diff --git a/python/python-examples/test/test_haversine_distance.py b/python/python-examples/test/test_haversine_distance.py
index d5f681c..5a29ceb 100644
--- a/python/python-examples/test/test_haversine_distance.py
+++ b/python/python-examples/test/test_haversine_distance.py
@@ -2,6 +2,7 @@
 Test for haversine distance.
 """
 import logging as log
+import numpy as np
 from math import radians
 import pytest
 from sklearn.metrics.pairwise import haversine_distances
@@ -12,6 +13,11 @@
     pytest.param(
         [-34.83333, -58.5166646], 
         [49.0083899664, 2.53844117956]
+    ),
+    # Google location vs USDA Location
+    pytest.param(
+        [39.0168311, -76.92883499999999],
+        [39.01644, -76.928925]
     )
 ])
 def test_calculate_haversince_distance(lat_long_origin:list, lat_long_destination:list):
@@ -28,4 +34,43 @@ def test_calculate_haversince_distance(lat_long_origin:list, lat_long_destinatio
     sklearn_distance = km_distance_matrix_result[0][1]
     log.info(f"Raw Calc={km_haversine_distance}, Sklearn Calc={sklearn_distance}")
     # Rounding both to 8 digits of accuracy...
-    assert km_haversine_distance == round(km_distance_matrix_result[0][1], 8)
\ No newline at end of file
+    assert km_haversine_distance == round(km_distance_matrix_result[0][1], 8)
+
+
+def test_shortest_distance():
+    # Lincoln Memorial Latlong
+    lincoln_memorial = [38.889248, -77.050636]
+    
+    # Ducinni's off U St
+    ducinnis_pizza = [38.91706701509112, -77.04118715785616]
+    
+    # Admo Jumbo Slice
+    jumbo_slice_pizza = [38.92105748065034, -77.04170211776945]
+    
+    # Manny Olgas Near U 
+    manny_olgas = [38.91543818061708, -77.03174726038766]
+
+    # Based off Haversine which is closest to Lincoln Memorial
+    dataset = [
+        {'name': 'Lincoln Memorial', 'geocode': lincoln_memorial},
+        {'name': "Ducinni's Pizza", 'geocode': ducinnis_pizza},
+        {'name': 'Jumbo Slice Pizza', 'geocode': jumbo_slice_pizza},
+        {'name': "Manny Olga's", 'geocode': manny_olgas}
+    ]
+
+    # Put all these in radians
+    radian_distances = list(map(lambda x: [radians(_) for _ in x['geocode']], dataset))
+
+    # Calculate the Haversine Distances
+    distance_matrix_result = haversine_distances(radian_distances)
+
+    # Pull out the first Entry in the 2D array
+    km_distance_matrix_result = distance_matrix_result * RADIUS_OF_EARTH / 1000
+    log.info(f"\nDistance Matrix in Kilometers:\n {km_distance_matrix_result}")
+
+    # Get the index of the minimum distance for the lincoln memorial
+    np_array = np.array(km_distance_matrix_result[0][1:])
+    idx_min = np.argmin(np_array)
+    # Closest Should be Ducinnis
+    assert idx_min + 1 == 1 
+    log.info(f"\nClosest Jumbo Slice spot to Lincoln Memorial by Haversine Distance is {dataset[idx_min + 1]['name']}")