How to Calculate Distance between Two Points using GEOPY
The Geopy library in Python makes it simple to calculate the geographical distance between two locations on Earth. Whether you’re working on logistics, mapping, or travel-based applications, this tutorial will guide you through multiple methods to determine the distance between two points using latitude and longitude.
How to Calculate Distance between Two Points using GEOPY.
Installing Geopy
Use the following command to install Geopy before using it:
pip install geopy
Once installed, you’re ready to use its powerful features for geographical calculations.
Methods to Calculate Distance Between Two Points
Geopy offers different ways to calculate distances, each suited to specific needs. Let’s go over each of these approaches individually:
Method 1: Using Geodesic Distance
The shortest path between two locations on the surface of the Earth is known as the geodesic distance. It takes the Earth’s ellipsoidal shape into account, ensuring high accuracy.
Example:
# Importing the geodesic module from geopy
from geopy.distance import geodesic as GD
# New York and Texas' latitudes and longitudes
New_York = (40.7128, -74.0060)
Texas = (31.9686, -99.9018)
# Calculating the geodesic distance in kilometers
print("The distance between New York and Texas is:", GD(New_York, Texas).km, "KM")
Output:
The distance between New York and Texas is: 2507.14797665193 KM
Method 2: Using Great Circle Distance
Assuming that Earth is a perfect sphere, the great circle distance is computed. While slightly less accurate than geodesic, it’s faster and often sufficient for many applications.
Example:
# Importing the great_circle module from geopy
from geopy.distance import great_circle as GC
# Latitude and longitude of New York and Texas
New_York = (40.7128, -74.0060)
Texas = (31.9686, -99.9018)
# Calculating the great circle distance in kilometers
print("The distance between New York and Texas is:", GC(New_York, Texas).km, "KM")
Output:
The distance between New York and Texas is: 2503.045970189156 KM
Method 3: Using the Haversine Formula
The Haversine formula calculates the orthodromic distance (shortest path) between two points on a sphere, based on their latitudes and longitudes. This approach gives precise results, especially for small distances.
How It Works:
Convert degrees of latitude and longitude to radians.
To find the central angle between two points, use the Haversine formula.
To find the distance, multiply by the radius of the Earth.
Example:
from math import radians, sin, cos, sqrt, asin
# Function to calculate distance in kilometers
def haversine_km(lat1, lon1, lat2, lon2):
# Convert degrees to radians
lat1, lon1, lat2, lon2 = map(radians, [lat1, lon1, lat2, lon2])
# Haversine formula
dlat = lat2 - lat1
dlon = lon2 - lon1
a = sin(dlat / 2)**2 + cos(lat1) * cos(lat2) * sin(dlon / 2)**2
c = 2 * asin(sqrt(a))
# Earth's radius in kilometers
r_km = 6371.0
return c * r_km
# Function to calculate distance in miles
def haversine_miles(lat1, lon1, lat2, lon2):
# Convert degrees to radians
lat1, lon1, lat2, lon2 = map(radians, [lat1, lon1, lat2, lon2])
# Haversine formula
dlat = lat2 - lat1
dlon = lon2 - lon1
a = sin(dlat / 2)**2 + cos(lat1) * cos(lat2) * sin(dlon / 2)**2
c = 2 * asin(sqrt(a))
# Earth's radius in miles
r_mi = 3963.0
return c * r_mi
# Coordinates of New York and Texas
lat1, lon1 = 40.7128, -74.0060 # New York
lat2, lon2 = 31.9686, -99.9018 # Texas
# Calculate distances
print("The distance between New York and Texas is:", haversine_km(lat1, lon1, lat2, lon2), "KM")
print("The distance between New York and Texas is:", haversine_miles(lat1, lon1, lat2, lon2), "Miles")
Output:
The distance between New York and Texas is: 2503.04243426357 KM
The distance between New York and Texas is: 1556.985899699659 Miles
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