Capstone_Final Project_Where_to_live_in_Delft.ipynb

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   "source": [
    "# Project Capstone: Which neighborhood to buy a house?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Investing in property has been a lucrative business over the past years. The average house prices in the, densely populated, Netherlands have been skyrocketing over the past years. An estimate done by several research institutions showed that between 2017 and 2019, the prices increased on average with  9.7%. This research will focus on the city Delft. “Why Delft?”, it is a beautiful city in the center of the Netherlands, with a strong technical university. This results in a population that is relatively young and therefore with many first-time buyers. This study will develop a model to determine good neighborhoods in Delft to settle for young adults and potentially have children. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Requirements:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to come up with a model that can predict if a neighborhood is suitable for a new family. We need to determine what is important for a new family. After a deep dive( and talking with the subject matter experts, aka. The author, cause I am in that situation), the following features have been selected:\n",
    "\n",
    "*\tVegetation (Trees)\n",
    "*\tWater (canals and ponds)\n",
    "*\tPlaygrounds\n",
    "*\tVenues (incl. object of interest)\n",
    "\n",
    "Based on these features, we can develop a model and evaluate the attractiveness of a neighborhood in which we want to invest. The data should contain at least: \n",
    "*\tLongitude and latitude, in order to decide in which neighborhood, they belong.\n",
    "*\tCount per neighborhood of the features discussed above. \n",
    "*\tOpen source, and freely accessible\n",
    "*\tWe need GEO data on the city of Delft.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data collection & understanding: "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above features were selected, among others, based on their availability. The municipality of Delft provides several open access data sets. These sources will cover: “Vegetation, Water, Playground”. The Four Square API will be used to collect venues per neighborhood. \n",
    "\n",
    "Before we can prepare the data, we need to study the different data sources:\n",
    "*\tVegetation dataset: list of trees which are taken care of by the municipality, additional information includes longitude, latitude, height, shape, level of maintenance, type, age etc. \n",
    "*\t Water dataset: list of water sources such as locations of canals and ponds, the level of maintenance, if it needs mowing and the caretaker etc. \n",
    "*\tPlaygrounds dataset: playgrounds with longitude, latitude, size, level of maintenance, caretaker and year of development etc. \n",
    "*\tFoursquare Api; Venues or object of interest within a certain radius. Additional information includes categories and names of the venues. \n",
    "\n",
    "The GEOdata per neighborhood is not available and has to be created from scratch. This is done using the tool: http://geojson.io/. With this tool, you can draw polygon on the map and later plot these polygons over the folium map. \n",
    "Data Preparation:\n",
    "The opensource data needs little preparation they are already provided in easy handling formats such as csv and Json.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Used packages:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "metadata": {},
   "outputs": [],
   "source": [
    "import folium\n",
    "from folium.features import DivIcon\n",
    "\n",
    "import pandas as pd\n",
    "from pandas.io.json import json_normalize\n",
    "\n",
    "import json\n",
    "import requests\n",
    "\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn import preprocessing\n",
    "\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## General Data Delft"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "General data from Delft has been collected by web searches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "metadata": {},
   "outputs": [
    {
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>Square Km</th>\n",
       "      <th>Population</th>\n",
       "      <th>Pop. density (Pop./KM)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Tanthof</th>\n",
       "      <td>51.982308</td>\n",
       "      <td>4.351760</td>\n",
       "      <td>5.86</td>\n",
       "      <td>17126.0</td>\n",
       "      <td>2900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Voorhof</th>\n",
       "      <td>51.996322</td>\n",
       "      <td>4.344202</td>\n",
       "      <td>5.02</td>\n",
       "      <td>30003.0</td>\n",
       "      <td>6000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Binnenstad</th>\n",
       "      <td>52.013576</td>\n",
       "      <td>4.364522</td>\n",
       "      <td>4.49</td>\n",
       "      <td>44456.0</td>\n",
       "      <td>9900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Buitenhof</th>\n",
       "      <td>52.009339</td>\n",
       "      <td>4.334157</td>\n",
       "      <td>2.06</td>\n",
       "      <td>17990.0</td>\n",
       "      <td>8700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Schieweg_Zuid</th>\n",
       "      <td>51.988527</td>\n",
       "      <td>4.383054</td>\n",
       "      <td>2.26</td>\n",
       "      <td>8213.0</td>\n",
       "      <td>3600.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "                     lat      long  Square Km  Population  \\\n",
       "Tanthof        51.982308  4.351760       5.86     17126.0   \n",
       "Voorhof        51.996322  4.344202       5.02     30003.0   \n",
       "Binnenstad     52.013576  4.364522       4.49     44456.0   \n",
       "Buitenhof      52.009339  4.334157       2.06     17990.0   \n",
       "Schieweg_Zuid  51.988527  4.383054       2.26      8213.0   \n",
       "\n",
       "               Pop. density (Pop./KM)  \n",
       "Tanthof                        2900.0  \n",
       "Voorhof                        6000.0  \n",
       "Binnenstad                     9900.0  \n",
       "Buitenhof                      8700.0  \n",
       "Schieweg_Zuid                  3600.0  "
      ]
     },
     "execution_count": 334,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#location of the centers, area and population of each neighborhood\n",
    "center_Location ={'Tanthof' : [51.982308,  4.351760,5.86,17126],\n",
    "                  'Voorhof' : [51.996322, 4.344202,5.02,30003],\n",
    "                  'Binnenstad' :[52.013576, 4.364522,4.49,44456],\n",
    "                  'Buitenhof':[52.009339, 4.334157,2.06,17990],\n",
    "                  'Schieweg_Zuid': [51.988527, 4.383054,2.26,8213]\n",
    "                 } \n",
    "   \n",
    "df_CL = pd.DataFrame(center_Location)\n",
    "df_CL = df_CL.transpose()\n",
    "df_CL.rename(columns={0:'lat',1:'long',2:'Square Km',3:'Population'},inplace=True)\n",
    "df_CL['Pop. density (Pop./KM)'] = round(df_CL['Population']/ df_CL['Square Km'],-2)\n",
    "df_CL"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's explore Delft a little bit more. If we plot the above dataframe on a map of Delft, so we can understand Delft's layout a bit better"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Json file decribing the polygons of Delft. \n",
    "Wijken_delft_json = r'C:\\Users\\perin\\OneDrive\\Desktop\\Kristian\\Neighboroughed Delft_3.json'\n",
    "\n",
    "#Coordinates of Delft\n",
    "latitude = 52.0050\n",
    "longitude = 4.3571"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "metadata": {},
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      ],
      "text/plain": [
       "<folium.folium.Map at 0x1b0ddc55390>"
      ]
     },
     "execution_count": 336,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create map of Delft using latitude and longitude values\n",
    "map_Delft = folium.Map(location=[latitude, longitude], zoom_start=13,tiles='OpenStreetMap')\n",
    "\n",
    "folium.GeoJson(Wijken_delft_json).add_to(map_Delft)\n",
    "\n",
    "# Add the color for the chloropleth:\n",
    "map_Delft.choropleth(\n",
    " geo_data= Wijken_delft_json,\n",
    " name='choropleth',\n",
    " data= df_CL,\n",
    " columns=[df_CL.index, 'Pop. density (Pop./KM)'],\n",
    " key_on='properties.name',\n",
    " fill_color='PuBuGn',\n",
    " fill_opacity=0.7,\n",
    " line_opacity=0.2,\n",
    " legend_name='Population Density'\n",
    ")\n",
    "folium.LayerControl().add_to(map_Delft)\n",
    "\n",
    "#add markers to map\n",
    "for lat, lng, name in zip(df_CL['lat'], df_CL['long'], df_CL.index):\n",
    "    label = '{}'.format(name)\n",
    "    label = folium.Popup(label, parse_html=True)\n",
    "    folium.CircleMarker(\n",
    "        [lat, lng],\n",
    "        radius=5,\n",
    "        popup=label,\n",
    "        color='Blue',\n",
    "        fill=True,\n",
    "        fill_color='#4868FF',\n",
    "        fill_opacity=0.7,\n",
    "        parse_html=False).add_to(map_Delft)      \n",
    "\n",
    "    folium.map.Marker(\n",
    "        [lat,lng],\n",
    "        icon=DivIcon(\n",
    "            icon_size=(150,36),\n",
    "            icon_anchor=(0,0),\n",
    "            html='<div style=\"font-size: 15pt\">%s</div>' % name\n",
    "            )\n",
    "    ).add_to(map_Delft)  \n",
    "map_Delft"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see on the map above, Delft Consist out of 5 neighborhoods. With 'Binnenstad' as the most densly populated area."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get Location of trees, playgrounds and water elements in Delft"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### In this section, we will collect the different data sources we will use for our further analysis\n",
    "As mentioned in the previous sections, we will look into: \n",
    "*\tVegetation (Trees)\n",
    "*\tWater (canals and ponds)\n",
    "*\tPlaygrounds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Datasources\n",
    "Trees = r'C:\\Users\\perin\\Downloads\\Bomen_in_beheer_door_gemeente_Delft.csv'\n",
    "playground = r'C:\\Users\\perin\\Downloads\\Speelplekken.csv'\n",
    "Water_elements_json = r'C:\\Users\\perin\\Downloads\\Wateren.geojson'\n",
    "\n",
    "#reading the data in pandas DataFrame\n",
    "df_playgroud_Delft = pd.read_csv(playground)\n",
    "df_playgroud_Delft = df_playgroud_Delft\n",
    "\n",
    "df_Trees_Delft = pd.read_csv(Trees)\n",
    "df_Trees_Delft = df_Trees_Delft\n",
    "\n",
    "#Reading the water data from a Json file this file will need some additional processing\n",
    "with open(Water_elements_json) as json_file:\n",
    "    water_data = json.load(json_file)\n",
    "\n",
    "#create an empty list in which will contain the final input\n",
    "Water_data= []\n",
    "\n",
    "for i in range(0,len(water_data['features'])):\n",
    "    Water_data.append([\n",
    "        (water_data['features'][i]['properties']['ELEMENTNUMMER']),\n",
    "        (water_data['features'][i]['properties']['WIJK']),\n",
    "        (water_data['features'][i]['properties']['OPPERVLAK'])\n",
    "        ])\n",
    "\n",
    "\n",
    "df_water = pd.DataFrame(Water_data, columns=['ObjectID','Wijk','Area'])\n",
    "df_total_water_per_wijk = df_water.groupby('Wijk').sum()\n",
    "df_total_water_per_wijk = df_total_water_per_wijk.reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting the collected Tree data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b0ddbe5c88>"
      ]
     },
     "execution_count": 338,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_Trees_Delt_plt = df_Trees_Delft['WIJKNAAM'].value_counts().rename_axis('Neighborhood').reset_index(name='Tree counts')\n",
    "df_Trees_Delt_plt.plot(kind='bar',x='Neighborhood',y='Tree counts',color='red')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, the dataframe data contains much more Neihborhoods as the ones defined in the pervious section. The neighborhoods have been split in to many sub-neighborhoods. e.g. east and west (oost en west). We will need to merge some neihborhoods. This does not only hold for the Tree data, but for all collected data. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pre-processing neighborhood data via mapping tables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Merging several neighborhoods into the sections we devided in the first step"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Voorhof' 'Tanthof' 'Binnenstad' 'Buitenhof' 'Schieweg_Zuid']\n",
      "['Buitenhof' 'Voorhof' 'Tanthof' 'Binnenstad' 'Schieweg_Zuid']\n",
      "['Binnenstad' 'Voorhof' 'Buitenhof' 'Tanthof' 'Schieweg_Zuid']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b0dd5b34e0>"
      ]
     },
     "execution_count": 339,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Tanthof = ['26 Abtswoude','Tanthof west 22','Tanthof Oost','Tanthof-West','Tanthof-Oost','Abtswoude','22 Tanthof-West','23 Tanthof-Oost'] \n",
    "Voorhof = ['24 Voorhof','Voorhof 24','Vrijenban 12','Voordijkshoorn 14','Voordijkshoorn','Voorhof','Vrijenban','12 Vrijenban','14 Voordijkshoorn']\n",
    "Schieweg_Zuid = ['28 Wippolder','27 Schieweg','Schieweg','Harnaschpolder','Wippolder 28','Wippolder','Schieweg Zuid','Schieweg Noord']\n",
    "Binnenstad=['Hof Van Delft','Binnenstad ','Hof Van Delft','Hof van Delft','11 Binnenstad','13 Hof van Delft']\n",
    "Buitenhof=['29 Ruiven','25 Buitenhof','Buitenhof','Delftse Hout','Ruiven',' ','16 Delftse Hout']\n",
    "\n",
    "Neighborhood_list = [Tanthof,Voorhof,Schieweg_Zuid,Binnenstad,Buitenhof]\n",
    "Neighborhood_list_name_list = ['Tanthof','Voorhof','Schieweg_Zuid','Binnenstad','Buitenhof']\n",
    "\n",
    "for i in range(0,len(Neighborhood_list)):\n",
    "    df_Trees_Delft['WIJKNAAM'].replace(Neighborhood_list[i],Neighborhood_list_name_list[i],inplace = True)\n",
    "    df_playgroud_Delft['Wijk'].replace(Neighborhood_list[i],Neighborhood_list_name_list[i],inplace = True)\n",
    "    df_total_water_per_wijk['Wijk'].replace(Neighborhood_list[i],Neighborhood_list_name_list[i],inplace = True)\n",
    "    \n",
    "print(df_playgroud_Delft['Wijk'].unique())\n",
    "print(df_Trees_Delft['WIJKNAAM'].unique())\n",
    "print(df_total_water_per_wijk['Wijk'].unique())\n",
    "\n",
    "#Lets plot the data again\n",
    "df_Trees_Delt_plt = df_Trees_Delft['WIJKNAAM'].value_counts().rename_axis('Neighborhood').reset_index(name='Tree counts')\n",
    "df_Trees_Delt_plt.plot(kind='bar',x='Neighborhood',y='Tree counts',color='red')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's merge all data cleaned data in one dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "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>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>Square Km</th>\n",
       "      <th>Population</th>\n",
       "      <th>Pop. density (Pop./KM)</th>\n",
       "      <th>Trees</th>\n",
       "      <th>Playgrounds</th>\n",
       "      <th>Water_Area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Tanthof</th>\n",
       "      <td>51.982308</td>\n",
       "      <td>4.351760</td>\n",
       "      <td>5.86</td>\n",
       "      <td>17126.0</td>\n",
       "      <td>2900.0</td>\n",
       "      <td>6081</td>\n",
       "      <td>52</td>\n",
       "      <td>143141.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Voorhof</th>\n",
       "      <td>51.996322</td>\n",
       "      <td>4.344202</td>\n",
       "      <td>5.02</td>\n",
       "      <td>30003.0</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>8105</td>\n",
       "      <td>53</td>\n",
       "      <td>280144.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Binnenstad</th>\n",
       "      <td>52.013576</td>\n",
       "      <td>4.364522</td>\n",
       "      <td>4.49</td>\n",
       "      <td>44456.0</td>\n",
       "      <td>9900.0</td>\n",
       "      <td>5340</td>\n",
       "      <td>33</td>\n",
       "      <td>51698.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Buitenhof</th>\n",
       "      <td>52.009339</td>\n",
       "      <td>4.334157</td>\n",
       "      <td>2.06</td>\n",
       "      <td>17990.0</td>\n",
       "      <td>8700.0</td>\n",
       "      <td>11673</td>\n",
       "      <td>33</td>\n",
       "      <td>589634.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Schieweg_Zuid</th>\n",
       "      <td>51.988527</td>\n",
       "      <td>4.383054</td>\n",
       "      <td>2.26</td>\n",
       "      <td>8213.0</td>\n",
       "      <td>3600.0</td>\n",
       "      <td>3856</td>\n",
       "      <td>32</td>\n",
       "      <td>58019.08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     lat      long  Square Km  Population  \\\n",
       "Tanthof        51.982308  4.351760       5.86     17126.0   \n",
       "Voorhof        51.996322  4.344202       5.02     30003.0   \n",
       "Binnenstad     52.013576  4.364522       4.49     44456.0   \n",
       "Buitenhof      52.009339  4.334157       2.06     17990.0   \n",
       "Schieweg_Zuid  51.988527  4.383054       2.26      8213.0   \n",
       "\n",
       "               Pop. density (Pop./KM)  Trees  Playgrounds  Water_Area  \n",
       "Tanthof                        2900.0   6081           52   143141.75  \n",
       "Voorhof                        6000.0   8105           53   280144.68  \n",
       "Binnenstad                     9900.0   5340           33    51698.36  \n",
       "Buitenhof                      8700.0  11673           33   589634.83  \n",
       "Schieweg_Zuid                  3600.0   3856           32    58019.08  "
      ]
     },
     "execution_count": 340,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_Delft = pd.concat([df_CL,\n",
    "                     df_Trees_Delft['WIJKNAAM'].value_counts(),\n",
    "                     df_playgroud_Delft['Wijk'].value_counts(),\n",
    "                     df_total_water_per_wijk.groupby('Wijk').sum()],\n",
    "                     axis=1, join='inner')\n",
    "                     \n",
    "df_Delft.rename(columns = {'WIJKNAAM':'Trees','Wijk':'Playgrounds','Area':'Water_Area'},inplace =True) \n",
    "df_Delft"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The prelimiary output of our analysis is shown in the table above. We still need to extend it based on the venues in the proximity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Foursquare: venue and object of interest data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the Foursquare API, we collect all venues and Object of interest data in the proximity of the central locations in each neighborhood"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "metadata": {},
   "outputs": [],
   "source": [
    "#login data for Foursquare API\n",
    "CLIENT_ID='UUTO0VO5CFK4L4XRUPKU0ZMIOYA041AWQ3MVZYJIOPJZAERU'\n",
    "CLIENT_SECRET='EY5P1YAVKYSAMKPTGKXRXHXEMMJIF5DOHBXNBKOGB2P04CAW'\n",
    "VERSION = '20180605' # Foursquare API version\n",
    "LIMIT = 100\n",
    "#this function will give nearby venues in a radius of 800ft.\n",
    "def getNearbyVenues(names, latitudes, longitudes, radius=800):\n",
    "    \n",
    "    venues_list=[]\n",
    "    for name, lat, lng in zip(names, latitudes, longitudes):\n",
    "        print(name)\n",
    "            \n",
    "        # create the API request URL\n",
    "        url = 'https://api.foursquare.com/v2/venues/explore?&client_id={}&client_secret={}&v={}&ll={},{}&radius={}&limit={}'.format(\n",
    "            CLIENT_ID, \n",
    "            CLIENT_SECRET, \n",
    "            VERSION, \n",
    "            lat, \n",
    "            lng, \n",
    "            radius, \n",
    "            LIMIT)\n",
    "            \n",
    "        # make the GET request\n",
    "        results = requests.get(url).json()[\"response\"]['groups'][0]['items']\n",
    "        \n",
    "        # return only relevant information for each nearby venue\n",
    "        venues_list.append([(\n",
    "            name, \n",
    "            lat, \n",
    "            lng, \n",
    "            v['venue']['name'], \n",
    "            v['venue']['location']['lat'], \n",
    "            v['venue']['location']['lng'],  \n",
    "            v['venue']['categories'][0]['name']) for v in results])\n",
    "\n",
    "    nearby_venues = pd.DataFrame([item for venue_list in venues_list for item in venue_list])\n",
    "    nearby_venues.columns = ['Neighborhood', \n",
    "                  'Neighborhood Latitude', \n",
    "                  'Neighborhood Longitude', \n",
    "                  'Venue', \n",
    "                  'Venue Latitude', \n",
    "                  'Venue Longitude', \n",
    "                  'Venue Category']\n",
    "    \n",
    "    return(nearby_venues)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we loop through the all locations and return the all the veneus in that area"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 342,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tanthof\n",
      "Voorhof\n",
      "Binnenstad\n",
      "Buitenhof\n",
      "Schieweg_Zuid\n"
     ]
    }
   ],
   "source": [
    "Delft_venues = getNearbyVenues(names=df_CL.index,\n",
    "                                   latitudes=df_CL['lat'],\n",
    "                                   longitudes=df_CL['long']\n",
    "                                  )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets look at some do some checks on each neighborhood. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 343,
   "metadata": {
    "scrolled": true
   },
   "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></th>\n",
       "      <th>Neighborhood Latitude</th>\n",
       "      <th>Neighborhood Longitude</th>\n",
       "      <th>Venue</th>\n",
       "      <th>Venue Latitude</th>\n",
       "      <th>Venue Longitude</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Venue Category</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"30\" valign=\"top\">Binnenstad</th>\n",
       "      <th>Athletics &amp; Sports</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BBQ Joint</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bakery</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bar</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Beer Store</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bistro</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bookstore</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bowling Alley</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Breakfast Spot</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brewery</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bridge</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Butcher</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Café</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cheese Shop</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chocolate Shop</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Church</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Clothing Store</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Coffee Shop</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Comfort Food Restaurant</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cupcake Shop</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Department Store</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dessert Shop</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Diner</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dutch Restaurant</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Electronics Store</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fast Food Restaurant</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Food</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Food Truck</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>French Restaurant</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Furniture / Home Store</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Schieweg_Zuid</th>\n",
       "      <th>Stadium</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tennis Court</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"9\" valign=\"top\">Tanthof</th>\n",
       "      <th>BBQ Joint</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bus Stop</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Department Store</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fast Food Restaurant</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Light Rail Station</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Park</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shop &amp; Service</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Supermarket</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tram Station</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"19\" valign=\"top\">Voorhof</th>\n",
       "      <th>Asian Restaurant</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Board Shop</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bowling Alley</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bus Stop</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chinese Restaurant</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Department Store</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Discount Store</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Drugstore</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Furniture / Home Store</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gym</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jewelry Store</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Light Rail Station</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Park</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pool Hall</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sandwich Place</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shopping Mall</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Skate Park</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Supermarket</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tram Station</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>102 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       Neighborhood Latitude  \\\n",
       "Neighborhood  Venue Category                                   \n",
       "Binnenstad    Athletics & Sports                           2   \n",
       "              BBQ Joint                                    1   \n",
       "              Bakery                                       3   \n",
       "              Bar                                          4   \n",
       "              Beer Store                                   1   \n",
       "              Bistro                                       1   \n",
       "              Bookstore                                    2   \n",
       "              Bowling Alley                                1   \n",
       "              Breakfast Spot                               1   \n",
       "              Brewery                                      1   \n",
       "              Bridge                                       1   \n",
       "              Butcher                                      1   \n",
       "              Café                                         6   \n",
       "              Cheese Shop                                  1   \n",
       "              Chocolate Shop                               2   \n",
       "              Church                                       1   \n",
       "              Clothing Store                               2   \n",
       "              Coffee Shop                                  2   \n",
       "              Comfort Food Restaurant                      1   \n",
       "              Cupcake Shop                                 1   \n",
       "              Department Store                             1   \n",
       "              Dessert Shop                                 1   \n",
       "              Diner                                        1   \n",
       "              Dutch Restaurant                             1   \n",
       "              Electronics Store                            1   \n",
       "              Fast Food Restaurant                         1   \n",
       "              Food                                         1   \n",
       "              Food Truck                                   1   \n",
       "              French Restaurant                            4   \n",
       "              Furniture / Home Store                       1   \n",
       "...                                                      ...   \n",
       "Schieweg_Zuid Stadium                                      1   \n",
       "              Tennis Court                                 1   \n",
       "Tanthof       BBQ Joint                                    1   \n",
       "              Bus Stop                                     2   \n",
       "              Department Store                             1   \n",
       "              Fast Food Restaurant                         3   \n",
       "              Light Rail Station                           1   \n",
       "              Park                                         3   \n",
       "              Shop & Service                               1   \n",
       "              Supermarket                                  3   \n",
       "              Tram Station                                 3   \n",
       "Voorhof       Asian Restaurant                             1   \n",
       "              Board Shop                                   1   \n",
       "              Bowling Alley                                1   \n",
       "              Bus Stop                                     1   \n",
       "              Chinese Restaurant                           1   \n",
       "              Department Store                             1   \n",
       "              Discount Store                               1   \n",
       "              Drugstore                                    1   \n",
       "              Furniture / Home Store                       1   \n",
       "              Gym                                          2   \n",
       "              Jewelry Store                                1   \n",
       "              Light Rail Station                           1   \n",
       "              Park                                         1   \n",
       "              Pool Hall                                    1   \n",
       "              Sandwich Place                               1   \n",
       "              Shopping Mall                                1   \n",
       "              Skate Park                                   1   \n",
       "              Supermarket                                  3   \n",
       "              Tram Station                                 1   \n",
       "\n",
       "                                       Neighborhood Longitude  Venue  \\\n",
       "Neighborhood  Venue Category                                           \n",
       "Binnenstad    Athletics & Sports                            2      2   \n",
       "              BBQ Joint                                     1      1   \n",
       "              Bakery                                        3      3   \n",
       "              Bar                                           4      4   \n",
       "              Beer Store                                    1      1   \n",
       "              Bistro                                        1      1   \n",
       "              Bookstore                                     2      2   \n",
       "              Bowling Alley                                 1      1   \n",
       "              Breakfast Spot                                1      1   \n",
       "              Brewery                                       1      1   \n",
       "              Bridge                                        1      1   \n",
       "              Butcher                                       1      1   \n",
       "              Café                                          6      6   \n",
       "              Cheese Shop                                   1      1   \n",
       "              Chocolate Shop                                2      2   \n",
       "              Church                                        1      1   \n",
       "              Clothing Store                                2      2   \n",
       "              Coffee Shop                                   2      2   \n",
       "              Comfort Food Restaurant                       1      1   \n",
       "              Cupcake Shop                                  1      1   \n",
       "              Department Store                              1      1   \n",
       "              Dessert Shop                                  1      1   \n",
       "              Diner                                         1      1   \n",
       "              Dutch Restaurant                              1      1   \n",
       "              Electronics Store                             1      1   \n",
       "              Fast Food Restaurant                          1      1   \n",
       "              Food                                          1      1   \n",
       "              Food Truck                                    1      1   \n",
       "              French Restaurant                             4      4   \n",
       "              Furniture / Home Store                        1      1   \n",
       "...                                                       ...    ...   \n",
       "Schieweg_Zuid Stadium                                       1      1   \n",
       "              Tennis Court                                  1      1   \n",
       "Tanthof       BBQ Joint                                     1      1   \n",
       "              Bus Stop                                      2      2   \n",
       "              Department Store                              1      1   \n",
       "              Fast Food Restaurant                          3      3   \n",
       "              Light Rail Station                            1      1   \n",
       "              Park                                          3      3   \n",
       "              Shop & Service                                1      1   \n",
       "              Supermarket                                   3      3   \n",
       "              Tram Station                                  3      3   \n",
       "Voorhof       Asian Restaurant                              1      1   \n",
       "              Board Shop                                    1      1   \n",
       "              Bowling Alley                                 1      1   \n",
       "              Bus Stop                                      1      1   \n",
       "              Chinese Restaurant                            1      1   \n",
       "              Department Store                              1      1   \n",
       "              Discount Store                                1      1   \n",
       "              Drugstore                                     1      1   \n",
       "              Furniture / Home Store                        1      1   \n",
       "              Gym                                           2      2   \n",
       "              Jewelry Store                                 1      1   \n",
       "              Light Rail Station                            1      1   \n",
       "              Park                                          1      1   \n",
       "              Pool Hall                                     1      1   \n",
       "              Sandwich Place                                1      1   \n",
       "              Shopping Mall                                 1      1   \n",
       "              Skate Park                                    1      1   \n",
       "              Supermarket                                   3      3   \n",
       "              Tram Station                                  1      1   \n",
       "\n",
       "                                       Venue Latitude  Venue Longitude  \n",
       "Neighborhood  Venue Category                                            \n",
       "Binnenstad    Athletics & Sports                    2                2  \n",
       "              BBQ Joint                             1                1  \n",
       "              Bakery                                3                3  \n",
       "              Bar                                   4                4  \n",
       "              Beer Store                            1                1  \n",
       "              Bistro                                1                1  \n",
       "              Bookstore                             2                2  \n",
       "              Bowling Alley                         1                1  \n",
       "              Breakfast Spot                        1                1  \n",
       "              Brewery                               1                1  \n",
       "              Bridge                                1                1  \n",
       "              Butcher                               1                1  \n",
       "              Café                                  6                6  \n",
       "              Cheese Shop                           1                1  \n",
       "              Chocolate Shop                        2                2  \n",
       "              Church                                1                1  \n",
       "              Clothing Store                        2                2  \n",
       "              Coffee Shop                           2                2  \n",
       "              Comfort Food Restaurant               1                1  \n",
       "              Cupcake Shop                          1                1  \n",
       "              Department Store                      1                1  \n",
       "              Dessert Shop                          1                1  \n",
       "              Diner                                 1                1  \n",
       "              Dutch Restaurant                      1                1  \n",
       "              Electronics Store                     1                1  \n",
       "              Fast Food Restaurant                  1                1  \n",
       "              Food                                  1                1  \n",
       "              Food Truck                            1                1  \n",
       "              French Restaurant                     4                4  \n",
       "              Furniture / Home Store                1                1  \n",
       "...                                               ...              ...  \n",
       "Schieweg_Zuid Stadium                               1                1  \n",
       "              Tennis Court                          1                1  \n",
       "Tanthof       BBQ Joint                             1                1  \n",
       "              Bus Stop                              2                2  \n",
       "              Department Store                      1                1  \n",
       "              Fast Food Restaurant                  3                3  \n",
       "              Light Rail Station                    1                1  \n",
       "              Park                                  3                3  \n",
       "              Shop & Service                        1                1  \n",
       "              Supermarket                           3                3  \n",
       "              Tram Station                          3                3  \n",
       "Voorhof       Asian Restaurant                      1                1  \n",
       "              Board Shop                            1                1  \n",
       "              Bowling Alley                         1                1  \n",
       "              Bus Stop                              1                1  \n",
       "              Chinese Restaurant                    1                1  \n",
       "              Department Store                      1                1  \n",
       "              Discount Store                        1                1  \n",
       "              Drugstore                             1                1  \n",
       "              Furniture / Home Store                1                1  \n",
       "              Gym                                   2                2  \n",
       "              Jewelry Store                         1                1  \n",
       "              Light Rail Station                    1                1  \n",
       "              Park                                  1                1  \n",
       "              Pool Hall                             1                1  \n",
       "              Sandwich Place                        1                1  \n",
       "              Shopping Mall                         1                1  \n",
       "              Skate Park                            1                1  \n",
       "              Supermarket                           3                3  \n",
       "              Tram Station                          1                1  \n",
       "\n",
       "[102 rows x 5 columns]"
      ]
     },
     "execution_count": 343,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Delft_venues.groupby(['Neighborhood','Venue Category']).count()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## data pre-processing for kmn clustering¶\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use KMN clustering to determine how simular the different neighborhoods are to each other"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 344,
   "metadata": {},
   "outputs": [],
   "source": [
    "# one hot encoding\n",
    "Delft_onehot = pd.get_dummies(Delft_venues[['Venue Category']], prefix=\"\", prefix_sep=\"\")\n",
    "\n",
    "# add neighborhood column back to dataframe\n",
    "Delft_onehot['Neighborhood'] = Delft_venues['Neighborhood'] \n",
    "\n",
    "# move neighborhood column to the first column\n",
    "fixed_columns = [Delft_onehot.columns[-1]] + list(Delft_onehot.columns[:-1])\n",
    "Delft_onehot = Delft_onehot[fixed_columns]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we will determine the frequency of each venue per neighborhood"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 345,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----Binnenstad----\n",
      "               venue  freq\n",
      "0               Café  0.06\n",
      "1                Pub  0.04\n",
      "2              Plaza  0.04\n",
      "3                Bar  0.04\n",
      "4  French Restaurant  0.04\n",
      "\n",
      "\n",
      "----Buitenhof----\n",
      "          venue  freq\n",
      "0      Pharmacy  0.25\n",
      "1   Supermarket  0.25\n",
      "2   Pizza Place  0.12\n",
      "3  Hockey Field  0.12\n",
      "4        Bakery  0.12\n",
      "\n",
      "\n",
      "----Schieweg_Zuid----\n",
      "              venue  freq\n",
      "0  Department Store  0.14\n",
      "1      Hockey Field  0.14\n",
      "2      Tennis Court  0.14\n",
      "3               Gym  0.14\n",
      "4           Stadium  0.14\n",
      "\n",
      "\n",
      "----Tanthof----\n",
      "                  venue  freq\n",
      "0          Tram Station  0.17\n",
      "1                  Park  0.17\n",
      "2           Supermarket  0.17\n",
      "3  Fast Food Restaurant  0.17\n",
      "4              Bus Stop  0.11\n",
      "\n",
      "\n",
      "----Voorhof----\n",
      "              venue  freq\n",
      "0       Supermarket  0.14\n",
      "1               Gym  0.09\n",
      "2  Asian Restaurant  0.05\n",
      "3        Skate Park  0.05\n",
      "4  Department Store  0.05\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "Delft_grouped = Delft_onehot.groupby('Neighborhood').mean().reset_index()\n",
    "num_top_venues = 5\n",
    "\n",
    "for hood in Delft_grouped['Neighborhood']:\n",
    "    print(\"----\"+hood+\"----\")\n",
    "    temp = Delft_grouped[Delft_grouped['Neighborhood'] == hood].T.reset_index()\n",
    "    temp.columns = ['venue','freq']\n",
    "    temp = temp.iloc[1:]\n",
    "    temp['freq'] = temp['freq'].astype(float)\n",
    "    temp = temp.round({'freq': 2})\n",
    "    print(temp.sort_values('freq', ascending=False).reset_index(drop=True).head(num_top_venues))\n",
    "    print('\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "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>Neighborhood</th>\n",
       "      <th>1st Most Common Venue</th>\n",
       "      <th>2nd Most Common Venue</th>\n",
       "      <th>3rd Most Common Venue</th>\n",
       "      <th>4th Most Common Venue</th>\n",
       "      <th>5th Most Common Venue</th>\n",
       "      <th>6th Most Common Venue</th>\n",
       "      <th>7th Most Common Venue</th>\n",
       "      <th>8th Most Common Venue</th>\n",
       "      <th>9th Most Common Venue</th>\n",
       "      <th>10th Most Common Venue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Binnenstad</td>\n",
       "      <td>Café</td>\n",
       "      <td>French Restaurant</td>\n",
       "      <td>Bar</td>\n",
       "      <td>Pub</td>\n",
       "      <td>Plaza</td>\n",
       "      <td>Bakery</td>\n",
       "      <td>Movie Theater</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>Restaurant</td>\n",
       "      <td>Coffee Shop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Buitenhof</td>\n",
       "      <td>Supermarket</td>\n",
       "      <td>Pharmacy</td>\n",
       "      <td>Hockey Field</td>\n",
       "      <td>Park</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>Bakery</td>\n",
       "      <td>Discount Store</td>\n",
       "      <td>Clothing Store</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Comfort Food Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Schieweg_Zuid</td>\n",
       "      <td>Hockey Field</td>\n",
       "      <td>Stadium</td>\n",
       "      <td>Department Store</td>\n",
       "      <td>Bus Stop</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>Gym</td>\n",
       "      <td>Tennis Court</td>\n",
       "      <td>Bar</td>\n",
       "      <td>Drugstore</td>\n",
       "      <td>Comfort Food Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Tanthof</td>\n",
       "      <td>Park</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Tram Station</td>\n",
       "      <td>Supermarket</td>\n",
       "      <td>Bus Stop</td>\n",
       "      <td>Department Store</td>\n",
       "      <td>Light Rail Station</td>\n",
       "      <td>Shop &amp; Service</td>\n",
       "      <td>BBQ Joint</td>\n",
       "      <td>Food</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Voorhof</td>\n",
       "      <td>Supermarket</td>\n",
       "      <td>Gym</td>\n",
       "      <td>Asian Restaurant</td>\n",
       "      <td>Bowling Alley</td>\n",
       "      <td>Tram Station</td>\n",
       "      <td>Drugstore</td>\n",
       "      <td>Discount Store</td>\n",
       "      <td>Department Store</td>\n",
       "      <td>Jewelry Store</td>\n",
       "      <td>Light Rail Station</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Neighborhood 1st Most Common Venue 2nd Most Common Venue  \\\n",
       "0     Binnenstad                  Café     French Restaurant   \n",
       "1      Buitenhof           Supermarket              Pharmacy   \n",
       "2  Schieweg_Zuid          Hockey Field               Stadium   \n",
       "3        Tanthof                  Park  Fast Food Restaurant   \n",
       "4        Voorhof           Supermarket                   Gym   \n",
       "\n",
       "  3rd Most Common Venue 4th Most Common Venue 5th Most Common Venue  \\\n",
       "0                   Bar                   Pub                 Plaza   \n",
       "1          Hockey Field                  Park           Pizza Place   \n",
       "2      Department Store              Bus Stop        Sandwich Place   \n",
       "3          Tram Station           Supermarket              Bus Stop   \n",
       "4      Asian Restaurant         Bowling Alley          Tram Station   \n",
       "\n",
       "  6th Most Common Venue 7th Most Common Venue 8th Most Common Venue  \\\n",
       "0                Bakery         Movie Theater        Sandwich Place   \n",
       "1                Bakery        Discount Store        Clothing Store   \n",
       "2                   Gym          Tennis Court                   Bar   \n",
       "3      Department Store    Light Rail Station        Shop & Service   \n",
       "4             Drugstore        Discount Store      Department Store   \n",
       "\n",
       "  9th Most Common Venue   10th Most Common Venue  \n",
       "0            Restaurant              Coffee Shop  \n",
       "1           Coffee Shop  Comfort Food Restaurant  \n",
       "2             Drugstore  Comfort Food Restaurant  \n",
       "3             BBQ Joint                     Food  \n",
       "4         Jewelry Store       Light Rail Station  "
      ]
     },
     "execution_count": 346,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def return_most_common_venues(row, num_top_venues):\n",
    "    row_categories = row.iloc[1:]\n",
    "    row_categories_sorted = row_categories.sort_values(ascending=False)\n",
    "    \n",
    "    return row_categories_sorted.index.values[0:num_top_venues]\n",
    "\n",
    "num_top_venues = 10\n",
    "\n",
    "indicators = ['st', 'nd', 'rd']\n",
    "\n",
    "# create columns according to number of top venues\n",
    "columns = ['Neighborhood']\n",
    "for ind in np.arange(num_top_venues):\n",
    "    try:\n",
    "        columns.append('{}{} Most Common Venue'.format(ind+1, indicators[ind]))\n",
    "    except:\n",
    "        columns.append('{}th Most Common Venue'.format(ind+1))\n",
    "\n",
    "# create a new dataframe\n",
    "neighborhoods_venues_sorted = pd.DataFrame(columns=columns)\n",
    "neighborhoods_venues_sorted['Neighborhood'] = Delft_grouped['Neighborhood']\n",
    "\n",
    "for ind in np.arange(Delft_grouped.shape[0]):\n",
    "    neighborhoods_venues_sorted.iloc[ind, 1:] = return_most_common_venues(Delft_grouped.iloc[ind, :], num_top_venues)\n",
    "\n",
    "neighborhoods_venues_sorted.shape\n",
    "\n",
    "neighborhoods_venues_sorted.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clustering of neighbourhood"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will cluster the neighbourhood in three clusters. After some additional research( not shown here) we determined that three was a good seperation number"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 347,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set number of clusters\n",
    "kclusters = 3\n",
    "\n",
    "Delft_grouped_clustering = Delft_grouped.drop('Neighborhood', 1)\n",
    "\n",
    "# run k-means clustering\n",
    "kmeans = KMeans(n_clusters=kclusters, random_state=0).fit(Delft_grouped_clustering)\n",
    "\n",
    "# check cluster labels generated for each row in the dataframe\n",
    "kmeans.labels_[0:10] \n",
    "\n",
    "# add clustering labels\n",
    "neighborhoods_venues_sorted.insert(1, 'Cluster Labels', kmeans.labels_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\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>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>Square Km</th>\n",
       "      <th>Population</th>\n",
       "      <th>Pop. density (Pop./KM)</th>\n",
       "      <th>Trees</th>\n",
       "      <th>Playgrounds</th>\n",
       "      <th>Water_Area</th>\n",
       "      <th>Cluster Labels</th>\n",
       "      <th>1st Most Common Venue</th>\n",
       "      <th>2nd Most Common Venue</th>\n",
       "      <th>3rd Most Common Venue</th>\n",
       "      <th>4th Most Common Venue</th>\n",
       "      <th>5th Most Common Venue</th>\n",
       "      <th>6th Most Common Venue</th>\n",
       "      <th>7th Most Common Venue</th>\n",
       "      <th>8th Most Common Venue</th>\n",
       "      <th>9th Most Common Venue</th>\n",
       "      <th>10th Most Common Venue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Tanthof</th>\n",
       "      <td>51.982308</td>\n",
       "      <td>4.351760</td>\n",
       "      <td>5.86</td>\n",
       "      <td>17126.0</td>\n",
       "      <td>2900.0</td>\n",
       "      <td>6081</td>\n",
       "      <td>52</td>\n",
       "      <td>143141.75</td>\n",
       "      <td>0</td>\n",
       "      <td>Park</td>\n",
       "      <td>Fast Food Restaurant</td>\n",
       "      <td>Tram Station</td>\n",
       "      <td>Supermarket</td>\n",
       "      <td>Bus Stop</td>\n",
       "      <td>Department Store</td>\n",
       "      <td>Light Rail Station</td>\n",
       "      <td>Shop &amp; Service</td>\n",
       "      <td>BBQ Joint</td>\n",
       "      <td>Food</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Voorhof</th>\n",
       "      <td>51.996322</td>\n",
       "      <td>4.344202</td>\n",
       "      <td>5.02</td>\n",
       "      <td>30003.0</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>8105</td>\n",
       "      <td>53</td>\n",
       "      <td>280144.68</td>\n",
       "      <td>0</td>\n",
       "      <td>Supermarket</td>\n",
       "      <td>Gym</td>\n",
       "      <td>Asian Restaurant</td>\n",
       "      <td>Bowling Alley</td>\n",
       "      <td>Tram Station</td>\n",
       "      <td>Drugstore</td>\n",
       "      <td>Discount Store</td>\n",
       "      <td>Department Store</td>\n",
       "      <td>Jewelry Store</td>\n",
       "      <td>Light Rail Station</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Binnenstad</th>\n",
       "      <td>52.013576</td>\n",
       "      <td>4.364522</td>\n",
       "      <td>4.49</td>\n",
       "      <td>44456.0</td>\n",
       "      <td>9900.0</td>\n",
       "      <td>5340</td>\n",
       "      <td>33</td>\n",
       "      <td>51698.36</td>\n",
       "      <td>0</td>\n",
       "      <td>Café</td>\n",
       "      <td>French Restaurant</td>\n",
       "      <td>Bar</td>\n",
       "      <td>Pub</td>\n",
       "      <td>Plaza</td>\n",
       "      <td>Bakery</td>\n",
       "      <td>Movie Theater</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>Restaurant</td>\n",
       "      <td>Coffee Shop</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Buitenhof</th>\n",
       "      <td>52.009339</td>\n",
       "      <td>4.334157</td>\n",
       "      <td>2.06</td>\n",
       "      <td>17990.0</td>\n",
       "      <td>8700.0</td>\n",
       "      <td>11673</td>\n",
       "      <td>33</td>\n",
       "      <td>589634.83</td>\n",
       "      <td>2</td>\n",
       "      <td>Supermarket</td>\n",
       "      <td>Pharmacy</td>\n",
       "      <td>Hockey Field</td>\n",
       "      <td>Park</td>\n",
       "      <td>Pizza Place</td>\n",
       "      <td>Bakery</td>\n",
       "      <td>Discount Store</td>\n",
       "      <td>Clothing Store</td>\n",
       "      <td>Coffee Shop</td>\n",
       "      <td>Comfort Food Restaurant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Schieweg_Zuid</th>\n",
       "      <td>51.988527</td>\n",
       "      <td>4.383054</td>\n",
       "      <td>2.26</td>\n",
       "      <td>8213.0</td>\n",
       "      <td>3600.0</td>\n",
       "      <td>3856</td>\n",
       "      <td>32</td>\n",
       "      <td>58019.08</td>\n",
       "      <td>1</td>\n",
       "      <td>Hockey Field</td>\n",
       "      <td>Stadium</td>\n",
       "      <td>Department Store</td>\n",
       "      <td>Bus Stop</td>\n",
       "      <td>Sandwich Place</td>\n",
       "      <td>Gym</td>\n",
       "      <td>Tennis Court</td>\n",
       "      <td>Bar</td>\n",
       "      <td>Drugstore</td>\n",
       "      <td>Comfort Food Restaurant</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     lat      long  Square Km  Population  \\\n",
       "Tanthof        51.982308  4.351760       5.86     17126.0   \n",
       "Voorhof        51.996322  4.344202       5.02     30003.0   \n",
       "Binnenstad     52.013576  4.364522       4.49     44456.0   \n",
       "Buitenhof      52.009339  4.334157       2.06     17990.0   \n",
       "Schieweg_Zuid  51.988527  4.383054       2.26      8213.0   \n",
       "\n",
       "               Pop. density (Pop./KM)  Trees  Playgrounds  Water_Area  \\\n",
       "Tanthof                        2900.0   6081           52   143141.75   \n",
       "Voorhof                        6000.0   8105           53   280144.68   \n",
       "Binnenstad                     9900.0   5340           33    51698.36   \n",
       "Buitenhof                      8700.0  11673           33   589634.83   \n",
       "Schieweg_Zuid                  3600.0   3856           32    58019.08   \n",
       "\n",
       "               Cluster Labels 1st Most Common Venue 2nd Most Common Venue  \\\n",
       "Tanthof                     0                  Park  Fast Food Restaurant   \n",
       "Voorhof                     0           Supermarket                   Gym   \n",
       "Binnenstad                  0                  Café     French Restaurant   \n",
       "Buitenhof                   2           Supermarket              Pharmacy   \n",
       "Schieweg_Zuid               1          Hockey Field               Stadium   \n",
       "\n",
       "              3rd Most Common Venue 4th Most Common Venue  \\\n",
       "Tanthof                Tram Station           Supermarket   \n",
       "Voorhof            Asian Restaurant         Bowling Alley   \n",
       "Binnenstad                      Bar                   Pub   \n",
       "Buitenhof              Hockey Field                  Park   \n",
       "Schieweg_Zuid      Department Store              Bus Stop   \n",
       "\n",
       "              5th Most Common Venue 6th Most Common Venue  \\\n",
       "Tanthof                    Bus Stop      Department Store   \n",
       "Voorhof                Tram Station             Drugstore   \n",
       "Binnenstad                    Plaza                Bakery   \n",
       "Buitenhof               Pizza Place                Bakery   \n",
       "Schieweg_Zuid        Sandwich Place                   Gym   \n",
       "\n",
       "              7th Most Common Venue 8th Most Common Venue  \\\n",
       "Tanthof          Light Rail Station        Shop & Service   \n",
       "Voorhof              Discount Store      Department Store   \n",
       "Binnenstad            Movie Theater        Sandwich Place   \n",
       "Buitenhof            Discount Store        Clothing Store   \n",
       "Schieweg_Zuid          Tennis Court                   Bar   \n",
       "\n",
       "              9th Most Common Venue   10th Most Common Venue  \n",
       "Tanthof                   BBQ Joint                     Food  \n",
       "Voorhof               Jewelry Store       Light Rail Station  \n",
       "Binnenstad               Restaurant              Coffee Shop  \n",
       "Buitenhof               Coffee Shop  Comfort Food Restaurant  \n",
       "Schieweg_Zuid             Drugstore  Comfort Food Restaurant  "
      ]
     },
     "execution_count": 348,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# merge the cluster data with the previously obtained dataframe to get the whole picture\n",
    "Delft_merged = df_Delft\n",
    "neighborhoods_venues_sorted.rename(columns ={'Neighborhood':'Wijk'},inplace=True)\n",
    "neighborhoods_venues_sorted.set_index('Wijk',inplace=True)\n",
    "Delft_merged = Delft_merged.join(neighborhoods_venues_sorted)\n",
    "Delft_merged"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This dataset will be used to determine the overall attractiveness for each neighborhood. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Normalization  and data prep for Attractiveness model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "here we created the final (data) format to design our model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Programs\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py:5890: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self._update_inplace(new_data)\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>Pop. density (Pop./KM)</th>\n",
       "      <th>Square Km</th>\n",
       "      <th>Trees/km</th>\n",
       "      <th>Playground/km</th>\n",
       "      <th>Water_Area/km</th>\n",
       "      <th>Cluster Labels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Tanthof</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.047005</td>\n",
       "      <td>Restuarant heavy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Voorhof</th>\n",
       "      <td>0.442857</td>\n",
       "      <td>0.778947</td>\n",
       "      <td>0.124649</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.161228</td>\n",
       "      <td>Restuarant heavy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Binnenstad</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.639474</td>\n",
       "      <td>0.032620</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Restuarant heavy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Buitenhof</th>\n",
       "      <td>0.828571</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>Shop heavy Area</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Schieweg_Zuid</th>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>0.144308</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.051537</td>\n",
       "      <td>Sport area heavy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Pop. density (Pop./KM)  Square Km  Trees/km  Playground/km  \\\n",
       "Tanthof                      0.000000   1.000000  0.000000       0.222222   \n",
       "Voorhof                      0.442857   0.778947  0.124649       0.444444   \n",
       "Binnenstad                   1.000000   0.639474  0.032620       0.000000   \n",
       "Buitenhof                    0.828571   0.000000  1.000000       1.000000   \n",
       "Schieweg_Zuid                0.100000   0.052632  0.144308       0.777778   \n",
       "\n",
       "               Water_Area/km    Cluster Labels  \n",
       "Tanthof             0.047005  Restuarant heavy  \n",
       "Voorhof             0.161228  Restuarant heavy  \n",
       "Binnenstad          0.000000  Restuarant heavy  \n",
       "Buitenhof           1.000000   Shop heavy Area  \n",
       "Schieweg_Zuid       0.051537  Sport area heavy  "
      ]
     },
     "execution_count": 349,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_Delft_Model = Delft_merged[['Square Km','Pop. density (Pop./KM)','Trees','Playgrounds','Water_Area','Cluster Labels']]\n",
    "df_Delft_Model['Cluster Labels'].replace({0:'Restuarant heavy',1:'Sport area heavy',2:'Shop heavy Area'},inplace =True)\n",
    "\n",
    "df_Delft_Model_f = df_Delft_Model[['Pop. density (Pop./KM)','Square Km']].join(\n",
    "    round(df_Delft_Model[['Trees','Playgrounds','Water_Area']].div(df_Delft_Model['Square Km'], axis=0)))\n",
    "    \n",
    "df_Delft_Model_f.rename(columns={'Trees':'Trees/km','Playgrounds':'Playground/km','Water_Area':'Water_Area/km'},inplace=True)\n",
    "\n",
    "#normalize the data\n",
    "columns_name =df_Delft_Model_f.columns\n",
    "index_names = df_Delft_Model_f.index\n",
    "\n",
    "x = df_Delft_Model_f.values #returns a numpy array\n",
    "min_max_scaler = preprocessing.MinMaxScaler()\n",
    "x_scaled = min_max_scaler.fit_transform(x)\n",
    "\n",
    "#rename columns and index\n",
    "df = pd.DataFrame(x_scaled,columns=columns_name,index=index_names)\n",
    "df_Delft_Final_norm = df.join(df_Delft_Model['Cluster Labels'])\n",
    "df_Delft_Final_norm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cluster values evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The KMN-model provided three cluster, meaning neighborhoods which are simular. Based on my **personal** preference, I will assign a certain weight to that value.Proximity to shops are very important to us when we want to go to do grocery shopping. Sports & restaurants facilities, we enjoy but dont need necessarily in our neighborhood. That is why we came up with the following valuations\n",
    "* v1=2 Restuarant heavy\n",
    "* v2=3 Shop heavy Area\n",
    "* v3=1 Sport_area heavy\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "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>Pop. density (Pop./KM)</th>\n",
       "      <th>Square Km</th>\n",
       "      <th>Trees/km</th>\n",
       "      <th>Playground/km</th>\n",
       "      <th>Water_Area/km</th>\n",
       "      <th>Cluster Labels</th>\n",
       "      <th>cluster_values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Tanthof</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.047005</td>\n",
       "      <td>Restuarant heavy</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Voorhof</th>\n",
       "      <td>0.442857</td>\n",
       "      <td>0.778947</td>\n",
       "      <td>0.124649</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.161228</td>\n",
       "      <td>Restuarant heavy</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Binnenstad</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.639474</td>\n",
       "      <td>0.032620</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Restuarant heavy</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Buitenhof</th>\n",
       "      <td>0.828571</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>Shop heavy Area</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Schieweg_Zuid</th>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>0.144308</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.051537</td>\n",
       "      <td>Sport area heavy</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Pop. density (Pop./KM)  Square Km  Trees/km  Playground/km  \\\n",
       "Tanthof                      0.000000   1.000000  0.000000       0.222222   \n",
       "Voorhof                      0.442857   0.778947  0.124649       0.444444   \n",
       "Binnenstad                   1.000000   0.639474  0.032620       0.000000   \n",
       "Buitenhof                    0.828571   0.000000  1.000000       1.000000   \n",
       "Schieweg_Zuid                0.100000   0.052632  0.144308       0.777778   \n",
       "\n",
       "               Water_Area/km    Cluster Labels  cluster_values  \n",
       "Tanthof             0.047005  Restuarant heavy               2  \n",
       "Voorhof             0.161228  Restuarant heavy               2  \n",
       "Binnenstad          0.000000  Restuarant heavy               2  \n",
       "Buitenhof           1.000000   Shop heavy Area               3  \n",
       "Schieweg_Zuid       0.051537  Sport area heavy               1  "
      ]
     },
     "execution_count": 350,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#assgined values\n",
    "\n",
    "v1=2 #Restuarant heavy\n",
    "v2=3 #Shop heavy Area\n",
    "v3=1 #Sport_area heavy\n",
    "\n",
    "df_Delft_Final_norm['cluster_values'] = df_Delft_Final_norm['Cluster Labels']\n",
    "df_Delft_Final_norm['cluster_values'].replace({'Restuarant heavy':v1,'Shop heavy Area':v2,'Sport area heavy':v3},inplace=True)\n",
    "df_Delft_Final_norm\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model development: Determination of the neighborhood attraction index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This model assumes a very basic level model.\n",
    "The Attraction index is calculated by the value in the normalized dataframe, multiplied by a weight factor.\n",
    "where the weight is a __personal__ opinion\n",
    "\n",
    "\\begin{equation*}\n",
    "Attraction-index = \\left( \\sum_{k=1}^n w_k p_k \\right)\n",
    "\\end{equation*}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 351,
   "metadata": {},
   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Attraction_index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Tanthof</th>\n",
       "      <td>0.189476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Voorhof</th>\n",
       "      <td>0.394113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Binnenstad</th>\n",
       "      <td>0.393921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Buitenhof</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Schieweg_Zuid</th>\n",
       "      <td>0.270031</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "               Attraction_index\n",
       "Tanthof                0.189476\n",
       "Voorhof                0.394113\n",
       "Binnenstad             0.393921\n",
       "Buitenhof              1.000000\n",
       "Schieweg_Zuid          0.270031"
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     "execution_count": 351,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "p0 = df_Delft_Final_norm['Pop. density (Pop./KM)']\n",
    "p1 = df_Delft_Final_norm['Trees/km']\n",
    "p2 = df_Delft_Final_norm['Playground/km']\n",
    "p3 = df_Delft_Final_norm['Water_Area/km']\n",
    "p4 = df_Delft_Final_norm['cluster_values']\n",
    "\n",
    "\n",
    "w0= 4\n",
    "w1= 1\n",
    "w2= 3\n",
    "w3= 5\n",
    "w4 = 1 # already assigend weight to this value in previous step\n",
    "\n",
    "#calculating the Attractiveness of a neibourhood\n",
    "Attractiveness_list = (w0*p0) +(w1*p1) + (w2*p2) + (w3*p3) + (w4*p4) \n",
    "Attractiveness_list=Attractiveness_list/Attractiveness_list.max()\n",
    "\n",
    "#convert output back to a dataframe\n",
    "df_attraction_index =pd.DataFrame(Attractiveness_list)\n",
    "df_attraction_index.rename(columns ={0:'Attraction_index'},inplace=True)\n",
    "df_attraction_index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Let's visualize our final result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 354,
   "metadata": {},
   "outputs": [
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    color_map_e3397f901dee4c4880c7d4f5fa91ff1f.x = d3.scale.linear()
              .domain([0.1894761045491105, 1.0])
              .range([0, 400]);

    color_map_e3397f901dee4c4880c7d4f5fa91ff1f.legend = L.control({position: 'topright'});
    color_map_e3397f901dee4c4880c7d4f5fa91ff1f.legend.onAdd = function (map) {var div = L.DomUtil.create('div', 'legend'); return div};
    color_map_e3397f901dee4c4880c7d4f5fa91ff1f.legend.addTo(map_044caf871ec545c3a061bd55e6105556);

    color_map_e3397f901dee4c4880c7d4f5fa91ff1f.xAxis = d3.svg.axis()
        .scale(color_map_e3397f901dee4c4880c7d4f5fa91ff1f.x)
        .orient("top")
        .tickSize(1)
        .tickValues([0.1894761045491105, 0.3245634204575921, 0.4596507363660737, 0.5947380522745553, 0.7298253681830369, 0.8649126840915184, 1.0]);

    color_map_e3397f901dee4c4880c7d4f5fa91ff1f.svg = d3.select(".legend.leaflet-control").append("svg")
        .attr("id", 'legend')
        .attr("width", 450)
        .attr("height", 40);

    color_map_e3397f901dee4c4880c7d4f5fa91ff1f.g = color_map_e3397f901dee4c4880c7d4f5fa91ff1f.svg.append("g")
        .attr("class", "key")
        .attr("transform", "translate(25,16)");

    color_map_e3397f901dee4c4880c7d4f5fa91ff1f.g.selectAll("rect")
        .data(color_map_e3397f901dee4c4880c7d4f5fa91ff1f.color.range().map(function(d, i) {
          return {
            x0: i ? color_map_e3397f901dee4c4880c7d4f5fa91ff1f.x(color_map_e3397f901dee4c4880c7d4f5fa91ff1f.color.domain()[i - 1]) : color_map_e3397f901dee4c4880c7d4f5fa91ff1f.x.range()[0],
            x1: i < color_map_e3397f901dee4c4880c7d4f5fa91ff1f.color.domain().length ? color_map_e3397f901dee4c4880c7d4f5fa91ff1f.x(color_map_e3397f901dee4c4880c7d4f5fa91ff1f.color.domain()[i]) : color_map_e3397f901dee4c4880c7d4f5fa91ff1f.x.range()[1],
            z: d
          };
        }))
      .enter().append("rect")
        .attr("height", 10)
        .attr("x", function(d) { return d.x0; })
        .attr("width", function(d) { return d.x1 - d.x0; })
        .style("fill", function(d) { return d.z; });

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</script> onload=\"this.contentDocument.open();this.contentDocument.write(atob(this.getAttribute('data-html')));this.contentDocument.close();\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
      ],
      "text/plain": [
       "<folium.folium.Map at 0x1b0db890ef0>"
      ]
     },
     "execution_count": 354,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create map of Delft using latitude and longitude values\n",
    "map_Delft = folium.Map(location=[latitude, longitude], zoom_start=13,tiles='OpenStreetMap')\n",
    "\n",
    "folium.GeoJson(Wijken_delft_json).add_to(map_Delft)\n",
    "\n",
    "# Add the color for the chloropleth:\n",
    "map_Delft.choropleth(\n",
    " geo_data= Wijken_delft_json,\n",
    " name='choropleth',\n",
    " data= df_attraction_index,\n",
    " columns=[df_attraction_index.index, 'Attraction_index'],\n",
    " key_on='properties.name',\n",
    " fill_color='PuBuGn',\n",
    " fill_opacity=0.7,\n",
    " line_opacity=0.2,\n",
    " legend_name='Attraction_index'\n",
    ")\n",
    "folium.LayerControl().add_to(map_Delft)\n",
    "\n",
    "#add markers to map\n",
    "for lat, lng, name in zip(df_CL['lat'], df_CL['long'], df_CL.index):\n",
    "    label = '{}'.format(name)\n",
    "    label = folium.Popup(label, parse_html=True)\n",
    "    folium.CircleMarker(\n",
    "        [lat, lng],\n",
    "        radius=5,\n",
    "        popup=label,\n",
    "        color='Blue',\n",
    "        fill=True,\n",
    "        fill_color='#4868FF',\n",
    "        fill_opacity=0.7,\n",
    "        parse_html=False).add_to(map_Delft)      \n",
    "\n",
    "    folium.map.Marker(\n",
    "        [lat,lng],\n",
    "        icon=DivIcon(\n",
    "            icon_size=(150,36),\n",
    "            icon_anchor=(0,0),\n",
    "            html='<div style=\"font-size: 15pt\">%s</div>' % name\n",
    "            )\n",
    "    ).add_to(map_Delft)  \n",
    "map_Delft"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Discussion & Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the created model, we can say that the best place to settle for our new home is the neighborhood __\"Buitenhof\"__. Many questions can be raised on the used model: Are the selected features appropriate? is the data complete? I think i can concluded that based on the available data, the results shown by the model are an rough indication. many more factors play a role; such as Crime numbers, average income, House values etc. Also the model is heavenly subjected to personal interpretation. What is important to the general, does not fit everyone. \n"
   ]
  }
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