The answers to Haversine Formula in Python (Bearing and Distance between two GPS points) provide Python implementations that answer your question.
Using the implementation below I performed 100,000 iterations in less than 1 second on an older laptop. I think for your purposes this should be sufficient. However, you should profile anything before you optimize for performance.
from math import radians, cos, sin, asin, sqrt def haversine(lon1, lat1, lon2, lat2): """ Calculate the great circle distance between two points on the earth (specified in decimal degrees) """ # convert decimal degrees to radians lon1, lat1, lon2, lat2 = map(radians, [lon1, lat1, lon2, lat2]) # haversine formula dlon = lon2 - lon1 dlat = lat2 - lat1 a = sin(dlat/2)**2 + cos(lat1) * cos(lat2) * sin(dlon/2)**2 c = 2 * asin(sqrt(a)) # Radius of earth in kilometers is 6371 km = 6371* c return km
To underestimate haversine(lat1, long1, lat2, long2) * 0.90
or whatever factor you want. I don’t see how introducing error to your underestimation is useful.