06_02 compare greater LA versus whole CA.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "217ffec8-ac33-4be1-aa36-e93d1a7a4689",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.cm as cm\n",
    "import pickle\n",
    "import seaborn as sns\n",
    "import geopandas as gpd\n",
    "from scipy.stats import pearsonr\n",
    "from scipy import stats\n",
    "\n",
    "pd.set_option('display.max_colwidth', 0)\n",
    "pd.set_option('display.max_rows', 500)\n",
    "pd.set_option('display.max_columns', 500)\n",
    "pd.set_option('display.width', 1000)\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "546a5e61-1ba4-48b1-9f3d-d1851a2ebdc2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "23212\n",
      "23141\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>place_id</th>\n",
       "      <th>home_scaled_entropy</th>\n",
       "      <th>home_class_dominant</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>25609839</td>\n",
       "      <td>0.096533</td>\n",
       "      <td>R_HispanicLatinx_low diversity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>31157976</td>\n",
       "      <td>0.096533</td>\n",
       "      <td>R_HispanicLatinx_low diversity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>27287495</td>\n",
       "      <td>0.096533</td>\n",
       "      <td>R_HispanicLatinx_low diversity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>25590381</td>\n",
       "      <td>0.472712</td>\n",
       "      <td>R_HispanicLatinx_moderate diversity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>25105312</td>\n",
       "      <td>0.472712</td>\n",
       "      <td>R_HispanicLatinx_moderate diversity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61963</th>\n",
       "      <td>31930253</td>\n",
       "      <td>0.312969</td>\n",
       "      <td>R_HispanicLatinx_low diversity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61964</th>\n",
       "      <td>25292594</td>\n",
       "      <td>0.289936</td>\n",
       "      <td>R_HispanicLatinx_low diversity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61965</th>\n",
       "      <td>25499120</td>\n",
       "      <td>0.562175</td>\n",
       "      <td>R_HispanicLatinx_moderate diversity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61966</th>\n",
       "      <td>25538751</td>\n",
       "      <td>0.762543</td>\n",
       "      <td>high diversity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61967</th>\n",
       "      <td>25529367</td>\n",
       "      <td>0.750106</td>\n",
       "      <td>high diversity</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>61968 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       place_id  home_scaled_entropy                  home_class_dominant\n",
       "0      25609839  0.096533             R_HispanicLatinx_low diversity     \n",
       "1      31157976  0.096533             R_HispanicLatinx_low diversity     \n",
       "2      27287495  0.096533             R_HispanicLatinx_low diversity     \n",
       "3      25590381  0.472712             R_HispanicLatinx_moderate diversity\n",
       "4      25105312  0.472712             R_HispanicLatinx_moderate diversity\n",
       "...         ...       ...                                             ...\n",
       "61963  31930253  0.312969             R_HispanicLatinx_low diversity     \n",
       "61964  25292594  0.289936             R_HispanicLatinx_low diversity     \n",
       "61965  25499120  0.562175             R_HispanicLatinx_moderate diversity\n",
       "61966  25538751  0.762543             high diversity                     \n",
       "61967  25529367  0.750106             high diversity                     \n",
       "\n",
       "[61968 rows x 3 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "home_diversity = pd.read_pickle(\"../output/CA_CBG_dominantRace.pkl\") #CBG level diversity\n",
    "print(home_diversity.shape[0])\n",
    "home_diversity = home_diversity[home_diversity['scaled_entropy'].notna()]\n",
    "print(home_diversity.shape[0])\n",
    "df_poi_CBG = pd.read_pickle('../output/LA_POI_belong_CBG.pkl') # each POI belongs to which CBG\n",
    "df_poi_CBG_merged = pd.merge(df_poi_CBG,home_diversity,on='block_group_id')\n",
    "df_poi_CBG_mergedneed = df_poi_CBG_merged[['place_id','scaled_entropy','class_dominant']]\n",
    "df_poi_CBG_mergedneed.rename(columns={'scaled_entropy': 'home_scaled_entropy','class_dominant':'home_class_dominant'}, inplace=True)\n",
    "df_poi_CBG_mergedneed # each POI's home based diversity"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d28cf91-fc22-4e72-af72-e2c766e11202",
   "metadata": {},
   "source": [
    "# 1. Greater LA\n",
    "Only use the normalized entropy. Do not consider the one weighted by activity duration. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "f6f80af8-2fe6-46fa-8b31-327d69294654",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "23888 28287 28330 22178\n"
     ]
    }
   ],
   "source": [
    "weekday = False #False True\n",
    "if weekday:\n",
    "    diversity_morning_GLA = pd.read_pickle(\"../output/onlyGreaterLAresidents/LA_POI_diversity_201903_weekday_morning.pkl\")  \n",
    "    diversity_noon_GLA = pd.read_pickle(\"../output/onlyGreaterLAresidents/LA_POI_diversity_201903_weekday_noon.pkl\")\n",
    "    diversity_afternoon_GLA = pd.read_pickle(\"../output/onlyGreaterLAresidents/LA_POI_diversity_201903_weekday_afternoon.pkl\")\n",
    "    diversity_evening_GLA = pd.read_pickle(\"../output/onlyGreaterLAresidents/LA_POI_diversity_201903_weekday_evening.pkl\")\n",
    "    print(diversity_morning_GLA.shape[0],diversity_noon_GLA.shape[0],diversity_afternoon_GLA.shape[0],diversity_evening_GLA.shape[0])\n",
    "    title = 'Weekday'\n",
    "else: #weekend\n",
    "    diversity_morning_GLA = pd.read_pickle(\"../output/onlyGreaterLAresidents/LA_POI_diversity_201903_weekend_morning.pkl\")  \n",
    "    diversity_noon_GLA = pd.read_pickle(\"../output/onlyGreaterLAresidents/LA_POI_diversity_201903_weekend_noon.pkl\")\n",
    "    diversity_afternoon_GLA = pd.read_pickle(\"../output/onlyGreaterLAresidents/LA_POI_diversity_201903_weekend_afternoon.pkl\")\n",
    "    diversity_evening_GLA = pd.read_pickle(\"../output/onlyGreaterLAresidents/LA_POI_diversity_201903_weekend_evening.pkl\")\n",
    "    print(diversity_morning_GLA.shape[0],diversity_noon_GLA.shape[0],diversity_afternoon_GLA.shape[0],diversity_evening_GLA.shape[0])\n",
    "    title = 'Weekend'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "c0d1f267-16c7-4e04-8777-a9c5ae1c1e0b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "***************\n",
      "count: 23888\n",
      "mean: 0.603\n",
      "std: 0.165\n",
      "***************\n",
      "count: 28287\n",
      "mean: 0.626\n",
      "std: 0.152\n",
      "***************\n",
      "count: 28330\n",
      "mean: 0.629\n",
      "std: 0.149\n",
      "***************\n",
      "count: 22178\n",
      "mean: 0.614\n",
      "std: 0.159\n"
     ]
    }
   ],
   "source": [
    "for item in [diversity_morning_GLA,diversity_noon_GLA,diversity_afternoon_GLA,diversity_evening_GLA]:\n",
    "    print('***************')\n",
    "    print('count:',item.shape[0])\n",
    "    print('mean:',round(item.scaled_entropy.mean(),3))\n",
    "    print('std:',round(item.scaled_entropy.std(),3))    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9d1744a-3bd7-429c-af7a-36ab7ee9e37c",
   "metadata": {},
   "source": [
    "# 2. Whole CA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "9469bcfe-56e9-48f4-83c7-ac8306ab0338",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24051 28393 28416 22288\n"
     ]
    }
   ],
   "source": [
    "weekday = False#False True\n",
    "if weekday:\n",
    "    diversity_morning = pd.read_pickle(\"../output/LA_POI_diversity_201903_weekday_morning.pkl\")  \n",
    "    diversity_noon = pd.read_pickle(\"../output/LA_POI_diversity_201903_weekday_noon.pkl\")\n",
    "    diversity_afternoon = pd.read_pickle(\"../output/LA_POI_diversity_201903_weekday_afternoon.pkl\")\n",
    "    diversity_evening = pd.read_pickle(\"../output/LA_POI_diversity_201903_weekday_evening.pkl\")\n",
    "    print(diversity_morning.shape[0],diversity_noon.shape[0],diversity_afternoon.shape[0],diversity_evening.shape[0])\n",
    "else: #weekend\n",
    "    diversity_morning = pd.read_pickle(\"../output/LA_POI_diversity_201903_weekend_morning.pkl\")  \n",
    "    diversity_noon = pd.read_pickle(\"../output/LA_POI_diversity_201903_weekend_noon.pkl\")\n",
    "    diversity_afternoon = pd.read_pickle(\"../output/LA_POI_diversity_201903_weekend_afternoon.pkl\")\n",
    "    diversity_evening = pd.read_pickle(\"../output/LA_POI_diversity_201903_weekend_evening.pkl\")\n",
    "    print(diversity_morning.shape[0],diversity_noon.shape[0],diversity_afternoon.shape[0],diversity_evening.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "c20a7323-e85f-49e6-8a5c-e0b5a8da11ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "***************\n",
      "count: 24051\n",
      "mean: 0.606\n",
      "std: 0.164\n",
      "***************\n",
      "count: 28393\n",
      "mean: 0.628\n",
      "std: 0.151\n",
      "***************\n",
      "count: 28416\n",
      "mean: 0.631\n",
      "std: 0.148\n",
      "***************\n",
      "count: 22288\n",
      "mean: 0.616\n",
      "std: 0.158\n"
     ]
    }
   ],
   "source": [
    "for item in [diversity_morning,diversity_noon,diversity_afternoon,diversity_evening]:\n",
    "    print('***************')\n",
    "    print('count:',item.shape[0])\n",
    "    print('mean:',round(item.scaled_entropy.mean(),3))\n",
    "    print('std:',round(item.scaled_entropy.std(),3))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c87d9c73-4c46-42ff-b375-d2c9d9f83a2b",
   "metadata": {},
   "source": [
    "# Mann-Whitney U statistic\n",
    "Sample size of two groups varies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "d1c6a351-fd1a-467e-b5c5-fd057d22fb6c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mann-Whitney U statistic: 289894941.0\n",
      "p-value: 0.083\n",
      "Mann-Whitney U statistic: 405454274.0\n",
      "p-value: 0.046\n",
      "Mann-Whitney U statistic: 406234881.5\n",
      "p-value: 0.056\n",
      "Mann-Whitney U statistic: 249622395.0\n",
      "p-value: 0.068\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import mannwhitneyu\n",
    "\n",
    "# diversity_morning.scaled_entropy, diversity_morning_GLA.scaled_entropy\n",
    "# diversity_noon.scaled_entropy, diversity_noon_GLA.scaled_entropy\n",
    "# diversity_afternoon.scaled_entropy, diversity_afternoon_GLA.scaled_entropy\n",
    "# diversity_evening.scaled_entropy, diversity_evening_GLA.scaled_entropy\n",
    "\n",
    "CAlist = [diversity_morning.scaled_entropy,diversity_noon.scaled_entropy,diversity_afternoon.scaled_entropy,diversity_evening.scaled_entropy]\n",
    "GLAlist = [diversity_morning_GLA.scaled_entropy,diversity_noon_GLA.scaled_entropy,diversity_afternoon_GLA.scaled_entropy,diversity_evening_GLA.scaled_entropy]\n",
    "\n",
    "for i in range(0,len(CAlist)):\n",
    "    statistic, p_value = mannwhitneyu(CAlist[i], GLAlist[i])\n",
    "    # Print the test statistic and p-value\n",
    "    print(\"Mann-Whitney U statistic:\", statistic)\n",
    "    print(\"p-value:\", round(p_value,3))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e505962-cd50-4d39-905b-f89d17dadef5",
   "metadata": {
    "tags": []
   },
   "source": [
    "# The Wilcoxon signed-rank test test (no need to run below)\n",
    "Groups come from the same population. Paired test."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "7c30c01b-e404-43aa-89ca-aba10e466540",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "44384 44287 44287\n",
      "0.6327605820650889 0.6311564291880429\n",
      "WilcoxonResult(statistic=9468954.0, pvalue=6.811028491203887e-254)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_merge1 = pd.merge(diversity_morning[['place_id','scaled_entropy']],diversity_morning_GLA[['place_id','scaled_entropy']],on='place_id')\n",
    "print(diversity_morning.shape[0], diversity_morning_GLA.shape[0], df_merge1.shape[0])\n",
    "print(df_merge1.scaled_entropy_x.mean(),df_merge1.scaled_entropy_y.mean())\n",
    "print(stats.wilcoxon(df_merge1.scaled_entropy_x, df_merge1.scaled_entropy_y))\n",
    "fig, ax = plt.subplots(figsize=(6,5))\n",
    "ax = sns.boxplot(data=df_merge1[['scaled_entropy_x','scaled_entropy_y']])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "ce0a0ec4-4248-4ba9-a3dd-dc0351ae7fac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "46503 46425 46425\n",
      "0.6492164630952061 0.6472287904738719\n",
      "WilcoxonResult(statistic=19985540.0, pvalue=0.0)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_merge1 = pd.merge(diversity_noon[['place_id','scaled_entropy']],diversity_noon_GLA[['place_id','scaled_entropy']],on='place_id')\n",
    "print(diversity_noon.shape[0], diversity_noon_GLA.shape[0], df_merge1.shape[0])\n",
    "print(df_merge1.scaled_entropy_x.mean(),df_merge1.scaled_entropy_y.mean())\n",
    "print(stats.wilcoxon(df_merge1.scaled_entropy_x, df_merge1.scaled_entropy_y))\n",
    "fig, ax = plt.subplots(figsize=(6,5))\n",
    "ax = sns.boxplot(data=df_merge1[['scaled_entropy_x','scaled_entropy_y']])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "dbed8a60-3298-48b8-965f-fe5ab944922a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "46914 46844 46844\n",
      "0.6457550470246142 0.6437508868843679\n",
      "WilcoxonResult(statistic=23521575.0, pvalue=0.0)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_merge1 = pd.merge(diversity_afternoon[['place_id','scaled_entropy']],diversity_afternoon_GLA[['place_id','scaled_entropy']],on='place_id')\n",
    "print(diversity_afternoon.shape[0], diversity_afternoon_GLA.shape[0], df_merge1.shape[0])\n",
    "print(df_merge1.scaled_entropy_x.mean(),df_merge1.scaled_entropy_y.mean())\n",
    "print(stats.wilcoxon(df_merge1.scaled_entropy_x, df_merge1.scaled_entropy_y))\n",
    "fig, ax = plt.subplots(figsize=(6,5))\n",
    "ax = sns.boxplot(data=df_merge1[['scaled_entropy_x','scaled_entropy_y']])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "cf844e42-9b4a-4019-8919-38b277d4bf29",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "41644 41512 41512\n",
      "0.6310616360614425 0.6290542256507899\n",
      "WilcoxonResult(statistic=13675382.0, pvalue=0.0)\n"
     ]
    },
    {
     "data": {
      "image/png": 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0fv16hYWFadSoUW7j+/XrJ7vd3qwdAAAEjseBIC0tTRUVFVq2bJlKS0s1atQobd68WYMGDZIklZaWtvmbBAAAILhYjDEm0EW0paqqSk6nU5WVlXI4HIEuB144fvy4UlNTJUnZ2dmKiooKcEUATsUc7Xra+xnKtQwAAACBAAAAEAgAAIAIBAAAQAQCAAAgAgEAABCBAAAAiEAAAABEIAAAACIQAAAAEQgAAIAIBAAAQAQCAAAgAgEAABCBAAAAiEAAAABEIAAAACIQAAAAEQgAAIAIBAAAQAQCAAAgAgEAABCBAAAAiEAAAABEIAAAACIQAAAAEQgQQD/72c8CXQIA4L8IBPCrgoIC139XV1frn//8Z+CKAQC4EAjgV3fffbfb/QULFgSmEABtYhWve4kIdAHoGGOMampqAl2GR+6///4W25csWaLf/va3fq7GO3a7XRaLJdBlAJ2upVW80aNHB64g+I3FGGMCXURbqqqq5HQ6VVlZKYfDEehygsLx48eVmpoa6DK6rezsbEVFRQW6DKDTTZw4sVlbbm5uACpBZ2nvZygrBADQyUJxBU/qOqt4rOB1DCsEISrU3nDas5qRnZ3th0o6B284OB1W8AKLFTx3rBB0cRaLpcu94Lva/gBAKCEQAEAns9vtIbXiJXWtVTy73R7oEkISgQAAOllXXMGTWMXr6vgdAgAAQCAAAAAEAgAAIAIBAAAQgQAAAIhAAAAARCAAAAAiEAAAABEIAACACAQAAEAEAgAAIAIBAAAQgQAAAIhAAAAARCAAAAAiEAAAABEIAACACAQAAEAEAgAAIAIBAAAQgQAAAIhAAAAARCAAAAAiEAAAABEIAACACAQAAEAEAgAAIAIBAAAQgQAAAKiDgSArK0tDhgyR3W5XUlKStm7d2uq2r7zyiiZPnqwzzzxTDodD48aNU3Z2docLBgAAnc/jQLBx40YtWLBAS5YsUX5+viZMmKApU6aoqKioxe1zc3M1efJkbd68WXl5ebriiit07bXXKj8/3+viAQBA57AYY4wnAy6++GKNHj1aq1atcrWNHDlS06dPV2ZmZrse47zzzlNaWpruvffeFvtra2tVW1vrul9VVaX4+HhVVlbK4XB4Ui6CxMSJE9vcJjc31w+VAGgJc7TrqqqqktPpbPMz1KMVgrq6OuXl5SklJcWtPSUlRdu3b2/XYzQ1Nam6ulq9e/dudZvMzEw5nU7XLT4+3pMyAQCAhzwKBOXl5WpsbFRsbKxbe2xsrA4dOtSux3j00Ud19OhRXX/99a1us3jxYlVWVrpuxcXFnpQJAAA8FNGRQRaLxe2+MaZZW0uef/55/f73v9frr7+ufv36tbqdzWaTzWbrSGkAAKADPFoh6Nu3r8LDw5utBpSVlTVbNfhfGzdu1Jw5c7Rp0yZdddVVnleKkBYeHu5VPwDAtzwKBFarVUlJScrJyXFrz8nJUXJycqvjnn/+ed1444167rnndM0113SsUoS0xsZGr/oBAL7l8SGDRYsWadasWRozZozGjRunp59+WkVFRcrIyJB04vj/N998o/Xr10s6EQbS09P1+OOP65JLLnGtLkRFRcnpdHbirgAAgI7yOBCkpaWpoqJCy5YtU2lpqUaNGqXNmzdr0KBBkqTS0lK33yR46qmn1NDQoNtuu0233Xabq3327Nlat26d93sAAAC85vHvEARCe79DieDFd5yB4MYc7bp88jsEQEdFR0d71Q/At3r06OFVP0IfgQB+cezYMa/6AfjW0aNHvepH6CMQwC/41wcQ3Nr67Rd+G6brIxDAL/jXBxDcTr1+TEf6EfoIBAAAgEAAAAAIBPATfroYAIIbgQB+0da1LtrqB+Bbdrvdq36EPgIB/KKkpMSrfgC+VVNT41U/Qh+BAH7Bvz6A4MZXg0EggF8QCIDgZrFYvOpH6CMQwC/4pUIguB0/ftyrfoQ+AgH8IiLi9BfWbKsfgG/xS4UgEMAvGhoavOoH4FvMURAI4Be9evXyqh+Ab3GeDwgE8Ivy8nKv+gH41pEjR7zqR+gjEMAvGhsbveoH4FtNTU1e9SP0EQjgF9HR0V71A/AtTvwFgQB+MWTIEK/6AfjW8OHDvepH6CMQwC+cTqdX/QB8q2fPnl71I/QRCOAXnFQIBLfvvvvOq36EPgIB/GL37t1e9QPwrT179njVj9BHIAAAAAQC+MePf/xjr/oB+NaYMWO86kfoIxDALzg+CQQ3zvMBgQB+ERZ2+pdaW/0AfIvLH4N3YfgFv1QIBDd+qRAEAvgFv4IGBLfw8HCv+hH6CATwCw4ZAMGtvr7eq36EPt6F4Rc//PCDV/0AfKu6utqrfoQ+AgH8gosbAcGNnxcHgQB+ERMT41U/AN9ijoJAAL/gK01AcGOOgkAAvzh8+LBX/QB8izkKAgH8Ijk52at+AL7FHAWBAH6RmJjoVT8A3xo5cqRX/Qh9BAL4xXvvvedVPwDf+tvf/uZVP0IfgQB+0aNHD6/6AfhW3759vepH6CMQwC969uzpVT8A3+rVq5dX/Qh9BAIAAEAggH9UVlZ61Q/At5ijIBDAL7iWARDcmKMgEMAvzj77bK/6AfgWcxQEAvhFQUGBV/0AfIs5CgIB/CIs7PQvtbb6AfgW1zIA78LwCy5/DAQ3Qjv4Pwy/4EdPgOA2cOBAr/oR+ggE8Ithw4Z51Q/At84991yv+hH6CATwi88++8yrfgC+xRwFgQB+ERsb61U/AN9ijoJAAL/Yv3+/V/0AfIs5CgIB/OInP/mJV/0AfIs5CgIB/OLw4cNe9QPwLeYoCATwi++//96rfgC+xRwFgQB+wVeagODGHAWBAH7BciQQ3JijIBDAL2JiYrzqB+BbzFEQCOAXq1ev9qofgG8xR0EggF8sWbLEq34AvsUcBYEAfvHJJ5941Q/At5ijIBDAL2bPnu1VPwDfYo6CQAC/OHjwoFf9AHyLOQoCAfzitdde86ofgG8xR2ExxphAF9GWqqoqOZ1OVVZWyuFwBLocdEB9fb0mTZrUav+7776ryMhIP1YE4FTM0a6rvZ+hHVohyMrK0pAhQ2S325WUlKStW7eedvsPPvhASUlJstvtGjp0qJ588smOPC1CWGRkpH7+85+32HfDDTfwRgMEGHMUHgeCjRs3asGCBVqyZIny8/M1YcIETZkyRUVFRS1uv3//fk2dOlUTJkxQfn6+7rnnHs2fP18vv/yy18UjtMybN6/F9ltvvdXPlQBoCXO0e/P4kMHFF1+s0aNHa9WqVa62kSNHavr06crMzGy2/d1336033nhDhYWFrraMjAz961//0o4dO9r1nBwy6BquvvpqHTt2rFl7dHS0tmzZEoCKAJyKOdo1+eSQQV1dnfLy8pSSkuLWnpKSou3bt7c4ZseOHc22T01N1c6dO1VfX9/imNraWlVVVbndENoOHz7c4huNJB07dozfSQcCjDkKjwJBeXm5GhsbFRsb69YeGxurQ4cOtTjm0KFDLW7f0NCg8vLyFsdkZmbK6XS6bvHx8Z6UiSA0Y8YMr/oB+BZzFB06qdBisbjdN8Y0a2tr+5baT1q8eLEqKytdt+Li4o6UiSDy4osvetUPwLeYo4jwZOO+ffsqPDy82WpAWVlZs1WAk84666wWt4+IiFCfPn1aHGOz2WSz2TwpDUGud+/eio6ObvX4ZO/evQNQFYCTmKPwaIXAarUqKSlJOTk5bu05OTlKTk5uccy4ceOabf/OO+9ozJgxfI2lm2ntpCROVgKCA3O0e/P4kMGiRYv0zDPPaM2aNSosLNTChQtVVFSkjIwMSSeW+9PT013bZ2Rk6ODBg1q0aJEKCwu1Zs0arV69WnfeeWfn7QVCxi9+8YvT3gcQWMzR7qtDv1SYlZWlhx9+WKWlpRo1apQee+wxTZw4UZJ044036sCBA3r//fdd23/wwQdauHChvvzyS/Xv31933323K0C0B1877FpOvlYkKTc3N4CVAGgJc7Rrae9nKD9dDABAF+bTny4GAABdC4EAAAAQCAAAAIEAAACIQAAAAEQgAAAAIhAAAAARCAAAgAgEAABAHl7tMFBO/phiVVVVgCsBACC0nPzsbOuHiUMiEFRXV0uS4uPjA1wJAAChqbq6Wk6ns9X+kLiWQVNTk0pKShQTEyOLxRLocuClqqoqxcfHq7i4mGtTAEGIOdq1GGNUXV2t/v37Kyys9TMFQmKFICwsTAMHDgx0GehkDoeDNxsgiDFHu47TrQycxEmFAACAQAAAAAgECACbzab77rtPNpst0KUAaAFztHsKiZMKAQCAb7FCAAAACAQAAIBAAAAARCAAAAAiEHQL69atU8+ePb1+HIvFotdee83rxwHgjjmKYEAgQNAaPHiwVqxYEegyALSCOdq1EAgQ0hobG9XU1BToMgC0gjkaOggEQeSll17S+eefr6ioKPXp00dXXXWVjh49Kklas2aNzjvvPNlsNsXFxen22293jVu+fLnOP/989ejRQ/Hx8Zo3b56OHDly2ud68803lZSUJLvdrqFDh2rp0qVqaGhw9e/Zs0cTJ06U3W5XYmKicnJyPNqXb775RmlpaerVq5f69OmjadOm6cCBA67+G2+8UdOnT9cjjzyiuLg49enTR7fddpvq6+slSZdffrkOHjyohQsXymKxuC5qdXJp9a233lJiYqJsNpsOHjyo77//Xunp6erVq5eio6M1ZcoU7dmzx/V8J8e99tprGj58uOx2uyZPnqzi4mJJ0oEDBxQWFqadO3e67cfKlSs1aNCgNi8bumzZMvXv318VFRWutuuuu04TJ07kzbALYY6G5hw1xmjYsGF65JFH3Nq/+OILhYWFae/evR797bosg6BQUlJiIiIizPLly83+/fvNZ599Zp544glTXV1tsrKyjN1uNytWrDBfffWV+eSTT8xjjz3mGvvYY4+Z9957z+zbt8+8++67JiEhwfzyl7909a9du9Y4nU7X/S1bthiHw2HWrVtn9u7da9555x0zePBg8/vf/94YY0xjY6MZNWqUufzyy01+fr754IMPzEUXXWQkmVdffbXNfTl69Kg599xzzc0332w+++wzs2vXLjNz5kyTkJBgamtrjTHGzJ492zgcDpORkWEKCwvNm2++aaKjo83TTz9tjDGmoqLCDBw40CxbtsyUlpaa0tJS175ERkaa5ORks23bNvPvf//bHDlyxFx33XVm5MiRJjc31xQUFJjU1FQzbNgwU1dX5zZuzJgxZvv27Wbnzp1m7NixJjk52VX35MmTzbx589z25aKLLjL33ntvm/vc0NBgxo0bZ6ZPn26MMWbVqlXG6XSaAwcOtDkWoYE5Gtpz9IEHHjCJiYlubQsXLjQTJ05sc2x3QSAIEnl5eUZSix8g/fv3N0uWLGn3Y23atMn06dPHdf9/32wmTJhgHnzwQbcxf/nLX0xcXJwxxpjs7GwTHh5uiouLXf1vv/12u99sVq9ebRISEkxTU5Orrba21kRFRZns7GxjzIk3m0GDBpmGhgbXNjNmzDBpaWmu+4MGDXJ7Uz25L5JMQUGBq2337t1Gktm2bZurrby83ERFRZlNmza5jfvoo49c2xQWFhpJ5uOPPzbGGLNx40bTq1cvU1NTY4wxpqCgwFgsFrN///4299kYY/bu3WtiYmLM3XffbaKjo81f//rXdo1DaGCOhvYcLSkpMeHh4a7HqqurM2eeeaZZt25dm2O7Cw4ZBIkLL7xQkyZN0vnnn68ZM2boz3/+s77//nuVlZWppKREkyZNanXsP/7xD02ePFkDBgxQTEyM0tPTVVFR4VrK/F95eXlatmyZzjjjDNftlltuUWlpqY4dO6bCwkKdffbZbpecHjduXLv3JS8vT//5z38UExPjevzevXurpqbGbWnuvPPOU3h4uOt+XFycysrK2nx8q9WqCy64wHW/sLBQERERuvjii11tffr0UUJCggoLC11tERERGjNmjOv+iBEj1LNnT9c206dPV0REhF599VVJJ5aAr7jiCg0ePLhd+z106FA98sgjeuihh3TttdfqhhtuaNc4hAbmaGjP0bi4OF1zzTVas2aNJOmtt95STU2NZsyY0ebY7oJAECTCw8OVk5Ojt99+W4mJiVq5cqUSEhL07bffnnbcwYMHNXXqVI0aNUovv/yy8vLy9MQTT0iS61jf/2pqatLSpUtVUFDgun3++efas2eP7HZ7i8fiTh4fbI+mpiYlJSW5PX5BQYF2796tmTNnuraLjIxs9hztOd4eFRXlVk9L9Z5s/9+6W9qPk21Wq1WzZs3S2rVrVVdXp+eee04333xzm/WcKjc3V+Hh4Tpw4IDb8V6EPuZo6M/RuXPn6oUXXtDx48e1du1apaWlKTo6ut3juzoCQRCxWCwaP368li5dqvz8fFmtVuXk5Gjw4MF69913Wxyzc+dONTQ06NFHH9Ull1yi4cOHq6Sk5LTPM3r0aH311VcaNmxYs1tYWJgSExNVVFTk9jg7duxo936MHj1ae/bsUb9+/Zo9vtPpbPfjWK1WNTY2trldYmKiGhoa9PHHH7vaKioqtHv3bo0cOdLV1tDQ4HZC0ldffaUffvhBI0aMcLXNnTtXf//735WVlaX6+nr99Kc/bXe9Gzdu1CuvvKL3339fxcXF+sMf/tDusQgNzFF3oTZHp06dqh49emjVqlV6++23PQ78XV4gj1fg/3300UfmgQceMJ9++qk5ePCg2bRpk7FarWbz5s1m3bp1xm63m8cff9zs3r3b5OXlmT/+8Y/GGGPy8/ONJLNixQqzd+9es379ejNgwAAjyXz//ffGmJZPWIqIiDD33Xef+eKLL8yuXbvMCy+84DoG2tjYaBITE82kSZNMQUGByc3NNUlJSR6fsHT55Zeb3Nxcs2/fPvP++++b+fPnu455zp4920ybNs1t3K9+9Stz2WWXue5PnjzZXHfddebrr7823333XYv7ctK0adNMYmKi2bp1qykoKDBXX311iycsjR071nz00UcmLy/PjBs3zlxyySXNHis5OdlYrVaTkZHR5r6eVFxcbHr16uX6//LOO++YyMhIs2PHjnY/BoIbczS05+hJ99xzj7FarWbEiBEej+3qCARBYteuXSY1NdWceeaZxmazmeHDh5uVK1e6+p988kmTkJBgIiMjTVxcnLnjjjtcfcuXLzdxcXEmKirKpKammvXr15/2zcaYE284ycnJJioqyjgcDjN27FjX2cPGGPPVV1+ZSy+91FitVjN8+HCzZcuWdr/ZGGNMaWmpSU9PN3379jU2m80MHTrU3HLLLaaystIY0743mx07dpgLLrjA2Gw2czK7tvZmc/jwYTNr1izjdDpdf4fdu3e7+k+Oe/nll83QoUON1Wo1V155ZYsniK1evdpIMp988km79rWpqclMmjTJpKamup2ktXDhQnPOOeeY6urqdj0OghtzNHTn6Kn27t1rJJmHH37Y47FdncWYNr5gDXQB69at04IFC/TDDz+0ue0DDzygF154QZ9//rnvCwMgyX9zdNu2bbr88sv19ddfKzY2tgOVdl0RgS4ACBZHjhxRYWGhVq5cyfF/IAh5M0dra2tVXFys3/3ud7r++usJAy3gpEJ47MEHH3T7OtSptylTpgS6vA67/fbbdemll+qyyy5rdrJRRkZGq/uckZERoIqBljFHm8/R559/XgkJCaqsrNTDDz8coD0IbhwygMcOHz6sw4cPt9gXFRWlAQMG+Lki3ysrK1NVVVWLfQ6HQ/369fNzRUDrmKPumKPtQyAAAAAcMgAAAAQCAAAgAgEAABCBAAAAiEAAAABEIAAAACIQAAAASf8H2JiogOvA0mkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_merge1 = pd.merge(diversity_evening[['place_id','scaled_entropy']],diversity_evening_GLA[['place_id','scaled_entropy']],on='place_id')\n",
    "print(diversity_evening.shape[0], diversity_evening_GLA.shape[0], df_merge1.shape[0])\n",
    "print(df_merge1.scaled_entropy_x.mean(),df_merge1.scaled_entropy_y.mean())\n",
    "print(stats.wilcoxon(df_merge1.scaled_entropy_x, df_merge1.scaled_entropy_y))\n",
    "fig, ax = plt.subplots(figsize=(6,5))\n",
    "ax = sns.boxplot(data=df_merge1[['scaled_entropy_x','scaled_entropy_y']])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e02f91c-7a5d-4287-990e-1a8962b4ab77",
   "metadata": {},
   "source": [
    "# No of POI visitors from Greater LA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0c2ae8ce-5b41-4b29-afca-f1d1339d50a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "751552\n",
      "751552\n"
     ]
    }
   ],
   "source": [
    "df_indi_home_loc_race = pd.read_pickle(\"./output/GreaterLA_Individual_race_composition.pkl\")\n",
    "print(df_indi_home_loc_race.shape[0]) # these are Greater LA local residents: 751552\n",
    "greaterLAid = df_indi_home_loc_race.cuebiq_id.unique().tolist()\n",
    "print(len(greaterLAid))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "9f1485a5-0e12-4ce3-a65b-b254e8e1e2e3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20220301_20220308\n",
      "20220309_20220316\n",
      "20220317_20220323\n",
      "20220324_20220331\n",
      "348220 239447\n",
      "CPU times: user 9.72 s, sys: 3.12 s, total: 12.8 s\n",
      "Wall time: 12.8 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "date = ['20220301_20220308','20220309_20220316','20220317_20220323','20220324_20220331']\n",
    "weekendays = [20220305,20220306,20220312,20220313,20220319,20220320,20220326,20220327]\n",
    "allvisit_pidlist, greaterLA_pidlist  = [], []\n",
    "\n",
    "weekday = False\n",
    "for d in date:\n",
    "    print(d)\n",
    "    df_visit_LA = pd.read_pickle(\"./output/LA_visit_with_poiCategory\"+str(d)+\".pkl\") # POI visits in LA\n",
    "    \n",
    "    if weekday:#1. select weekdays\n",
    "        df_visit_LA = df_visit_LA[~df_visit_LA.event_date.isin(weekendays)]        \n",
    "    else:#2. select weekends\n",
    "        df_visit_LA = df_visit_LA[df_visit_LA.event_date.isin(weekendays)] \n",
    "        \n",
    "    df_visit_LA = df_visit_LA[['cuebiq_id','place_id']]\n",
    "    allvisit_pidlist.extend(df_visit_LA.cuebiq_id.unique().tolist())\n",
    "    \n",
    "    df_visit_LA = df_visit_LA[df_visit_LA['cuebiq_id'].isin(greaterLAid)]\n",
    "    greaterLA_pidlist.extend(df_visit_LA.cuebiq_id.unique().tolist())\n",
    "\n",
    "print(len(set(allvisit_pidlist)),len(set(greaterLA_pidlist)))\n",
    "#weekend: 348220 239447 = 68.76%\n",
    "#weekday: 459880 287134 = 62.44%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "99ee594a-e01b-46c7-ac66-a2723dcea2fb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b1ba5c34-a032-4580-9c7d-e1426ba5aa44",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "language": "python",
   "name": "python3"
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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   "name": "python",
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   "pygments_lexer": "ipython3",
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