diff --git a/install.sh b/install.sh
new file mode 100755
index 0000000..306fa06
--- /dev/null
+++ b/install.sh
@@ -0,0 +1,2 @@
+#!/bin/sh
+pip install -r requirements.txt
\ No newline at end of file
diff --git a/notebooks/ex09_geolocation_moving_obj.csv b/notebooks/ex09_geolocation_moving_obj.csv
new file mode 100644
index 0000000..833362e
--- /dev/null
+++ b/notebooks/ex09_geolocation_moving_obj.csv
@@ -0,0 +1,6 @@
+cab_26,43.602508,39.715685,14:47:44
+cab_112,43.582243,39.752077,14:47:55
+cab_26,43.607480,39.721521,14:49:11
+cab_112,43.579258,39.758944,14:49:51
+cab_112,43.574906,39.766325,14:51:53
+cab_26,43.612203,39.720491,14:52:48
\ No newline at end of file
diff --git a/notebooks/sn09_analyzing_location_data.ipynb b/notebooks/sn09_analyzing_location_data.ipynb
new file mode 100644
index 0000000..d100a76
--- /dev/null
+++ b/notebooks/sn09_analyzing_location_data.ipynb
@@ -0,0 +1,718 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "d22b455e-8d88-49cd-82e6-55838d526cbc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import geocoder\n",
+    "\n",
+    "geocode_res = geocoder.osm('1 Apple Park Way, Cupertino, California, United States')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "1caf59dc-c1f8-4bc7-b478-39edf657d9b8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[-122.010747, 37.3317424]\n"
+     ]
+    }
+   ],
+   "source": [
+    "coordinates = geocode_res.geometry['coordinates']\n",
+    "print(coordinates)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "ddedf165-a1cd-4fb8-9d42-175eb5a56dc5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>cab</th>\n",
+       "      <th>lat</th>\n",
+       "      <th>long</th>\n",
+       "      <th>tm</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>cab_26</td>\n",
+       "      <td>43.602508</td>\n",
+       "      <td>39.715685</td>\n",
+       "      <td>14:47:44</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>cab_112</td>\n",
+       "      <td>43.582243</td>\n",
+       "      <td>39.752077</td>\n",
+       "      <td>14:47:55</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>cab_26</td>\n",
+       "      <td>43.607480</td>\n",
+       "      <td>39.721521</td>\n",
+       "      <td>14:49:11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>cab_112</td>\n",
+       "      <td>43.579258</td>\n",
+       "      <td>39.758944</td>\n",
+       "      <td>14:49:51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>cab_112</td>\n",
+       "      <td>43.574906</td>\n",
+       "      <td>39.766325</td>\n",
+       "      <td>14:51:53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>cab_26</td>\n",
+       "      <td>43.612203</td>\n",
+       "      <td>39.720491</td>\n",
+       "      <td>14:52:48</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       cab        lat       long        tm\n",
+       "0   cab_26  43.602508  39.715685  14:47:44\n",
+       "1  cab_112  43.582243  39.752077  14:47:55\n",
+       "2   cab_26  43.607480  39.721521  14:49:11\n",
+       "3  cab_112  43.579258  39.758944  14:49:51\n",
+       "4  cab_112  43.574906  39.766325  14:51:53\n",
+       "5   cab_26  43.612203  39.720491  14:52:48"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "df = pd.read_csv(\"ex09_geolocation_moving_obj.csv\", names=['cab', 'lat', 'long', 'tm'])\n",
+    "display(df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "9f5412aa-d334-45a5-af1f-b430f161413f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>cab</th>\n",
+       "      <th>lat</th>\n",
+       "      <th>long</th>\n",
+       "      <th>tm</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>cab_26</td>\n",
+       "      <td>43.612203</td>\n",
+       "      <td>39.720491</td>\n",
+       "      <td>14:52:48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>cab_112</td>\n",
+       "      <td>43.574906</td>\n",
+       "      <td>39.766325</td>\n",
+       "      <td>14:51:53</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       cab        lat       long        tm\n",
+       "5   cab_26  43.612203  39.720491  14:52:48\n",
+       "4  cab_112  43.574906  39.766325  14:51:53"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "latestrows = df.sort_values(['cab', 'tm'], ascending=False).drop_duplicates('cab')\n",
+    "display(latestrows)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "dee21ad0-9ffb-42b5-bdc4-c7fcfa828427",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[['cab_26', 43.612203, 39.720491, '14:52:48'],\n",
+       " ['cab_112', 43.574906, 39.766325, '14:51:53']]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "latestrows_numpy = latestrows.values\n",
+    "latestrows = latestrows_numpy.tolist()\n",
+    "display(latestrows)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "eaf3773d-9880-4026-86e0-0758a998fa2b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "cab_26: 4636\n",
+      "cab_112: 1015\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[['cab_26', 43.612203, 39.720491, '14:52:48', 4636],\n",
+       " ['cab_112', 43.574906, 39.766325, '14:51:53', 1015]]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# calculate the distance between each cab and a pickup place\n",
+    "from geopy.distance import distance\n",
+    "\n",
+    "pick_up = (43.578854, 39.754995)\n",
+    "\n",
+    "for i, row in enumerate(latestrows):\n",
+    "    lat = row[1]\n",
+    "    long = row[2]\n",
+    "    cab = (lat, long)\n",
+    "    dist_meters = distance(pick_up, cab).m\n",
+    "\n",
+    "    print(row[0] + ':', round(dist_meters))\n",
+    "    latestrows[i].append(round(dist_meters))\n",
+    "\n",
+    "display(latestrows)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "e12cf848-4505-40c1-ae6e-a9a2c2c962e1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The closest cab is:  cab_112 . Distance in meters:  1015\n"
+     ]
+    }
+   ],
+   "source": [
+    "closest_cab = min(latestrows, key=lambda x: x[4])\n",
+    "print('The closest cab is: ', closest_cab[0], '. Distance in meters: ', closest_cab[4])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3e8ca7ad-0680-4a7a-96e5-1ff5cecbf2d3",
+   "metadata": {},
+   "source": [
+    "Not always the closest object to the target is the most efficient solution. Some obstacles may infer in the time elapsed to reach the objective.\n",
+    "\n",
+    "If obstacles makes traject to the objective longer an alternative is to divide the area into a set of polygons, and then use these polygons to check which objects share a polygon with the target."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "84aacde9-d089-43ff-a99e-adea84c74ba4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "cab_26 within the polygon: False\n",
+      "cab_112 within the polygon: True\n",
+      "pick_up within the polygon: True\n"
+     ]
+    }
+   ],
+   "source": [
+    "from shapely.geometry import Point, Polygon\n",
+    "\n",
+    "coords = [\n",
+    "    (46.082991, 38.987384),\n",
+    "    (46.075489, 38.987599),\n",
+    "    (46.079395, 38.997684),\n",
+    "    (46.073822, 39.007297),\n",
+    "    (46.081741, 39.008842)\n",
+    "]\n",
+    "poly = Polygon(coords)\n",
+    "cab_26 = Point(46.073852, 38.991890)\n",
+    "cab_112 = Point(46.078228, 39.003949)\n",
+    "pick_up = Point(46.080074, 38.991289)\n",
+    "\n",
+    "print('cab_26 within the polygon:', cab_26.within(poly))\n",
+    "print('cab_112 within the polygon:', cab_112.within(poly))\n",
+    "print('pick_up within the polygon:', pick_up.within(poly))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "65fc62a7-8a59-47e4-b144-679f361b1dce",
+   "metadata": {},
+   "source": [
+    "The best approach is to use both calculations:\n",
+    "\n",
+    "1. Determine which polygon each cab is in and use that determination to decide how to calculate the distance from that cab to the pick-up location. Then proceed to:\n",
+    "   1. Calculate direct straight line distance if the cab is in the same polygon as the pick-up location.\n",
+    "   2. The distance by way of the entry point if it’s in an adjacent polygon."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "0c45585e-bab3-4432-8aca-8056ea3b15fc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1544\n"
+     ]
+    }
+   ],
+   "source": [
+    "entry_point = Point(46.075357, 39.000298)\n",
+    "\n",
+    "if cab_26.within(poly):\n",
+    "    dist = distance((pick_up.x, pick_up.y), (cab_26.x,cab_26.y)).m\n",
+    "else:\n",
+    "    dist = distance((cab_26.x,cab_26.y), (entry_point.x,entry_point.y)).m + distance((entry_point.x,entry_point.y), (pick_up.x, pick_up.y)).m\n",
+    "\n",
+    "print(round(dist))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "cc38e942-8042-47d2-8478-ba25a6b357cb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['cab_14', 'cab_79', 'cab_104', 'cab_45']"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "orders = [\n",
+    "    ('order_039', 'open', 'cab_14'),\n",
+    "    ('order_034', 'open', 'cab_79'),\n",
+    "    ('order_032', 'open', 'cab_104'),\n",
+    "    ('order_026', 'closed', 'cab_79'),\n",
+    "    ('order_021', 'open', 'cab_45'),\n",
+    "    ('order_018', 'closed', 'cab_26'),\n",
+    "    ('order_008', 'closed', 'cab_112')\n",
+    "]\n",
+    "\n",
+    "df_orders = pd.DataFrame(orders, columns =['order','status','cab'])\n",
+    "df_orders_open = df_orders[df_orders['status']=='open']\n",
+    "unavailable_list = [x[2] for x in orders if x[1] == 'open']\n",
+    "display(unavailable_list)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "abf62615-a79f-445f-bd43-cf08e49185e2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[('cab_26', 2165), ('cab_112', 2861)]\n",
+      "('cab_26', 2165)\n"
+     ]
+    }
+   ],
+   "source": [
+    "pick_up = 46.083822, 38.967845\n",
+    "cab_26 = 46.073852, 38.991890\n",
+    "cab_112 = 46.078228, 39.003949\n",
+    "cab_104 = 46.071226, 39.004947\n",
+    "cab_14 = 46.004859, 38.095825\n",
+    "cab_79 = 46.088621, 39.033929\n",
+    "cab_45 = 46.141225, 39.124934\n",
+    "cabs = {'cab_26': cab_26, 'cab_112': cab_112, 'cab_14': cab_14,\n",
+    "        'cab_104': cab_104, 'cab_79': cab_79, 'cab_45': cab_45}\n",
+    "\n",
+    "dist_list = []\n",
+    "\n",
+    "for cab_name, cab_loc in cabs.items():\n",
+    "  if cab_name not in unavailable_list:\n",
+    "    dist = distance(pick_up, cab_loc).m\n",
+    "    dist_list.append((cab_name, round(dist)))\n",
+    "\n",
+    "print(dist_list)\n",
+    "print(min(dist_list, key=lambda x: x[1]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "d3ab7e20-3999-4d18-af08-7433dc36d53f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Cabs data with Baby Seat\n",
+    "cabs_list = [\n",
+    "    ('cab_14',1),\n",
+    "    ('cab_79',0),\n",
+    "    ('cab_104',0),\n",
+    "    ('cab_45',1),\n",
+    "    ('cab_26',0),\n",
+    "    ('cab_112',1)\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "73212eb9-2128-4ec7-8564-51f0ec569f28",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_cabs = pd.DataFrame(cabs_list, columns =['cab', 'seat'])\n",
+    "df_dist = pd.DataFrame(dist_list, columns =['cab', 'dist'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "563b1942-13d7-4726-b175-0feaf9050f1b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>cab</th>\n",
+       "      <th>seat</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>cab_14</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>cab_79</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>cab_104</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>cab_45</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>cab_26</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>cab_112</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       cab  seat\n",
+       "0   cab_14     1\n",
+       "1   cab_79     0\n",
+       "2  cab_104     0\n",
+       "3   cab_45     1\n",
+       "4   cab_26     0\n",
+       "5  cab_112     1"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>cab</th>\n",
+       "      <th>dist</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>cab_26</td>\n",
+       "      <td>2165</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>cab_112</td>\n",
+       "      <td>2861</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       cab  dist\n",
+       "0   cab_26  2165\n",
+       "1  cab_112  2861"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "display(df_cabs)\n",
+    "display(df_dist)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "0425a9c4-1f5e-4b16-a7e4-c9ab3e2bf031",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.merge(df_cabs, df_dist, on='cab', how='inner')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "ae908c91-a73b-4ade-a49f-c52ec0ed92ba",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>cab</th>\n",
+       "      <th>seat</th>\n",
+       "      <th>dist</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>cab_26</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2165</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>cab_112</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2861</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       cab  seat  dist\n",
+       "0   cab_26     0  2165\n",
+       "1  cab_112     1  2861"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "display(df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "dbd40a14-f4ad-482e-907c-8249de11ec3d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[('cab_112', 1, 2861)]\n"
+     ]
+    }
+   ],
+   "source": [
+    "result_list = list(df.itertuples(index=False,name=None))\n",
+    "result_list = [x for x in result_list if x[1] == 1]\n",
+    "print(result_list)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.17"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/requirements.txt b/requirements.txt
index a48644d..0cd065c 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,4 +1,6 @@
 Cartopy == 0.23.0
+geocoder == 1.38.1
+geopy == 2.4.1
 h5py == 3.10.0
 jupyter == 1.0.0
 keras == 3.3.3
@@ -10,5 +12,6 @@ pandas == 2.2.1
 pillow == 10.3.0
 scikit-learn == 1.4.0
 seaborn == 0.13.2
+shapely == 2.0.6
 spacy == 3.7.4
 yfinance == 0.2.38