diff --git a/src/features/location_doryab/location_base.py b/src/features/location_doryab/location_base.py
index be4d3ed3..1a8aee82 100644
--- a/src/features/location_doryab/location_base.py
+++ b/src/features/location_doryab/location_base.py
@@ -25,14 +25,11 @@ def base_location_features(location_data, day_segment, requested_features, dbsca
 
             location_data = location_data[(location_data['double_latitude']!=0.0) & (location_data['double_longitude']!=0.0)]
 
-            # exclude dates when "double_latitude" and "double_longitude" values are constant
-            location_data = dropDatesSameLocationAllDay(location_data)
-
             if "locationvariance" in features_to_compute:
                 location_features["location_" + day_segment + "_locationvariance"] = location_data.groupby(['local_date'])['double_latitude'].var() + location_data.groupby(['local_date'])['double_longitude'].var()
             
             if "loglocationvariance" in features_to_compute:
-                location_features["location_" + day_segment + "_loglocationvariance"] = np.log10(location_data.groupby(['local_date'])['double_latitude'].var() + location_data.groupby(['local_date'])['double_longitude'].var())
+                location_features["location_" + day_segment + "_loglocationvariance"] = (location_data.groupby(['local_date'])['double_latitude'].var() + location_data.groupby(['local_date'])['double_longitude'].var()).apply(lambda x: np.log10(x) if x > 0 else None)
 
             
             preComputedDistanceandSpeed = pd.DataFrame()
@@ -128,13 +125,6 @@ def base_location_features(location_data, day_segment, requested_features, dbsca
 
     return location_features
 
-def dropDatesSameLocationAllDay(data):
-    data_grouped = data.groupby(["local_date"])["double_latitude", "double_longitude"].var()
-    drop_dates = data_grouped[((data_grouped["double_latitude"] == 0) & (data_grouped["double_longitude"] == 0)) | (data_grouped["double_latitude"].isnull()) | (data_grouped["double_longitude"].isnull())].index
-    data.set_index(["local_date"], inplace = True)
-    if not drop_dates.empty:
-        data.drop(drop_dates, axis = 0, inplace = True)
-    return data.reset_index()
 
 def distance_to_degrees(d):
     #Just an approximation, but speeds up clustering by a huge amount and doesnt introduce much error
@@ -156,9 +146,11 @@ def get_all_travel_distances_meters_speed(locationData,threshold):
     lat_lon_temp['time_diff'] = lat_lon_temp['time_after'] - lat_lon_temp['time_before']
     lat_lon_temp['timeInSeconds'] = lat_lon_temp['time_diff'].apply(lambda x: x.total_seconds())
 
-    lat_lon_temp = lat_lon_temp[lat_lon_temp['timeInSeconds'] <= 300] 
-    # if lat_lon_temp.empty:
-    #     return pd.Series([0]), pd.DataFrame({"speed": [0], "speedTag": ["Static"]})
+    lat_lon_temp = lat_lon_temp[lat_lon_temp['timeInSeconds'] <= 300]
+
+    if lat_lon_temp.empty:
+        return pd.Series(), pd.DataFrame({"speed": [], "speedTag": []})
+    
     lat_lon_temp['distances'] = lat_lon_temp.apply(haversine, axis=1)  # meters
     lat_lon_temp['speed']  = (lat_lon_temp['distances'] / lat_lon_temp['timeInSeconds'] )
     lat_lon_temp['speed'] = lat_lon_temp['speed'].replace(np.inf, np.nan) * 3.6