_Districte	Nom_Districte	Codi_Barri	Nom_Barri	Taxa_mil_hab	Birth_rate	NOM
0	2020	1	Ciutat Vella	1	el Raval	Taxa per mil habitants	9.7	el Raval
1	2020	1	Ciutat Vella	2	el Barri Gòtic	Taxa per mil habitants	6.0	el Barri Gòtic
2	2020	1	Ciutat Vella	3	la Barceloneta	Taxa per mil habitants	5.7	la Barceloneta
3	2020	1	Ciutat Vella	4	Sant Pere, Santa Caterina i la Ribera	Taxa per mil habitants	5.6	Sant Pere, Santa Caterina i la Ribera
4	2020	2	Eixample	5	el Fort Pienc	Taxa per mil habitants	5.4	el Fort Pienc
...	...	...	...	...	...	...	...	...
68	2020	10	Sant Martí	69	Diagonal Mar i el Front Marítim del Poblenou	Taxa per mil habitants	8.6	Diagonal Mar i el Front Marítim del Poblenou
69	2020	10	Sant Martí	70	el Besòs i el Maresme	Taxa per mil habitants	7.2	el Besòs i el Maresme
70	2020	10	Sant Martí	71	Provençals del Poblenou	Taxa per mil habitants	7.4	Provençals del Poblenou
71	2020	10	Sant Martí	72	Sant Martí de Provençals	Taxa per mil habitants	6.0	Sant Martí de Provençals
72	2020	10	Sant Martí	73	la Verneda i la Pau	Taxa per mil habitants	6.1	la Verneda i la Pau

73 rows × 8 columns

BCN_map = folium.Map(location=[41.39, 2.17], zoom_start=12) # in location we must add the city's coordinates
choropleth = folium.Choropleth(geo_data=data, # GeoJson Coordinates
                   data=birthrate_20,   # The table which contains the values we are analysing
                   columns=['NOM', 'Birth_rate'],
                   key_on='feature.properties.NOM', # We chose the key we need in the GeoJson file
                   fill_color='YlOrRd',
                   fill_opacity=0.7,
                   line_opacity=0.4,
                   legend_name='Birth rate').add_to(BCN_map)
choropleth.geojson.add_child(folium.features.GeoJsonTooltip(['NOM', 'Birth_rate'], 
aliases=['Neiboorhood','Birth rate']))
BCN_map

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BCN_map.save("choropleth maps/birth_rate_20.html")

Life expecatancy obtained from: https://opendata-ajuntament.barcelona.cat/data/es/dataset/est-sp-esp-vida

life_expectancy = pd.read_excel("Life_expectancy.xlsx")
life_expectancy['NOM'] = renta['NOM']
for n in range(73):
    data['features'][n]['properties']['Life_expectancy']= life_expectancy['Life_expectancy'].iloc[n]
life_expectancy

	Barrios	Life_expectancy	NOM
0	1     1. el Raval	81.6	el Raval
1	1     2. el Barri Gòtic	82.5	el Barri Gòtic
2	1     3. la Barceloneta	80.5	la Barceloneta
3	1     4. Sant Pere, Santa Caterina i la Ribera	83.5	Sant Pere, Santa Caterina i la Ribera
4	2     5. el Fort Pienc	84.7	el Fort Pienc
...	...	...	...
68	10     69. Diagonal Mar i el Front Marítim del...	85.9	Diagonal Mar i el Front Marítim del Poblenou
69	10     70. el Besòs i el Maresme	84.5	el Besòs i el Maresme
70	10     71. Provençals del Poblenou	85.1	Provençals del Poblenou
71	10     72. Sant Martí de Provençals	85.8	Sant Martí de Provençals
72	10     73. la Verneda i la Pau	84.7	la Verneda i la Pau

73 rows × 3 columns

life_expectancy.nsmallest(3, 'Life_expectancy')

	Barrios	Life_expectancy	NOM
55	8     56. Vallbona	78.1	Vallbona
52	8     53. la Trinitat Nova	79.6	la Trinitat Nova
11	3     12. la Marina del Prat Vermell - Zona Fr...	79.8	la Marina del Prat Vermell

life_expectancy.nlargest(3, 'Life_expectancy')

	Barrios	Life_expectancy	NOM
20	4     21. Pedralbes	87.7	Pedralbes
66	10     67. la Vila Olímpica del Poblenou	87.4	la Vila Olímpica del Poblenou
19	4     20. la Maternitat i Sant Ramon	86.7	la Maternitat i Sant Ramon

BCN_map = folium.Map(location=[41.39, 2.17], zoom_start=12) # in location we must add the city's coordinates
choropleth = folium.Choropleth(geo_data=data, # GeoJson Coordinates
                   data=life_expectancy,   # The table which contains the values we are analysing
                   columns=['NOM', 'Life_expectancy'],
                   key_on='feature.properties.NOM', # We chose the key we need in the GeoJson file
                   fill_color='YlOrRd',
                   fill_opacity=0.7,
                   line_opacity=0.4,
                   legend_name='Life_expectancy').add_to(BCN_map)
choropleth.geojson.add_child(folium.features.GeoJsonTooltip(['NOM', 'Life_expectancy'], 
aliases=['Neiboorhood','Life expectancy 2015-2019']))
BCN_map

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BCN_map.save("choropleth maps/life_expectancy_19.html")

List of hospitals and centres of primary health service of the city of Barcelona: https://opendata-ajuntament.barcelona.cat/data/en/dataset/sanitat-hospitals-atencio-primaria

from folium.plugins import MarkerCluster

# generate the clusters of datas
hospitals = pd.read_csv("opendatabcn_sanitat_hospitals-i-centres-atencio-primaria.csv")
hospitals_cluster = folium.Map(location=[41.39, 2.17], zoom_start=12)
hospitals_cluster.add_child(MarkerCluster(hospitals[['geo_epgs_4326_x', 'geo_epgs_4326_y']], popups = hospitals['name'].tolist()))
hospitals_cluster

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hospitals_cluster.save("choropleth maps/hospitals_cluster.html")