Hello,
I am new to Plotly and i am wondering if there is any way to plot data on a map object such as go.Scattergeo() with projection_type="natural earth". Not scatterpoints or choropleths but on the whole map maybe as a heatmap or contour.
Something like this:
Thank you !
Hey @alex1000,
Welcome to the community.
You can plot your data as a contour but as far as I know you need to install geojsoncontour package.
https://pypi.org/project/geojsoncontour/
Here’s an example:
DATA:
x y z
26.7750 38.3250 118.0000
26.8250 38.3250 228.0000
26.8250 38.3750 334.0000
26.8750 38.3250 334.0000
26.8750 38.3750 416.0000
26.9250 38.3250 554.0000
26.9250 38.3750 416.0000
26.9250 38.4250 224.0000
26.9750 38.3750 415.0000
26.9750 38.4250 544.0000
27.0250 38.3750 112.0000
27.0250 38.4250 119.0000
27.0250 38.4750 554.0000
27.0750 38.3750 817.0000
27.0750 38.4250 925.0000
27.0750 38.4750 120.0000
27.1250 38.3750 317.0000
27.1250 38.4250 244.0000
27.1250 38.4750 137.0000
27.1750 38.3750 884.0000
27.1750 38.4250 842.0000
27.1750 38.4750 635.0000
27.2250 38.4250 720.0000
27.2250 38.4750 522.0000
27.2750 38.4250 114.0000
CODE:
import numpy as np
import pandas as pd
from scipy.interpolate import griddata
#
import json
import geojsoncontour
#
import plotly.graph_objs as go
import matplotlib.pyplot as plt
import matplotlib as mpl
#Read data
data = pd.read_csv('data.txt', header=0, delimiter='\t')
#Create meshgrid for x,y
xi = np.linspace(min(data['x']), max(data['x']), num=100)
yi = np.linspace(min(data['y']), max(data['y']), num=100)
x_grid, y_grid = np.meshgrid(xi,yi)
#Grid data
z_grid = griddata((data['x'],data['y']),data['z'],(x_grid,y_grid),method='cubic')
#Mpl contourf
cs=plt.contourf(x_grid,y_grid,z_grid,cmap=plt.cm.jet)
plt.colorbar(cs)
plt.show()
# Convert matplotlib contourf to geojson
geojson = geojsoncontour.contourf_to_geojson(contourf=cs)
my_geojson = json.loads(geojson)
#Filling array with Z and Id for each geojson spatial object. Z value from contour plot will be stored as title
arr_temp=np.ones([len(my_geojson["features"]),2])
for i in range(0, len(my_geojson["features"])):
my_geojson["features"][i]["id"]=i
arr_temp[i,0]=i
arr_temp[i,1]=my_geojson["features"][i]["properties"]["title"].split("-")[0]
df_contour=pd.DataFrame(arr_temp, columns=["Id","Z"])
fig = go.Figure(go.Choropleth ( locations=df_contour.Id,
z= df_contour.Z,
colorscale="jet",
geojson= my_geojson) )
fig.update_layout(geo = dict(projection = dict(type = 'natural earth')))
fig.show()
OUTPUT:
Useful Links:
https://www.tjansson.dk/2018/10/contour-map-in-folium/
https://towardsdatascience.com/visualizing-spatial-data-with-geojson-heatmaps-1fbe2063ab86
https://stackoverflow.com/questions/69443849/adding-geojson-contours-as-layers-on-plotly-density-mapbox
Have a nice day.
