jupyter/city_distances.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Distances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import folium"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('../data/ru_lat_lng_clear_v2.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>country</th>\n",
       "      <th>subcountry</th>\n",
       "      <th>geonameid</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Yoshkar-Ola</td>\n",
       "      <td>Russia</td>\n",
       "      <td>Mariy-El</td>\n",
       "      <td>466806</td>\n",
       "      <td>56.631595</td>\n",
       "      <td>47.886178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Yaroslavl</td>\n",
       "      <td>Russia</td>\n",
       "      <td>Jaroslavl</td>\n",
       "      <td>468902</td>\n",
       "      <td>57.626578</td>\n",
       "      <td>39.893858</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          name country subcountry  geonameid        lat        lng\n",
       "0  Yoshkar-Ola  Russia   Mariy-El     466806  56.631595  47.886178\n",
       "1    Yaroslavl  Russia  Jaroslavl     468902  57.626578  39.893858"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[great circle distance](https://en.wikipedia.org/wiki/Great-circle_distance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "from math import cos, sin, sqrt, atan\n",
    "def angleDist(lat1, lng1, lat2, lng2):\n",
    "    lat1 = math.radians(lat1)\n",
    "    lng1 = math.radians(lng1)\n",
    "    lat2 = math.radians(lat2)\n",
    "    lng2 = math.radians(lng2)\n",
    "    phi1 = lat1\n",
    "    phi2 = lat2\n",
    "    dl = (lng1 - lng2)\n",
    "    n = (sqrt((cos(phi2)*sin(dl))**2\n",
    "             +(cos(phi1)*sin(phi1)-sin(phi1)*cos(phi2)*cos(dl))**2))\n",
    "    d = sin(phi1)*sin(phi2)+cos(phi1)*cos(phi2)*cos(dl)\n",
    "    sigma = atan(n/d)\n",
    "    \n",
    "    return sigma"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add ang dist?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "mat = pd.crosstab(df['name'], df['name']).astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(df.shape[0]):\n",
    "    for j in range(i + 1, df.shape[0]):\n",
    "        r1 = df.iloc[i]\n",
    "        r2 = df.iloc[j]\n",
    "        dist = angleDist(r1['lat'], r1['lng'], r2['lat'], r2['lng'])\n",
    "        mat[r1['name']][r2['name']] = dist\n",
    "        mat[r2['name']][r1['name']] = dist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "mat.to_csv('../data/ru_dist_mat_v2.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "mat_cpy = mat.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dropping large distances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import folium"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "mat = pd.read_csv('../data/ru_dist_mat_v2.csv', index_col='name')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(80, 80)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mat.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "dist_vals = pd.Series(mat.values.ravel())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6400,)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dist_vals.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "sns.distplot(dist_vals);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "sns.boxplot(dist_vals);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5671884640526746\n"
     ]
    }
   ],
   "source": [
    "q = dist_vals.quantile(0.80)\n",
    "print(q)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "dist_q = dist_vals[dist_vals < q]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "sns.boxplot(dist_q);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5120,)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dist_q.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "dist_4 = dist_vals[dist_vals < 0.4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "sns.boxplot(dist_4);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4362,)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dist_4.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Removing edges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "mat_cpy = mat.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "mat = mat_cpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(mat.shape[0]):\n",
    "    for j in range(i + 1, mat.shape[0]):\n",
    "        r = mat.iloc[i, j]\n",
    "        if (r >= q):\n",
    "            mat.iloc[i, j] = -1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "mat.to_csv('../data/ru_dist_mat_v2.csv')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}