I am trying to create a map for "Ecuador" country with the coordinates 1.8312° S, 78.1834° W in Folium map using the below code
map = folium.Map(location=[1.8312, 78.1834], zoom_start=12)
map
The map is not appearing and getting below empty output
When I tried for another country example: US, I am getting an output. Not sure why I am not getting for Ecuador. I am using Jupyter notebook.
The coordinates you used (1.8312, 78.1834) refer to south and west. Folium needs north and east.
Therefore, use:
import folium
m = folium.Map(location=[-0.22985, -78.52495], zoom_start=12)
m
and you get your map:
Related
I having trouble getting some air pollution data to show different colors in a chloropleth map using folium. Please let me know where my code may be throwing an error. I think it is the key_on parameter but need help.
This is how my map turns out.
enter image description here
What I would like is for the mean concentration of the air pollution data to show up on the map but the map is still greyed out.
Here are the files I used:
Geojson file - Used "download zip" in upper right of this website https://gist.github.com/miguelpaz/edbc79fc55447ae736704654b3b2ef90#file-uhf42-geojson
Data file - Exported data from here https://a816-dohbesp.nyc.gov/IndicatorPublic/VisualizationData.aspx?id=2023,719b87,122,Summarize
Here is my code:
import geopandas as gpd
import folium
#clean pollution data
pm_df1 = pd.read_csv('/work/Fine Particulate Matter (PM2.5).csv',header = 5, usecols = ['GeoTypeName', 'Borough','Geography', 'Geography ID','Mean (mcg per cubic meter)'], nrows = 140)
#limit dataframe to rows with neighborhood (UHF 42) that matches geojson file
pm_df2 = pm_df1[(pm_df1['GeoTypeName'] == 'Neighborhood (UHF 42)')]
pm_df2
#clean geojson file
uhf_df2 = gpd.read_file('/work/uhf42.geojson', driver='GeoJSON')
uhf_df2.head()
#drop row 1 that has no geography
uhf_df3 = uhf_df2.iloc[1:]
uhf_df3.head()
## create a map
pm_testmap = folium.Map(location=[40.65639,-73.97379], tiles = "cartodbpositron", zoom_start=10)
# generate choropleth map
pm_testmap.choropleth(
geo_data=uhf_df3,
data=pm_df2,
columns=['Geography', 'Mean (mcg per cubic meter)'],
key_on='feature.properties.uhf_neigh', #think this is where I mess up.
fill_color='BuPu',
fill_opacity=0.2,
line_opacity=0.7,
legend_name='Average dust concentration',
smooth_factor=0)
# display map
pm_testmap
The problem with key_on is right as you think.
Both data have the name of UHF written on them, but in a completely different form.
In order to link these two, the data must first be preprocessed.
I don't know your data.
It would be nice if you could df.head() the two data to show them, but I'll explain based on the data I checked through the link you provided.
In your geojson file, uhf_neigh simply says Northeast Bronx. However, your PM data appears to have the region listed as Bronx: Northeast Bronx. The following process seems to be necessary to unify your local name before plotting map.
uhf_df2['UHF_NEIGH'] = uhf_df2['BOROUGH']+ ': ' + uhf_df2['UHF_NEIGH']
I tried to run it with your data and code, but it was not even displaying the map. There should be no problem in your code because you have associated the place name in the data frame with the place name in geojson. I gave up on the string association and changed the association to a place name code association, and the map was displayed. The provided csv file failed to load, so I deleted the unnecessary lines and loaded it. Also, I read the file as a json file instead of geopandas.
import pandas as pd
import geopandas as gpd
import json
import folium
pm_df1 = pd.read_csv('./data/test_20211221.csv')
pm_df1 = pm_df1[['GeoTypeName', 'Borough', 'Geography', 'Geography ID', 'Mean (mcg per cubic meter)']]
pm_df2 = pm_df1[(pm_df1['GeoTypeName'] == 'Neighborhood (UHF 42)')]
with open('./data/uhf42.geojson') as f:
uhf_df3 = json.load(f)
pm_testmap = folium.Map(location=[40.65639,-73.97379], tiles = "cartodbpositron", zoom_start=10)
# generate choropleth map
pm_testmap.choropleth(
geo_data=uhf_df3,
data=pm_df2,
columns=['Geography ID', 'Mean (mcg per cubic meter)'],
key_on='feature.properties.uhfcode', #think this is where I mess up.
fill_color='BuPu',
fill_opacity=0.2,
line_opacity=0.7,
legend_name='Average dust concentration',
smooth_factor=0)
# display map
pm_testmap
I am trying to load in a shapefile with the use of geopandas. I have read the file in, but when I go to add the layer to the ipyleaflet map, an error comes up that says 'GeoDataFrame' object has no attribute 'model_id'.
I have tried many files and it just will not read with the geodataframe.
from ipyleaflet import Map, basemaps
fire_was = gpd.read_file('fire_wa.shp')
#add map
center = [47.409824923593575,-120.43401014843751]zoom = 7
m = Map(basemap=basemaps.OpenStreetMap.Mapnik, center=center, zoom=zoom)
m.add_layer(fire_was)
m
Any ideas?
I figured it out. The example I had seen did not show the below but checked more of the documentation of ipyleaflet. You must use GeoData to load the geodataframe.
from ipyleaflet import Map, GeoData, basemaps, LayersControl
fire_was = gpd.read_file('fire_wa.shp') firewa = GeoData(geo_dataframe = fire_was)
m = Map(center =(47.409824923593575,-120.43401014843751), zoom =7,basemap= basemaps.Esri.WorldTopoMap)
m.add_layer(firewa)
m
I have a dataframe created on a csv file about Italian Covid-19 spread all over regions. I was trying to create a px.choropleth plot in which showing Total Positive values for every regions in Italy.
This the code tried:
italy_regions=[i for i in region['Region'].unique()]
fig = px.choropleth(italy_last, locations="Country",
locationmode=italy_regions,
color=np.log(italy_last["TotalPositive"]),
hover_name="Region", hover_data=['TotalPositive'],
color_continuous_scale="Sunsetdark",
title='Regions with Positive Cases')
fig.update(layout_coloraxis_showscale=False)
fig.show()
Now I report some info: 'Country' is the name given to my dataframe and is filled only with the same values: 'Italy'. If I only input 'location="Country"' the graph is fine and I can see Italy colored into the world map.
The problems start when I try to make pyplot color my regions. As I'm a newbye in pyplot express, I read some examples and I thought I had to create a list of italian regions names and then put into 'choropleth' as input for 'barmode'.
Clearly I'm wrong.
So, what is the procedure to follow to make it run (if any)?
In case of need, I can provide both the csv file that the jupyter file I'm working on.
You need to provide a geojson with Italian region borders as geojson parameter to plotly.express.choropleth, for instance this one
https://gist.githubusercontent.com/datajournalism-it/48e29e7c87dca7eb1d29/raw/2636aeef92ba0770a073424853f37690064eb0ea/regioni.geojson
If you use this one, you need to explicitly pass featureidkey='properties.NOME_REG' as a parameter of plotly.express.choropleth.
Working example:
import pandas as pd
import requests
import plotly.express as px
regions = ['Piemonte', 'Trentino-Alto Adige', 'Lombardia', 'Puglia', 'Basilicata',
'Friuli Venezia Giulia', 'Liguria', "Valle d'Aosta", 'Emilia-Romagna',
'Molise', 'Lazio', 'Veneto', 'Sardegna', 'Sicilia', 'Abruzzo',
'Calabria', 'Toscana', 'Umbria', 'Campania', 'Marche']
# Create a dataframe with the region names
df = pd.DataFrame(regions, columns=['NOME_REG'])
# For demonstration, create a column with the length of the region's name
df['name_length'] = df['NOME_REG'].str.len()
# Read the geojson data with Italy's regional borders from github
repo_url = 'https://gist.githubusercontent.com/datajournalism-it/48e29e7c87dca7eb1d29/raw/2636aeef92ba0770a073424853f37690064eb0ea/regioni.geojson'
italy_regions_geo = requests.get(repo_url).json()
# Choropleth representing the length of region names
fig = px.choropleth(data_frame=df,
geojson=italy_regions_geo,
locations='NOME_REG', # name of dataframe column
featureidkey='properties.NOME_REG', # path to field in GeoJSON feature object with which to match the values passed in to locations
color='name_length',
color_continuous_scale="Magma",
scope="europe",
)
fig.update_geos(showcountries=False, showcoastlines=False, showland=False, fitbounds="locations")
fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0})
fig.show()
Output image
I have a data frame for UK data that looks something like this:
longitude latitude region priority
51.307733 -0.75708898 South East High
51.527477 -0.20646542 London Medium
51.725135 0.4747223 East of England Low
This dataframe is several thousand rows long. I want a heatmap of the UK broken down by the regions and colour intensity to be dependent on the priority in each region.
I would like to know the best way to turn this into a heatmap of the UK. I have tried geoPandas and Plotly but I have no functioning knowledge of these. Are these the best way to do it or is there a tool out there that you can simply upload your data to and it will plot it for you? Thanks!
For this kind of job i use to go with folium, which is great to work with maps,
But for the heatMap you have to have your "priority" column as float!
import folium
from folium import plugins
from folium.plugins import HeatMap
my_map = folium.Map(location=[51.5074, 0.1278],
zoom_start = 13) # for UK
your_dataframe['latitude'] = your_dataframe['latitude'].astype(float)
your_dataframe['longitude'] = your_dataframe['longitude'].astype(float)
your_dataframe['priority'] = your_dataframe['priority'].astype(float)
heat_df = your_dataframe[['latitude', 'longitude','priority']]
heat_df = heat_df.dropna(axis=0, subset=['latitude','longitude','priority'])
# List comprehension to make out list of lists
heat_data = [[row['latitude'],row['longitude'],row['priority']] for index, row in heat_df.iterrows()]
my_map.add_children(plugins.HeatMap(heat_data))
my_map.save('map.html')
and then you have to open map.html with yout browser
I have created a geoDataFrame using, and would like to create a Folium Map, plotting the population eat for each country. Do I have to create the Json file, or I can directly use the geoDataFrame file?
import folium
import fiona
import geopandas as gpd
world = fiona.open(gpd.datasets.get_path('naturalearth_lowres'))
world = gpd.GeoDataFrame.from_features([feature for feature in world])
world = world[(world.pop_est > 0) & (world.name != "Antarctica")]
I used folium.map and geojson function, but it failed to create correct JSON files.
Thanks for the help!
The m.cholopleth() code in #joris's answer is now deprecated. The following code produces the same result using the new folium.Chloropleth() function:
m = folium.Map()
folium.Choropleth(world, data=world,
key_on='feature.properties.name',
columns=['name', 'pop_est'],
fill_color='YlOrBr').add_to(m)
folium.LayerControl().add_to(m)
m
In recent releases of folium, you don't need to convert the GeoDataFrame to geojson, but you can pass it directly. Connecting the population column to color the polygons is still somewhat tricky to get correct:
m = folium.Map()
m.choropleth(world, data=world, key_on='feature.properties.name',
columns=['name', 'pop_est'], fill_color='YlOrBr')
m