Heat World Map with MatPlotLib

Question

I am trying to combine a heat map with a world map i created. What i got is a CSV file with 3 columns. The first column contains the name of a country and the second and third contain the latitude respectively the longitude. Now i wrote a class which plots the point according to this coordinates on a world map. That works fine but what i want now is a heat map, because having just a few points everything looks fine but i am going to have a great many of points. So depending on the count of points in a country and specified borders, the heat map should be realized. ``` import csv class toMap: def setMap(self): filename = 'log.csv' lats, lons = [], [] with open(filename) as f: reader = csv.reader(f) next(reader) for row in reader: lats.append(float(row[1])) lons.append(float(row[2])) from mpl_toolkits.basemap import Basemap import matplotlib.pyplot as plt import numpy as np map = Basemap(projection='robin', resolution='l', area_thresh=1000.0, lat_0=0, lon_0=-130) map.drawcoastlines() ma…

Accepted Answer

Following the same logic from my comment above, I made some changes in your code to get the kind of map you want.

My solution uses cartopy library.

So here's your code, with my changes (and comments):

```
import csv

class toMap:

def setMap(self):
    # --- Save Countries, Latitudes and Longitudes ---
    filename = 'log.csv'
    pais, lats, lons = [], [], []

with open(filename) as f:
        reader = csv.reader(f)

next(reader)

for row in reader:
            pais.append(str(row[0]))
            lats.append(float(row[1]))
            lons.append(float(row[2]))

#count the number of times a country is in the list
    unique_pais = set(pais)
    unique_pais = list(unique_pais)

c_numero = []

for p in unique_pais:
        c_numero.append(pais.count(p))
        print p, pais.count(p)

maximo = max(c_numero)

# --- Build Map ---
    import cartopy.crs as ccrs
    import cartopy.io.shapereader as shpreader
    import matplotlib.pyplot as plt
    import matplotlib as mpl
    import numpy as np

cmap = mpl.cm.Blues

# --- Using the shapereader ---
    test = 0
    shapename = 'admin_0_countries'
    countries_shp = shpreader.natural_earth(resolution='110m',
                                            category='cultural', name=shapename)

ax = plt.axes(projection=ccrs.Robinson())
    for country in shpreader.Reader(countries_shp).records():
        nome = country.attributes['name_long']
        if nome in unique_pais:
            i = unique_pais.index(nome)
            numero = c_numero[i]
            ax.add_geometries(country.geometry, ccrs.PlateCarree(),
                              facecolor=cmap(numero / float(maximo), 1),
                              label=nome)
            test = test + 1

else:
            ax.add_geometries(country.geometry, ccrs.PlateCarree(),
                              facecolor='#FAFAFA',
                              label=nome)

if test != len(unique_pais):
        print "check the way you are writting your country names!"

plt.show()

def main():
    m = toMap()
    m.setMap()
```

I've made a custom log.csv file with some countries, following your logic, and here's my map:

(I've used the Blues colormap, and the maximum of the scale is defined according to the maximum number of times a country appears in your csv file.)

According to the example image you had before editing your question, I think this is exactly what you want!

Heat World Map with MatPlotLib

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