190519-职住分析.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 居住/工作位置识别\n",
    "\n",
    "(1) 居住位置判断规则\n",
    "* 工作日发生\n",
    "* 18:00:00~8:00:00\n",
    "* 次数最多的停驻位置\n",
    "\n",
    "(2) 工作位置判断规则\n",
    "* 工作日发生\n",
    "* 9:00:00~16:00:00\n",
    "* 17个工作日中至少有12天位于同一位置"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import geopandas as gpd\n",
    "import shapely\n",
    "import datetime\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "plt.rcParams['font.family'] = ['sans-serif']\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "\n",
    "pd.set_option('max_rows',1000)\n",
    "pd.set_option('max_columns',50)\n",
    "\n",
    "os.chdir('E:\\\\zy城市群数据分析\\\\190519导出职住数据')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (0) 栅格对应的数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# grid = gpd.read_file('../0栅格数据/栅格-乡镇对应/joinRes.shp')          # 栅格数据，已经与乡镇数据连接起来了\n",
    "# town = gpd.read_file('../0栅格数据/9市街道区划/9市街道区划-合并.shp')   # 乡镇数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "grid = gpd.read_file('../0栅格数据/栅格-乡镇对应/grid_new.shp')\n",
    "town = gpd.read_file('../0栅格数据/栅格-乡镇对应/town_new.shp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>id</th>\n",
       "      <th>LONCOL</th>\n",
       "      <th>LATCOL</th>\n",
       "      <th>HBLON</th>\n",
       "      <th>HBLAT</th>\n",
       "      <th>name</th>\n",
       "      <th>County</th>\n",
       "      <th>City</th>\n",
       "      <th>Province</th>\n",
       "      <th>GridRing</th>\n",
       "      <th>TownRing</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>281</td>\n",
       "      <td>118.357348</td>\n",
       "      <td>31.931298</td>\n",
       "      <td>星甸街道</td>\n",
       "      <td>浦口区</td>\n",
       "      <td>南京市</td>\n",
       "      <td>江苏省</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((118.36 31.92904945779842, 118.354695...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>282</td>\n",
       "      <td>118.357348</td>\n",
       "      <td>31.935794</td>\n",
       "      <td>星甸街道</td>\n",
       "      <td>浦口区</td>\n",
       "      <td>南京市</td>\n",
       "      <td>江苏省</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((118.36 31.93354606300484, 118.354695...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>283</td>\n",
       "      <td>118.357348</td>\n",
       "      <td>31.940291</td>\n",
       "      <td>星甸街道</td>\n",
       "      <td>浦口区</td>\n",
       "      <td>南京市</td>\n",
       "      <td>江苏省</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((118.36 31.93804266821126, 118.354695...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>281</td>\n",
       "      <td>118.362652</td>\n",
       "      <td>31.931298</td>\n",
       "      <td>星甸街道</td>\n",
       "      <td>浦口区</td>\n",
       "      <td>南京市</td>\n",
       "      <td>江苏省</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((118.3653043927706 31.92904945779842,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>282</td>\n",
       "      <td>118.362652</td>\n",
       "      <td>31.935794</td>\n",
       "      <td>星甸街道</td>\n",
       "      <td>浦口区</td>\n",
       "      <td>南京市</td>\n",
       "      <td>江苏省</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((118.3653043927706 31.93354606300484,...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  LONCOL  LATCOL       HBLON      HBLAT  name County City Province  \\\n",
       "0   0       0     281  118.357348  31.931298  星甸街道    浦口区  南京市      江苏省   \n",
       "1   1       0     282  118.357348  31.935794  星甸街道    浦口区  南京市      江苏省   \n",
       "2   2       0     283  118.357348  31.940291  星甸街道    浦口区  南京市      江苏省   \n",
       "3   3       1     281  118.362652  31.931298  星甸街道    浦口区  南京市      江苏省   \n",
       "4   4       1     282  118.362652  31.935794  星甸街道    浦口区  南京市      江苏省   \n",
       "\n",
       "   GridRing  TownRing                                           geometry  \n",
       "0         0         0  POLYGON ((118.36 31.92904945779842, 118.354695...  \n",
       "1         0         0  POLYGON ((118.36 31.93354606300484, 118.354695...  \n",
       "2         0         0  POLYGON ((118.36 31.93804266821126, 118.354695...  \n",
       "3         0         0  POLYGON ((118.3653043927706 31.92904945779842,...  \n",
       "4         0         0  POLYGON ((118.3653043927706 31.93354606300484,...  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>Province</th>\n",
       "      <th>City</th>\n",
       "      <th>County</th>\n",
       "      <th>label</th>\n",
       "      <th>TownRing</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>嘉定工业区</td>\n",
       "      <td>上海市</td>\n",
       "      <td>上海市</td>\n",
       "      <td>嘉定区</td>\n",
       "      <td>嘉定工业区|嘉定区</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((121.2578519644719 31.3711403539198, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>菊园新区管委会</td>\n",
       "      <td>上海市</td>\n",
       "      <td>上海市</td>\n",
       "      <td>嘉定区</td>\n",
       "      <td>菊园新区管委会|嘉定区</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((121.2691103726473 31.40221438071714,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>嘉定镇街道</td>\n",
       "      <td>上海市</td>\n",
       "      <td>上海市</td>\n",
       "      <td>嘉定区</td>\n",
       "      <td>嘉定镇街道|嘉定区</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((121.255658116194 31.39191372165894, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>新成路街道</td>\n",
       "      <td>上海市</td>\n",
       "      <td>上海市</td>\n",
       "      <td>嘉定区</td>\n",
       "      <td>新成路街道|嘉定区</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((121.2758621911989 31.40147191184149,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>马陆镇</td>\n",
       "      <td>上海市</td>\n",
       "      <td>上海市</td>\n",
       "      <td>嘉定区</td>\n",
       "      <td>马陆镇|嘉定区</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((121.3417180535397 31.33828939627281,...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      name Province City County        label  TownRing  \\\n",
       "0    嘉定工业区      上海市  上海市    嘉定区    嘉定工业区|嘉定区         0   \n",
       "1  菊园新区管委会      上海市  上海市    嘉定区  菊园新区管委会|嘉定区         0   \n",
       "2    嘉定镇街道      上海市  上海市    嘉定区    嘉定镇街道|嘉定区         0   \n",
       "3    新成路街道      上海市  上海市    嘉定区    新成路街道|嘉定区         0   \n",
       "4      马陆镇      上海市  上海市    嘉定区      马陆镇|嘉定区         0   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON ((121.2578519644719 31.3711403539198, ...  \n",
       "1  POLYGON ((121.2691103726473 31.40221438071714,...  \n",
       "2  POLYGON ((121.255658116194 31.39191372165894, ...  \n",
       "3  POLYGON ((121.2758621911989 31.40147191184149,...  \n",
       "4  POLYGON ((121.3417180535397 31.33828939627281,...  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "town.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (1) 职住识别"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = pd.read_csv('./zy_stop_count.csv.gz',compression='gzip')\n",
    "data.columns = ['loncol_500','latcol_500','stopNum','dateNum','tw','th','user_id','rank_id']\n",
    "data.drop('rank_id',axis=1,inplace=True)\n",
    "data = data.reindex(columns=['user_id','loncol_500','latcol_500','stopNum','dateNum','tw','th'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>user_id</th>\n",
       "      <th>loncol_500</th>\n",
       "      <th>latcol_500</th>\n",
       "      <th>stopNum</th>\n",
       "      <th>dateNum</th>\n",
       "      <th>tw</th>\n",
       "      <th>th</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>409</td>\n",
       "      <td>265</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>38751</td>\n",
       "      <td>90129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>413</td>\n",
       "      <td>274</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1919</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>411</td>\n",
       "      <td>267</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>9376</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>82</td>\n",
       "      <td>267</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>31097</td>\n",
       "      <td>11982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>86</td>\n",
       "      <td>281</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>8267</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  loncol_500  latcol_500  stopNum  dateNum     tw     th\n",
       "0        1         409         265        4        3  38751  90129\n",
       "1        1         413         274        1        1   1919      0\n",
       "2        1         411         267        1        1   9376      0\n",
       "3        3          82         267        5        3  31097  11982\n",
       "4        3          86         281        2        1   8267      0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0.  , 0.02, 0.04, 0.06, 0.08, 0.1 , 0.12, 0.14, 0.16]),\n",
       " <a list of 9 Text yticklabel objects>)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA40AAAFZCAYAAAA8ZMopAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb7f4978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 简单的频率分布分析\n",
    "import matplotlib as mpl\n",
    "def plot_hist(series, num_bins, color_bins, color_line, alpha=1, label='', linestyle='dashed', marker='o', markerfacecolor='r', markersize=2): \n",
    "    # 将hist设置为函数\n",
    "    # num_bins = 30\n",
    "    mu, sigma = series.agg(['mean','std'])\n",
    "    n, bins, patchs = plt.hist(series, num_bins, normed=1, color=color_bins, alpha=alpha,label=label)\n",
    "    y = mpl.mlab.normpdf(bins,mu,sigma)\n",
    "    plt.plot(bins,y,color=color_line, linestyle=linestyle, marker=marker, markerfacecolor=markerfacecolor, markersize=markersize)\n",
    "\n",
    "    \n",
    "def rgb(R,G,B): return \"#%02X%02X%02X\" % (R,G,B)\n",
    "fig, ax = plt.subplots(figsize=(15,5))\n",
    "plt.subplot(1,2,1)\n",
    "# data.iloc[:10000,:].groupby('user_id').stopNum.max().hist(bins=30)\n",
    "plot_hist(data.groupby('user_id').dateNum.max(),num_bins=22,color_bins=rgb(252,216,103),color_line=rgb(123,99,0))\n",
    "plt.title('最大停驻天数频率分布',fontsize=18)\n",
    "plt.xlabel('停驻天数',fontsize=16)\n",
    "plt.ylabel('用户比例',fontsize=16)\n",
    "plt.xticks(fontsize=14)\n",
    "plt.yticks(fontsize=14)\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "# data.iloc[:10000,:].groupby('user_id').dateNum.max().hist(bins=30)\n",
    "plot_hist(data.groupby('user_id').stopNum.max(),num_bins=50,color_bins=rgb(252,216,103),color_line=rgb(123,99,0))\n",
    "plt.title('最大停驻次数频率分布',fontsize=18)\n",
    "plt.xlabel('停驻次数',fontsize=16)\n",
    "plt.ylabel('用户比例',fontsize=16)\n",
    "plt.xticks(fontsize=14)\n",
    "plt.yticks(fontsize=14)\n",
    "\n",
    "# plt.savefig('C:\\\\Users\\Administrator\\Desktop\\c1.png',bbox_inches='tight',pad_inches=0,dpi=600)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 考虑同地规则：若工作与居住相同，则取下一个"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# date_max = data.groupby('user_id').dateNum.max()\n",
    "# userFilter = date_max[date_max >= 10].index.tolist()  # 筛选满足停驻天数条件的用户数据\n",
    "# dataFilter = data[data.user_id.isin(userFilter)]\n",
    "\n",
    "# 识别居住与工作位置栅格,为简化计算，采用记录id的方式\n",
    "t0 = datetime.datetime.now()\n",
    "homeID = list()\n",
    "workID = list()\n",
    "for user,udata in data.groupby('user_id'):\n",
    "#     print(user)\n",
    "    hdata = udata[udata.th == udata.th.max()]   # 最有可能在家的停驻\n",
    "    homeID.append([user, hdata.index.values[0]])# 居住\n",
    "    wdata = udata[udata.tw == udata.tw.max()]   # 最有可能工作的停驻\n",
    "    if len(udata)>1 and wdata[['loncol_500','latcol_500']].values.tolist() == hdata[['loncol_500','latcol_500']].values.tolist():\n",
    "        wdata = udata[udata.tw == udata.tw.nlargest(2).iloc[1]]   # 第二大的停驻天数  函数df.nlargest()\n",
    "        workID.append([user, wdata.index.values[0]])\n",
    "    elif len(udata)>1 and wdata[['loncol_500','latcol_500']].values.tolist() != hdata[['loncol_500','latcol_500']].values.tolist():\n",
    "        workID.append([user, wdata.index.values[0]])  # 1.只有一行，2.工作与家不一样\n",
    "    else:\n",
    "        workID.append([user,''])\n",
    "print((datetime.datetime.now()-t0).seconds / 60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# pd.DataFrame(homeID).to_csv('/home_id.csv',index=False)\n",
    "# pd.DataFrame(workID).to_csv('/work_id.csv',index=False)\n",
    "# pd.DataFrame(homeID).to_csv('C:\\\\Users\\\\Administrator\\\\Desktop\\\\home_id.csv',index=False)`\n",
    "# pd.DataFrame(workID).to_csv('C:\\\\Users\\\\Administrator\\\\Desktop\\\\work_id.csv',index=False)\n",
    "\n",
    "# 将index转化为数据\n",
    "homeID = pd.read_csv('./home_id.csv')\n",
    "homeID.columns=['user_id','home_id']\n",
    "home_data = data.loc[homeID.home_id.values]\n",
    "\n",
    "workID = pd.read_csv('./work_id.csv')\n",
    "workID.columns=['user_id','work_id']\n",
    "workID.dropna(inplace=True)\n",
    "work_data = data.loc[workID.work_id.values]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 不考虑同地规则"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "home_data = data[data.groupby('user_id').th.transform(max) == data.th]\n",
    "work_data = data[data.groupby('user_id').tw.transform(max) == data.tw]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 居住工作识别结果处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "home_data = home_data[home_data.dateNum >= 10]\n",
    "work_data = work_data[work_data.dateNum >= 10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "hw = pd.merge(home_data,work_data,how='outer',on='user_id',suffixes=('_h','_w'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "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>user_id</th>\n",
       "      <th>loncol_500_h</th>\n",
       "      <th>latcol_500_h</th>\n",
       "      <th>stopNum_h</th>\n",
       "      <th>dateNum_h</th>\n",
       "      <th>tw_h</th>\n",
       "      <th>th_h</th>\n",
       "      <th>loncol_500_w</th>\n",
       "      <th>latcol_500_w</th>\n",
       "      <th>stopNum_w</th>\n",
       "      <th>dateNum_w</th>\n",
       "      <th>tw_w</th>\n",
       "      <th>th_w</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9</td>\n",
       "      <td>550.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>2066.0</td>\n",
       "      <td>389978.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16</td>\n",
       "      <td>571.0</td>\n",
       "      <td>116.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>189777.0</td>\n",
       "      <td>444145.0</td>\n",
       "      <td>571.0</td>\n",
       "      <td>116.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>189777.0</td>\n",
       "      <td>444145.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>31</td>\n",
       "      <td>562.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>3004.0</td>\n",
       "      <td>432008.0</td>\n",
       "      <td>572.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>130919.0</td>\n",
       "      <td>34530.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>34</td>\n",
       "      <td>591.0</td>\n",
       "      <td>146.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>746446.0</td>\n",
       "      <td>578.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>444386.0</td>\n",
       "      <td>23663.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>38</td>\n",
       "      <td>86.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>102081.0</td>\n",
       "      <td>52917.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>102081.0</td>\n",
       "      <td>52917.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  loncol_500_h  latcol_500_h  stopNum_h  dateNum_h      tw_h  \\\n",
       "0        9         550.0         150.0       21.0       14.0    2066.0   \n",
       "1       16         571.0         116.0       23.0       13.0  189777.0   \n",
       "2       31         562.0         160.0       26.0       19.0    3004.0   \n",
       "3       34         591.0         146.0       38.0       20.0       0.0   \n",
       "4       38          86.0         280.0       11.0       11.0  102081.0   \n",
       "\n",
       "       th_h  loncol_500_w  latcol_500_w  stopNum_w  dateNum_w      tw_w  \\\n",
       "0  389978.0           NaN           NaN        NaN        NaN       NaN   \n",
       "1  444145.0         571.0         116.0       23.0       13.0  189777.0   \n",
       "2  432008.0         572.0         120.0       13.0       11.0  130919.0   \n",
       "3  746446.0         578.0         111.0       21.0       19.0  444386.0   \n",
       "4   52917.0          86.0         280.0       11.0       11.0  102081.0   \n",
       "\n",
       "       th_w  \n",
       "0       NaN  \n",
       "1  444145.0  \n",
       "2   34530.0  \n",
       "3   23663.0  \n",
       "4   52917.0  "
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hw.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "hw1 = hw[(~hw.th_h.isnull()) & (~hw.tw_w.isnull())]   # 成功识别居住与工作\n",
    "hw2 = hw[(~hw.th_h.isnull()) & (hw.tw_w.isnull())]    # 成功识别居住，无法识别工作\n",
    "hw3 = hw[(hw.th_h.isnull()) & (~hw.tw_w.isnull())]    # 成功识别工作，无法识别居住   \n",
    "hw4 = data[data.user_id.isin(set(data.user_id.unique()) - set(hw.user_id.unique()))]  # 无法识别居住和工作"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "23726274 7330357 6479944 596422 253991 16395917\n"
     ]
    }
   ],
   "source": [
    "# 总体情况\n",
    "print(data.user_id.nunique(), hw.user_id.nunique(), hw1.user_id.nunique(), hw2.user_id.nunique(), hw3.user_id.nunique(), hw4.user_id.nunique())\n",
    "# 7330357 6479944 596422 253991"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 从第一类用户中筛选有用信息\n",
    "hw_total = hw[['user_id','loncol_500_h','latcol_500_h','loncol_500_w','latcol_500_w']].reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 栅格转乡镇: 注意有些栅格对应对个乡镇，导致新表比旧表多\n",
    "hw_totalTown_temp = hw_total.merge(grid[['LONCOL','LATCOL','name','County','City','Province','GridRing','TownRing']],how='left',\\\n",
    "                               left_on=['loncol_500_h','latcol_500_h'],right_on=['LONCOL','LATCOL'])\n",
    "hw_totalTown_temp2 = hw_totalTown_temp.merge(grid[['LONCOL','LATCOL','name','County','City','Province','GridRing','TownRing']],how='left',\\\n",
    "                               left_on=['loncol_500_w','latcol_500_w'],right_on=['LONCOL','LATCOL'],suffixes=('_h', '_w'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "hw_total_town = hw_totalTown_temp2[['user_id','loncol_500_h','latcol_500_h','name_h','County_h','City_h','Province_h','GridRing_h','TownRing_h',\\\n",
    "                                    'loncol_500_w','latcol_500_w','name_w','County_w','City_w','Province_w','GridRing_w','TownRing_w']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "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>user_id</th>\n",
       "      <th>loncol_500_h</th>\n",
       "      <th>latcol_500_h</th>\n",
       "      <th>name_h</th>\n",
       "      <th>County_h</th>\n",
       "      <th>City_h</th>\n",
       "      <th>Province_h</th>\n",
       "      <th>GridRing_h</th>\n",
       "      <th>TownRing_h</th>\n",
       "      <th>loncol_500_w</th>\n",
       "      <th>latcol_500_w</th>\n",
       "      <th>name_w</th>\n",
       "      <th>County_w</th>\n",
       "      <th>City_w</th>\n",
       "      <th>Province_w</th>\n",
       "      <th>GridRing_w</th>\n",
       "      <th>TownRing_w</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9</td>\n",
       "      <td>550.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>马陆镇</td>\n",
       "      <td>嘉定区</td>\n",
       "      <td>上海市</td>\n",
       "      <td>上海市</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16</td>\n",
       "      <td>571.0</td>\n",
       "      <td>116.0</td>\n",
       "      <td>虹桥镇</td>\n",
       "      <td>闵行区</td>\n",
       "      <td>上海市</td>\n",
       "      <td>上海市</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>571.0</td>\n",
       "      <td>116.0</td>\n",
       "      <td>虹桥镇</td>\n",
       "      <td>闵行区</td>\n",
       "      <td>上海市</td>\n",
       "      <td>上海市</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>31</td>\n",
       "      <td>562.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>罗店镇</td>\n",
       "      <td>宝山区</td>\n",
       "      <td>上海市</td>\n",
       "      <td>上海市</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>572.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>仙霞新村街道</td>\n",
       "      <td>长宁区</td>\n",
       "      <td>上海市</td>\n",
       "      <td>上海市</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>34</td>\n",
       "      <td>591.0</td>\n",
       "      <td>146.0</td>\n",
       "      <td>新江湾城街道</td>\n",
       "      <td>杨浦区</td>\n",
       "      <td>上海市</td>\n",
       "      <td>上海市</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>578.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>漕河泾街道</td>\n",
       "      <td>徐汇区</td>\n",
       "      <td>上海市</td>\n",
       "      <td>上海市</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>38</td>\n",
       "      <td>86.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>秣陵街道</td>\n",
       "      <td>江宁区</td>\n",
       "      <td>南京市</td>\n",
       "      <td>江苏省</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>秣陵街道</td>\n",
       "      <td>江宁区</td>\n",
       "      <td>南京市</td>\n",
       "      <td>江苏省</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  loncol_500_h  latcol_500_h  name_h County_h City_h Province_h  \\\n",
       "0        9         550.0         150.0     马陆镇      嘉定区    上海市        上海市   \n",
       "1       16         571.0         116.0     虹桥镇      闵行区    上海市        上海市   \n",
       "2       31         562.0         160.0     罗店镇      宝山区    上海市        上海市   \n",
       "3       34         591.0         146.0  新江湾城街道      杨浦区    上海市        上海市   \n",
       "4       38          86.0         280.0    秣陵街道      江宁区    南京市        江苏省   \n",
       "\n",
       "   GridRing_h  TownRing_h  loncol_500_w  latcol_500_w  name_w County_w City_w  \\\n",
       "0         0.0         0.0           NaN           NaN     NaN      NaN    NaN   \n",
       "1         1.0         1.0         571.0         116.0     虹桥镇      闵行区    上海市   \n",
       "2         0.0         0.0         572.0         120.0  仙霞新村街道      长宁区    上海市   \n",
       "3         1.0         1.0         578.0         111.0   漕河泾街道      徐汇区    上海市   \n",
       "4         0.0         0.0          86.0         280.0    秣陵街道      江宁区    南京市   \n",
       "\n",
       "  Province_w  GridRing_w  TownRing_w  \n",
       "0        NaN         NaN         NaN  \n",
       "1        上海市         1.0         1.0  \n",
       "2        上海市         1.0         1.0  \n",
       "3        上海市         1.0         1.0  \n",
       "4        江苏省         0.0         0.0  "
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hw_total_town.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3145309"
      ]
     },
     "execution_count": 243,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hw_total_town[hw_total_town.City_h == '上海市'].user_id.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2834328"
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hw_total_town[(hw_total_town.City_h == '上海市') & (~hw_total_town.City_w.isnull())].user_id.nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 居住与工作位置统计结果导入---栅格层面"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "hw1_hcnt = hw1.groupby(['loncol_500_h','latcol_500_h']).user_id.count().reset_index()\n",
    "hw1_hcnt.rename(columns={'user_id':'cnt'},inplace=True)\n",
    "hw1_hcnt_grid = grid.merge(hw1_hcnt,left_on=['LONCOL','LATCOL'],right_on=['loncol_500_h','latcol_500_h'],how='left')\n",
    "hw1_hcnt_grid.cnt.fillna(0,inplace=True)\n",
    "hw1_hcnt_grid.cnt = hw1_hcnt_grid.cnt.astype(int)\n",
    "hw1_hcnt_grid.to_file('../190519导出职住数据-new/ArcGIS图/hw1_homecnt.shp',encoding='gb2312')\n",
    "\n",
    "hw1_wcnt = hw1.groupby(['loncol_500_w','latcol_500_w']).user_id.count().reset_index()\n",
    "hw1_wcnt.rename(columns={'user_id':'cnt'},inplace=True)\n",
    "hw1_wcnt_grid = grid.merge(hw1_wcnt,left_on=['LONCOL','LATCOL'],right_on=['loncol_500_w','latcol_500_w'],how='left')\n",
    "hw1_wcnt_grid.cnt.fillna(0,inplace=True)\n",
    "hw1_wcnt_grid.cnt = hw1_wcnt_grid.cnt.astype(int)\n",
    "hw1_wcnt_grid.to_file('../190519导出职住数据-new/ArcGIS图/hw1_workcnt.shp',encoding='gb2312')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "hw2_cnt = hw2.groupby(['loncol_500_h','latcol_500_h']).user_id.count().reset_index()\n",
    "hw2_cnt.rename(columns={'user_id':'cnt'},inplace=True)\n",
    "hw2_cnt_grid = grid.merge(hw2_cnt,left_on=['LONCOL','LATCOL'],right_on=['loncol_500_h','latcol_500_h'],how='left')\n",
    "hw2_cnt_grid.cnt.fillna(0,inplace=True)\n",
    "hw2_cnt_grid.cnt = hw2_cnt_grid.cnt.astype(int)\n",
    "hw2_cnt_grid.to_file('../190519导出职住数据-new/ArcGIS图/hw2_homecnt.shp',encoding='gb2312')\n",
    "\n",
    "hw3_cnt = hw3.groupby(['loncol_500_w','latcol_500_w']).user_id.count().reset_index()\n",
    "hw3_cnt.rename(columns={'user_id':'cnt'},inplace=True)\n",
    "hw3_cnt_grid = grid.merge(hw3_cnt,left_on=['LONCOL','LATCOL'],right_on=['loncol_500_w','latcol_500_w'],how='left')\n",
    "hw3_cnt_grid.cnt.fillna(0,inplace=True)\n",
    "hw3_cnt_grid.cnt = hw3_cnt_grid.cnt.astype(int)\n",
    "hw3_cnt_grid.to_file('../190519导出职住数据-new/ArcGIS图/hw3_workcnt.shp',encoding='gb2312')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 居住与工作位置统计结果导入---乡镇层面"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "hw1_hcnt_town = hw1_hcnt_grid.groupby(['County','name']).cnt.sum().reset_index()\n",
    "hw1_wcnt_town = hw1_wcnt_grid.groupby(['County','name']).cnt.sum().reset_index()\n",
    "hw1_hcnt_townshp = town.merge(hw1_hcnt_town, on=['County','name'],how='inner')\n",
    "hw1_wcnt_townshp = town.merge(hw1_wcnt_town, on=['County','name'],how='inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "hw2_cnt_town = hw2_cnt_grid.groupby(['County','name']).cnt.sum().reset_index()\n",
    "hw2_cnt_townshp = town.merge(hw2_cnt_town, on=['County','name'],how='inner')\n",
    "hw3_cnt_town = hw3_cnt_grid.groupby(['County','name']).cnt.sum().reset_index()\n",
    "hw3_cnt_townshp = town.merge(hw3_cnt_town, on=['County','name'],how='inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "hw1_hcnt_townshp.to_file('../190519导出职住数据-new/ArcGIS图/hw1_homecnt_town.shp',encoding='gb2312')\n",
    "hw1_wcnt_townshp.to_file('../190519导出职住数据-new/ArcGIS图/hw1_workcnt_town.shp',encoding='gb2312')\n",
    "hw2_cnt_townshp.to_file('../190519导出职住数据-new/ArcGIS图/hw2_homecnt_town.shp',encoding='gb2312')\n",
    "hw3_cnt_townshp.to_file('../190519导出职住数据-new/ArcGIS图/hw3_workcnt_town.shp',encoding='gb2312')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (2) 对hw1进行人口分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 从第一类用户中筛选有用信息\n",
    "hwBaseGrid = hw1[['user_id','loncol_500_h','latcol_500_h','loncol_500_w','latcol_500_w']].reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 栅格转乡镇: 注意有些栅格对应对个乡镇，导致新表比旧表多\n",
    "hwBaseTownH = hwBaseGrid.merge(grid[['LONCOL','LATCOL','name','County','City','Province','GridRing','TownRing']],how='left',\\\n",
    "                               left_on=['loncol_500_h','latcol_500_h'],right_on=['LONCOL','LATCOL'])\n",
    "hwBaseTownW = hwBaseTownH.merge(grid[['LONCOL','LATCOL','name','County','City','Province','GridRing','TownRing']],how='left',\\\n",
    "                               left_on=['loncol_500_w','latcol_500_w'],right_on=['LONCOL','LATCOL'],suffixes=('_h', '_w'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "hwBaseTown = hwBaseTownW[['user_id','loncol_500_h','latcol_500_h','name_h','County_h','City_h','Province_h','GridRing_h','TownRing_h',\\\n",
    "                                    'loncol_500_w','latcol_500_w','name_w','County_w','City_w','Province_w','GridRing_w','TownRing_w']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "799\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>user_id</th>\n",
       "      <th>loncol_500_h</th>\n",
       "      <th>latcol_500_h</th>\n",
       "      <th>name_h</th>\n",
       "      <th>County_h</th>\n",
       "      <th>City_h</th>\n",
       "      <th>Province_h</th>\n",
       "      <th>GridRing_h</th>\n",
       "      <th>TownRing_h</th>\n",
       "      <th>loncol_500_w</th>\n",
       "      <th>latcol_500_w</th>\n",
       "      <th>name_w</th>\n",
       "      <th>County_w</th>\n",
       "      <th>City_w</th>\n",
       "      <th>Province_w</th>\n",
       "      <th>GridRing_w</th>\n",
       "      <th>TownRing_w</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11647</th>\n",
       "      <td>55124</td>\n",
       "      <td>387.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>387.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13918</th>\n",
       "      <td>65889</td>\n",
       "      <td>384.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>384.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34411</th>\n",
       "      <td>163890</td>\n",
       "      <td>366.0</td>\n",
       "      <td>506.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>366.0</td>\n",
       "      <td>506.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39799</th>\n",
       "      <td>189749</td>\n",
       "      <td>441.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>444.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>汾湖镇</td>\n",
       "      <td>吴江区</td>\n",
       "      <td>苏州市</td>\n",
       "      <td>江苏省</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40398</th>\n",
       "      <td>192552</td>\n",
       "      <td>442.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>442.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       user_id  loncol_500_h  latcol_500_h name_h County_h City_h Province_h  \\\n",
       "11647    55124         387.0          51.0    NaN      NaN    NaN        NaN   \n",
       "13918    65889         384.0          49.0    NaN      NaN    NaN        NaN   \n",
       "34411   163890         366.0         506.0    NaN      NaN    NaN        NaN   \n",
       "39799   189749         441.0          66.0    NaN      NaN    NaN        NaN   \n",
       "40398   192552         442.0          48.0    NaN      NaN    NaN        NaN   \n",
       "\n",
       "       GridRing_h  TownRing_h  loncol_500_w  latcol_500_w name_w County_w  \\\n",
       "11647         NaN         NaN         387.0          51.0    NaN      NaN   \n",
       "13918         NaN         NaN         384.0          49.0    NaN      NaN   \n",
       "34411         NaN         NaN         366.0         506.0    NaN      NaN   \n",
       "39799         NaN         NaN         444.0          73.0    汾湖镇      吴江区   \n",
       "40398         NaN         NaN         442.0          48.0    NaN      NaN   \n",
       "\n",
       "      City_w Province_w  GridRing_w  TownRing_w  \n",
       "11647    NaN        NaN         NaN         NaN  \n",
       "13918    NaN        NaN         NaN         NaN  \n",
       "34411    NaN        NaN         NaN         NaN  \n",
       "39799    苏州市        江苏省         0.0         0.0  \n",
       "40398    NaN        NaN         NaN         NaN  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 栅格没有对应的乡镇\n",
    "print(len(hwBaseTown[pd.isnull(hwBaseTown).any(axis=1)]))\n",
    "hwBaseTown[pd.isnull(hwBaseTown).any(axis=1)].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6479944"
      ]
     },
     "execution_count": 245,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hwBaseTown.user_id.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 城市用户量统计\n",
    "hw_pop = hwBaseTown.groupby('City_h').user_id.count().reset_index().dropna()\n",
    "hw_pop.rename(columns={'City_h':'city','user_id':'cnt'},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 城市2017年人口\n",
    "pop = pd.DataFrame(\\\n",
    "[['上海市',2418.33,18.89],\\\n",
    "['南京市',833.50,14.86],\\\n",
    "['无锡市',655.30,13.8],\\\n",
    "['常州市',471.73,10.94],\\\n",
    "['苏州市',1068.36,22.24],\\\n",
    "['南通市',730.50,8.17],\\\n",
    "['扬州市',450.82,9.42],\\\n",
    "['镇江市',318.63,9.44],\\\n",
    "['泰州市',465.19,7.02]], columns=['city','population','originRate'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [],
   "source": [
    "hw_poprate = pd.merge(hw_pop, pop, on='city')\n",
    "hw_poprate['rate'] = hw_poprate.cnt / hw_poprate.population / 100\n",
    "hw_poprate.sort_values('rate',ascending=False,inplace=True)\n",
    "hw_poprate.reset_index(drop=True,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "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>city</th>\n",
       "      <th>cnt</th>\n",
       "      <th>population</th>\n",
       "      <th>originRate</th>\n",
       "      <th>rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>上海市</td>\n",
       "      <td>2941575</td>\n",
       "      <td>2418.33</td>\n",
       "      <td>18.89</td>\n",
       "      <td>12.163663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>苏州市</td>\n",
       "      <td>1275357</td>\n",
       "      <td>1068.36</td>\n",
       "      <td>22.24</td>\n",
       "      <td>11.937521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>南京市</td>\n",
       "      <td>762192</td>\n",
       "      <td>833.50</td>\n",
       "      <td>14.86</td>\n",
       "      <td>9.144475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>无锡市</td>\n",
       "      <td>496736</td>\n",
       "      <td>655.30</td>\n",
       "      <td>13.80</td>\n",
       "      <td>7.580284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>常州市</td>\n",
       "      <td>276196</td>\n",
       "      <td>471.73</td>\n",
       "      <td>10.94</td>\n",
       "      <td>5.854959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>镇江市</td>\n",
       "      <td>166877</td>\n",
       "      <td>318.63</td>\n",
       "      <td>9.44</td>\n",
       "      <td>5.237329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>扬州市</td>\n",
       "      <td>214405</td>\n",
       "      <td>450.82</td>\n",
       "      <td>9.42</td>\n",
       "      <td>4.755889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>南通市</td>\n",
       "      <td>297801</td>\n",
       "      <td>730.50</td>\n",
       "      <td>8.17</td>\n",
       "      <td>4.076674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>泰州市</td>\n",
       "      <td>157506</td>\n",
       "      <td>465.19</td>\n",
       "      <td>7.02</td>\n",
       "      <td>3.385842</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  city      cnt  population  originRate       rate\n",
       "0  上海市  2941575     2418.33       18.89  12.163663\n",
       "1  苏州市  1275357     1068.36       22.24  11.937521\n",
       "2  南京市   762192      833.50       14.86   9.144475\n",
       "3  无锡市   496736      655.30       13.80   7.580284\n",
       "4  常州市   276196      471.73       10.94   5.854959\n",
       "5  镇江市   166877      318.63        9.44   5.237329\n",
       "6  扬州市   214405      450.82        9.42   4.755889\n",
       "7  南通市   297801      730.50        8.17   4.076674\n",
       "8  泰州市   157506      465.19        7.02   3.385842"
      ]
     },
     "execution_count": 247,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hw_poprate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmgAAAE3CAYAAAAe3732AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f4d828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10,5))\n",
    "def rgb(R,G,B): return \"#%02X%02X%02X\" % (R,G,B)\n",
    "plt.bar(range(9),hw_poprate.originRate,color=rgb(252,216,103))\n",
    "for i,v in enumerate(hw_poprate.originRate): ax.text(i,v+0.4, str(v)+'%',color='b',horizontalalignment='center',verticalalignment='center',\\\n",
    "                                                     fontsize=14, fontweight='bold')\n",
    "\n",
    "plt.bar(range(9),hw_poprate.rate,color=rgb(123,99,0))\n",
    "for i,v in enumerate(hw_poprate.rate): ax.text(i,v+0.5, str(round(v,2))+'%',color='b',horizontalalignment='center',verticalalignment='center',\\\n",
    "                                                     fontsize=14, fontweight='bold')\n",
    "    \n",
    "plt.xticks(range(9),hw_poprate.city.values,fontsize=14)\n",
    "plt.yticks(fontsize=14)\n",
    "plt.ylim([0,25])\n",
    "# plt.xlabel('研究范围内城市',fontsize=16)\n",
    "plt.ylabel('占总人口比率 (%)',fontsize=16)\n",
    "plt.legend(['原始手机用户','第一类用户'],fontsize=16)\n",
    "plt.savefig('C:\\\\Users\\Administrator\\Desktop\\渗透率.png',bbox_inches='tight',pad_inches=0,dpi=600)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (3) 职住识别验证-上海用户"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 上海市16个区的人口,根据《上海2018年统计年鉴》,单位：万人\n",
    "pop_sh = pd.DataFrame(\\\n",
    "[['浦东新区',552.84],\\\n",
    "['黄浦区',65.48],\\\n",
    "['徐汇区',108.83],\\\n",
    "['长宁区',69.37],\\\n",
    "['静安区',106.62],\\\n",
    "['普陀区',128.47],\\\n",
    "['虹口区',79.90],\\\n",
    "['杨浦区',131.34],\\\n",
    "['闵行区',253.43],\\\n",
    "['宝山区',203.08],\\\n",
    "['嘉定区',158.18],\\\n",
    "['金山区',80.14],\\\n",
    "['松江区',175.13],\\\n",
    "['青浦区',120.53],\\\n",
    "['奉贤区',115.53],\\\n",
    "['崇明区',69.46]],columns=['County','population'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 提取手机数据中居住和工作均在上海的用户\n",
    "hw_SH = hwBaseTown[(hwBaseTown.City_h=='上海市') & (hwBaseTown.City_w=='上海市')]\n",
    "hwSH_towncnt = hw_SH.groupby(['name_h','name_w']).user_id.count().reset_index()        # 乡镇层面\n",
    "hwSH_towncnt.rename(columns={'user_id':'cnt'},inplace=True)\n",
    "hwSH_countycnt = hw_SH.groupby(['County_h','County_w']).user_id.count().reset_index()  #区县层面\n",
    "hwSH_countycnt.rename(columns={'user_id':'cnt'},inplace=True)\n",
    "\n",
    "# 按区扩样\n",
    "user_mobile = hwSH_countycnt.groupby('County_h').cnt.sum().reset_index()\n",
    "user_expand = pd.merge(user_mobile,pop_sh,left_on='County_h',right_on='County')\n",
    "user_expand['factor'] = user_expand.population / user_expand.cnt * 10000  # 扩样因子\n",
    "\n",
    "# 扩样后的用户量\n",
    "hwSH_countycnt2 = pd.merge(hwSH_countycnt, user_expand[['County_h','factor']],on='County_h')\n",
    "hwSH_countycnt2['new_cnt'] = hwSH_countycnt2.cnt * hwSH_countycnt2.factor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 2009年居民出行调查数据,直接用家庭行政区和单位行政区\n",
    "xz_survey = pd.read_csv('E:\\\\zhpy\\\\2travel_survey_2009SH\\\\hw_cnt_sh.csv',encoding='gb2312')  # 已经扩样后的职住数据\n",
    "county_combine = pd.merge(hwSH_countycnt2,xz_survey,left_on=['County_h','County_w'],right_on=['H_XZ','W_XZ'],suffixes=['_mobile','_hts'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1, 2, 3, 4, 5, 6, 7, 8]), <a list of 9 Text yticklabel objects>)"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWYAAAFSCAYAAADBzXWtAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1052bac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(5,5))\n",
    "xsh = county_combine.new_cnt_hts.values.reshape((-1,1))\n",
    "ysh = county_combine.new_cnt_mobile.values.reshape((-1,1))\n",
    "plt.scatter(np.log10(xsh),np.log10(ysh), marker='o', linewidths=1, c='b',alpha=0.5)\n",
    "plt.plot(range(10),range(10),color='r')\n",
    "plt.xlim([0,8])\n",
    "plt.ylim([0,8])\n",
    "# plt.title('上海职住量比较',fontsize=18)\n",
    "plt.xlabel('居民出行调查数据扩样用户数',fontsize=16)\n",
    "plt.ylabel('手机信令数据扩样用户数',fontsize=16)\n",
    "plt.xticks(range(9),['$10^{}$'.format(x) for x in range(9)])\n",
    "plt.yticks(range(9),['$10^{}$'.format(x) for x in range(9)])\n",
    "plt.xticks(fontsize=14)\n",
    "plt.yticks(fontsize=14)\n",
    "\n",
    "# plt.savefig('C:\\\\Users\\Administrator\\Desktop\\z1.png',bbox_inches='tight',pad_inches=0,dpi=600)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (3) 职住识别验证-南京用户"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pop_nj = pd.DataFrame(\\\n",
    "[['玄武区',60.02],\\\n",
    "['秦淮区',100.03],\\\n",
    "['建邺区',47.26],\\\n",
    "['鼓楼区',116.84],\\\n",
    "['浦口区',79.88],\\\n",
    "['栖霞区',71.79],\\\n",
    "['雨花台区',45.45],\\\n",
    "['江宁区',124.85],\\\n",
    "['六合区',96.35],\\\n",
    "['溧水区',46.39],\\\n",
    "['高淳区',44.64]],columns=['County','population'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 提取手机数据中居住和工作均在南京的用户\n",
    "hw_NJ = hwBaseTown[(hwBaseTown.City_h=='南京市') & (hwBaseTown.City_w=='南京市')]\n",
    "hwNJ_countycnt = hw_NJ.groupby(['County_h','County_w']).user_id.count().reset_index()  #区县层面\n",
    "hwNJ_countycnt.rename(columns={'user_id':'cnt'},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 按区扩样\n",
    "user_mobile_nj = hwNJ_countycnt.groupby('County_h').cnt.sum().reset_index()\n",
    "user_expand_nj = pd.merge(user_mobile_nj,pop_nj,left_on='County_h',right_on='County')\n",
    "user_expand_nj['factor'] = user_expand_nj.population / user_expand_nj.cnt * 10000  # 扩样因子\n",
    "\n",
    "# 扩样后的用户量\n",
    "hwNJ_countycnt2 = pd.merge(hwNJ_countycnt, user_expand_nj[['County_h','factor']],on='County_h')\n",
    "hwNJ_countycnt2['new_cnt'] = hwNJ_countycnt2.cnt * hwNJ_countycnt2.factor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 南京居民调查数据\n",
    "hwNJ_ncnt = pd.read_csv('E:\\\\zhpy\\\\2travel_survey_2009SH\\\\hw_cnt_nj.csv',encoding='gb2312')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1, 2, 3, 4, 5, 6, 7]), <a list of 8 Text yticklabel objects>)"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWYAAAFTCAYAAAAKkaYIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x104c85c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "combine = pd.merge(hwNJ_countycnt2,hwNJ_ncnt,left_on=['County_h','County_w'],right_on=['H_XZ','W_XZ'],suffixes=['_mobile','_hts'])\n",
    "fig, ax = plt.subplots(figsize=(5,5))\n",
    "x = combine.new_cnt_hts.values.reshape((-1,1))\n",
    "y = combine.new_cnt_mobile.values.reshape((-1,1))\n",
    "plt.scatter(np.log10(x),np.log10(y), marker='o', linewidths=1, c='b',alpha=0.5)\n",
    "plt.plot(range(10),range(10),color='r')\n",
    "plt.xlim([0,7])\n",
    "plt.ylim([0,7])\n",
    "# plt.title('上海职住量比较',fontsize=18)\n",
    "plt.xlabel('居民出行调查数据扩样用户数 (万人)',fontsize=16)\n",
    "plt.ylabel('手机信令数据扩样用户数 (万人)',fontsize=16)\n",
    "plt.xticks(range(8),['$10^{}$'.format(x) for x in range(8)])\n",
    "plt.yticks(range(8),['$10^{}$'.format(x) for x in range(8)])\n",
    "plt.xticks(fontsize=14)\n",
    "plt.yticks(fontsize=14)\n",
    "\n",
    "# plt.savefig('C:\\\\Users\\Administrator\\Desktop\\z2.png',bbox_inches='tight',pad_inches=0,dpi=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuwAAAFgCAYAAADguizCAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nH0y6xsX57VdpAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ce6390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12,5))\n",
    "plt.subplot(1,2,1)\n",
    "xsh = county_combine.new_cnt_hts.values.reshape((-1,1))\n",
    "ysh = county_combine.new_cnt_mobile.values.reshape((-1,1))\n",
    "plt.plot(range(10),range(10),color='r')\n",
    "plt.scatter(np.log10(xsh),np.log10(ysh), marker='o', linewidths=1, c='b',alpha=0.5)\n",
    "\n",
    "plt.xlim([0,8])\n",
    "plt.ylim([0,8])\n",
    "plt.title('(a) 上海市',fontsize=18)\n",
    "plt.xlabel('居民出行调查数据扩样用户数',fontsize=16)\n",
    "plt.ylabel('手机信令数据扩样用户数',fontsize=16)\n",
    "plt.xticks(range(9),['$10^{}$'.format(x) for x in range(9)])\n",
    "plt.yticks(range(9),['$10^{}$'.format(x) for x in range(9)])\n",
    "plt.xticks(fontsize=14)\n",
    "plt.yticks(fontsize=14)\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "combine = pd.merge(hwNJ_countycnt2,hwNJ_ncnt,left_on=['County_h','County_w'],right_on=['H_XZ','W_XZ'],suffixes=['_mobile','_hts'])\n",
    "x = combine.new_cnt_hts.values.reshape((-1,1))\n",
    "y = combine.new_cnt_mobile.values.reshape((-1,1))\n",
    "plt.plot(range(10),range(10),color='r')\n",
    "plt.scatter(np.log10(x),np.log10(y), marker='o', linewidths=1, c='b',alpha=0.5)\n",
    "\n",
    "plt.xlim([0,8])\n",
    "plt.ylim([0,8])\n",
    "plt.title('(b) 南京市',fontsize=18)\n",
    "plt.xlabel('居民出行调查数据扩样用户数',fontsize=16)\n",
    "plt.ylabel('手机信令数据扩样用户数',fontsize=16)\n",
    "plt.xticks(range(9),['$10^{}$'.format(x) for x in range(9)])\n",
    "plt.yticks(range(9),['$10^{}$'.format(x) for x in range(9)])\n",
    "plt.xticks(fontsize=14)\n",
    "plt.yticks(fontsize=14)\n",
    "plt.subplots_adjust(wspace=0.3)\n",
    "\n",
    "\n",
    "# plt.savefig('C:\\\\Users\\Administrator\\Desktop\\z1.png',bbox_inches='tight',pad_inches=0,dpi=600)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (4) hw1分析---上海市域、外环"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>user_id</th>\n",
       "      <th>loncol_500_h</th>\n",
       "      <th>latcol_500_h</th>\n",
       "      <th>name_h</th>\n",
       "      <th>County_h</th>\n",
       "      <th>City_h</th>\n",
       "      <th>Province_h</th>\n",
       "      <th>GridRing_h</th>\n",
       "      <th>TownRing_h</th>\n",
       "      <th>loncol_500_w</th>\n",
       "      <th>latcol_500_w</th>\n",
       "      <th>name_w</th>\n",
       "      <th>County_w</th>\n",
       "      <th>City_w</th>\n",
       "      <th>Province_w</th>\n",
       "      <th>GridRing_w</th>\n",
       "      <th>TownRing_w</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16</td>\n",
       "      <td>571.0</td>\n",
       "      <td>116.0</td>\n",
       "      <td>虹桥镇</td>\n",
       "      <td>闵行区</td>\n",
       "      <td>上海市</td>\n",
       "      <td>上海市</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>571.0</td>\n",
       "      <td>116.0</td>\n",
       "      <td>虹桥镇</td>\n",
       "      <td>闵行区</td>\n",
       "      <td>上海市</td>\n",
       "      <td>上海市</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>31</td>\n",
       "      <td>562.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>罗店镇</td>\n",
       "      <td>宝山区</td>\n",
       "      <td>上海市</td>\n",
       "      <td>上海市</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>572.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>仙霞新村街道</td>\n",
       "      <td>长宁区</td>\n",
       "      <td>上海市</td>\n",
       "      <td>上海市</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>34</td>\n",
       "      <td>591.0</td>\n",
       "      <td>146.0</td>\n",
       "      <td>新江湾城街道</td>\n",
       "      <td>杨浦区</td>\n",
       "      <td>上海市</td>\n",
       "      <td>上海市</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>578.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>漕河泾街道</td>\n",
       "      <td>徐汇区</td>\n",
       "      <td>上海市</td>\n",
       "      <td>上海市</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>38</td>\n",
       "      <td>86.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>秣陵街道</td>\n",
       "      <td>江宁区</td>\n",
       "      <td>南京市</td>\n",
       "      <td>江苏省</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>秣陵街道</td>\n",
       "      <td>江宁区</td>\n",
       "      <td>南京市</td>\n",
       "      <td>江苏省</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>40</td>\n",
       "      <td>205.0</td>\n",
       "      <td>376.0</td>\n",
       "      <td>汤汪乡</td>\n",
       "      <td>广陵区</td>\n",
       "      <td>扬州市</td>\n",
       "      <td>江苏省</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>197.0</td>\n",
       "      <td>381.0</td>\n",
       "      <td>文汇街道</td>\n",
       "      <td>邗江区</td>\n",
       "      <td>扬州市</td>\n",
       "      <td>江苏省</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  loncol_500_h  latcol_500_h  name_h County_h City_h Province_h  \\\n",
       "0       16         571.0         116.0     虹桥镇      闵行区    上海市        上海市   \n",
       "1       31         562.0         160.0     罗店镇      宝山区    上海市        上海市   \n",
       "2       34         591.0         146.0  新江湾城街道      杨浦区    上海市        上海市   \n",
       "3       38          86.0         280.0    秣陵街道      江宁区    南京市        江苏省   \n",
       "4       40         205.0         376.0     汤汪乡      广陵区    扬州市        江苏省   \n",
       "\n",
       "   GridRing_h  TownRing_h  loncol_500_w  latcol_500_w  name_w County_w City_w  \\\n",
       "0         1.0         1.0         571.0         116.0     虹桥镇      闵行区    上海市   \n",
       "1         0.0         0.0         572.0         120.0  仙霞新村街道      长宁区    上海市   \n",
       "2         1.0         1.0         578.0         111.0   漕河泾街道      徐汇区    上海市   \n",
       "3         0.0         0.0          86.0         280.0    秣陵街道      江宁区    南京市   \n",
       "4         0.0         0.0         197.0         381.0    文汇街道      邗江区    扬州市   \n",
       "\n",
       "  Province_w  GridRing_w  TownRing_w  \n",
       "0        上海市         1.0         1.0  \n",
       "1        上海市         1.0         1.0  \n",
       "2        上海市         1.0         1.0  \n",
       "3        江苏省         0.0         0.0  \n",
       "4        江苏省         0.0         0.0  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 799个栅格没有乡镇对应\n",
    "hwBaseTown.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### $1 上海市区(外环)---栅格层面"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# shanghai outer ring，不需要剔除空值，都有对应的栅格\n",
    "shor1 = hwBaseTown[(hwBaseTown.GridRing_h ==1) & (hwBaseTown.GridRing_w == 1)]    # 在市区居住、在市区工作\n",
    "shor2 = hwBaseTown[(hwBaseTown.GridRing_h ==1) & (hwBaseTown.GridRing_w == 0)]    # 在市区居住、在市区外工作\n",
    "shor3 = hwBaseTown[(hwBaseTown.GridRing_h ==0) & (hwBaseTown.GridRing_w == 1)]    # 在市区外居住、在市区工作\n",
    "shor4 = hwBaseTown[(hwBaseTown.GridRing_h ==0) & (hwBaseTown.GridRing_w == 0)]    # 在市区外居住、在市区外工作"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 内向通勤\n",
    "shor3_hcnt = shor3.groupby(['loncol_500_h','latcol_500_h']).user_id.count().reset_index()\n",
    "shor3_hcnt.rename(columns={'user_id':'cnt'},inplace=True)\n",
    "shor3_hcnt_grid = grid.merge(shor3_hcnt,left_on=['LONCOL','LATCOL'],right_on=['loncol_500_h','latcol_500_h'],how='inner')\n",
    "shor3_hcnt_grid.to_file('../190622上海通勤率分析/ArcGIS图/shor3_homecnt.shp',encoding='gb2312')\n",
    "\n",
    "shor3_wcnt = shor3.groupby(['loncol_500_w','latcol_500_w']).user_id.count().reset_index()\n",
    "shor3_wcnt.rename(columns={'user_id':'cnt'},inplace=True)\n",
    "shor3_wcnt_grid = grid.merge(shor3_wcnt,left_on=['LONCOL','LATCOL'],right_on=['loncol_500_w','latcol_500_w'],how='inner')\n",
    "shor3_wcnt_grid.to_file('../190622上海通勤率分析/ArcGIS图/shor3_workcnt.shp',encoding='gb2312')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 外向通勤\n",
    "shor2_hcnt = shor2.groupby(['loncol_500_h','latcol_500_h']).user_id.count().reset_index()\n",
    "shor2_hcnt.rename(columns={'user_id':'cnt'},inplace=True)\n",
    "shor2_hcnt_grid = grid.merge(shor2_hcnt,left_on=['LONCOL','LATCOL'],right_on=['loncol_500_h','latcol_500_h'],how='inner')\n",
    "shor2_hcnt_grid.to_file('../190622上海通勤率分析/ArcGIS图/shor2_homecnt.shp',encoding='gb2312')\n",
    "\n",
    "shor2_wcnt = shor2.groupby(['loncol_500_w','latcol_500_w']).user_id.count().reset_index()\n",
    "shor2_wcnt.rename(columns={'user_id':'cnt'},inplace=True)\n",
    "shor2_wcnt_grid = grid.merge(shor2_wcnt,left_on=['LONCOL','LATCOL'],right_on=['loncol_500_w','latcol_500_w'],how='inner')\n",
    "shor2_wcnt_grid.to_file('../190622上海通勤率分析/ArcGIS图/shor2_workcnt.shp',encoding='gb2312')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### $2 上海市区(外环)---街道层面"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# shanghai outer ring town\n",
    "hwBaseTown2 = hwBaseTown.dropna()    # 对于街道，先剔除栅格无对应的数据\n",
    "\n",
    "short1 = hwBaseTown2[(hwBaseTown2.TownRing_h ==1) & (hwBaseTown2.TownRing_w == 1)]    # 在市区居住、在市区工作\n",
    "short2 = hwBaseTown2[(hwBaseTown2.TownRing_h ==1) & (hwBaseTown2.TownRing_w == 0)]    # 在市区居住、在市区外工作\n",
    "short3 = hwBaseTown2[(hwBaseTown2.TownRing_h ==0) & (hwBaseTown2.TownRing_w == 1)]    # 在市区外居住、在市区工作\n",
    "short4 = hwBaseTown2[(hwBaseTown2.TownRing_h ==0) & (hwBaseTown2.TownRing_w == 0)]    # 在市区外居住、在市区外工作\n",
    "\n",
    "# 内向通勤\n",
    "short3_hcnt = short3.groupby(['name_h','County_h']).user_id.count().reset_index()\n",
    "short3_hcnt.rename(columns={'user_id':'cnt'},inplace=True)\n",
    "short3_hcnt_town = town.merge(short3_hcnt,left_on=['name','County'],right_on=['name_h','County_h'],how='inner')\n",
    "short3_hcnt_town.to_file('../190622上海通勤率分析/ArcGIS图/short3_homecnt_town.shp',encoding='gb2312')\n",
    "\n",
    "# 外向通勤\n",
    "short2_wcnt = short2.groupby(['name_w','County_w']).user_id.count().reset_index()\n",
    "short2_wcnt.rename(columns={'user_id':'cnt'},inplace=True)\n",
    "short2_wcnt = pd.merge(short2_wcnt,pop_town_sh,left_on=['name_w','County_w'],right_on=['name','County'],how='inner')\n",
    "short2_wcnt['rate'] = short2_wcnt.cnt / short2_wcnt.population\n",
    "short2_wcnt_town = town.merge(short2_wcnt[['name_w','County_w','rate']],left_on=['name','County'],right_on=['name_w','County_w'],how='inner')\n",
    "short2_wcnt_town.to_file('../190622上海通勤率分析/ArcGIS图/short2_workcnt_town.shp',encoding='gb2312')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "hwBaseTown2 = hwBaseTown.dropna()    # 对于街道，先剔除栅格无对应的数据\n",
    "\n",
    "# 上海乡镇街道人口，columns=['County', 'name', 'population']\n",
    "pop_town_sh = pd.read_csv('../0基础数据/上海乡镇人口数据.csv',encoding='gb2312')\n",
    "\n",
    "# 扩样\n",
    "hw1_town_user = hwBaseTown2.groupby(['County_h','name_h']).user_id.count().reset_index()\n",
    "hw1_town_user.rename(columns={'user_id':'user_num'},inplace=True)\n",
    "hw1_town_user = pd.merge(hw1_town_user,pop_town_sh,left_on=['County_h','name_h'],right_on=['County','name'],how='left')\n",
    "hwBaseTown2 = pd.merge(hwBaseTown2,hw1_town_user,on=['County_h','name_h'],how='left')\n",
    "hwBaseTown2['expand'] = hwBaseTown2.population / hwBaseTown2.user_num\n",
    "\n",
    "short1 = hwBaseTown2[(hwBaseTown2.TownRing_h ==1) & (hwBaseTown2.TownRing_w == 1)]    # 在市区居住、在市区工作\n",
    "short2 = hwBaseTown2[(hwBaseTown2.TownRing_h ==1) & (hwBaseTown2.TownRing_w == 0)]    # 在市区居住、在市区外工作\n",
    "short3 = hwBaseTown2[(hwBaseTown2.TownRing_h ==0) & (hwBaseTown2.TownRing_w == 1)]    # 在市区外居住、在市区工作\n",
    "short4 = hwBaseTown2[(hwBaseTown2.TownRing_h ==0) & (hwBaseTown2.TownRing_w == 0)]    # 在市区外居住、在市区外工作\n",
    "\n",
    "# 内向通勤\n",
    "short3_hcnt = short3.groupby(['name_h','County_h']).expand.sum().reset_index()\n",
    "short3_hcnt.rename(columns={'expand':'cnt'},inplace=True)\n",
    "short3_hcnt = pd.merge(short3_hcnt,pop_town_sh,left_on=['name_h','County_h'],right_on=['name','County'],how='inner')\n",
    "short3_hcnt['rate'] = short3_hcnt.cnt / short3_hcnt.population\n",
    "short3_hcnt_town = town.merge(short3_hcnt[['name_h','County_h','rate']],left_on=['name','County'],right_on=['name_h','County_h'],how='inner')\n",
    "short3_hcnt_town.to_file('../190622上海通勤率分析/ArcGIS图-扩样/short3_homecnt_town.shp',encoding='gb2312')\n",
    "\n",
    "# 外向通勤\n",
    "short2_wcnt = short2.groupby(['name_w','County_w']).expand.sum().reset_index()\n",
    "short2_wcnt.rename(columns={'expand':'cnt'},inplace=True)\n",
    "short2_wcnt = pd.merge(short2_wcnt,pop_town_sh,left_on=['name_w','County_w'],right_on=['name','County'],how='inner')\n",
    "short2_wcnt['rate'] = short2_wcnt.cnt / short2_wcnt.population\n",
    "short2_wcnt_town = town.merge(short2_wcnt[['name_w','County_w','rate']],left_on=['name','County'],right_on=['name_w','County_w'],how='inner')\n",
    "short2_wcnt_town.to_file('../190622上海通勤率分析/ArcGIS图-扩样/short2_workcnt_town.shp',encoding='gb2312')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "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>user_id</th>\n",
       "      <th>loncol_500_h</th>\n",
       "      <th>latcol_500_h</th>\n",
       "      <th>name_h</th>\n",
       "      <th>County_h</th>\n",
       "      <th>City_h</th>\n",
       "      <th>Province_h</th>\n",
       "      <th>GridRing_h</th>\n",
       "      <th>TownRing_h</th>\n",
       "      <th>loncol_500_w</th>\n",
       "      <th>latcol_500_w</th>\n",
       "      <th>name_w</th>\n",
       "      <th>County_w</th>\n",
       "      <th>City_w</th>\n",
       "      <th>Province_w</th>\n",
       "      <th>GridRing_w</th>\n",
       "      <th>TownRing_w</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16</td>\n",
       "      <td>571.0</td>\n",
       "      <td>116.0</td>\n",
       "      <td>虹桥镇</td>\n",
       "      <td>闵行区</td>\n",
       "      <td>上海市</td>\n",
       "      <td>上海市</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>571.0</td>\n",
       "      <td>116.0</td>\n",
       "      <td>虹桥镇</td>\n",
       "      <td>闵行区</td>\n",
       "      <td>上海市</td>\n",
       "      <td>上海市</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>31</td>\n",
       "      <td>562.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>罗店镇</td>\n",
       "      <td>宝山区</td>\n",
       "      <td>上海市</td>\n",
       "      <td>上海市</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>572.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>仙霞新村街道</td>\n",
       "      <td>长宁区</td>\n",
       "      <td>上海市</td>\n",
       "      <td>上海市</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>34</td>\n",
       "      <td>591.0</td>\n",
       "      <td>146.0</td>\n",
       "      <td>新江湾城街道</td>\n",
       "      <td>杨浦区</td>\n",
       "      <td>上海市</td>\n",
       "      <td>上海市</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>578.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>漕河泾街道</td>\n",
       "      <td>徐汇区</td>\n",
       "      <td>上海市</td>\n",
       "      <td>上海市</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>38</td>\n",
       "      <td>86.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>秣陵街道</td>\n",
       "      <td>江宁区</td>\n",
       "      <td>南京市</td>\n",
       "      <td>江苏省</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>秣陵街道</td>\n",
       "      <td>江宁区</td>\n",
       "      <td>南京市</td>\n",
       "      <td>江苏省</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>40</td>\n",
       "      <td>205.0</td>\n",
       "      <td>376.0</td>\n",
       "      <td>汤汪乡</td>\n",
       "      <td>广陵区</td>\n",
       "      <td>扬州市</td>\n",
       "      <td>江苏省</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>197.0</td>\n",
       "      <td>381.0</td>\n",
       "      <td>文汇街道</td>\n",
       "      <td>邗江区</td>\n",
       "      <td>扬州市</td>\n",
       "      <td>江苏省</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  loncol_500_h  latcol_500_h  name_h County_h City_h Province_h  \\\n",
       "0       16         571.0         116.0     虹桥镇      闵行区    上海市        上海市   \n",
       "1       31         562.0         160.0     罗店镇      宝山区    上海市        上海市   \n",
       "2       34         591.0         146.0  新江湾城街道      杨浦区    上海市        上海市   \n",
       "3       38          86.0         280.0    秣陵街道      江宁区    南京市        江苏省   \n",
       "4       40         205.0         376.0     汤汪乡      广陵区    扬州市        江苏省   \n",
       "\n",
       "   GridRing_h  TownRing_h  loncol_500_w  latcol_500_w  name_w County_w City_w  \\\n",
       "0         1.0         1.0         571.0         116.0     虹桥镇      闵行区    上海市   \n",
       "1         0.0         0.0         572.0         120.0  仙霞新村街道      长宁区    上海市   \n",
       "2         1.0         1.0         578.0         111.0   漕河泾街道      徐汇区    上海市   \n",
       "3         0.0         0.0          86.0         280.0    秣陵街道      江宁区    南京市   \n",
       "4         0.0         0.0         197.0         381.0    文汇街道      邗江区    扬州市   \n",
       "\n",
       "  Province_w  GridRing_w  TownRing_w  \n",
       "0        上海市         1.0         1.0  \n",
       "1        上海市         1.0         1.0  \n",
       "2        上海市         1.0         1.0  \n",
       "3        江苏省         0.0         0.0  \n",
       "4        江苏省         0.0         0.0  "
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hwBaseTown.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "City_w\n",
       "上海市    2846531\n",
       "苏州市      13555\n",
       "南通市        772\n",
       "无锡市        256\n",
       "南京市        130\n",
       "常州市         79\n",
       "泰州市         54\n",
       "镇江市         22\n",
       "扬州市         22\n",
       "Name: user_id, dtype: int64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hsh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "hsh = hwBaseTown[hwBaseTown.City_h == '上海市'].groupby('City_w').count().user_id.sort_values(ascending=False)\n",
    "wsh = hwBaseTown[hwBaseTown.City_w == '上海市'].groupby('City_h').count().user_id.sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "City_h\n",
       "上海市    2846531\n",
       "苏州市      13950\n",
       "南通市       2477\n",
       "无锡市        231\n",
       "南京市        104\n",
       "常州市         59\n",
       "镇江市         24\n",
       "泰州市         19\n",
       "扬州市         19\n",
       "Name: user_id, dtype: int64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wsh"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 上海市域---栅格层面"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame(grid.drop(columns='geometry')).to_csv('C:\\\\Users\\\\Administrator\\\\Desktop\\\\grid_town.csv',encoding='gbk',index=False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}