Choroplèthes avec geopandas

Visualiser des données géospatiales en Python

Mary van Valkenburg

Data Science Program Manager, Nashville Software School

graphique de cartes de couleurs séquentielles

Choroplèthe avec GeoDataFrame.plot()

districts_with_counts.plot(column = 'school_density', legend = True)
plt.title('Schools per decimal degrees squared area')
plt.xlabel('longitude')
plt.ylabel('latitude');

choroplèthe des densités d'écoles avec la carte de couleurs viridis

Choroplèthe avec GeoDataFrame.plot()

districts_with_counts.plot(column = 'school_density', cmap = 'BuGn', edgecolor = 'black', legend = True)
plt.title('Schools per decimal degrees squared area')
plt.xlabel('longitude')
plt.ylabel('latitude');

choroplèthe des densités d'écoles avec une carte bleu-vert

Surface en kilomètres carrés

# starting CRS
print(school_districts.crs)

epsg:4326

# convert to EPSG 3857
school_districts = school_districts.to_crs(epsg = 3857)
print(school_districts.crs)

epsg:3857

Surface en kilomètres carrés

# define a variable for m^2 to km^2
sqm_to_sqkm = 10**6

school_districts['area'] = school_districts.area / sqm_to_sqkm
school_districts.head(2)

district    geometry                             area
1          (POLYGON ((-965.055 4353528.766...    563.134380
3          (POLYGON ((-965.823 4356392.677...    218.369949

# change crs back to 4326
school_districts = school_districts.to_crs(epsg = 4326)
print(school_districts.crs)

epsg:4326

print(school_districts.head(2))

district      geometry                       area
1             (POLYGON ((-86.771 36.383...   563.134380
3             (POLYGON ((-86.753 36.404...   218.369949

# spatial join to get districts that contain schools
schools_in_districts = gpd.sjoin(school_districts, schools_geo, predicate = 'contains')

# aggregate to get counts
school_counts = schools_in_districts.groupby(['district']).size()

# convert school_counts to a df 
school_counts_df = school_counts.to_frame()
school_counts_df = school_counts_df.reset_index(level=0)
school_counts_df.columns = ['district', 'school_count']

# merge
districts_with_counts = pd.merge(school_districts,
                                 school_counts_df, on = 'district')

districts_with_counts.head(1)

district  geometry                       area       school_count
1         (POLYGON ((-86.771 36.383..    563.134380    30

Calculer la densité d'écoles

# create school_density
districts_with_counts['school_density'] = districts_with_counts.apply(
    lambda row: row.school_count/row.area, axis = 1)

# plot it
districts_with_counts.plot(column = 'school_density', cmap = 'BuGn', 
                           edgecolor = 'black', legend = True)
plt.title('Schools per kilometers squared')
plt.xlabel('longitude')
plt.ylabel('latitude')
plt.show();

choroplèthe des densités d'écoles par kilomètre carré

Passons à la pratique !

Visualiser des données géospatiales en Python