diff --git a/lecture_material/17-viz-2/vis_2.ipynb b/lecture_material/17-viz-2/vis_2.ipynb
index 761bceffe77c42f530b550a86557057a4ac4a716..50a67cb4f895d77affa9843ff65a465e3811dcf3 100644
--- a/lecture_material/17-viz-2/vis_2.ipynb
+++ b/lecture_material/17-viz-2/vis_2.ipynb
@@ -97,7 +97,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/tmp/ipykernel_13458/2175405820.py:2: FutureWarning: The geopandas.dataset module is deprecated and will be removed in GeoPandas 1.0. You can get the original 'naturalearth_lowres' data from https://www.naturalearthdata.com/downloads/110m-cultural-vectors/.\n",
+      "/tmp/ipykernel_22480/2175405820.py:2: FutureWarning: The geopandas.dataset module is deprecated and will be removed in GeoPandas 1.0. You can get the original 'naturalearth_lowres' data from https://www.naturalearthdata.com/downloads/110m-cultural-vectors/.\n",
       "  path = gpd.datasets.get_path(\"naturalearth_lowres\")\n"
      ]
     }
@@ -345,7 +345,7 @@
     }
    ],
    "source": [
-    "# First country's geometry\n",
+    "# First country's name and geometry\n",
     "print(gdf.index[0])\n",
     "gdf[\"geometry\"].iat[0]"
    ]
@@ -378,7 +378,7 @@
     }
    ],
    "source": [
-    "# Second country's geometry\n",
+    "# Second country's name and geometry\n",
     "print(gdf.index[1])\n",
     "gdf[\"geometry\"].iat[1]"
    ]
@@ -519,24 +519,6 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "id": "962a552b-94a6-4690-8018-f82173dd6096",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create a map where countries with >100M people are red, others are gray\n",
-    "\n",
-    "# Add a new column called color to gdf and set default value to \"lightgray\"\n",
-    "\n",
-    "# Boolean indexing to set color to red for countries with \"pop_est\" > 1e8\n",
-    "\n",
-    "# Create the plot\n",
-    "# ax = gdf.plot(figsize=(8,4), color=gdf[\"color\"])\n",
-    "# ax.set_axis_off()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
    "id": "11214c90",
    "metadata": {},
    "outputs": [
@@ -573,7 +555,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 17,
    "id": "33e92def-a7db-4d90-adc8-a3d01d472ac1",
    "metadata": {},
    "outputs": [
@@ -583,7 +565,7 @@
        "shapely.geometry.polygon.Polygon"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -609,7 +591,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 18,
    "id": "61716db9",
    "metadata": {},
    "outputs": [
@@ -622,7 +604,7 @@
        "<POLYGON ((0 0, 1.2 1, 2 0, 0 0))>"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -634,7 +616,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 19,
    "id": "6a36021a-8653-4698-a0ba-818c14091d79",
    "metadata": {},
    "outputs": [
@@ -644,7 +626,7 @@
        "shapely.geometry.polygon.Polygon"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -655,7 +637,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 20,
    "id": "bddd958d-6fde-42df-8ae3-459e25fa3f04",
    "metadata": {},
    "outputs": [
@@ -668,7 +650,7 @@
        "<POLYGON ((1 0, 1 1, 0 1, 0 0, 1 0))>"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -680,7 +662,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 21,
    "id": "eb7ade8d-96d2-4770-bce9-b3e35996b0b8",
    "metadata": {},
    "outputs": [
@@ -690,7 +672,7 @@
        "shapely.geometry.polygon.Polygon"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -701,7 +683,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 22,
    "id": "e5308c61-b1fa-433b-bb05-485ca7bd23da",
    "metadata": {},
    "outputs": [
@@ -714,7 +696,7 @@
        "<POINT (5 5)>"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -726,7 +708,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 23,
    "id": "5c669619-af78-477d-b807-3e6d99278f2f",
    "metadata": {},
    "outputs": [
@@ -736,7 +718,7 @@
        "shapely.geometry.point.Point"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -747,7 +729,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 24,
    "id": "f015d27e-8fd8-446f-992f-4f7a88cc582d",
    "metadata": {},
    "outputs": [
@@ -760,7 +742,7 @@
        "<POLYGON ((6 5, 5.995 4.902, 5.981 4.805, 5.957 4.71, 5.924 4.617, 5.882 4.5...>"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -772,7 +754,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 25,
    "id": "39fe5752-8038-46cc-b4a6-320e6a78bbd1",
    "metadata": {},
    "outputs": [
@@ -782,7 +764,7 @@
        "shapely.geometry.polygon.Polygon"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -793,7 +775,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 26,
    "id": "800cc48d-c241-4439-9161-ff309e50373f",
    "metadata": {},
    "outputs": [
@@ -806,7 +788,7 @@
        "<POLYGON ((0 -3, -0.293 -2.986, -0.584 -2.943, -0.868 -2.872, -1.145 -2.773,...>"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -818,7 +800,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 27,
    "id": "6b9478d3-7cc9-4e80-ae84-e56d7bfd0b71",
    "metadata": {},
    "outputs": [
@@ -828,7 +810,7 @@
        "shapely.geometry.polygon.Polygon"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -853,7 +835,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 28,
    "id": "d1c5f9f7",
    "metadata": {},
    "outputs": [
@@ -866,7 +848,7 @@
        "<POLYGON ((1.2 1, 2 0, 1 0, 0 0, 0 1, 1 1, 1 0.833, 1.2 1))>"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -877,7 +859,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 29,
    "id": "8a2d3357",
    "metadata": {},
    "outputs": [
@@ -890,7 +872,7 @@
        "<POLYGON ((1 0.833, 1 0, 0 0, 1 0.833))>"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -901,7 +883,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 30,
    "id": "153a6b72",
    "metadata": {},
    "outputs": [
@@ -914,7 +896,7 @@
        "<POLYGON ((1.2 1, 2 0, 1 0, 1 0.833, 1.2 1))>"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -925,7 +907,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 31,
    "id": "9082b54a",
    "metadata": {},
    "outputs": [
@@ -938,7 +920,7 @@
        "<POLYGON ((0 1, 1 1, 1 0.833, 0 0, 0 1))>"
       ]
      },
-     "execution_count": 32,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -949,7 +931,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 32,
    "id": "59493a5b",
    "metadata": {},
    "outputs": [
@@ -959,7 +941,7 @@
        "True"
       ]
      },
-     "execution_count": 33,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -978,7 +960,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 33,
    "id": "7a87b70f",
    "metadata": {},
    "outputs": [
@@ -991,7 +973,7 @@
        "<POLYGON ((0.83 0.692, 0.757 0.757, 0.362 1.194, 0.011 1.667, -0.292 2.172, ...>"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1002,7 +984,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 34,
    "id": "e04daa60",
    "metadata": {},
    "outputs": [
@@ -1012,7 +994,7 @@
        "True"
       ]
      },
-     "execution_count": 35,
+     "execution_count": 34,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1031,7 +1013,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 35,
    "id": "410b08cf",
    "metadata": {},
    "outputs": [],
@@ -1050,7 +1032,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 36,
    "id": "24f4a32b-9ed4-468b-a194-ebe0395fcdb6",
    "metadata": {},
    "outputs": [
@@ -1072,7 +1054,7 @@
        "Length: 177, dtype: bool"
       ]
      },
-     "execution_count": 37,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1083,7 +1065,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 37,
    "id": "ff455178-84da-45bf-b5b9-a71a7f9160f7",
    "metadata": {},
    "outputs": [
@@ -1093,7 +1075,7 @@
        "<Axes: >"
       ]
      },
-     "execution_count": 38,
+     "execution_count": 37,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -1115,7 +1097,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 38,
    "id": "03aa8da8-72f4-45fd-8931-e410d1204226",
    "metadata": {},
    "outputs": [
@@ -1125,7 +1107,7 @@
        "<Axes: >"
       ]
      },
-     "execution_count": 39,
+     "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -1155,7 +1137,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 39,
    "id": "d7226fd9-3bb2-48c3-a5e9-5ce1ca0dd88f",
    "metadata": {},
    "outputs": [
@@ -1177,7 +1159,7 @@
        "Length: 177, dtype: geometry"
       ]
      },
-     "execution_count": 40,
+     "execution_count": 39,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1188,7 +1170,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 40,
    "id": "108b8b8a",
    "metadata": {},
    "outputs": [
@@ -1198,7 +1180,7 @@
        "<Axes: >"
       ]
      },
-     "execution_count": 41,
+     "execution_count": 40,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -1227,7 +1209,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 41,
    "id": "8c0dd051-a808-4fd4-b5ff-fee6ed580753",
    "metadata": {},
    "outputs": [
@@ -1249,7 +1231,7 @@
        "Length: 177, dtype: bool"
       ]
      },
-     "execution_count": 42,
+     "execution_count": 41,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1269,7 +1251,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 42,
    "id": "55a76a00",
    "metadata": {},
    "outputs": [
@@ -1322,7 +1304,7 @@
        "dtype: geometry"
       ]
      },
-     "execution_count": 43,
+     "execution_count": 42,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1334,7 +1316,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 43,
    "id": "08c59df7",
    "metadata": {},
    "outputs": [
@@ -1344,7 +1326,7 @@
        "<Axes: >"
       ]
      },
-     "execution_count": 44,
+     "execution_count": 43,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -1373,7 +1355,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 44,
    "id": "eb5c83b7",
    "metadata": {},
    "outputs": [
@@ -1381,7 +1363,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/tmp/ipykernel_13458/1089923979.py:3: UserWarning: Geometry is in a geographic CRS. Results from 'centroid' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.\n",
+      "/tmp/ipykernel_22480/1089923979.py:3: UserWarning: Geometry is in a geographic CRS. Results from 'centroid' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.\n",
       "\n",
       "  eur.centroid.plot(ax=ax)\n"
      ]
@@ -1392,7 +1374,7 @@
        "<Axes: >"
       ]
      },
-     "execution_count": 45,
+     "execution_count": 44,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -1432,7 +1414,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 45,
    "id": "89251896",
    "metadata": {},
    "outputs": [
@@ -1452,7 +1434,7 @@
        "- Prime Meridian: Greenwich"
       ]
      },
-     "execution_count": 46,
+     "execution_count": 45,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1474,7 +1456,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 46,
    "id": "5451e78f-ebaa-4bb3-af34-24ffe92d0582",
    "metadata": {},
    "outputs": [],
@@ -1484,7 +1466,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 47,
    "id": "586038b1",
    "metadata": {},
    "outputs": [
@@ -1507,7 +1489,7 @@
        "- Prime Meridian: Greenwich"
       ]
      },
-     "execution_count": 48,
+     "execution_count": 47,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1520,7 +1502,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 48,
    "id": "d46c124c-9aff-4c2f-81fe-08885b606800",
    "metadata": {},
    "outputs": [
@@ -1530,7 +1512,7 @@
        "<Axes: >"
       ]
      },
-     "execution_count": 49,
+     "execution_count": 48,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -1562,7 +1544,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 49,
    "id": "045b9c33",
    "metadata": {},
    "outputs": [
@@ -1572,7 +1554,7 @@
        "<Axes: >"
       ]
      },
-     "execution_count": 50,
+     "execution_count": 49,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -1602,7 +1584,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 50,
    "id": "c0b72aff",
    "metadata": {},
    "outputs": [
@@ -1610,7 +1592,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/tmp/ipykernel_13458/2170279601.py:3: UserWarning: Geometry is in a geographic CRS. Results from 'centroid' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.\n",
+      "/tmp/ipykernel_22480/2170279601.py:3: UserWarning: Geometry is in a geographic CRS. Results from 'centroid' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.\n",
       "\n",
       "  eur.centroid.to_crs(\"EPSG:3035\").plot(ax=ax, color=\"r\")  # red => miscalculated\n"
      ]
@@ -1621,7 +1603,7 @@
        "<Axes: >"
       ]
      },
-     "execution_count": 51,
+     "execution_count": 50,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -1644,7 +1626,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 51,
    "id": "ca9e306e",
    "metadata": {},
    "outputs": [
@@ -1654,7 +1636,7 @@
        "shapely.geometry.multipolygon.MultiPolygon"
       ]
      },
-     "execution_count": 52,
+     "execution_count": 51,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1665,7 +1647,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 52,
    "id": "f489c88d-5964-4358-b8c4-fc80d28b5491",
    "metadata": {},
    "outputs": [
@@ -1684,7 +1666,7 @@
        " object)"
       ]
      },
-     "execution_count": 53,
+     "execution_count": 52,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1703,7 +1685,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 53,
    "id": "3e4874d9",
    "metadata": {},
    "outputs": [
@@ -1756,7 +1738,7 @@
        "dtype: float64"
       ]
      },
-     "execution_count": 54,
+     "execution_count": 53,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1775,7 +1757,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 54,
    "id": "85ee20c2",
    "metadata": {},
    "outputs": [
@@ -1828,7 +1810,7 @@
        "dtype: float64"
       ]
      },
-     "execution_count": 55,
+     "execution_count": 54,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1841,7 +1823,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 55,
    "id": "cd600837",
    "metadata": {},
    "outputs": [
@@ -1849,7 +1831,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/tmp/ipykernel_13458/3689342177.py:2: UserWarning: Geometry is in a geographic CRS. Results from 'area' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.\n",
+      "/tmp/ipykernel_22480/3689342177.py:2: UserWarning: Geometry is in a geographic CRS. Results from 'area' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.\n",
       "\n",
       "  eur.area\n"
      ]
@@ -1903,7 +1885,7 @@
        "dtype: float64"
       ]
      },
-     "execution_count": 56,
+     "execution_count": 55,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1943,7 +1925,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 56,
    "id": "a6f80847",
    "metadata": {},
    "outputs": [],
@@ -1953,7 +1935,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 57,
    "id": "7f8595be",
    "metadata": {},
    "outputs": [
@@ -1976,7 +1958,7 @@
        "- Prime Meridian: Greenwich"
       ]
      },
-     "execution_count": 58,
+     "execution_count": 57,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1987,7 +1969,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 58,
    "id": "9ebd361f",
    "metadata": {},
    "outputs": [],
@@ -2024,7 +2006,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 59,
    "id": "3a095d5e",
    "metadata": {},
    "outputs": [],
@@ -2035,7 +2017,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 60,
    "id": "248be81e",
    "metadata": {},
    "outputs": [
@@ -2061,7 +2043,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 61,
    "id": "3a609d81",
    "metadata": {},
    "outputs": [],
@@ -2085,7 +2067,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 62,
    "id": "6b0b2aa0",
    "metadata": {},
    "outputs": [
@@ -2095,7 +2077,7 @@
        "<Response [200]>"
       ]
      },
-     "execution_count": 63,
+     "execution_count": 62,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2108,7 +2090,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 63,
    "id": "bee28b41",
    "metadata": {},
    "outputs": [],
@@ -2118,7 +2100,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 64,
    "id": "0bae00e7",
    "metadata": {},
    "outputs": [],
@@ -2129,7 +2111,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 65,
    "id": "47173ec2",
    "metadata": {},
    "outputs": [],
@@ -2147,7 +2129,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 66,
    "id": "ac7b9482-5512-49e4-b0b8-7f9ed34b5844",
    "metadata": {},
    "outputs": [],
@@ -2158,7 +2140,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 67,
    "id": "8e9b49d2-3e0d-4a39-9dcf-13b288a165e7",
    "metadata": {},
    "outputs": [
@@ -2178,7 +2160,7 @@
        "dtype: object"
       ]
      },
-     "execution_count": 68,
+     "execution_count": 67,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2199,7 +2181,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 68,
    "id": "095b9ebe-b583-4098-a3a2-47dd46610d59",
    "metadata": {},
    "outputs": [
@@ -2246,7 +2228,7 @@
        "0  1300, East 9th Avenue, 99202, East 9th Avenue,...  "
       ]
      },
-     "execution_count": 69,
+     "execution_count": 68,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2258,7 +2240,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 69,
    "id": "e52ee0fc-d73c-4942-9bec-d1586a702f68",
    "metadata": {},
    "outputs": [
@@ -2268,7 +2250,7 @@
        "'1300, East 9th Avenue, 99202, East 9th Avenue, Spokane, WA, United States'"
       ]
      },
-     "execution_count": 70,
+     "execution_count": 69,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2287,7 +2269,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 70,
    "id": "cf3f590b-8d00-46c4-ac57-6f2f61509f68",
    "metadata": {},
    "outputs": [
@@ -2319,7 +2301,7 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>POINT (-89.29503 43.13823)</td>\n",
+       "      <td>POINT (-89.29287 43.13960)</td>\n",
        "      <td>East Washington Avenue, 53701, Madison, Wiscon...</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -2328,13 +2310,13 @@
       ],
       "text/plain": [
        "                     geometry  \\\n",
-       "0  POINT (-89.29503 43.13823)   \n",
+       "0  POINT (-89.29287 43.13960)   \n",
        "\n",
        "                                             address  \n",
        "0  East Washington Avenue, 53701, Madison, Wiscon...  "
       ]
      },
-     "execution_count": 71,
+     "execution_count": 70,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2354,7 +2336,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 71,
    "id": "54103e4a-5ac2-4f19-811b-385c02623ed2",
    "metadata": {},
    "outputs": [
@@ -2401,7 +2383,7 @@
        "0  800, Madison Street, 54650, Madison Street, On...  "
       ]
      },
-     "execution_count": 72,
+     "execution_count": 71,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2412,7 +2394,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 72,
    "id": "66072f4b-2286-4d66-ba2c-18ccd2e37ed6",
    "metadata": {},
    "outputs": [
@@ -2459,7 +2441,7 @@
        "0  University of Wisconsin-Whitewater, 800, West ...  "
       ]
      },
-     "execution_count": 73,
+     "execution_count": 72,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2470,7 +2452,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 73,
    "id": "cf982302",
    "metadata": {},
    "outputs": [
@@ -2490,7 +2472,7 @@
        "dtype: object"
       ]
      },
-     "execution_count": 74,
+     "execution_count": 73,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2514,7 +2496,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 74,
    "id": "ab0e699f",
    "metadata": {},
    "outputs": [
@@ -2565,6 +2547,11 @@
        "      <td>700, Vernon Avenue, Rolling Meadows, Madison, ...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>POINT EMPTY</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
        "      <th>5</th>\n",
        "      <td>POINT (-89.40073 43.05908)</td>\n",
        "      <td>900, Delaplaine Court, Greenbush, Madison, Dan...</td>\n",
@@ -2576,10 +2563,15 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
-       "      <td>POINT (-89.39917 43.06979)</td>\n",
+       "      <td>POINT (-89.39919 43.06913)</td>\n",
        "      <td>East Campus Mall, State-Langdon, Bowens Additi...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>POINT EMPTY</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
        "      <th>9</th>\n",
        "      <td>POINT (-89.50622 43.05752)</td>\n",
        "      <td>West Towne Mall, Madison, Dane County, Wiscons...</td>\n",
@@ -2594,9 +2586,11 @@
        "1  POINT (-89.33876 43.04856)   \n",
        "2  POINT (-89.39985 43.07217)   \n",
        "3  POINT (-89.30584 43.08825)   \n",
+       "4                 POINT EMPTY   \n",
        "5  POINT (-89.40073 43.05908)   \n",
        "6  POINT (-89.50167 43.05667)   \n",
-       "7  POINT (-89.39917 43.06979)   \n",
+       "7  POINT (-89.39919 43.06913)   \n",
+       "8                 POINT EMPTY   \n",
        "9  POINT (-89.50622 43.05752)   \n",
        "\n",
        "                                             address  \n",
@@ -2604,19 +2598,21 @@
        "1  6400, Bridge Road, Bridge-Lakepoint, Monona, D...  \n",
        "2  800, West Johnson Street, State-Langdon, Bowen...  \n",
        "3  700, Vernon Avenue, Rolling Meadows, Madison, ...  \n",
+       "4                                               None  \n",
        "5  900, Delaplaine Court, Greenbush, Madison, Dan...  \n",
        "6  6900, Odana Road, Madison, Dane County, Wiscon...  \n",
        "7  East Campus Mall, State-Langdon, Bowens Additi...  \n",
+       "8                                               None  \n",
        "9  West Towne Mall, Madison, Dane County, Wiscons...  "
       ]
      },
-     "execution_count": 75,
+     "execution_count": 74,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "incidents = gpd.tools.geocode(fixed_addrs, provider=\"nominatim\", user_agent=\"cs320bot\").dropna()\n",
+    "incidents = gpd.tools.geocode(fixed_addrs, provider=\"nominatim\", user_agent=\"cs320bot\")\n",
     "incidents"
    ]
   },
@@ -2630,7 +2626,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 75,
    "id": "41a7d12e-73be-4442-b43c-285229e6cfdb",
    "metadata": {},
    "outputs": [
@@ -2692,7 +2688,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
-       "      <td>POINT (-89.39917 43.06979)</td>\n",
+       "      <td>POINT (-89.39919 43.06913)</td>\n",
        "      <td>East Campus Mall, State-Langdon, Bowens Additi...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -2712,7 +2708,7 @@
        "3  POINT (-89.30584 43.08825)   \n",
        "5  POINT (-89.40073 43.05908)   \n",
        "6  POINT (-89.50167 43.05667)   \n",
-       "7  POINT (-89.39917 43.06979)   \n",
+       "7  POINT (-89.39919 43.06913)   \n",
        "9  POINT (-89.50622 43.05752)   \n",
        "\n",
        "                                             address  \n",
@@ -2726,7 +2722,7 @@
        "9  West Towne Mall, Madison, Dane County, Wiscons...  "
       ]
      },
-     "execution_count": 76,
+     "execution_count": 75,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2748,7 +2744,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 76,
    "id": "843bbba2-3de5-4cc0-b76b-92e7bebdd379",
    "metadata": {},
    "outputs": [
@@ -2775,13 +2771,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 77,
    "id": "1a04c2b0",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
diff --git a/lecture_material/17-viz-2/vis_2_lec_001.ipynb b/lecture_material/17-viz-2/vis_2_lec_001.ipynb
index a530b4c2668fbbc8d7e0d3a09f652eed5488f1bc..c7712fd5c032c28f35b14ee49ee6e79e96ff34b9 100644
--- a/lecture_material/17-viz-2/vis_2_lec_001.ipynb
+++ b/lecture_material/17-viz-2/vis_2_lec_001.ipynb
@@ -131,9 +131,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# First country's geometry\n",
-    "print(gdf.index[0])\n",
-    "gdf[\"geometry\"].iat[0]"
+    "# First country's name and geometry\n"
    ]
   },
   {
@@ -143,7 +141,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Second country's geometry\n",
+    "# Second country's name geometry\n",
     "print(gdf.index[1])\n",
     "gdf[\"geometry\"].iat[1]"
    ]
@@ -156,7 +154,7 @@
    "outputs": [],
    "source": [
     "# Geometry for \"United States of America\"\n",
-    "gdf.at[<row_index>, <column_name>]"
+    "# gdf.at[<row_index>, <column_name>]"
    ]
   },
   {
@@ -167,10 +165,9 @@
    "outputs": [],
    "source": [
     "# Type of Tanzania's geometry\n",
-    "print(gdf.index[1], type(gdf[\"geometry\"].iat[1]))\n",
     "\n",
-    "# Type of United States of America's geometry\n",
-    "print(\"United States of America\", type(gdf.at[\"United States of America\", \"geometry\"]))"
+    "\n",
+    "# Type of United States of America's geometry\n"
    ]
   },
   {
@@ -244,24 +241,6 @@
     "# ax.set_axis_off()"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "11214c90",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create a map where countries with >100M people are red, others are gray\n",
-    "\n",
-    "# Add a new column called color to gdf and set default value to \"lightgray\"\n",
-    "gdf[\"color\"] = \"lightgray\"\n",
-    "# Boolean indexing to set color to red for countries with \"pop_est\" > 1e8\n",
-    "gdf.loc[gdf[\"pop_est\"] > 1e8, \"color\"] = \"red\"\n",
-    "# Create the plot\n",
-    "ax = gdf.plot(figsize=(8,4), color=gdf[\"color\"])\n",
-    "ax.set_axis_off()"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "5a32c52c-509f-4f7f-8dd6-bc7fe53c64fd",
@@ -302,7 +281,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "triangle = Polygon([(0, 0), (1.2, 1), (2, 0)])   # triangle\n",
+    "triangle =    # triangle\n",
     "triangle"
    ]
   },
@@ -323,7 +302,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "box1 = box(0, 0, 1, 1) # not a type; just a function that creates box\n",
+    "box1 =     # not a type; just a function that creates box\n",
     "box1"
    ]
   },
@@ -344,7 +323,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "point = Point(5, 5)\n",
+    "point = \n",
     "point"
    ]
   },
@@ -365,7 +344,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "circle = point.buffer(1)\n",
+    "# use buffer to create a circle from a point\n",
+    "circle = \n",
     "circle"
    ]
   },
@@ -443,8 +423,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# difference of triangle and box1\n",
-    "# subtraction"
+    "# difference of triangle and box1"
    ]
   },
   {
@@ -690,16 +669,6 @@
     "- https://spatialreference.org/ref/?search=europe"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "5451e78f-ebaa-4bb3-af34-24ffe92d0582",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Setting CRS to \"EPSG:3035\"\n"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -878,9 +847,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "water = gpd.read_file(\"Lakes_and_Rivers.zip\").to_crs(city.crs)\n",
-    "fire = gpd.read_file(\"Fire_Stations.zip\").to_crs(city.crs)\n",
-    "police = gpd.read_file(\"Police_Stations.zip\").to_crs(city.crs)"
+    "water =  \n",
+    "fire =  \n",
+    "police ="
    ]
   },
   {
@@ -1027,7 +996,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "addrs = re.findall(r' <p>(.*?)<br>', r.text)\n",
+    "addrs = re.findall(r'', r.text)\n",
     "addrs = pd.Series(addrs)\n",
     "addrs"
    ]
@@ -1043,22 +1012,22 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "095b9ebe-b583-4098-a3a2-47dd46610d59",
+   "id": "e52ee0fc-d73c-4942-9bec-d1586a702f68",
    "metadata": {},
    "outputs": [],
    "source": [
-    "geo_info = gpd.tools.geocode(\"1300 East Washington Ave\")\n",
-    "geo_info"
+    "geo_info[\"address\"].loc[0]"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "e52ee0fc-d73c-4942-9bec-d1586a702f68",
+   "id": "095b9ebe-b583-4098-a3a2-47dd46610d59",
    "metadata": {},
    "outputs": [],
    "source": [
-    "geo_info[\"address\"].loc[0]"
+    "geo_info = gpd.tools.geocode(\"1300 East Washington Ave\")\n",
+    "geo_info"
    ]
   },
   {
@@ -1138,7 +1107,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "incidents = gpd.tools.geocode(fixed_addrs, provider=\"nominatim\", user_agent=\"cs320bot\").dropna()\n",
+    "incidents = \n",
     "incidents"
    ]
   },
@@ -1216,7 +1185,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.8.10"
   }
  },
  "nbformat": 4,
diff --git a/lecture_material/17-viz-2/vis_2_lec_002.ipynb b/lecture_material/17-viz-2/vis_2_lec_002.ipynb
index a530b4c2668fbbc8d7e0d3a09f652eed5488f1bc..c7712fd5c032c28f35b14ee49ee6e79e96ff34b9 100644
--- a/lecture_material/17-viz-2/vis_2_lec_002.ipynb
+++ b/lecture_material/17-viz-2/vis_2_lec_002.ipynb
@@ -131,9 +131,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# First country's geometry\n",
-    "print(gdf.index[0])\n",
-    "gdf[\"geometry\"].iat[0]"
+    "# First country's name and geometry\n"
    ]
   },
   {
@@ -143,7 +141,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Second country's geometry\n",
+    "# Second country's name geometry\n",
     "print(gdf.index[1])\n",
     "gdf[\"geometry\"].iat[1]"
    ]
@@ -156,7 +154,7 @@
    "outputs": [],
    "source": [
     "# Geometry for \"United States of America\"\n",
-    "gdf.at[<row_index>, <column_name>]"
+    "# gdf.at[<row_index>, <column_name>]"
    ]
   },
   {
@@ -167,10 +165,9 @@
    "outputs": [],
    "source": [
     "# Type of Tanzania's geometry\n",
-    "print(gdf.index[1], type(gdf[\"geometry\"].iat[1]))\n",
     "\n",
-    "# Type of United States of America's geometry\n",
-    "print(\"United States of America\", type(gdf.at[\"United States of America\", \"geometry\"]))"
+    "\n",
+    "# Type of United States of America's geometry\n"
    ]
   },
   {
@@ -244,24 +241,6 @@
     "# ax.set_axis_off()"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "11214c90",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create a map where countries with >100M people are red, others are gray\n",
-    "\n",
-    "# Add a new column called color to gdf and set default value to \"lightgray\"\n",
-    "gdf[\"color\"] = \"lightgray\"\n",
-    "# Boolean indexing to set color to red for countries with \"pop_est\" > 1e8\n",
-    "gdf.loc[gdf[\"pop_est\"] > 1e8, \"color\"] = \"red\"\n",
-    "# Create the plot\n",
-    "ax = gdf.plot(figsize=(8,4), color=gdf[\"color\"])\n",
-    "ax.set_axis_off()"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "5a32c52c-509f-4f7f-8dd6-bc7fe53c64fd",
@@ -302,7 +281,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "triangle = Polygon([(0, 0), (1.2, 1), (2, 0)])   # triangle\n",
+    "triangle =    # triangle\n",
     "triangle"
    ]
   },
@@ -323,7 +302,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "box1 = box(0, 0, 1, 1) # not a type; just a function that creates box\n",
+    "box1 =     # not a type; just a function that creates box\n",
     "box1"
    ]
   },
@@ -344,7 +323,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "point = Point(5, 5)\n",
+    "point = \n",
     "point"
    ]
   },
@@ -365,7 +344,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "circle = point.buffer(1)\n",
+    "# use buffer to create a circle from a point\n",
+    "circle = \n",
     "circle"
    ]
   },
@@ -443,8 +423,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# difference of triangle and box1\n",
-    "# subtraction"
+    "# difference of triangle and box1"
    ]
   },
   {
@@ -690,16 +669,6 @@
     "- https://spatialreference.org/ref/?search=europe"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "5451e78f-ebaa-4bb3-af34-24ffe92d0582",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Setting CRS to \"EPSG:3035\"\n"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -878,9 +847,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "water = gpd.read_file(\"Lakes_and_Rivers.zip\").to_crs(city.crs)\n",
-    "fire = gpd.read_file(\"Fire_Stations.zip\").to_crs(city.crs)\n",
-    "police = gpd.read_file(\"Police_Stations.zip\").to_crs(city.crs)"
+    "water =  \n",
+    "fire =  \n",
+    "police ="
    ]
   },
   {
@@ -1027,7 +996,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "addrs = re.findall(r' <p>(.*?)<br>', r.text)\n",
+    "addrs = re.findall(r'', r.text)\n",
     "addrs = pd.Series(addrs)\n",
     "addrs"
    ]
@@ -1043,22 +1012,22 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "095b9ebe-b583-4098-a3a2-47dd46610d59",
+   "id": "e52ee0fc-d73c-4942-9bec-d1586a702f68",
    "metadata": {},
    "outputs": [],
    "source": [
-    "geo_info = gpd.tools.geocode(\"1300 East Washington Ave\")\n",
-    "geo_info"
+    "geo_info[\"address\"].loc[0]"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "e52ee0fc-d73c-4942-9bec-d1586a702f68",
+   "id": "095b9ebe-b583-4098-a3a2-47dd46610d59",
    "metadata": {},
    "outputs": [],
    "source": [
-    "geo_info[\"address\"].loc[0]"
+    "geo_info = gpd.tools.geocode(\"1300 East Washington Ave\")\n",
+    "geo_info"
    ]
   },
   {
@@ -1138,7 +1107,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "incidents = gpd.tools.geocode(fixed_addrs, provider=\"nominatim\", user_agent=\"cs320bot\").dropna()\n",
+    "incidents = \n",
     "incidents"
    ]
   },
@@ -1216,7 +1185,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.8.10"
   }
  },
  "nbformat": 4,