11. Folium#
Folium is a powerful Python library that helps you create several types of Leaflet maps. By default, Folium creates a map in a separate HTML file.
Advantages#
Folium makes easy to visualize data that’s been manipulated in Python on an interactive leaflet map. It enables both the binding of data to a map for choropleth visualizations as well as passing rich vector/raster/HTML visualizations as markers on the map.
The library has a number of built-in tilesets from OpenStreetMap, Mapbox, and Stamen, and supports custom tilesets with Mapbox or Cloudmade API keys. folium supports both Image, Video, GeoJSON and TopoJSON overlays.
Starting point#
Folium.Map(location = [LAT0,LON0], zoom_start = 12)
location has a specific coordinates and zoom shows the distance from the coordinate.
import pandas as pd
#from pandas import Series, DataFrame
import numpy as np
import matplotlib.pyplot as plt
import chardet
import folium as fm
from folium import Marker, GeoJson
from folium.plugins import MarkerCluster, HeatMap, StripePattern
import geopandas as gpd
from geopandas import GeoSeries
from shapely.geometry import Point, LineString
import branca as br
---------------------------------------------------------------------------
ModuleNotFoundError Traceback (most recent call last)
Cell In[1], line 5
3 import numpy as np
4 import matplotlib.pyplot as plt
----> 5 import chardet
7 import folium as fm
8 from folium import Marker, GeoJson
ModuleNotFoundError: No module named 'chardet'
Mining Data#
#Gettting the character format
base = open(r'../_data/Folium/MINING.csv', 'rb').read()
det = chardet.detect(base)
charenc = det['encoding']
charenc
'UTF-8-SIG'
# Geographical information of mining
MINING = pd.read_csv( r'../_data/Folium/MINING.csv', encoding = charenc)
MINING
| UBIGEO | MÉTODO DE EXPLOTACIÓN | TITULAR | UNIDAD | REGION | PROVINCIA | DISTRITO | PRODUCTO | LONGITUD | LATITUD | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 20201 | MINERÍA SUBTERRÁNEA | COMPAÑIA MINERA LINCUNA S.A | HUANCAPETI | ANCASH | AIJA | AIJA | As, Bi, Mn, Pb, Zn, Au, Ag | -77.531000 | -9.753000 |
| 1 | 220902 | MINERíA NO METÁLICA | QUIMPAC S.A. | SALINAS PILLUANA | SAN MARTIN | SAN MARTIN | ALBERTO LEVEAU | Sal | -76.267000 | -6.742000 |
| 2 | 200502 | MINERíA NO METÁLICA | COMPAÑIA MINERA AGREGADOS CALCAREOS S.A. | CERRO BLANCO | PIURA | PAITA | AMOTAPE | Bentonita | -81.027000 | -4.836000 |
| 3 | 210802 | MINERÍA SUBTERRÁNEA | MINSUR S.A. | QUENAMARI-SAN RAFAEL | PUNO | MELGAR | ANTAUTA | Sn | -70.491600 | -14.133500 |
| 4 | 150502 | MINERíA NO METÁLICA | COMPAÑIA MINERA LAS CAMELIAS S.A. | PROMESA 345 | LIMA | CAÑETE | ASIA | Arcillas | -76.530467 | -12.736480 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 137 | 120810 | MINERÍA SUBTERRÁNEA | COMPAÑIA MINERA ARGENTUM S.A. | MANUELITA | JUNIN | YAULI | YAULI | Pb, Zn, Ag, Cu | -76.096077 | -11.628891 |
| 138 | 120810 | MINERÍA SUBTERRÁNEA | VOLCAN COMPAÑÍA MINERA S.A.A. | SAN CRISTOBAL | JUNIN | YAULI | YAULI | Cu, Pb, Zn, Ag | -75.484539 | -12.018773 |
| 139 | 120810 | MINERíA NO METÁLICA | MINERA CHINALCO PERU S.A. | TUNSHURUCO | JUNIN | YAULI | YAULI | Caliza | -76.139792 | -11.656098 |
| 140 | 60508 | MINERíA NO METÁLICA | CEMENTOS PACASMAYO S.A.A. | TEMBLADERA | CAJAMARCA | CONTUMAZA | YONAN | Caliza | -79.124000 | -7.247000 |
| 141 | 40128 | MINERíA NO METÁLICA | YURA S.A. | YURA | AREQUIPA | AREQUIPA | YURA | Caliza, Pizarra, Puzolana, Yeso | -71.778723 | -16.206498 |
142 rows × 10 columns
# Mining activities in Yauli - Junín
MINING1 = MINING[MINING.DISTRITO == "YAULI"]
MINING1
| UBIGEO | MÉTODO DE EXPLOTACIÓN | TITULAR | UNIDAD | REGION | PROVINCIA | DISTRITO | PRODUCTO | LONGITUD | LATITUD | |
|---|---|---|---|---|---|---|---|---|---|---|
| 134 | 120810 | MINERÍA SUBTERRÁNEA | COMPAÑIA MINERA CASAPALCA S.A. | AMERICANA | JUNIN | YAULI | YAULI | Zn, Ag, Cu, Pb | -76.199576 | -11.698612 |
| 135 | 120810 | MINERÍA SUBTERRÁNEA | COMPAÑIA MINERA ARGENTUM S.A. | ANTICONA | JUNIN | YAULI | YAULI | Zn, Ag, Cu, Pb | -76.171596 | -11.637246 |
| 136 | 120810 | MINERÍA SUBTERRÁNEA | VOLCAN COMPAÑÍA MINERA S.A.A. | CARAHUACRA | JUNIN | YAULI | YAULI | Zn, Ag, Cu, Pb | -76.070389 | -11.740413 |
| 137 | 120810 | MINERÍA SUBTERRÁNEA | COMPAÑIA MINERA ARGENTUM S.A. | MANUELITA | JUNIN | YAULI | YAULI | Pb, Zn, Ag, Cu | -76.096077 | -11.628891 |
| 138 | 120810 | MINERÍA SUBTERRÁNEA | VOLCAN COMPAÑÍA MINERA S.A.A. | SAN CRISTOBAL | JUNIN | YAULI | YAULI | Cu, Pb, Zn, Ag | -75.484539 | -12.018773 |
| 139 | 120810 | MINERíA NO METÁLICA | MINERA CHINALCO PERU S.A. | TUNSHURUCO | JUNIN | YAULI | YAULI | Caliza | -76.139792 | -11.656098 |
# # Loop to save coordinates from mining location
# for j in range(0,len(MINING1)):
# globals()[f'LON{j}'] = MINING1.iloc[j,8]
# globals()[f'LAT{j}'] = MINING1.iloc[j,9]
#LAT1
1. First Map and some additional details#
1.1 Define the first parameters#
zoom_start = 12
lat_mining = MINING1["LATITUD"].mean()
long_mining = MINING1["LONGITUD"].mean()
a = fm.Map( location = [lat_mining, long_mining], tiles="OpenStreetMap", zoom_start = zoom_start, control_scale=True)
a
Make this Notebook Trusted to load map: File -> Trust Notebook
Add Markers#
tooltip = "Click me!"
fm.Marker( [-11.637246, -76.171596],
popup="<i>COMPAÑIA MINERA CASAPALCA S.A.</i>",
tooltip=tooltip
).add_to(a)
fm.Marker( [-11.628891, -76.096077 ],
popup="<b><i>COMPAÑIA MINERA ARGENTUM S.A.</i></b>",
tooltip=tooltip
).add_to(a)
a
Make this Notebook Trusted to load map: File -> Trust Notebook
Ballon Marker#
# b. Set the markers
for index, row in MINING1.iterrows():
fm.Marker([row['LATITUD'], row['LONGITUD']], popup= row['TITULAR'],
radius = 50,
icon=fm.Icon(color="red", icon="info-sign"),
color="#3186cc",
fill=True,
fill_color="#3186cc",
tooltip=tooltip).add_to(a)
a
Make this Notebook Trusted to load map: File -> Trust Notebook
Circle Marker#
tooltip = "Click"
#Mineral type in a list
#mineral = ["CASAPALCA (Zinc)","ARGENTUM (Cobre)","VOLCAN (Mercurio)", "ARGENTUM (Plomo)", "VOLCAN (Plata)", "CHINALCO (Caliza)"]
# Adding circle and mark.
# popup save information
# CircleMarker uses Pixels
for _, row in MINING1.iterrows():
fm.Circle([row['LATITUD'], row['LONGITUD']], popup=row['TITULAR'],
radius = 10000,
fill=True,
fill_color="#3186cc",
tooltip=tooltip).add_to(a)
a
Make this Notebook Trusted to load map: File -> Trust Notebook
# tooltip = "Click"
# #Mineral type in a list
# mineral = ["CASAPALCA (Zinc)","ARGENTUM (Cobre)","VOLCAN (Mercurio)", "ARGENTUM (Plomo)", "VOLCAN (Plata)", "CHINALCO (Caliza)"]
# # Adding circle and mark.
# # popup save information
# for i in range(0,len(MINING1)):
# fm.CircleMarker([globals()[f'LAT{i}'], globals()[f'LON{i}']], popup= mineral[i],
# radius = 100,
# fill=True,
# fill_color="#3186cc",
# tooltip=tooltip).add_to(a)
# for i in range(0,len(MINING1)):
# fm.Marker([globals()[f'LAT{i}'], globals()[f'LON{i}']], popup= mineral[i],
# radius = 50,
# icon=fm.Icon(color="red", icon="info-sign"),
# color="#3186cc",
# fill=True,
# fill_color="#3186cc",
# tooltip=tooltip).add_to(a)
# a
Using a Different Tiles#
zoom_start = 10
lat_mining = MINING1["LATITUD"].mean()
long_mining = MINING1["LONGITUD"].mean()
# "cartodbpositron"
a = fm.Map(location = [lat_mining, long_mining], tiles="Stamen Terrain", zoom_start = zoom_start, control_scale=True)
# b. Set the markers
for index, row in MINING1.iterrows():
fm.Marker([row['LATITUD'], row['LONGITUD']], popup= row['TITULAR'],
radius = 50,
icon=fm.Icon(color="red", icon="info-sign"),
color="#3186cc",
fill=True,
fill_color="#3186cc",
tooltip=tooltip).add_to(a)
# Circles
for _, row in MINING1.iterrows():
fm.Circle([row['LATITUD'], row['LONGITUD']], popup=row['TITULAR'],
radius = 10000,
fill=True,
fill_color="#3186cc",
tooltip=tooltip).add_to(a)
a
Make this Notebook Trusted to load map: File -> Trust Notebook
Peru GeoJson Files#
District Level#
# Yoy must upload coordinates base on GeoJson format to use package Choropleth.
distritos = gpd.read_file(r'../_data/folium/peru_distrital_simple.geojson')
distritos
| OBJECTID | IDDIST | IDDPTO | IDPROV | NOMBDIST | NOMBPROV | NOMBDEP | DCTO | LEY | FECHA | NOM_CAP | SHAPE_LENG | SHAPE_AREA | SHAPE_LE_1 | SHAPE_AR_1 | AREA_MINAM | geometry | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 230110 | 23 | 2301 | CORONEL GREGORIO ALBARRACIN LANCHIPA | TACNA | TACNA | LEY | 27415 | 02/02/2001 | ALFONSO UGARTE | 0.570510 | 0.016140 | 0.570195 | 0.015990 | 18834.14 | POLYGON ((-70.14409 -18.09309, -70.17512 -18.1... |
| 1 | 2 | 230108 | 23 | 2301 | POCOLLAY | TACNA | TACNA | LEY | 13069 | 15/01/1959 | POCOLLAY | 0.883871 | 0.022816 | 0.897169 | 0.022961 | 27073.52 | POLYGON ((-69.90467 -17.95829, -69.98287 -18.0... |
| 2 | 3 | 230103 | 23 | 2301 | CALANA | TACNA | TACNA | LEY | S/N | 20/08/1872 | CALANA | 0.446736 | 0.009458 | 0.445963 | 0.009383 | 11063.99 | POLYGON ((-70.09201 -17.98026, -70.17243 -18.0... |
| 3 | 4 | 230101 | 23 | 2301 | TACNA | TACNA | TACNA | - | - | EPOCA INDEP. | TACNA | 2.758951 | 0.209156 | 2.758123 | 0.209177 | 246365.27 | POLYGON ((-70.23500 -17.99231, -70.23710 -18.0... |
| 4 | 5 | 230109 | 23 | 2301 | SAMA | TACNA | TACNA | - | - | EPOCA INDEP. | LAS YARAS | 1.515506 | 0.096789 | 1.513660 | 0.096766 | 113953.51 | POLYGON ((-70.42374 -17.88983, -70.51323 -17.9... |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 1829 | 1830 | 160303 | 16 | 1603 | TIGRE | LORETO | LORETO | LEY | 9815 | 02/07/1943 | INTUTU | 9.211057 | 1.637168 | 9.179725 | 1.637432 | 2011378.30 | POLYGON ((-74.96515 -2.36565, -74.94959 -2.458... |
| 1830 | 1831 | 160107 | 16 | 1601 | NAPO | MAYNAS | LORETO | LEY | 9815 | 02/07/1943 | SANTA CLOTILDE | 11.380550 | 1.986357 | 11.359436 | 1.985362 | 2440805.01 | POLYGON ((-72.93790 -2.83906, -72.97311 -2.930... |
| 1831 | 1832 | 160109 | 16 | 1601 | PUTUMAYO | MAYNAS | LORETO | LEY | 9815 | 02/07/1943 | SAN ANTONIO DEL ESTRECHO | 16.256407 | 2.884865 | 16.232424 | 2.884722 | 3555516.31 | POLYGON ((-73.53075 -1.45793, -73.47857 -1.522... |
| 1832 | 1833 | 160110 | 16 | 1601 | TORRES CAUSANA | MAYNAS | LORETO | LEY | 9815 | 02/07/1943 | PANTOJA | 6.592491 | 0.609698 | 6.572157 | 0.609290 | 749185.08 | POLYGON ((-74.87370 -0.95012, -74.88779 -1.006... |
| 1833 | 1834 | 160114 | 16 | 1601 | TENIENTE MANUEL CLAVERO | MAYNAS | LORETO | LEY | 28362 | 19/10/2004 | S0PLIN VARGAS | 7.442130 | 0.742758 | 7.429279 | 0.741488 | 911972.65 | POLYGON ((-73.53075 -1.45793, -73.58899 -1.460... |
1834 rows × 17 columns
distritos1 = distritos[['IDDIST', 'geometry']]
distritos1 = distritos1 .rename({'IDDIST':'UBIGEO1'}, axis =1 )
distritos1['UBIGEO1'] = distritos1['UBIGEO1'].astype(str).astype(np.int64)
distritos1
| UBIGEO1 | geometry | |
|---|---|---|
| 0 | 230110 | POLYGON ((-70.14409 -18.09309, -70.17512 -18.1... |
| 1 | 230108 | POLYGON ((-69.90467 -17.95829, -69.98287 -18.0... |
| 2 | 230103 | POLYGON ((-70.09201 -17.98026, -70.17243 -18.0... |
| 3 | 230101 | POLYGON ((-70.23500 -17.99231, -70.23710 -18.0... |
| 4 | 230109 | POLYGON ((-70.42374 -17.88983, -70.51323 -17.9... |
| ... | ... | ... |
| 1829 | 160303 | POLYGON ((-74.96515 -2.36565, -74.94959 -2.458... |
| 1830 | 160107 | POLYGON ((-72.93790 -2.83906, -72.97311 -2.930... |
| 1831 | 160109 | POLYGON ((-73.53075 -1.45793, -73.47857 -1.522... |
| 1832 | 160110 | POLYGON ((-74.87370 -0.95012, -74.88779 -1.006... |
| 1833 | 160114 | POLYGON ((-73.53075 -1.45793, -73.58899 -1.460... |
1834 rows × 2 columns
Departamenta levels#
#administrative regions
dpto = gpd.read_file(r'../_data/folium/peru_departamental_simple.geojson')
dpto1 = dpto[['FIRST_IDDP', 'geometry']]
dpto1 = dpto1.rename({'FIRST_IDDP':'UBIGEO2'}, axis =1 )
dpto1['UBIGEO2'] = dpto1['UBIGEO2'] + "0000"
dpto1['UBIGEO2'] = dpto1['UBIGEO2'].astype(str).astype(np.int64)
dpto1
| UBIGEO2 | geometry | |
|---|---|---|
| 0 | 10000 | POLYGON ((-77.75893 -6.96451, -77.84586 -6.976... |
| 1 | 20000 | POLYGON ((-77.31749 -8.53015, -77.28903 -8.589... |
| 2 | 30000 | POLYGON ((-72.47177 -14.66140, -72.57725 -14.6... |
| 3 | 40000 | POLYGON ((-75.07333 -15.44294, -75.04965 -15.4... |
| 4 | 50000 | POLYGON ((-74.34595 -12.17374, -74.32187 -12.2... |
| 5 | 60000 | POLYGON ((-79.32259 -7.02568, -79.29663 -6.999... |
| 6 | 70000 | POLYGON ((-77.18710 -11.82836, -77.12605 -11.8... |
| 7 | 80000 | POLYGON ((-72.47177 -14.66140, -72.46170 -14.6... |
| 8 | 90000 | POLYGON ((-75.05905 -14.12962, -75.10884 -14.0... |
| 9 | 100000 | POLYGON ((-77.31749 -8.53015, -77.26408 -8.467... |
| 10 | 110000 | POLYGON ((-74.76230 -15.09805, -74.81592 -15.1... |
| 11 | 120000 | POLYGON ((-74.57300 -10.91260, -74.50838 -10.9... |
| 12 | 130000 | POLYGON ((-78.64492 -8.96923, -78.65166 -8.914... |
| 13 | 140000 | POLYGON ((-80.62713 -6.37208, -80.37622 -6.006... |
| 14 | 150000 | POLYGON ((-76.90625 -10.27419, -76.85639 -10.3... |
| 15 | 160000 | POLYGON ((-73.98303 -7.53436, -74.03380 -7.484... |
| 16 | 170000 | POLYGON ((-70.38847 -13.19239, -70.46425 -13.1... |
| 17 | 180000 | POLYGON ((-71.13944 -17.82148, -71.20565 -17.7... |
| 18 | 190000 | POLYGON ((-76.27886 -10.92345, -76.24117 -11.0... |
| 19 | 200000 | POLYGON ((-79.21007 -4.96524, -79.24509 -5.001... |
| 20 | 210000 | MULTIPOLYGON (((-68.91839 -16.40372, -68.91840... |
| 21 | 220000 | POLYGON ((-75.98496 -8.32215, -76.08718 -8.336... |
| 22 | 230000 | POLYGON ((-69.64132 -17.28745, -69.57522 -17.2... |
| 23 | 240000 | POLYGON ((-80.48733 -4.08313, -80.52435 -4.107... |
| 24 | 250000 | POLYGON ((-70.61388 -9.91686, -70.66763 -9.972... |
Data to plot- IDH, GDP , Poverty Rate#
# Uplaoad economic indicators such us IDH, GDP per - capita, Poverty rate
base = open(r'../_data/Folium/Poverty.csv', 'rb').read()
det = chardet.detect(base)
charenc = det['encoding']
poverty = pd.read_csv( r'../_data/Folium/Poverty.csv', encoding = charenc)
poverty
| UBIGEO1 | UBIGEO2 | DEPARTAMENTO | PROVINCIA | DISTRITO | POVERTY_RATE | PBI_PC | IDH | |
|---|---|---|---|---|---|---|---|---|
| 0 | 10101 | 10000 | AMAZONAS | CHACHAPOYAS | CHACHAPOYAS | 9.034625 | 3449.564733 | 41.8 |
| 1 | 10102 | 10000 | AMAZONAS | CHACHAPOYAS | ASUNCIÓN | 36.519949 | 3449.564733 | 41.8 |
| 2 | 10103 | 10000 | AMAZONAS | CHACHAPOYAS | BALSAS | 45.732962 | 3449.564733 | 41.8 |
| 3 | 10104 | 10000 | AMAZONAS | CHACHAPOYAS | CHETO | 39.169782 | 3449.564733 | 41.8 |
| 4 | 10105 | 10000 | AMAZONAS | CHACHAPOYAS | CHILIQUÍN | 53.045662 | 3449.564733 | 41.8 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 1868 | 250302 | 250000 | UCAYALI | PADRE ABAD | IRAZOLA | 12.899560 | 4059.106491 | 48.4 |
| 1869 | 250303 | 250000 | UCAYALI | PADRE ABAD | CURIMANÁ | 9.014545 | 4059.106491 | 48.4 |
| 1870 | 250304 | 250000 | UCAYALI | PADRE ABAD | NESHUYA | 11.946857 | 4059.106491 | 48.4 |
| 1871 | 250305 | 250000 | UCAYALI | PADRE ABAD | ALEXANDER VON HUMBOLDT | 12.899560 | 4059.106491 | 48.4 |
| 1872 | 250401 | 250000 | UCAYALI | PURÚS | PURÚS | 31.928338 | 4059.106491 | 48.4 |
1873 rows × 8 columns
#Check variables´ types to make math operations, merge datasets, etc
poverty.dtypes
UBIGEO1 int64
UBIGEO2 int64
DEPARTAMENTO object
PROVINCIA object
DISTRITO object
POVERTY_RATE float64
PBI_PC object
IDH object
dtype: object
# From district level to administrative region
poverty2 = poverty.drop_duplicates(subset=['UBIGEO2'])
# Drop callao
poverty2 = poverty2.drop(689)
poverty2['IDH'] = poverty2['IDH'].astype(str).astype(float)
poverty2['IDH'] = poverty2['IDH'].astype(str).astype(float)
poverty2['PBI_PC'] = poverty2['PBI_PC'].astype(str).astype(float)
poverty2
| UBIGEO1 | UBIGEO2 | DEPARTAMENTO | PROVINCIA | DISTRITO | POVERTY_RATE | PBI_PC | IDH | |
|---|---|---|---|---|---|---|---|---|
| 0 | 10101 | 10000 | AMAZONAS | CHACHAPOYAS | CHACHAPOYAS | 9.034625 | 3449.564733 | 41.8 |
| 84 | 20101 | 20000 | ÁNCASH | HUARAZ | HUARAZ | 10.242455 | 3151.903885 | 51.6 |
| 250 | 30101 | 30000 | APURÍMAC | ABANCAY | ABANCAY | 19.900498 | 6100.316981 | 41.1 |
| 334 | 40101 | 40000 | AREQUIPA | AREQUIPA | AREQUIPA | 0.715245 | 9006.816781 | 64.3 |
| 443 | 50101 | 50000 | AYACUCHO | HUAMANGA | AYACUCHO | 15.054646 | 3201.644414 | 43.3 |
| 562 | 60101 | 60000 | CAJAMARCA | CAJAMARCA | CAJAMARCA | 19.748740 | 2081.118853 | 42.5 |
| 696 | 80101 | 80000 | CUSCO | CUSCO | CUSCO | 8.214426 | 3396.604469 | 51.2 |
| 808 | 90101 | 90000 | HUANCAVELICA | HUANCAVELICA | HUANCAVELICA | 17.073401 | 2654.809240 | 38.4 |
| 908 | 100101 | 100000 | HUÁNUCO | HUÁNUCO | HUANUCO | 13.066336 | 2189.070100 | 45.4 |
| 992 | 110101 | 110000 | ICA | ICA | ICA | 2.165357 | 7595.170228 | 60.0 |
| 1035 | 120101 | 120000 | JUNÍN | HUANCAYO | HUANCAYO | 6.563487 | 4473.586854 | 51.1 |
| 1159 | 130101 | 130000 | LA LIBERTAD | TRUJILLO | TRUJILLO | 4.989521 | 3936.456002 | 54.8 |
| 1242 | 140101 | 140000 | LAMBAYEQUE | CHICLAYO | CHICLAYO | 7.491534 | 3849.700330 | 53.4 |
| 1280 | 150101 | 150000 | LIMA | LIMA | LIMA | 7.958984 | 13182.516370 | 70.7 |
| 1450 | 160101 | 160000 | LORETO | MAYNAS | IQUITOS | 8.626166 | 1505.223823 | 48.3 |
| 1503 | 170101 | 170000 | MADRE DE DIOS | TAMBOPATA | TAMBOPATA | 4.787605 | 4161.142764 | 61.4 |
| 1514 | 180101 | 180000 | MOQUEGUA | MARISCAL NIETO | MOQUEGUA | 7.023362 | 7244.112850 | 65.9 |
| 1534 | 190101 | 190000 | PASCO | PASCO | CHAUPIMARCA | 24.613999 | 2628.772378 | 47.8 |
| 1563 | 200101 | 200000 | PIURA | PIURA | PIURA | 9.786115 | 3092.914978 | 51.3 |
| 1628 | 210101 | 210000 | PUNO | PUNO | PUNO | 12.829234 | 2441.895177 | 46.6 |
| 1738 | 220101 | 220000 | SAN MARTÍN | MOYOBAMBA | MOYOBAMBA | 16.339630 | 2433.887759 | 48.3 |
| 1815 | 230101 | 230000 | TACNA | TACNA | TACNA | 7.691274 | 5080.827164 | 59.0 |
| 1843 | 240101 | 240000 | TUMBES | TUMBES | TUMBES | 8.706752 | 2200.674031 | 55.5 |
| 1856 | 250101 | 250000 | UCAYALI | CORONEL PORTILLO | CALLERÍA | 6.229848 | 4059.106491 | 48.4 |
2. Choropleth#
poverty
| UBIGEO1 | UBIGEO2 | DEPARTAMENTO | PROVINCIA | DISTRITO | POVERTY_RATE | PBI_PC | IDH | |
|---|---|---|---|---|---|---|---|---|
| 0 | 10101 | 10000 | AMAZONAS | CHACHAPOYAS | CHACHAPOYAS | 9.034625 | 3449.564733 | 41.8 |
| 1 | 10102 | 10000 | AMAZONAS | CHACHAPOYAS | ASUNCIÓN | 36.519949 | 3449.564733 | 41.8 |
| 2 | 10103 | 10000 | AMAZONAS | CHACHAPOYAS | BALSAS | 45.732962 | 3449.564733 | 41.8 |
| 3 | 10104 | 10000 | AMAZONAS | CHACHAPOYAS | CHETO | 39.169782 | 3449.564733 | 41.8 |
| 4 | 10105 | 10000 | AMAZONAS | CHACHAPOYAS | CHILIQUÍN | 53.045662 | 3449.564733 | 41.8 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 1868 | 250302 | 250000 | UCAYALI | PADRE ABAD | IRAZOLA | 12.899560 | 4059.106491 | 48.4 |
| 1869 | 250303 | 250000 | UCAYALI | PADRE ABAD | CURIMANÁ | 9.014545 | 4059.106491 | 48.4 |
| 1870 | 250304 | 250000 | UCAYALI | PADRE ABAD | NESHUYA | 11.946857 | 4059.106491 | 48.4 |
| 1871 | 250305 | 250000 | UCAYALI | PADRE ABAD | ALEXANDER VON HUMBOLDT | 12.899560 | 4059.106491 | 48.4 |
| 1872 | 250401 | 250000 | UCAYALI | PURÚS | PURÚS | 31.928338 | 4059.106491 | 48.4 |
1873 rows × 8 columns
# government palace coordinates
lat_palacio = -12.0757538
long_palacio = -76.9863174
zoom_start = 5
z = fm.Map(location = [lat_palacio, long_palacio], tiles='cartodbpositron', zoom_start = zoom_start)
# Mandatory: geo_data in GeoJson format
# columns: variables from economics indicators data set
# Atention !!! key_on: commom variable between geodata and data "feature.properties.(name of variable)"
fm.Choropleth(
geo_data=distritos1,
data=poverty,
columns=['UBIGEO1', 'POVERTY_RATE'],
key_on="feature.properties.UBIGEO1",
fill_color="YlOrRd",
fill_opacity=0.8,
line_opacity=0.2,
legend_name="Poverty Rate (%)",
smooth_factor=0,
Highlight= True,
line_color = "#0000",
overlay=True,
nan_fill_color = "White" # fill white missing values
).add_to(z)
#fm.LayerControl().add_to(z)
# Save in a html format
#z.save("Poverty_Map.html")
z
Make this Notebook Trusted to load map: File -> Trust Notebook
# Add quantile on legend
bins = list(poverty2["IDH"].quantile([0, 0.2, 0.4, 0.6,0.8, 1]))
bins
[38.4, 44.56, 48.32, 51.54, 59.400000000000006, 70.7]
lat_palacio = -12.0757538
long_palacio = -76.9863174
z = fm.Map(location = [lat_palacio, long_palacio], zoom_start = 5)
fm.Choropleth(geo_data=dpto1,
data=poverty2,
columns=["UBIGEO2", "IDH"],
key_on="feature.properties.UBIGEO2",
fill_color="Reds",
fill_opacity=0.8,
legend_name="Human development index",
bins = bins,
reset = True
).add_to(z)
fm.LayerControl().add_to(z)
z
z.save("IDH_map_peru.html")
lat_palacio = -12.0757538
long_palacio = -76.9863174
#add quantils
bins = list(poverty2["IDH"].quantile([0, 0.2, 0.4, 0.6,0.8, 1]))
z = fm.Map(location = [lat_palacio, long_palacio], zoom_start = 5)
fm.Choropleth(geo_data=dpto1,
data=poverty2,
columns=["UBIGEO2", "IDH"],
key_on="feature.properties.UBIGEO2",
fill_color="Reds",
fill_opacity=0.8,
legend_name="Human development index",
bins = bins,
reset = True
).add_to(z)
#Merge both dataset to add informaction by region
data_both = pd.merge(dpto1, poverty2, how="inner", on="UBIGEO2")
data_both = data_both.to_json() # from pandas to Json
# Color and opacity of each region
style_function = lambda x: {'fillColor': '#ffffff',
'color':'#000000',
'fillOpacity': 0.1,
'weight': 0.1}
# Color and opacity of each selected region
highlight_function = lambda x: {'fillColor': '#000000',
'color':'#000000',
'fillOpacity': 0.5,
'weight': 0.1}
details = fm.features.GeoJson(
data = data_both,
style_function=style_function,
control=False,
highlight_function=highlight_function,
tooltip=fm.features.GeoJsonTooltip(
fields=['DEPARTAMENTO','IDH'], #Variable selection
aliases=['Administrative Region', 'Human Development Index'], # renames
style=("background-color: white; color: #333333; font-family: arial; font-size: 12px; padding: 10px;")
)
)
# Add new features
z.add_child(details)
z.keep_in_front(details)
z
Make this Notebook Trusted to load map: File -> Trust Notebook
3. Heat Map and Interactive Map#
#Gettting the character format
base = open(r'../_data/Folium/enaho.csv', 'rb').read()
det = chardet.detect(base)
charenc = det['encoding']
ENAHO = pd.read_csv( r'../_data/Folium/enaho.csv', encoding = charenc)
ENAHO
| conglome | vivienda | hogar | dominio | registro_sunarp | nbi1 | nbi2 | nbi3 | nbi4 | nbi5 | longitud | latitud | prog_mujer | prog_juntos | prog_juvenil | beca18 | bonogas | bono_ind | bono_uni | retiro_afp | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 6024 | 1 | 11 | 8 | NaN | 0 | 0 | 0 | 0 | 0 | -77.133652 | -11.825158 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 |
| 1 | 6024 | 15 | 11 | 8 | 1.0 | 0 | 0 | 0 | 0 | 0 | -77.133652 | -11.825158 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 2 | 6024 | 27 | 11 | 8 | 1.0 | 0 | 0 | 0 | 0 | 0 | -77.133652 | -11.825158 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 3 | 6024 | 41 | 11 | 8 | 2.0 | 0 | 0 | 0 | 0 | 0 | -77.133652 | -11.825158 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 1.0 | 1.0 |
| 4 | 6024 | 68 | 11 | 8 | 1.0 | 0 | 0 | 0 | 0 | 0 | -77.133652 | -11.825158 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 4063 | 18524 | 338 | 11 | 8 | NaN | 0 | 0 | 0 | 0 | 0 | -76.981087 | -11.797198 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 |
| 4064 | 18524 | 341 | 11 | 8 | NaN | 0 | 0 | 0 | 0 | 0 | -76.981087 | -11.797198 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 4065 | 18524 | 440 | 11 | 8 | NaN | 0 | 0 | 0 | 0 | 0 | -76.981087 | -11.797198 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 |
| 4066 | 18524 | 452 | 11 | 8 | NaN | 0 | 0 | 0 | 0 | 0 | -76.981087 | -11.797198 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 4067 | 18524 | 462 | 11 | 8 | NaN | 0 | 0 | 0 | 0 | 1 | -76.981087 | -11.797198 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
4068 rows × 20 columns
# beneficiary households of the universal bond
bono_uni= ENAHO[ENAHO.bono_uni == 1]
bono_uni
| conglome | vivienda | hogar | dominio | registro_sunarp | nbi1 | nbi2 | nbi3 | nbi4 | nbi5 | longitud | latitud | prog_mujer | prog_juntos | prog_juvenil | beca18 | bonogas | bono_ind | bono_uni | retiro_afp | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 3 | 6024 | 41 | 11 | 8 | 2.0 | 0 | 0 | 0 | 0 | 0 | -77.133652 | -11.825158 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 1.0 | 1.0 |
| 4 | 6024 | 68 | 11 | 8 | 1.0 | 0 | 0 | 0 | 0 | 0 | -77.133652 | -11.825158 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 |
| 5 | 6024 | 108 | 11 | 8 | 1.0 | 0 | 0 | 0 | 0 | 0 | -77.133652 | -11.825158 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 |
| 21 | 6027 | 101 | 11 | 8 | NaN | 1 | 0 | 0 | 0 | 0 | -77.138649 | -11.830967 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 |
| 32 | 6029 | 92 | 11 | 8 | NaN | 0 | 0 | 0 | 0 | 0 | -77.129189 | -11.841843 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 4052 | 18512 | 52 | 11 | 8 | NaN | 0 | 0 | 0 | 0 | 0 | -77.039070 | -11.836553 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 1.0 |
| 4056 | 18512 | 98 | 11 | 8 | NaN | 0 | 0 | 1 | 0 | 1 | -77.039070 | -11.836553 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 |
| 4060 | 18524 | 3 | 11 | 8 | NaN | 0 | 0 | 0 | 0 | 0 | -76.981087 | -11.797198 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 |
| 4061 | 18524 | 5 | 11 | 8 | NaN | 0 | 0 | 0 | 0 | 0 | -76.981087 | -11.797198 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 |
| 4065 | 18524 | 440 | 11 | 8 | NaN | 0 | 0 | 0 | 0 | 0 | -76.981087 | -11.797198 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 |
432 rows × 20 columns
# Coordinamtes in a list
hogares = list(zip(bono_uni['latitud'], bono_uni['longitud']))
hogares
Show code cell output
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(-11.941335, -76.956573),
(-11.941335, -76.956573),
(-11.941335, -76.956573),
(-12.016133, -76.873047),
(-12.069239, -77.058716),
(-12.015506, -77.075218),
(-12.015506, -77.075218),
(-12.026865, -77.052299),
(-12.065591, -76.994637),
(-12.181662, -77.004402),
(-11.84586, -77.103714),
(-11.84586, -77.103714),
(-11.84586, -77.103714),
(-11.84586, -77.103714),
(-11.953346, -77.075386),
(-11.953346, -77.075386),
(-11.953346, -77.075386),
(-12.216764, -76.950737),
(-12.156192, -76.933136),
(-12.480495, -76.793121),
(-11.844765, -77.108894),
(-11.844765, -77.108894),
(-11.844765, -77.108894),
(-12.01957, -76.837631),
(-11.903223, -76.955154),
(-11.903223, -76.955154),
(-11.903223, -76.955154),
(-12.078948, -76.74971),
(-12.080252, -77.017815),
(-12.020346, -77.075302),
(-12.061877, -77.056992),
(-12.077748, -77.00621),
(-12.077748, -77.00621),
(-12.183489, -77.011665),
(-11.995973, -77.1064),
(-12.00456, -77.071831),
(-12.18486, -77.002609),
(-12.18486, -77.002609),
(-12.171728, -76.940987),
(-11.815562, -77.136421),
(-11.865419, -77.062553),
(-11.865419, -77.062553),
(-12.220265, -76.919044),
(-12.220265, -76.919044),
(-11.947125, -77.011978),
(-11.947125, -77.011978),
(-11.904354, -76.971199),
(-11.904354, -76.971199),
(-12.00824, -76.816681),
(-12.00824, -76.816681),
(-12.00824, -76.816681),
(-12.022938, -77.084244),
(-12.022938, -77.084244),
(-12.032175, -76.9963),
(-12.032175, -76.9963),
(-12.032175, -76.9963),
(-11.849547, -77.009445),
(-11.849547, -77.009445),
(-12.219831, -76.996338),
(-12.029696, -77.091751),
(-12.04606, -76.964653),
(-12.06182, -77.049118),
(-12.06182, -77.049118),
(-12.06182, -77.049118),
(-11.869674, -77.017708),
(-11.869674, -77.017708),
(-11.952466, -77.086853),
(-11.952466, -77.086853),
(-11.952466, -77.086853),
(-11.994779, -77.094841),
(-11.971277, -77.083199),
(-12.041748, -77.051529),
(-12.143287, -76.980064),
(-12.143287, -76.980064),
(-12.143287, -76.980064),
(-11.869614, -77.009827),
(-11.953676, -77.098991),
(-12.000566, -76.921173),
(-12.000566, -76.921173),
(-12.000566, -76.921173),
(-12.28418, -76.854202),
(-11.861197, -77.103958),
(-11.861197, -77.103958),
(-11.939064, -76.950546),
(-11.939064, -76.950546),
(-12.13063, -76.932968),
(-12.13063, -76.932968),
(-12.13063, -76.932968),
(-12.13063, -76.932968),
(-12.13063, -76.932968),
(-12.072483, -77.04789),
(-12.072483, -77.04789),
(-12.090082, -77.005638),
(-12.127042, -76.99295),
(-11.987869, -77.090599),
(-12.035714, -77.088928),
(-12.035714, -77.088928),
(-11.995614, -77.009514),
(-12.088955, -77.03299),
(-12.088955, -77.03299),
(-12.075452, -77.019257),
(-12.075452, -77.019257),
(-11.875884, -77.023659),
(-11.966658, -77.092644),
(-11.91429, -77.043983),
(-11.91429, -77.043983),
(-11.991151, -76.853195),
(-12.024905, -76.892906),
(-12.064677, -77.024765),
(-12.235798, -76.916191),
(-12.154671, -76.958199),
(-12.154671, -76.958199),
(-11.866397, -77.021126),
(-11.866397, -77.021126),
(-11.866397, -77.021126),
(-11.866397, -77.021126),
(-11.939857, -76.965118),
(-11.939857, -76.965118),
(-11.939857, -76.965118),
(-12.048248, -76.89975),
(-12.033723, -76.956528),
(-11.994685, -76.938454),
(-11.994685, -76.938454),
(-11.961085, -77.073326),
(-11.961085, -77.073326),
(-11.961085, -77.073326),
(-11.836553, -77.03907),
(-11.836553, -77.03907),
(-11.797198, -76.981087),
(-11.797198, -76.981087),
(-11.797198, -76.981087)]
# List of tiles
z = fm.Map(location = [lat_palacio, long_palacio], zoom_start = 12)
# Cluster Map
MarkerCluster( hogares, name = 'Cluster' ).add_to(z)
z
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# Heat Map
HeatMap(data=bono_uni[['latitud', 'longitud']], radius=20, name = 'Heatmap').add_to(z)
z
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#Add different kind of tiles
Tiles = ["stamenterrain","stamenwatercolor","cartodbpositron","openstreetmap", "cartodbdark_matter"]
for i in Tiles:
fm.TileLayer(i, name = i, control = True).add_to(z)
fm.LayerControl(collapsed=False).add_to(z)
z.save("cluster_heatmap_bono.html")
z
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# beneficiary households of the universal bond
bono_uni= ENAHO[ENAHO.bono_uni == 1]
# Coordinamtes in a list
hogares = list(zip(bono_uni['latitud'], bono_uni['longitud']))
# List of tiles
Tiles = ["stamenterrain","stamenwatercolor","cartodbpositron","openstreetmap", "cartodbdark_matter"]
z = fm.Map(location = [lat_palacio, long_palacio], zoom_start = 12)
# Heat Map
HeatMap(data=bono_uni[['latitud', 'longitud']], radius=20, name = 'Heatmap').add_to(z)
# Cluster Map
MarkerCluster(hogares, name = 'Cluster').add_to(z)
#Add different kind of tiles
for i in Tiles:
fm.TileLayer(i, name = i, control = True).add_to(z)
fm.LayerControl(collapsed=False).add_to(z)
z.save("cluster_heatmap_bono.html")
4. Marker#
bonogas = ENAHO[ENAHO.bonogas == 1]
bn = fm.Map(location = [lat_palacio, long_palacio], zoom_start = 12, control_scale=True)
# Loop for rows
for idx, row in bonogas.iterrows():
Marker([row['latitud'], row['longitud']]).add_to(bn)
bn
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5. Circles#
base1 = ENAHO[ENAHO['nbi1'] == 1]
base2 = ENAHO[ENAHO['nbi2'] == 1]
base3 = ENAHO[ENAHO['nbi3'] == 1]
base4 = ENAHO[ENAHO['nbi4'] == 1]
base5 = ENAHO[ENAHO['nbi5'] == 1]
globals()["base1"]
| conglome | vivienda | hogar | dominio | registro_sunarp | nbi1 | nbi2 | nbi3 | nbi4 | nbi5 | longitud | latitud | prog_mujer | prog_juntos | prog_juvenil | beca18 | bonogas | bono_ind | bono_uni | retiro_afp | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 21 | 6027 | 101 | 11 | 8 | NaN | 1 | 0 | 0 | 0 | 0 | -77.138649 | -11.830967 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 |
| 35 | 6032 | 87 | 11 | 8 | 1.0 | 1 | 0 | 0 | 0 | 0 | -77.128754 | -11.836511 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 128 | 6057 | 16 | 11 | 8 | NaN | 1 | 0 | 0 | 0 | 0 | -77.124878 | -11.897841 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 132 | 6057 | 96 | 11 | 8 | NaN | 1 | 0 | 0 | 0 | 0 | -77.124878 | -11.897841 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 506 | 6152 | 46 | 11 | 8 | NaN | 1 | 0 | 0 | 0 | 0 | -77.144478 | -12.059699 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 4010 | 18210 | 16 | 11 | 8 | 1.0 | 1 | 0 | 0 | 0 | 0 | -76.928871 | -12.239233 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 4037 | 18215 | 115 | 11 | 8 | NaN | 1 | 0 | 1 | 0 | 0 | -76.950340 | -12.125921 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 4053 | 18512 | 90 | 11 | 8 | NaN | 1 | 0 | 0 | 0 | 0 | -77.039070 | -11.836553 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 4059 | 18512 | 124 | 11 | 8 | NaN | 1 | 0 | 0 | 0 | 0 | -77.039070 | -11.836553 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 4062 | 18524 | 335 | 11 | 8 | NaN | 1 | 0 | 0 | 0 | 0 | -76.981087 | -11.797198 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
75 rows × 20 columns
#unsatisfied basic need
#nbi1: Inadequate housing
#nbi2: Overcrowded housing
#nbi3: Housing without SSHH
#nbi4: School non-attendance
#nbi5: High economic dependency
base1 = ENAHO[ENAHO['nbi1'] == 1]
base2 = ENAHO[ENAHO['nbi2'] == 1]
base3 = ENAHO[ENAHO['nbi3'] == 1]
base4 = ENAHO[ENAHO['nbi4'] == 1]
base5 = ENAHO[ENAHO['nbi5'] == 1]
m = fm.Map(location = [lat_palacio, long_palacio], zoom_start = 12.5, control_scale=True)
colors = ['forestgreen','darkred', 'blue', 'lime']
for j in range(1,5):
for idx, row in globals()[f'base{j}'].iterrows():
fm.Circle([row['latitud'], row['longitud']], radius = 200, color = colors[j-1]).add_to(m)
m
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# Marks and unsatisfied basic need
m = fm.Map(location = [lat_palacio, long_palacio], zoom_start = 11, control_scale=True)
colors = ['purple','lightgreen', 'red', 'darkblue', 'orange']
nbi = ['Vivienda inadecuada','Vivienda con hacinamiento', 'Vivienda sin SS.HH', 'Inasistencia escolar', 'Alta depenencia Económica']
for j in range(1,5):
for idx, row in globals()[f'base{j}'].iterrows():
Marker([row['latitud'], row['longitud']], icon=fm.Icon(color=colors[j-1]), popup= nbi[j-1]).add_to(m)
m
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6. Interactive Map part II#
# Solidaridad Hospital Centers
base = open(r'../_data/Folium/Solidaridad_Center.csv', 'rb').read()
det = chardet.detect(base)
charenc = det['encoding']
h_solidaridad = pd.read_csv( r'../_data/Folium/Solidaridad_Center.csv', encoding = charenc)
h_solidaridad
| Health_center | distrito | direction | Schedule | phone | especialidades | available_beds | Prueba_Covid | Centro_vacunacion | latitud | longitud | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | SISOL001 | ATE | Av. Matropolitana 187 | 7:00-19:00 | (01) 352-0798 | 35 | 15 | No | Si | -12.033665 | -76.942156 |
| 1 | SISOL002 | Santa Anita | Av. Cultura 808 | 7:00 - 20:00 | (01) 231 3162 | 30 | 5 | Si | Si | -12.046247 | -76.968734 |
| 2 | SISOL003 | Agustino | Av. Cesar Vallejo con J.C Mariátegui | 7:00 - 20:0 | (01) 385 0954 | 35 | 20 | Si | No | -12.039829 | -76.997270 |
| 3 | SISOL004 | La Victoria | c. Manco Capac 218 | 8:00-18:00 | (01) 426 4618 | 30 | 10 | No | Si | -12.060868 | -77.029709 |
| 4 | SISOL005 | Cercado de Lima | Jirón Camaná 7000 | 8:00-20:00 | (01) 272 9422 | 28 | 11 | Si | Si | -12.049331 | -77.035446 |
| 5 | SISOL006 | Rimac | Av. Tupac Amaru Cdra.16 | 7:30-18:30 | (01) 534 8556 | 20 | 25 | No | No | -12.013246 | -77.052052 |
| 6 | SISOL007 | Los Olivos | Av. Universitaria 15304 | 7:30-18:30 | (01) 771 4269 | 30 | 15 | Si | Si | -11.979238 | -77.076962 |
| 7 | SISOL008 | Breña | Av. Colonial 2001 | 7:00-19:00 | (01) 336 8228 | 25 | 20 | Si | Si | -12.049191 | -77.068206 |
| 8 | SISOL009 | Magdalena del Mar | Jr. Bolognesi 2013 | 7:00 - 20:00 | (01) 263 6103 | 20 | 5 | No | Si | -12.092302 | -77.070088 |
| 9 | SISOL010 | Surquillo | Av. Angamos 734 | 8:00-18:00 | (01) 243 1120 | 34 | 15 | Si | Si | -12.113604 | -77.023852 |
| 10 | SISOL011 | Chorrillos | Av. Fernando Teran | 7:00-19:00 | (01) 467 1684 | 40 | 25 | No | Si | -12.167886 | -77.019242 |
| 11 | SISOL012 | Villa María del Triunfo | Av. Pastor Sevilla con Óvalo Pumacahua | 6:00-18:00 | (01) 292 3504 | 38 | 17 | Si | Si | -12.170943 | -76.946157 |
| 12 | SISOL013 | Lince | Av. César Canevaro | 8:00-20:00 | (01) 472 6755 | 31 | 20 | Si | No | -12.083984 | -77.039484 |
# Function create table by each Health center using html. This funtion will be aplly by each row
# Almost alway each code on html requires a beginnig <p> and ending </p>
def visual_html(i):
# information by Health center
district = h_solidaridad['distrito'].iloc[i]
direction = h_solidaridad['direction'].iloc[i]
atencion = h_solidaridad['Schedule'].iloc[i]
phone = h_solidaridad['phone'].iloc[i]
espec = h_solidaridad['especialidades'].iloc[i]
beds = h_solidaridad['available_beds'].iloc[i]
covid = h_solidaridad['Prueba_Covid'].iloc[i]
vacunation = h_solidaridad['Centro_vacunacion'].iloc[i]
Health_center = h_solidaridad['Health_center'].iloc[i]
# Color by each column of table
left_col_colour = "#FA8072"
right_col_colour = "#BDC3C7"
html = """<!DOCTYPE html>
<html>
<head>
<p> Solidaridad Health Center </p>
</head>
<table style="height: 126px; width: 350px;"> <!-- Comment: Create a teable. -->
<!-- Add information -->
<tbody>
<tr>
<!-- Add color by column -->
<td style="background-color: """+ left_col_colour +""";"><span style="color: #ffffff;">District of Lima</span></td>
<td style="width: 150px;background-color: """+ right_col_colour +""";">{}</td>""".format(district) + """
</tr>
<tr>
<td style="background-color: """+ left_col_colour +""";"><span style="color: #ffffff;">Direction</span></td>
<td style="width: 150px;background-color: """+ right_col_colour +""";">{}</td>""".format(direction) + """
</tr>
<tr>
<td style="background-color: """+ left_col_colour +""";"><span style="color: #ffffff;">Openning Hour</span></td>
<td style="width: 150px;background-color: """+ right_col_colour +""";">{}</td>""".format(atencion) + """
</tr>
<tr>
<td style="background-color: """+ left_col_colour +""";"><span style="color: #ffffff;">Phone - number</span></td>
<td style="width: 150px;background-color: """+ right_col_colour +""";">{}</td>""".format(phone) + """
</tr>
<tr>
<td style="background-color: """+ left_col_colour +""";"><span style="color: #ffffff;">Number of medical specialties</span></td>
<td style="width: 150px;background-color: """+ right_col_colour +""";">{}</td>""".format(espec) + """
</tr>
<tr>
<td style="background-color: """+ left_col_colour +""";"><span style="color: #ffffff;">Available beds</span></td>
<td style="width: 150px;background-color: """+ right_col_colour +""";">{}</td>""".format(beds) + """
</tr>
<tr>
<td style="background-color: """+ left_col_colour +""";"><span style="color: #ffffff;">Covid test</span></td>
<td style="width: 150px;background-color: """+ right_col_colour +""";">{}</td>""".format(covid) + """
</tr>
<tr>
<td style="background-color: """+ left_col_colour +""";"><span style="color: #ffffff;">Vaccination center</span></td>
<td style="width: 150px;background-color: """+ right_col_colour +""";">{}</td>""".format(vacunation) + """
</tr>
</tbody>
</table>
</html>
"""
return html
ubication = h_solidaridad['latitud'].mean(), h_solidaridad['longitud'].mean() # Average point
sol = fm.Map(location = ubication, zoom_start=12)
for i in range(0,len(h_solidaridad)):
html = visual_html(i)
iframe = br.element.IFrame(html=html,width=350,height=300)
popup = fm.Popup(iframe, parse_html=True)
fm.Marker([h_solidaridad['latitud'].iloc[i],h_solidaridad['longitud'].iloc[i]],
popup=popup, icon=fm.Icon(color= 'blue', icon='medkit', prefix="fa")).add_to(sol)
sol.save("hospital_solidaridad.html")
sol
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# Alternative
def visual_html(i):
html="""
<h4>Direction: </h4>""" + str(h_solidaridad.iloc[i]['distrito']) + " - " + str(h_solidaridad.iloc[i]['direction']) +\
"""<h4>Phone - number:</h4>""" + str(h_solidaridad.iloc[i]['phone']) +\
"""<h4>Openning hour:</h4>""" + str(h_solidaridad.iloc[i]['Schedule']) +\
"""<h4>Number of medical specialties:</h4>""" + str(h_solidaridad.iloc[i]['especialidades']) +\
"""<h4>Available_beds:</h4>""" + str(h_solidaridad.iloc[i]['available_beds']) +\
"""<h4>Covid Test:</h4>""" + str(h_solidaridad.iloc[i]['Prueba_Covid']) +\
"""<h4>Vaccination center:</h4>""" + str(h_solidaridad.iloc[i]['Centro_vacunacion'])
return html
ubication = h_solidaridad['latitud'].mean(), h_solidaridad['longitud'].mean()
sol = fm.Map(location = ubication, zoom_start=12)
for i in range(0,len(h_solidaridad)):
html = visual_html(i)
iframe = br.element.IFrame(html=html,width=350,height=300)
popup = fm.Popup(iframe,parse_html=True)
fm.Marker([h_solidaridad['latitud'].iloc[i],h_solidaridad['longitud'].iloc[i]],
popup=popup,icon=fm.Icon(color= 'red', icon='medkit', prefix="fa")).add_to(sol)
sol.save("hospitals.html")
sol
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References:#
MINEM Geografic mining centers#
http://www.minem.gob.pe/_publicaSector.php?idSector=1&idCategoria=24
Poverty map at distric level#
Geo-spatial information#
Folium#
https://python-visualization.github.io/folium/index.html
https://www.kaggle.com/alexisbcook/interactive-maps
https://www.kaggle.com/dabaker/fancy-folium
https://towardsdatascience.com/how-to-step-up-your-folium-choropleth-map-skills-17cf6de7c6fe
https://www.kaggle.com/mbnb8317/ds4c-tutorial-all-about-folium-pydeck