test_google_correlation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting numpy cores to 1\n",
      "Running code on rambo.stanford.edu; at Stanford=True\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ERROR:fbprophet:Importing plotly failed. Interactive plots will not work.\n"
     ]
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "import helper_methods_for_aggregate_data_analysis as helper\n",
    "import numpy as np\n",
    "import random\n",
    "import datetime\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "import pandas as pd\n",
    "from covid_constants_and_util import *\n",
    "from scipy.stats import pearsonr, linregress\n",
    "from model_experiments import *\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "FIGURES_DIRECTORY = './covid_figures_for_paper/'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load data: Google, SafeGraph POI, ACS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Static variables\n",
    "# Google method: \"The baseline is the median value, for the corresponding day of the week, \n",
    "# during the 5-week period Jan 3–Feb 6, 2020.\"\n",
    "# note: Jan 3 is a Friday\n",
    "BASELINE_DATES = helper.list_datetimes_in_range(datetime.datetime(2020, 1, 3), datetime.datetime(2020, 2, 6))\n",
    "# Feb 28 bc this is the closest Friday to Mar 1, the start of our modeling\n",
    "# May 2 bc this is as far as the Google and SafeGraph data go\n",
    "TEST_DATES = helper.list_datetimes_in_range(datetime.datetime(2020, 2, 28), datetime.datetime(2020, 5, 2))\n",
    "\n",
    "GOOGLE_DATE_VALS = [dt.strftime('%Y-%m-%d') for dt in TEST_DATES]  # need to match these values in the 'date' column\n",
    "POI_BASELINE_COLS = ['%s.%s.%s' % (dt.year, dt.month, dt.day) for dt in BASELINE_DATES]\n",
    "POI_TEST_COLS = ['%s.%s.%s' % (dt.year, dt.month, dt.day) for dt in TEST_DATES]\n",
    "SDM_BASELINE_COLS = ['%s.%s.%s_completely_home_device_count' % (dt.year, dt.month, dt.day) for dt in BASELINE_DATES]\n",
    "SDM_TEST_COLS = ['%s.%s.%s_completely_home_device_count' % (dt.year, dt.month, dt.day) for dt in TEST_DATES]\n",
    "\n",
    "assert(TEST_DATES[0].weekday() == BASELINE_DATES[0].weekday())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/dfs/scratch1/safegraph_homes/safegraph_env_v3/safegraph_env_v3/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3214: DtypeWarning: Columns (3) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  if (yield from self.run_code(code, result)):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Index(['country_region_code', 'country_region', 'sub_region_1', 'sub_region_2',\n",
       "       'date', 'retail_and_recreation_percent_change_from_baseline',\n",
       "       'grocery_and_pharmacy_percent_change_from_baseline',\n",
       "       'parks_percent_change_from_baseline',\n",
       "       'transit_stations_percent_change_from_baseline',\n",
       "       'workplaces_percent_change_from_baseline',\n",
       "       'residential_percent_change_from_baseline'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load Google mobility data\n",
    "google_df = helper.load_google_mobility_data()\n",
    "must_have_cols = ['date', 'sub_region_1']  # column for state\n",
    "google_df = google_df.dropna(subset=must_have_cols)\n",
    "google_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020-02-15\n",
      "2020-05-02\n"
     ]
    }
   ],
   "source": [
    "google_dates = google_df.date.unique()\n",
    "print(min(google_dates))\n",
    "print(max(google_dates))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading chunk 0 from /dfs/scratch1/safegraph_homes/all_aggregate_data/chunks_with_demographic_annotations/chunk_1.2017-3.2020_c2.h5\n",
      "Dates range from 2019-01-01 00:00:00 to 2020-05-02 00:00:00\n",
      "Loaded chunk 0 in 19.448 seconds\n",
      "Reading chunk 1 from /dfs/scratch1/safegraph_homes/all_aggregate_data/chunks_with_demographic_annotations/chunk_1.2017-3.2020_c2.h5\n",
      "Dates range from 2019-01-01 00:00:00 to 2020-05-02 00:00:00\n",
      "Loaded chunk 1 in 21.538 seconds\n",
      "Reading chunk 2 from /dfs/scratch1/safegraph_homes/all_aggregate_data/chunks_with_demographic_annotations/chunk_1.2017-3.2020_c2.h5\n",
      "Dates range from 2019-01-01 00:00:00 to 2020-05-02 00:00:00\n",
      "Loaded chunk 2 in 20.179 seconds\n",
      "Reading chunk 3 from /dfs/scratch1/safegraph_homes/all_aggregate_data/chunks_with_demographic_annotations/chunk_1.2017-3.2020_c2.h5\n",
      "Dates range from 2019-01-01 00:00:00 to 2020-05-02 00:00:00\n",
      "Loaded chunk 3 in 21.501 seconds\n",
      "Reading chunk 4 from /dfs/scratch1/safegraph_homes/all_aggregate_data/chunks_with_demographic_annotations/chunk_1.2017-3.2020_c2.h5\n",
      "Dates range from 2019-01-01 00:00:00 to 2020-05-02 00:00:00\n",
      "Loaded chunk 4 in 22.173 seconds\n",
      "Concatenated 5 chunks in 0.531 seconds\n"
     ]
    }
   ],
   "source": [
    "# load SafeGraph POI data\n",
    "must_have_cols = POI_BASELINE_COLS + POI_TEST_COLS + ['sub_category', 'region']  # necessary \n",
    "may_have_cols = must_have_cols + ['top_category', 'location_name']  # nice to have\n",
    "chunks = helper.load_multiple_chunks(range(5), cols=may_have_cols)\n",
    "chunks = chunks.dropna(subset=must_have_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading social distancing metrics for 35 datetimes; using version v2\n",
      "Total time to load social distancing metrics: 165.675 seconds; total rows 219731\n",
      "Loading social distancing metrics for 65 datetimes; using version v2\n",
      "Total time to load social distancing metrics: 262.212 seconds; total rows 219849\n"
     ]
    },
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>census_block_group</th>\n",
       "      <th>2020.1.3_device_count</th>\n",
       "      <th>2020.1.3_distance_traveled_from_home</th>\n",
       "      <th>2020.1.3_completely_home_device_count</th>\n",
       "      <th>2020.1.3_full_time_work_behavior_devices</th>\n",
       "      <th>2020.1.4_device_count</th>\n",
       "      <th>2020.1.4_distance_traveled_from_home</th>\n",
       "      <th>2020.1.4_completely_home_device_count</th>\n",
       "      <th>2020.1.4_full_time_work_behavior_devices</th>\n",
       "      <th>2020.1.5_device_count</th>\n",
       "      <th>...</th>\n",
       "      <th>2020.4.30_completely_home_device_count</th>\n",
       "      <th>2020.4.30_full_time_work_behavior_devices</th>\n",
       "      <th>2020.5.1_device_count</th>\n",
       "      <th>2020.5.1_distance_traveled_from_home</th>\n",
       "      <th>2020.5.1_completely_home_device_count</th>\n",
       "      <th>2020.5.1_full_time_work_behavior_devices</th>\n",
       "      <th>2020.5.2_device_count</th>\n",
       "      <th>2020.5.2_distance_traveled_from_home</th>\n",
       "      <th>2020.5.2_completely_home_device_count</th>\n",
       "      <th>2020.5.2_full_time_work_behavior_devices</th>\n",
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       "      <td>19907.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>18999.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>...</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>14645.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>14606.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10210601014</td>\n",
       "      <td>166.0</td>\n",
       "      <td>7639.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>159.0</td>\n",
       "      <td>20310.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>166.0</td>\n",
       "      <td>...</td>\n",
       "      <td>38.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>155.0</td>\n",
       "      <td>3548.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>5046.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10950303005</td>\n",
       "      <td>81.0</td>\n",
       "      <td>9414.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>9323.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>...</td>\n",
       "      <td>21.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>9371.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>9252.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11010055032</td>\n",
       "      <td>83.0</td>\n",
       "      <td>9012.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>6648.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>...</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>7338.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>5306.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>5.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>40210002061</td>\n",
       "      <td>505.0</td>\n",
       "      <td>14810.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>501.0</td>\n",
       "      <td>9414.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>505.0</td>\n",
       "      <td>...</td>\n",
       "      <td>159.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>494.0</td>\n",
       "      <td>8651.0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>470.0</td>\n",
       "      <td>8304.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>7.0</td>\n",
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       "<p>5 rows × 401 columns</p>\n",
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      "text/plain": [
       "   census_block_group  2020.1.3_device_count  \\\n",
       "0         10139533002                   58.0   \n",
       "1         10210601014                  166.0   \n",
       "2         10950303005                   81.0   \n",
       "3         11010055032                   83.0   \n",
       "4         40210002061                  505.0   \n",
       "\n",
       "   2020.1.3_distance_traveled_from_home  \\\n",
       "0                               19907.0   \n",
       "1                                7639.0   \n",
       "2                                9414.0   \n",
       "3                                9012.0   \n",
       "4                               14810.0   \n",
       "\n",
       "   2020.1.3_completely_home_device_count  \\\n",
       "0                                    8.0   \n",
       "1                                   31.0   \n",
       "2                                   14.0   \n",
       "3                                   17.0   \n",
       "4                                  107.0   \n",
       "\n",
       "   2020.1.3_full_time_work_behavior_devices  2020.1.4_device_count  \\\n",
       "0                                       1.0                   59.0   \n",
       "1                                      14.0                  159.0   \n",
       "2                                       6.0                   92.0   \n",
       "3                                       1.0                   82.0   \n",
       "4                                      19.0                  501.0   \n",
       "\n",
       "   2020.1.4_distance_traveled_from_home  \\\n",
       "0                               18999.0   \n",
       "1                               20310.0   \n",
       "2                                9323.0   \n",
       "3                                6648.0   \n",
       "4                                9414.0   \n",
       "\n",
       "   2020.1.4_completely_home_device_count  \\\n",
       "0                                    9.0   \n",
       "1                                   30.0   \n",
       "2                                    8.0   \n",
       "3                                   20.0   \n",
       "4                                  141.0   \n",
       "\n",
       "   2020.1.4_full_time_work_behavior_devices  2020.1.5_device_count  ...  \\\n",
       "0                                       1.0                   59.0  ...   \n",
       "1                                       1.0                  166.0  ...   \n",
       "2                                       1.0                   84.0  ...   \n",
       "3                                       7.0                   89.0  ...   \n",
       "4                                      10.0                  505.0  ...   \n",
       "\n",
       "   2020.4.30_completely_home_device_count  \\\n",
       "0                                     8.0   \n",
       "1                                    38.0   \n",
       "2                                    21.0   \n",
       "3                                    32.0   \n",
       "4                                   159.0   \n",
       "\n",
       "   2020.4.30_full_time_work_behavior_devices  2020.5.1_device_count  \\\n",
       "0                                        1.0                   53.0   \n",
       "1                                       10.0                  155.0   \n",
       "2                                        2.0                   96.0   \n",
       "3                                        1.0                  114.0   \n",
       "4                                       24.0                  494.0   \n",
       "\n",
       "   2020.5.1_distance_traveled_from_home  \\\n",
       "0                               14645.0   \n",
       "1                                3548.0   \n",
       "2                                9371.0   \n",
       "3                                7338.0   \n",
       "4                                8651.0   \n",
       "\n",
       "   2020.5.1_completely_home_device_count  \\\n",
       "0                                    7.0   \n",
       "1                                   28.0   \n",
       "2                                   21.0   \n",
       "3                                   32.0   \n",
       "4                                  195.0   \n",
       "\n",
       "   2020.5.1_full_time_work_behavior_devices  2020.5.2_device_count  \\\n",
       "0                                       1.0                   52.0   \n",
       "1                                       4.0                  142.0   \n",
       "2                                       1.0                   95.0   \n",
       "3                                       3.0                  100.0   \n",
       "4                                      11.0                  470.0   \n",
       "\n",
       "   2020.5.2_distance_traveled_from_home  \\\n",
       "0                               14606.0   \n",
       "1                                5046.0   \n",
       "2                                9252.0   \n",
       "3                                5306.0   \n",
       "4                                8304.0   \n",
       "\n",
       "   2020.5.2_completely_home_device_count  \\\n",
       "0                                   11.0   \n",
       "1                                   28.0   \n",
       "2                                   16.0   \n",
       "3                                   30.0   \n",
       "4                                  162.0   \n",
       "\n",
       "   2020.5.2_full_time_work_behavior_devices  \n",
       "0                                       1.0  \n",
       "1                                       1.0  \n",
       "2                                       1.0  \n",
       "3                                       5.0  \n",
       "4                                       7.0  \n",
       "\n",
       "[5 rows x 401 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load SafeGraph Social Distancing Metrics data\n",
    "baseline_sdm = helper.load_social_distancing_metrics(BASELINE_DATES)\n",
    "must_have_cols = ['census_block_group'] + SDM_BASELINE_COLS\n",
    "baseline_sdm = baseline_sdm.dropna(subset=must_have_cols)\n",
    "\n",
    "test_sdm = helper.load_social_distancing_metrics(TEST_DATES)\n",
    "must_have_cols = ['census_block_group'] + SDM_TEST_COLS\n",
    "test_sdm = test_sdm.dropna(subset=must_have_cols)\n",
    "\n",
    "sdm_data = baseline_sdm.join(test_sdm.set_index('census_block_group'), on='census_block_group', how='inner')\n",
    "sdm_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "220333 rows of 2018 1-year ACS data read\n",
      "217739 rows of 2017 5-year ACS data read\n"
     ]
    },
    {
     "data": {
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       "      <th></th>\n",
       "      <th>census_block_group</th>\n",
       "      <th>total_cbg_population_2018_1YR</th>\n",
       "      <th>state_code</th>\n",
       "      <th>county_code</th>\n",
       "      <th>state_name</th>\n",
       "      <th>GEOID</th>\n",
       "      <th>p_black_2017_5YR</th>\n",
       "      <th>p_white_2017_5YR</th>\n",
       "      <th>p_asian_2017_5YR</th>\n",
       "      <th>median_household_income_2017_5YR</th>\n",
       "      <th>block_group_area_in_square_miles_2017_5YR</th>\n",
       "      <th>people_per_mile_2017_5YR</th>\n",
       "      <th>TRACTCE</th>\n",
       "      <th>Metropolitan/Micropolitan Statistical Area</th>\n",
       "      <th>CBSA Title</th>\n",
       "      <th>State Name</th>\n",
       "      <th>people_per_mile_hybrid</th>\n",
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       "      <th>0</th>\n",
       "      <td>10010201001</td>\n",
       "      <td>636</td>\n",
       "      <td>01</td>\n",
       "      <td>1001</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>15000US010010201001</td>\n",
       "      <td>0.037572</td>\n",
       "      <td>0.916185</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>34907.0</td>\n",
       "      <td>1.651735</td>\n",
       "      <td>418.953424</td>\n",
       "      <td>20100.0</td>\n",
       "      <td>Metropolitan Statistical Area</td>\n",
       "      <td>Montgomery, AL</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>385.049679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10010201002</td>\n",
       "      <td>1287</td>\n",
       "      <td>01</td>\n",
       "      <td>1001</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>15000US010010201002</td>\n",
       "      <td>0.060711</td>\n",
       "      <td>0.830876</td>\n",
       "      <td>0.019081</td>\n",
       "      <td>76875.0</td>\n",
       "      <td>2.149942</td>\n",
       "      <td>536.293534</td>\n",
       "      <td>20100.0</td>\n",
       "      <td>Metropolitan Statistical Area</td>\n",
       "      <td>Montgomery, AL</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>598.620796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10010202001</td>\n",
       "      <td>810</td>\n",
       "      <td>01</td>\n",
       "      <td>1001</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>15000US010010202001</td>\n",
       "      <td>0.635294</td>\n",
       "      <td>0.332353</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>24063.0</td>\n",
       "      <td>0.794749</td>\n",
       "      <td>1283.423916</td>\n",
       "      <td>20200.0</td>\n",
       "      <td>Metropolitan Statistical Area</td>\n",
       "      <td>Montgomery, AL</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>1019.189580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10010202002</td>\n",
       "      <td>1218</td>\n",
       "      <td>01</td>\n",
       "      <td>1001</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>15000US010010202002</td>\n",
       "      <td>0.465278</td>\n",
       "      <td>0.490451</td>\n",
       "      <td>0.019097</td>\n",
       "      <td>48382.0</td>\n",
       "      <td>0.491497</td>\n",
       "      <td>2343.861068</td>\n",
       "      <td>20200.0</td>\n",
       "      <td>Metropolitan Statistical Area</td>\n",
       "      <td>Montgomery, AL</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>2478.144775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10010203001</td>\n",
       "      <td>2641</td>\n",
       "      <td>01</td>\n",
       "      <td>1001</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>15000US010010203001</td>\n",
       "      <td>0.255577</td>\n",
       "      <td>0.590607</td>\n",
       "      <td>0.009785</td>\n",
       "      <td>55476.0</td>\n",
       "      <td>1.496373</td>\n",
       "      <td>1707.461531</td>\n",
       "      <td>20300.0</td>\n",
       "      <td>Metropolitan Statistical Area</td>\n",
       "      <td>Montgomery, AL</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>1764.933818</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   census_block_group  total_cbg_population_2018_1YR state_code  county_code  \\\n",
       "0         10010201001                            636         01         1001   \n",
       "1         10010201002                           1287         01         1001   \n",
       "2         10010202001                            810         01         1001   \n",
       "3         10010202002                           1218         01         1001   \n",
       "4         10010203001                           2641         01         1001   \n",
       "\n",
       "  state_name                GEOID  p_black_2017_5YR  p_white_2017_5YR  \\\n",
       "0    Alabama  15000US010010201001          0.037572          0.916185   \n",
       "1    Alabama  15000US010010201002          0.060711          0.830876   \n",
       "2    Alabama  15000US010010202001          0.635294          0.332353   \n",
       "3    Alabama  15000US010010202002          0.465278          0.490451   \n",
       "4    Alabama  15000US010010203001          0.255577          0.590607   \n",
       "\n",
       "   p_asian_2017_5YR  median_household_income_2017_5YR  \\\n",
       "0          0.000000                           34907.0   \n",
       "1          0.019081                           76875.0   \n",
       "2          0.000000                           24063.0   \n",
       "3          0.019097                           48382.0   \n",
       "4          0.009785                           55476.0   \n",
       "\n",
       "   block_group_area_in_square_miles_2017_5YR  people_per_mile_2017_5YR  \\\n",
       "0                                   1.651735                418.953424   \n",
       "1                                   2.149942                536.293534   \n",
       "2                                   0.794749               1283.423916   \n",
       "3                                   0.491497               2343.861068   \n",
       "4                                   1.496373               1707.461531   \n",
       "\n",
       "   TRACTCE Metropolitan/Micropolitan Statistical Area      CBSA Title  \\\n",
       "0  20100.0              Metropolitan Statistical Area  Montgomery, AL   \n",
       "1  20100.0              Metropolitan Statistical Area  Montgomery, AL   \n",
       "2  20200.0              Metropolitan Statistical Area  Montgomery, AL   \n",
       "3  20200.0              Metropolitan Statistical Area  Montgomery, AL   \n",
       "4  20300.0              Metropolitan Statistical Area  Montgomery, AL   \n",
       "\n",
       "  State Name  people_per_mile_hybrid  \n",
       "0    Alabama              385.049679  \n",
       "1    Alabama              598.620796  \n",
       "2    Alabama             1019.189580  \n",
       "3    Alabama             2478.144775  \n",
       "4    Alabama             1764.933818  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load ACS data\n",
    "acs_df = helper.load_and_reconcile_multiple_acs_data()\n",
    "acs_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Figure out category mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sub_category\n",
       "Full-Service Restaurants                                      357128\n",
       "Limited-Service Restaurants                                   118887\n",
       "Religious Organizations                                       113950\n",
       "Gasoline Stations with Convenience Stores                     104097\n",
       "Fitness and Recreational Sports Centers                        96441\n",
       "Elementary and Secondary Schools                               85311\n",
       "Snack and Nonalcoholic Beverage Bars                           81330\n",
       "Nature Parks and Other Similar Institutions                    74130\n",
       "Supermarkets and Other Grocery (except Convenience) Stores     70565\n",
       "Child Day Care Services                                        68296\n",
       "dtype: int64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# top 10 most common sub categories\n",
    "chunks.groupby('sub_category').size().sort_values(ascending=False)[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['All Other Transit and Ground Passenger Transportation']"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find sub_category types by searching for key words\n",
    "sub_categories = list(chunks['sub_category'].unique())\n",
    "[c for c in sub_categories if type(c)==str and 'transit' in c.lower()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "scrolled": false
   },
   "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>location_name</th>\n",
       "      <th>sub_category</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>safegraph_place_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [location_name, sub_category]\n",
       "Index: []"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find sub_category types by examples of POIs\n",
    "pois = chunks[(chunks['location_name'].str.contains('Union Square')) & (chunks['location_name'].str.contains('123')) & (chunks['sub_category'] != 'Full-Service Restaurants')]\n",
    "pois.head(100)[['location_name', 'sub_category']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "# mapping of Google category to SafeGraph sub_category (more specific than top_category)\n",
    "# see https://www.google.com/covid19/mobility/data_documentation.html for descriptions \n",
    "# of what each Google category means\n",
    "# see https://www.naics.com/naics-code-description for descriptions of what the SafeGraph\n",
    "# (aka NAICS) categories mean\n",
    "CATEGORY_MAPPING = {\n",
    "    # from Google: \"restaurants, cafes, shopping centers, theme parks, museums, libraries, \n",
    "    # and movie theaters\"\n",
    "    'Retail & recreation':['Full-Service Restaurants',\n",
    "                           'Limited-Service Restaurants',  # e.g. Subway\n",
    "                           'Snack and Nonalcoholic Beverage Bars',  # e.g. cafes, juiceries\n",
    "                           'Drinking Places (Alcoholic Beverages)',  # bars\n",
    "                           'Malls',  # shopping centers\n",
    "                           'Amusement and Theme Parks',\n",
    "                           'Museums',\n",
    "                           'Libraries and Archives',\n",
    "                           'Motion Picture Theaters (except Drive-Ins)'],\n",
    "    \n",
    "     # from Google: \"grocery markets, food warehouses, farmers markets, specialty food shops, \n",
    "     # drug stores, and pharmacies\"\n",
    "     'Grocery & pharmacy':['Supermarkets and Other Grocery (except Convenience) Stores',\n",
    "                           'Food (Health) Supplement Stores',\n",
    "                           'Fish and Seafood Markets',\n",
    "                           'All Other Specialty Food Stores',\n",
    "                           'Pharmacies and Drug Stores'],\n",
    "    \n",
    "     # from Google: \"local parks, national parks, public beaches, marinas, dog parks, \n",
    "     # plazas, and public gardens\"\n",
    "     'Parks':['Nature Parks and Other Similar Institutions'],  # includes parks, beaches, gardens \n",
    "}\n",
    "\n",
    "GOOGLE_CATEGORIES = ['Retail & recreation', 'Grocery & pharmacy', 'Parks', 'Residential']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "# considered, but removed\n",
    "removed = ['Personal Care Services', # saunas, tanning salons, ear piercing services\n",
    "           'Home Health Care Services', # skilled nursing services in the home\n",
    "           'Miscellaneous Nondurable Goods Merchant Wholesalers',  # merchant wholesale distribution e.g pet supplies, christmas trees\n",
    "           'Miscellaneous Durable Goods Merchant Wholesalers', \n",
    "           'Warehousing and Storage', # seems less focused on food\n",
    "           'Drugs and Druggists\\' Sundries Merchant Wholesalers',  # I've decided to exclude wholesalers          \n",
    "]\n",
    "stores = ['Sporting Goods, Hobby, and Musical Instrument Stores',\n",
    "          'Clothing Stores',\n",
    "          'Automotive Parts, Accessories, and Tire Stores',\n",
    "          'Other Miscellaneous Store Retailers',\n",
    "          'Furniture Stores',\n",
    "          'Office Supplies, Stationery, and Gift Stores',\n",
    "          'Jewelry, Luggage, and Leather Goods Stores',\n",
    "          'Beer, Wine, and Liquor Stores',\n",
    "          'Book Stores and News Dealers',\n",
    "          'Shoe Stores']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 611997 POIs in category Retail & recreation\n",
      "Found 112114 POIs in category Grocery & pharmacy\n",
      "Found 74130 POIs in category Parks\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.34801274184273173, 0.055763479125853936, 0.025878149513024747]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check how much each category contributes to overall POI visits\n",
    "baseline_sum = np.sum(chunks[POI_BASELINE_COLS].values)\n",
    "y = []\n",
    "for category in CATEGORY_MAPPING:\n",
    "    sub_categories = set(CATEGORY_MAPPING[category])\n",
    "    pois = chunks[chunks['sub_category'].isin(sub_categories)]\n",
    "    print('Found %d POIs in category %s' % (len(pois), category))\n",
    "    category_sum = np.sum(pois[POI_BASELINE_COLS].values)  # sum over POIs\n",
    "    y.append(category_sum / baseline_sum)\n",
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Compare trends within one state, over days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_google_trend_for_state(df, state, category=None,\n",
    "                               make_plot=False, ax=None, color=None):\n",
    "    if category is not None:\n",
    "        assert category in GOOGLE_CATEGORIES\n",
    "        if category == 'Retail & recreation':\n",
    "            cols = 'retail_and_recreation_percent_change_from_baseline'\n",
    "        elif category == 'Grocery & pharmacy':\n",
    "            cols = 'grocery_and_pharmacy_percent_change_from_baseline'\n",
    "        elif category == 'Parks':\n",
    "            cols = 'parks_percent_change_from_baseline'\n",
    "        else:\n",
    "            cols = 'residential_percent_change_from_baseline'\n",
    "    else:\n",
    "        cols = ['retail_and_recreation_percent_change_from_baseline',\n",
    "                'grocery_and_pharmacy_percent_change_from_baseline',\n",
    "                'parks_percent_change_from_baseline',\n",
    "                'residential_percent_change_from_baseline']\n",
    "            \n",
    "    state_df = df[(df['sub_region_1'] == state) & (df['sub_region_2'].isnull())]\n",
    "    state_df = state_df[state_df['date'].isin(set(GOOGLE_DATE_VALS))]\n",
    "    if len(state_df) != len(TEST_DATES):\n",
    "        print('Missing dates in Google data for %s: found %d rows, wanted %d' % \n",
    "              (state, len(state_df), len(TEST_DATES)))\n",
    "        return None\n",
    "    state_df = state_df.sort_values(by='date')\n",
    "    y = state_df[cols].values  # either 1D or 2D; length is dates\n",
    "    \n",
    "    if make_plot:\n",
    "        assert(category is not None and ax is not None and color is not None)\n",
    "        smoothed_y = apply_smoothing(y)\n",
    "        ax.plot_date(TEST_DATES, smoothed_y, linestyle='-', marker='.', color=color)\n",
    "        ax.fill_between(TEST_DATES, 0, smoothed_y, alpha=0.3, color=color)\n",
    "        if category == 'Residential':\n",
    "            ax.set_ylim(-50, 150)\n",
    "        else:\n",
    "            ax.set_ylim(-80, 80)\n",
    "        ax.xaxis.set_major_locator(mdates.DayLocator(interval=14))\n",
    "        ax.xaxis.set_major_formatter(mdates.DateFormatter('%m-%d'))\n",
    "        ax.grid(alpha=0.5)\n",
    "        ax.set_title('%s (Google data)' % category)\n",
    "    return y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(65, 4)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "y = get_google_trend_for_state(google_df, 'California', category='Parks', make_plot=True, ax=ax, color='blue')\n",
    "y_mat = get_google_trend_for_state(google_df, 'California')\n",
    "print(y_mat.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_percent_change_trend(df, baseline_cols, test_cols):\n",
    "    baseline_vals = np.sum(df[baseline_cols].values, axis=0)  # sum over POIs or CBGs -> 1 x num_days\n",
    "    baseline_week = []\n",
    "    # iterate through the days of the week, 0=first day of baseline cols (a Friday)\n",
    "    for i in range(7):\n",
    "        day_idxs = np.arange(i, len(baseline_cols), 7)\n",
    "        median = np.median(baseline_vals[day_idxs])\n",
    "        baseline_week.append(median)        \n",
    "    test_vals = np.sum(df[test_cols].values, axis=0)\n",
    "    y = []\n",
    "    for i, v in enumerate(test_vals):\n",
    "        baseline = baseline_week[i % 7]\n",
    "        diff = v - baseline\n",
    "        y.append(100. * diff / baseline)\n",
    "    return np.array(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_safegraph_poi_trend_for_state(df, state, category=None,\n",
    "                                      make_plot=False, ax=None, color=None,\n",
    "                                      verbose=True):\n",
    "    if category is not None:\n",
    "        assert category in CATEGORY_MAPPING\n",
    "        sub_categories = set(CATEGORY_MAPPING[category])\n",
    "        state_df = df[(df['region'] == state) & (df['sub_category']).isin(sub_categories)]\n",
    "        if verbose: \n",
    "            print('Found %d POIs in state %s and category %s' % (len(state_df), state, category))\n",
    "    else:\n",
    "        state_df = df[df['region'] == state]\n",
    "        if verbose: \n",
    "            print('Found %d POIs in state %s' % (len(state_df), state))\n",
    "    if len(state_df) == 0:\n",
    "        return None\n",
    "    \n",
    "    y = compute_percent_change_trend(df, POI_BASELINE_COLS, POI_TEST_COLS)\n",
    "    if make_plot:\n",
    "        assert(ax is not None and color is not None)\n",
    "        smoothed_y = apply_smoothing(y)\n",
    "        ax.plot_date(TEST_DATES, smoothed_y, linestyle='-', marker='.', color=color)\n",
    "        ax.fill_between(TEST_DATES, 0, smoothed_y, alpha=0.3, color=color)\n",
    "        ax.set_ylim(-80, 80)\n",
    "        ax.xaxis.set_major_locator(mdates.DayLocator(interval=14))\n",
    "        ax.xaxis.set_major_formatter(mdates.DateFormatter('%m-%d'))\n",
    "        ax.grid(alpha=0.5)\n",
    "        if category is None:\n",
    "            ax.set_title('All POIs in state')\n",
    "        else:\n",
    "            ax.set_title('%s (SafeGraph data)' % category)\n",
    "    return y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 8639 POIs in state CA and category Parks\n",
      "Found 281691 POIs in state CA\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1,2)\n",
    "y = get_safegraph_poi_trend_for_state(chunks, 'CA', category='Parks', make_plot=True, ax=axes[0], color='forestgreen')\n",
    "y_mat = get_safegraph_poi_trend_for_state(chunks, 'CA', make_plot=True, ax=axes[1], color='blue')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_safegraph_at_home_trend_for_state(sdm_df, acs_df, state,\n",
    "                                          make_plot=False, ax=None, color=None,\n",
    "                                          verbose=True):\n",
    "    cbgs_in_state = set(acs_df[acs_df['state_name'] == state].census_block_group.values)\n",
    "    state_df = sdm_df[sdm_df['census_block_group'].isin(cbgs_in_state)]\n",
    "    if verbose:\n",
    "        print('Found %d out of %d CBGs in state %s' % (len(state_df), len(cbgs_in_state), state))\n",
    "    if len(state_df) == 0:\n",
    "        return None\n",
    "    y = compute_percent_change_trend(state_df, SDM_BASELINE_COLS, SDM_TEST_COLS)\n",
    "    if make_plot:\n",
    "        assert(ax is not None and color is not None)\n",
    "        smoothed_y = apply_smoothing(y)\n",
    "        ax.plot_date(TEST_DATES, smoothed_y, linestyle='-', marker='.', color=color)\n",
    "        ax.fill_between(TEST_DATES, 0, smoothed_y, alpha=0.3, color=color)\n",
    "        ax.set_ylim(-50, 150)\n",
    "        ax.xaxis.set_major_locator(mdates.DayLocator(interval=14))\n",
    "        ax.xaxis.set_major_formatter(mdates.DateFormatter('%m-%d'))\n",
    "        ax.grid(alpha=0.5)\n",
    "        ax.set_title('Completely at home (SafeGraph Data)')\n",
    "    return y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 22984 out of 23212 CBGs in state California\n"
     ]
    },
    {
     "data": {
      "image/png": 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+F9z339C7P0y9ECpOgumfhWFj4OUHLPA7l4AHfOcytWoBLH85+lkEtqyBW74OD/8CXrwnO9U8G5bDbd+GI4dh6kUwoCy6v7Mvh67d4YGfwrEjme/LdUpepeNcJpbPh4dvgYFDYdyH4dB+KBsJxV3hjadh2Uu23KuPZtaKprYa/jITtAmkCLr1iKm/B7r3grM+CfPuh/tvgpEToXKyt9pxJ/CA71w6aqthweNWui+tgDMugZKyE5epmAC7t9r7xmPWiibdAFz9hgV7AFXYVQtDRpy4zPBxUD4B1i6GtW95U033AV6l41yqaqth9vUW7BEYf/oHgz1YSb8oUqYSK3Gn6/266HaKi23b8QwYEt54U033QR7wnUtVzUorsUcc2Bt/udIKuOgq6DMAunaDYWPT29+RQ7BmEZSWwynnwoVX2bbjGTYmWtVT3CWzk4zrdFIK+CLyJxHZKSIrYqYNFJE5IrI2/B0QpouI/FJE1onIMhH5cLYT71xeVE4GIkG1hdI2WGCeegEcPQyrXktvf2/PhSMHYcIZMGV64mAf2d+U8+z99M94dY47Qaol/NnApc2mfQd4XlXHA8+HzwCfAMaH1wzgd+kn07l2ZMBQQK3E3VJpO2L4eLvJunhO6vtqbISFT8Kg4TBsdHLrnDTNbhpvWZP6/lynllLAV9WXgd3NJl8B3B7e3w5cGTP9DjULgRIRGZZJYp1rF9Yvsb8TprUe7MGuAkadArWrYW9d68vHql4I9Tth1GTo1jO5dbp0hYrx8O4Ku7JwLshGK50yVd0GoKrbRCRy16gcqI1ZbnOYti12ZRGZgV0BUF5eTk1NTRaS1L7U1aX4I++kOks+lC59hZ7delLbYwjUH0hqna5DJlC+dhF1Lz7IvqmXJJ0XQ196gOJe/dlSMjrpfQH0LB1L2cZV7HjlKQ6Nab+1qZ3lmMiGtsiLXDbLlDjT9AMTVGcBswCqqqq0srIyh0nKn876vVLV4fOhqQl2rIPBFVQOHgBFSV4kl/SGlUMYVPMWg678OpBEXmyqhl0bYdJHqBwy6MR2963pOwmWPUfZ5mVw4aeTXy8POvwxkUW5zotstNLZEamqCX93humbgdiGwhXA1izsz7n82f6utcoZVJ58sI8YO9WqZ95d3vqytdXwxG+hSzcYNSm1YA9WjVRxEmxaBYeTvzJwnVs2Av7jwDXh/TXAYzHTrw6tdc4C9kaqfpzrsCL190NaaJmTyKjJUFRsI122pLYabr/eOlc1Hku/Hr5yso3rs+KV9NZ3nU6qzTLvARYAJ4nIZhH5GvBj4GMishb4WPgM8BSwAVgH3Ar8Y9ZS7Vy+rHsbSoZAyeDU1+3eE8rHwztv0v/tZxKPr7N0ngVqsErQXbXxl2vNkFG2z9hxflxBS6kOX1W/lGDWRXGWVeCf0kmUc+3S4YMWpEdPsaqWdJSOgNpqSpY+CytfOHEY5aZGmP8wLHo2unxr7fxbUlQEIyba8MkH3ofe/dLbjus0fCwd55JVs9yC8uCK1OvUIxqPAqFFQ8MxeOSXcNGXbbtz/wJ7d1nde/l4OLQPykYl1/QzkVGTYN1bsOxlOPuT6W/HdQoe8J1L1rolVrIfnGaJGyyAF3dBGxvtnLF/DzxwU3S+FMG4qekPw9Dc4BHW6WvBY1AxznveFjgfS8e5ZKhaSXnQcOjZN/3tlFbAhVdRP+FsuPgrcOX/tqGMozuC3TsyTu5xdVvg2FF4/z27EdwWj1907ZYHfOeSsXubNaksrbB69UyUVrB37BnRbZ10hg10JmKteNKts49nx6bosMoNPnpmofMqHeeSse5t+zs4g/r0REKpnx2bLNhnUmffXNlIO5lEHr/oo2cWNC/hO5eM5fOtLlw/0Fk8O0orYPI52Q32ke3GDvAWb9x+VzA84DvXmpqVsPkd6wD1ykPw3uZ8pyg1pRVw2kWARh+56AqSB3znWrNifvR9U6NVvXQ0g4ZDzz6wckG+U+LyyAO+c62K/ExycFO1rYhYa6Dt62Fffb5T4/LEA75zrXn/PejVFyZ/JLkHnrRXIyfaaJ9erVOwPOA715KmJhtxclB5648XbO8GlYdqnTQftdie1VbD/Ie8n0ErvFmmcy3ZVWvDCw8cmv5wCu1FpFpn7WKr1ulbku8UZUdkdNGGY/a0r9jxidwJvITvXEs2rba/AzvJ0zmPV+vMy3dKsqdmZXR00YZj8NL9dnPdfYAHfOdasnGVVYOUDGl92Y4gUq2z6tV8pyR7esWOAiqw/m2Y9X9g6UtezdOMV+k415JNq62zUrIPEG/vRGzI5HWdqFpn9ULo2t2GrR450R4U//ZceOQXNr9LN7jmv7yaBy/hu46orW7Q1e+0FjoDh6b+OMP2LFKt8/ivO37pd/MaG9Ru7FQ47WIbHXTcVJhQFV2m0ccQiuhER7ErCLXVMPt6eP5OuP0HuQ1YG1fZ30GdpP7+uDA8xNrF2cnD2mp4+cH8nDxeug+697LSfexJuXyc9ZkAu6rxMYQAD/iuo9mw3J7zCtBwFF591EqrubBptVUVDBiam+3ny86YRyY2Hsus9LvoWbjtu/DCXbk/ATcXKd2PngL9Sk+cFxlDqEtXe+/VOUAW6vBF5CTgvphJY4DrgRLg74FdYfp3VfWpTPfnCtyRAyd+rn4d/nAtnHEpHNpvJbls/bg3rYYBZdC9d3a2117EjqCp2MPVU7W/3p7QteSF6LSGo3ZCbqvgmqh0HzF4hM1bvwwO7rPOcwUu44Cvqu8AUwFEpBjYAjwC/C1ws6r+NNN9OAfAkUPW8qL/YHsE4PCxVs++dB48+XtbJls36A6+b23wTzoj8/Hv25tI6Xfla7B1HeyrS37djavsqqpmuQX48gmwbX20GeTGlaCfzX2fhUjp/uQzP1i6jzVyIqx9C1a8AtM+kds0gT0VbetaO9G0w6uKbLfSuQhYr6obpaN3UnHtz6uPwIG9cM4V0VLp4BFw6ACsfMU+R27QZfpj62zt75srrYBzPwNP/g7m3gkTz279xnRtNdz+n6EKTWDqBXZzdM92G1Bu7y7YsNSqdy76cm7T//RtUNzVmsu2lO7SEXYV0BYBf9UCuP8n9l6K4NxPQ8V4q0LL5pVnBrId8L8I3BPz+ZsicjWwCLhWVfc0X0FEZgAzAMrLy6mpqclykvKvri6FElQnlkk+FB/cS/lrj3FoyFh29RkB9dGqne59hzO0qBhpakRF2N59EEcyPI4GLF9Iv6JiNvYYcsK+sqXu4NGsbzMdvcedzeBlz7Jrzn0cOOnsFpcdNP8h+ob7JQrUHzzM3n1HoMsAKB8Aw5XSI8foM/8h9tTvha7dOTx0HEeGVCbcZjrHRO+1bzB4yxoU0DeeZgc9OTIg8Yl54JCx9Nm8mto1q9EcNq8te+UxIltXbULmP2jvAUQ4UDmVI0PGII3HKDpykEMjJp+QN20RJ7IW8EWkG3A5cF2Y9DvgRuz73gj8DPhq8/VUdRYwC6CqqkorKyuzlaR2pbN+r1SlnQ+P/waaGuk9eRq9B/U7cV7JOOhzFcy7H+k7kGHTzs80mTBnCwwoo7J0gN34y4HKknZwb6D/h6FmMYOXPcfgiz5jVWLx7NsNm0OrJQQpLmbAqHEMaP4dzvsUPHsbA5bPBSQMddByFVvKx8QzvwqpANFGhh3cCaPHJV5+3BSoXc6ovRvtXk+uHNob3lj+UFYJW9chAKr0qVlCn3ffPr54yap58DcnDgOR6ziRzVY6nwDeUtUdAKq6Q1UbVbUJuBWYlsV9uUKycxO8/bxV4wweEX+ZwSNgwhn27NnYVijpOHoYtm2w6oIcBft2Q8QejnKgHl59LP4yTU3wyC+tRc/UC+DUjyYeNbS4GCoiAUyz3wZ+fz3s3GjpTvYZwENG2tPKVuawd/HOTVa1VTklmj+Tz4k+q7i4C5z/xWb9A47Zk9TaUDardL5ETHWOiAxT1W3h46eAFVnclysUtdXw6K+svnb86fbDSWTMqVaXv+gZuOzv09/n5jV2E7Kz1t83N2yMBe9XHoKmBhh32okl8gWPW938KdPhpGnR9u2JDB8Dq16zh6cXFWW3Dfzi5+x/M/VCOxEl8wzgoiI7CW1cYfd7eubgymr5fAvs4z8MpeXR6c2fVdylK6xfAo2NgForp6pLYUiCgkyWZaWELyK9gI8BD8dM/omILBeRZcAFwL9mY1+uHcpVz9faapj9n1C31UqKkQdxJ9KnxEr6K1/NrG1+pNTVWmDrLESsVcmxIzDvfvjz92HxHMvDt5+HuXdYEBt/enJ5UloBF3wRirrY2D3ZulnZcAzefAaGjLKTUirPAB450Y6fXJTyVe2YGTwCSgafOK/5s4ojLaRO/SicfQUgMPv7dmXaBrJSwlfVg8CgZtO+ko1tu3Zu42q443oLDsVdsjtmyfql0SCvas0kWysJjZ0KC5+ANYvg5DRqEWurYcnz9n7RM9BvYMceAz9ZRw5F3zc1whO/hedmw5GDNm33dti/G3r0Sm57ZZWW/6teg23vwrDRmadx9ULYv8eaYnbtntq6ZZFqnVeg6uOZpyXW5jVQv8OCeKJ7ILFKK6LHVP9S6zV+67cpGVsFxR/PaWse72nr0tfYAE/NCh14mrJfX1u3Nfq+OMlHC444yS6bFz+X3j7XvmUnF+i4z69NR6QzloiVzMdXWYCM0KbU82JClV0RvHx/dtK48EnoO9D6YKSqqNj6DNRWR09i2bJivuXd8LGprzugzKqnDu2j/4oXc95b2QO+S8+xo9bmeEdNdFo262u3v2ttp8tGwSnnJv9owS7dbDTIDUvh0L40dhzTf6SjPr82HZGqhikfhYuuslLwWZ+MOQmkkRc9+8CoSfDOm/B+hk0ON6+BLWvsf9uzT3rbGDnRqoUe/VX2gmpjI6x41W4Mt9QBrCXh6kog5wO9ecB3qTtyCO7+of2QJ38EzvucXWL36A3D0ijlNNfUCI//Frr3hA9dCFPOS61aZeyH7Ifz1vOp73vXJujWyzoideTn16YjUX3zlBZa5bRm4ln2/3zlkczS9vr/2DE26uT0e/FGWlytXpi9knTNCmvhNGxsyw0KWhKurpTQmieHA715wHepWfc2/OZf4N0V8KHzYdLZdol99uXWC3ZeFi7fX3/KuvyfdKYNTZyq0groXQJvPp3azeRjR+z7DRtt362Qgn0izU8Cqeo/GIaOhqUvWHPXdOzbbTdbKyakX4oG2LU5+j5bJenlL9uJaNiY9LdRWgEf/QL1Y6flfNx+D/guebXVcNeNNka8CAwaHr1JVT7eSjmvPQZ7dqS/j1ULYM7tMGCYNbNMpzQnYjcN63emNorj+iUW9IdWdvzn17YnE8+yq8I3nk5v/efvsquEkrLMWk6VjbT7ExGZlqSPHbWrhbJR1kIsE6Xl7B394ZwPv+AB3yVv5WvRG5oovLf1xPlVl9jNvSd+l972N66GB26yH/fendYqJF3dww1HTaHzz+qF9mSrIaPS36/7oLJKa43y2qOpj5tf/UZ0RM5lL8F7m1tcvEWlFXZ/ov9gu980qLz1dVqy8Am7Adx3UIdpwusB3yXvcKR1Q4KbeH1KrO57w1J4+JbUftjHjtiIl5ETSjqtQmJVTIiW0ouLWy/NNTbAO4us2acPo5tdIlBxko1A+sLdyV9xNTXCM7ed+DnTVlOlFXDmJ+3/vSBBz+Jk1FbbdwG7l5XJiagNecB3yVGFTatsuIGWWs2UhdLxsnnJ/7D318Pt19sNUyki4QklFaUVcM6V9n5kEiMVblwFh/db+jtIaa1DOZ6nKVxxzXvAquWkKP2WQvEMGmYdyRbPab0zXyJvzbVCCYB2nOa7/hBzl5yt66w34JTzLOAn8t6W6PuGoy0PVVxbbdVEK16xJpSnXQwDhlhVUTJd5lszcqKlu2a5dRxq6Qbw6oU2fENZFjoIuQ8aWhl6QDda+8PWrrg2LLMGACNOts50u7dn55iImHiW3dB/+3mrikzFgb1W1QRkpXDShjzgu+Qsnx86r7QwKiGc+DQlOPEmWazIs2kjjys8ZTpMCF33yyqzlmxOmW4nnbl3wuf/Pf4yTU325KzBIzK/+ebiK62Ai/7aWmDt3wO9+ydedn89PHyz9XKeMt3q3DNpBRPP8PGWhgVPpBaQZFKBAAAVi0lEQVTwVeGxX1vd/akfBSS7J6Ic8yod17qmRutNOGRU683iIm23T5kOPfrAqw/b4+WaW/RsNNgjdhMtF1UpfUqstc/qBSdefcTaus6a/pWNSr8ttWtdaQWc/wV7/8Tv4y+zcTX86bt2zJz6UQv2uVBUZAPB1W2xprjJeuOpMGzHmXDyWZk1Wc0DD/iudTUrrdQ1PMnOJaUVVjI777NWVfPwzTGte7BL+2Uvhw8Shk3IYcuYyedaNcLcO+PPX73Q6omHenVOzvXuD5POgXeXxVSLBBuWwe1hIDFV6wCXS2M/ZG3o596ZXH+N7TXw3O12nEyo6pCPvvSA71qXbueSQcMt2K5720r0YE3sHvyZ3Tg785Mtj62eLb37wZip8M4bHxwrX9Wqc0rLod+g+Ou77Jp0NvTqB0/falePqnaT/94fR0c5Vc19y5cu3WD4ONi+ofX+GhuWwx0/sALPlOnpD++QZ3796lp27KiNeFhWmV799uSP2EOun7mNssGVsH2djTtSdUnuLtcTpWP9Erj/v+GKb0ZvJC+fb4O0jT618z/spL0o7gKnfxzmP8jQJ2+B4iJrodV3IDQcgSZNfrC8TPUKT09TtUYG1W+c2Mig4ZiV/ufdD6hdCcZerXYwHvBdy9Yutl6Sw8elV8deVGR1na8+TM/t6wCxsczbMtgDHNyLdRbbArddFx1e99gR+7txJYyb2qHqYzu0Hr0AocfuUIofM9VKzgf3nvjAkFyrGG/t6JtCI4MFj1tT0D4ldtyvXWytciJU7Sox0ZPX2jkP+K5ly+fboGiZtJzZ16zH7P698ZfLpR2bwtOkg94lVq8fGQaiKXT08oDfNk5oty7Qp791eOvVt23/B5Hetzs2WZBfu/jEh6QMHGbNe99ZZO3tO1ATzHg84LvE1i+x+u2yysx6n0ZGA2xsDA93zsMPpmykVRM0hR/tGaEp3gt3R6d14B9yhxP+H9FjIo/DWcQ+kGR/fRhkTQGxK9sp063ndlteeeSIB3wXX2013PUj6024swZ2b03/QA9NNes3rmPAqHH5+cFEmos2/9HGm+Zyrz0cE/E0LxhEntQVe1LowLIW8EWkBtgHNAINqlolIgOB+4BKoAb4vKruydY+XQ7VrIzWazZp5tUdpRXs7TKAASU5eIB0Cmn4wHfoJD/kDqk9HBPNJSoYdBLZbpZ5gapOVdWq8Pk7wPOqOh54Pnx2HUHsTal8VcM4lw+ZPgOgHct1O/wrgNvD+9uBK3O8P5ctkefJjj618J785Fwnlc06fAWeExEF/qCqs4AyVd0GoKrbRGRI85VEZAYwA6C8vJyamposJql9qKvL8HmeeTB80Ry0fxnbxp1r7dPrD2S8zbqDR7OQss7B88J4PgRNTdQdboAcx79sBvyPqOrWENTniEhSg6GHE8MsgKqqKq2srMxiktqPDvW9ttfAnq0w6RwqS7M7mFhle6qvzTPPC+P5gA022NiQ8ziRtSodVd0a/u4EHgGmATtEZBhA+LszW/tzObRsnvUoLJ+Q75Q457IoKwFfRHqLSN/Ie+DjwArgceCasNg1QAaPmHFtoqnRxs4ZMhJK2rg3rHMup7JVpVMGPCL2SLkuwN2q+oyIvAncLyJfAzYBn8vS/lyu1Ky0nrHjTvOxZZzrZLIS8FV1A/ChONPrgIuysQ/XRpbNs3FmhrfyoBPnXIfjwyO7qKNHYNUCG+/bn/zkXKfjAd9FvfMGHM1gZEznXLvmAd+Z2mp46V7o3suf/ORcJ+WDpzkL9rf/wB4AIQIH6jMbHdM51y55Cd9Zy5yG8EDxyAMenHOdjgd8B5WT7WEgYI+f84HSnOuUvErH2eMGVe1B3lMv9IHSnOukvITvYOVr9nfyuR32WZ3OudZ5wHew8hUoGQKlw/OdEudcDnnAL3T1O2HzGntubbee+U6Ncy6HPOAXukh1zvCx+U2Hcy7nPOAXupWvwoAyGDgs3ylxzuWYB/xCtnsbbF0HZaOhW498p8Y5l2Me8AuZV+c4V1A84BeyFa9YVc7AoflOiXOuDXjAL1TvbYEdNTC00sa/d851eh7wC9Vr4WmTvfrlNx3OuTbjAb8Q1VbDW3Pt/dvPw3ub85se51ybyDjgi8gIEXlRRFaLyEoR+d9h+kwR2SIiS8LrssyT67Ji6UuA2vumRtixKZ+pcc61kWwMntYAXKuqb4lIX2CxiMwJ825W1Z9mYR8um7auD2/Enmzlo2M6VxAyDviqug3YFt7vE5HVQHmm23U5snGVtb0fNQn6DYaho3x0TOcKRFaHRxaRSuA04HXgI8A3ReRqYBF2FbAnzjozgBkA5eXl1NTUZDNJ7UJdXV2+k3Bc2bOz6da9F5tHnYX2Dk+1qj/QJvuuO3i0TfbTEXheGM+HoKmJusMNkOP4l7WALyJ9gIeAb6nq+yLyO+BGrLL4RuBnwFebr6eqs4BZAFVVVVpZWZmtJLUr7eJ71ayAbWth4lmMGl5mjzNsY5Ulvdt8n+2V54XxfAAaG6CxIedxIiutdESkKxbs71LVhwFUdYeqNqpqE3ArMC0b+3IZePFe6NkHxnwoL8HeOZdf2WilI8BtwGpV/XnM9NjRuD4FrMh0Xy4D7y6HjSth9KnQd2C+U+Ocy4NsVOl8BPgKsFxEloRp3wW+JCJTsSqdGuDrWdiXS4dqKN33hdFTvHTvXIHKRiudV4g+AjvWU5lu22XJ6/8Dm1ZZsPfSvXMFy3vadnbvvAnP/Mneb1wFdVvymx7nXN54wO/MNlXDQz/neK9abfJetc4VsKy2w3fthCq88TQ8+yfo3guKjlmw9161zhU0D/idzfqlMOd22P4uDB0NH7oAGo/BzloL9t6r1rmC5QE/ntpqqFkJlZNhxMn5Tk1im6ph1WvQrTsc3A/vLoO6rTZPBCZURR9uMnhE/tLpnGsXPOA3t/YtuPtHVi3SpQtcc4NNb28ngFUL4IGbLJ0AxV2s+iZCgfpdUD4+L8lzzrU/HvBjHTsKT/7e6rsBGo7BX26EhsPQpNClK1zzX/kN+o0NsOBxeOHuaLAHK80PGwvz7rMhj72+3jnXjAf8iKYmeOxXsHcXSJEFUxEoEpsH0HAUXrgHPvUvtlxbl/oXPQsv3gMH9sKgctizPXoztmKC1c9feJW1xPH6eudcMx7wI1682x7qffKZMHwsvLc1WkJ+4W4rWYPVk//87+xkoLRNqX/HRnhqlrWjBzshTZkOXbt9MLiXVnigd87F5QEfYPEcmP+QjRE/8Wzo0QvKKqPzY0vNxV3hjadg9zab13AU5vwF/uof4PD+7JX6a6th5WuwcyNsWA5FsV0mFHZvh8nneHB3ziXNA/6CJ+DZP8OAoXDq+Rbsm2teaj79YzGlfrFhC37zz9ExaoozLPVvqobZ34tWJZVPsNfiZ7x+3jmXtsIO+MvmWecksDr5w/uhT0nr6zWvK+/RGxY+CbtqbX7jMSvppxvwX7o3GuwRa1o59lToP9Dr551zaSvcgL+/Hp66Nfo5MuxAsoG0eal/6gXw/N3Q1GA3fEvK0kvXO2/ChqXRewTFxTC0Mv4+nXMuBYUZ8I8dhfv+G44etrpx1cyrSUor4KKrYONqWL8E5syG0ZOhz4Dkt7FrMzx8Cwwog5PPstY4Xpp3zmVJ4QV8VXjit3ZTdOqFMGi4BdpsBNZICXzEydbq5/YfwKVfo//KN6H43JareA4fgHt/bCX70y468aaxc85lQWEF/NpqexDIhqVw0hkw/sPQpRsMyfIN0CEj4Jwr4ZWH4M7/okSBpc/BZ/4VunaHretgzKnRE8DGVfD4b6zlz7TLYMio7KbHOecopIC/cTXcfr3VsYtA2WgL9rky4iQYPh62rrWnwzQeg/t/Ep3/4j0W/Lt2g4P7bJoUQe8SfyKVcy4nOv94+Ko2Ps4DN1mwhzDOzM7c73vy2VBUbKPRSxEMHHbi/N4l0LXHiWl9zx9Q4pzLjZyX8EXkUuAXQDHwR1X9ca73CVj1zZKXYMs7sL0GevaJDplQ3Ebt2Esr4KK/pn7jOgaMGmfTXrg72pb+jEs+OM3b1zvnciSnAV9EioHfAB8DNgNvisjjqroqZzs9fMB6zb72aHRwscopcMq51s6+rceFL61gb5cBDCjpbZ/jjXXj498459pArkv404B1qroBQETuBa4Ashvwmxrh9adgyQvwXi00NsbMFOg3EPoOsFe+x4WP15be29c759pArgN+OVAb83kzcGbsAiIyA5gBUF5eTk1NTco76bt6PoNefzjUlQv15ZPov+0dpKkJLSpiR3E/jmxrgzr7BOqONsGhA3nbf3vh+RDleWE8HyKUuoYiSCP+pSLXAT9ecxM94YPqLGAWQFVVlVZWVqa+l02LAEFQQBgwcjyc879g8xqkYgLDho1JfZvZtG0nlcOG5DcN7YHnQ5TnhfF8iNpZR1rxLwW5Dvibgdg6lApga9b3MvoUG6a4scGe/DTpLGvjPvmcrO8qLe8fjj5qsJB5PkR5XhjPh6j3D+d8F7kO+G8C40VkNLAF+CJwVdb3MuJkG52yvT2G0Dnn2pGcBnxVbRCRbwLPYs0y/6SqK3OysxEne6B3zrkW5Lwdvqo+BTyV6/0455xrWefvaeuccw7wgO+ccwXDA75zzhUID/jOOVcgPOA751yB8IDvnHMFwgO+c84VCA/4zjlXIDzgO+dcgfCA75xzBcIDvnPOFQgP+M45VyA84DvnXIHwgO+ccwXCA75zzhUID/jOOVcgPOA751yB8IDvnHMFIqOALyI3iUi1iCwTkUdEpCRMrxSRQyKyJLx+n53kOuecS1emJfw5wCmqeiqwBrguZt56VZ0aXt/IcD/OOecylFHAV9XnVLUhfFwIVGSeJOecc7mQzTr8rwJPx3weLSJvi8g8EZmexf0455xLQ5fWFhCRucDQOLO+p6qPhWW+BzQAd4V524CRqlonIqcDj4rIZFV9P872ZwAzAMrLy6mpqUnri7RndXV1+U5Cu+D5EOV5YTwfotoiL1oN+Kp6cUvzReQa4JPARaqqYZ0jwJHwfrGIrAcmAIvibH8WMAugqqpKKysrU/wKHUNn/V6p8nyI8rwwng9Ruc6LTFvpXAp8G7hcVQ/GTB8sIsXh/RhgPLAhk30555zLTKsl/Fb8GugOzBERgIWhRc55wA0i0gA0At9Q1d0Z7ss551wGMgr4qjouwfSHgIcy2bZzzrns8p62zjlXIDzgO+dcgfCA75xzBcIDvnPOFQgP+M45VyA84DvnXIHwgO+ccwXCA75zzhUID/jOOVcgPOA751yB8IDvnHMFwgO+c84VCA/4zjlXIDzgO+dcgfCA75xzBcIDvnPOFQgP+M45VyA84DvnXIHwgO+ccwUio4AvIjNFZIuILAmvy2LmXSci60TkHRG5JPOkOuecy0RGDzEPblbVn8ZOEJFJwBeBycBwYK6ITFDVxizszznnXBpyVaVzBXCvqh5R1XeBdcC0HO3LOedcErJRwv+miFwNLAKuVdU9QDmwMGaZzWHaB4jIDGBG+LhfRN7JQpram1LgvXwnoh3wfIjyvDCeD1GZ5MWoZBZqNeCLyFxgaJxZ3wN+B9wIaPj7M+CrgMRZXuNtX1VnAbOSSWxHJSKLVLUq3+nIN8+HKM8L4/kQ1RZ50WrAV9WLk9mQiNwKPBk+bgZGxMyuALamnDrnnHNZk2krnWExHz8FrAjvHwe+KCLdRWQ0MB54I5N9Oeecy0ymdfg/EZGpWHVNDfB1AFVdKSL3A6uABuCfCryFTqeuskqB50OU54XxfIjKeV6Iatyqdeecc52M97R1zrkC4QHfOecKhAf8FInIpWG4iHUi8p0w7TYRWSoiy0TkQRHpk2Dd00VkeVj3lyIiYfp9McNT1IjIkrb8TulINx9EpJeI/I+IVIvIShH5ccy880TkLRFpEJHPtuX3yUS8vIiZ9ysR2d/CunGPiZj5/y4iKiKluUp/NmWYFz8Skdrmy4jISBF5UUTeDsfWZYm20V4k+H3MFpF3Y37rUxOsO1pEXheRtSE2dAvT/01EVoU8eF5Ekmp7fwJV9VeSL6AYWA+MAboBS4FJQL+YZX4OfCfB+m8AZ2P9FJ4GPhFnmZ8B1+f7u+YqH4BewAXhfTdgfiQfgErgVOAO4LP5/p6Z5EWYVwX8BdjfwvoJjwmsafOzwEagNN/ftQ3y4ixgWPNlsJuZ/xDeTwJq8v1d08kHYHYyxzVwP/DF8P73Md/9AqBXeP8PwH2pps1L+KmZBqxT1Q2qehS4F7hCVd8HCKWznsTpZBaasPZT1QVq/7E7gCubLSPA54F7cvs1MpZ2PqjqQVV9Mbw/CryF9dNAVWtUdRnQ1DZfIyvi5oWIFAM3Af+RaMUkjombw/odpWVF2nkBoKoLVXVbvFlAv/C+P+2/T0/cfEhmxfDbuRB4MEy6nXBMqOqLqnowTF9I+N2kwgN+asqB2pjPx4eMEJE/A9uBk4FfJVh3c7x1Y0wHdqjq2mwlOEcyyYfjRKQE+Cvg+dwks00kyotvAo8nCGCx68Y9JkTkcmCLqi7NbnJzKpO8aMlM4Msishl4CvjnTBLZBhL+PoAfhSqZm0Wke5x1BwH1qtoQZ91YX8OuCFPiAT81CYeMUNW/xUYGXQ18IZV1Y3yJ9l+6h8zywTYg0gX7rr9U1Q25SGQbiZcX3YHP0coJL8G6KiK9sKFLrs8wbW0tk7xoyZeA2apaAVwG/EVE2nPsSvT7uA4rCJ0BDAS+ncK60QVEvoxVkd2UasLac6a1Ry0OGaHWuew+4DMiUhxzc+aGsG5FonVDAPx0WL+9yyQfImYBa1X1ljZJce7Ey4saYBywTkRqgF7h5l2yx8RYYDSwNKxfAbwlIvHGtGpPMsmLlnwNq9dGVRcAPbCBxtqruL8PVd2m5gjwZ8IIwiLybMiHP2KDp5WEeHB83ciGRORirDBwedhOavJ9g6MjvbCeyRuwH2PkZsxkYFyYL8BPgZ8mWP9N7MZU5AbdZTHzLgXm5fs7tlE+/BB4CChKMH82Heembdy8aLZMSzcqEx4TMcvU0DFu2maUF4mWCfnyN+H9RCwASr6/b6r5AAwL8wW4BfhxgvUf4MSbtv8Y3p+G3Qwen3ba8p05He2FXVKuCRn/Pewq6VVgOTaW0F3EtFZptm5VWGY98OvYgzYEuW/k+/vlOh+wEotiVT5LwuvvwrwzsNLRAaAOWJnv75lOXsSZ31LAT3hMxCzTIQJ+FvLiJ+H/3xT+zgzTJ4Vja2k4Xj6e7++ZTj4AL8T8Pu4E+iRYdwzWemtdCP7dw/S5wI6Y383jqabLh1ZwzrkC4XX4zjlXIDzgO+dcgfCA75xzBcIDvnPOFQgP+M45VyA84DvnXIHwgO+ccwXi/wNH6MOkpRC+uAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "y = get_safegraph_at_home_trend_for_state(sdm_data, acs_df, 'California', make_plot=True, ax=ax, color='coral')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compare_plots_for_state(state, categories=None):\n",
    "    assert state in codes_to_states\n",
    "    if categories is None:\n",
    "        categories = GOOGLE_CATEGORIES\n",
    "    state_name = codes_to_states[state]\n",
    "    n = len(categories)\n",
    "    fig = plt.figure(figsize=(5*n, 8))\n",
    "    colors = ['royalblue', 'coral', 'forestgreen', 'darkorchid']\n",
    "    ax_idx = 1\n",
    "    for i, category in enumerate(categories):\n",
    "        ax = fig.add_subplot(2, n, i+1)\n",
    "        ax_idx += 1\n",
    "        google_state_name = 'District of Columbia' if state_name == 'Washington, D.C.' else state_name\n",
    "        google_v = get_google_trend_for_state(google_df, google_state_name, category=category,\n",
    "                                              make_plot=True, ax=ax, color=colors[i])\n",
    "        ax = fig.add_subplot(2, n, i+1+n)\n",
    "        ax_idx += 1\n",
    "        if category == 'Residential':\n",
    "            safegraph_v = get_safegraph_at_home_trend_for_state(sdm_data, acs_df, state_name,\n",
    "                                                                make_plot=True, ax=ax, color=colors[i])\n",
    "        else:\n",
    "            safegraph_v = get_safegraph_poi_trend_for_state(chunks, state, category=category,\n",
    "                                                            make_plot=True, ax=ax, color=colors[i])\n",
    "        if google_v is not None and safegraph_v is not None:\n",
    "            nan_idx = np.isnan(google_v)\n",
    "            print('Num nan in Google data:', np.sum(nan_idx))\n",
    "            google_v = google_v[~nan_idx]\n",
    "            safegraph_v = safegraph_v[~nan_idx]\n",
    "            r, p = pearsonr(google_v, safegraph_v)\n",
    "            print('%s: pearson r=%.2f, p=%.4f' % (category, r, p))\n",
    "            slope, intercept, r_value, _, _ = linregress(safegraph_v, google_v)\n",
    "            print('%s: slope=%.2f, intercept=%.2f, r^2=%.2f' % (category, slope, intercept, r_value**2))\n",
    "    fig.suptitle(state_name.upper(), fontsize=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 76634 POIs in state CA and category Retail & recreation\n",
      "Num nan in Google data: 0\n",
      "Retail & recreation: pearson r=0.95, p=0.0000\n",
      "Retail & recreation: slope=1.03, intercept=11.95, r^2=0.90\n",
      "Found 13099 POIs in state CA and category Grocery & pharmacy\n",
      "Num nan in Google data: 0\n",
      "Grocery & pharmacy: pearson r=0.83, p=0.0000\n",
      "Grocery & pharmacy: slope=0.56, intercept=16.01, r^2=0.70\n",
      "Found 8639 POIs in state CA and category Parks\n",
      "Num nan in Google data: 0\n",
      "Parks: pearson r=0.82, p=0.0000\n",
      "Parks: slope=0.79, intercept=11.45, r^2=0.68\n",
      "Found 22984 out of 23212 CBGs in state California\n",
      "Num nan in Google data: 0\n",
      "Residential: pearson r=0.88, p=0.0000\n",
      "Residential: slope=0.20, intercept=3.35, r^2=0.77\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "compare_plots_for_state('CA')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 40234 POIs in state NY and category Retail & recreation\n",
      "Num nan in Google data: 0\n",
      "Retail & recreation: pearson r=0.96, p=0.0000\n",
      "Retail & recreation: slope=1.28, intercept=16.69, r^2=0.92\n",
      "Found 7399 POIs in state NY and category Grocery & pharmacy\n",
      "Num nan in Google data: 0\n",
      "Grocery & pharmacy: pearson r=0.76, p=0.0000\n",
      "Grocery & pharmacy: slope=0.63, intercept=17.51, r^2=0.58\n",
      "Found 3556 POIs in state NY and category Parks\n",
      "Num nan in Google data: 0\n",
      "Parks: pearson r=0.57, p=0.0000\n",
      "Parks: slope=0.82, intercept=24.27, r^2=0.32\n",
      "Found 14998 out of 15463 CBGs in state New York\n",
      "Num nan in Google data: 0\n",
      "Residential: pearson r=0.91, p=0.0000\n",
      "Residential: slope=0.21, intercept=2.92, r^2=0.83\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "compare_plots_for_state('NY')\n",
    "fn = os.path.join(FIGURES_DIRECTORY, 'safegraph_google_comparison_ny.pdf')\n",
    "plt.savefig(fn, dpi=300, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 1492 POIs in state SD and category Retail & recreation\n",
      "Num nan in Google data: 0\n",
      "Retail & recreation: pearson r=0.93, p=0.0000\n",
      "Retail & recreation: slope=0.90, intercept=18.64, r^2=0.87\n",
      "Found 324 POIs in state SD and category Grocery & pharmacy\n",
      "Num nan in Google data: 0\n",
      "Grocery & pharmacy: pearson r=0.69, p=0.0000\n",
      "Grocery & pharmacy: slope=0.41, intercept=22.50, r^2=0.48\n",
      "Found 199 POIs in state SD and category Parks\n",
      "Num nan in Google data: 0\n",
      "Parks: pearson r=-0.13, p=0.3190\n",
      "Parks: slope=-0.31, intercept=28.57, r^2=0.02\n",
      "Found 652 out of 654 CBGs in state South Dakota\n",
      "Num nan in Google data: 0\n",
      "Residential: pearson r=0.82, p=0.0000\n",
      "Residential: slope=0.18, intercept=3.65, r^2=0.67\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "compare_plots_for_state('SD')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 1760 POIs in state DE and category Retail & recreation\n",
      "Num nan in Google data: 0\n",
      "Retail & recreation: pearson r=0.96, p=0.0000\n",
      "Retail & recreation: slope=1.00, intercept=19.14, r^2=0.92\n",
      "Found 348 POIs in state DE and category Grocery & pharmacy\n",
      "Num nan in Google data: 0\n",
      "Grocery & pharmacy: pearson r=0.85, p=0.0000\n",
      "Grocery & pharmacy: slope=0.57, intercept=14.33, r^2=0.72\n",
      "Found 102 POIs in state DE and category Parks\n",
      "Num nan in Google data: 2\n",
      "Parks: pearson r=0.37, p=0.0032\n",
      "Parks: slope=0.56, intercept=34.38, r^2=0.13\n",
      "Found 554 out of 574 CBGs in state Delaware\n",
      "Num nan in Google data: 0\n",
      "Residential: pearson r=0.86, p=0.0000\n",
      "Residential: slope=0.21, intercept=1.53, r^2=0.73\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "compare_plots_for_state('DE')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compare_timeseries_for_all_states():\n",
    "    all_results = []\n",
    "    for state, state_name in codes_to_states.items():\n",
    "        google_state_name = 'District of Columbia' if state_name == 'Washington, D.C.' else state_name\n",
    "        google_trends = get_google_trend_for_state(google_df, google_state_name)\n",
    "        if google_trends is not None:\n",
    "            results = [state_name]\n",
    "            num_success = 0 \n",
    "            for i, category in enumerate(GOOGLE_CATEGORIES):\n",
    "                google_v = google_trends[:, i]\n",
    "                if category == 'Residential':\n",
    "                    safegraph_v = get_safegraph_at_home_trend_for_state(sdm_data, acs_df, state_name, verbose=False)\n",
    "                else:\n",
    "                    safegraph_v = get_safegraph_poi_trend_for_state(chunks, state, category=category, verbose=False)\n",
    "                category_results = [np.nan, np.nan, np.nan, np.nan]\n",
    "                if safegraph_v is not None:\n",
    "                    nan_idx = np.isnan(google_v)\n",
    "                    if np.sum(nan_idx) <= 3:  # not missing many data points\n",
    "                        google_v = google_v[~nan_idx]\n",
    "                        safegraph_v = safegraph_v[~nan_idx]\n",
    "                        r, p = pearsonr(google_v, safegraph_v)\n",
    "                        slope, intercept, _, _, _ = linregress(safegraph_v, google_v)\n",
    "                        category_results = [r, p, slope, intercept]\n",
    "                        num_success += 1\n",
    "                results.extend(category_results)\n",
    "            print('%s: succeeded on %d categories' % (state_name, num_success))\n",
    "            all_results.append(results)\n",
    "    cols = ['state_name']\n",
    "    for cat in GOOGLE_CATEGORIES:\n",
    "        cols.extend(['%s_pearson_r' % cat, '%s_pearson_p' % cat, \n",
    "                     '%s_regression_slope' % cat, '%s_regression_intercept' % cat])\n",
    "    results_df = pd.DataFrame(all_results, columns=cols)\n",
    "    return results_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Alabama: succeeded on 4 categories\n",
      "Alaska: succeeded on 4 categories\n",
      "Missing dates in Google data for American Samoa: found 0 rows, wanted 65\n",
      "Arizona: succeeded on 4 categories\n",
      "Arkansas: succeeded on 4 categories\n",
      "California: succeeded on 4 categories\n",
      "Colorado: succeeded on 4 categories\n",
      "Connecticut: succeeded on 4 categories\n",
      "Delaware: succeeded on 4 categories\n",
      "Washington, D.C.: succeeded on 4 categories\n",
      "Missing dates in Google data for Federated States Of Micronesia: found 0 rows, wanted 65\n",
      "Florida: succeeded on 4 categories\n",
      "Georgia: succeeded on 4 categories\n",
      "Missing dates in Google data for Guam: found 0 rows, wanted 65\n",
      "Hawaii: succeeded on 4 categories\n",
      "Idaho: succeeded on 4 categories\n",
      "Illinois: succeeded on 4 categories\n",
      "Indiana: succeeded on 4 categories\n",
      "Iowa: succeeded on 4 categories\n",
      "Kansas: succeeded on 4 categories\n",
      "Kentucky: succeeded on 4 categories\n",
      "Louisiana: succeeded on 4 categories\n",
      "Maine: succeeded on 4 categories\n",
      "Missing dates in Google data for Marshall Islands: found 0 rows, wanted 65\n",
      "Maryland: succeeded on 4 categories\n",
      "Massachusetts: succeeded on 4 categories\n",
      "Michigan: succeeded on 4 categories\n",
      "Minnesota: succeeded on 4 categories\n",
      "Mississippi: succeeded on 4 categories\n",
      "Missouri: succeeded on 4 categories\n",
      "Montana: succeeded on 4 categories\n",
      "Nebraska: succeeded on 4 categories\n",
      "Nevada: succeeded on 4 categories\n",
      "New Hampshire: succeeded on 4 categories\n",
      "New Jersey: succeeded on 4 categories\n",
      "New Mexico: succeeded on 4 categories\n",
      "New York: succeeded on 4 categories\n",
      "North Carolina: succeeded on 4 categories\n",
      "North Dakota: succeeded on 4 categories\n",
      "Missing dates in Google data for Northern Mariana Islands: found 0 rows, wanted 65\n",
      "Ohio: succeeded on 4 categories\n",
      "Oklahoma: succeeded on 4 categories\n",
      "Oregon: succeeded on 4 categories\n",
      "Missing dates in Google data for Palau: found 0 rows, wanted 65\n",
      "Pennsylvania: succeeded on 4 categories\n",
      "Missing dates in Google data for Puerto Rico: found 0 rows, wanted 65\n",
      "Rhode Island: succeeded on 4 categories\n",
      "South Carolina: succeeded on 4 categories\n",
      "South Dakota: succeeded on 4 categories\n",
      "Tennessee: succeeded on 4 categories\n",
      "Texas: succeeded on 4 categories\n",
      "Utah: succeeded on 4 categories\n",
      "Vermont: succeeded on 4 categories\n",
      "Missing dates in Google data for Virgin Islands: found 0 rows, wanted 65\n",
      "Virginia: succeeded on 4 categories\n",
      "Washington: succeeded on 4 categories\n",
      "West Virginia: succeeded on 4 categories\n",
      "Wisconsin: succeeded on 4 categories\n",
      "Wyoming: succeeded on 4 categories\n"
     ]
    }
   ],
   "source": [
    "results_df = compare_timeseries_for_all_states()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "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>state_name</th>\n",
       "      <th>Retail &amp; recreation_pearson_r</th>\n",
       "      <th>Retail &amp; recreation_pearson_p</th>\n",
       "      <th>Retail &amp; recreation_regression_slope</th>\n",
       "      <th>Retail &amp; recreation_regression_intercept</th>\n",
       "      <th>Grocery &amp; pharmacy_pearson_r</th>\n",
       "      <th>Grocery &amp; pharmacy_pearson_p</th>\n",
       "      <th>Grocery &amp; pharmacy_regression_slope</th>\n",
       "      <th>Grocery &amp; pharmacy_regression_intercept</th>\n",
       "      <th>Parks_pearson_r</th>\n",
       "      <th>Parks_pearson_p</th>\n",
       "      <th>Parks_regression_slope</th>\n",
       "      <th>Parks_regression_intercept</th>\n",
       "      <th>Residential_pearson_r</th>\n",
       "      <th>Residential_pearson_p</th>\n",
       "      <th>Residential_regression_slope</th>\n",
       "      <th>Residential_regression_intercept</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Delaware</td>\n",
       "      <td>0.957258</td>\n",
       "      <td>1.195717e-35</td>\n",
       "      <td>1.004807</td>\n",
       "      <td>19.143343</td>\n",
       "      <td>0.846951</td>\n",
       "      <td>6.103204e-19</td>\n",
       "      <td>0.570838</td>\n",
       "      <td>14.332617</td>\n",
       "      <td>0.366196</td>\n",
       "      <td>0.003161</td>\n",
       "      <td>0.562821</td>\n",
       "      <td>34.376979</td>\n",
       "      <td>0.855615</td>\n",
       "      <td>1.117279e-19</td>\n",
       "      <td>0.207107</td>\n",
       "      <td>1.533048</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  state_name  Retail & recreation_pearson_r  Retail & recreation_pearson_p  \\\n",
       "7   Delaware                       0.957258                   1.195717e-35   \n",
       "\n",
       "   Retail & recreation_regression_slope  \\\n",
       "7                              1.004807   \n",
       "\n",
       "   Retail & recreation_regression_intercept  Grocery & pharmacy_pearson_r  \\\n",
       "7                                 19.143343                      0.846951   \n",
       "\n",
       "   Grocery & pharmacy_pearson_p  Grocery & pharmacy_regression_slope  \\\n",
       "7                  6.103204e-19                             0.570838   \n",
       "\n",
       "   Grocery & pharmacy_regression_intercept  Parks_pearson_r  Parks_pearson_p  \\\n",
       "7                                14.332617         0.366196         0.003161   \n",
       "\n",
       "   Parks_regression_slope  Parks_regression_intercept  Residential_pearson_r  \\\n",
       "7                0.562821                   34.376979               0.855615   \n",
       "\n",
       "   Residential_pearson_p  Residential_regression_slope  \\\n",
       "7           1.117279e-19                      0.207107   \n",
       "\n",
       "   Residential_regression_intercept  \n",
       "7                          1.533048  "
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_df[results_df['state_name'] == 'Delaware']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Retail & recreation: taking median over 51 states...\n",
      "median correlation: 0.958, median slope: 0.980\n",
      "\n",
      "Grocery & pharmacy: taking median over 51 states...\n",
      "median correlation: 0.752, median slope: 0.467\n",
      "\n",
      "Parks: taking median over 51 states...\n",
      "median correlation: 0.281, median slope: 0.369\n",
      "\n",
      "Residential: taking median over 51 states...\n",
      "median correlation: 0.868, median slope: 0.190\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# all states\n",
    "for cat in GOOGLE_CATEGORIES:\n",
    "    cols = ['%s_pearson_r' % cat, '%s_regression_slope' % cat]\n",
    "    values = results_df[cols].values\n",
    "    print('%s: taking median over %d states...' % (cat, len(values)))\n",
    "    medians = np.median(values, axis=0)\n",
    "    print('median correlation: %.3f, median slope: %.3f' % (medians[0], medians[1]))\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['California', 'Delaware', 'Washington, D.C.', 'Florida', 'Georgia',\n",
       "       'Illinois', 'Indiana', 'Maryland', 'New Jersey', 'New York',\n",
       "       'Pennsylvania', 'Texas', 'Virginia', 'West Virginia', 'Wisconsin'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# states we model\n",
    "msas = ['New York-Newark-Jersey City, NY-NJ-PA', \n",
    "        'Los Angeles-Long Beach-Anaheim, CA', \n",
    "        'Chicago-Naperville-Elgin, IL-IN-WI',\n",
    "        'Philadelphia-Camden-Wilmington, PA-NJ-DE-MD',\n",
    "        'Dallas-Fort Worth-Arlington, TX',\n",
    "        'Washington-Arlington-Alexandria, DC-VA-MD-WV',\n",
    "        'Miami-Fort Lauderdale-West Palm Beach, FL',\n",
    "        'Houston-The Woodlands-Sugar Land, TX',\n",
    "        'San Francisco-Oakland-Hayward, CA', \n",
    "        'Atlanta-Sandy Springs-Roswell, GA']\n",
    "states_of_interest = acs_df[acs_df['CBSA Title'].isin(set(msas))].state_name.unique()\n",
    "states_of_interest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Retail & recreation: taking median over 15 states...\n",
      "median correlation: 0.959, median slope: 1.023\n",
      "\n",
      "Grocery & pharmacy: taking median over 15 states...\n",
      "median correlation: 0.789, median slope: 0.554\n",
      "\n",
      "Parks: taking median over 15 states...\n",
      "median correlation: 0.442, median slope: 0.604\n",
      "\n",
      "Residential: taking median over 15 states...\n",
      "median correlation: 0.877, median slope: 0.191\n",
      "\n"
     ]
    }
   ],
   "source": [
    "in_msa_df = results_df[results_df['state_name'].isin(set(states_of_interest))]\n",
    "for cat in GOOGLE_CATEGORIES:\n",
    "    cols = ['%s_pearson_r' % cat, '%s_regression_slope' % cat]\n",
    "    values = in_msa_df[cols].values\n",
    "    print('%s: taking median over %d states...' % (cat, len(values)))\n",
    "    medians = np.median(values, axis=0)\n",
    "    print('median correlation: %.3f, median slope: %.3f' % (medians[0], medians[1]))\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "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>state_name</th>\n",
       "      <th>Retail &amp; recreation_pearson_r</th>\n",
       "      <th>Retail &amp; recreation_pearson_p</th>\n",
       "      <th>Retail &amp; recreation_regression_slope</th>\n",
       "      <th>Retail &amp; recreation_regression_intercept</th>\n",
       "      <th>Grocery &amp; pharmacy_pearson_r</th>\n",
       "      <th>Grocery &amp; pharmacy_pearson_p</th>\n",
       "      <th>Grocery &amp; pharmacy_regression_slope</th>\n",
       "      <th>Grocery &amp; pharmacy_regression_intercept</th>\n",
       "      <th>Parks_pearson_r</th>\n",
       "      <th>Parks_pearson_p</th>\n",
       "      <th>Parks_regression_slope</th>\n",
       "      <th>Parks_regression_intercept</th>\n",
       "      <th>Residential_pearson_r</th>\n",
       "      <th>Residential_pearson_p</th>\n",
       "      <th>Residential_regression_slope</th>\n",
       "      <th>Residential_regression_intercept</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>California</td>\n",
       "      <td>0.947221</td>\n",
       "      <td>7.898395e-33</td>\n",
       "      <td>1.031001</td>\n",
       "      <td>11.945741</td>\n",
       "      <td>0.834025</td>\n",
       "      <td>6.384223e-18</td>\n",
       "      <td>0.559891</td>\n",
       "      <td>16.012202</td>\n",
       "      <td>0.823706</td>\n",
       "      <td>3.615767e-17</td>\n",
       "      <td>0.787384</td>\n",
       "      <td>11.449165</td>\n",
       "      <td>0.875685</td>\n",
       "      <td>1.374674e-21</td>\n",
       "      <td>0.203662</td>\n",
       "      <td>3.345597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Delaware</td>\n",
       "      <td>0.957258</td>\n",
       "      <td>1.195717e-35</td>\n",
       "      <td>1.004807</td>\n",
       "      <td>19.143343</td>\n",
       "      <td>0.846951</td>\n",
       "      <td>6.103204e-19</td>\n",
       "      <td>0.570838</td>\n",
       "      <td>14.332617</td>\n",
       "      <td>0.366196</td>\n",
       "      <td>3.160752e-03</td>\n",
       "      <td>0.562821</td>\n",
       "      <td>34.376979</td>\n",
       "      <td>0.855615</td>\n",
       "      <td>1.117279e-19</td>\n",
       "      <td>0.207107</td>\n",
       "      <td>1.533048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Florida</td>\n",
       "      <td>0.962932</td>\n",
       "      <td>1.467108e-37</td>\n",
       "      <td>1.022565</td>\n",
       "      <td>16.122249</td>\n",
       "      <td>0.814290</td>\n",
       "      <td>1.599654e-16</td>\n",
       "      <td>0.590494</td>\n",
       "      <td>15.873331</td>\n",
       "      <td>0.905158</td>\n",
       "      <td>4.321963e-25</td>\n",
       "      <td>1.169148</td>\n",
       "      <td>24.263266</td>\n",
       "      <td>0.884939</td>\n",
       "      <td>1.389795e-22</td>\n",
       "      <td>0.191162</td>\n",
       "      <td>0.470551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Georgia</td>\n",
       "      <td>0.947609</td>\n",
       "      <td>6.297593e-33</td>\n",
       "      <td>0.879459</td>\n",
       "      <td>16.275178</td>\n",
       "      <td>0.681525</td>\n",
       "      <td>4.132334e-10</td>\n",
       "      <td>0.404414</td>\n",
       "      <td>14.581061</td>\n",
       "      <td>0.442402</td>\n",
       "      <td>2.243411e-04</td>\n",
       "      <td>0.603579</td>\n",
       "      <td>34.504787</td>\n",
       "      <td>0.868215</td>\n",
       "      <td>7.682924e-21</td>\n",
       "      <td>0.165623</td>\n",
       "      <td>1.604118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Illinois</td>\n",
       "      <td>0.964294</td>\n",
       "      <td>4.604410e-38</td>\n",
       "      <td>1.083845</td>\n",
       "      <td>18.986266</td>\n",
       "      <td>0.709521</td>\n",
       "      <td>3.688698e-11</td>\n",
       "      <td>0.553664</td>\n",
       "      <td>21.779869</td>\n",
       "      <td>0.511620</td>\n",
       "      <td>1.326596e-05</td>\n",
       "      <td>0.946835</td>\n",
       "      <td>47.381127</td>\n",
       "      <td>0.898885</td>\n",
       "      <td>2.949216e-24</td>\n",
       "      <td>0.182900</td>\n",
       "      <td>1.953261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Indiana</td>\n",
       "      <td>0.956117</td>\n",
       "      <td>2.695297e-35</td>\n",
       "      <td>0.979735</td>\n",
       "      <td>20.591139</td>\n",
       "      <td>0.741135</td>\n",
       "      <td>1.672516e-12</td>\n",
       "      <td>0.466603</td>\n",
       "      <td>17.654970</td>\n",
       "      <td>0.069554</td>\n",
       "      <td>5.819409e-01</td>\n",
       "      <td>0.136604</td>\n",
       "      <td>45.786986</td>\n",
       "      <td>0.877294</td>\n",
       "      <td>9.353158e-22</td>\n",
       "      <td>0.182249</td>\n",
       "      <td>0.575392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Maryland</td>\n",
       "      <td>0.956466</td>\n",
       "      <td>2.107677e-35</td>\n",
       "      <td>0.938894</td>\n",
       "      <td>14.210561</td>\n",
       "      <td>0.825071</td>\n",
       "      <td>2.893571e-17</td>\n",
       "      <td>0.541821</td>\n",
       "      <td>13.050155</td>\n",
       "      <td>0.350765</td>\n",
       "      <td>4.173954e-03</td>\n",
       "      <td>0.558542</td>\n",
       "      <td>41.767287</td>\n",
       "      <td>0.885756</td>\n",
       "      <td>1.124495e-22</td>\n",
       "      <td>0.197413</td>\n",
       "      <td>2.848279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>New Jersey</td>\n",
       "      <td>0.951656</td>\n",
       "      <td>5.317839e-34</td>\n",
       "      <td>1.264750</td>\n",
       "      <td>17.662452</td>\n",
       "      <td>0.720012</td>\n",
       "      <td>1.384636e-11</td>\n",
       "      <td>0.575841</td>\n",
       "      <td>15.356388</td>\n",
       "      <td>0.496504</td>\n",
       "      <td>2.593961e-05</td>\n",
       "      <td>1.018766</td>\n",
       "      <td>53.383107</td>\n",
       "      <td>0.935028</td>\n",
       "      <td>4.574815e-30</td>\n",
       "      <td>0.171052</td>\n",
       "      <td>1.625929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>New York</td>\n",
       "      <td>0.958218</td>\n",
       "      <td>5.934346e-36</td>\n",
       "      <td>1.282837</td>\n",
       "      <td>16.686860</td>\n",
       "      <td>0.762783</td>\n",
       "      <td>1.533918e-13</td>\n",
       "      <td>0.628047</td>\n",
       "      <td>17.506867</td>\n",
       "      <td>0.568931</td>\n",
       "      <td>7.624445e-07</td>\n",
       "      <td>0.815737</td>\n",
       "      <td>24.271184</td>\n",
       "      <td>0.909284</td>\n",
       "      <td>1.134798e-25</td>\n",
       "      <td>0.209915</td>\n",
       "      <td>2.917576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>0.971245</td>\n",
       "      <td>5.581747e-41</td>\n",
       "      <td>1.109267</td>\n",
       "      <td>18.954130</td>\n",
       "      <td>0.850095</td>\n",
       "      <td>3.337055e-19</td>\n",
       "      <td>0.590021</td>\n",
       "      <td>15.652213</td>\n",
       "      <td>0.339366</td>\n",
       "      <td>5.682647e-03</td>\n",
       "      <td>0.603522</td>\n",
       "      <td>41.271464</td>\n",
       "      <td>0.875479</td>\n",
       "      <td>1.443731e-21</td>\n",
       "      <td>0.187793</td>\n",
       "      <td>2.489996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Texas</td>\n",
       "      <td>0.965332</td>\n",
       "      <td>1.845921e-38</td>\n",
       "      <td>0.954121</td>\n",
       "      <td>18.599329</td>\n",
       "      <td>0.788840</td>\n",
       "      <td>6.037280e-15</td>\n",
       "      <td>0.518735</td>\n",
       "      <td>17.771400</td>\n",
       "      <td>0.754733</td>\n",
       "      <td>3.838292e-13</td>\n",
       "      <td>0.906788</td>\n",
       "      <td>30.297577</td>\n",
       "      <td>0.886070</td>\n",
       "      <td>1.036122e-22</td>\n",
       "      <td>0.179868</td>\n",
       "      <td>1.835418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Virginia</td>\n",
       "      <td>0.967023</td>\n",
       "      <td>3.920319e-39</td>\n",
       "      <td>0.911842</td>\n",
       "      <td>15.769805</td>\n",
       "      <td>0.840196</td>\n",
       "      <td>2.136201e-18</td>\n",
       "      <td>0.464954</td>\n",
       "      <td>14.919707</td>\n",
       "      <td>0.335810</td>\n",
       "      <td>6.242597e-03</td>\n",
       "      <td>0.384163</td>\n",
       "      <td>35.213729</td>\n",
       "      <td>0.877309</td>\n",
       "      <td>9.319320e-22</td>\n",
       "      <td>0.191697</td>\n",
       "      <td>1.047625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Washington, D.C.</td>\n",
       "      <td>0.958851</td>\n",
       "      <td>3.702772e-36</td>\n",
       "      <td>1.291933</td>\n",
       "      <td>12.509105</td>\n",
       "      <td>0.888633</td>\n",
       "      <td>5.266506e-23</td>\n",
       "      <td>0.676978</td>\n",
       "      <td>10.157027</td>\n",
       "      <td>0.840552</td>\n",
       "      <td>2.002580e-18</td>\n",
       "      <td>1.187316</td>\n",
       "      <td>13.522048</td>\n",
       "      <td>0.780487</td>\n",
       "      <td>1.786225e-14</td>\n",
       "      <td>0.287868</td>\n",
       "      <td>9.664520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>West Virginia</td>\n",
       "      <td>0.960138</td>\n",
       "      <td>1.387822e-36</td>\n",
       "      <td>0.987814</td>\n",
       "      <td>23.229444</td>\n",
       "      <td>0.739630</td>\n",
       "      <td>1.957434e-12</td>\n",
       "      <td>0.414920</td>\n",
       "      <td>14.035592</td>\n",
       "      <td>0.310630</td>\n",
       "      <td>1.178810e-02</td>\n",
       "      <td>0.355327</td>\n",
       "      <td>26.708559</td>\n",
       "      <td>0.814097</td>\n",
       "      <td>1.647611e-16</td>\n",
       "      <td>0.169905</td>\n",
       "      <td>2.180638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Wisconsin</td>\n",
       "      <td>0.967078</td>\n",
       "      <td>3.723101e-39</td>\n",
       "      <td>1.089094</td>\n",
       "      <td>18.990292</td>\n",
       "      <td>0.783084</td>\n",
       "      <td>1.281446e-14</td>\n",
       "      <td>0.530223</td>\n",
       "      <td>16.500610</td>\n",
       "      <td>-0.093049</td>\n",
       "      <td>4.609829e-01</td>\n",
       "      <td>-0.197724</td>\n",
       "      <td>33.233041</td>\n",
       "      <td>0.885623</td>\n",
       "      <td>1.163977e-22</td>\n",
       "      <td>0.202249</td>\n",
       "      <td>2.549195</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          state_name  Retail & recreation_pearson_r  \\\n",
       "4         California                       0.947221   \n",
       "7           Delaware                       0.957258   \n",
       "9            Florida                       0.962932   \n",
       "10           Georgia                       0.947609   \n",
       "13          Illinois                       0.964294   \n",
       "14           Indiana                       0.956117   \n",
       "20          Maryland                       0.956466   \n",
       "30        New Jersey                       0.951656   \n",
       "32          New York                       0.958218   \n",
       "38      Pennsylvania                       0.971245   \n",
       "43             Texas                       0.965332   \n",
       "46          Virginia                       0.967023   \n",
       "8   Washington, D.C.                       0.958851   \n",
       "48     West Virginia                       0.960138   \n",
       "49         Wisconsin                       0.967078   \n",
       "\n",
       "    Retail & recreation_pearson_p  Retail & recreation_regression_slope  \\\n",
       "4                    7.898395e-33                              1.031001   \n",
       "7                    1.195717e-35                              1.004807   \n",
       "9                    1.467108e-37                              1.022565   \n",
       "10                   6.297593e-33                              0.879459   \n",
       "13                   4.604410e-38                              1.083845   \n",
       "14                   2.695297e-35                              0.979735   \n",
       "20                   2.107677e-35                              0.938894   \n",
       "30                   5.317839e-34                              1.264750   \n",
       "32                   5.934346e-36                              1.282837   \n",
       "38                   5.581747e-41                              1.109267   \n",
       "43                   1.845921e-38                              0.954121   \n",
       "46                   3.920319e-39                              0.911842   \n",
       "8                    3.702772e-36                              1.291933   \n",
       "48                   1.387822e-36                              0.987814   \n",
       "49                   3.723101e-39                              1.089094   \n",
       "\n",
       "    Retail & recreation_regression_intercept  Grocery & pharmacy_pearson_r  \\\n",
       "4                                  11.945741                      0.834025   \n",
       "7                                  19.143343                      0.846951   \n",
       "9                                  16.122249                      0.814290   \n",
       "10                                 16.275178                      0.681525   \n",
       "13                                 18.986266                      0.709521   \n",
       "14                                 20.591139                      0.741135   \n",
       "20                                 14.210561                      0.825071   \n",
       "30                                 17.662452                      0.720012   \n",
       "32                                 16.686860                      0.762783   \n",
       "38                                 18.954130                      0.850095   \n",
       "43                                 18.599329                      0.788840   \n",
       "46                                 15.769805                      0.840196   \n",
       "8                                  12.509105                      0.888633   \n",
       "48                                 23.229444                      0.739630   \n",
       "49                                 18.990292                      0.783084   \n",
       "\n",
       "    Grocery & pharmacy_pearson_p  Grocery & pharmacy_regression_slope  \\\n",
       "4                   6.384223e-18                             0.559891   \n",
       "7                   6.103204e-19                             0.570838   \n",
       "9                   1.599654e-16                             0.590494   \n",
       "10                  4.132334e-10                             0.404414   \n",
       "13                  3.688698e-11                             0.553664   \n",
       "14                  1.672516e-12                             0.466603   \n",
       "20                  2.893571e-17                             0.541821   \n",
       "30                  1.384636e-11                             0.575841   \n",
       "32                  1.533918e-13                             0.628047   \n",
       "38                  3.337055e-19                             0.590021   \n",
       "43                  6.037280e-15                             0.518735   \n",
       "46                  2.136201e-18                             0.464954   \n",
       "8                   5.266506e-23                             0.676978   \n",
       "48                  1.957434e-12                             0.414920   \n",
       "49                  1.281446e-14                             0.530223   \n",
       "\n",
       "    Grocery & pharmacy_regression_intercept  Parks_pearson_r  Parks_pearson_p  \\\n",
       "4                                 16.012202         0.823706     3.615767e-17   \n",
       "7                                 14.332617         0.366196     3.160752e-03   \n",
       "9                                 15.873331         0.905158     4.321963e-25   \n",
       "10                                14.581061         0.442402     2.243411e-04   \n",
       "13                                21.779869         0.511620     1.326596e-05   \n",
       "14                                17.654970         0.069554     5.819409e-01   \n",
       "20                                13.050155         0.350765     4.173954e-03   \n",
       "30                                15.356388         0.496504     2.593961e-05   \n",
       "32                                17.506867         0.568931     7.624445e-07   \n",
       "38                                15.652213         0.339366     5.682647e-03   \n",
       "43                                17.771400         0.754733     3.838292e-13   \n",
       "46                                14.919707         0.335810     6.242597e-03   \n",
       "8                                 10.157027         0.840552     2.002580e-18   \n",
       "48                                14.035592         0.310630     1.178810e-02   \n",
       "49                                16.500610        -0.093049     4.609829e-01   \n",
       "\n",
       "    Parks_regression_slope  Parks_regression_intercept  Residential_pearson_r  \\\n",
       "4                 0.787384                   11.449165               0.875685   \n",
       "7                 0.562821                   34.376979               0.855615   \n",
       "9                 1.169148                   24.263266               0.884939   \n",
       "10                0.603579                   34.504787               0.868215   \n",
       "13                0.946835                   47.381127               0.898885   \n",
       "14                0.136604                   45.786986               0.877294   \n",
       "20                0.558542                   41.767287               0.885756   \n",
       "30                1.018766                   53.383107               0.935028   \n",
       "32                0.815737                   24.271184               0.909284   \n",
       "38                0.603522                   41.271464               0.875479   \n",
       "43                0.906788                   30.297577               0.886070   \n",
       "46                0.384163                   35.213729               0.877309   \n",
       "8                 1.187316                   13.522048               0.780487   \n",
       "48                0.355327                   26.708559               0.814097   \n",
       "49               -0.197724                   33.233041               0.885623   \n",
       "\n",
       "    Residential_pearson_p  Residential_regression_slope  \\\n",
       "4            1.374674e-21                      0.203662   \n",
       "7            1.117279e-19                      0.207107   \n",
       "9            1.389795e-22                      0.191162   \n",
       "10           7.682924e-21                      0.165623   \n",
       "13           2.949216e-24                      0.182900   \n",
       "14           9.353158e-22                      0.182249   \n",
       "20           1.124495e-22                      0.197413   \n",
       "30           4.574815e-30                      0.171052   \n",
       "32           1.134798e-25                      0.209915   \n",
       "38           1.443731e-21                      0.187793   \n",
       "43           1.036122e-22                      0.179868   \n",
       "46           9.319320e-22                      0.191697   \n",
       "8            1.786225e-14                      0.287868   \n",
       "48           1.647611e-16                      0.169905   \n",
       "49           1.163977e-22                      0.202249   \n",
       "\n",
       "    Residential_regression_intercept  \n",
       "4                           3.345597  \n",
       "7                           1.533048  \n",
       "9                           0.470551  \n",
       "10                          1.604118  \n",
       "13                          1.953261  \n",
       "14                          0.575392  \n",
       "20                          2.848279  \n",
       "30                          1.625929  \n",
       "32                          2.917576  \n",
       "38                          2.489996  \n",
       "43                          1.835418  \n",
       "46                          1.047625  \n",
       "8                           9.664520  \n",
       "48                          2.180638  \n",
       "49                          2.549195  "
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "in_msa_df = in_msa_df.sort_values(by='state_name')\n",
    "in_msa_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Not using anymore: 2. Compare trends across states, aggregated over a recent week"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_trend_for_all_states_and_category(df, category, mode='google',\n",
    "                                          print_n=8):\n",
    "    assert(mode in {'google', 'safegraph'})\n",
    "    dates = helper.list_datetimes_in_range(datetime.datetime(2020, 4, 3),\n",
    "                                           datetime.datetime(2020, 4, 10))\n",
    "    if mode == 'google':\n",
    "        all_states = df.sub_region_1.unique()\n",
    "        state2val = {}\n",
    "        for state in all_states:\n",
    "            values = get_google_trend_for_state_and_category(df, state, dates, category)\n",
    "            if state == 'District of Columbia':\n",
    "                state = 'Washington, D.C.'\n",
    "            state2val[state] = np.mean(values)  # take the mean from the week\n",
    "    else:\n",
    "        all_states = df.region.unique()\n",
    "        state2val = {}\n",
    "        for state in all_states:\n",
    "            if state in codes_to_states:\n",
    "                values = get_safegraph_trend_for_state_and_category(df, state, dates, category, verbose=False)\n",
    "                if values is not None:\n",
    "                    state = codes_to_states[state]\n",
    "                    state2val[state] = np.mean(values) \n",
    "            else:\n",
    "                print('Could not find state name for code %s -> skipping' % state)\n",
    "        \n",
    "    if print_n > 0:\n",
    "        state_names = list(state2val.keys())\n",
    "        sorted_states = sorted(state_names, key=lambda x:state2val[x])\n",
    "        print('%s: %d states with biggest drop' % (mode.capitalize(), print_n))\n",
    "        print_out = ['%s (%d)' % (s, state2val[s]) for s in sorted_states[:print_n]]\n",
    "        print(', '.join(print_out))\n",
    "        print('%s: %d states with smallest drop' % (mode.capitalize(), print_n))\n",
    "        print_out = ['%s (%d)' % (s, state2val[s]) for s in sorted_states[:-print_n-1:-1]]\n",
    "        print(', '.join(print_out))\n",
    "    return state2val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing category RETAIL & RECREATION...\n",
      "Google: 20 states with biggest drop\n",
      "Washington, D.C. (-63), New Jersey (-61), New York (-59), Michigan (-53), Vermont (-53), Hawaii (-51), Massachusetts (-50), Florida (-48), California (-47), Maine (-47), Pennsylvania (-46), Wisconsin (-46), Rhode Island (-45), Connecticut (-45), Illinois (-45), Minnesota (-44), North Dakota (-44), Nevada (-44), Colorado (-44), New Hampshire (-43)\n",
      "Google: 20 states with smallest drop\n",
      "Arkansas (-30), Oklahoma (-34), Mississippi (-34), Wyoming (-34), Kentucky (-35), Nebraska (-36), Idaho (-36), South Dakota (-36), North Carolina (-37), South Carolina (-37), Tennessee (-37), Alabama (-37), New Mexico (-37), West Virginia (-37), Utah (-38), Indiana (-38), Kansas (-39), Missouri (-39), Arizona (-39), Virginia (-39)\n",
      "Safegraph: 20 states with biggest drop\n",
      "Guam (-99), Puerto Rico (-91), Washington, D.C. (-79), New York (-71), Massachusetts (-70), Vermont (-69), Michigan (-68), Florida (-68), New Jersey (-67), California (-67), Rhode Island (-66), Minnesota (-66), Alaska (-65), Nevada (-65), Maryland (-64), Pennsylvania (-64), Wisconsin (-64), Illinois (-64), Connecticut (-63), Colorado (-63)\n",
      "Safegraph: 20 states with smallest drop\n",
      "Utah (-52), Wyoming (-53), Oklahoma (-53), Montana (-55), Kentucky (-55), Mississippi (-55), Idaho (-55), Arkansas (-55), Nebraska (-57), South Carolina (-57), New Mexico (-57), Iowa (-57), West Virginia (-57), Delaware (-57), Tennessee (-58), North Carolina (-58), North Dakota (-58), South Dakota (-58), Ohio (-59), Kansas (-59)\n",
      "Comparing 51 states...\n",
      "Pearson corr = 0.874 (p=0.000)\n",
      "\n",
      "Testing category GROCERY & PHARMACY...\n",
      "Google: 20 states with biggest drop\n",
      "Washington, D.C. (-31), Vermont (-29), Hawaii (-27), Florida (-23), Delaware (-21), Maryland (-21), Massachusetts (-21), Michigan (-21), California (-21), Maine (-20), Pennsylvania (-20), Colorado (-18), New York (-17), New Hampshire (-17), Rhode Island (-17), Texas (-17), Nevada (-16), Connecticut (-16), Wisconsin (-15), Arizona (-15)\n",
      "Google: 20 states with smallest drop\n",
      "Iowa (0), South Dakota (-1), Arkansas (-2), Kentucky (-2), Alabama (-4), Wyoming (-4), Nebraska (-5), New Jersey (-5), Oklahoma (-5), Tennessee (-5), South Carolina (-6), Missouri (-6), Mississippi (-6), New Mexico (-7), Idaho (-8), Louisiana (-8), North Carolina (-8), Utah (-9), North Dakota (-9), Montana (-9)\n",
      "Safegraph: 20 states with biggest drop\n",
      "Guam (-91), Puerto Rico (-82), Washington, D.C. (-59), New York (-47), Massachusetts (-47), Colorado (-43), New Jersey (-42), Rhode Island (-40), Florida (-40), New Mexico (-39), Connecticut (-39), Maine (-39), California (-38), Vermont (-37), Montana (-37), Texas (-37), Washington (-37), Oregon (-36), Michigan (-36), Hawaii (-35)\n",
      "Safegraph: 20 states with smallest drop\n",
      "South Carolina (-17), Alabama (-18), South Dakota (-20), Iowa (-20), New Hampshire (-20), Missouri (-21), Arkansas (-22), Oklahoma (-22), Kentucky (-23), Delaware (-24), North Carolina (-24), Louisiana (-24), Utah (-25), Wyoming (-25), Tennessee (-25), Mississippi (-25), Wisconsin (-26), North Dakota (-28), Indiana (-28), Ohio (-28)\n",
      "Comparing 51 states...\n",
      "Pearson corr = 0.663 (p=0.000)\n",
      "\n",
      "Testing category PARKS...\n",
      "Google: 20 states with biggest drop\n",
      "Hawaii (-66), Washington, D.C. (-60), Florida (-51), California (-47), Nevada (-44), Texas (-34), New York (-25), Arizona (-22), South Carolina (-21), Louisiana (-20), Mississippi (-9), Georgia (-6), New Mexico (-5), Vermont (-1), New Jersey (-1), North Dakota (0), Illinois (1), Massachusetts (1), Alabama (2), Tennessee (2)\n",
      "Google: 20 states with smallest drop\n",
      "Ohio (70), Iowa (65), Idaho (62), Wisconsin (57), Indiana (55), Kansas (54), Kentucky (50), South Dakota (48), Nebraska (44), Utah (38), Michigan (35), Montana (33), Minnesota (32), Arkansas (32), Washington (22), Missouri (21), Wyoming (18), Oregon (17), North Carolina (17), Connecticut (16)\n",
      "Safegraph: 20 states with biggest drop\n",
      "Maine (-71), Rhode Island (-69), California (-68), New Jersey (-67), Florida (-65), Kansas (-63), Hawaii (-63), Massachusetts (-62), Connecticut (-60), South Carolina (-60), Washington, D.C. (-58), South Dakota (-58), Arizona (-58), Michigan (-58), Texas (-58), Georgia (-56), Virginia (-55), Louisiana (-55), New York (-55), Nevada (-55)\n",
      "Safegraph: 20 states with smallest drop\n",
      "Kentucky (-13), Idaho (-30), Iowa (-31), Montana (-32), Nebraska (-35), Alaska (-36), Arkansas (-37), New Mexico (-38), Ohio (-38), West Virginia (-41), Oklahoma (-43), North Dakota (-43), Wisconsin (-44), Utah (-44), Washington (-45), Missouri (-46), Vermont (-46), North Carolina (-47), Pennsylvania (-47), Minnesota (-48)\n",
      "Comparing 51 states...\n",
      "Pearson corr = 0.561 (p=0.000)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for category in CATEGORY_MAPPING:\n",
    "    print('Testing category %s...' % category.upper())\n",
    "    google_state2val = get_trend_for_all_states_and_category(google_df, category, mode='google', print_n=20)\n",
    "    safegraph_state2val = get_trend_for_all_states_and_category(chunks, category, mode='safegraph', print_n=20)\n",
    "    # only compare the states that appear in both datasets\n",
    "    state_overlap = set(google_state2val.keys()).intersection(set(safegraph_state2val.keys()))\n",
    "    print('Comparing %d states...' % (len(state_overlap)))\n",
    "    state_overlap = sorted(state_overlap)\n",
    "    google_y = [google_state2val[s] for s in state_overlap]\n",
    "    safegraph_y = [safegraph_state2val[s] for s in state_overlap]\n",
    "    r, p = pearsonr(google_y, safegraph_y)\n",
    "    print('Pearson corr = %.3f (p=%.3f)' % (r, p))\n",
    "    print()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "safegraph",
   "language": "python",
   "name": "safegraph"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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