diff --git a/data/constants.py b/data/constants.py
index 2bfaeec564c2cec4d1e3548bcfa606b04ed4ec2d..93f9d0ca941ea4f4b80a8a7d566fb54a7ae051ff 100644
--- a/data/constants.py
+++ b/data/constants.py
@@ -3,11 +3,11 @@ import pyproj
 # TODO: Pandas is quite good at working with categorical data. Refactor everything to make use of that.
 # It will not only be more readable but will also bring a speedup!
 
-CH1903 = pyproj.Proj("EPSG:21781")
+CH1903 = pyproj.Proj("epsg:21781")
 LV05 = CH1903
-CH1903_PLUS = pyproj.Proj("EPSG:2056")
+CH1903_PLUS = pyproj.Proj("epsg:2056")
 LV95 = CH1903_PLUS
-WGS84 = pyproj.Proj("EPSG:4326")
+WGS84 = pyproj.Proj("epsg:4326")
 
 MAXIMUM_HOUSEHOLD_SIZE = 12
 MINIMUM_AGE_PER_HOUSEHOLD = 16
diff --git a/data/spatial/cantons.py b/data/spatial/cantons.py
index e434e3c9664641ffd7600ff21ba8d52856424fa2..fdb923060406ddbaa62bb1bed9d39798e239c532 100644
--- a/data/spatial/cantons.py
+++ b/data/spatial/cantons.py
@@ -9,9 +9,12 @@ def execute(context):
     # Load data
     data_path = context.config("data_path")
 
-    df = gpd.read_file("%s/municipality_borders/gd-b-00.03-875-gg18/ggg_2018-LV95/shp/g1k18.shp" % data_path,
-                       encoding="latin1"
-                       ).to_crs("EPSG:2056")
+    df = gpd.read_file(
+        "%s/municipality_borders/gd-b-00.03-875-gg18/ggg_2018-LV95/shp/g1k18.shp" % data_path,
+        encoding="latin1"
+    ).to_crs("epsg:2056")
+
+    df.crs = "epsg:2056"
 
     df = df.rename({"KTNR": "canton_id", "KTNAME": "canton_name"}, axis=1)
     df = df[["canton_id", "canton_name", "geometry"]]
diff --git a/data/spatial/municipalities.py b/data/spatial/municipalities.py
index 1dafdfc0f0d6509378e6da2efe91094df939d31d..35754d5444dab34ff3bcfa809297e489ba7096b2 100644
--- a/data/spatial/municipalities.py
+++ b/data/spatial/municipalities.py
@@ -2,6 +2,7 @@ import geopandas as gpd
 import numpy as np
 import pandas as pd
 from sklearn.neighbors import KDTree
+import data.spatial.utils
 
 
 def configure(context):
@@ -35,7 +36,10 @@ def execute(context):
         df = gpd.read_file(
             "%s/%s" % (data_path, shapefile),
             encoding="latin1"
-        ).to_crs("EPSG:2056")
+        ).to_crs("epsg:2056")
+        
+        df.crs = "epsg:2056"
+
         df.loc[:, "municipality_id"] = df[id_field]
         df.loc[:, "municipality_name"] = df[name_field]
         df.loc[:, "year"] = year
diff --git a/data/spatial/nuts.py b/data/spatial/nuts.py
index 2f1bce07bfa5213d3815cf68df65044a633a774b..abdbb9127d0a97eae4fab946f7dfb0ada8b3426d 100644
--- a/data/spatial/nuts.py
+++ b/data/spatial/nuts.py
@@ -34,8 +34,8 @@ def execute(context):
             "%s/%s" % (data_path, shapefile),
             encoding="utf-8"
         )
-        df.crs = "EPSG:4326"
-        df = df.to_crs("EPSG:2056")
+        df.crs = "epsg:4326"
+        df = df.to_crs("epsg:2056")
 
         df.loc[:, "nuts_id"] = df[id_field]
         df.loc[:, "nuts_name"] = df[name_field]
diff --git a/data/spatial/ovgk.py b/data/spatial/ovgk.py
index c49f9c0834ce6aead2e78ce97985035ca8fcf9f3..99431bdfb0b3f120478879654ac137d3e61e789a 100644
--- a/data/spatial/ovgk.py
+++ b/data/spatial/ovgk.py
@@ -11,7 +11,7 @@ def configure(context):
 def execute(context):
     input_path = "%s/ov_guteklasse/LV95/Oev_Gueteklassen_ARE.shp" % context.config("data_path")
     df = gpd.read_file(input_path)
-    df.crs = "EPSG:2056"
+    df.crs = "epsg:2056"
     df = df[["KLASSE", "geometry"]].rename({"KLASSE": "ovgk"}, axis=1)
     return df
 
diff --git a/data/spatial/postal_codes.py b/data/spatial/postal_codes.py
index 263f37302f18601d85bad436badd56798836d5ce..7a73a2f4c85563679a3e527e901366386cb70008 100644
--- a/data/spatial/postal_codes.py
+++ b/data/spatial/postal_codes.py
@@ -9,10 +9,12 @@ def execute(context):
     df = gpd.read_file(
         "%s/postal_codes/PLZO_SHP_LV95/PLZO_PLZ.shp" % data_path,
         encoding = "latin1"
-    ).to_crs("EPSG:2056")
+    ).to_crs("epsg:2056")
+
+    df.crs = "epsg:2056"
 
     df["postal_code"] = df["PLZ"]
     df = df.sort_values(by="postal_code").reset_index()
     df = df[["postal_code", "geometry"]]
 
-    return df
\ No newline at end of file
+    return df
diff --git a/data/spatial/quarters.py b/data/spatial/quarters.py
index 3a0d285fec31232fe2e04135ccaab2dc799629c3..8cca366c767619ea15ac5830f3eb654c3dfb527c 100644
--- a/data/spatial/quarters.py
+++ b/data/spatial/quarters.py
@@ -12,7 +12,9 @@ def execute(context):
     df = gpd.read_file(
         "%s/statistical_quarter_borders/shp/quart17.shp" % data_path,
         encoding = "latin1"
-    ).to_crs("EPSG:2056")
+    ).to_crs("epsg:2056")
+
+    df.crs = "epsg:2056"
 
     df["quarter_id"] = df["GMDEQNR"]
     df["quarter_name"] = df["NAME"]
diff --git a/data/spatial/swiss_border.py b/data/spatial/swiss_border.py
index a236c9b5607ad1e6cac167762c9f6b4e97e9888b..785ba04a3f6f1a932266139d08234366e89b3501 100644
--- a/data/spatial/swiss_border.py
+++ b/data/spatial/swiss_border.py
@@ -9,6 +9,8 @@ def execute(context):
     df = gpd.read_file(
         "%s/municipality_borders/gd-b-00.03-875-gg18/ggg_2018-LV95/shp/g1l18.shp" % data_path,
         encoding = "latin1"
-    ).to_crs("EPSG:2056")
+    ).to_crs("epsg:2056")
 
-    return df["geometry"]
\ No newline at end of file
+    df.crs = "epsg:2056"
+
+    return df["geometry"]
diff --git a/data/spatial/utils.py b/data/spatial/utils.py
index 761062557cccd8006318ce430eb2fcf425cf769d..f4b1c338ca9e4e6ded19c3371f51db1c3fe368b8 100644
--- a/data/spatial/utils.py
+++ b/data/spatial/utils.py
@@ -4,7 +4,6 @@ import pandas as pd
 import shapely.geometry as geo
 from sklearn.neighbors import KDTree
 
-
 def sample_coordinates(row, count):
     samples = []
     bounds = row["geometry"].bounds
@@ -19,22 +18,24 @@ def sample_coordinates(row, count):
     return np.array(list(map(lambda p: (p.x, p.y), samples[:count])))
 
 
-def to_gpd(context, df, x="x", y="y", crs="EPSG:2056"):
+def to_gpd(context, df, x="x", y="y", crs="epsg:2056", coord_type=""):
     df["geometry"] = [
         geo.Point(*coord) for coord in context.progress(
             zip(df[x], df[y]), total=len(df),
-            label="Converting coordinates"
+            label="Converting %s coordinates" % coord_type
         )]
     df = gpd.GeoDataFrame(df)
     df.crs = crs
 
-    if not crs == "EPSG:2056":
-        df = df.to_crs("EPSG:2056")
+    if not crs == "epsg:2056":
+        df = df.to_crs("epsg:2056")
+        df.crs = "epsg:2056"
 
     return df
 
 
-def impute(context, df_points, df_zones, point_id_field, zone_id_field, fix_by_distance=True, chunk_size=10000):
+def impute(context, df_points, df_zones, point_id_field, zone_id_field, fix_by_distance=True, chunk_size=10000,
+           zone_type="", point_type=""):
     assert (type(df_points) == gpd.GeoDataFrame)
     assert (type(df_zones) == gpd.GeoDataFrame)
 
@@ -46,7 +47,8 @@ def impute(context, df_points, df_zones, point_id_field, zone_id_field, fix_by_d
     df_points = df_points[[point_id_field, "geometry"]]
     df_zones = df_zones[[zone_id_field, "geometry"]]
 
-    print("Imputing %d zones into %d points by spatial join..." % (len(df_zones), len(df_points)))
+    print("Imputing %d %s zones onto %d %s points by spatial join..."
+          % (len(df_zones), zone_type, len(df_points), point_type))
 
     result = []
     chunk_count = max(1, int(len(df_points) / chunk_size))
diff --git a/data/statpop/statpop.py b/data/statpop/statpop.py
index 8982faeac20d03c663428c4c21a83803e8be1fb5..411eddda7465c5650430ab93670c54a079d877b1 100644
--- a/data/statpop/statpop.py
+++ b/data/statpop/statpop.py
@@ -93,24 +93,23 @@ def execute(context):
     df_cantons = context.stage("data.spatial.cantons")
 
     df_spatial = pd.DataFrame(df[["person_id", "home_x", "home_y"]])
-    df_spatial = data.spatial.utils.to_gpd(context, df_spatial, "home_x", "home_y")
+    df_spatial = data.spatial.utils.to_gpd(context, df_spatial, "home_x", "home_y", coord_type="home")
 
     # Impute municipalities
-    df_spatial = data.spatial.utils.impute(context, df_spatial, df_municipalities, "person_id", "municipality_id")[[
-        "person_id", "municipality_id", "geometry"
-    ]]
+    df_spatial = (data.spatial.utils.impute(context, df_spatial, df_municipalities, "person_id", "municipality_id",
+                                            zone_type="municipality", point_type="home")[
+        ["person_id", "municipality_id", "geometry"]])
     df_spatial["municipality_id"] = df_spatial["municipality_id"].astype(np.int)
 
     # Impute quarters
     df_spatial = (data.spatial.utils.impute(context, df_spatial, df_quarters, "person_id", "quarter_id",
-                                            fix_by_distance=False)[
-        ["person_id", "municipality_id", "quarter_id", "geometry"]]
-    )
+                                            fix_by_distance=False, zone_type="quarter", point_type="home")[
+        ["person_id", "municipality_id", "quarter_id", "geometry"]])
 
     # Impute cantons
-    df_spatial = data.spatial.utils.impute(context, df_spatial, df_cantons, "person_id", "canton_id")[[
-        "person_id", "municipality_id", "quarter_id", "canton_id", "geometry"
-    ]]
+    df_spatial = (data.spatial.utils.impute(context, df_spatial, df_cantons, "person_id", "canton_id",
+                                            zone_type="canton", point_type="home")[
+        ["person_id", "municipality_id", "quarter_id", "canton_id", "geometry"]])
 
     # Impute municipality types
     df_spatial = data.spatial.municipality_types.impute(df_spatial, df_municipality_types)
diff --git a/matsim/network/convert_hafas.py b/matsim/network/convert_hafas.py
index cdd98f54afcfa7ba5fb5d0e365e4bda1cd2eff32..ef8593902fe46a45d91d5e771e059bcc7050f37e 100644
--- a/matsim/network/convert_hafas.py
+++ b/matsim/network/convert_hafas.py
@@ -12,7 +12,7 @@ def execute(context):
     # Create MATSim schedule
 
     java(jar, "org.matsim.pt2matsim.run.Hafas2TransitSchedule", [
-        "%s/hafas" % context.config("data_path"), "EPSG:2056",
+        "%s/hafas" % context.config("data_path"), "epsg:2056",
         "%s/transit_schedule.xml.gz" % context.cache_path,
         "%s/transit_vehicles.xml.gz" % context.cache_path,
         context.config("hafas_date")
diff --git a/matsim/network/convert_osm.py b/matsim/network/convert_osm.py
index 14e467419d69a889395d5656151d206f2fa63386..1d4e54f265da4be82e6f73e057fee8913c03ef1c 100644
--- a/matsim/network/convert_osm.py
+++ b/matsim/network/convert_osm.py
@@ -23,7 +23,7 @@ def execute(context):
     )
     content = content.replace(
         '<param name="outputCoordinateSystem" value="null" />',
-        '<param name="outputCoordinateSystem" value="EPSG:2056" />'
+        '<param name="outputCoordinateSystem" value="epsg:2056" />'
     )
     content = content.replace(
         '<param name="outputNetworkFile" value="null" />',
diff --git a/matsim/network/plausibility.py b/matsim/network/plausibility.py
index 82706da97a10b318c94b0bfb5f41ad96fb1251f3..463859915cd9354dd6808e1abe2f3aa1527286d9 100644
--- a/matsim/network/plausibility.py
+++ b/matsim/network/plausibility.py
@@ -14,7 +14,7 @@ def execute(context):
 
     java(jar, "org.matsim.pt2matsim.run.CheckMappedSchedulePlausibility", [
         "-Djava.io.tmpdir=%s/java_tmp" % tmp_path,
-        paths["schedule"], paths["network"], "EPSG:2056", context.cache_path
+        paths["schedule"], paths["network"], "epsg:2056", context.cache_path
     ], cwd = context.cache_path)
 
     assert(os.path.exists("%s/allPlausibilityWarnings.csv" % context.cache_path))