Learn Aggregating Spatial Data | Spatial Analysis Techniques

Swipe to show menu

Aggregating spatial data allows you to summarize and analyze geographic features based on shared attributes. With geopandas, you can use the groupby() method together with aggregation functions to answer questions like "What is the total area of parks in each city?" or "How many schools are in each district?" These techniques are essential for extracting meaningful insights from complex geospatial datasets.


              123456789101112131415161718192021222324252627
            
import geopandas as gpd
import pandas as pd

# 1. Load the open-source global dataset from the public URL
url = "https://raw.githubusercontent.com/nvkelso/natural-earth-vector/master/geojson/ne_110m_admin_0_countries.geojson"
world = gpd.read_file(url)

# 2. Clean up column names to uppercase
world.columns = world.columns.str.upper()

# 3. Tell GeoPandas to look at the uppercase 'GEOMETRY' column now
world = world.set_geometry("GEOMETRY")

# EPSG:8857 is the Equal Earth projection, great for global land area calculations
world['CALC_AREA'] = world.to_crs(epsg=8857).geometry.area

# 5. Perform the Aggregation (The 'groupby' step)
continent_summary = world.groupby('CONTINENT').agg(
    Total_Population=('POP_EST', 'sum'),
    Average_Country_Size=('CALC_AREA', 'mean'),
    Total_Countries=('CONTINENT', 'count')
).reset_index()

# 6. Interpret the results
print("Spatial Aggregation Results by Continent ")
pd.set_option('display.float_format', lambda x: '%.2f' % x)
print(continent_summary.to_string(index=False))

After performing aggregation, you interpret the results in a spatial context. For instance, by grouping countries by continent and summing their populations, you can compare population distributions globally. Calculating the mean area of countries per continent helps you understand spatial patterns, such as which continents have larger or smaller average country sizes. Aggregation is not limited to counts or sums; you can also compute averages, minimums, maximums, or custom statistics, depending on your analysis goals. These summaries are especially valuable when visualized on a map, making patterns and trends immediately apparent.

Everything was clear?

Thanks for your feedback!

Section 2. Chapter 3

Ask AI

Ask anything or try one of the suggested questions to begin our chat

Section 2. Chapter 3