Capstone Project - The Battle of Neighborhoods.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Capstone Project - The Battle of Neighborhoods"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction/Business Problem"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Introduction where you discuss the business problem and who would be interested in this project."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### \"Would you recommend a location in Hong Kong to open a new cinema?\"  \n",
    "My boss, the stakeholder wants to **open a new cinema as company's new business** and ask me this question.\n",
    "  \n",
    "He explains that in customer point of view, watching movie is a part of whole afternoon or night activities. Cinema should has **many restaurants and shopping places nearby**. Transportation is also an important factor. Customer can walk to cinema within **5 minutes** from **public transport facilities** such as bus stop and metro station.  \n",
    "  \n",
    "He wants me to concentrate on selection of cinema location according to its nearby environment. Cinema facility and rental price is not my concern. He lists out his **top 10 favorite cinemas** in Hong Kong with rating.  \n",
    "\n",
    "I work with my teammates and select **5 possible locations** to build the cinema. Which location should be suggested to the stakeholder?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data where you describe the data that will be used to solve the problem and the source of the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "According to the question, I need to find following data to resolve the problem."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. Geographic coordinate of Hong Kong cinemas\n",
    "\n",
    "I need to **compare 5 possible locations with current cinemas** in Hong Kong. Therefore, I need to find a list of Hong Kong cinema and cinemas' geographic coordinates. Luckily, I can find the list and coordinates from the website https://hkmovie6.com/cinema ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import necessary library\n",
    "import json\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2018-09-27 01:29:16--  https://hkmovie6.com/api/cinemas/lists\n",
      "Resolving hkmovie6.com (hkmovie6.com)... 104.27.132.135, 104.27.133.135, 2606:4700:30::681b:8487, ...\n",
      "Connecting to hkmovie6.com (hkmovie6.com)|104.27.132.135|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: unspecified [application/json]\n",
      "Saving to: ‘hk_cinema_list.json’\n",
      "\n",
      "hk_cinema_list.json     [ <=>                  ]  51.74K   291KB/s   in 0.2s   \n",
      "\n",
      "2018-09-27 01:29:17 (291 KB/s) - ‘hk_cinema_list.json’ saved [52984]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Download the cinema list\n",
    "!wget -O hk_cinema_list.json https://hkmovie6.com/api/cinemas/lists"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert the JSON data into DataFrmae\n",
    "cinemas_json = None\n",
    "with open('hk_cinema_list.json', 'r') as f:\n",
    "    cinemas_json = json.load(f)\n",
    "    \n",
    "cinemas = []\n",
    "for data in cinemas_json['data']:\n",
    "    cinemas.append({\n",
    "        'Name': data['name'],\n",
    "        'Address': data['address'],\n",
    "        'Latitude': data['lat'],\n",
    "        'Longitude': data['lon']\n",
    "    })\n",
    "df_cinemas = pd.DataFrame(cinemas, columns=['Name','Address','Latitude','Longitude'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 68 cinemas in Hong Kong\n"
     ]
    }
   ],
   "source": [
    "print('There are {} cinemas in Hong Kong'.format(len(df_cinemas)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>Name</th>\n",
       "      <th>Address</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Emperor Cinemas - Entertainment Building</td>\n",
       "      <td>3/F, Emperor Cinemas Entertainment Building, 3...</td>\n",
       "      <td>22.281453</td>\n",
       "      <td>114.154230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>The Coronet @ Emperor Cinemas - Entertainment ...</td>\n",
       "      <td>3/F, Emperor Cinemas Entertainment Building, 3...</td>\n",
       "      <td>22.281453</td>\n",
       "      <td>114.154230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Emperor Cinemas - Tuen Mun</td>\n",
       "      <td>3/F, New Town Commercial Arcade, 2 Tuen Lee St...</td>\n",
       "      <td>22.390776</td>\n",
       "      <td>113.975983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Broadway Circuit - CYBERPORT</td>\n",
       "      <td>Shop L1 - 3, Level 1, The Arcade, 100 Cyberpor...</td>\n",
       "      <td>22.261067</td>\n",
       "      <td>114.129825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Broadway Circuit - PALACE IFC</td>\n",
       "      <td>Podium L1, IFC Mall, 8 Finance Street, Central</td>\n",
       "      <td>22.285545</td>\n",
       "      <td>114.157979</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                Name  \\\n",
       "0           Emperor Cinemas - Entertainment Building   \n",
       "1  The Coronet @ Emperor Cinemas - Entertainment ...   \n",
       "2                         Emperor Cinemas - Tuen Mun   \n",
       "3                       Broadway Circuit - CYBERPORT   \n",
       "4                      Broadway Circuit - PALACE IFC   \n",
       "\n",
       "                                             Address   Latitude   Longitude  \n",
       "0  3/F, Emperor Cinemas Entertainment Building, 3...  22.281453  114.154230  \n",
       "1  3/F, Emperor Cinemas Entertainment Building, 3...  22.281453  114.154230  \n",
       "2  3/F, New Town Commercial Arcade, 2 Tuen Lee St...  22.390776  113.975983  \n",
       "3  Shop L1 - 3, Level 1, The Arcade, 100 Cyberpor...  22.261067  114.129825  \n",
       "4     Podium L1, IFC Mall, 8 Finance Street, Central  22.285545  114.157979  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cinemas.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. Geographic coordinates of 5 possible cinema addresses\n",
    "I also need to know the geographic coordinates of 5 possible cinemas. I can use Google Map API to find this information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "possible_locations = [\n",
    "    { 'Location': 'L1', 'Address': 'Sau Mau Ping Shopping Centre, Sau Mau Ping'},\n",
    "    { 'Location': 'L2', 'Address': 'Tuen Mun Ferry, Tuen Mun'},\n",
    "    { 'Location': 'L3', 'Address': 'Un Chau Shopping Centre, Cheung Sha Wan'},\n",
    "    { 'Location': 'L4', 'Address': 'Prosperity Millennia Plaza, North Point'},\n",
    "    { 'Location': 'L5', 'Address': 'Tsuen Fung Centre Shopping Arcade, Tsuen Wan'},\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting googlemaps\n",
      "  Downloading https://files.pythonhosted.org/packages/5a/3d/13b4230f3c1b8a586cdc8d8179f3c6af771c11247f8de9c166d1ab37f51d/googlemaps-3.0.2.tar.gz\n",
      "Requirement not upgraded as not directly required: requests<3.0,>=2.11.1 in /home/jupyterlab/conda/lib/python3.6/site-packages (from googlemaps) (2.18.4)\n",
      "Requirement not upgraded as not directly required: chardet<3.1.0,>=3.0.2 in /home/jupyterlab/conda/lib/python3.6/site-packages (from requests<3.0,>=2.11.1->googlemaps) (3.0.4)\n",
      "Requirement not upgraded as not directly required: idna<2.7,>=2.5 in /home/jupyterlab/conda/lib/python3.6/site-packages (from requests<3.0,>=2.11.1->googlemaps) (2.6)\n",
      "Requirement not upgraded as not directly required: urllib3<1.23,>=1.21.1 in /home/jupyterlab/conda/lib/python3.6/site-packages (from requests<3.0,>=2.11.1->googlemaps) (1.22)\n",
      "Requirement not upgraded as not directly required: certifi>=2017.4.17 in /home/jupyterlab/conda/lib/python3.6/site-packages (from requests<3.0,>=2.11.1->googlemaps) (2018.8.24)\n",
      "Building wheels for collected packages: googlemaps\n",
      "  Running setup.py bdist_wheel for googlemaps ... \u001b[?25ldone\n",
      "\u001b[?25h  Stored in directory: /home/jupyterlab/.cache/pip/wheels/3c/3f/25/ce6d7722dba07e5d4a12d27ab38f3d7add65ef43171b02c819\n",
      "Successfully built googlemaps\n",
      "\u001b[31mdistributed 1.21.8 requires msgpack, which is not installed.\u001b[0m\n",
      "Installing collected packages: googlemaps\n",
      "Successfully installed googlemaps-3.0.2\n"
     ]
    }
   ],
   "source": [
    "# install the google map api client library\n",
    "!pip install -U googlemaps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "google_act = None\n",
    "with open('google_map_act.json', 'r') as f:\n",
    "    google_act = json.load(f)\n",
    "    \n",
    "GOOGLE_MAP_API_KEY = google_act['api_key']    \n",
    "\n",
    "import googlemaps\n",
    "gmaps = googlemaps.Client(key=GOOGLE_MAP_API_KEY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Retrieve geolocation and create the dataframe of pending cinema addresses\n",
    "def getLatLng(address):\n",
    "    latlnt = gmaps.geocode('{}, Hong Kong'.format(address))\n",
    "    return (latlnt[0]['geometry']['location']['lat'], latlnt[0]['geometry']['location']['lng'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dataframe of 5 possible locations with geographic coordinates information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>Location</th>\n",
       "      <th>Address</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>L1</td>\n",
       "      <td>Sau Mau Ping Shopping Centre, Sau Mau Ping</td>\n",
       "      <td>22.319503</td>\n",
       "      <td>114.232187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>L2</td>\n",
       "      <td>Tuen Mun Ferry, Tuen Mun</td>\n",
       "      <td>22.371780</td>\n",
       "      <td>113.966039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>L3</td>\n",
       "      <td>Un Chau Shopping Centre, Cheung Sha Wan</td>\n",
       "      <td>22.337280</td>\n",
       "      <td>114.156457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>L4</td>\n",
       "      <td>Prosperity Millennia Plaza, North Point</td>\n",
       "      <td>22.291698</td>\n",
       "      <td>114.208168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>L5</td>\n",
       "      <td>Tsuen Fung Centre Shopping Arcade, Tsuen Wan</td>\n",
       "      <td>22.372112</td>\n",
       "      <td>114.119317</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Location                                       Address   Latitude  \\\n",
       "0       L1    Sau Mau Ping Shopping Centre, Sau Mau Ping  22.319503   \n",
       "1       L2                      Tuen Mun Ferry, Tuen Mun  22.371780   \n",
       "2       L3       Un Chau Shopping Centre, Cheung Sha Wan  22.337280   \n",
       "3       L4       Prosperity Millennia Plaza, North Point  22.291698   \n",
       "4       L5  Tsuen Fung Centre Shopping Arcade, Tsuen Wan  22.372112   \n",
       "\n",
       "    Longitude  \n",
       "0  114.232187  \n",
       "1  113.966039  \n",
       "2  114.156457  \n",
       "3  114.208168  \n",
       "4  114.119317  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for loc in possible_locations:        \n",
    "    (lat, lng) = getLatLng(loc['Address'])\n",
    "    loc['Latitude'] = lat\n",
    "    loc['Longitude'] = lng\n",
    "    \n",
    "df_possible_locations = pd.DataFrame(possible_locations, columns=['Location', 'Address', 'Latitude', 'Longitude'])\n",
    "df_possible_locations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3. Favorite cinema list of stakeholder"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The favorite cinema list is an important information that I can **use it as profile to select the best location**.  \n",
    "Stakeholder further explains that the rating is range of 1.0 (worst) to 5.0 (best) values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>Name</th>\n",
       "      <th>Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Boradway Circuit - MONGKONG</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Boradway Circuit - The ONE</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Grand Ocean</td>\n",
       "      <td>4.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>The Grand Cinema</td>\n",
       "      <td>3.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AMC Pacific Place</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>UA IMAX @ Airport</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Name  Rating\n",
       "0  Boradway Circuit - MONGKONG     4.5\n",
       "1   Boradway Circuit - The ONE     4.5\n",
       "2                  Grand Ocean     4.3\n",
       "3             The Grand Cinema     3.4\n",
       "4            AMC Pacific Place     2.3\n",
       "5            UA IMAX @ Airport     1.5"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boss_favorite = [\n",
    "    {'Name': 'Boradway Circuit - MONGKONG', 'Rating': 4.5},\n",
    "    {'Name': 'Boradway Circuit - The ONE', 'Rating': 4.5},\n",
    "    {'Name': 'Grand Ocean', 'Rating': 4.3},\n",
    "    {'Name': 'The Grand Cinema', 'Rating': 3.4},\n",
    "    {'Name': 'AMC Pacific Place', 'Rating': 2.3},\n",
    "    {'Name': 'UA IMAX @ Airport', 'Rating': 1.5},\n",
    "]\n",
    "\n",
    "df_boss_favorite = pd.DataFrame(boss_favorite, columns=['Name','Rating'])\n",
    "df_boss_favorite"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4. Eating, Shopping and Public transportation facility around cinema\n",
    "The recommended cinema location needs to have many eating and shopping venues nearby. Convenient public transport is also required.  \n",
    "I can use FourSquare API to find these venues around the location. \n",
    "\n",
    "5 minutes walking distance is about 500m. I think it is the suitable distance to search nearby venues."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, the API provides maximum 50 results only, so it is better to search venues by category. Following categories will be used for finding the target venues. Full list of categories: https://developer.foursquare.com/docs/resources/categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "cinema = df_cinemas.loc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Use the first cinema \"Emperor Cinemas - Entertainment Building\" in the list as example to explore venues nearyby\n"
     ]
    }
   ],
   "source": [
    "print('Use the first cinema \"{}\" in the list as example to explore venues nearyby'.format(cinema['Name']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "fs_categories = {\n",
    "    'Food': '4d4b7105d754a06374d81259',\n",
    "    'Shop & Service': '4d4b7105d754a06378d81259',\n",
    "    'Bus Stop': '52f2ab2ebcbc57f1066b8b4f',\n",
    "    'Metro Station': '4bf58dd8d48988d1fd931735',\n",
    "    'Nightlife Spot': '4d4b7105d754a06376d81259',\n",
    "    'Arts & Entertainment': '4d4b7104d754a06370d81259'\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting foursquare\n",
      "  Downloading https://files.pythonhosted.org/packages/7e/9f/21ef283c50eb576eaebb0525d8a988baffe4d59ac2bbb1f9d84434bdf616/foursquare-1%212016.9.12.tar.gz\n",
      "Requirement already satisfied: requests>=2.1 in /home/jupyterlab/conda/lib/python3.6/site-packages (from foursquare) (2.18.4)\n",
      "Requirement already satisfied: six in /home/jupyterlab/conda/lib/python3.6/site-packages (from foursquare) (1.11.0)\n",
      "Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /home/jupyterlab/conda/lib/python3.6/site-packages (from requests>=2.1->foursquare) (3.0.4)\n",
      "Requirement already satisfied: idna<2.7,>=2.5 in /home/jupyterlab/conda/lib/python3.6/site-packages (from requests>=2.1->foursquare) (2.6)\n",
      "Requirement already satisfied: urllib3<1.23,>=1.21.1 in /home/jupyterlab/conda/lib/python3.6/site-packages (from requests>=2.1->foursquare) (1.22)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /home/jupyterlab/conda/lib/python3.6/site-packages (from requests>=2.1->foursquare) (2018.8.24)\n",
      "Building wheels for collected packages: foursquare\n",
      "  Running setup.py bdist_wheel for foursquare ... \u001b[?25ldone\n",
      "\u001b[?25h  Stored in directory: /home/jupyterlab/.cache/pip/wheels/c1/a4/ff/e07a4f4f02ef7189c5b1e0738a09131f6c5f2de811ce3a39a0\n",
      "Successfully built foursquare\n",
      "\u001b[31mdistributed 1.21.8 requires msgpack, which is not installed.\u001b[0m\n",
      "Installing collected packages: foursquare\n",
      "Successfully installed foursquare-1!2016.9.12\n"
     ]
    }
   ],
   "source": [
    "# Install FourSquare client library\n",
    "!pip install foursquare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "fs_act = None\n",
    "with open('fs_act.json') as json_data:\n",
    "    fs_act = json.load(json_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "import foursquare\n",
    "from pandas.io.json import json_normalize # tranform JSON file into a pandas dataframe\n",
    "fs = foursquare.Foursquare(client_id=fs_act['client_id'], client_secret=fs_act['client_secret'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "RADIUS = 500 # 500m, around 5 minutes walking time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a function to search nearby information and convert the result as dataframe\n",
    "def venues_nearby(latitude, longitude, category):    \n",
    "    results = fs.venues.search(\n",
    "        params = {\n",
    "            'query': category, \n",
    "            'll': '{},{}'.format(latitude, longitude),\n",
    "            'radius': RADIUS,\n",
    "            'categoryId': fs_categories[category]\n",
    "        }\n",
    "    )    \n",
    "    df = json_normalize(results['venues'])\n",
    "    cols = ['Name','Latitude','Longitude','Tips','Users','Visits']    \n",
    "    if( len(df) == 0 ):        \n",
    "        df = pd.DataFrame(columns=cols)\n",
    "    else:        \n",
    "        df = df[['name','location.lat','location.lng','stats.tipCount','stats.usersCount','stats.visitsCount']]\n",
    "        df.columns = cols\n",
    "    print('{} \"{}\" venues are found within {}m of location'.format(len(df), category, RADIUS))\n",
    "    return df\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Find number of MTR station around the cinema"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 \"Metro Station\" venues are found within 500m of location\n"
     ]
    },
    {
     "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>Name</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Tips</th>\n",
       "      <th>Users</th>\n",
       "      <th>Visits</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MTR Central Station (港鐵中環站)</td>\n",
       "      <td>22.281911</td>\n",
       "      <td>114.158406</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MTR Hong Kong Station (港鐵香港站)</td>\n",
       "      <td>22.284926</td>\n",
       "      <td>114.158314</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Name   Latitude   Longitude  Tips  Users  Visits\n",
       "0    MTR Central Station (港鐵中環站)  22.281911  114.158406     0      0       0\n",
       "1  MTR Hong Kong Station (港鐵香港站)  22.284926  114.158314     0      0       0"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "venues_nearby(cinema['Latitude'], cinema['Longitude'], 'Metro Station').head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Find number of bus station around the cinema"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30 \"Bus Stop\" venues are found within 500m of location\n"
     ]
    },
    {
     "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>Name</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Tips</th>\n",
       "      <th>Users</th>\n",
       "      <th>Visits</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Seymour Road / Robinson Road Bus Stop 西摩道／羅便臣道巴士站</td>\n",
       "      <td>22.280465</td>\n",
       "      <td>114.150347</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Douglas Street Bus Stop 德忌利士街巴士站</td>\n",
       "      <td>22.283273</td>\n",
       "      <td>114.156910</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HSBC Headquarters Bus Stop 匯豐總行巴士站</td>\n",
       "      <td>22.280577</td>\n",
       "      <td>114.159446</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Dr. Sun Yat-Sen Museum Bus Stop 孫中山紀念館巴士站</td>\n",
       "      <td>22.279132</td>\n",
       "      <td>114.152743</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Hang Seng Bank Head Office Bus Stop 恒生銀行總行巴士站</td>\n",
       "      <td>22.283998</td>\n",
       "      <td>114.156038</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                Name   Latitude   Longitude  \\\n",
       "0  Seymour Road / Robinson Road Bus Stop 西摩道／羅便臣道巴士站  22.280465  114.150347   \n",
       "1                   Douglas Street Bus Stop 德忌利士街巴士站  22.283273  114.156910   \n",
       "2                 HSBC Headquarters Bus Stop 匯豐總行巴士站  22.280577  114.159446   \n",
       "3          Dr. Sun Yat-Sen Museum Bus Stop 孫中山紀念館巴士站  22.279132  114.152743   \n",
       "4      Hang Seng Bank Head Office Bus Stop 恒生銀行總行巴士站  22.283998  114.156038   \n",
       "\n",
       "   Tips  Users  Visits  \n",
       "0     0      0       0  \n",
       "1     0      0       0  \n",
       "2     0      0       0  \n",
       "3     0      0       0  \n",
       "4     0      0       0  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "venues_nearby(cinema['Latitude'], cinema['Longitude'], 'Bus Stop').head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Find eating places around the cinema"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25 \"Food\" venues are found within 500m of location\n"
     ]
    },
    {
     "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>Name</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Tips</th>\n",
       "      <th>Users</th>\n",
       "      <th>Visits</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mana! Fast Slow Food</td>\n",
       "      <td>22.282921</td>\n",
       "      <td>114.154651</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Good Luck Thai Food (鴻運泰國美食)</td>\n",
       "      <td>22.281165</td>\n",
       "      <td>114.155296</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Chiu Lung Fast Food (昭隆美食)</td>\n",
       "      <td>22.282659</td>\n",
       "      <td>114.156753</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Soul Food</td>\n",
       "      <td>22.281668</td>\n",
       "      <td>114.152495</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Sun Hing Fast Food (新興美食)</td>\n",
       "      <td>22.282521</td>\n",
       "      <td>114.156717</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Name   Latitude   Longitude  Tips  Users  Visits\n",
       "0          Mana! Fast Slow Food  22.282921  114.154651     0      0       0\n",
       "1  Good Luck Thai Food (鴻運泰國美食)  22.281165  114.155296     0      0       0\n",
       "2    Chiu Lung Fast Food (昭隆美食)  22.282659  114.156753     0      0       0\n",
       "3                     Soul Food  22.281668  114.152495     0      0       0\n",
       "4     Sun Hing Fast Food (新興美食)  22.282521  114.156717     0      0       0"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "venues_nearby(cinema['Latitude'], cinema['Longitude'], 'Food').head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12 \"Arts & Entertainment\" venues are found within 500m of location\n"
     ]
    },
    {
     "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>Name</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Tips</th>\n",
       "      <th>Users</th>\n",
       "      <th>Visits</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Tai Kwun Centre for Heritage and Arts (大館古蹟及藝術館)</td>\n",
       "      <td>22.281668</td>\n",
       "      <td>114.154216</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Wah Tung China Arts Limited (華通陶瓷藝術有限公司)</td>\n",
       "      <td>22.283046</td>\n",
       "      <td>114.152723</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ravenel Fine Arts Limited 睿芙奧</td>\n",
       "      <td>22.281819</td>\n",
       "      <td>114.156906</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>KONG Arts Space</td>\n",
       "      <td>22.281751</td>\n",
       "      <td>114.153300</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>State Of The Arts</td>\n",
       "      <td>22.282225</td>\n",
       "      <td>114.155006</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               Name   Latitude   Longitude  \\\n",
       "0  Tai Kwun Centre for Heritage and Arts (大館古蹟及藝術館)  22.281668  114.154216   \n",
       "1          Wah Tung China Arts Limited (華通陶瓷藝術有限公司)  22.283046  114.152723   \n",
       "2                     Ravenel Fine Arts Limited 睿芙奧  22.281819  114.156906   \n",
       "3                                   KONG Arts Space  22.281751  114.153300   \n",
       "4                                 State Of The Arts  22.282225  114.155006   \n",
       "\n",
       "   Tips  Users  Visits  \n",
       "0     0      0       0  \n",
       "1     0      0       0  \n",
       "2     0      0       0  \n",
       "3     0      0       0  \n",
       "4     0      0       0  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "venues_nearby(cinema['Latitude'], cinema['Longitude'], 'Arts & Entertainment').head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With above data, I can build a **content-based recommender systems** to resolve the problem.  \n",
    "\n",
    "Combine with FourSquare API on counting how many different venues (Food, Transport, Night Life) and Hong Kong cinema list, a **cinema nearby venues matrix** can be built. Stakeholder's favorite list is the **profile** to combine with cinema nearby venues matrix to become a **weighted matrix of favorite cinema**.\n",
    "\n",
    "The weighted matrix can be applied on **5 possible locations with venues information** to generate a ranking result. The **the top one** on the ranking list can be recommended to the stakeholder.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Methodology "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Methodology section which represents the main component of the report where you discuss and describe any exploratory data analysis that you did, any inferential statistical testing that you performed, and what machine learnings were used and why."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TBD"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Results section where you discuss the results."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TBD"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Discussion "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Discussion section where you discuss any observations you noted and any recommendations you can make based on the results.TBC"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TBD"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Conclusion section where you conclude the report."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TBD"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}