I want to calculate the geo-distance between latitude-longitude.
I had checked this thread Vectorizing Haversine distance calculation in Python but when I am using it for two different set of coordinates, I m getting an error.
df1 size can be in millions and if there is any other way to calculate accurate geo distance in less time then it would be really helpful.
length1 = 1000
d1 = np.random.uniform(-90, 90, length1)
d2 = np.random.uniform(-180, 180, length1)
length2 = 100
d3 = np.random.uniform(-90, 90, length2)
d4 = np.random.uniform(-180, 180, length2)
coords = tuple(zip(d1, d2))
df1 = pd.DataFrame({'coordinates':coords})
coords = tuple(zip(d3, d4))
df2 = pd.DataFrame({'coordinates':coords})
def get_diff(df1, df2):
data1 = np.array(df1['coordinates'].tolist())
data2 = np.array(df2['coordinates'].tolist())
lat1 = data1[:,0]
lng1 = data1[:,1]
lat2 = data2[:,0]
lng2 = data2[:,1]
#print(lat1.shape)
#print(lng1.shape)
#print(lat2.shape)
#print(lng2.shape)
diff_lat = lat1[:,None] - lat2
diff_lng = lng1[:,None] - lng2
#print(diff_lat.shape)
#print(diff_lng.shape)
d = np.sin(diff_lat/2)**2 + np.cos(lat1[:,None])*np.cos(lat1) * np.sin(diff_lng/2)**2
return 2 * 6371 * np.arcsin(np.sqrt(d))
get_diff(df1, df2)
ValueError Traceback (most recent call last)
<ipython-input-58-df06c7cff72c> in <module>
----> 1 get_diff(df1, df2)
<ipython-input-57-9bd8f10189e6> in get_diff(df1, df2)
26 print(diff_lat.shape)
27 print(diff_lng.shape)
---> 28 d = np.sin(diff_lat/2)**2 + np.cos(lat1[:,None])*np.cos(lat1) * np.sin(diff_lng/2)**2
29 return 2 * 6371 * np.arcsin(np.sqrt(d))
ValueError: operands could not be broadcast together with shapes (1000,1000) (1000,100)
Pairwise haversine distances
Here's a vectorized way with
broadcasting
based onthis post
-Hence, to solve your case to get all pairwise haversine distances, it would be -
Elementwise haversine distances
For element-wise haversine distance computations between two data, such that each data holds latitude and longitude in two columns each or lists of two elements each, we would skip some of the extensions to
2D
and end up with something like this -Sample run with a dataframe holding the two data in two columns -
Those slight differences are largely because
haversine
library assumes6371.0088
as the earth radius, while we are taking it as6371
here.