Nearest landmark computation based on lat/long coordinates

get_nearest_landmark.py

import math

def haversine(lat1, long1, lat2, long2):
    # Radius of the Earth in kilometers
    R = 6371

    # Convert latitude and longitude from degrees to radians
    lat1, long1, lat2, long2 = map(math.radians, [lat1, long1, lat2, long2])

    # Differences in latitude and longitude
    dlat = lat2 - lat1
    dlong = long2 - long1

    # Haversine formula
    a = math.sin(dlat/2)**2 + math.cos(lat1) * math.cos(lat2) * math.sin(dlong/2)**2
    c = 2 * math.atan2(math.sqrt(a), math.sqrt(1 - a))
    distance = R * c

    return distance

def find_nearest_landmark(user_lat, user_long, landmarks):
    nearest_landmark = None
    min_distance = float('inf')

    for landmark in landmarks:
        landmark_lat = landmark['latitude']
        landmark_long = landmark['longitude']
        distance = haversine(user_lat, user_long, landmark_lat, landmark_long)

        if distance < min_distance:
            min_distance = distance
            nearest_landmark = landmark

    return nearest_landmark, min_distance

# Example usage:
user_lat = 40.7128
user_long = -74.0060
landmarks = [
    {"name": "Statue of Liberty", "latitude": 40.6892, "longitude": -74.0445},
    {"name": "Empire State Building", "latitude": 40.748817, "longitude": -73.985428},
    # Add more landmarks to the list
]

nearest, distance = find_nearest_landmark(user_lat, user_long, landmarks)
print(f"The nearest landmark is {nearest['name']} at a distance of {distance} km.")