Calculate Euclidean Distance Using Python OSMnx Distance Module

Last Updated : 18 Mar, 2024

Euclidean space is defined as the line segment length between two points. The distance can be calculated using the coordinate points and the Pythagoras theorem. In this article, we will see how to calculate Euclidean distances between Points Using the OSMnx distance module.

Syntax of osmnx.distance.euclidean() Function

The vectorized function to calculate the Euclidean distance between two points’ coordinates or between arrays of points’ coordinates is as follows:

osmnx.distance.euclidean(y1, x1, y2, x2)

Parameters:

y1 (float or numpy.array of float) – first point’s y coordinate
x1 (float or numpy.array of float) – first point’s x coordinate
y2 (float or numpy.array of float) – second point’s y coordinate
x2 (float or numpy.array of float) – second point’s x coordinate
Note: For accurate results, use projected coordinates rather than decimal degrees

Returns: dist – distance from each (x1, y1) to each (x2, y2) in coordinates’ units

Return Type: Float or numpy.array of float

Calculate Euclidean Distance Using Python OSMnx Distance Module

Below, are the example of how to calculate Euclidean distances between Points Using OSMnx distance module in Python:

Geographic Coordinate Reference System uses latitude and longitude to specify a location on Earth. Projected Coordinate System, on the other hand, employs a two-dimensional XY plane for Euclidean distance calculations, using northing (Y) and easting (X) values in meters. Northing is the distance from the equator, and easting is the distance from the central meridian (longitude). The output is the Euclidean distance in meters.

Create GeoDataFrame with Geographic Coordinates

In below code Using geopandas and shapely, this code creates a GeoDataFrame named gdf from latitude and longitude coordinates for Thiruvananthapuram and Kollam, specifying the coordinate reference system as EPSG:4326.

Python3

import geopandas as gd from shapely.geometry import Point  # location coordinates dictionary tvm_lat, tvm_lon = 8.506, 76.96153 kol_lat, kol_lon = 8.88795, 76.59550 coordinate_dict = {'name': ['Thiruvananthapuram', 'Kollam'],                    'geometry': [Point(tvm_lon, tvm_lat),                                 Point(kol_lon, kol_lat)]}  # convert to geodataframe # EPSG:4326 is a geographic coordinate system gdf = gd.GeoDataFrame(coordinate_dict, crs="EPSG:4326") print(gdf)

Output

                name                  geometry 0  Thiruvananthapuram  POINT (76.96153 8.50600) 1              Kollam  POINT (76.59550 8.88795)

Applying Coordinate Projection

As a next step, we can convert the geographic coordinates to projected coordinates using to_crs() functionality. Here we will be using EPSG:7781 Coordintae Reference System (CRS).

below, code applies a coordinate projection to transform the GeoDataFrame `gdf` from EPSG:4326 to EPSG:7781, and then prints the transformed GeoDataFrame `gdf_7781`.

Python3

# apply projection gdf_7781 = gdf.geometry.to_crs("EPSG:7781")

Output:

0   POINT (1105863.487 779603.870) 1    POINT (1065495.736 821765.127) Name: geometry, dtype: geometry

Calculating Euclidean Distance

In below code we uses OSMnx and GeoPandas to convert geographic coordinates of Thiruvananthapuram and Kollam to a projected coordinate system (EPSG:7781) and then calculates the Euclidean distance between the two locations using the OSMnx library.

Python3

import osmnx as ox  import geopandas as gd from shapely.geometry import Point  # location coordinates dictionary tvm_lat, tvm_lon = 8.506, 76.96153 kol_lat, kol_lon = 8.88795, 76.59550 coordinate_dict = {'name': ['Thiruvananthapuram', 'Kollam'],                    'geometry': [Point(tvm_lon, tvm_lat),                                 Point(kol_lon, kol_lat)]}  # convert to geodataframe # EPSG:4326 is a geographic coordinate system gdf = gd.GeoDataFrame(coordinate_dict, crs="EPSG:4326")  # apply projection gdf_7781 = gdf.geometry.to_crs("EPSG:7781")  # projected coordinates tvm_lon_utm = gdf_7781.geometry[0].x tvm_lat_utm = gdf_7781.geometry[0].y kol_lon_utm = gdf_7781.geometry[1].x kol_lat_utm = gdf_7781.geometry[1].y  # calculate eucledian distance ox.distance.euclidean(kol_lon_utm, kol_lat_utm, tvm_lon_utm, tvm_lat_utm)

Output:

58370.59962990761

So, the eucledian distance between Kollam and Thiruvananthapuram is 58370.59 meters or 58.37 KM.

Pass as Array of Calculated Euclidean Distance

In below code we uses NumPy, OSMnx, and GeoPandas to convert latitude and longitude coordinates of Thiruvananthapuram, Mumbai, and New Delhi to a projected coordinate system (EPSG:24378) and calculates the Euclidean distance between Mumbai and Thiruvananthapuram.

Python3

import numpy as np import osmnx as ox import geopandas as gd from shapely.geometry import Point  # lat-lon points # Thiruvananthapuram tvm_lat, tvm_lon = 8.50606, 76.96153 # Mumbai mum_lat, mum_lon = 19.0760, 72.8777 # New Delhi nwdelhi_lat, nwdelhi_lon = 28.6139, 77.2090  # coordinate dictionary coordinate_dict = {'name': ['Thiruvananthapuram', 'Mumbai', 'New Delhi'],                    'geometry': [Point(tvm_lon, tvm_lat),                                 Point(mum_lon, mum_lat),                                 Point(nwdelhi_lon, nwdelhi_lat)]}  # convert dictionary to geodataframe gdf = gd.GeoDataFrame(coordinate_dict, crs="EPSG:4326") # convert to projected coordinates gdf_24378 = gdf.geometry.to_crs("EPSG:24378")  # projected coordinates # Thiruvananthapuram proj_tvm_lon = gdf_24378.geometry[0].x proj_tvm_lat = gdf_24378.geometry[0].y # Mumbai proj_mum_lon = gdf_24378.geometry[1].x proj_mum_lat = gdf_24378.geometry[1].y # New Delhi proj_dlh_lon = gdf_24378.geometry[2].x proj_dlh_lat = gdf_24378.geometry[2].y  # set from and to projected points from_lat = np.array([proj_tvm_lat, proj_dlh_lat]) from_lon = np.array([proj_tvm_lon, proj_dlh_lon]) to_lat = np.array([proj_mum_lat, proj_tvm_lat]) to_lon = np.array([proj_mum_lon, proj_tvm_lon])  # calculate the euclidean distance ox.distance.euclidean(from_lon, from_lat, to_lon, to_lat)

Output

array([1314897.39984368, 2298605.77047159])

From the output, we can conclude that the euclidean distance between Thrivananthapuram and Mumbai is 1314897.39 meters or 1314.897 KM and the euclidean distance between New Delhi and Thiruvananthapuram is 2298605.77 meters or 2298.605 KM.

Find the Nearest Edge to a Point Using Python OSMnx Distance Module

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Practice Tags :

Calculate Euclidean Distance Using Python OSMnx Distance Module

Syntax of osmnx.distance.euclidean() Function

Calculate Euclidean Distance Using Python OSMnx Distance Module

Create GeoDataFrame with Geographic Coordinates

Applying Coordinate Projection

Calculating Euclidean Distance

Pass as Array of Calculated Euclidean Distance

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