Spatial Visualization with Python

Loading packages

import geopandas as gpd
import folium
import pandas as pd

Reading spatial data

world = gpd.read_file(gpd.datasets.get_path("naturalearth_lowres"))
print("Geometry Column Name:", world.geometry.name)
print("Dataset Size:", world.shape)
world.head()
world.plot()
world.geometry.head()
C:\Users\DELL\AppData\Local\Temp\ipykernel_11228\541073294.py:1: FutureWarning:

The geopandas.dataset module is deprecated and will be removed in GeoPandas 1.0. You can get the original 'naturalearth_lowres' data from https://www.naturalearthdata.com/downloads/110m-cultural-vectors/.

Geometry Column Name: geometry
Dataset Size: (177, 6)
0    MULTIPOLYGON (((180.00000 -16.06713, 180.00000...
1    POLYGON ((33.90371 -0.95000, 34.07262 -1.05982...
2    POLYGON ((-8.66559 27.65643, -8.66512 27.58948...
3    MULTIPOLYGON (((-122.84000 49.00000, -122.9742...
4    MULTIPOLYGON (((-122.84000 49.00000, -120.0000...
Name: geometry, dtype: geometry

world.crs.name
'WGS 84'

Centroid

world["geometry"].centroid.head() # method 1
C:\Users\DELL\AppData\Local\Temp\ipykernel_11228\3721365273.py:1: UserWarning:

Geometry is in a geographic CRS. Results from 'centroid' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.


0    POINT (163.85316 -17.31631)
1      POINT (34.75299 -6.25773)
2     POINT (-12.13783 24.29117)
3     POINT (-98.14238 61.46908)
4    POINT (-112.59944 45.70563)
dtype: geometry
world.centroid.plot(markersize=1) # method 2
C:\Users\DELL\AppData\Local\Temp\ipykernel_11228\2075499188.py:1: UserWarning:

Geometry is in a geographic CRS. Results from 'centroid' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.

unary union operation

combined_geometry = world.unary_union
combined_geometry

Bounding polygon

world.envelope.head()
0    POLYGON ((-180.00000 -18.28799, 180.00000 -18....
1    POLYGON ((29.34000 -11.72094, 40.31659 -11.720...
2    POLYGON ((-17.06342 20.99975, -8.66512 20.9997...
3    POLYGON ((-140.99778 41.67511, -52.64810 41.67...
4    POLYGON ((-171.79111 18.91619, -66.96466 18.91...
dtype: geometry

Read GeoJson file

us_states_geo = gpd.read_file("datasets/us-states.json")
us_states_geo.head()
id name geometry
0 01 Alabama POLYGON ((-87.35930 35.00118, -85.60667 34.984...
1 02 Alaska MULTIPOLYGON (((-131.60202 55.11798, -131.5691...
2 04 Arizona POLYGON ((-109.04250 37.00026, -109.04798 31.3...
3 05 Arkansas POLYGON ((-94.47384 36.50186, -90.15254 36.496...
4 06 California POLYGON ((-123.23326 42.00619, -122.37885 42.0...

Read .csv file

world_happiness = pd.read_csv("datasets/world-happiness-report-2019.csv")
world_happiness.head()
Country (region) Ladder SD of Ladder Positive affect Negative affect Social support Freedom Corruption Generosity Log of GDP\nper capita Healthy life\nexpectancy
0 Finland 1 4 41.0 10.0 2.0 5.0 4.0 47.0 22.0 27.0
1 Denmark 2 13 24.0 26.0 4.0 6.0 3.0 22.0 14.0 23.0
2 Norway 3 8 16.0 29.0 3.0 3.0 8.0 11.0 7.0 12.0
3 Iceland 4 9 3.0 3.0 1.0 7.0 45.0 3.0 15.0 13.0
4 Netherlands 5 1 12.0 25.0 15.0 19.0 12.0 7.0 12.0 18.0

Merge data files

world_total_data = world.merge(world_happiness, left_on = "name", 
right_on = "Country (region)")
world_total_data.head()
pop_est continent name iso_a3 gdp_md_est geometry Country (region) Ladder SD of Ladder Positive affect Negative affect Social support Freedom Corruption Generosity Log of GDP\nper capita Healthy life\nexpectancy
0 58005463.0 Africa Tanzania TZA 63177 POLYGON ((33.90371 -0.95000, 34.07262 -1.05982... Tanzania 153 122 78.0 50.0 131.0 78.0 34.0 49.0 125.0 118.0
1 37589262.0 North America Canada CAN 1736425 MULTIPOLYGON (((-122.84000 49.00000, -122.9742... Canada 9 23 18.0 49.0 20.0 9.0 11.0 14.0 19.0 8.0
2 18513930.0 Asia Kazakhstan KAZ 181665 POLYGON ((87.35997 49.21498, 86.59878 48.54918... Kazakhstan 60 40 81.0 5.0 19.0 80.0 57.0 57.0 47.0 88.0
3 33580650.0 Asia Uzbekistan UZB 57921 POLYGON ((55.96819 41.30864, 55.92892 44.99586... Uzbekistan 41 99 19.0 15.0 11.0 1.0 18.0 29.0 104.0 83.0
4 270625568.0 Asia Indonesia IDN 1119190 MULTIPOLYGON (((141.00021 -2.60015, 141.01706 ... Indonesia 92 108 9.0 104.0 94.0 48.0 129.0 2.0 83.0 98.0

Choropleth Map

from plotnine import ggplot, geom_map, aes, scale_fill_cmap, theme, labs

chart = ggplot(world_total_data) + \
    geom_map(aes(fill="Freedom", map_id="name", geometry="geometry")) + \
    scale_fill_cmap(cmap_name="Blues") + \
    labs(title="Freedom") + \
    theme(figure_size=(12, 6))

print(chart)

chart = ggplot(world_total_data) + \
    geom_map(aes(fill="Healthy life\nexpectancy", map_id="name", geometry="geometry")) + \
    scale_fill_cmap(cmap_name="Blues") + \
    labs(title="Healthy life and expectancy") + \
    theme(figure_size=(12, 6))

print(chart)

Reading

https://pythongis.org/part2/chapter-06/nb/01-geodataframe.html