Measuring Areas and Distances

Understanding how to measure areas and distances is a key part of working with spatial data — and it’s no coincidence that this section comes right after the blocks on coordinate systems.

That’s because accurate calculations of distance, area, and even buffer zones all depend heavily on the coordinate reference system (CRS) you’re using.

If your data is in a geographic CRS (like WGS 84), coordinates are expressed in degrees, which are not suitable for direct distance or area calculations.
To get correct results in meters or square meters, your data needs to be projected into an appropriate projected CRS, like UTM.

In this section, we’ll explore how to measure distances and areas correctly — and why choosing the right projection is essential.

Import libraries

import geopandas as gpd
import pandas as pd
import osmnx as ox

Export City Boundary

admin_border = ox.geocode_to_gdf('Vienna, Austria')
admin_border.explore(tiles='cartodbpositron')

Make this Notebook Trusted to load map: File -> Trust Notebook

In GeoPandas, every GeoDataFrame has a special attribute called .geometry, which stores the geometric shapes of the spatial features — for example, points, lines, or polygons.
This attribute gives you direct access to the geometry of each object in your dataset.

Once you have access to the geometry, you can perform various spatial operations.
One particularly useful method is .area, which calculates the area of each polygon feature.

But here’s an important note: the .area method returns correct values only if your data is in a projected CRS (like UTM), where coordinates are in meters.
If your data is still in degrees (e.g., EPSG:4326), the area values will be meaningless.

Now that we’ve reprojected our dataset into a suitable UTM zone, we can safely use the .area method to calculate the area of each polygon.

Let’s create a new column called "area" in our admin_okrug GeoDataFrame and store the calculated area values there:

admin_okrug["area"] = admin_okrug.geometry.area

admin_border['area'] = admin_border.geometry.area

admin_border.head()

/var/folders/ry/9bb7wrz54vq_kn2ytlj6ynzm0000gn/T/ipykernel_49378/1924210537.py:1: UserWarning: Geometry is in a geographic CRS. Results from 'area' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.

  admin_border['area'] = admin_border.geometry.area

	geometry	bbox_west	bbox_south	bbox_east	bbox_north	place_id	osm_type	osm_id	lat	lon	class	type	place_rank	importance	addresstype	name	display_name	area
0	POLYGON ((16.18183 48.17112, 16.1819 48.17103,...	16.181831	48.117907	16.577513	48.322668	408340724	relation	109166	48.208354	16.372504	boundary	administrative	7	0.818783	city	Vienna	Vienna, Austria	0.050199

And here’s the problem — the area values look strange and way too small. What’s going on?

Let’s think back to the coordinate system of this layer… it’s geographic, which means the units are in degrees, not meters.
As a result, all area calculations are also in degrees² — which isn’t meaningful for real-world measurements.

🛑 Always pay attention to the “UserWarning” you might see when calculating areas (like we did above). It often points out that the results are likely incorrect because the data is still in a geographic CRS. It also reminds you that you need to reproject your data to a suitable projected CRS to get accurate results.

admin_border_utm = admin_border.to_crs(admin_border.estimate_utm_crs())

admin_border_utm['area_utm'] = admin_border_utm.geometry.area

admin_border_utm.head()

	geometry	bbox_west	bbox_south	bbox_east	bbox_north	place_id	osm_type	osm_id	lat	lon	class	type	place_rank	importance	addresstype	name	display_name	area	area_utm
0	POLYGON ((587866.661 5335995.06, 587872.091 53...	16.181831	48.117907	16.577513	48.322668	408340724	relation	109166	48.208354	16.372504	boundary	administrative	7	0.818783	city	Vienna	Vienna, Austria	0.050199	4.146378e+08

Now that looks more realistic — and yes, the values are in square meters!

This kind of notation might not be familiar to everyone, so let’s break it down with an example:
4.146378e+08 means 4.146378 × 10⁸, i.e., 414,637,800 square meters, which is 414.6378 square kilometers (≈ 414.6 km²).

Summary

In this section, we covered how to measure areas and distances correctly in GeoPandas.

We learned to:

• Recognize that geographic CRS (e.g., EPSG:4326) stores coordinates in degrees, which aren’t suitable for measurements.

• Reproject data to an appropriate projected CRS (e.g., UTM) using to get results in meters and square meters.

With the right CRS, distance, area, and buffer calculations become accurate and reliable.