diff --git a/src/great_circle_distances.ipynb b/src/great_circle_distances.ipynb
index b8024db..ba1b701 100644
--- a/src/great_circle_distances.ipynb
+++ b/src/great_circle_distances.ipynb
@@ -30,39 +30,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 134,
    "metadata": {},
    "outputs": [],
    "source": [
-    "def cartesian_to_gcs(v):\n",
-    "    \"\"\"Change the basis used to represent\n",
-    "    a *unit* vector in R^3 (3-dimensional Euclidean space)\n",
-    "    from the standard basis for R^3 into a basis\n",
-    "    for a geographical coordinate system (GCS).  Output is\n",
-    "    in degrees. Only the latitude and longitude are returned\n",
-    "    (in that order).\n",
-    "\n",
-    "    Args:\n",
-    "        v: numpy.ndarray\n",
-    "\n",
-    "    Returns:\n",
-    "        numpy.ndarray\n",
-    "    \"\"\"\n",
-    "    # https://en.wikipedia.org/wiki/Spherical_coordinate_system#In_geography\n",
-    "    x = v[0]\n",
-    "    y = v[1]\n",
-    "    z = v[2]\n",
-    "    # Assume v has L-2 norm of 1.\n",
-    "    r = 1.0\n",
-    "    # https://stackoverflow.com/questions/50906886/convert-spherical-to-geographical-coordinate-system\n",
-    "    # Calculate the latitude\n",
-    "    theta = np.arccos(z / r) * 180.0 / np.pi\n",
-    "    latitude = theta - 90.0\n",
-    "    # Calculate the longitude\n",
-    "    phi = np.sign(y) * np.arccos(x / np.sqrt(x**2 + y**2)) * 180.0 / np.pi\n",
-    "    longitude = phi\n",
-    "    return np.array([latitude, longitude])\n",
-    "\n",
     "def get_uniformly_distributed_points_on_sphere(n:int):\n",
     "    \"\"\"The points are returned with respect to the\n",
     "    standard basis for R^3 as an numpy.ndarray.\n",
@@ -118,6 +89,7 @@
     "def get_central_angle(n1, n2):\n",
     "    \"\"\"Given the so-called n-vectors n1 and n2,\n",
     "    find the central angle between them.\n",
+    "    Returns the angle in degrees.\n",
     "    \n",
     "    Args:\n",
     "        n1: numpy.ndarray\n",
@@ -131,7 +103,7 @@
     "            ord=2\n",
     "        ),\n",
     "        np.dot(n1, n2)\n",
-    "    )\n",
+    "    ) * 180 / np.pi\n",
     "\n",
     "    return delta_sigma\n",
     "\n",
@@ -148,7 +120,7 @@
     "    See: https://en.m.wikipedia.org/wiki/N-vector\n",
     "    \"\"\"\n",
     "    delta_sigma = get_central_angle(n1=p, n2=q)\n",
-    "    d = r * delta_sigma\n",
+    "    d = r * np.abs(delta_sigma)\n",
     "\n",
     "    return d\n",
     "\n",
@@ -182,7 +154,7 @@
     "\n",
     "    return dist_mat\n",
     "\n",
-    "def n_vector_to_lat_long(v):\n",
+    "def to_lat_long(v):\n",
     "    \"\"\"Convert n-vectors to their two-dimensional\n",
     "    representation on a sphere.\n",
     "    \n",
@@ -200,12 +172,44 @@
     "\n",
     "    See: https://en.m.wikipedia.org/wiki/N-vector\n",
     "    \"\"\"\n",
-    "    n_x = v[0]\n",
-    "    n_y = v[1]\n",
-    "    n_z = v[2]\n",
-    "    latitude = np.arctan2(n_z / np.sqrt(n_x**2 + n_y**2))\n",
-    "    longitude = np.arctan2(n_y / n_x)\n",
-    "\n"
+    "    n_x = v[:, 0]\n",
+    "    n_y = v[:, 1]\n",
+    "    n_z = v[:, 2]\n",
+    "    latitude = np.arctan2(n_z, np.sqrt(n_x**2 + n_y**2)) * 180 / np.pi\n",
+    "    longitude = np.arctan2(n_y, n_x) * 180 / np.pi\n",
+    "\n",
+    "    return np.column_stack((latitude, longitude))\n",
+    "\n",
+    "def to_n_vector(p):\n",
+    "    \"\"\"Given a matrix p \n",
+    "    where each row of p represents a point\n",
+    "    in geocentric coordinates (such as in the WGS 84 CRS),\n",
+    "    and where the 0th column is the latitude\n",
+    "    and the 1st column is the longitude,\n",
+    "    find the associated n-vector for each row of p.\n",
+    "    Everything should be in degrees.\n",
+    "\n",
+    "    Args:\n",
+    "        p: numpy.ndarray\n",
+    "\n",
+    "    Returns:\n",
+    "        numpy.ndarray\n",
+    "\n",
+    "    See: \n",
+    "    https://en.wikipedia.org/wiki/N-vector#Converting_latitude/longitude_to_n-vector\n",
+    "    https://en.wikipedia.org/wiki/Geographic_coordinate_conversion#From_ECEF_to_geodetic_coordinates\n",
+    "    \"\"\"\n",
+    "    latitude = p[:, 0]\n",
+    "    longitude = p[:, 1]\n",
+    "\n",
+    "    latitude_in_radians = latitude * np.pi / 180\n",
+    "    longitude_in_radians = longitude * np.pi / 180\n",
+    "    # trig functions are in radians\n",
+    "    x = np.cos(latitude_in_radians) * np.cos(longitude_in_radians)\n",
+    "    y = np.cos(latitude_in_radians) * np.sin(longitude_in_radians)\n",
+    "    z = np.sin(latitude_in_radians)\n",
+    "\n",
+    "    return np.column_stack((x, y, z))"
    ]
   },
   {
@@ -217,143 +221,122 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 138,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "my_points: \n",
-      " [[-0.02586324  0.99380063  0.1081268 ]\n",
-      " [ 0.80855533  0.57040455  0.14448851]\n",
-      " [-0.41383937 -0.82778741  0.37882051]]\n"
-     ]
+     "data": {
+      "text/plain": [
+       "array([[ 0.86193681,  0.04628469,  0.50489866],\n",
+       "       [-0.82105693,  0.21950517,  0.52695635],\n",
+       "       [-0.65625093,  0.70562806,  0.26725225],\n",
+       "       [-0.83458621,  0.54637923, -0.07025376],\n",
+       "       [-0.88455107,  0.44819874, -0.12917929],\n",
+       "       [-0.84111925,  0.3512144 , -0.41129899],\n",
+       "       [ 0.9813084 , -0.19085949,  0.02462691],\n",
+       "       [-0.19737118,  0.88274138, -0.42639451],\n",
+       "       [-0.5928383 ,  0.08187432,  0.80114877],\n",
+       "       [-0.39474526, -0.28447135,  0.87364308]])"
+      ]
+     },
+     "execution_count": 138,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "my_points = get_uniformly_distributed_points_on_sphere(3)\n",
-    "print(f\"my_points: \\n\", my_points)"
+    "my_points = get_uniformly_distributed_points_on_sphere(10)\n",
+    "my_points"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 142,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[0.02586324, 0.97450268, 2.45119334],\n",
-       "       [0.80855533, 0.57040455, 2.42196398],\n",
-       "       [0.41383937, 0.82778741, 0.37882051]])"
+       "array([[  30.3246254 ,    3.07374385],\n",
+       "       [  31.80003761,  165.03232277],\n",
+       "       [  15.50082482,  132.9235584 ],\n",
+       "       [  -4.02856235,  146.7884017 ],\n",
+       "       [  -7.42216933,  153.12883663],\n",
+       "       [ -24.28646174,  157.33673091],\n",
+       "       [   1.41116077,  -11.00632701],\n",
+       "       [ -25.23896311,  102.60340669],\n",
+       "       [  53.23994201,  172.13686933],\n",
+       "       [  60.88478882, -144.2217672 ]])"
       ]
      },
-     "execution_count": 56,
+     "execution_count": 142,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "great_circle_dist_matrix(vecs=my_points, r=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# https://en.m.wikipedia.org/wiki/Great-circle_distance\n",
-    "# Before loop:\n",
-    "# Fix a point on the unit-sphere.\n",
-    "fixed_point = vecs_on_sphere[:, 0]\n",
-    "\n",
-    "delta_sigma = np.empty(shape=vecs_on_sphere.shape[1] - 1)\n",
-    "# Get a vector of greatest-circle\n",
-    "# distances to the other points.\n",
-    "for j in range(1, vecs_on_sphere.shape[1]):\n",
-    "    delta_sigma[j - 1] = np.arctan2(\n",
-    "        np.linalg.norm(np.cross(fixed_point, vecs_on_sphere[:, j]), ord=2),\n",
-    "        np.dot(fixed_point, vecs_on_sphere[:, j])\n",
-    "    )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Now, scale to the size of the Earth.\n",
-    "distances = MEAN_RADIUS_OF_EARTH * np.abs(delta_sigma)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "distances.sort()\n",
-    "delta_sigma.sort()"
+    "my_points_as_lat_long = to_lat_long(my_points)\n",
+    "my_points_as_lat_long"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 143,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0.86193681,  0.04628469,  0.50489866],\n",
+       "       [-0.82105693,  0.21950517,  0.52695635],\n",
+       "       [-0.65625093,  0.70562806,  0.26725225],\n",
+       "       [-0.83458621,  0.54637923, -0.07025376],\n",
+       "       [-0.88455107,  0.44819874, -0.12917929],\n",
+       "       [-0.84111925,  0.3512144 , -0.41129899],\n",
+       "       [ 0.9813084 , -0.19085949,  0.02462691],\n",
+       "       [-0.19737118,  0.88274138, -0.42639451],\n",
+       "       [-0.5928383 ,  0.08187432,  0.80114877],\n",
+       "       [-0.39474526, -0.28447135,  0.87364308]])"
+      ]
+     },
+     "execution_count": 143,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "distances[NUM_WORLD_LOCATIONS - 10: ]"
+    "my_points_as_n_vec = to_n_vector(my_points_as_lat_long)\n",
+    "my_points_as_n_vec"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 141,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'cartopy' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m/home/justin/Documents/schoolwork/ISYE_6413_Design_of_Exp/doe_group_project/src/great_circle_distances.ipynb Cell 8\u001b[0m line \u001b[0;36m2\n\u001b[1;32m      <a href='vscode-notebook-cell:/home/justin/Documents/schoolwork/ISYE_6413_Design_of_Exp/doe_group_project/src/great_circle_distances.ipynb#X14sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m \u001b[39m# https://github.com/SciTools/cartopy/issues/1325\u001b[39;00m\n\u001b[0;32m----> <a href='vscode-notebook-cell:/home/justin/Documents/schoolwork/ISYE_6413_Design_of_Exp/doe_group_project/src/great_circle_distances.ipynb#X14sZmlsZQ%3D%3D?line=1'>2</a>\u001b[0m cartopy\u001b[39m.\u001b[39mconfig[\u001b[39m\"\u001b[39m\u001b[39mdata_dir\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39m=\u001b[39m Path(DATA_PATH, \u001b[39m\"\u001b[39m\u001b[39mraw_data\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m      <a href='vscode-notebook-cell:/home/justin/Documents/schoolwork/ISYE_6413_Design_of_Exp/doe_group_project/src/great_circle_distances.ipynb#X14sZmlsZQ%3D%3D?line=2'>3</a>\u001b[0m land_on_earth \u001b[39m=\u001b[39m shapely\u001b[39m.\u001b[39munion_all(\n\u001b[1;32m      <a href='vscode-notebook-cell:/home/justin/Documents/schoolwork/ISYE_6413_Design_of_Exp/doe_group_project/src/great_circle_distances.ipynb#X14sZmlsZQ%3D%3D?line=3'>4</a>\u001b[0m     geometries\u001b[39m=\u001b[39m\u001b[39mlist\u001b[39m(shapereader\u001b[39m.\u001b[39mReader(natural_earth_data)\u001b[39m.\u001b[39mgeometries())\n\u001b[1;32m      <a href='vscode-notebook-cell:/home/justin/Documents/schoolwork/ISYE_6413_Design_of_Exp/doe_group_project/src/great_circle_distances.ipynb#X14sZmlsZQ%3D%3D?line=4'>5</a>\u001b[0m )\n\u001b[1;32m      <a href='vscode-notebook-cell:/home/justin/Documents/schoolwork/ISYE_6413_Design_of_Exp/doe_group_project/src/great_circle_distances.ipynb#X14sZmlsZQ%3D%3D?line=6'>7</a>\u001b[0m shapely\u001b[39m.\u001b[39mprepare(\n\u001b[1;32m      <a href='vscode-notebook-cell:/home/justin/Documents/schoolwork/ISYE_6413_Design_of_Exp/doe_group_project/src/great_circle_distances.ipynb#X14sZmlsZQ%3D%3D?line=7'>8</a>\u001b[0m     geometry\u001b[39m=\u001b[39mland_on_earth\n\u001b[1;32m      <a href='vscode-notebook-cell:/home/justin/Documents/schoolwork/ISYE_6413_Design_of_Exp/doe_group_project/src/great_circle_distances.ipynb#X14sZmlsZQ%3D%3D?line=8'>9</a>\u001b[0m )\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'cartopy' is not defined"
+     ]
+    }
+   ],
    "source": [
     "# https://github.com/SciTools/cartopy/issues/1325\n",
-    "cartopy.config[\"data_dir\"] = Path(DATA_PATH, \"raw_data\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# https://datascience.stackexchange.com/questions/112104/sampling-from-earths-landmass\n",
-    "natural_earth_data = shapereader.natural_earth(\n",
-    "    resolution=\"10m\",\n",
-    "    category=\"physical\",\n",
-    "    name=\"land\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(natural_earth_data)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
+    "cartopy.config[\"data_dir\"] = Path(DATA_PATH, \"raw_data\")\n",
     "land_on_earth = shapely.union_all(\n",
     "    geometries=list(shapereader.Reader(natural_earth_data).geometries())\n",
     ")\n",
     "\n",
     "shapely.prepare(\n",
     "    geometry=land_on_earth\n",
-    ")"
+    ")\n",
+    "land_on_earth.contains(shapely.geometry.Point(33.792978, -84.412071))"
    ]
   },
   {
@@ -362,23 +345,55 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "land_on_earth.contains(shapely.geometry.Point(33.792978, -84.412071))"
+    "ecoregions_2017 = geopandas.read_file(filename=Path(DATA_PATH, \"raw_data\", \"Ecoregions2017\"))"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "geopandas.geodataframe.GeoDataFrame"
+      ]
+     },
+     "execution_count": 105,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "ecoregions_2017 = geopandas.read_file(filename=Path(DATA_PATH, \"raw_data\", \"Ecoregions2017\"))"
+    "type(ecoregions_2017)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Geographic 2D CRS: EPSG:4326>\n",
+       "Name: WGS 84\n",
+       "Axis Info [ellipsoidal]:\n",
+       "- Lat[north]: Geodetic latitude (degree)\n",
+       "- Lon[east]: Geodetic longitude (degree)\n",
+       "Area of Use:\n",
+       "- name: World.\n",
+       "- bounds: (-180.0, -90.0, 180.0, 90.0)\n",
+       "Datum: World Geodetic System 1984 ensemble\n",
+       "- Ellipsoid: WGS 84\n",
+       "- Prime Meridian: Greenwich"
+      ]
+     },
+     "execution_count": 109,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "ecoregions_2017.crs"
    ]
@@ -387,7 +402,28 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 103,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "ecoregions_2017.plot()"
    ]
@@ -396,7 +432,83 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<bound method NDFrame.head of      OBJECTID                                           ECO_NAME  BIOME_NUM  \\\n",
+       "0         1.0                                 Adelie Land tundra       11.0   \n",
+       "1         2.0             Admiralty Islands lowland rain forests        1.0   \n",
+       "2         3.0  Aegean and Western Turkey sclerophyllous and m...       12.0   \n",
+       "3         4.0                       Afghan Mountains semi-desert       13.0   \n",
+       "4         5.0                   Ahklun and Kilbuck Upland Tundra       11.0   \n",
+       "..        ...                                                ...        ...   \n",
+       "842     848.0                      Sulawesi lowland rain forests        1.0   \n",
+       "843     212.0                       East African montane forests        1.0   \n",
+       "844     224.0                                Eastern Arc forests        1.0   \n",
+       "845      79.0                        Borneo montane rain forests        1.0   \n",
+       "846     376.0                    Kinabalu montane alpine meadows       10.0   \n",
+       "\n",
+       "                                         BIOME_NAME        REALM ECO_BIOME_  \\\n",
+       "0                                            Tundra   Antarctica       AN11   \n",
+       "1    Tropical & Subtropical Moist Broadleaf Forests  Australasia       AU01   \n",
+       "2          Mediterranean Forests, Woodlands & Scrub   Palearctic       PA12   \n",
+       "3                        Deserts & Xeric Shrublands   Palearctic       PA13   \n",
+       "4                                            Tundra     Nearctic       NE11   \n",
+       "..                                              ...          ...        ...   \n",
+       "842  Tropical & Subtropical Moist Broadleaf Forests  Australasia       AU01   \n",
+       "843  Tropical & Subtropical Moist Broadleaf Forests   Afrotropic       AF01   \n",
+       "844  Tropical & Subtropical Moist Broadleaf Forests   Afrotropic       AF01   \n",
+       "845  Tropical & Subtropical Moist Broadleaf Forests  Indomalayan       IN01   \n",
+       "846                 Montane Grasslands & Shrublands  Indomalayan       IN10   \n",
+       "\n",
+       "     NNH  ECO_ID  SHAPE_LENG  SHAPE_AREA                           NNH_NAME  \\\n",
+       "0      1     117    9.749780    0.038948                     Half Protected   \n",
+       "1      2     135    4.800349    0.170599  Nature Could Reach Half Protected   \n",
+       "2      4     785  162.523044   13.844952                   Nature Imperiled   \n",
+       "3      4     807   15.084037    1.355536                   Nature Imperiled   \n",
+       "4      1     404   22.590087    8.196573                     Half Protected   \n",
+       "..   ...     ...         ...         ...                                ...   \n",
+       "842    2     156  150.744361    9.422097  Nature Could Reach Half Protected   \n",
+       "843    3       8  157.848926    5.010930               Nature Could Recover   \n",
+       "844    3       9   34.153333    0.890325               Nature Could Recover   \n",
+       "845    2     220   38.280990    9.358407  Nature Could Reach Half Protected   \n",
+       "846    3     313    5.722539    0.352694               Nature Could Recover   \n",
+       "\n",
+       "       COLOR COLOR_BIO COLOR_NNH    LICENSE  \\\n",
+       "0    #63CFAB   #9ED7C2   #257339  CC-BY 4.0   \n",
+       "1    #70A800   #38A700   #7BC141  CC-BY 4.0   \n",
+       "2    #FF7F7C   #FE0000   #EE1E23  CC-BY 4.0   \n",
+       "3    #FA774D   #CC6767   #EE1E23  CC-BY 4.0   \n",
+       "4    #4C82B6   #9ED7C2   #257339  CC-BY 4.0   \n",
+       "..       ...       ...       ...        ...   \n",
+       "842  #70A800   #38A700   #7BC141  CC-BY 4.0   \n",
+       "843  #13ED00   #38A700   #F9A91B  CC-BY 4.0   \n",
+       "844  #267400   #38A700   #F9A91B  CC-BY 4.0   \n",
+       "845  #23DB01   #38A700   #7BC141  CC-BY 4.0   \n",
+       "846  #C48832   #D6C39D   #F9A91B  CC-BY 4.0   \n",
+       "\n",
+       "                                              geometry  \n",
+       "0    MULTIPOLYGON (((158.71410 -69.60657, 158.71264...  \n",
+       "1    MULTIPOLYGON (((147.28819 -2.57589, 147.27150 ...  \n",
+       "2    MULTIPOLYGON (((26.88659 35.32161, 26.88297 35...  \n",
+       "3    MULTIPOLYGON (((65.48655 34.71401, 65.52872 34...  \n",
+       "4    MULTIPOLYGON (((-160.26404 58.64097, -160.2673...  \n",
+       "..                                                 ...  \n",
+       "842  MULTIPOLYGON (((117.33111 -7.53306, 117.30525 ...  \n",
+       "843  MULTIPOLYGON (((36.73750 -3.13000, 36.73750 -3...  \n",
+       "844  MULTIPOLYGON (((36.38000 -8.96583, 36.38000 -8...  \n",
+       "845  MULTIPOLYGON (((112.82394 -0.50660, 112.82298 ...  \n",
+       "846  MULTIPOLYGON (((116.52616 6.11011, 116.52734 6...  \n",
+       "\n",
+       "[847 rows x 16 columns]>"
+      ]
+     },
+     "execution_count": 104,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "ecoregions_2017.head"
    ]
@@ -405,7 +517,29 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0      POINT (149.78004 -68.39917)\n",
+       "1       POINT (146.93374 -2.10620)\n",
+       "2        POINT (22.23609 37.36933)\n",
+       "3        POINT (65.19977 34.56284)\n",
+       "4      POINT (-159.90268 60.04211)\n",
+       "                  ...             \n",
+       "842     POINT (124.82377 -1.83335)\n",
+       "843       POINT (33.72785 4.21125)\n",
+       "844      POINT (36.89917 -6.39917)\n",
+       "845      POINT (115.19245 2.45332)\n",
+       "846      POINT (116.58859 6.10447)\n",
+       "Length: 847, dtype: geometry"
+      ]
+     },
+     "execution_count": 106,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# Points have longitude first and latitude second.\n",
     "ecoregions_2017[\"geometry\"].representative_point()"