Entrance_location_identification/issue solving.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7b3fc22c",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Identifying the entrance locations for selected POIs\n",
    "\n",
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "111902b3",
   "metadata": {},
   "source": [
    "### Why we need to identify the entrance locations for some POIs ?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7278d6aa",
   "metadata": {},
   "source": [
    "- 100m Walking Radius (R) given for a DOP may not capture POIs with large land areas. Increasing the  R as a remedy tends to capture unnecessary POIs. \n",
    "- More suitable solution is to identify the possible entering points or passenger DOPs for these POIs  \n",
    "- Developed solution is two-fold. <br/>\n",
    "    1.)  Identifying the possible POIs with large land areas from total POI dataset. (Pre-requsite) <br/>\n",
    "    2.)  Identifying the possible entering points for the selected POIs  \n",
    "\n",
    "The following image shows the Complete process\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "25583f69",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 7,
     "metadata": {
      "image/png": {
       "height": 700,
       "width": 400
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(\"poi_selection_graph.png\", width=400, height=700)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "351551b2",
   "metadata": {},
   "source": [
    "## Package Import "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7aa80b73",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import math\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import geopandas as gpd\n",
    "from shapely.geometry import Point\n",
    "import seaborn as sns\n",
    "import folium\n",
    "from math import cos, sqrt \n",
    "pd.options.mode.chained_assignment = None\n",
    "from IPython.display import Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ec07afaf",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.cluster import DBSCAN\n",
    "from sklearn.neighbors import NearestNeighbors"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c5abbec",
   "metadata": {},
   "source": [
    "## Importing data "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca800859",
   "metadata": {},
   "source": [
    "### POI/places data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "e04f76d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import the original/raw POI data\n",
    "\n",
    "places = pd.read_csv('Original_POI_data.csv',index_col = 0)\n",
    "places.reset_index(inplace = True, drop = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70b468d4",
   "metadata": {},
   "source": [
    "### Taxi data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "d46ea2b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import the taxi data and convert it to a geodataframe \n",
    "taxidata = pd.read_csv('Taxi_trip_data.csv', index_col = 0, parse_dates = [13])\n",
    "geometry = [Point(xy) for xy in zip(taxidata.dropoff_long, taxidata.dropoff_lat)]\n",
    "taxidata  = gpd.GeoDataFrame(taxidata, crs=\"EPSG:4326\", geometry=geometry)\n",
    "taxidata.crs = \"EPSG:4326\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61120f84",
   "metadata": {},
   "source": [
    "### Road network data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "3b839ab8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import filter the road network for the selected area \n",
    "\n",
    "# import road network data/ shape file \n",
    "slroads = gpd.read_file('road network\\\\hotosm_lka_roads_lines.shp',crs = {'init': 'epsg:4326'})\n",
    "\n",
    "# import land area shape file \n",
    "sri_lanka = gpd.read_file('land area\\\\lka_admbnda_adm2_slsd_20200305.shp',crs = {'init': 'epsg:4326'})\n",
    "\n",
    "# filter for the required land area \n",
    "colombo = sri_lanka[sri_lanka['ADM2_EN']=='Colombo']\n",
    "colombo.reset_index(drop=True, inplace=True)\n",
    "mask = slroads.within(colombo.loc[0,'geometry'])\n",
    "colombo_roads = slroads.loc[mask]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b93b4a85",
   "metadata": {},
   "source": [
    "## PreRequisite - Selecting the POIs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c10104dc",
   "metadata": {},
   "source": [
    "It is assumed that a POI should have a higher attraction level and considerable distance from the nearest road to get selected.  This causes to select only impactful POIs that is neglected through the allowed maximum Walking Radius (R) around the DOP of a trip. The two assumptions are assessed by using the <b> ‘number of reviews’ </b> attribute provided with the poi data and measured <b> ‘linear distance from the nearest road’ </b> respectively. Use of <b> k-means </b> clustering to these two features and selecting the clusters with higher number of reviews and higher distances assist in selecting the required POIs. The advantage of using a clustering method for this purpose comes with its capability to group similar data and providing the ability to select the required groups. \n",
    "\n",
    "\n",
    "Providing a Radius (R) for a POI assist to select the taxi trips that had attracted to the surrounding of it using the taxi DOPs. Then, Density-Based Spatial Clustering of Applications with Noise  <b> (DBSCAN) </b> algorithm can be used with its optimized parameters for the DOPs to capture the clustered trip attraction points around that POI. Two parameters as maximum distance between two DOPs and minimum number of DOPS that need to select the clusters are required to optimize the algorithm. This assist to select the possible entering locations by taking the center of the cluster with the minimum distance from the POI. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01978788",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Cluster and Filter for Attractiveness"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9398d224",
   "metadata": {},
   "source": [
    "KNN clustering is used for the 'no of ratinngs' attribute of Places dataset to identify what kind of groups are within the attribute, which may help to remove the groups or clusters of places that are not much attractive."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c666cd6e",
   "metadata": {},
   "source": [
    "To do that, it is required to turn the Null values to 0 in the 'no of ratings' attibute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "b9864c8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "for idx,row in places.iterrows():\n",
    "    if math.isnan(row['no_of_ratings']) == True:\n",
    "            places.loc[idx,'no_of_ratings'] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "570d3458",
   "metadata": {},
   "outputs": [],
   "source": [
    "Xy = pd.DataFrame(places[['no_of_ratings']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e2b756fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "d91dfddf",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = scaler.fit_transform(Xy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "aa45a1e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "inertialist = []\n",
    "nclusters = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20]\n",
    "for i in nclusters:\n",
    "    model = KMeans(n_clusters=i)\n",
    "    model.fit(X)\n",
    "    inert = model.inertia_\n",
    "    inertialist.append(inert)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "cc77a9e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1ce265b3af0>]"
      ]
     },
     "execution_count": 39,
     "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": [
    "plt.plot(nclusters , inertialist)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2cc52f7",
   "metadata": {},
   "source": [
    "it is decided to go with 15 clusters "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0bffd8d3",
   "metadata": {},
   "source": [
    "checking what would be the optimal number of clusters is done. However, the optimal is mainly selected referrring the minimum, maximum value of ratings from the obtained from the clusters. In that case it was decided to selected 14 clusters and remove "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "a20b6eb3",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = KMeans(n_clusters=15, random_state=32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "50bcb686",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(n_clusters=15, random_state=32)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "ff8556fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "clusterlabels = model.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "5bc49f62",
   "metadata": {},
   "outputs": [],
   "source": [
    "Xy['cluster_name'] = clusterlabels.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "30f3d4ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cluster_name\n",
       "0        84.0\n",
       "1     11428.0\n",
       "2      1373.0\n",
       "3     18746.0\n",
       "4      3612.0\n",
       "5       621.0\n",
       "6      2770.0\n",
       "7      7493.0\n",
       "8        22.0\n",
       "9       374.0\n",
       "10      937.0\n",
       "11     1959.0\n",
       "12      198.0\n",
       "13     9011.0\n",
       "14     4691.0\n",
       "Name: no_of_ratings, dtype: float64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xy.groupby('cluster_name')['no_of_ratings'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "2e265a3c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[    0  2124]\n",
      " [    1     3]\n",
      " [    2    68]\n",
      " [    3     1]\n",
      " [    4    18]\n",
      " [    5   164]\n",
      " [    6    23]\n",
      " [    7     3]\n",
      " [    8 25689]\n",
      " [    9   277]\n",
      " [   10   115]\n",
      " [   11    41]\n",
      " [   12   684]\n",
      " [   13     2]\n",
      " [   14     7]]\n"
     ]
    }
   ],
   "source": [
    "(unique, counts) = np.unique(clusterlabels, return_counts=True)\n",
    "frequencies = np.asarray((unique, counts)).T\n",
    "print(frequencies)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "563c5289",
   "metadata": {},
   "source": [
    "since the cluster 0 has a maximum number of 36 ratings, it was decided to remove that cluster, the following helps to identify the number of places belongs to each cluster. it can be seen that majority of the places belong to the removed cluster "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c395a180",
   "metadata": {},
   "source": [
    "then we remove the unwanted cluster and take the data (with the indexes) to go for the second clustering, which is based on the distance from the road  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "b2d015aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "Xy =  Xy[Xy['cluster_name'] != 8]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa59b0f3",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Cluster and Filter for distance"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5bcd710a",
   "metadata": {},
   "source": [
    "before do this, we have to measure the linear distance a places from the selected places data has from the roads. and that is done in the following first "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "4a4ba24c",
   "metadata": {},
   "outputs": [],
   "source": [
    "places = places.loc[Xy.index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "d0f92f33",
   "metadata": {},
   "outputs": [],
   "source": [
    "# measure the distance from the selected road network\n",
    "for idx,row in places.iterrows():\n",
    "    Point_DOP = Point(row['lng'],row['lat'])\n",
    "    Buffer = Point_DOP.buffer(100*0.001/110) \n",
    "    \n",
    "    idxlists = []\n",
    "    for idxx,roww in colombo_roads.iterrows():   \n",
    "       if Buffer.intersects(roww['geometry']) == True:\n",
    "            idxlists.append(idxx)      \n",
    "    places.loc[idx,'no_of_roads'] = len(idxlists)\n",
    "    \n",
    "    if len(idxlists) > 0:\n",
    "        \n",
    "        selectedroadss = colombo_roads.loc[idxlists,:]\n",
    "        \n",
    "        distancelist = []\n",
    "        for idxxx,rowss in selectedroadss.iterrows():\n",
    "            distance = (Point_DOP.distance(rowss['geometry']))*10000\n",
    "            distancelist.append(distance)\n",
    "        places.loc[idx,'road_min_distace'] = min(distancelist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "63078311",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>place_id</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>types</th>\n",
       "      <th>vicinity</th>\n",
       "      <th>no_of_ratings</th>\n",
       "      <th>avg_rating</th>\n",
       "      <th>geometry</th>\n",
       "      <th>type_1</th>\n",
       "      <th>dual_1</th>\n",
       "      <th>dual_2</th>\n",
       "      <th>no_of_roads</th>\n",
       "      <th>road_min_distace</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Khairiya Muslim Girl's Grand College</td>\n",
       "      <td>ChIJS6PgLw9Z4joRADXcRN0MYh0</td>\n",
       "      <td>6.929464</td>\n",
       "      <td>79.868975</td>\n",
       "      <td>['school', 'point_of_interest', 'establishment']</td>\n",
       "      <td>219 Sri Vajiragnana Mawatha, Colombo</td>\n",
       "      <td>27.0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>POINT (79.8689747 6.929464200000002)</td>\n",
       "      <td>school</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.829677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Makutarama Myanmar Temple</td>\n",
       "      <td>ChIJVxc36ghZ4joRVrufIFGSRhA</td>\n",
       "      <td>6.929036</td>\n",
       "      <td>79.870371</td>\n",
       "      <td>['place_of_worship', 'point_of_interest', 'est...</td>\n",
       "      <td>Reservoir Road, Colombo</td>\n",
       "      <td>66.0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>POINT (79.87037119999999 6.9290365)</td>\n",
       "      <td>place_of_worship</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.258524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Clifton Balika Maha Vidyalaya</td>\n",
       "      <td>ChIJMSvg0A5Z4joR6Y2kwXHSD4w</td>\n",
       "      <td>6.926980</td>\n",
       "      <td>79.868559</td>\n",
       "      <td>['school', 'point_of_interest', 'establishment']</td>\n",
       "      <td>Colombo</td>\n",
       "      <td>32.0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>POINT (79.86855919999999 6.9269799)</td>\n",
       "      <td>school</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.050879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Ananda Balika Vidyalaya</td>\n",
       "      <td>ChIJ89pvRwlZ4joR-8vJh0v2hB8</td>\n",
       "      <td>6.926378</td>\n",
       "      <td>79.869846</td>\n",
       "      <td>['school', 'point_of_interest', 'establishment']</td>\n",
       "      <td>01000 Maligakanda Road, Colombo</td>\n",
       "      <td>82.0</td>\n",
       "      <td>4.3</td>\n",
       "      <td>POINT (79.8698462 6.9263778)</td>\n",
       "      <td>school</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.078381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>Mahabodhi Viharaya</td>\n",
       "      <td>ChIJ-RmXNglZ4joRJURgbkCDwyM</td>\n",
       "      <td>6.926740</td>\n",
       "      <td>79.868784</td>\n",
       "      <td>['place_of_worship', 'point_of_interest', 'est...</td>\n",
       "      <td>Maligakanda Road, Colombo</td>\n",
       "      <td>71.0</td>\n",
       "      <td>4.6</td>\n",
       "      <td>POINT (79.8687838 6.926740199999998)</td>\n",
       "      <td>place_of_worship</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.842632</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    name                     place_id  \\\n",
       "0   Khairiya Muslim Girl's Grand College  ChIJS6PgLw9Z4joRADXcRN0MYh0   \n",
       "36             Makutarama Myanmar Temple  ChIJVxc36ghZ4joRVrufIFGSRhA   \n",
       "37         Clifton Balika Maha Vidyalaya  ChIJMSvg0A5Z4joR6Y2kwXHSD4w   \n",
       "45               Ananda Balika Vidyalaya  ChIJ89pvRwlZ4joR-8vJh0v2hB8   \n",
       "52                    Mahabodhi Viharaya  ChIJ-RmXNglZ4joRJURgbkCDwyM   \n",
       "\n",
       "         lat        lng                                              types  \\\n",
       "0   6.929464  79.868975   ['school', 'point_of_interest', 'establishment']   \n",
       "36  6.929036  79.870371  ['place_of_worship', 'point_of_interest', 'est...   \n",
       "37  6.926980  79.868559   ['school', 'point_of_interest', 'establishment']   \n",
       "45  6.926378  79.869846   ['school', 'point_of_interest', 'establishment']   \n",
       "52  6.926740  79.868784  ['place_of_worship', 'point_of_interest', 'est...   \n",
       "\n",
       "                                vicinity  no_of_ratings  avg_rating  \\\n",
       "0   219 Sri Vajiragnana Mawatha, Colombo           27.0         4.4   \n",
       "36               Reservoir Road, Colombo           66.0         4.2   \n",
       "37                               Colombo           32.0         3.8   \n",
       "45       01000 Maligakanda Road, Colombo           82.0         4.3   \n",
       "52             Maligakanda Road, Colombo           71.0         4.6   \n",
       "\n",
       "                                geometry            type_1 dual_1 dual_2  \\\n",
       "0   POINT (79.8689747 6.929464200000002)            school    NaN    NaN   \n",
       "36   POINT (79.87037119999999 6.9290365)  place_of_worship    NaN    NaN   \n",
       "37   POINT (79.86855919999999 6.9269799)            school    NaN    NaN   \n",
       "45          POINT (79.8698462 6.9263778)            school    NaN    NaN   \n",
       "52  POINT (79.8687838 6.926740199999998)  place_of_worship    NaN    NaN   \n",
       "\n",
       "    no_of_roads  road_min_distace  \n",
       "0           1.0          0.829677  \n",
       "36          4.0          5.258524  \n",
       "37          3.0          1.050879  \n",
       "45          6.0          5.078381  \n",
       "52          4.0          1.842632  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "places.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "be48b12b",
   "metadata": {},
   "outputs": [],
   "source": [
    "for idx,row in places.iterrows():\n",
    "    if math.isnan(row['road_min_distace']) == True:\n",
    "            places.loc[idx,'road_min_distace'] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "928bed8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "Xy = pd.DataFrame(places[['road_min_distace']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "d5b61c70",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "43c8e07f",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = scaler.fit_transform(Xy)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f64e6fb7",
   "metadata": {},
   "source": [
    "checking what would be the optimal number of clusters is done. However, the optimal is mainly selected referrring the maximum values for the distance from the obtained from the clusters. In that case it was decided to selected 14 clusters and remove "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "ae6fe3fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "inertialist = []\n",
    "nclusters = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20]\n",
    "for i in nclusters:\n",
    "    model = KMeans(n_clusters=i)\n",
    "    model.fit(X)\n",
    "    inert = model.inertia_\n",
    "    inertialist.append(inert)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "4e639fc5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1ce2688db50>]"
      ]
     },
     "execution_count": 66,
     "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": [
    "plt.plot(nclusters , inertialist)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2a14535",
   "metadata": {},
   "source": [
    "It is decided to go with 15 clusters "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "533b752c",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = KMeans(n_clusters=15, random_state=32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "c0825b3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(n_clusters=15, random_state=32)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "7d0effbf",
   "metadata": {},
   "outputs": [],
   "source": [
    "clusterlabels = model.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "c012042e",
   "metadata": {},
   "outputs": [],
   "source": [
    "Xy['cluster_name'] = clusterlabels.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "f283672e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cluster_name\n",
       "0     1.674899\n",
       "1     4.814240\n",
       "2     0.611971\n",
       "3     3.406968\n",
       "4     1.116543\n",
       "5     8.518076\n",
       "6     2.837493\n",
       "7     4.097493\n",
       "8     2.364207\n",
       "9     6.710782\n",
       "10    0.318823\n",
       "11    0.866451\n",
       "12    1.385197\n",
       "13    5.521592\n",
       "14    1.990449\n",
       "Name: road_min_distace, dtype: float64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xy.groupby('cluster_name')['road_min_distace'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "e7404771",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  0 398]\n",
      " [  1  45]\n",
      " [  2 343]\n",
      " [  3 102]\n",
      " [  4 535]\n",
      " [  5  14]\n",
      " [  6 159]\n",
      " [  7  78]\n",
      " [  8 236]\n",
      " [  9  20]\n",
      " [ 10 278]\n",
      " [ 11 507]\n",
      " [ 12 493]\n",
      " [ 13  39]\n",
      " [ 14 283]]\n"
     ]
    }
   ],
   "source": [
    "(unique, counts) = np.unique(clusterlabels, return_counts=True)\n",
    "frequencies = np.asarray((unique, counts)).T\n",
    "print(frequencies)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16318c19",
   "metadata": {},
   "source": [
    "Since the cluster x has reletively lower distance, we remove that cluster and take the rest of the clusters for our analysis. These finally filtered data are the most suitable POIs to identify entering locations in improving the Trip purpose inference results. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "88ec4157",
   "metadata": {},
   "outputs": [],
   "source": [
    "Xy =  Xy[Xy['cluster_name'] != 10]\n",
    "Xy =  Xy[Xy['cluster_name'] != 11]\n",
    "Xy =  Xy[Xy['cluster_name'] != 2]\n",
    "Xy =  Xy[Xy['cluster_name'] != 4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "b397dd22",
   "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>road_min_distace</th>\n",
       "      <th>cluster_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>5.258524</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>5.078381</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>1.842632</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>1.804010</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>1.508724</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29131</th>\n",
       "      <td>1.488900</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29149</th>\n",
       "      <td>1.333438</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29150</th>\n",
       "      <td>1.333438</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29160</th>\n",
       "      <td>1.261168</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29185</th>\n",
       "      <td>1.896624</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1867 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       road_min_distace  cluster_name\n",
       "36             5.258524            13\n",
       "45             5.078381            13\n",
       "52             1.842632            14\n",
       "55             1.804010            14\n",
       "95             1.508724             0\n",
       "...                 ...           ...\n",
       "29131          1.488900             0\n",
       "29149          1.333438            12\n",
       "29150          1.333438            12\n",
       "29160          1.261168            12\n",
       "29185          1.896624            14\n",
       "\n",
       "[1867 rows x 2 columns]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "934eb331",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([   36,    45,    52,    55,    95,   100,   104,   108,   150,\n",
       "              165,\n",
       "            ...\n",
       "            29039, 29057, 29092, 29110, 29126, 29131, 29149, 29150, 29160,\n",
       "            29185],\n",
       "           dtype='int64', length=1867)"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xy.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d4be1a72",
   "metadata": {},
   "outputs": [],
   "source": [
    "places = places.loc[Xy.index]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9cef4067",
   "metadata": {},
   "source": [
    "## Entering Location identification"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfc09f21",
   "metadata": {},
   "source": [
    "DBSCAN algorithm is used to identify the clusters of drop off points, which helps to do it by two parameters as eps and minimum number of gps points. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35076767",
   "metadata": {},
   "source": [
    "Estimating the eps value that could be used in clustering the taxi drop off points is done. Here it is done only for a sample Place. When trying out different samples, it was obvisous that the 0.0002 is the most viable value that can be used.  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "acda7938",
   "metadata": {},
   "source": [
    "### Eps value estimation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "a29b6fac",
   "metadata": {},
   "outputs": [],
   "source": [
    "place_index = 'ChIJS6PgLw9Z4joRADXcRN0MYh0'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "5382741c",
   "metadata": {},
   "outputs": [],
   "source": [
    "places.set_index('place_id', inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "b80fb58a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Selecting a sample point to check the eps value \n",
    "POI = places.loc[place_index,['lat','lng']].values \n",
    "Point_POI = Point(POI[1],POI[0]) # convert to point type\n",
    "Buffer = Point_POI.buffer(200) # create the buffer for the Point giving the radius \n",
    "mask = taxidata.within(Buffer) ## filter the data for the required buffer zone \n",
    "radius_data = taxidata.loc[mask]\n",
    "\n",
    "X = radius_data[['dropoff_lat','dropoff_long']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "e97b00d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "neigh = NearestNeighbors(n_neighbors = 2)\n",
    "nbrs = neigh.fit(X)\n",
    "distances, indices = nbrs.kneighbors(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "aa0575f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1ce42e3f640>]"
      ]
     },
     "execution_count": 89,
     "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": [
    "distances = np.sort(distances, axis=0)\n",
    "distances = distances[:,1]\n",
    "plt.plot(distances)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13d6a10f",
   "metadata": {},
   "source": [
    "The y values at the arm bend of the above curve can be identified as the best value the eps. because it shows the number of taxi drop off points that got into the clusters "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "481a6299",
   "metadata": {},
   "source": [
    "### Assigning the entering location"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b32af18c",
   "metadata": {},
   "source": [
    "After determining the eps value, we can go to our main task, which is identifying the entering locations for attractive big land area places. Here we cluster the taxi trip dops around a radius of 200m of radius (except for few places which has a higher distance from the road (cluster no 1) ).  Definitely more clusters can be found for a one place. In that case the cluster with the minimum distance from the considered place is selected. \n",
    "\n",
    "Another fact is that although we cannot take a exact center location for the cluster after observations it was decided that a viable center point can be obtained just by taking the minimum of both lat and long values. In that case that value is used in this analysis. This value also given as the possible entering location. \n",
    "\n",
    "There are possibilities that some places might have one or more entering locations. For an instance, hospitals. In that  case, the distance to all of these clusters are measured and if the distance gap is not substantial(here we assumed it as 20m) it was decided to keep them all as entering locations. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "cab3d816",
   "metadata": {},
   "outputs": [],
   "source": [
    "## smalll fun to calculate the starightline distance, source: STACKOVERFLOW\n",
    "def qick_distance(Lat1, Long1, Lat2, Long2):\n",
    "    \"\"\"smalll fun to calculate the starightlits distance\"\"\" \n",
    "    \n",
    "    x = Lat2 - Lat1\n",
    "    y = (Long2 - Long1) * cos((Lat2 + Lat1)*0.00872664626)  \n",
    "    return 111.319 * sqrt(x*x + y*y)*1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aeac0aad",
   "metadata": {},
   "outputs": [],
   "source": [
    "for idx,row in places.iterrows():\n",
    "   \n",
    "    place_index = idx ## input the index of the POI \n",
    "    \n",
    "    if row['cluster_labels'] == 1:\n",
    "        radius = 400*0.001/110\n",
    "    else:\n",
    "        radius = 200*0.001/110 #(110m = 0.001) (##idk about this convertion but it is right)\n",
    "\n",
    "    POI = places.loc[place_index,['lat','lng']].values \n",
    "\n",
    "    Point_POI = Point(POI[1],POI[0]) # convert to point type\n",
    "\n",
    "    Buffer = Point_POI.buffer(radius) # create the buffer for the Point giving the radius \n",
    "\n",
    "    mask = taxidata.within(Buffer) ## filter the data for the required buffer zone \n",
    "    radius_data = taxidata.loc[mask]\n",
    "\n",
    "    X = radius_data[['dropoff_lat','dropoff_long']]\n",
    "    \n",
    "    if X.shape[0] == 0:\n",
    "        \n",
    "        continue\n",
    "    \n",
    "    model = DBSCAN(eps=0.0002, min_samples = 10)\n",
    "    \n",
    "    model.fit(X)\n",
    "    \n",
    "    clusterlabels = model.labels_\n",
    "    \n",
    "    radius_data['cluster_no'] = clusterlabels.tolist()\n",
    "    \n",
    "    clusters = np.sort(radius_data['cluster_no'].unique())\n",
    "    \n",
    "    grouped_clusters = radius_data.groupby('cluster_no')\n",
    "    \n",
    "    clusters_for_POI = pd.DataFrame()\n",
    "    \n",
    "    for i, cluster in enumerate(clusters):\n",
    "        selected_cluster = grouped_clusters.get_group(cluster)\n",
    "        mean_lat, mean_lng  =  selected_cluster['dropoff_lat'].mean() , selected_cluster['dropoff_long'].mean()\n",
    "        clusters_for_POI.loc[i,'cluster'] = cluster\n",
    "        clusters_for_POI.loc[i,'lat'] = mean_lat\n",
    "        clusters_for_POI.loc[i,'lng'] = mean_lng\n",
    "        clusters_for_POI.loc[i,'distance'] = qick_distance(POI[0], POI[1], mean_lat, mean_lng)\n",
    "    clusters_for_POI =  clusters_for_POI.drop(0)\n",
    "    \n",
    "    if clusters_for_POI.shape[0] != 0: ## process if only there are good clusters. \n",
    "        \n",
    "        clusters_for_POI['distance_difference'] = clusters_for_POI['distance'] - clusters_for_POI['distance'].min()\n",
    "\n",
    "        for idxx,rows in clusters_for_POI.iterrows(): # drop all the clusters that has a distance more than 20m and keep the rest \n",
    "            if rows['distance_difference'] > 20:\n",
    "                clusters_for_POI.drop(idxx, inplace = True)\n",
    "        \n",
    "        for idxxx,rowss in clusters_for_POI.iterrows() : # save the cluster locations as new places in the places dataset \n",
    "            new_index = place_index +str(idxxx)\n",
    "            places.loc[new_index,:] = places.loc[place_index,:]\n",
    "            places.loc[new_index,['lat_1','lng_1']] = rowss['lat'], rowss['lng']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "09aebe32",
   "metadata": {},
   "outputs": [],
   "source": [
    "places.to_csv('Entrance_identified_POIs.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a3b67fc",
   "metadata": {
    "tags": []
   },
   "source": [
    "## EDA"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "753d999b",
   "metadata": {},
   "source": [
    "### Visualizing clustered DOPs around a POI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2a0c1765",
   "metadata": {},
   "outputs": [],
   "source": [
    "place_index = 'ChIJY-4CjWNZ4joRGZP7dE2cZZ8' ## input the index of the trip DOP want to consider \n",
    "radius = 200*0.001/110 #(110m = 0.001) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a846905b",
   "metadata": {},
   "outputs": [],
   "source": [
    "POI = places.loc[place_index,['lat','lng']].values \n",
    "Point_POI = Point(POI[1],POI[0]) # convert to point type\n",
    "Buffer = Point_POI.buffer(radius) # create the buffer for the Point giving the radius \n",
    "mask = taxidata.within(Buffer) ## filter the data for the required buffer zone \n",
    "radius_data = taxidata.loc[mask]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "556d30a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "grouped_clusters = radius_data.groupby('cluster_no')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f7be7429",
   "metadata": {},
   "outputs": [],
   "source": [
    "## mapping \n",
    "m = folium.Map(location=POI,zoom_start=25)\n",
    "folium.Marker(list(POI), icon=folium.Icon(color='red',icon='none')).add_to(m)\n",
    "for idx, row in radius_data.iterrows():\n",
    "    folium.Marker([row['dropoff_lat'], row['dropoff_long']]).add_to(m)\n",
    "## , popup=row['name']+str(\"\\n\")+row['type_1']+str(\"\\n\")+str(row['no_of_ratings']\n",
    "folium.GeoJson(Buffer).add_to(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dd746a77",
   "metadata": {},
   "outputs": [],
   "source": [
    "## mapping \n",
    "m = folium.Map(location=POI,zoom_start=25)\n",
    "folium.Marker(list(POI), icon=folium.Icon(color='red',icon='none')).add_to(m)\n",
    "clusters = np.sort(radius_data['cluster_no'].unique())\n",
    "grouped_clusters = radius_data.groupby('cluster_no')\n",
    "colors = ['black','orange','yellow', 'beige', 'darkblue', 'darkgreen', 'cadetblue','lightgreen', 'gray']\n",
    "for i, cluster in enumerate(clusters):\n",
    "    selected_cluster = grouped_clusters.get_group(cluster)\n",
    "    mean_lat, mean_lng  =  selected_cluster['dropoff_lat'].mean() , selected_cluster['dropoff_long'].mean()\n",
    "    folium.Marker([mean_lat, mean_lng],  icon=folium.Icon(color=colors[i+1],icon='none')).add_to(m)\n",
    "    for idx, row in  selected_cluster.iterrows():\n",
    "        folium.Marker([row['dropoff_lat'], row['dropoff_long']],  icon=folium.Icon(color=colors[i],icon='none')).add_to(m)\n",
    "## , popup=row['name']+str(\"\\n\")+row['type_1']+str(\"\\n\")+str(row['no_of_ratings']\n",
    "folium.GeoJson(Buffer).add_to(m)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "191d88b6",
   "metadata": {},
   "source": []
  }
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