Basic Mapping with Geopandas

Analyzing US Census Data in Python

Lee Hachadoorian

Asst. Professor of Instruction, Temple University

Geospatial Data - Further Learning

Loading Geospatial Data

import geopandas as gpd

# Load a geospatial file
geo_state = gpd.read_file("state_computer_use.gpkg")

type(geo_state)

geopandas.geodataframe.GeoDataFrame

Geopandas DataFrames

print(geo_state.columns)

Index(['state', 'postal', 'name', 'geometry', 'total', 'has_computer',
       'desktop_laptop', 'desktop_laptop_only', 'portable_device',
       'portable_device_only', 'no_computer'],
      dtype='object')

Geopandas DataFrames

print(geo_state.head())

  state postal  ... portable_device_only no_computer
0    06     CA  ...              1052406     1263635
1    08     CO  ...               148749      168639
2    11     DC  ...                23554       32916
3    16     ID  ...                46565       67454
4    17     IL  ...               415840      640062

[5 rows x 11 columns]

Geopandas geometry Column

print(geo_state["geometry"].head())

0    (POLYGON ((-1716661.572795197 -1091585.2669098...
1    (POLYGON ((-787764.8711845676 -679501.90042785...
2    (POLYGON ((1950824.825659711 -402441.066172522...
3    (POLYGON ((-1357254.591159301 77405.1771214068...
4    (POLYGON ((720437.1241238854 -496471.759394428...
Name: geometry, dtype: object

Geopandas Plotting

geo_state.plot()

A map of the continental United States.

Choropleth Maps

geo_state.plot(column = "has_computer")

A map of the continental United States, with states colored by the number of households with computers.

Choropleth Maps

geo_state.plot(column = "has_computer", cmap = "YlOrRd")

A map of the continental United States, with states colored by the number of households with computers.

Choropleth Maps

geo_state["pct_has_computer"] = 100 * geo_state["has_computer"]/geo_state["total"]

geo_state.plot(column = "pct_has_computer", cmap = "YlOrRd")

A map of the continental United States, with states colored by the proportion of households with computers.

Ramps of 18 sequential light-to-dark colormaps available in matplotlib.

Matplotlib Sequential Colormaps

https://matplotlib.org/users/colormaps.html

Let's practice!

Analyzing US Census Data in Python