spatial_temporal_Portland.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jianq\\Anaconda3\\lib\\site-packages\\pysal\\explore\\segregation\\network\\network.py:16: UserWarning: You need pandana and urbanaccess to work with segregation's network module\n",
      "You can install them with  `pip install urbanaccess pandana` or `conda install -c udst pandana urbanaccess`\n",
      "  \"You need pandana and urbanaccess to work with segregation's network module\\n\"\n",
      "C:\\Users\\jianq\\Anaconda3\\lib\\site-packages\\pysal\\model\\spvcm\\abstracts.py:10: UserWarning: The `dill` module is required to use the sqlite backend fully.\n",
      "  from .sqlite import head_to_sql, start_sql\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "from shapely import wkt\n",
    "import seaborn as sbn\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pysal.lib import examples\n",
    "import pysal as ps\n",
    "import geopandas as gpd\n",
    "from sklearn import cluster\n",
    "from sklearn.preprocessing import scale\n",
    "from scipy.special import factorial\n",
    "#sns.set(style=\"whitegrid\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final=pd.read_csv(\"Final_merged_city_portland.csv\") #ask for update csv\n",
    "df_final=df_final.reset_index()\n",
    "df_final=df_final.drop(columns=['index'])\n",
    "\n",
    "df_final['geometry'] = df_final['geometry'].apply(wkt.loads)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "      <th>geo_id</th>\n",
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       "      <th>collegefemale</th>\n",
       "      <th>...</th>\n",
       "      <th>EmploymentS</th>\n",
       "      <th>EstablishmentS</th>\n",
       "      <th>ratio_65</th>\n",
       "      <th>ratio_22_50</th>\n",
       "      <th>bus_ratio</th>\n",
       "      <th>bus_ratio_total</th>\n",
       "      <th>public_ratio</th>\n",
       "      <th>car_ratio</th>\n",
       "      <th>bicy_ratio</th>\n",
       "      <th>walked_ratio</th>\n",
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       "   Unnamed: 0       geo_id                                      centroid  \\\n",
       "0        2432  41051010402  POINT (-122.3356996936241 45.48566422279789)   \n",
       "1        2435  41051010402  POINT (-122.3356996936241 45.48566422279789)   \n",
       "2        2438  41051010402  POINT (-122.3356996936241 45.48566422279789)   \n",
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       "4        2444  41051010402   POINT (-122.335683996136 45.48565935229686)   \n",
       "\n",
       "   year                                           geometry  total_tran   pop  \\\n",
       "0  2010  (POLYGON ((-122.3868792758254 45.5190298016209...        2601  5717   \n",
       "1  2011  (POLYGON ((-122.3868792758254 45.5190298016209...        2826  6037   \n",
       "2  2012  (POLYGON ((-122.3868792758254 45.5190298016209...        2868  6153   \n",
       "3  2013  (POLYGON ((-122.3868792758254 45.5190298016209...        2876  6022   \n",
       "4  2014  (POLYGON ((-122.3868792758254 45.5190298016209...        3078  6181   \n",
       "\n",
       "   edut  collegemale  collegefemale  ...  EmploymentS  EstablishmentS  \\\n",
       "0  3699          294            356  ...          0.0             0.0   \n",
       "1  4024          368            476  ...          0.0             0.0   \n",
       "2  4090          369            507  ...          0.0             0.0   \n",
       "3  3931          296            404  ...          0.0             0.0   \n",
       "4  4094          316            457  ...          0.0             0.0   \n",
       "\n",
       "   ratio_65  ratio_22_50  bus_ratio  bus_ratio_total  public_ratio  car_ratio  \\\n",
       "0  0.391464     0.091307   0.490196         0.009612      0.019608   0.891580   \n",
       "1  0.405831     0.108001   0.305970         0.014508      0.047417   0.866596   \n",
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       "4  0.387316     0.108720   0.264368         0.007472      0.028265   0.875569   \n",
       "\n",
       "   bicy_ratio  walked_ratio  \n",
       "0    0.005767      0.014994  \n",
       "1    0.030432      0.009908  \n",
       "2    0.033473      0.004533  \n",
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     },
     "execution_count": 3,
     "metadata": {},
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   ],
   "source": [
    "df_final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2608, 23)"
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     "execution_count": 4,
     "metadata": {},
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   "source": [
    "df_final.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0', 'geo_id', 'centroid', 'year', 'geometry', 'total_tran',\n",
       "       'pop', 'edut', 'collegemale', 'collegefemale', 'households', 'income',\n",
       "       'zipcode', 'EmploymentS', 'EstablishmentS', 'ratio_65', 'ratio_22_50',\n",
       "       'bus_ratio', 'bus_ratio_total', 'public_ratio', 'car_ratio',\n",
       "       'bicy_ratio', 'walked_ratio'],\n",
       "      dtype='object')"
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     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df_final.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final3=df_final.groupby(['year'], as_index=False).agg(\n",
    "    { 'EmploymentS':'mean', 'EstablishmentS':'mean'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
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       "      <th>year</th>\n",
       "      <th>EmploymentS</th>\n",
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       "      <td>74.739936</td>\n",
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       "      <td>1</td>\n",
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       "      <td>75.180485</td>\n",
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       "      <td>2</td>\n",
       "      <td>2012</td>\n",
       "      <td>1264.806553</td>\n",
       "      <td>61.859742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2013</td>\n",
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       "      <td>62.531839</td>\n",
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       "      <td>4</td>\n",
       "      <td>2014</td>\n",
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      "text/plain": [
       "   year  EmploymentS  EstablishmentS\n",
       "0  2010  1557.070061       74.739936\n",
       "1  2011  1598.731753       75.180485\n",
       "2  2012  1264.806553       61.859742\n",
       "3  2013  1284.351071       62.531839\n",
       "4  2014  1316.261484       63.682289"
      ]
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     "execution_count": 7,
     "metadata": {},
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   ],
   "source": [
    "df_final3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
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       "      <th>ratio_65</th>\n",
       "      <th>ratio_22_50</th>\n",
       "      <th>bus_ratio</th>\n",
       "      <th>bus_ratio_total</th>\n",
       "      <th>public_ratio</th>\n",
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       "      <th>bicy_ratio</th>\n",
       "      <th>walked_ratio</th>\n",
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       "      <td>41051010402</td>\n",
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       "      <td>0.321739</td>\n",
       "      <td>0.012865</td>\n",
       "      <td>0.039986</td>\n",
       "      <td>0.858832</td>\n",
       "      <td>0.025730</td>\n",
       "      <td>0.012517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2444</td>\n",
       "      <td>41051010402</td>\n",
       "      <td>POINT (-122.335683996136 45.48565935229686)</td>\n",
       "      <td>2014</td>\n",
       "      <td>(POLYGON ((-122.3868792758254 45.5190298016209...</td>\n",
       "      <td>3078</td>\n",
       "      <td>6181</td>\n",
       "      <td>4094</td>\n",
       "      <td>316</td>\n",
       "      <td>457</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.387316</td>\n",
       "      <td>0.108720</td>\n",
       "      <td>0.264368</td>\n",
       "      <td>0.007472</td>\n",
       "      <td>0.028265</td>\n",
       "      <td>0.875569</td>\n",
       "      <td>0.016244</td>\n",
       "      <td>0.023392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>2447</td>\n",
       "      <td>41051010402</td>\n",
       "      <td>POINT (-122.3356858461854 45.48565778402043)</td>\n",
       "      <td>2015</td>\n",
       "      <td>(POLYGON ((-122.3868792758254 45.5190298016209...</td>\n",
       "      <td>3063</td>\n",
       "      <td>6349</td>\n",
       "      <td>4184</td>\n",
       "      <td>346</td>\n",
       "      <td>517</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.382107</td>\n",
       "      <td>0.118916</td>\n",
       "      <td>0.596026</td>\n",
       "      <td>0.029383</td>\n",
       "      <td>0.049298</td>\n",
       "      <td>0.841659</td>\n",
       "      <td>0.016650</td>\n",
       "      <td>0.026118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>2450</td>\n",
       "      <td>41051010402</td>\n",
       "      <td>POINT (-122.3356596646475 45.48564052912985)</td>\n",
       "      <td>2016</td>\n",
       "      <td>(POLYGON ((-122.3868792758254 45.5190298016209...</td>\n",
       "      <td>3190</td>\n",
       "      <td>6566</td>\n",
       "      <td>4383</td>\n",
       "      <td>334</td>\n",
       "      <td>540</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.362473</td>\n",
       "      <td>0.128389</td>\n",
       "      <td>0.813187</td>\n",
       "      <td>0.023197</td>\n",
       "      <td>0.028527</td>\n",
       "      <td>0.878056</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.033856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>2453</td>\n",
       "      <td>41051010402</td>\n",
       "      <td>POINT (-122.3356596646475 45.48564052912985)</td>\n",
       "      <td>2017</td>\n",
       "      <td>(POLYGON ((-122.3868792758254 45.5190298016209...</td>\n",
       "      <td>3456</td>\n",
       "      <td>6981</td>\n",
       "      <td>4633</td>\n",
       "      <td>399</td>\n",
       "      <td>485</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.355966</td>\n",
       "      <td>0.133792</td>\n",
       "      <td>0.867188</td>\n",
       "      <td>0.032118</td>\n",
       "      <td>0.037037</td>\n",
       "      <td>0.882523</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.035880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>2536</td>\n",
       "      <td>41051010500</td>\n",
       "      <td>POINT (-122.08527042624 45.52768157713402)</td>\n",
       "      <td>2010</td>\n",
       "      <td>(POLYGON ((-122.3970989671113 45.5726005840231...</td>\n",
       "      <td>1765</td>\n",
       "      <td>3944</td>\n",
       "      <td>2580</td>\n",
       "      <td>258</td>\n",
       "      <td>313</td>\n",
       "      <td>...</td>\n",
       "      <td>255.555556</td>\n",
       "      <td>17.888889</td>\n",
       "      <td>0.259381</td>\n",
       "      <td>0.107759</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.012465</td>\n",
       "      <td>0.012465</td>\n",
       "      <td>0.803966</td>\n",
       "      <td>0.002266</td>\n",
       "      <td>0.031161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>2539</td>\n",
       "      <td>41051010500</td>\n",
       "      <td>POINT (-122.08527042624 45.52768157713402)</td>\n",
       "      <td>2011</td>\n",
       "      <td>(POLYGON ((-122.3970989671113 45.5726005840231...</td>\n",
       "      <td>1796</td>\n",
       "      <td>4138</td>\n",
       "      <td>2803</td>\n",
       "      <td>247</td>\n",
       "      <td>284</td>\n",
       "      <td>...</td>\n",
       "      <td>249.166667</td>\n",
       "      <td>17.250000</td>\n",
       "      <td>0.301112</td>\n",
       "      <td>0.106573</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.004454</td>\n",
       "      <td>0.004454</td>\n",
       "      <td>0.827951</td>\n",
       "      <td>0.005011</td>\n",
       "      <td>0.027283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>2542</td>\n",
       "      <td>41051010500</td>\n",
       "      <td>POINT (-122.08527042624 45.52768157713402)</td>\n",
       "      <td>2012</td>\n",
       "      <td>(POLYGON ((-122.3970989671113 45.5726005840231...</td>\n",
       "      <td>1748</td>\n",
       "      <td>4229</td>\n",
       "      <td>2899</td>\n",
       "      <td>230</td>\n",
       "      <td>205</td>\n",
       "      <td>...</td>\n",
       "      <td>121.388889</td>\n",
       "      <td>9.583333</td>\n",
       "      <td>0.299598</td>\n",
       "      <td>0.110192</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.004005</td>\n",
       "      <td>0.004005</td>\n",
       "      <td>0.810069</td>\n",
       "      <td>0.007437</td>\n",
       "      <td>0.037757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>2545</td>\n",
       "      <td>41051010500</td>\n",
       "      <td>POINT (-122.08527042624 45.52768157713402)</td>\n",
       "      <td>2013</td>\n",
       "      <td>(POLYGON ((-122.3970989671113 45.5726005840231...</td>\n",
       "      <td>1633</td>\n",
       "      <td>4543</td>\n",
       "      <td>3145</td>\n",
       "      <td>277</td>\n",
       "      <td>276</td>\n",
       "      <td>...</td>\n",
       "      <td>172.500000</td>\n",
       "      <td>12.138889</td>\n",
       "      <td>0.293418</td>\n",
       "      <td>0.146599</td>\n",
       "      <td>0.215686</td>\n",
       "      <td>0.006736</td>\n",
       "      <td>0.031231</td>\n",
       "      <td>0.775873</td>\n",
       "      <td>0.006124</td>\n",
       "      <td>0.033680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>2548</td>\n",
       "      <td>41051010500</td>\n",
       "      <td>POINT (-122.085281180618 45.52768840231451)</td>\n",
       "      <td>2014</td>\n",
       "      <td>(POLYGON ((-122.3970989671113 45.5726005840231...</td>\n",
       "      <td>1452</td>\n",
       "      <td>4308</td>\n",
       "      <td>2985</td>\n",
       "      <td>240</td>\n",
       "      <td>244</td>\n",
       "      <td>...</td>\n",
       "      <td>115.000000</td>\n",
       "      <td>11.500000</td>\n",
       "      <td>0.285283</td>\n",
       "      <td>0.169684</td>\n",
       "      <td>0.243243</td>\n",
       "      <td>0.006198</td>\n",
       "      <td>0.025482</td>\n",
       "      <td>0.787190</td>\n",
       "      <td>0.008264</td>\n",
       "      <td>0.036501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>2551</td>\n",
       "      <td>41051010500</td>\n",
       "      <td>POINT (-122.0852811612068 45.52768836206592)</td>\n",
       "      <td>2015</td>\n",
       "      <td>(POLYGON ((-122.3970989671113 45.5726005840231...</td>\n",
       "      <td>1789</td>\n",
       "      <td>4186</td>\n",
       "      <td>2867</td>\n",
       "      <td>298</td>\n",
       "      <td>292</td>\n",
       "      <td>...</td>\n",
       "      <td>127.777778</td>\n",
       "      <td>12.777778</td>\n",
       "      <td>0.260631</td>\n",
       "      <td>0.179885</td>\n",
       "      <td>0.180000</td>\n",
       "      <td>0.005031</td>\n",
       "      <td>0.027949</td>\n",
       "      <td>0.842929</td>\n",
       "      <td>0.014533</td>\n",
       "      <td>0.029067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>2554</td>\n",
       "      <td>41051010500</td>\n",
       "      <td>POINT (-122.0852409017989 45.52764142881911)</td>\n",
       "      <td>2016</td>\n",
       "      <td>(POLYGON ((-122.3970989671113 45.5726005840229...</td>\n",
       "      <td>1497</td>\n",
       "      <td>3886</td>\n",
       "      <td>2693</td>\n",
       "      <td>256</td>\n",
       "      <td>270</td>\n",
       "      <td>...</td>\n",
       "      <td>191.666667</td>\n",
       "      <td>16.611111</td>\n",
       "      <td>0.234174</td>\n",
       "      <td>0.214617</td>\n",
       "      <td>0.145833</td>\n",
       "      <td>0.004676</td>\n",
       "      <td>0.032064</td>\n",
       "      <td>0.794255</td>\n",
       "      <td>0.008684</td>\n",
       "      <td>0.038744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>2557</td>\n",
       "      <td>41051010500</td>\n",
       "      <td>POINT (-122.0852430195605 45.52765726906909)</td>\n",
       "      <td>2017</td>\n",
       "      <td>(POLYGON ((-122.3970989671113 45.5726005840229...</td>\n",
       "      <td>1535</td>\n",
       "      <td>3912</td>\n",
       "      <td>2732</td>\n",
       "      <td>245</td>\n",
       "      <td>306</td>\n",
       "      <td>...</td>\n",
       "      <td>63.888889</td>\n",
       "      <td>6.388889</td>\n",
       "      <td>0.224949</td>\n",
       "      <td>0.214468</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.037785</td>\n",
       "      <td>0.833876</td>\n",
       "      <td>0.005863</td>\n",
       "      <td>0.016938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>2537</td>\n",
       "      <td>41051010500</td>\n",
       "      <td>POINT (-122.08527042624 45.52768157713402)</td>\n",
       "      <td>2010</td>\n",
       "      <td>(POLYGON ((-122.3970989671113 45.5726005840231...</td>\n",
       "      <td>1765</td>\n",
       "      <td>3944</td>\n",
       "      <td>2580</td>\n",
       "      <td>258</td>\n",
       "      <td>313</td>\n",
       "      <td>...</td>\n",
       "      <td>536.475410</td>\n",
       "      <td>53.647541</td>\n",
       "      <td>0.259381</td>\n",
       "      <td>0.107759</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.012465</td>\n",
       "      <td>0.012465</td>\n",
       "      <td>0.803966</td>\n",
       "      <td>0.002266</td>\n",
       "      <td>0.031161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>2540</td>\n",
       "      <td>41051010500</td>\n",
       "      <td>POINT (-122.08527042624 45.52768157713402)</td>\n",
       "      <td>2011</td>\n",
       "      <td>(POLYGON ((-122.3970989671113 45.5726005840231...</td>\n",
       "      <td>1796</td>\n",
       "      <td>4138</td>\n",
       "      <td>2803</td>\n",
       "      <td>247</td>\n",
       "      <td>284</td>\n",
       "      <td>...</td>\n",
       "      <td>526.721312</td>\n",
       "      <td>52.672131</td>\n",
       "      <td>0.301112</td>\n",
       "      <td>0.106573</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.004454</td>\n",
       "      <td>0.004454</td>\n",
       "      <td>0.827951</td>\n",
       "      <td>0.005011</td>\n",
       "      <td>0.027283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>2543</td>\n",
       "      <td>41051010500</td>\n",
       "      <td>POINT (-122.08527042624 45.52768157713402)</td>\n",
       "      <td>2012</td>\n",
       "      <td>(POLYGON ((-122.3970989671113 45.5726005840231...</td>\n",
       "      <td>1748</td>\n",
       "      <td>4229</td>\n",
       "      <td>2899</td>\n",
       "      <td>230</td>\n",
       "      <td>205</td>\n",
       "      <td>...</td>\n",
       "      <td>575.491803</td>\n",
       "      <td>49.745902</td>\n",
       "      <td>0.299598</td>\n",
       "      <td>0.110192</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.004005</td>\n",
       "      <td>0.004005</td>\n",
       "      <td>0.810069</td>\n",
       "      <td>0.007437</td>\n",
       "      <td>0.037757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>2546</td>\n",
       "      <td>41051010500</td>\n",
       "      <td>POINT (-122.08527042624 45.52768157713402)</td>\n",
       "      <td>2013</td>\n",
       "      <td>(POLYGON ((-122.3970989671113 45.5726005840231...</td>\n",
       "      <td>1633</td>\n",
       "      <td>4543</td>\n",
       "      <td>3145</td>\n",
       "      <td>277</td>\n",
       "      <td>276</td>\n",
       "      <td>...</td>\n",
       "      <td>565.737705</td>\n",
       "      <td>48.770492</td>\n",
       "      <td>0.293418</td>\n",
       "      <td>0.146599</td>\n",
       "      <td>0.215686</td>\n",
       "      <td>0.006736</td>\n",
       "      <td>0.031231</td>\n",
       "      <td>0.775873</td>\n",
       "      <td>0.006124</td>\n",
       "      <td>0.033680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>2549</td>\n",
       "      <td>41051010500</td>\n",
       "      <td>POINT (-122.085281180618 45.52768840231451)</td>\n",
       "      <td>2014</td>\n",
       "      <td>(POLYGON ((-122.3970989671113 45.5726005840231...</td>\n",
       "      <td>1452</td>\n",
       "      <td>4308</td>\n",
       "      <td>2985</td>\n",
       "      <td>240</td>\n",
       "      <td>244</td>\n",
       "      <td>...</td>\n",
       "      <td>555.983607</td>\n",
       "      <td>47.795082</td>\n",
       "      <td>0.285283</td>\n",
       "      <td>0.169684</td>\n",
       "      <td>0.243243</td>\n",
       "      <td>0.006198</td>\n",
       "      <td>0.025482</td>\n",
       "      <td>0.787190</td>\n",
       "      <td>0.008264</td>\n",
       "      <td>0.036501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>2552</td>\n",
       "      <td>41051010500</td>\n",
       "      <td>POINT (-122.0852811612068 45.52768836206592)</td>\n",
       "      <td>2015</td>\n",
       "      <td>(POLYGON ((-122.3970989671113 45.5726005840231...</td>\n",
       "      <td>1789</td>\n",
       "      <td>4186</td>\n",
       "      <td>2867</td>\n",
       "      <td>298</td>\n",
       "      <td>292</td>\n",
       "      <td>...</td>\n",
       "      <td>487.704918</td>\n",
       "      <td>44.868852</td>\n",
       "      <td>0.260631</td>\n",
       "      <td>0.179885</td>\n",
       "      <td>0.180000</td>\n",
       "      <td>0.005031</td>\n",
       "      <td>0.027949</td>\n",
       "      <td>0.842929</td>\n",
       "      <td>0.014533</td>\n",
       "      <td>0.029067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>2555</td>\n",
       "      <td>41051010500</td>\n",
       "      <td>POINT (-122.0852409017989 45.52764142881911)</td>\n",
       "      <td>2016</td>\n",
       "      <td>(POLYGON ((-122.3970989671113 45.5726005840229...</td>\n",
       "      <td>1497</td>\n",
       "      <td>3886</td>\n",
       "      <td>2693</td>\n",
       "      <td>256</td>\n",
       "      <td>270</td>\n",
       "      <td>...</td>\n",
       "      <td>536.475410</td>\n",
       "      <td>45.844262</td>\n",
       "      <td>0.234174</td>\n",
       "      <td>0.214617</td>\n",
       "      <td>0.145833</td>\n",
       "      <td>0.004676</td>\n",
       "      <td>0.032064</td>\n",
       "      <td>0.794255</td>\n",
       "      <td>0.008684</td>\n",
       "      <td>0.038744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>2558</td>\n",
       "      <td>41051010500</td>\n",
       "      <td>POINT (-122.0852430195605 45.52765726906909)</td>\n",
       "      <td>2017</td>\n",
       "      <td>(POLYGON ((-122.3970989671113 45.5726005840229...</td>\n",
       "      <td>1535</td>\n",
       "      <td>3912</td>\n",
       "      <td>2732</td>\n",
       "      <td>245</td>\n",
       "      <td>306</td>\n",
       "      <td>...</td>\n",
       "      <td>292.622951</td>\n",
       "      <td>29.262295</td>\n",
       "      <td>0.224949</td>\n",
       "      <td>0.214468</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.037785</td>\n",
       "      <td>0.833876</td>\n",
       "      <td>0.005863</td>\n",
       "      <td>0.016938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>2088</td>\n",
       "      <td>41051009603</td>\n",
       "      <td>POINT (-122.4608485441421 45.52931048858725)</td>\n",
       "      <td>2010</td>\n",
       "      <td>(POLYGON ((-122.4661255423961 45.5395284963269...</td>\n",
       "      <td>1619</td>\n",
       "      <td>3391</td>\n",
       "      <td>2240</td>\n",
       "      <td>9</td>\n",
       "      <td>118</td>\n",
       "      <td>...</td>\n",
       "      <td>4.444444</td>\n",
       "      <td>0.251852</td>\n",
       "      <td>0.404011</td>\n",
       "      <td>0.132704</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.071032</td>\n",
       "      <td>0.071032</td>\n",
       "      <td>0.815318</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.017295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>2091</td>\n",
       "      <td>41051009603</td>\n",
       "      <td>POINT (-122.4608485441421 45.52931048858725)</td>\n",
       "      <td>2011</td>\n",
       "      <td>(POLYGON ((-122.4661255423961 45.5395284963269...</td>\n",
       "      <td>1827</td>\n",
       "      <td>3986</td>\n",
       "      <td>2435</td>\n",
       "      <td>46</td>\n",
       "      <td>137</td>\n",
       "      <td>...</td>\n",
       "      <td>4.166667</td>\n",
       "      <td>0.251852</td>\n",
       "      <td>0.419719</td>\n",
       "      <td>0.104867</td>\n",
       "      <td>0.962264</td>\n",
       "      <td>0.083744</td>\n",
       "      <td>0.087028</td>\n",
       "      <td>0.846743</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.006568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>2094</td>\n",
       "      <td>41051009603</td>\n",
       "      <td>POINT (-122.4608485441421 45.52931048858725)</td>\n",
       "      <td>2012</td>\n",
       "      <td>(POLYGON ((-122.4661255423961 45.5395284963269...</td>\n",
       "      <td>1681</td>\n",
       "      <td>3641</td>\n",
       "      <td>2288</td>\n",
       "      <td>96</td>\n",
       "      <td>139</td>\n",
       "      <td>...</td>\n",
       "      <td>3.222222</td>\n",
       "      <td>0.207407</td>\n",
       "      <td>0.424334</td>\n",
       "      <td>0.109585</td>\n",
       "      <td>0.978723</td>\n",
       "      <td>0.109459</td>\n",
       "      <td>0.111838</td>\n",
       "      <td>0.852469</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>2097</td>\n",
       "      <td>41051009603</td>\n",
       "      <td>POINT (-122.4608485441421 45.52931048858725)</td>\n",
       "      <td>2013</td>\n",
       "      <td>(POLYGON ((-122.4661255423961 45.5395284963269...</td>\n",
       "      <td>1554</td>\n",
       "      <td>3573</td>\n",
       "      <td>2322</td>\n",
       "      <td>109</td>\n",
       "      <td>153</td>\n",
       "      <td>...</td>\n",
       "      <td>3.277778</td>\n",
       "      <td>0.198148</td>\n",
       "      <td>0.414218</td>\n",
       "      <td>0.119507</td>\n",
       "      <td>0.952096</td>\n",
       "      <td>0.102317</td>\n",
       "      <td>0.107465</td>\n",
       "      <td>0.826898</td>\n",
       "      <td>0.005148</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>2100</td>\n",
       "      <td>41051009603</td>\n",
       "      <td>POINT (-122.4607256890022 45.52919102699193)</td>\n",
       "      <td>2014</td>\n",
       "      <td>(POLYGON ((-122.4661255423961 45.5395284963269...</td>\n",
       "      <td>1747</td>\n",
       "      <td>3983</td>\n",
       "      <td>2473</td>\n",
       "      <td>164</td>\n",
       "      <td>157</td>\n",
       "      <td>...</td>\n",
       "      <td>3.092593</td>\n",
       "      <td>0.201852</td>\n",
       "      <td>0.381622</td>\n",
       "      <td>0.107206</td>\n",
       "      <td>0.920455</td>\n",
       "      <td>0.046365</td>\n",
       "      <td>0.050372</td>\n",
       "      <td>0.871208</td>\n",
       "      <td>0.004579</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>2103</td>\n",
       "      <td>41051009603</td>\n",
       "      <td>POINT (-122.4607256890022 45.52919102699193)</td>\n",
       "      <td>2015</td>\n",
       "      <td>(POLYGON ((-122.4661255423961 45.5395284963269...</td>\n",
       "      <td>1767</td>\n",
       "      <td>3855</td>\n",
       "      <td>2407</td>\n",
       "      <td>167</td>\n",
       "      <td>148</td>\n",
       "      <td>...</td>\n",
       "      <td>3.351852</td>\n",
       "      <td>0.220370</td>\n",
       "      <td>0.375097</td>\n",
       "      <td>0.112840</td>\n",
       "      <td>0.838095</td>\n",
       "      <td>0.049802</td>\n",
       "      <td>0.059423</td>\n",
       "      <td>0.867006</td>\n",
       "      <td>0.004527</td>\n",
       "      <td>0.003396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>2106</td>\n",
       "      <td>41051009603</td>\n",
       "      <td>POINT (-122.4607567884648 45.52925465690799)</td>\n",
       "      <td>2016</td>\n",
       "      <td>(POLYGON ((-122.4656775426601 45.5398394984017...</td>\n",
       "      <td>1721</td>\n",
       "      <td>3683</td>\n",
       "      <td>2448</td>\n",
       "      <td>132</td>\n",
       "      <td>153</td>\n",
       "      <td>...</td>\n",
       "      <td>3.666667</td>\n",
       "      <td>0.229630</td>\n",
       "      <td>0.370893</td>\n",
       "      <td>0.118110</td>\n",
       "      <td>0.831461</td>\n",
       "      <td>0.042998</td>\n",
       "      <td>0.051714</td>\n",
       "      <td>0.855898</td>\n",
       "      <td>0.015107</td>\n",
       "      <td>0.016851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>2109</td>\n",
       "      <td>41051009603</td>\n",
       "      <td>POINT (-122.4607256890022 45.52919102699193)</td>\n",
       "      <td>2017</td>\n",
       "      <td>(POLYGON ((-122.4661255423961 45.5395284963269...</td>\n",
       "      <td>1766</td>\n",
       "      <td>4044</td>\n",
       "      <td>2533</td>\n",
       "      <td>119</td>\n",
       "      <td>177</td>\n",
       "      <td>...</td>\n",
       "      <td>2.129630</td>\n",
       "      <td>0.190741</td>\n",
       "      <td>0.370425</td>\n",
       "      <td>0.116716</td>\n",
       "      <td>0.792683</td>\n",
       "      <td>0.036806</td>\n",
       "      <td>0.046433</td>\n",
       "      <td>0.852208</td>\n",
       "      <td>0.024915</td>\n",
       "      <td>0.018120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>2328</td>\n",
       "      <td>41051010100</td>\n",
       "      <td>POINT (-122.444171605879 45.52613866368701)</td>\n",
       "      <td>2010</td>\n",
       "      <td>(POLYGON ((-122.4566475168594 45.5394885343066...</td>\n",
       "      <td>3791</td>\n",
       "      <td>8126</td>\n",
       "      <td>5080</td>\n",
       "      <td>277</td>\n",
       "      <td>330</td>\n",
       "      <td>...</td>\n",
       "      <td>1920.000000</td>\n",
       "      <td>108.800000</td>\n",
       "      <td>0.399335</td>\n",
       "      <td>0.126261</td>\n",
       "      <td>0.574230</td>\n",
       "      <td>0.054075</td>\n",
       "      <td>0.094170</td>\n",
       "      <td>0.798206</td>\n",
       "      <td>0.009496</td>\n",
       "      <td>0.045898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>2332</td>\n",
       "      <td>41051010100</td>\n",
       "      <td>POINT (-122.444171605879 45.52613866368701)</td>\n",
       "      <td>2011</td>\n",
       "      <td>(POLYGON ((-122.4566475168594 45.5394885343066...</td>\n",
       "      <td>3788</td>\n",
       "      <td>8357</td>\n",
       "      <td>5385</td>\n",
       "      <td>255</td>\n",
       "      <td>395</td>\n",
       "      <td>...</td>\n",
       "      <td>1800.000000</td>\n",
       "      <td>108.800000</td>\n",
       "      <td>0.376571</td>\n",
       "      <td>0.135695</td>\n",
       "      <td>0.625298</td>\n",
       "      <td>0.069166</td>\n",
       "      <td>0.110612</td>\n",
       "      <td>0.756072</td>\n",
       "      <td>0.010032</td>\n",
       "      <td>0.040127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>2336</td>\n",
       "      <td>41051010100</td>\n",
       "      <td>POINT (-122.444171605879 45.52613866368701)</td>\n",
       "      <td>2012</td>\n",
       "      <td>(POLYGON ((-122.4566475168594 45.5394885343066...</td>\n",
       "      <td>3755</td>\n",
       "      <td>8244</td>\n",
       "      <td>5425</td>\n",
       "      <td>410</td>\n",
       "      <td>447</td>\n",
       "      <td>...</td>\n",
       "      <td>1392.000000</td>\n",
       "      <td>89.600000</td>\n",
       "      <td>0.392407</td>\n",
       "      <td>0.158297</td>\n",
       "      <td>0.694190</td>\n",
       "      <td>0.060453</td>\n",
       "      <td>0.087084</td>\n",
       "      <td>0.796538</td>\n",
       "      <td>0.005326</td>\n",
       "      <td>0.033555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>2340</td>\n",
       "      <td>41051010100</td>\n",
       "      <td>POINT (-122.444171605879 45.52613866368701)</td>\n",
       "      <td>2013</td>\n",
       "      <td>(POLYGON ((-122.4566475168594 45.5394885343066...</td>\n",
       "      <td>3998</td>\n",
       "      <td>8876</td>\n",
       "      <td>5712</td>\n",
       "      <td>303</td>\n",
       "      <td>437</td>\n",
       "      <td>...</td>\n",
       "      <td>1416.000000</td>\n",
       "      <td>85.600000</td>\n",
       "      <td>0.389928</td>\n",
       "      <td>0.141505</td>\n",
       "      <td>0.809384</td>\n",
       "      <td>0.069035</td>\n",
       "      <td>0.085293</td>\n",
       "      <td>0.827414</td>\n",
       "      <td>0.005253</td>\n",
       "      <td>0.001251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>2344</td>\n",
       "      <td>41051010100</td>\n",
       "      <td>POINT (-122.444171605879 45.52613866368701)</td>\n",
       "      <td>2014</td>\n",
       "      <td>(POLYGON ((-122.4566475168594 45.5394885343066...</td>\n",
       "      <td>4193</td>\n",
       "      <td>9761</td>\n",
       "      <td>6268</td>\n",
       "      <td>344</td>\n",
       "      <td>476</td>\n",
       "      <td>...</td>\n",
       "      <td>1336.000000</td>\n",
       "      <td>87.200000</td>\n",
       "      <td>0.376908</td>\n",
       "      <td>0.141379</td>\n",
       "      <td>0.838068</td>\n",
       "      <td>0.070355</td>\n",
       "      <td>0.083949</td>\n",
       "      <td>0.859289</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.005008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>2348</td>\n",
       "      <td>41051010100</td>\n",
       "      <td>POINT (-122.444171605879 45.52613866368701)</td>\n",
       "      <td>2015</td>\n",
       "      <td>(POLYGON ((-122.4566475168594 45.5394885343066...</td>\n",
       "      <td>3883</td>\n",
       "      <td>9222</td>\n",
       "      <td>5964</td>\n",
       "      <td>355</td>\n",
       "      <td>636</td>\n",
       "      <td>...</td>\n",
       "      <td>1448.000000</td>\n",
       "      <td>95.200000</td>\n",
       "      <td>0.360442</td>\n",
       "      <td>0.135654</td>\n",
       "      <td>0.829060</td>\n",
       "      <td>0.074942</td>\n",
       "      <td>0.090394</td>\n",
       "      <td>0.868658</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.009786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>2352</td>\n",
       "      <td>41051010100</td>\n",
       "      <td>POINT (-122.444171605879 45.52613866368701)</td>\n",
       "      <td>2016</td>\n",
       "      <td>(POLYGON ((-122.4566475168594 45.5394885343066...</td>\n",
       "      <td>3996</td>\n",
       "      <td>9090</td>\n",
       "      <td>5926</td>\n",
       "      <td>435</td>\n",
       "      <td>647</td>\n",
       "      <td>...</td>\n",
       "      <td>1584.000000</td>\n",
       "      <td>99.200000</td>\n",
       "      <td>0.373047</td>\n",
       "      <td>0.137954</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.076827</td>\n",
       "      <td>0.076827</td>\n",
       "      <td>0.885135</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.011011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>2356</td>\n",
       "      <td>41051010100</td>\n",
       "      <td>POINT (-122.444171605879 45.52613866368701)</td>\n",
       "      <td>2017</td>\n",
       "      <td>(POLYGON ((-122.4566475168594 45.5394885343066...</td>\n",
       "      <td>3898</td>\n",
       "      <td>8755</td>\n",
       "      <td>6112</td>\n",
       "      <td>363</td>\n",
       "      <td>551</td>\n",
       "      <td>...</td>\n",
       "      <td>920.000000</td>\n",
       "      <td>82.400000</td>\n",
       "      <td>0.382524</td>\n",
       "      <td>0.145060</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.081324</td>\n",
       "      <td>0.081324</td>\n",
       "      <td>0.879169</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.009492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>2360</td>\n",
       "      <td>41051010200</td>\n",
       "      <td>POINT (-122.4781619568552 45.56548173898273)</td>\n",
       "      <td>2010</td>\n",
       "      <td>(POLYGON ((-122.5865501307936 45.5992380830268...</td>\n",
       "      <td>2572</td>\n",
       "      <td>5335</td>\n",
       "      <td>3811</td>\n",
       "      <td>239</td>\n",
       "      <td>251</td>\n",
       "      <td>...</td>\n",
       "      <td>382.222222</td>\n",
       "      <td>21.659259</td>\n",
       "      <td>0.339831</td>\n",
       "      <td>0.158013</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.019829</td>\n",
       "      <td>0.019829</td>\n",
       "      <td>0.855754</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.008165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41</td>\n",
       "      <td>2363</td>\n",
       "      <td>41051010200</td>\n",
       "      <td>POINT (-122.4781619568552 45.56548173898273)</td>\n",
       "      <td>2011</td>\n",
       "      <td>(POLYGON ((-122.5865501307936 45.5992380830268...</td>\n",
       "      <td>2592</td>\n",
       "      <td>5319</td>\n",
       "      <td>3861</td>\n",
       "      <td>305</td>\n",
       "      <td>318</td>\n",
       "      <td>...</td>\n",
       "      <td>358.333333</td>\n",
       "      <td>21.659259</td>\n",
       "      <td>0.337845</td>\n",
       "      <td>0.159052</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.021991</td>\n",
       "      <td>0.021991</td>\n",
       "      <td>0.856867</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.018904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42</td>\n",
       "      <td>2366</td>\n",
       "      <td>41051010200</td>\n",
       "      <td>POINT (-122.4781619568552 45.56548173898273)</td>\n",
       "      <td>2012</td>\n",
       "      <td>(POLYGON ((-122.5865501307936 45.5992380830268...</td>\n",
       "      <td>2472</td>\n",
       "      <td>5573</td>\n",
       "      <td>4037</td>\n",
       "      <td>267</td>\n",
       "      <td>329</td>\n",
       "      <td>...</td>\n",
       "      <td>277.111111</td>\n",
       "      <td>17.837037</td>\n",
       "      <td>0.327292</td>\n",
       "      <td>0.159340</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.010113</td>\n",
       "      <td>0.010113</td>\n",
       "      <td>0.902913</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.007282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43</td>\n",
       "      <td>2369</td>\n",
       "      <td>41051010200</td>\n",
       "      <td>POINT (-122.4781619568552 45.56548173898273)</td>\n",
       "      <td>2013</td>\n",
       "      <td>(POLYGON ((-122.5865501307936 45.5992380830268...</td>\n",
       "      <td>2456</td>\n",
       "      <td>5638</td>\n",
       "      <td>4185</td>\n",
       "      <td>247</td>\n",
       "      <td>314</td>\n",
       "      <td>...</td>\n",
       "      <td>281.888889</td>\n",
       "      <td>17.040741</td>\n",
       "      <td>0.329195</td>\n",
       "      <td>0.159631</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.006107</td>\n",
       "      <td>0.006107</td>\n",
       "      <td>0.899023</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.008143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>44</td>\n",
       "      <td>2372</td>\n",
       "      <td>41051010200</td>\n",
       "      <td>POINT (-122.4781708493315 45.56549471573069)</td>\n",
       "      <td>2014</td>\n",
       "      <td>(POLYGON ((-122.5865501307936 45.5992380830268...</td>\n",
       "      <td>2700</td>\n",
       "      <td>5872</td>\n",
       "      <td>4467</td>\n",
       "      <td>336</td>\n",
       "      <td>239</td>\n",
       "      <td>...</td>\n",
       "      <td>265.962963</td>\n",
       "      <td>17.359259</td>\n",
       "      <td>0.332766</td>\n",
       "      <td>0.166723</td>\n",
       "      <td>0.461538</td>\n",
       "      <td>0.008889</td>\n",
       "      <td>0.019259</td>\n",
       "      <td>0.894815</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.021481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>2375</td>\n",
       "      <td>41051010200</td>\n",
       "      <td>POINT (-122.4781708493358 45.5654947157295)</td>\n",
       "      <td>2015</td>\n",
       "      <td>(POLYGON ((-122.5865501307936 45.5992380830268...</td>\n",
       "      <td>2772</td>\n",
       "      <td>6101</td>\n",
       "      <td>4521</td>\n",
       "      <td>407</td>\n",
       "      <td>207</td>\n",
       "      <td>...</td>\n",
       "      <td>288.259259</td>\n",
       "      <td>18.951852</td>\n",
       "      <td>0.358138</td>\n",
       "      <td>0.156532</td>\n",
       "      <td>0.504348</td>\n",
       "      <td>0.020924</td>\n",
       "      <td>0.041486</td>\n",
       "      <td>0.881313</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.020202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>46</td>\n",
       "      <td>2378</td>\n",
       "      <td>41051010200</td>\n",
       "      <td>POINT (-122.4781708493357 45.56549471572949)</td>\n",
       "      <td>2016</td>\n",
       "      <td>(POLYGON ((-122.5865501307936 45.5992380830268...</td>\n",
       "      <td>2745</td>\n",
       "      <td>6213</td>\n",
       "      <td>4529</td>\n",
       "      <td>440</td>\n",
       "      <td>220</td>\n",
       "      <td>...</td>\n",
       "      <td>315.333333</td>\n",
       "      <td>19.748148</td>\n",
       "      <td>0.363753</td>\n",
       "      <td>0.156768</td>\n",
       "      <td>0.486726</td>\n",
       "      <td>0.020036</td>\n",
       "      <td>0.041166</td>\n",
       "      <td>0.906011</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.011658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>47</td>\n",
       "      <td>2381</td>\n",
       "      <td>41051010200</td>\n",
       "      <td>POINT (-122.4781708493357 45.56549471572949)</td>\n",
       "      <td>2017</td>\n",
       "      <td>(POLYGON ((-122.5865501307936 45.5992380830268...</td>\n",
       "      <td>2989</td>\n",
       "      <td>6251</td>\n",
       "      <td>4542</td>\n",
       "      <td>449</td>\n",
       "      <td>208</td>\n",
       "      <td>...</td>\n",
       "      <td>183.148148</td>\n",
       "      <td>16.403704</td>\n",
       "      <td>0.377860</td>\n",
       "      <td>0.177252</td>\n",
       "      <td>0.614286</td>\n",
       "      <td>0.028772</td>\n",
       "      <td>0.046838</td>\n",
       "      <td>0.887253</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.011710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48</td>\n",
       "      <td>2400</td>\n",
       "      <td>41051010304</td>\n",
       "      <td>POINT (-122.4240876987692 45.53441166972644)</td>\n",
       "      <td>2010</td>\n",
       "      <td>(POLYGON ((-122.4336984445508 45.5366666241537...</td>\n",
       "      <td>1968</td>\n",
       "      <td>4620</td>\n",
       "      <td>2652</td>\n",
       "      <td>95</td>\n",
       "      <td>83</td>\n",
       "      <td>...</td>\n",
       "      <td>93.333333</td>\n",
       "      <td>5.288889</td>\n",
       "      <td>0.419264</td>\n",
       "      <td>0.064502</td>\n",
       "      <td>0.585366</td>\n",
       "      <td>0.060976</td>\n",
       "      <td>0.104167</td>\n",
       "      <td>0.840447</td>\n",
       "      <td>0.003557</td>\n",
       "      <td>0.019817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49</td>\n",
       "      <td>2402</td>\n",
       "      <td>41051010304</td>\n",
       "      <td>POINT (-122.4240876987692 45.53441166972644)</td>\n",
       "      <td>2011</td>\n",
       "      <td>(POLYGON ((-122.4336984445508 45.5366666241537...</td>\n",
       "      <td>2065</td>\n",
       "      <td>4631</td>\n",
       "      <td>2655</td>\n",
       "      <td>62</td>\n",
       "      <td>82</td>\n",
       "      <td>...</td>\n",
       "      <td>87.500000</td>\n",
       "      <td>5.288889</td>\n",
       "      <td>0.404880</td>\n",
       "      <td>0.057223</td>\n",
       "      <td>0.519802</td>\n",
       "      <td>0.050847</td>\n",
       "      <td>0.097821</td>\n",
       "      <td>0.853269</td>\n",
       "      <td>0.003390</td>\n",
       "      <td>0.015012</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>50 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0       geo_id                                      centroid  \\\n",
       "0         2432  41051010402  POINT (-122.3356996936241 45.48566422279789)   \n",
       "1         2435  41051010402  POINT (-122.3356996936241 45.48566422279789)   \n",
       "2         2438  41051010402  POINT (-122.3356996936241 45.48566422279789)   \n",
       "3         2441  41051010402  POINT (-122.3356996936241 45.48566422279789)   \n",
       "4         2444  41051010402   POINT (-122.335683996136 45.48565935229686)   \n",
       "5         2447  41051010402  POINT (-122.3356858461854 45.48565778402043)   \n",
       "6         2450  41051010402  POINT (-122.3356596646475 45.48564052912985)   \n",
       "7         2453  41051010402  POINT (-122.3356596646475 45.48564052912985)   \n",
       "8         2536  41051010500    POINT (-122.08527042624 45.52768157713402)   \n",
       "9         2539  41051010500    POINT (-122.08527042624 45.52768157713402)   \n",
       "10        2542  41051010500    POINT (-122.08527042624 45.52768157713402)   \n",
       "11        2545  41051010500    POINT (-122.08527042624 45.52768157713402)   \n",
       "12        2548  41051010500   POINT (-122.085281180618 45.52768840231451)   \n",
       "13        2551  41051010500  POINT (-122.0852811612068 45.52768836206592)   \n",
       "14        2554  41051010500  POINT (-122.0852409017989 45.52764142881911)   \n",
       "15        2557  41051010500  POINT (-122.0852430195605 45.52765726906909)   \n",
       "16        2537  41051010500    POINT (-122.08527042624 45.52768157713402)   \n",
       "17        2540  41051010500    POINT (-122.08527042624 45.52768157713402)   \n",
       "18        2543  41051010500    POINT (-122.08527042624 45.52768157713402)   \n",
       "19        2546  41051010500    POINT (-122.08527042624 45.52768157713402)   \n",
       "20        2549  41051010500   POINT (-122.085281180618 45.52768840231451)   \n",
       "21        2552  41051010500  POINT (-122.0852811612068 45.52768836206592)   \n",
       "22        2555  41051010500  POINT (-122.0852409017989 45.52764142881911)   \n",
       "23        2558  41051010500  POINT (-122.0852430195605 45.52765726906909)   \n",
       "24        2088  41051009603  POINT (-122.4608485441421 45.52931048858725)   \n",
       "25        2091  41051009603  POINT (-122.4608485441421 45.52931048858725)   \n",
       "26        2094  41051009603  POINT (-122.4608485441421 45.52931048858725)   \n",
       "27        2097  41051009603  POINT (-122.4608485441421 45.52931048858725)   \n",
       "28        2100  41051009603  POINT (-122.4607256890022 45.52919102699193)   \n",
       "29        2103  41051009603  POINT (-122.4607256890022 45.52919102699193)   \n",
       "30        2106  41051009603  POINT (-122.4607567884648 45.52925465690799)   \n",
       "31        2109  41051009603  POINT (-122.4607256890022 45.52919102699193)   \n",
       "32        2328  41051010100   POINT (-122.444171605879 45.52613866368701)   \n",
       "33        2332  41051010100   POINT (-122.444171605879 45.52613866368701)   \n",
       "34        2336  41051010100   POINT (-122.444171605879 45.52613866368701)   \n",
       "35        2340  41051010100   POINT (-122.444171605879 45.52613866368701)   \n",
       "36        2344  41051010100   POINT (-122.444171605879 45.52613866368701)   \n",
       "37        2348  41051010100   POINT (-122.444171605879 45.52613866368701)   \n",
       "38        2352  41051010100   POINT (-122.444171605879 45.52613866368701)   \n",
       "39        2356  41051010100   POINT (-122.444171605879 45.52613866368701)   \n",
       "40        2360  41051010200  POINT (-122.4781619568552 45.56548173898273)   \n",
       "41        2363  41051010200  POINT (-122.4781619568552 45.56548173898273)   \n",
       "42        2366  41051010200  POINT (-122.4781619568552 45.56548173898273)   \n",
       "43        2369  41051010200  POINT (-122.4781619568552 45.56548173898273)   \n",
       "44        2372  41051010200  POINT (-122.4781708493315 45.56549471573069)   \n",
       "45        2375  41051010200   POINT (-122.4781708493358 45.5654947157295)   \n",
       "46        2378  41051010200  POINT (-122.4781708493357 45.56549471572949)   \n",
       "47        2381  41051010200  POINT (-122.4781708493357 45.56549471572949)   \n",
       "48        2400  41051010304  POINT (-122.4240876987692 45.53441166972644)   \n",
       "49        2402  41051010304  POINT (-122.4240876987692 45.53441166972644)   \n",
       "\n",
       "    year                                           geometry  total_tran   pop  \\\n",
       "0   2010  (POLYGON ((-122.3868792758254 45.5190298016209...        2601  5717   \n",
       "1   2011  (POLYGON ((-122.3868792758254 45.5190298016209...        2826  6037   \n",
       "2   2012  (POLYGON ((-122.3868792758254 45.5190298016209...        2868  6153   \n",
       "3   2013  (POLYGON ((-122.3868792758254 45.5190298016209...        2876  6022   \n",
       "4   2014  (POLYGON ((-122.3868792758254 45.5190298016209...        3078  6181   \n",
       "5   2015  (POLYGON ((-122.3868792758254 45.5190298016209...        3063  6349   \n",
       "6   2016  (POLYGON ((-122.3868792758254 45.5190298016209...        3190  6566   \n",
       "7   2017  (POLYGON ((-122.3868792758254 45.5190298016209...        3456  6981   \n",
       "8   2010  (POLYGON ((-122.3970989671113 45.5726005840231...        1765  3944   \n",
       "9   2011  (POLYGON ((-122.3970989671113 45.5726005840231...        1796  4138   \n",
       "10  2012  (POLYGON ((-122.3970989671113 45.5726005840231...        1748  4229   \n",
       "11  2013  (POLYGON ((-122.3970989671113 45.5726005840231...        1633  4543   \n",
       "12  2014  (POLYGON ((-122.3970989671113 45.5726005840231...        1452  4308   \n",
       "13  2015  (POLYGON ((-122.3970989671113 45.5726005840231...        1789  4186   \n",
       "14  2016  (POLYGON ((-122.3970989671113 45.5726005840229...        1497  3886   \n",
       "15  2017  (POLYGON ((-122.3970989671113 45.5726005840229...        1535  3912   \n",
       "16  2010  (POLYGON ((-122.3970989671113 45.5726005840231...        1765  3944   \n",
       "17  2011  (POLYGON ((-122.3970989671113 45.5726005840231...        1796  4138   \n",
       "18  2012  (POLYGON ((-122.3970989671113 45.5726005840231...        1748  4229   \n",
       "19  2013  (POLYGON ((-122.3970989671113 45.5726005840231...        1633  4543   \n",
       "20  2014  (POLYGON ((-122.3970989671113 45.5726005840231...        1452  4308   \n",
       "21  2015  (POLYGON ((-122.3970989671113 45.5726005840231...        1789  4186   \n",
       "22  2016  (POLYGON ((-122.3970989671113 45.5726005840229...        1497  3886   \n",
       "23  2017  (POLYGON ((-122.3970989671113 45.5726005840229...        1535  3912   \n",
       "24  2010  (POLYGON ((-122.4661255423961 45.5395284963269...        1619  3391   \n",
       "25  2011  (POLYGON ((-122.4661255423961 45.5395284963269...        1827  3986   \n",
       "26  2012  (POLYGON ((-122.4661255423961 45.5395284963269...        1681  3641   \n",
       "27  2013  (POLYGON ((-122.4661255423961 45.5395284963269...        1554  3573   \n",
       "28  2014  (POLYGON ((-122.4661255423961 45.5395284963269...        1747  3983   \n",
       "29  2015  (POLYGON ((-122.4661255423961 45.5395284963269...        1767  3855   \n",
       "30  2016  (POLYGON ((-122.4656775426601 45.5398394984017...        1721  3683   \n",
       "31  2017  (POLYGON ((-122.4661255423961 45.5395284963269...        1766  4044   \n",
       "32  2010  (POLYGON ((-122.4566475168594 45.5394885343066...        3791  8126   \n",
       "33  2011  (POLYGON ((-122.4566475168594 45.5394885343066...        3788  8357   \n",
       "34  2012  (POLYGON ((-122.4566475168594 45.5394885343066...        3755  8244   \n",
       "35  2013  (POLYGON ((-122.4566475168594 45.5394885343066...        3998  8876   \n",
       "36  2014  (POLYGON ((-122.4566475168594 45.5394885343066...        4193  9761   \n",
       "37  2015  (POLYGON ((-122.4566475168594 45.5394885343066...        3883  9222   \n",
       "38  2016  (POLYGON ((-122.4566475168594 45.5394885343066...        3996  9090   \n",
       "39  2017  (POLYGON ((-122.4566475168594 45.5394885343066...        3898  8755   \n",
       "40  2010  (POLYGON ((-122.5865501307936 45.5992380830268...        2572  5335   \n",
       "41  2011  (POLYGON ((-122.5865501307936 45.5992380830268...        2592  5319   \n",
       "42  2012  (POLYGON ((-122.5865501307936 45.5992380830268...        2472  5573   \n",
       "43  2013  (POLYGON ((-122.5865501307936 45.5992380830268...        2456  5638   \n",
       "44  2014  (POLYGON ((-122.5865501307936 45.5992380830268...        2700  5872   \n",
       "45  2015  (POLYGON ((-122.5865501307936 45.5992380830268...        2772  6101   \n",
       "46  2016  (POLYGON ((-122.5865501307936 45.5992380830268...        2745  6213   \n",
       "47  2017  (POLYGON ((-122.5865501307936 45.5992380830268...        2989  6251   \n",
       "48  2010  (POLYGON ((-122.4336984445508 45.5366666241537...        1968  4620   \n",
       "49  2011  (POLYGON ((-122.4336984445508 45.5366666241537...        2065  4631   \n",
       "\n",
       "    edut  collegemale  collegefemale  ...  EmploymentS  EstablishmentS  \\\n",
       "0   3699          294            356  ...     0.000000        0.000000   \n",
       "1   4024          368            476  ...     0.000000        0.000000   \n",
       "2   4090          369            507  ...     0.000000        0.000000   \n",
       "3   3931          296            404  ...     0.000000        0.000000   \n",
       "4   4094          316            457  ...     0.000000        0.000000   \n",
       "5   4184          346            517  ...     0.000000        0.000000   \n",
       "6   4383          334            540  ...     0.000000        0.000000   \n",
       "7   4633          399            485  ...     0.000000        0.000000   \n",
       "8   2580          258            313  ...   255.555556       17.888889   \n",
       "9   2803          247            284  ...   249.166667       17.250000   \n",
       "10  2899          230            205  ...   121.388889        9.583333   \n",
       "11  3145          277            276  ...   172.500000       12.138889   \n",
       "12  2985          240            244  ...   115.000000       11.500000   \n",
       "13  2867          298            292  ...   127.777778       12.777778   \n",
       "14  2693          256            270  ...   191.666667       16.611111   \n",
       "15  2732          245            306  ...    63.888889        6.388889   \n",
       "16  2580          258            313  ...   536.475410       53.647541   \n",
       "17  2803          247            284  ...   526.721312       52.672131   \n",
       "18  2899          230            205  ...   575.491803       49.745902   \n",
       "19  3145          277            276  ...   565.737705       48.770492   \n",
       "20  2985          240            244  ...   555.983607       47.795082   \n",
       "21  2867          298            292  ...   487.704918       44.868852   \n",
       "22  2693          256            270  ...   536.475410       45.844262   \n",
       "23  2732          245            306  ...   292.622951       29.262295   \n",
       "24  2240            9            118  ...     4.444444        0.251852   \n",
       "25  2435           46            137  ...     4.166667        0.251852   \n",
       "26  2288           96            139  ...     3.222222        0.207407   \n",
       "27  2322          109            153  ...     3.277778        0.198148   \n",
       "28  2473          164            157  ...     3.092593        0.201852   \n",
       "29  2407          167            148  ...     3.351852        0.220370   \n",
       "30  2448          132            153  ...     3.666667        0.229630   \n",
       "31  2533          119            177  ...     2.129630        0.190741   \n",
       "32  5080          277            330  ...  1920.000000      108.800000   \n",
       "33  5385          255            395  ...  1800.000000      108.800000   \n",
       "34  5425          410            447  ...  1392.000000       89.600000   \n",
       "35  5712          303            437  ...  1416.000000       85.600000   \n",
       "36  6268          344            476  ...  1336.000000       87.200000   \n",
       "37  5964          355            636  ...  1448.000000       95.200000   \n",
       "38  5926          435            647  ...  1584.000000       99.200000   \n",
       "39  6112          363            551  ...   920.000000       82.400000   \n",
       "40  3811          239            251  ...   382.222222       21.659259   \n",
       "41  3861          305            318  ...   358.333333       21.659259   \n",
       "42  4037          267            329  ...   277.111111       17.837037   \n",
       "43  4185          247            314  ...   281.888889       17.040741   \n",
       "44  4467          336            239  ...   265.962963       17.359259   \n",
       "45  4521          407            207  ...   288.259259       18.951852   \n",
       "46  4529          440            220  ...   315.333333       19.748148   \n",
       "47  4542          449            208  ...   183.148148       16.403704   \n",
       "48  2652           95             83  ...    93.333333        5.288889   \n",
       "49  2655           62             82  ...    87.500000        5.288889   \n",
       "\n",
       "    ratio_65  ratio_22_50  bus_ratio  bus_ratio_total  public_ratio  \\\n",
       "0   0.391464     0.091307   0.490196         0.009612      0.019608   \n",
       "1   0.405831     0.108001   0.305970         0.014508      0.047417   \n",
       "2   0.393954     0.109052   0.266667         0.011158      0.041841   \n",
       "3   0.366490     0.108602   0.321739         0.012865      0.039986   \n",
       "4   0.387316     0.108720   0.264368         0.007472      0.028265   \n",
       "5   0.382107     0.118916   0.596026         0.029383      0.049298   \n",
       "6   0.362473     0.128389   0.813187         0.023197      0.028527   \n",
       "7   0.355966     0.133792   0.867188         0.032118      0.037037   \n",
       "8   0.259381     0.107759   1.000000         0.012465      0.012465   \n",
       "9   0.301112     0.106573   1.000000         0.004454      0.004454   \n",
       "10  0.299598     0.110192   1.000000         0.004005      0.004005   \n",
       "11  0.293418     0.146599   0.215686         0.006736      0.031231   \n",
       "12  0.285283     0.169684   0.243243         0.006198      0.025482   \n",
       "13  0.260631     0.179885   0.180000         0.005031      0.027949   \n",
       "14  0.234174     0.214617   0.145833         0.004676      0.032064   \n",
       "15  0.224949     0.214468   0.000000         0.000000      0.037785   \n",
       "16  0.259381     0.107759   1.000000         0.012465      0.012465   \n",
       "17  0.301112     0.106573   1.000000         0.004454      0.004454   \n",
       "18  0.299598     0.110192   1.000000         0.004005      0.004005   \n",
       "19  0.293418     0.146599   0.215686         0.006736      0.031231   \n",
       "20  0.285283     0.169684   0.243243         0.006198      0.025482   \n",
       "21  0.260631     0.179885   0.180000         0.005031      0.027949   \n",
       "22  0.234174     0.214617   0.145833         0.004676      0.032064   \n",
       "23  0.224949     0.214468   0.000000         0.000000      0.037785   \n",
       "24  0.404011     0.132704   1.000000         0.071032      0.071032   \n",
       "25  0.419719     0.104867   0.962264         0.083744      0.087028   \n",
       "26  0.424334     0.109585   0.978723         0.109459      0.111838   \n",
       "27  0.414218     0.119507   0.952096         0.102317      0.107465   \n",
       "28  0.381622     0.107206   0.920455         0.046365      0.050372   \n",
       "29  0.375097     0.112840   0.838095         0.049802      0.059423   \n",
       "30  0.370893     0.118110   0.831461         0.042998      0.051714   \n",
       "31  0.370425     0.116716   0.792683         0.036806      0.046433   \n",
       "32  0.399335     0.126261   0.574230         0.054075      0.094170   \n",
       "33  0.376571     0.135695   0.625298         0.069166      0.110612   \n",
       "34  0.392407     0.158297   0.694190         0.060453      0.087084   \n",
       "35  0.389928     0.141505   0.809384         0.069035      0.085293   \n",
       "36  0.376908     0.141379   0.838068         0.070355      0.083949   \n",
       "37  0.360442     0.135654   0.829060         0.074942      0.090394   \n",
       "38  0.373047     0.137954   1.000000         0.076827      0.076827   \n",
       "39  0.382524     0.145060   1.000000         0.081324      0.081324   \n",
       "40  0.339831     0.158013   1.000000         0.019829      0.019829   \n",
       "41  0.337845     0.159052   1.000000         0.021991      0.021991   \n",
       "42  0.327292     0.159340   1.000000         0.010113      0.010113   \n",
       "43  0.329195     0.159631   1.000000         0.006107      0.006107   \n",
       "44  0.332766     0.166723   0.461538         0.008889      0.019259   \n",
       "45  0.358138     0.156532   0.504348         0.020924      0.041486   \n",
       "46  0.363753     0.156768   0.486726         0.020036      0.041166   \n",
       "47  0.377860     0.177252   0.614286         0.028772      0.046838   \n",
       "48  0.419264     0.064502   0.585366         0.060976      0.104167   \n",
       "49  0.404880     0.057223   0.519802         0.050847      0.097821   \n",
       "\n",
       "    car_ratio  bicy_ratio  walked_ratio  \n",
       "0    0.891580    0.005767      0.014994  \n",
       "1    0.866596    0.030432      0.009908  \n",
       "2    0.857043    0.033473      0.004533  \n",
       "3    0.858832    0.025730      0.012517  \n",
       "4    0.875569    0.016244      0.023392  \n",
       "5    0.841659    0.016650      0.026118  \n",
       "6    0.878056    0.000000      0.033856  \n",
       "7    0.882523    0.000000      0.035880  \n",
       "8    0.803966    0.002266      0.031161  \n",
       "9    0.827951    0.005011      0.027283  \n",
       "10   0.810069    0.007437      0.037757  \n",
       "11   0.775873    0.006124      0.033680  \n",
       "12   0.787190    0.008264      0.036501  \n",
       "13   0.842929    0.014533      0.029067  \n",
       "14   0.794255    0.008684      0.038744  \n",
       "15   0.833876    0.005863      0.016938  \n",
       "16   0.803966    0.002266      0.031161  \n",
       "17   0.827951    0.005011      0.027283  \n",
       "18   0.810069    0.007437      0.037757  \n",
       "19   0.775873    0.006124      0.033680  \n",
       "20   0.787190    0.008264      0.036501  \n",
       "21   0.842929    0.014533      0.029067  \n",
       "22   0.794255    0.008684      0.038744  \n",
       "23   0.833876    0.005863      0.016938  \n",
       "24   0.815318    0.000000      0.017295  \n",
       "25   0.846743    0.000000      0.006568  \n",
       "26   0.852469    0.000000      0.000000  \n",
       "27   0.826898    0.005148      0.000000  \n",
       "28   0.871208    0.004579      0.000000  \n",
       "29   0.867006    0.004527      0.003396  \n",
       "30   0.855898    0.015107      0.016851  \n",
       "31   0.852208    0.024915      0.018120  \n",
       "32   0.798206    0.009496      0.045898  \n",
       "33   0.756072    0.010032      0.040127  \n",
       "34   0.796538    0.005326      0.033555  \n",
       "35   0.827414    0.005253      0.001251  \n",
       "36   0.859289    0.000000      0.005008  \n",
       "37   0.868658    0.000000      0.009786  \n",
       "38   0.885135    0.000000      0.011011  \n",
       "39   0.879169    0.000000      0.009492  \n",
       "40   0.855754    0.000000      0.008165  \n",
       "41   0.856867    0.000000      0.018904  \n",
       "42   0.902913    0.000000      0.007282  \n",
       "43   0.899023    0.000000      0.008143  \n",
       "44   0.894815    0.000000      0.021481  \n",
       "45   0.881313    0.000000      0.020202  \n",
       "46   0.906011    0.000000      0.011658  \n",
       "47   0.887253    0.000000      0.011710  \n",
       "48   0.840447    0.003557      0.019817  \n",
       "49   0.853269    0.003390      0.015012  \n",
       "\n",
       "[50 rows x 23 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final.iloc[:50,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final1=df_final.groupby(['geo_id'], as_index=False).agg(\n",
    "    { 'EmploymentS':'mean', 'EstablishmentS':'mean','geometry':'first'})  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "        geo_id  EmploymentS  EstablishmentS  \\\n",
       "0  41051000100  4093.842436      233.650918   \n",
       "1  41051000200   853.065138       48.326562   \n",
       "2  41051000301  1552.518125       88.608023   \n",
       "3  41051000302   145.966677        8.613696   \n",
       "4  41051000401   543.987224       37.206859   \n",
       "\n",
       "                                            geometry  \n",
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       "1  (POLYGON ((-122.6511506479096 45.4903237698433...  \n",
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      ]
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     "execution_count": 10,
     "metadata": {},
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   "source": [
    "df_final1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final2=df_final.groupby(['geo_id'], as_index=False).agg(\n",
    "    { 'EmploymentS':'mean', 'EstablishmentS':'mean','geometry':'first','total_tran':'mean',\n",
    "     \n",
    "       'pop':'mean', 'edut':'mean', 'collegemale':'mean', 'collegefemale':'mean', 'households':'mean',\n",
    "     'income':'mean', 'ratio_65':'mean',\n",
    "       'ratio_22_50':'mean', 'bus_ratio':'mean',\n",
    "       'bus_ratio_total':'mean', 'public_ratio':'mean', 'car_ratio':'mean',\n",
    "     'bicy_ratio':'mean',\n",
    "       'walked_ratio':'mean'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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       "      <td>492.500</td>\n",
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       "      <td>0.049731</td>\n",
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       "        geo_id  EmploymentS  EstablishmentS  \\\n",
       "0  41051000100  4093.842436      233.650918   \n",
       "1  41051000200   853.065138       48.326562   \n",
       "2  41051000301  1552.518125       88.608023   \n",
       "3  41051000302   145.966677        8.613696   \n",
       "4  41051000401   543.987224       37.206859   \n",
       "\n",
       "                                            geometry  total_tran       pop  \\\n",
       "0  (POLYGON ((-122.6685096758439 45.4896287092521...    3243.250  6095.875   \n",
       "1  (POLYGON ((-122.6511506479096 45.4903237698433...    3135.125  6157.500   \n",
       "2  (POLYGON ((-122.6435166338574 45.4903107958509...    2580.875  5330.125   \n",
       "3  (POLYGON ((-122.6376585383979 45.4743597792970...    3633.000  6929.875   \n",
       "4  (POLYGON ((-122.6205705527944 45.4827708556041...    1994.375  3549.625   \n",
       "\n",
       "       edut  collegemale  collegefemale  households    income  ratio_65  \\\n",
       "0  4667.250      615.000        733.125     170.125  2826.250  0.464817   \n",
       "1  4660.500      653.750        825.875     173.250  2954.750  0.488260   \n",
       "2  3346.750      423.375        387.625     130.500  2109.375  0.449649   \n",
       "3  4937.750      824.375        858.625     175.500  2621.875  0.385256   \n",
       "4  2708.625      410.500        492.500     141.500  1502.125  0.518228   \n",
       "\n",
       "   ratio_22_50  bus_ratio  bus_ratio_total  public_ratio  car_ratio  \\\n",
       "0     0.121203   0.953060         0.084304      0.087963   0.677976   \n",
       "1     0.134470   0.957985         0.107039      0.111167   0.671435   \n",
       "2     0.131125   0.964928         0.139973      0.144679   0.497451   \n",
       "3     0.137374   0.972658         0.055748      0.057137   0.758092   \n",
       "4     0.103065   0.991553         0.113903      0.115135   0.727155   \n",
       "\n",
       "   bicy_ratio  walked_ratio  \n",
       "0    0.095493      0.041783  \n",
       "1    0.091021      0.025325  \n",
       "2    0.050301      0.186356  \n",
       "3    0.074766      0.036437  \n",
       "4    0.049731      0.015462  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>geo_id</th>\n",
       "      <th>EmploymentS</th>\n",
       "      <th>EstablishmentS</th>\n",
       "      <th>geometry</th>\n",
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       "      <td>4093.842436</td>\n",
       "      <td>233.650918</td>\n",
       "      <td>MULTIPOLYGON (((-122.66851 45.48963, -122.6654...</td>\n",
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       "      <td>MULTIPOLYGON (((-122.64352 45.49031, -122.6395...</td>\n",
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       "      <td>41051000302</td>\n",
       "      <td>145.966677</td>\n",
       "      <td>8.613696</td>\n",
       "      <td>MULTIPOLYGON (((-122.63766 45.47436, -122.6344...</td>\n",
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       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>41051000401</td>\n",
       "      <td>543.987224</td>\n",
       "      <td>37.206859</td>\n",
       "      <td>MULTIPOLYGON (((-122.62057 45.48277, -122.6205...</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "        geo_id  EmploymentS  EstablishmentS  \\\n",
       "0  41051000100  4093.842436      233.650918   \n",
       "1  41051000200   853.065138       48.326562   \n",
       "2  41051000301  1552.518125       88.608023   \n",
       "3  41051000302   145.966677        8.613696   \n",
       "4  41051000401   543.987224       37.206859   \n",
       "\n",
       "                                            geometry  \n",
       "0  MULTIPOLYGON (((-122.66851 45.48963, -122.6654...  \n",
       "1  MULTIPOLYGON (((-122.65115 45.49032, -122.6493...  \n",
       "2  MULTIPOLYGON (((-122.64352 45.49031, -122.6395...  \n",
       "3  MULTIPOLYGON (((-122.63766 45.47436, -122.6344...  \n",
       "4  MULTIPOLYGON (((-122.62057 45.48277, -122.6205...  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdf =gpd.GeoDataFrame(df_final1, geometry='geometry', crs={'init': 'epsg:4326'})\n",
    "gdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>geo_id</th>\n",
       "      <th>EmploymentS</th>\n",
       "      <th>EstablishmentS</th>\n",
       "      <th>geometry</th>\n",
       "      <th>total_tran</th>\n",
       "      <th>pop</th>\n",
       "      <th>edut</th>\n",
       "      <th>collegemale</th>\n",
       "      <th>collegefemale</th>\n",
       "      <th>households</th>\n",
       "      <th>income</th>\n",
       "      <th>ratio_65</th>\n",
       "      <th>ratio_22_50</th>\n",
       "      <th>bus_ratio</th>\n",
       "      <th>bus_ratio_total</th>\n",
       "      <th>public_ratio</th>\n",
       "      <th>car_ratio</th>\n",
       "      <th>bicy_ratio</th>\n",
       "      <th>walked_ratio</th>\n",
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       "  </thead>\n",
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       "      <td>41051000100</td>\n",
       "      <td>4093.842436</td>\n",
       "      <td>233.650918</td>\n",
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       "      <td>3243.250</td>\n",
       "      <td>6095.875</td>\n",
       "      <td>4667.250</td>\n",
       "      <td>615.000</td>\n",
       "      <td>733.125</td>\n",
       "      <td>170.125</td>\n",
       "      <td>2826.250</td>\n",
       "      <td>0.464817</td>\n",
       "      <td>0.121203</td>\n",
       "      <td>0.953060</td>\n",
       "      <td>0.084304</td>\n",
       "      <td>0.087963</td>\n",
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       "      <td>0.095493</td>\n",
       "      <td>0.041783</td>\n",
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       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>41051000200</td>\n",
       "      <td>853.065138</td>\n",
       "      <td>48.326562</td>\n",
       "      <td>MULTIPOLYGON (((-122.65115 45.49032, -122.6493...</td>\n",
       "      <td>3135.125</td>\n",
       "      <td>6157.500</td>\n",
       "      <td>4660.500</td>\n",
       "      <td>653.750</td>\n",
       "      <td>825.875</td>\n",
       "      <td>173.250</td>\n",
       "      <td>2954.750</td>\n",
       "      <td>0.488260</td>\n",
       "      <td>0.134470</td>\n",
       "      <td>0.957985</td>\n",
       "      <td>0.107039</td>\n",
       "      <td>0.111167</td>\n",
       "      <td>0.671435</td>\n",
       "      <td>0.091021</td>\n",
       "      <td>0.025325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>41051000301</td>\n",
       "      <td>1552.518125</td>\n",
       "      <td>88.608023</td>\n",
       "      <td>MULTIPOLYGON (((-122.64352 45.49031, -122.6395...</td>\n",
       "      <td>2580.875</td>\n",
       "      <td>5330.125</td>\n",
       "      <td>3346.750</td>\n",
       "      <td>423.375</td>\n",
       "      <td>387.625</td>\n",
       "      <td>130.500</td>\n",
       "      <td>2109.375</td>\n",
       "      <td>0.449649</td>\n",
       "      <td>0.131125</td>\n",
       "      <td>0.964928</td>\n",
       "      <td>0.139973</td>\n",
       "      <td>0.144679</td>\n",
       "      <td>0.497451</td>\n",
       "      <td>0.050301</td>\n",
       "      <td>0.186356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>41051000302</td>\n",
       "      <td>145.966677</td>\n",
       "      <td>8.613696</td>\n",
       "      <td>MULTIPOLYGON (((-122.63766 45.47436, -122.6344...</td>\n",
       "      <td>3633.000</td>\n",
       "      <td>6929.875</td>\n",
       "      <td>4937.750</td>\n",
       "      <td>824.375</td>\n",
       "      <td>858.625</td>\n",
       "      <td>175.500</td>\n",
       "      <td>2621.875</td>\n",
       "      <td>0.385256</td>\n",
       "      <td>0.137374</td>\n",
       "      <td>0.972658</td>\n",
       "      <td>0.055748</td>\n",
       "      <td>0.057137</td>\n",
       "      <td>0.758092</td>\n",
       "      <td>0.074766</td>\n",
       "      <td>0.036437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>41051000401</td>\n",
       "      <td>543.987224</td>\n",
       "      <td>37.206859</td>\n",
       "      <td>MULTIPOLYGON (((-122.62057 45.48277, -122.6205...</td>\n",
       "      <td>1994.375</td>\n",
       "      <td>3549.625</td>\n",
       "      <td>2708.625</td>\n",
       "      <td>410.500</td>\n",
       "      <td>492.500</td>\n",
       "      <td>141.500</td>\n",
       "      <td>1502.125</td>\n",
       "      <td>0.518228</td>\n",
       "      <td>0.103065</td>\n",
       "      <td>0.991553</td>\n",
       "      <td>0.113903</td>\n",
       "      <td>0.115135</td>\n",
       "      <td>0.727155</td>\n",
       "      <td>0.049731</td>\n",
       "      <td>0.015462</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        geo_id  EmploymentS  EstablishmentS  \\\n",
       "0  41051000100  4093.842436      233.650918   \n",
       "1  41051000200   853.065138       48.326562   \n",
       "2  41051000301  1552.518125       88.608023   \n",
       "3  41051000302   145.966677        8.613696   \n",
       "4  41051000401   543.987224       37.206859   \n",
       "\n",
       "                                            geometry  total_tran       pop  \\\n",
       "0  MULTIPOLYGON (((-122.66851 45.48963, -122.6654...    3243.250  6095.875   \n",
       "1  MULTIPOLYGON (((-122.65115 45.49032, -122.6493...    3135.125  6157.500   \n",
       "2  MULTIPOLYGON (((-122.64352 45.49031, -122.6395...    2580.875  5330.125   \n",
       "3  MULTIPOLYGON (((-122.63766 45.47436, -122.6344...    3633.000  6929.875   \n",
       "4  MULTIPOLYGON (((-122.62057 45.48277, -122.6205...    1994.375  3549.625   \n",
       "\n",
       "       edut  collegemale  collegefemale  households    income  ratio_65  \\\n",
       "0  4667.250      615.000        733.125     170.125  2826.250  0.464817   \n",
       "1  4660.500      653.750        825.875     173.250  2954.750  0.488260   \n",
       "2  3346.750      423.375        387.625     130.500  2109.375  0.449649   \n",
       "3  4937.750      824.375        858.625     175.500  2621.875  0.385256   \n",
       "4  2708.625      410.500        492.500     141.500  1502.125  0.518228   \n",
       "\n",
       "   ratio_22_50  bus_ratio  bus_ratio_total  public_ratio  car_ratio  \\\n",
       "0     0.121203   0.953060         0.084304      0.087963   0.677976   \n",
       "1     0.134470   0.957985         0.107039      0.111167   0.671435   \n",
       "2     0.131125   0.964928         0.139973      0.144679   0.497451   \n",
       "3     0.137374   0.972658         0.055748      0.057137   0.758092   \n",
       "4     0.103065   0.991553         0.113903      0.115135   0.727155   \n",
       "\n",
       "   bicy_ratio  walked_ratio  \n",
       "0    0.095493      0.041783  \n",
       "1    0.091021      0.025325  \n",
       "2    0.050301      0.186356  \n",
       "3    0.074766      0.036437  \n",
       "4    0.049731      0.015462  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdf2 =gpd.GeoDataFrame(df_final2, geometry='geometry', crs={'init': 'epsg:4326'})\n",
    "gdf2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(171, 19)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdf2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "qW = ps.lib.weights.Queen.from_dataframe(gdf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "qW2 = ps.lib.weights.Queen.from_dataframe(gdf2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "qW.transform = 'r'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "qW2.transform = 'r'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "import libpysal as lps\n",
    "y = gdf['EstablishmentS']\n",
    "ylag = lps.weights.lag_spatial(qW, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import mapclassify as mc\n",
    "ylagq5 = mc.Quantiles(ylag, k=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(1, figsize=(9, 9))\n",
    "gdf.assign(cl=ylagq5.yb).plot(column='cl', categorical=True, \\\n",
    "        k=5, cmap='GnBu', linewidth=0.1, ax=ax, \\\n",
    "        edgecolor='white', legend=True)\n",
    "ax.set_axis_off()\n",
    "plt.title(\"Spatial Lag establishment size\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2304x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gdf['lag_est'] = ylag\n",
    "f,ax = plt.subplots(2,1,figsize=(4*8,6))\n",
    "gdf.plot(column='EstablishmentS', ax=ax[0], edgecolor='k',\n",
    "        scheme=\"quantiles\", legend=True, k=5, cmap='GnBu')\n",
    "ax[0].axis(gdf.total_bounds[np.asarray([0,2,1,3])])\n",
    "ax[0].set_title(\"establishment size\")\n",
    "\n",
    "gdf.plot(column='lag_est', ax=ax[1], edgecolor='k',\n",
    "        scheme='quantiles', cmap='GnBu', legend=True, k=5)\n",
    "ax[1].axis(gdf.total_bounds[np.asarray([0,2,1,3])])\n",
    "ax[1].set_title(\"Spatial establishment size\")\n",
    "ax[0].axis('off')\n",
    "ax[1].axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "40.91676617512499"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "85"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yb = y > y.median()\n",
    "sum(yb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "yb = y > y.median()\n",
    "labels = [\"0 Low\", \"1 High\"]\n",
    "yb = [labels[i] for i in 1*yb]\n",
    "gdf['yb'] = yb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c818558a08>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12,10), subplot_kw={'aspect':'equal'})\n",
    "gdf.plot(column='yb', cmap='binary', edgecolor='grey', legend=True, ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "import esda\n",
    "yb = 1 * (y > y.median()) # convert back to binary\n",
    "wq =  lps.weights.Queen.from_dataframe(gdf)\n",
    "wq.transform = 'b'\n",
    "np.random.seed(12345)\n",
    "jc = esda.join_counts.Join_Counts(yb, wq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "162"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jc.bb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "132"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jc.ww"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "235"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jc.bw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "529"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jc.bb + jc.ww + jc.bw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "529.0"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wq.s0 / 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "129"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jc.mean_bb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'BB Counts')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sbn.kdeplot(jc.sim_bb, shade=True)\n",
    "plt.vlines(jc.bb, 0, 0.075, color='r')\n",
    "plt.vlines(jc.mean_bb, 0,0.075)\n",
    "plt.xlabel('BB Counts')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.001"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jc.p_sim_bb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "wq.transform = 'r'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = gdf['EstablishmentS']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.26449658439944207"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Moran's I (Global)\n",
    "np.random.seed(12345)\n",
    "mi = esda.moran.Moran(y, wq)\n",
    "mi.I\n",
    "#positive related"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Again, our value for the statistic needs to be interpreted against a reference distribution under the null of CSR. \n",
    "#PySAL uses a similar approach as we saw in the join count analysis: random spatial permutations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, \"Moran's I\")"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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oT+w3MMdbUJTF++cX8PCW41zoGfA6jkjMUYHHie31baT6fVSXJP7493j3rq2gu3+IhzbriBSRiVTgcWJbXSuLS7MTZvrYqaosyOT6qkK+v+2EjkgRmSC52iBOtXb1c7ipk+XlyTP+Pd6nrqmgf2iY72yq9zqKSExRgceBsdPnE/3470uZk5fBB6qLeWTHSZo6+ryOIxIzVOBx4PX6VjJT/Swoes9UM0njk2vKGRpx/POrdV5HEYkZKvA4sPVoK0vLcvH7Emv62OkoyU1n/eJifvLGKc6c1xwpIqACj3mn23s4fb43YaePnY5fWVMOwD9t1F64CKjAY972+lYgcaePnY7C7DRuXVrK4zVnONHa7XUcEc+pwGPc9vo28jJSqMjP8DpKTLh79Rz8fuPbrxz1OoqI51TgMcw5x/a6NpbNyU245dNmKj8zlQ8vK+XpvWepa+70Oo6Ip1TgMexocxctXf1Je/jgpXx85RxSAz7+YYP2wiW5qcBj2La60fHvZJr/ZCpyM1K4c3kZ/7W/gUMNHV7HEfGMCjyGba9voyQnjeKcdK+jxJyPrSwjK9XP/9VYuCQxFXiMGhoeYcexNh19cgnZaQFuW1bKi283crJNR6RIclKBx6gD5zro7BvS8Mll3LF8Nj4zvqv5wiVJqcBj1Njx38vKVOCXkp+Zyo2LivhZzWnOd2u+cEk+KvAYta2ulbkFGeRlpnodJaZ97Ooy+gZH+PEbJ72OIhJ1KvAY1Dc4TM2J8yzT+PcVVRZkcnX5LH604xRDwyNexxGJKhV4DNpz6gL9QyMs1/wnU3L7slIaO/p4+VCz11FEokoFHoO217fiM41/T9U1c/Mpyk7lkddPeB1FJKpU4DFoW10rVcXZZKYGvI4SF/w+49arStlW36bT6yWpqMBjTFf/EG+dvsgyDZ9MywevKiHgM/5z52mvo4hEjQo8xrx5vI1h5zT/yTTNykhhzdw8fr77LIN6M1OShAo8xmyrayPFbywuzfE6Sty5ZXEJbd0DbDrc4nUUkahQgceY7fWtLC7NITWgb810raqcxayMFB6r0TCKJAe1RAxp6+rnUEOn5j+ZoYDPx02LithY20xbV7/XcUQiLuQCNzO/me0xs+fCESiZvX6sDUDrX4bglsXFDI04ntp7zusoIhEXjj3wLwOHwvA4SW97fRsZKX4WFmd7HSVuVRZksrA4i8dqTuOc8zqOSESFVOBmVgF8DHg4PHGS27a6VpaW5eD3afm0UNyyuJjaxk7ePqfFHiSxhboH/o/A14BLHrdlZg+YWY2Z1bS06OiASzl7oZeTbT0a/w6DG6qKCPiMx3ed8TqKSETNuMDN7C6g2Tm363LbOececs6tc86tKy4ununTJbzt7yyfpgIPVXZagHXz83lyz1n6h4a9jiMSMaHsgd8IfMLMTgA/BT5kZj8KS6oktL2+jdyMABX5GV5HSQi3LC7mYu+gjgmXhDbjAnfOfcM5V+Gcmw/cB2x0zv1G2JIlEeccW+taWV42C59p/Dscri7PIzcjwNN7z3odRSRidBx4DKhv6aKls1/Tx4aR32dct6CQlw8209E36HUckYgIS4E75zY55+4Kx2Mlo9eOjI5/r6zQ+Hc43bSoiIHhEV440Oh1FJGI0B54DHjtcDNz8tIpzkn3OkpCWVSSzezcNJ7eo2EUSUwqcI/1DQ7zxvF2VlbkeR0l4ZgZN1QVsb2+jeaOPq/jiISdCtxjbxxvp39ohFUq8Ii4cVERDnjmLZ1aL4lHBe6x1w63kOI3lpZp+thImJOXwcKiLJ7S0SiSgFTgHtt8pIWlZbmkBfxeR0lYN1QVceBsB/UtXV5HEQkrFbiHzl7opa6lS8MnEXZ9VSEGejNTEo4K3EObj4yeJajDByOrICuV5eW5PLX3nGYolISiAvfQa4dbKMpOpTxPp89H2o1VRZxq72Hv6QteRxEJGxW4RwaHR9ha18rKijxMp89H3PsXFJDiN57WQg+SQFTgHtl7+gJd/UMaPomSzNQA18zN59m3zjGkVeslQajAPfLa4RZ8Bis0/3fU3LioiLbuAbYGp+4ViXcqcI+8dqSF6pIcstICXkdJGqsr88hK82sYRRKGCtwDTR197D97kVWVOnwwmlL8Pq5bUMgLBxrp6h/yOo5IyFTgHnj5UBMA6+ble5wk+dy8uJjewWGe39fgdRSRkKnAPbDhYBOluWlafccD1SXZzMlL52c1p72OIhIyFXiUdfUPsa2ulbVz83X4oAfMjFuqi6k5eZ5jOrVe4pwKPMo2H2lhcNixbn6B11GS1k3VxfgMrVovcU8FHmUvvd1ITnqAxaWafdArBVmprK7M42c1pxkY0jHhEr9U4FE0ODzCxtpm1lTm4fdp+MRLty8rpbVrgBfe1nJrEr9U4FG083g7HX1DrJun4ROvrazIozQ3jUdeP+F1FJEZU4FH0UsHm0j1+7hap897zmfGbUtL2XniPLWNHV7HEZkRFXiUOOfYcLCJFeW5pKdo8YZYsH5xCal+Hz/YdsLrKCIzogKPkoMNHZy90MtaDZ/EjOz0ADcvLuKJ3Wdo0qLHEodU4FGy4WATBlwzV6fPx5K7Vs5heMTx8JZjXkcRmTYVeJS8cKCR6tJs8jJTvY4i45TmpnP9wkJ+9MYpLvQMeB1HZFpU4FFQ19xJbWMn1y8s9DqKTOITq8vpHRjm4S3HvY4iMi0q8Ch49q0GDLhWBR6T5hZkcn1VIQ9vPUbjRY2FS/xQgUeYc47n9p1jaVku+Ro+iVn3ratkaNjxdy8d9jqKyJTNuMDNrNLMXjWzQ2b2tpl9OZzBEsWhhk7qW7q5vkp737GsJDedO5bP5vFdZ3j73EWv44hMSSh74EPA7zvnlgLXAV8ws2XhiZU4nt13Dp+NLqorse2eNeXkZAT4oyf2ad1MiQszLnDnXINzbnfwcidwCCgPV7BEMDzieHL3WVZV5JGbnuJ1HLmC7LQAv33DAg6c7eDhrXpDU2JfWMbAzWw+sAZ4Y5LbHjCzGjOraWlpCcfTxY0dx9po7OjjA9XFXkeRKbp2QQHvm5/P3790hKNNnV7HEbmskAvczLKBJ4CvOOfeM6mEc+4h59w659y64uLkKrIndp0hK9XPWi2dFjfMjM/euICMVD+/+6Nd9Axo7UyJXSEVuJmlMFreP3bO/Tw8kRJDV/8QvzjQyHULC0kN6GCfeJKXmcoXP7iIYy3d/MmTB3DOeR1JZFKhHIViwHeBQ865vw9fpMTwi/0N9A4Oc/Pi5PqrI1GsKJ/FvWsreHLPWb6nya4kRoWya3gj8BngQ2a2N/jx0TDlinuPvnmKslnpVJdkex1FZuieNeW8f34Bf/VfB3m1ttnrOCLvEcpRKFudc+acW+mcWx38eD6c4eLVoYYOdp+6wG1LS7VwcRzzmfHg+irmFWbxxUd3c7hRb2pKbNHgbAT85I1TpPiNm3X0SdxLT/HzBx9eQqrfx2d/sJPWrn6vI4m8QwUeZt39Q/x8zxmuW1hIdnrA6zgSBgVZqfz+h5fQ0tnP7zyyi/6hYa8jiQAq8LB7cs9ZuvuHuW1pqddRJIyqirN5cH0Vu06e5xtP7NeRKRITtIsYRiMjju9uPU5VcZbevExA1y0s5NyFXh7bdYbq0hweXF/ldSRJctoDD6NXaps53trNR68u05uXCepX1pRzQ1Uh33qhlhffbvQ6jiQ5FXgY/fvmYxRlp3LtAs08mKjMjN+5uYqqkmy+8tM9HDirmQvFOyrwMNl7+gJvnmjnzuVl+H3a+05kqQEfv3/7YrLSAjzwHzW0d2spNvGGCjxMvv3yEbLTAnzoqhKvo0gU5GWm8tXbFtPS1c8Xf7Jb08+KJ1TgYbD39AVePdzCx1aWkZHq9zqORMnC4mw+d9NCtte38a0XtZKPRJ8KPAy+/fIRctID3LFsttdRJMpuWVzMh5eV8tDmYzz71jmv40iSUYGHaOeJdl493MJHr9bed7L6zHXzWDI7h689vo9DDe+ZUVkkYlTgIRgZcfz5s29TmJXKR1Zo7ztZBfw+vnJrNekpPh54pIaLPYNeR5IkoQIPwc/3nOXA2Q7ue/9c0gLJs/f9hV//BF/49U94HSOm5GWm8pXbFtNwoY8v/3QPwyM6U1OC1q8f/YgAFfgMdfQN8jcv1LKoOIsbtOK8AItLc/jN6+ez6UgLf/ncQZ1uLxGnU+ln6H8/X0tbVz9fvrUan866lKDblpbQeLGXH2w/QUluGp9fv8jrSJLAVOAzsL2+lUffPMVdK8uoKtacJ/JLZsanr5vHxd5BvvXCYdICfj530wKvY0mCUoFP08XeQb722D5m56Zz79oKr+NIDPKZ8bu3VDE47PjL5w7SNzjM59dXaX4cCTuNgU+Dc44/fOwtGjr6eHB9VVK9cSnTE/D7+NKt1dxQVcj/efEwf/zkAQaGdLamhJf2wKfh4S3HeelgE5+5bh6LS3O8jiMxzu8zvrB+EUXZaTz65imONHXy7ftWU5Gf6XU0SRDaA5+iFw408tfPH+L98wt0zLdMmc9n/Nr75/J7H1rEwXMd3PmPW/hZzWlGdJihhIEKfArePN7Olx7dw6KSbD7/QY1lyvTdUFXENz95NRX5GXzt8X3c8y/b2HWy3etYEudU4Fewva6V+7//JkXZqfzBHUs07i0zVpKbzp/etYzPr6/izPlePvWvr/PFn+zmVFuP19EkTmkM/DJeONDAlx7dS2luGt/46FJy01O8jiRxzmfGB6qLed/8Ap7bd47n9jXw/P4G7lo5h9+9pYplc3K9jihxRAU+ieERxz9sOML/e7WORSXZfO2OJeSovCWM0lP83Lu2kg9dVcovDjSw4WATz7x1jpuri/j0dfP44JISUgP6A1kuTwU+wfHWbv7wsbeoOXmeDy4p5rduWKBfJImYgqxUPn3tPO5eXc7LB5t48e1GNj+yi7yMFD62sox71pRzzdx8rfIkk1KBB/UMDPHQ5mN8Z1M9fp/x4C1VfKC6SG9YSlRkpwW4Z005H181h/1nL7DlaCuP7zrDj984RU56gPfPL2BpWS5LZuewZHYO8wuztGMhKvCegSEeffM0//ZaPc2d/Vy7oIDfvH4+BVmpXkeTJOT3Gasr81ldmU/vwDB7T5/nwLkODjd18urhZsaOPjSgOCeNivwM5uRlUJ6fQXne6MfY13rPJvElZYE756ht7ORnNaf5+e6zXOwdZGlZDp9fv4gls3WCjsSGjFQ/11cVcX1VEQCDwyOcu9DLqfYemjr6aO0aoLWrn5oT53nhQCNDE44tz04LMCcvfbTY8zMoz8tkTl76O6VfkpOuoZk4F1KBm9mdwLcBP/Cwc+6bYUkVAe3dA+w6eZ6dJ9rZcLCJ463dBHzGuvn5fGRFmc6slJiX4vcxrzCLeYVZ77ltxDk6egdp7ep/p9jHPh9r7WbniXa6+offdR+/z5idm055fgYVwZKfM24vviI/g/QUHTYby2Zc4GbmB/4ZuB04A+w0s2eccwfDFW6qnHP0DY7Q0TfIxd5B2roGON3ew6n2Hk6293Dw3EXqW7oBCPiMpWW5fPbGBVy7sEB/ZkpC8JmRl5lKXmYqi0om36Z3YJjWrn7auvtp6RygrTtY8p39HGvpor17gIkniBbnpDG3IJPK/AwqCzIpz8ugMDuNgqxUCrNSyc1IISPFT1rAh09781EXyh74+4E659wxADP7KXA3EPYC/8bP97G9ro2hEcfwiGNoZIShYffO14PDI+/58xHAZ1CSk055fjqfvnYuV83Ooao4W2/+hGjsjd0Uv35h40lKRoDcjAALi9+7Bw+jh8+2dw/Q0tVPa2c/TZ39NHf00dzZz/b6NlrfOveegh8vNeAjPeAjPcWP32f4fYbPDJ/Bd3/rfZp6OQJCKfBy4PS4r88A107cyMweAB4AmDt37oyeaG5BFj1zhwn4fAR8ht9vo599Rorfh99n5KQHmJWRwqyMFPIzU6nMHx3vC/hV1uG2a8dWryOIBwaHR2jq6KO9e4C27gHaugbo7Bukb3CEvsFh+oaG6R8coXdgmGHnGHGOkRHHiCO5h2I2bYrYQ4dS4JPtfr3n/2fn3EPAQwDr1q2b0Qw+D66vmsndRCSMUvw+KvIzNZtiDAll9/QMUCoCvYsAAAfHSURBVDnu6wrgXGhxRERkqkIp8J1AtZktMLNU4D7gmfDEEhGRK5nxEIpzbsjMvgi8yOhhhN9zzr0dtmQiInJZIR0H7px7Hng+TFlERGQadIiGiEicUoGLiMQpFbiISJxSgYuIxClzLnqrY5tZC3DyEjcXAa1RCzM1sZgJlGu6YjFXLGYC5ZqOaGaa55wrnnhlVAv8csysxjm3zusc48ViJlCu6YrFXLGYCZRrOmIhk4ZQRETilApcRCROxVKBP+R1gEnEYiZQrumKxVyxmAmUazo8zxQzY+AiIjI9sbQHLiIi06ACFxGJU1EtcDMrMLMNZnY0+Dn/Etu9YGYXzOy5Cdf/wMyOm9ne4MfqGMi0wMzeCN7/P4NT64ZsGrnuD25z1MzuH3f9JjM7PO61usRKiVPOc2fw8erM7OuT3J4W/PfXBV+P+eNu+0bw+sNmdkcoOcKRyczmm1nvuNfmO+HKNMVcN5vZbjMbMrN7J9w26fczBnINj3u9wjZt9BQy/XczO2hm+8zsFTObN+42L1+ry+WKyGs1Kedc1D6AbwFfD17+OvA3l9juVuDjwHMTrv8BcG+MZfoZcF/w8neAB6OVCygAjgU/5wcv5wdv2wSsC1MWP1APLARSgbeAZRO2+TzwneDl+4D/DF5eFtw+DVgQfBy/x5nmAwci9DM+lVzzgZXAf4z/eb7c99PLXMHbujx6rT4IZAYvPzjue+j1azVprki9Vpf6iPYQyt3AD4OXfwjcM9lGzrlXgM5Yz2RmBnwIePxK949QrjuADc65dufceWADcGeYnn+8dxawds4NAGMLWF8q7+PArcHX527gp865fufccaAu+HheZoqkK+Zyzp1wzu0DRibcN5Lfz1ByRcpUMr3qnOsJfrmD0ZW/wPvX6lK5oiraBV7qnGsACH6eyZ/1fxX8s+UfzCzN40yFwAXn3FDw6zOMLvYcDlPJNdnC0uOf//vBP+P+NMTiutLzvGub4OtxkdHXZyr3jXYmgAVmtsfMXjOzD4Qhz3RyReK+kX7sdDOrMbMdZhaunZTpZvoc8IsZ3jdauSAyr9WkQlrQYTJm9jIwe5Kb/iQMD/8NoJHRP2seAv4I+AsPM01pYedL3jn0XJd7/k87586aWQ7wBPAZRv80nomp/DsvtU1Ir9FlhJKpAZjrnGszs7XAU2a23DnXEaVckbhvpB97rnPunJktBDaa2X7nXH20MpnZbwDrgFume98o54LIvFaTCnuBO+duu9RtZtZkZmXOuQYzKwOap/nYDcGL/Wb2feAPPM7UCuSZWSC4hzethZ3DkOsMsH7c1xWMjn3jnDsb/NxpZj9h9M/CmRb4VBawHtvmjJkFgFlA+xTvG9VMbnSgsh/AObfLzOqBxUBNlHJd7r7rJ9x3UxgyjT32jL8Pzrlzwc/HzGwTsIbRceKIZzKz2xjdqbnFOdc/7r7rJ9x3U4h5wpErUq/VpKI9hPIMMPZu8f3A09O5c7DIxsae7wEOeJkpWASvAmPv2E/73xRirheBD5tZvo0epfJh4EUzC5hZEYCZpQB3EdprNZUFrMfnvRfYGHx9ngHuCx4RsgCoBt4MIUvImcys2Mz8AMG9pGpG3wQLh1AW+570++l1rmCetODlIuBG4GA0MpnZGuDfgE8458bvxHj6Wl0qVwRfq8lF693S0d9lCoFXgKPBzwXB69cBD4/bbgvQAvQy+r/hHcHrNwL7GS2jHwHZMZBpIaOFVAc8BqRF+bX6bPC564DfDl6XBewC9gFvA98mxCM/gI8CRxjdk/iT4HV/wegPMEB68N9fF3w9Fo67758E73cY+EgYf55mlAn4VPB1eQvYDXw8zD/nV8r1vuDPUDfQBrx9ue+n17mAG4K/d28FP38uipleBpqAvcGPZ2LktZo0VyRfq8k+dCq9iEic0pmYIiJxSgUuIhKnVOAiInFKBS4iEqdU4CIicUoFLnHHzJyZPTLu64CZtdiEmSIj9NybbNxMi1fY9s/MbEonm4nMhApc4lE3sMLMMoJf3w6cnc4DBM/MFIlrKnCJV78APha8/GvAo2M32Ohc6k8FJz3bYWYrg9f/mZk9ZGYvAf9ho/OCb7HRObB3m9kNwe3WB/e0HzezWjP78bjJwNqBYTPz2+j89AfMbL+ZfTV6/3SRUdoLkXj1U+B/BodNVgLfA8ZmFfxzYI9z7h4z+xCjc8CMLf6xFrjJOddrZpnA7c65PjOrZvQ/gXXB7dYAyxmdA2Mbo6dEb3XOfRIgOAlWuXNuRfDrvMj+c0XeS3vgEpfc6LzV8xnd+35+ws03AY8Et9sIFJrZrOBtzzjneoOXU4B/N7P9jJ5yv2zcY7zpnDvjnBth9FTp+ROe4xiw0Mz+yczuBMIxk6HItKjAJZ49A/wt44ZPgi43HWj3uOu+yuh8FqsY3fMevxxe/7jLw0z4a9WNLiKwitEZ8L4APDy96CKhU4FLPPse8BfOuf0Trt8MfBpGx7OBVjf5XN+zgIbgXvZnGF1Ka0qCM835nHNPAH8KXDP9+CKh0Ri4xC3n3BlGZ1qc6M8YXY1oH9DDL6eUnehfgCfM7FcZnRa4+xLbTaY8+BxjO0HfmMZ9RcJCsxGKiMQpDaGIiMQpFbiISJxSgYuIxCkVuIhInFKBi4jEKRW4iEicUoGLiMSp/w98RvflJf/z1QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sbn.kdeplot(mi.sim, shade=True)\n",
    "plt.vlines(mi.I, 0, 1, color='r')\n",
    "plt.vlines(mi.EI, 0,1)\n",
    "plt.xlabel(\"Moran's I\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.001"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mi.p_sim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "lag_price = lps.weights.lag_spatial(wq, gdf['EstablishmentS'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "price = gdf['EstablishmentS']\n",
    "b, a = np.polyfit(price, lag_price, 1)\n",
    "f, ax = plt.subplots(1, figsize=(9, 9))\n",
    "\n",
    "plt.plot(price, lag_price, '.', color='firebrick')\n",
    "\n",
    " # dashed vert at mean of the price\n",
    "plt.vlines(price.mean(), lag_price.min(), lag_price.max(), linestyle='--')\n",
    " # dashed horizontal at mean of lagged price\n",
    "plt.hlines(lag_price.mean(), price.min(), price.max(), linestyle='--')\n",
    "\n",
    "# red line of best fit using global I as slope\n",
    "plt.plot(price, a + b*price, 'r')\n",
    "plt.title('Moran Scatterplot')\n",
    "plt.ylabel('Spatial Lag of Establishment size')\n",
    "plt.xlabel('Establishment size')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Moran's I (Local)\n",
    "li = esda.moran.Moran_Local(y, wq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(li.p_sim < 0.05).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "sig = li.p_sim < 0.05\n",
    "hotspot = sig * li.q==1\n",
    "coldspot = sig * li.q==3\n",
    "doughnut = sig * li.q==2\n",
    "diamond = sig * li.q==4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "spots = ['n.sig.', 'hot spot']\n",
    "labels = [spots[i] for i in hotspot*1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = gdf\n",
    "from matplotlib import colors\n",
    "hmap = colors.ListedColormap(['red', 'lightgrey'])\n",
    "f, ax = plt.subplots(1, figsize=(9, 9))\n",
    "df.assign(cl=labels).plot(column='cl', categorical=True, \\\n",
    "        k=2, cmap=hmap, linewidth=0.1, ax=ax, \\\n",
    "        edgecolor='white', legend=True)\n",
    "ax.set_axis_off()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "spots = ['n.sig.', 'cold spot']\n",
    "labels = [spots[i] for i in coldspot*1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = df\n",
    "from matplotlib import colors\n",
    "hmap = colors.ListedColormap(['blue', 'lightgrey'])\n",
    "f, ax = plt.subplots(1, figsize=(9, 9))\n",
    "df.assign(cl=labels).plot(column='cl', categorical=True, \\\n",
    "        k=2, cmap=hmap, linewidth=0.1, ax=ax, \\\n",
    "        edgecolor='white', legend=True)\n",
    "ax.set_axis_off()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "spots = ['n.sig.', 'doughnut']\n",
    "labels = [spots[i] for i in doughnut*1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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IG1fKrgeNViYV1sMBgEm/NnwGvTGmfTXGjeP4FKX0OkIoVErtXOx45xxLkuR9Usp3lkqlz3V1df2Hjo6O8mrY5vF4PCvF3bt3nyqXyz8vpXybc47c73gIofse40FmPW+fmFYbcD8QQrQxpme1xtdaHzTGtEdR9BJCqKn3ylrbVS6XPz04OPjCrVu3fhI2YaWHx+NZ/9y9e/cHr1y58ucTExPfFkK8cyWcgZT1POe1nPX85mxoh4AxNmWtbWpLf7lYa7NxHD9KCBkJw/A0QqipXRWt9ZHp6ek/7uvr++/9/f1vBO8YeDyedUB/f/8P9PX1/WWhUPhbIcRbnXMr+r/JObee57yWs95CBtVsaIfAORes1bW01j1a6x5K6T1K6agQ4mQzf0hSytdJKV9XLpdfYIz9QXt7+//b1dXlyxU9Hs+aUSqVSKFQeH2SJO8QQvzMKv/v9IufBVhvSYWz3Llz541a6x3w8geIar+SJPmCMYa3ykaA2YS9c9ba0Fq71TnXhhAqU0rLQohd1e8vpXQAALgxpnOlPd9qKKV3KKVjQoiHnXNNvz+EkFHG2H/KZrP/cceOHadXyz6Px7O5SZ2AN8Rx/CPGmB/RWq9Jh1fG2LePHDny9FpcayOybh2CJiAXL16cNsa0TMugvb39spTSxXF8tN7rQRBcllIeoZTeZYxNxnF8DAAgLbG8JoR4ZAVjY/MghExQSq8hhDqcc0wI0dQfHULIMsb+hhDy17lc7pvbt29/HryWgcfjuQ9a5QRUwxj7zpEjR1611tfdKGxkh4ClDsGa7hAEQXAeY5xzznULITILHcs5v4IxLidJctw5Ny88wxgrEUImnHNFAEiklCdW00EIw/AlAOhOkmRJbZUZY9cxxt+qkkm+uToWejyejUaxWMxMTEy8RghxyjmXA4DIORcBQKbyHQAia+3hVjgB1TDGvnvkyJEfaKUN65mN7BCEqUOwZnkQlNIxY0x7vcm9EVEUvdhM7wIAgCAIBjHGI0mSnFy+lU1d5wbGeMIYs18p1bn4GS+DELKU0tOEkG8xxr7Z3t7+tx0dHeOrZavH41lfxHFMC4XC03EcP2WMebUx5snVKrFeaRhj/3TkyBEv1NaAjewQZC5cuDBtrV2TrFGM8STnfKBReKARCCEbBMGtZiSHK4RheM05VzLG7NVa348q12K2GcbYZYyxVkr1GmPCpY6BMS5TSr9LCPlWGIbPYYyHgyAoBkEwFYbhCGNMgA83eDwbmoGBgVfGcfwarfWrrLWv0lp3t9qm5UAIGcrlcu/Ys2fPd1pty3pkIzsEudQhaHgAxrjEGLuMEHLW2sBau2exCZZSWtBaz1s1h2F4IUmSY8sxlFJ60xizZznhgCiKLhtjjHMuZ63tMsbklmPDYmCMi4yxqxjjKEmS3uUkPYZheDpJklOVx4QQgxAqAkAxbUda7+ciQqiUfi+mjtfVtra2F7PZbGnFbtDj8SyJyclJPDk5+WNJkvwbKeXrWm3PSoEQisMw/N3Ozs7/q6ura6rV9qwnNrJDQPr7+/95aj8GAJxOYhhSCWSt9Y+Vy+XZrlgIIeCc9wPAsLWWG2MOYIxHCCFjANADAE4IsTOTybwUx/Fh51yWMXaTEDIZx/F99SOIouiFOI5fcT9jIIRizvmlJEmaCkEsF0JIkRByC2MsnHOdUsq9zTgzYRjeS5JkUeXEJm2IMcZXMMaXEEIXKaWXcrnclSiKXspmsxu6JNXjWc9MTk5mx8fH3yulfL9S6pFW27NaZLPZ/+3AgQNfaLUd64mN7BAsypUrV35HCPFzjV5HCEG9+w+C4IIxZjfGGCml8ithC0IoYYyNSSmXlNBXD875PYTQOADItJHRkRUwsSGEkJgQcidd7VuEEAaAwDmXBYC29DmNEJqUUh5YrUTPIAguWmu3I4QuYIxfpJS+kMvlnu/u7j4HPizh8dwXw8PDO6anp9+vlPpZrfXuVtuz2mCMJ9va2l63e/fuF1pty3phPQsTrQQLKgU2coacc1lKqUEIyZVyCJxzIQDMboEHQTCAMZ5IkuThpY4lhNgJALMr8TAM+wFgDACMUuqwMWZFbK5gjImMMb2LHYcQ2oExjlfy2tVYa7Na6y4AeDUAvFoIAaVSCcbHx/sRQi9gjF8MguCFtra273Z0dAytlh0ez4PEwMDA8VKp9HNSyv/ZWtvWanvWCudc5JxbtRytjciD7hAs6/6UUvsqP0dRdDaO4xXJ+pdS9mYymdNaa6aUehgAtnDOb1frA3DO+621gVJqW7PjJkmyGwB2ZzKZl1baGVgKzjkghNwwxiwr12IxEEJ1700ptRsAdgPAW5MkgWKxqIeGhi5gjF8ghLwYRdGLuVzuu/l8Xq6GXR7PRqS/v/+1cRx/MG0ctKoy6+sRzvmX9+zZ8z9abcd6wjsEiyCl7OacvwgATmvde79JfeVy+VQURWellAhmtt0hl8udU0op51xeCHGIEDLIOR8UQvQAAERRdN45h4wxe+rtWCCEXC6Xe0kIkUUI6aWURa40GONktcZGCJU553eFEAu2lLbWUmvtIwDwCAC8t1wuw+Tk5ATG+B8ppd/IZrN/tX379nOrZafHs14plUpkbGzsx9NEwR9stT2txDm3oI7MZuSBziHo6+v7Eynlu1dqPErpXUrpsFLq6P0oJCKEVBAEN4QQDbfhEUKOMXaLMVYol8vHnHMcIeQopbcxxmMIIWat3a21pkEQ9FdCD5TSUhRFV40xgda6Q0q5Y7l2LgfO+UUhxJLDIEshiqI+YwyVUh5czvkIIaCUniOEfINz/o2Ojo7/ns/nVy3U4fG0mkKhkJuYmPiXaaLggg71ZoFz/szhw4ff02o71hMPtEMwODh43Fq7paqEDsHL94wopW1jY2NfWep7QCkddM5ljDHLjrdhjCcZY0MLOQUAAGEYvtioqiCVRIY4juckKmKMp4IguJUkyUnO+RhC6I5zzhljdq92/XAURX1xHC+ab7AShGF40VobSimb1nioByFkjBDyd4yxb+Ryub/u7u6+tlI2ejytZHh4eNf09PS/UUr9a631ilQAPSi0tbW9Y+/evV9ptR3riQfaIViMYrG49+bNm7eWcy7GOKaUXieE2OXmGCCEZBiGlxudn8lkzsRx/HC9+F4QBBeNMbsaOSWc8+eFEI/XPh+G4T0AGLLWdtzvRFoLY2zIWuuMMT0rOe5ihGF4wRgTKaXu+35SJcYLGOPTGOMzQRCcbm9v/6e2tjavxujZMAwMDJwslUoflFL+i82UKNgshJB77e3tT+/cufN6q21ZT2xqh2BiYuJIf3//pfsdJ5vNXi6VSssu/Yui6KVanQPO+VkhRF1HIYqiMwAASqn9Wuu6f+xhGL6QJMmCuge5XO6MUkoDADbG7Lqf3YMwDM9JKXdaa7uWO8b9EkXRea11rjopdCXAGEtCyKXUSTjNOT/T1tb2vba2tsmVvI7Hc5+g/v7+15XL5Q8opd7Wylyi9U4+n3//vn37fq/Vdqw3NrVDMDY2dnxgYGBFksuCILjonGtTSi1LZ4BSepNSGidJ8jDGeJoQIpRSW6uPQQjpMAzPx3F8Kn2sGGN9GGMrhDhavZPAOX9BCLGgQ1B7TFq+OAIzugLUWqucc8w516WU2tNoHISQDoLgzlLkmVeTMAzPaa3bVrORCsZYEEIuYIxPE0LORFH03Uwm82x7e3tj6UyPBwBKpVJ2enr6kSRJDmutDznnDllrDyCECgihGxjjm5TSW0EQ3Glra7uVzWaHAKCuGFepVOJTU1OPJ0nyqFLqXVLKf7bGt7MhYYx9r7Oz823btm0baLUt64lN7RAMDw8/Ojw8/OJKjZdKFO90zgXLHSMIgj7OeXF6enrOZI4xngyCYDBJkro7EYSQKUrpdYRQW5IkB5t0CJ4TQnxfg3spaq1nKyqCIBjHGN92zlGt9b7a8kbG2CjG+JbWeq8xZl3onEdRdFZr3a6UWqte67cxxn9PKf1WPp//xtatW/125CZmcnJyR7FYPCGlfMgYc9haW5n8DzbbNwQhBISQIkJoFABGEEJjADCKEJLW2qPW2pOtLDXeyBBCBimlXwuC4K/27dv3X1ptz3pgUzsEQ0NDrxwZGfneSo5JKb3tnLvvngNpR8IpIcQxQsggQogopZpKCqKU3gvDcFAptUMI0bDKYBGHYDwVAZpDGIZnhRDHgyC4BgBTzrltSqldzjkMMBOD55xfcs6VnXNdy60EWEmiKLpijEmUUsdWs710NQghyxh7gRDy9Uwm88yOHTvOr8V1PWsOGhsbO1oqlXqVUoestb3OuYecc4e11nuW0xPEs7bk8/n37Nu375lW27Ee2NQOwfj4eGStPVT9XO0fcKFQ+I9SynnJeQvRzOq8WQghY5zz/jiOH1nKPxfG2B2t9R5K6V2EUAIAFmrkfYMgKAohspXHCCEEAARmKjDicrl8vHbcMAzPJUlyourxpDGGEkL6AaBorY2stbu01u1hGPanoknrAsbYCCHkjlJqnzFmy1pdFyEkOOd/mMvl/n1PT0/Ldw2EEEGxWOyN43gPxrjc1dX1nTAMVavt2mgMDw93T01N/W6SJO9stS2e5UEIGejs7PxnPT09V1tty3pgUyeddHV1xQBwdqFjxsfHl1yfjjGmCCG3EqsDY8yWcrm8hVJ6gxBihBCHGh0bBMEgQuiec45Ya/cSQhLnXIQQKmOMp2tljRFCzymlGiVDNvUHYq3lSqmwdpy0mmE8l8uNSynbtdY7rLXLDqWsBEqpbqVUN0LIhGF4zlpLlVJHV9spds7xJEneL6V8T6lU+mJbW9vnuru7B1f1olUMDw/vLxaLP6G1foNzbp+1dqcxZlaUZWpqahxj/E8Y4+9yzr/X3t7+bZ8wuTD9/f2vLhaLX9Jar2ofEc/qwjn/994ZeJlN7RA0yZJniziOHyGElAghEyvRzAgAQGt9QGtd2bKft/WdzWavlsvlg8652pK/EAC6AAAopXcIIa5J/YSmkuMaOT1p18OdjLFEax0CgA3D8C4AjFlrNWNsi5Ryu1KqqVjqSuKcI5VdDs75IADc01ofMMbMa3u9klhr83Ecf1RK+d7p6enf7ujo+M2urq6J1bhWoVBoL5VK7y6Xyz+itX69tbZhw6k0NPRGAHhjkiQwPT0dE0JeIIR8lzH2vXw+/w9dXV39q2HnBgTdvHnzI+Vy+VettV7pboPDOfehvCo2dcigGfr6+v5OSvma5Zy7Wqp9mUzmdLlcPlV1natKqS3W2kUntEo5YhAElxBCQfr5z/slYIzFUspM2hFyVsyJMVaO4/hY1XFKa80a/R4FQRBLKeeoOqZVADsxxsMIoRgADEKIOufyANAlpexc699LhJDmnF9wzlnnXGSt7bDWbrXWrlrOAaX0Duf8811dXb/b3t5+35LPpVKJFAqF/ymO43dqrd8KM63AlTGm6b4Y9UAIGUrp+SAInjlw4MD/DZu0s+TIyMjWycnJ30mS5CdabYtnZYii6LcfeuihD7XajvWC3yFYnGU3xKGUgrVWa63pSk5w5XL5kTAMX1RKHWKM3RJCHEq7KS5K1bZ9IoQ4usChl5RS8xICKaW1HrVb6r0ZY9rTVWldzQJCSIwxHkUITSKEBCGEpFnZPUKIVelO5pyjSZLM6f1OCLFhGA4650YBwDXShVguWus9WuvfEEL83PDw8F8xxv4xn89/e8uWLQuKZSVJkpmcnDwex/EBY8xBa+0B59wBa+2R2lJLzvk1a23H/VS+OOeIUuoRpdQjN27caD9w4MD/AZvMKejv73+qWCz+vtZ6ob8ZzwYjSZIP9PX17QrD8O/27t37hVbb02q8Q7A4YrknlkqlhxFClZr1foTQBEKIAkB3uqW+XFCSJI8RQkZgZkJuettdKXU8m82eVWrRHLKmQgYIoRWvu0/bLe8BgD1RFM3ZDaGUjmOMhyilZWstds61WWt7lFLZBYZcrh04VV3sCcNwxcpTa9FaH9JaHxJC/C+lUsmOjo6exRg/yxh7FiFU0lrvt9YedM4ddM7tt9buM8Y0DAFUI4R4iHN+WSm13Vp7385UqVT6+I0bN9CBAwc+AZvAKUjzL34qSZKPW2tX/HfM01qcc1RK+XaM8XSrbVkP+DYokwsAACAASURBVJDBIgwNDR11znWkZXWVXgizW+iEkP2jo6N/uNRxCSFTqZMQp04CQgiFy+kDwDm/4ZyTUspFE5yCIBiklCbpVrirChlUJnYHM6GBaaVUG8zcM6TfEWNssqKOmOYPOMaYUkpRAABK6XSqX4DS+0TWWoCZ7WsEAAhjPBtGSJMva38JHQC4KIomi8XiiXp5CmEYXkmS5HA6BhBChjDGIwihBCHEnHOdSqmd1toVcXqbUX5cz3DOrxhjOlaql0U2m/21AwcOfBweQKdgbGysZ2pq6u1KqbcppV67GVsDbzYQQioMw89zzv909+7d/9Rqe1qF3yFYhO3bty8obTw2Nja2nHGNMW3GmNlYfBRFfVrrZZXCVRQCwzC8qLXOLqTQJ6XswRifTpLkVKNjAACccxeklPvrvFQWQtSOPyqE2Jqed7H6PEpprLWek0NAKb2zWKOVKIrOF4vFY42SFp1z01U/g9Z6OwBsJ4RMMcauM8amjTE61XAYQwgphFAIAFuFED3LcIQ39MQnhDiMEHJRFPVZa+ViLaQXo1QqfSzdKfgF2ODvDQBAoVDonJyc/FEp5dvSJEyfMLiJcM6xOI5/Id0F8g6Bp7UkSXKYMdYXBME9xlhea02ttTRVKGOEEBHH8SOLjPEwQsiGYXjaWhtJKevuNiRJciqTyZwxxhxKdzsQAGCMcQwAVkqZg8b/5OtN0NXb181MDgsewzm/kiRJ7yIiQnPsYIwNI4QGlVJHkyR5lDEmtNYcAHanX7OkFSCDadhBOueysEh+wpITJdYhzjlU2YGKouialDJ/PwmHpVLpo6lT8DHYgE5BsVjMjI2NvVVK+aNa6x82xrS32iZPa7HWPl0qlUg2m60rFf2g4x2C+6TZWO5iOOeQlPIIxthaa8cYYwPW2oyUcnYlFwTBZQAIpZQNm/c453Bl9Z+W1N01xuyoXZHHcXycc35VCFEdZggAAAghxSAIBELoudrxKaVZY8w9AHAIIQiCQJRKpYXUCOdNFISQTLpyH0mS5GTNa4OEEMo5n9UYr4QmqsdDCGHG2GQURXeNMXlrLU/7OSyaPGeMyRpjHjLGiOrPj1JawBgPE0IkY4wKIQJr7Q6l1AO3Wozj+KEwDM/cbwVCqVT6tzdu3ICN4hTEcUxHR0ffnCTJW7XWP3K/9+95cEAISc75721WZwDAOwQrwYom1VlrsbW221rbWRv/llIeSSfMQjM180KIHgDoAZhZEVprp5VSvdbajHOOSCl7OOe3hBBzHAxjTE4pZYQQr6wz7JUa5+JuzeuLxlsr4QXO+ZxzMcYlQogol8sLNkkKw7CPUmqFENHU1NRs2CUIgpvGmLDZ9suEkOvGmNmyUK11JwB0AgAYY/qklA8hhIBSOgAAIef8Oeccd851aa13VOSaNxqpvPSF2qqK5ZI6BfjAgQP/FtahUzA5OYmLxeIPlcvlHzXG/KjWet2oZ3rWBxjjiVwu96/27t37lVbb0kq8Q3CfEEIUpXQcZt5L5pwjaYdABDAT314OjZLhjDE9lNIrQRCMSSkbqhbWEsfxQ6m9pTAMTydJcspa266UIpzzyzU7BQD1QwP1nq91iIIoispxHDezqp4zFqX0lpTyWL0DGWOT6QTenSRJbxAEZ2p3A6SU+wkhE4yxPgBYtAVyKulcFyllbxRFZ+M4Pqm13qG1ntMTgjE2Rgi5pbXeorVe0XbLqw3n/NxKOQMVSqXSR9Lwwc/D+nAK0L17954sl8tvN8a8TSnV9N+KZ/NAKR3EGD+byWR+fffu3f/Qantaja8yWFkwzDgGVCkVSCkz1lo+MjLyiXK5/P6VughCCPL5/KU4jg8AgFRKLbnbWRRFp6vbKIdheF5KedQYE+ZyuXPGGOqca6teBae9DhxjbFApVVmFOwBwlFKptQ4g/Z2aORQQY8wqpQjAzNY/IcSl5YIAadiBEDJljMmlcXrHGCsXi8VDqZ2XjDEylRiedQCCIDgjpaw7qYVheMkYs49Set0Yg5xzWYSQZIyVtdYWZlrJ4jAMiVKKGGOUc67DGLOzOoQQhuHzSZIs2MeCc/68EGJJvS5azUr22qiFMXYRIXQbIXQbY3ybEHKHMXY7l8vdaG9vvwWr6CwUCoVtU1NTr1FKPWmM+SGl1Io6PZ6NDyGkEATBb1NKz0RRdCObzb60mUMEtfgdgpXFwoyQkWSMlRljEwAAQ0NDxZW8iHMOlFJca80QQiXG2IBSqmFXw3rEcXwql8u9WCqVHkkzbB/FGE9zzi9Za1kcxw0FWBBCcZIkc7blG3VHdM6VqhsoZTKZC0mSzNkFYIzFVQ4GIITuhGH4vNZ61wJ2NEw4dM4VKaWX4zh+tOb5u0qpXVWPz0gpT3DOhxFCQ0EQXGSMITsDxRjnUqXGRpcCWB+r4TmEYTgIAMPOOQMvN7VClFKntUaMMSylVKtRTqeUehgA5qlzTkxMwMDAwHjqLNypchhuc877c7nclXw+P1yxFWY+X5x+J1JKorUOnHPMWsvS39GtQoh9SqnvN8Y8aYx5Rav7ZXjWN8aYTufcQcbYf922bdsLrbZnveEdgjVgNXZh0t0BcM51BUFwpuIQMMamUv2AhiCEXBiGLxWLxcc4532p7PEWa21eCPFoLpc7vdD51tplt3ZWSm2tfa52YnLO2SZW3Q3j9865HinlnF0TQkhsjKl1WDAAgBBiGwBsAwCI47m9rBhjCmM8CgBTCKGkapJFaX6BQwg9XwkRpbsosz0eMMZgjKHGmP3LbYmdxvzvWGvzUsq66o5BEAwTQvqVUj2p6FW9PIqzQoiTQgiIouhckiTH1ioPIi0NrahTznHUisUijI+PO0LIFLzsBOA0/FZ5PPt35Hc1PfdDkiT/Qgjx49baH9y9e/c/ttqe9YR3CNaGVdPDB5iZdNra2s5IKQOlVHsYhqNJkhwEACCElBljV51zKk2Ia7PWbonj+DEAACFEL2NslFJ6RSn1UBiGZ+I4PkApnYA0HAAALgxDWyqVup1zQCm1AFCCKtEiQohNyxZR1XmIMaaEeFnsMQgChTGeqrafUkoQQom1lqcTaTOJmnXfU875IEKISynnlJAxxq7UiZsv2o1SKcUAYEf6Ve96zy3kvHDOx6SUW9JJ/TrGeBJjTK21xlrrnHMZa20bALRbazPWWsAYa0rpLYTQBABExpi9WuuM1rorjXkOIYQqUpPYOZdRSh1BCKGFhIcwxrOTfxzHJzjntwCgJISom7exljjnkNbal/151gTnXFAulz9XKpWe9iGDl/EOwdqwqg6BEKK3XC6HhJBhSulYkiQPM8buAQAYY7YulkCmlNpKKSW5XO50uVx+qF43xGKxCIQQPTOkgeowAABAEAQ36iUEIoTmLOeEELSidFjBGOO01mEq82wIIYsuARsJFiGE7iGEMgBQOzHW02pei9WxBZgpBxVCHAyC4Fwcx7OlpGEYvlhRmEzLOGNjDJZSPlQ9CKXUAQBorWcrRypEUTSMMX5BKdVwYo+i6KUkSeaEXyrVJVEU9SmlslrrFenM6fFsBKSUT46Ojr4rm83+p1bbsl7wDsEagDFmTcSil03a+AeMMdswxhYAIAgCCQCjWut76WEIZlbyVGttGGNGStkJAAohpDHGZHp6+jGM8VQURS/Vxt/T8SkAUMbYvBtBCDVqAmUZY7ecc+1a646Feh8458AYQ4wxsx47xjhmjF1CCFUSFhNjzG6EUN3JHGPMpJTztCHqCQs1ciqWQio7vdDrtauPWjtmbXDOQW1nyMUghAwDwECj5EfG2BhCaLje51khjuNehJCglF7TWj/U6DiP50HD9zCYi3cI1oC9e/f+70qpX7LWhtZaboyZEzM3xhwfHBz8zytxLa11J2PsljEGE0IwAIBS6mClsU0URZeklEcRQtcwxgWt9S5jzI4wDM8TQoaNMdviOH6Uc35WSnnIOTdvghJCbA2C4Gp12WM9ZwdjPEYIKVVWopzz4XS7e8hau6uyvc0Yu6m1npVSJoSg1BG4SCkNSqXSY7Vj53K5EYTQC865vHMuwBgDIaSktd6GENKMsRsAs70SrLW2MwiC6xVVxtQeizG+WbmFitkIIVwdu67scjDGhFKq0kjKpSGYxcIbta/P+ZtbglNS15tkjPXHcdywYoAxNiylzFBKbwFApfpja20zKOccJ4RMaq2bNMfj2fhIKZ8GgL9otR3rBe8QrAGMsYQx1rDmfWBgYMVip845DgDcGEMqzXgIIRNhGJ5PkuS4UupA2rnPaq23MMZGjDE7kiQ5zhi7UlFuE0KcpJRec8511ibjOecoACQIIccYuwoA5SAIGGPsrNa6kjBArLW70vg5OOfmJO8hhGwmk3khSZLD1trZ7XxK6WQqLRxTSlm5XG4kvxxqrY9XHkdRdEsp1aOUqpt014ArSqn9zR6MEOpLkmTO8UEQ3FnktDkOQSqTPGfYZq9fDyHEI5TSe5TS0XqhIa01rdVJ4JxfYYwxQkgxSZLZ8EVteMfjedARQry/UCj8emdn53CrbVkPeIdgfbCisWytdU8YhleVUt0AAMaYDmttexiGZ4QQjzDGwjiOH+acPwepMh8AAEJI14zzECFkMJPJvKi1zlRi3ZTS22EYIozxWKXjoLV2oFa8BwAgCILrWut50sbOOWytxYQQwRjDAPA8ACBr7f7p6elTQRBcLZfLdTsd1sNaO6KUWqpAUNOTcRAE1+I4PlznpQX/htKqhOrHc5w/Y0wPxrhgrV1QeZJzfpMQIhewuW4LbITQvC1RIcRhSuk9mNFjqBznVqI9ssezkbDWtk9NTf14Z2fn77TalvWAdwjWB/cdy66Qy+XOCyF64zieo8zmnENCiJOMsUvOuUrpH0qS5CFKaX8q5zovD8AY06O1npZSHo6i6KxzjiVJcnRqak6hQGUreh4IoXEAmOcQZDKZ00KIXmNMpl4porW2OwzD/jiO9zS41TkXNMYcShMbF5Q9rrGtaUcMITRVe02AmQoJa22lfr7eeXMeW2vzGGNIW0KD1npbEATX0nyOhgghDqZtpZdEvc8ll8v1F4vF3dUS1ISQyQehgZPHs1SKxeJvXr9+/cDBgwc/2mpbWo13CNYBTcShm0ZrbdJSuXrXQQihtnGWH0xw9jZFCCGSeYEgZB2zwyG1liP0HEoD75XzKKWBlBLiOD5Zb9zK8PWeNMZ0EEKmjTFzdAGcc5JSeqkqQbG6gREAAMIY0/b29mRycrLeyrz2vjtyudwNjPE5AACMMbLWzkvgq9pxQJRSAwAXU3swenn2JKk9Nu0GiTHGdStFtNaB1rphgxxCyO1aGxa7l0ZDLfO8eZ+L1nqqznMd+Xz+HkKor1GXTI/nQSTt6/JBAPg4VO2abUa8Q7AOwBg7Sumcuv6ql5FzDqpq9CHtiIgBZmLxhJAJrXUXY+wWQqg2Rj0HIcTOAnZQ0rBzZq6YWSTnCR7Pl6bCen3gnXNnm7iNuk5NKiFcriocmDnYWtRIPhdjHAdBcCFJkoPGmFwYhnecc5NCiOp497zzpJT5SqIj5/yCEOL4vIOqcM5dl1LW7dTIGBtTSm2psqmRWNOCq2o039A6Ty3sJGCMK3kbS6bBor9u5qCUMlmoCsTjeVAxxmTu3r37ll27dv05rEP10bXCOwTrgJ6enn/s6emp3g5GUOMcXL9+/b9aa3c653ZqrbeGYXjPOVc2xuzSWncFQTCtlNpBCLm80LUymcy5vba0tcyCl8okBOWgfVrqfbtA3LbW1i1NY4xpzvlFKeVuACDOOZzuNlR6D4xJKbdwzqtl/hxjbEAIsQNjHKSvuVRJMYcQaphIyRi7WF1GlyTJHgDYwzmfgFRPgFKqCCGDkIogOecgCAIi5UzUA2NMEUJ2ESW+hn/4tbkLlFJkrR2pPY5S6uo9X/V6iVJa77zK9as/64YwxsbTEsZ6NmPO+ZQQYl5IgVJKrbU3oEoOmFKKGWOVZMjKZpDTWh8khNR2r/R4Hnicc1AoFL5SKpW+HYbhb7W3t/9pe3v7pnOOvUOwPqko/c1irXWVqgEAAGPMnBauVVK9C/4SW2udSZIejkXnYMCmhHHd2zm+FWjDHecvWGu3V+v9Y4yTJEnqihXNGuscUkrNK090zmml1JwdB2PMlNY6xznPc85vVnogIIQIADDG2FS5XK7tvAgAAEKI2aQ359yolLJWnvdK5Yc4jns55/0AUBBCLBTqaAqtNdRTAUQIlRdSB1RKjS70OgBAFEWTC72OEAIp5fZK3kE9GGPFesJChJBBpdScvAqM8aRSak5uRqpD0C+lPF51XIFSeidVRUTW2r3GmE5r7Wz4IgiCq0qpfavRF8HjWWuklK+WUr46SZLnpqenP7R79+5nGxyK+vv73yilPJnJZP6up6fnOXgAdha8Q7BBsAvNBjWH1nuy0tGwIlBTijouCOkeAwDoUsXpRMyUnzHGhtLmOIMAM+2dS6XSK9MxHGNMpKvn2ZVtunqeXY1XmTp/bzwV6hFCVCbzUSHE/srrzrmSMWbBsEdKvW3vOWqEQojdALCbc37DWquUUrWx8ZVYASy2ul80cdEYYznnIwihAeecds51pZPsbAvtdFJuOOkaYxp97vV6GtTLR7BVcsiQlpO213aUTNUUywihEkLIJElyqLpzpsfzICCl/D6l1DeSJPkipfR7lNIhSmkshNitlHqFMeYtSqkTAADlchniOP6lAwcO/Gqr7b5fvEOwcWjK+6yXKR6G4Tml1LZqtbotILfkiHtpFIe7IkzCYqoegDEeTJLkFKTyuISQcYSQcc4RzvnF6k6FlVg4xthZazHGWAGAq0gPE0IwIaSiS+DS4+dM5BjjOTsd0Pzv5LwJsFEdvRDiAOe8niLZQu9pQ0XBGhab8JupZHBCiG5I5ZY5589TSrsIITedc9YYs5cQMm6MOVTv5DAMR5IkmberQiktJ0kyb9cAYzyvmsQ5FymlDoVheBoArBDiEefcPMchVVPMAMDszk/zvqrHs3FwzmWSJPlIM8cqpeaJp21EvEOwcWjqv251xhpCCKIouhzH8dF5SWlGT7kkeXQrKusY42wURS9aa3dLKedsrxtjuqIoOhvH8cla1cKK75G2FIU6CozSGDNHRjgIgjkOgbV2Tv08QmjZW88LaRYsY0u72VlusR2CRfMDap04hJBTSrUppWZX50EQlMIwfCFt/btTa72leogmbQUAAGNMXSfFOUdTZ3BJ1IavPJ7NhlLqzXfu3Hl6z54932q1LfeDdwg2Dk2VwxBCgiiKBpxzsVJqW3U8Pt2WvgMApPKP3zlHjTE0juPHoii64pybF++WUm5LJYgPBEEwobWeI2BDCJk0xsyrTkAzQHqdynMOY2wJIUMAoAghKAiC6VTAxxFCVBAEN2CBCdlam6+nIVC95V2Lc47U9pNYYi+Dusc2Mcai16itOqg3Zrlc3gMAs3H/9LPs11pvEULsDYJgqrZpVCNtCKXUUc75VSFE3R2HpZC+p/N6RywGpbSAEEq01j0r0VPC42klaffE3xwbG3vtli1bFswJWs94h2Dj0NQ/zVKpdBwhBIQQnXaxi51zeWvtjupt6WoYYyKbzd5upF9gjNkehuGLxpjHKKVCaz1nslFK9VSaKiGEXBiG14UQ+40xhyil1dvTjhDitNZOKbUjPXfOtZRSwBi7oZRasMlOEAQjlNICzIQgdFp+6RhjN9PSOQczToVLr4sxxjZ1PGx635oQUl1S6RBCJJ2gHKV0KM2LQBhjyjm/AjMhAJomQVKMsQaAOVUEnHMthKDpe9dGKR2q2JF+AULIBkFQkFJ2aK331tzeop915bNECOkgCM4ihNoAIJ/egwMASwgxte8vwIxzpJTqZIwNK6Uaaig0Q9oOe5JSekVKeWwx5wBjPMk5v13RtOCc37HWJkqpRbUmPJ71jFLqsdHR0b+P4/h9u3fv/qdW27McvEPwAEIImWKMXRdC7G1G218pxScmJg5zzi/ncrmLQoiDGOMyAEwCQBkhJAkhwDl/zjlHrLXba4aYUzqnlLKNnAuMsTbGLCays+iEqGeYo+6XZtTvr3c8IcRaa3HNDsGV2okI40pepG1KxZBSWqhjR3ExVcG0JNJprespMTYdAnDOUSHEyVwudz5JkurPgTjnUD1RKAAAa+0WjPFNhFDZOTdvd2cpCCF2AcCuVBr74UbhGYzxOKV0ulrgSgixJwzDF+s5Lh7PRkMpdWp6evovh4aGHtu+fXt/q+1ZKt4h2GBQSkdhZlVsMcZFmPkMLUJoGiEUOue2CiG6tdaPhmF4ZinNfpxzxBgDSikOAByq+hwEQXBGSvlYFEWXmrDRaK2lMSaodxmA2YkXAOompS23t8NC59ULMcybeK21kM1m7xhjCs45k6oUOgDYXi9BD2bepyXDOT+dJEmjRKR5dmGMFcZ4NO03UXnDEABg5xwWQmRrs/2ttTQMw6vGmLrX0VrvD4LgvJTyGKyAfHaSJI8wxqYJIVecc2WYec+Rc44jhBJr7Q4p5Zx+E1EUXamuMmGM3UYIFQGgIpKVN8ZkvaqyZ6NgjNkax/HrAOCPWm3LUvEOwQaBEIIrHQtrXwvD8PlqIZ8Kzrm6inSc85sIoUkAwEqpfRWNAWNMt7V2Kk1EnJO1XgkJJEnSSwgZMcY0rK3XWiPnHGGM9SOEJmBmsuHW2gxjjGqthXOOVW1tz4YbgiAY1lp3U0orIkeu6vvsrJBu2dey2Kq8kXLfHFKHal6jJErpOMa4H2NsnHNdUso9ACCgKuO+xua6EEJKSql9QRD0YYy1cy6Al3dZEEKom3N+MwiCRCkVAABXSm3TWu/gnJ+tp6sQBMH1OI5PRVF0Jo7j6lLBBZMjnXOZZt+XZlBK5ZVSxxY7DiGkOeeX4jieVZ+Mouil6kqYCpzzWGttmyxH9XhaCkLIMMautdqO5eAdgg0Cxvh2qVR6R73XrLV1RYOstVsAABhjMpPJ3JyamjronKMIoUJldYoxngjD8EySJI9gjJExZpcxpq/m2kklpu+cw4yxOws5BOm1ibV2NwDMyUA3xlyqqTyYM7FjjJWUcp7IUS3pqr2WeeV0FRpMektaFWutuwBgdseFEJJQSscopTestcQ5t7WeOFAt2Wz2upQSkiSpK5gUBMEgQmioWCyerFVatNbuwBhPW2vnhQEAAOI4fiSTyZzVWueVUrW5CfNIQweqwW7OqsE5v1DdrhljHNeTkSaElJ1z9xqVXHo8640oin5j165d/9BqO5bDirbd9awelSS8WsIwPCulrJuQpbXem81m7xFCJgBmMtMzmcwZIcQpSuktAABrbYcQ4nAQBOcxxoPOOUMISarHYYz1VcehhRCPhWF4jhAiMcam6stijB0hhGKMIW1tPE4IURhjl4YJOMYYGn1Za7dns9nz6diAEKr7lUoTz7lf55yApdHIIWjKUTDGhEmSHEqS5BVSylNKqV2U0tJCORKMseFisbi7kTMQRdFLSqmtQohT9WSXlVJbgyC4WSfcMfu4XC6fJIRMMsYmAYCGYXiWcz5Z0YeouYc2hNAE5/yl2vbXqwnGGDjnLxJCBtLEyMv11DCDILhS6U/h8WwE6rUc3yggH5vbGPT19T0jpXxX9XPpxD1ljGmYKR6G4b0kSWbb3CKERBiGV5xzQZIkc5T7KKXXMMaRlHJnzRhnqldzFTDGoDLt524pfAIAoIehm7ny+P7qUkNr7Wy+QOV5zvn56sZDnPOJalniIAimrbWCUnpPa71Ha90OqbBRJeufUnpRa32EUprAzM6ATlUQTRoqMYyxcrrb4FK1P3DO2aoqBF6vXTKlNNZaL7pL0Yh65zPGyhjjAUrppNYapWJPlZeRc4465zoBwNRKDdcjk8mcKZfL1ToF16SUcyozoig6myTJ0UqSX1p9kgBADAAxxriIMS4657rTMk6XJMmaagoQQqYxxrK6kRRjbABjPGCt7VFK7VzofI9nHeKiKPrtbDb76z09PbXdTtc1PmSwQQiCIMYYn4WqlSAhBBljNKX0jnOuw1q7NZ08q5nj8TnneBzHJxhj/VEU3U6SZE9VF0VT6wyk59TNQrfWQiBKex1qk8a5YFDafYcRKhpjcrXHVSOl3F7d/VApFVU/ttaOCyH2CSG2IoR0FEUX4zg+Xu28EkKcMQan+geNsuRv1JvwKzDGVvyPFWOsgiCYoJRed84J51zAOWfFYvGIc+4hIRbexKCUXm/mOnXaSc/z7OM4Psk571NKbbfWtjvnQGsdAkAIVQmjFcIwXPOsaGNMvvqzrzRwatQJ0+PZAKA4jj8khPjp6enp/w8hdJsx9g/79u37b602bDG8Q7BBUErxRk16crncJGNsvFAotKeCNbe11j1a611a6wznfFII0V4z3m6lFGQymWvOufY4jrcqpQ6l3QhrBW4aJnMZJdvCCA2UtNuhHUJA2U0w5kSj4ytD1jyeIxqEEJqdNdOyuuN1cgAWK12EVCOgIYt0QlwShJAxxtgdxlg4PT19FABmQzxSSuCc3zLGOK31fgAAzvlVYwytPF4qCKFxAKh2duqGOYQQvWlyZ8kYs+5X2845gjFeaujH41l3WGvbhRA/BQAgpZwYGBh45Y4dO6622q6F8A7BBqHeCrBCsVhsD8OwDPCyYA0AQBiG15xzBUop1Vofrs3SxhhPKaW2KqXa02tgQsh1pdScTG/n3ARUTXD1rKv8lJBAMkgaH1r/3mons0XjWIwxhRB6ASEUKaUO1kokAwBUrzzrQQjRADCAMR5LnRDknOOEkDwAbNFaL5rVjhAynPMzaeOkoFgs1o13CyH2BUEwW7KJMdZCiENRFL1kjKEAEDjnuHNu0Wtyzu+kCY6zYIwbvulKqd2EkAnG2FWl1ELx+HURP0wVMa/63AHPg4K1tqNYLP4SAPx0q21ZCO8QbBwWWzXNWyEmSfIQAIAQAjDGMgiCixhjJoQ4RCkdYoz1a61DxlgZrXhrVQAACyJJREFUIRQDgKnWB5gdGKHyQhfGgIqVuaSMgq7KVgTn/BZCqAAA1jlXUdBDGGMCAOWKKE+tr1PH+bG1q3mlVCSlPAEwk6EeBMG5tGlPTyVnwlq74OSKECporR+DBs5O2hBpFCFUcs4lMFPzzwEgSvUJxgFgS5IkjyGEhNa6c17PiLn31RFF0SVrrav0Iqgus0uTKBdU6Imi6KUkSU7WNh6SUh4MguCSlPJovfOMMR3W2ijVHZhXuloxYaFrrxWMsXvVLbg9ngcBrfWPTExM5Ds6OtZt0qF3CDYIjDENAOehql4dUuEXAECEEEMpLcFMHTuueh1XpHittW3OOdzR0XGxUCg8rJSqVRwEALhc+8RiiacMXEUzAMaU3ddFSCWPYCxJkrqx4CiKTlNKBczICrcZY9oq1wmCgMJM4psDAEMpNcYYkjoKlZbAsw6QtXbWOUjfqztBEBQAgKbdFrtSRb5iOuHa9P1YsOGRECIPM3LAjdgPMNMRkhAy1qjao4JSqkcpVa8dceU+gFLaUDcg1YeYV6cPMKdb4QtKqX2U0ukqZwxBqkeAMV5286i1AmN8zzm37sMbHs9SMMZ0FgqFX0yS5M/y+fxz2Wy2qf40a4l3CDYIWmu8wMoOEEKDWuuGk001ExMTPZzzG0KIeQl31tqubDZ7q1QqzQrzOOfC2uOqYciaygaFdgjRKLpMlXIL2SOlPOmcs9baeb+DtU13tNaiNiTAOR+BOnDObwNAoVwuH69erQdBMCilnNcieCVgjA0nSVJ3Zb4MGjoExpjFciJokiSvQAgJQsitcrm81MS89dJkyP9f8jyQlEqlXyiVSr8wMTExQgj5m2w2+4s7d+680Wq7KqyLLUJPU6zYP2vnHHLO6YpCYDVa626E0EQmkzlTdXxt5cIcmJs7TGwAhBDft1hb3IW215cKIWSCc/6SlHJPWsM/Z+xUJGm1WElP3wHMCPJUOz2c8+FmFAABZipJyuXyK6IoOr2Cdq0ZvhTa86Cjte4WQvz/7d3PbyRHFQfwV1VdXTU9OI53Y4fd/FhLUSRYxAYth1wDZ/4CDtw4kAsSx0gcOXEJF4QQipA4BYkLQkLiHyASymKvlIDwWmFxkskCEy87M+6u6vrBwdV277hnPA7rXU/4fqTWTM/0zJTblvtNd733vj0ajd4aDAanphk/LojEl8cj/fZmrX2Zc+5XVlb+SkTKGBMpNTIqy/J57/1Gv99/11r7fAjhmXnlbXMKuj3pf8LzlXnn2YkOJzBqrXerqtrknJt0+aBzcmGWZWEymczsGaC1vm2tfdEY03k6PTm3b79VVX1Fa/1+Xdfrp1Vw7FIUxba1dpUxVnHOPWOscM69aIwpsiwbcs4HzrkrZ20TXJblK1rr7abV9WkuyoGYMVZkWfYxET11WpMogGVW1/Vr+/v7fzLGvL65ufnrJz0eBARLQkpZMca2qGMmeIwxSikFEX3cKgoU6LDjXRFCWO+YlT7WWn8wGo06UxmJiJxzGed8WNf1s0qpfWPMidx1IqKc/BrR8aXp+55fW+W8XuAa/WbqqnhgjOmaz0BERN77rhn0Mc/zPSIqu4omdTjXs2FVVV0XQjzQWm8ZY66n/gQLCSH4WemHafLh5a7nFhzXK7MKS3W4EJcMjDFHBbOklGPO+T8ZYw+qqpoX8AEspRDC5clk8tbu7u7NlZWVn2xsbAye1FhQqXBJ7Ozs/NwY891Zz2utP6mqauY1eynlhHN+jzE2llJSXdfPVVV14kAjhBhJKXdDCCrNWGe9Xm+LiFbKsnzp5Dsf+qi3vjdx4aiV73U23nXWzNxeCOFjjGyRNsNdlf/6/f5Wmiew0CQ5IYSNMeYdnRUfOSnlPhHdq+t6oXkFjLEqy7KPmn4R56HX622n7ISZ+1sp9YkxZqF5KE+ClPJuXdcnmk4BfF4IIe4VRfHDa9eu/YKeQBowzhAsj7kTyhhjQyKa+c+8rut+nuc1ET09Go2Omt4URbFd1/UVxtgkhLASQlib/iZWluXXtNa3T7zpQ6IlIpKc7Wec7UehPpQxBDoMOjljTNJxdkTM87y01gohBKPDkxws/RyMpjocCiGassdH24UQQpZlgzRXQNDh37IkIhlC6E0f+FN3xaYXQmSM1al2f5N10Iyv/dlHY6DjVsNZjFF3BRbN+3vv15RS+9779kaMWgF4OxBP77ehtd5q7/vWeNtj9enWMcZcjLF5Lqb9wNOtSKmJnDEmnHMvFEXxnrX2i2mSKJtaqJWp4tMSWrehKQ2d1mf9s5rVY6GdGSPSEme87mjft/ZTzPN8SMdZE037ap/WZy6MMda63PJQdk7XfpjzXBNMsa7PnHqs+QGo6/7jNN3zY2o90mFZ8ObvPEytEx3/vqeX0H6P9uunFpq63ynG+JT3fu58pc877/2z4/H4p4PB4NMrV6785nF/PgKC5TF34lpq+DNsOhy2ZVm2l2XZuKqqLzPGrBCi9t5LrfX7BwcHC11frqrqRq/X2y7LsnP7jDEhGNEX8uyX37j2zA8W+5HOhXDO9aqquhRCUN77LAUkMc/z+3meH0gpa0oHVTqlPXCHzDmnq6q65L3XqZiT4ZwbIYSVUto0WXP6HyDz3gvnXOackyEEHULIvfdZCEE03Rv5YSGIWghR5Xn+QCll01ibwOVRmz6IzTvY/7/rTPmdtXjvmXMuS7/jPAWTMoTAY4w8xsjTfdGspzM4Dz3WrNNxCrFIjwtKAXe6f7R9sw1jTGxsbPyBiCrGWOCch3TrsyybdZDvevzc7e3tvVbX9cxLmP+DM10unFcEri2ldz9yQgj/JIIBIlwyWBo7OztvGmO+P28bpdStdg14IcS+1vpD51xIBXWk936Fc65jjHX6Jj2rD0Cnoih+zxh7m3M+EkI8kFL+Rym1nxf9fxPj0fpgn9ayPP2dAADgIsEZguUxtxYAERFj7DkioizLRkKIHSmlGI/HJ77Re++pKIrb3vsNY8yZAoKyLL+5trb2xtWrV7e6ni/kqS0GAADgAkIdguXRmXbHGIu9Xu+W1vpfMcaeUupT59yKMeamc25mW+SDg4Mb1toNrfV7ZxlEjFGNRqOf3b9//0yBBAAAXGwICJZEOuV/gtZ6uyzLm1VVrRtjnjLGtNML56aRxRi5MeZlpdTds4ylrutXh8Phj8/yGgAAuNgQECyPE5MKtdZ/mVXbPjn195vy5YdnHUxZlq/fvXv3QnfuAgCAxSEgWBKpPS9lWbanlHo3dc2b22d40cp21tobWutbKa1sYZPJ5M07d+78aDAYvEoXpKgNAAB8NggIloSU0mmt7zjnXjDGfL0syy9xzrM8z8uUM9xloRSSpimOlPLvQoiFW3OmmgVvDIfDd3Z3d39FCAoAAJYWsgyWhLV21Vr7UBfAqqq+SnRYwY0x1pwtaA7KUQhhlFIPprsHzvmMl5RSt7z3Z+2St3DuLgAAXEwICJbHzCYvs8q5Wmup3+9/YK1duEqac25TCFF673unb03EGHNKqbcvX778PUJBGwCApYVLBstj4WY5bSGE6ixf3r33lzjn/1BK/W2hQeX5O+vr699ZW1sbf5bxAQDAxYAzBEsiz/Pfcc7/3C5NSof15akpa5oeY0QkhBC9PM+pLMtaKfVHerg07VEJ1KnX5Wk9MsZEv9/freu6qTTUrvPf1PWPMUY6ODj41urq6m/PfScAAMC5QeliAAAAwCUDAAAAQEAAAAAAhIAAAAAACAEBAAAAEAICAAAAIAQEAAAAQAgIAAAAgBAQAAAAACEgAAAAAEJAAAAAAISAAAAAAAgBAQAAABDRfwEsy4NaQ+XHogAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 648x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = df\n",
    "from matplotlib import colors\n",
    "hmap = colors.ListedColormap(['lightblue', 'lightgrey'])\n",
    "f, ax = plt.subplots(1, figsize=(9, 9))\n",
    "df.assign(cl=labels).plot(column='cl', categorical=True, \\\n",
    "        k=2, cmap=hmap, linewidth=0.1, ax=ax, \\\n",
    "        edgecolor='white', legend=True)\n",
    "ax.set_axis_off()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "spots = ['n.sig.', 'diamond']\n",
    "labels = [spots[i] for i in diamond*1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = df\n",
    "from matplotlib import colors\n",
    "hmap = colors.ListedColormap(['pink', 'lightgrey'])\n",
    "f, ax = plt.subplots(1, figsize=(9, 9))\n",
    "df.assign(cl=labels).plot(column='cl', categorical=True, \\\n",
    "        k=2, cmap=hmap, linewidth=0.1, ax=ax, \\\n",
    "        edgecolor='white', legend=True)\n",
    "ax.set_axis_off()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 2, 2, 1, 1, 0, 0, 0,\n",
       "       0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0,\n",
       "       3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 3, 0, 3, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 1, 0])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sig = 1 * (li.p_sim < 0.05)\n",
    "hotspot = 1 * (sig * li.q==1)\n",
    "coldspot = 3 * (sig * li.q==3)\n",
    "doughnut = 2 * (sig * li.q==2)\n",
    "diamond = 4 * (sig * li.q==4)\n",
    "spots = hotspot + coldspot + doughnut + diamond\n",
    "spots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "spot_labels = [ '0 ns', '1 hot spot', '2 doughnut', '3 cold spot', '4 diamond']\n",
    "labels = [spot_labels[i] for i in spots]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import colors\n",
    "hmap = colors.ListedColormap([ 'lightgrey', 'red', 'lightblue', 'blue', 'pink'])\n",
    "f, ax = plt.subplots(1, figsize=(9, 9))\n",
    "df.assign(cl=labels).plot(column='cl', categorical=True, \\\n",
    "        k=2, cmap=hmap, linewidth=0.1, ax=ax, \\\n",
    "        edgecolor='white', legend=True)\n",
    "ax.set_axis_off()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['geo_id', 'EmploymentS', 'EstablishmentS', 'geometry', 'total_tran',\n",
       "       'pop', 'edut', 'collegemale', 'collegefemale', 'households', 'income',\n",
       "       'ratio_65', 'ratio_22_50', 'bus_ratio', 'bus_ratio_total',\n",
       "       'public_ratio', 'car_ratio', 'bicy_ratio', 'walked_ratio'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdf2.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "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>EstablishmentS</th>\n",
       "      <th>total_tran</th>\n",
       "      <th>pop</th>\n",
       "      <th>edut</th>\n",
       "      <th>collegemale</th>\n",
       "      <th>collegefemale</th>\n",
       "      <th>households</th>\n",
       "      <th>income</th>\n",
       "      <th>ratio_65</th>\n",
       "      <th>ratio_22_50</th>\n",
       "      <th>bus_ratio</th>\n",
       "      <th>bus_ratio_total</th>\n",
       "      <th>public_ratio</th>\n",
       "      <th>car_ratio</th>\n",
       "      <th>bicy_ratio</th>\n",
       "      <th>walked_ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>EstablishmentS</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.010765</td>\n",
       "      <td>-0.058005</td>\n",
       "      <td>0.019406</td>\n",
       "      <td>0.159578</td>\n",
       "      <td>0.069694</td>\n",
       "      <td>0.049947</td>\n",
       "      <td>0.197374</td>\n",
       "      <td>0.362523</td>\n",
       "      <td>-0.030987</td>\n",
       "      <td>-0.088874</td>\n",
       "      <td>0.334046</td>\n",
       "      <td>0.457452</td>\n",
       "      <td>-0.472984</td>\n",
       "      <td>0.135422</td>\n",
       "      <td>0.507925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>total_tran</td>\n",
       "      <td>-0.010765</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.912688</td>\n",
       "      <td>0.946363</td>\n",
       "      <td>0.615589</td>\n",
       "      <td>0.645998</td>\n",
       "      <td>0.651593</td>\n",
       "      <td>0.903007</td>\n",
       "      <td>0.089370</td>\n",
       "      <td>-0.114917</td>\n",
       "      <td>0.106394</td>\n",
       "      <td>0.163674</td>\n",
       "      <td>0.120726</td>\n",
       "      <td>0.116117</td>\n",
       "      <td>0.000465</td>\n",
       "      <td>-0.137638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>pop</td>\n",
       "      <td>-0.058005</td>\n",
       "      <td>0.912688</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.958546</td>\n",
       "      <td>0.345582</td>\n",
       "      <td>0.370063</td>\n",
       "      <td>0.571089</td>\n",
       "      <td>0.857712</td>\n",
       "      <td>-0.167666</td>\n",
       "      <td>-0.004248</td>\n",
       "      <td>0.031435</td>\n",
       "      <td>0.023222</td>\n",
       "      <td>0.000665</td>\n",
       "      <td>0.297726</td>\n",
       "      <td>-0.245764</td>\n",
       "      <td>-0.190328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>edut</td>\n",
       "      <td>0.019406</td>\n",
       "      <td>0.946363</td>\n",
       "      <td>0.958546</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.529231</td>\n",
       "      <td>0.544621</td>\n",
       "      <td>0.633180</td>\n",
       "      <td>0.934828</td>\n",
       "      <td>-0.026685</td>\n",
       "      <td>0.045693</td>\n",
       "      <td>0.036980</td>\n",
       "      <td>0.083267</td>\n",
       "      <td>0.079433</td>\n",
       "      <td>0.193788</td>\n",
       "      <td>-0.132271</td>\n",
       "      <td>-0.154698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>collegemale</td>\n",
       "      <td>0.159578</td>\n",
       "      <td>0.615589</td>\n",
       "      <td>0.345582</td>\n",
       "      <td>0.529231</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.902181</td>\n",
       "      <td>0.600162</td>\n",
       "      <td>0.641574</td>\n",
       "      <td>0.358966</td>\n",
       "      <td>-0.005564</td>\n",
       "      <td>0.086926</td>\n",
       "      <td>0.202671</td>\n",
       "      <td>0.230903</td>\n",
       "      <td>-0.337277</td>\n",
       "      <td>0.367961</td>\n",
       "      <td>0.181080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>collegefemale</td>\n",
       "      <td>0.069694</td>\n",
       "      <td>0.645998</td>\n",
       "      <td>0.370063</td>\n",
       "      <td>0.544621</td>\n",
       "      <td>0.902181</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.554723</td>\n",
       "      <td>0.607945</td>\n",
       "      <td>0.327157</td>\n",
       "      <td>-0.021633</td>\n",
       "      <td>0.186325</td>\n",
       "      <td>0.214775</td>\n",
       "      <td>0.171703</td>\n",
       "      <td>-0.259858</td>\n",
       "      <td>0.380765</td>\n",
       "      <td>0.063212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>households</td>\n",
       "      <td>0.049947</td>\n",
       "      <td>0.651593</td>\n",
       "      <td>0.571089</td>\n",
       "      <td>0.633180</td>\n",
       "      <td>0.600162</td>\n",
       "      <td>0.554723</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.679171</td>\n",
       "      <td>0.053121</td>\n",
       "      <td>0.109397</td>\n",
       "      <td>0.104192</td>\n",
       "      <td>0.158934</td>\n",
       "      <td>0.203291</td>\n",
       "      <td>-0.013414</td>\n",
       "      <td>0.063217</td>\n",
       "      <td>0.016178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>income</td>\n",
       "      <td>0.197374</td>\n",
       "      <td>0.903007</td>\n",
       "      <td>0.857712</td>\n",
       "      <td>0.934828</td>\n",
       "      <td>0.641574</td>\n",
       "      <td>0.607945</td>\n",
       "      <td>0.679171</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.108103</td>\n",
       "      <td>0.076131</td>\n",
       "      <td>-0.022193</td>\n",
       "      <td>0.180828</td>\n",
       "      <td>0.265682</td>\n",
       "      <td>-0.032680</td>\n",
       "      <td>-0.072336</td>\n",
       "      <td>0.095124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ratio_65</td>\n",
       "      <td>0.362523</td>\n",
       "      <td>0.089370</td>\n",
       "      <td>-0.167666</td>\n",
       "      <td>-0.026685</td>\n",
       "      <td>0.358966</td>\n",
       "      <td>0.327157</td>\n",
       "      <td>0.053121</td>\n",
       "      <td>0.108103</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.484744</td>\n",
       "      <td>0.150948</td>\n",
       "      <td>0.605613</td>\n",
       "      <td>0.653642</td>\n",
       "      <td>-0.607124</td>\n",
       "      <td>0.679478</td>\n",
       "      <td>0.308324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ratio_22_50</td>\n",
       "      <td>-0.030987</td>\n",
       "      <td>-0.114917</td>\n",
       "      <td>-0.004248</td>\n",
       "      <td>0.045693</td>\n",
       "      <td>-0.005564</td>\n",
       "      <td>-0.021633</td>\n",
       "      <td>0.109397</td>\n",
       "      <td>0.076131</td>\n",
       "      <td>-0.484744</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.115243</td>\n",
       "      <td>-0.326774</td>\n",
       "      <td>-0.235582</td>\n",
       "      <td>0.265058</td>\n",
       "      <td>-0.361301</td>\n",
       "      <td>-0.058144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>bus_ratio</td>\n",
       "      <td>-0.088874</td>\n",
       "      <td>0.106394</td>\n",
       "      <td>0.031435</td>\n",
       "      <td>0.036980</td>\n",
       "      <td>0.086926</td>\n",
       "      <td>0.186325</td>\n",
       "      <td>0.104192</td>\n",
       "      <td>-0.022193</td>\n",
       "      <td>0.150948</td>\n",
       "      <td>-0.115243</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.543040</td>\n",
       "      <td>0.082200</td>\n",
       "      <td>0.091746</td>\n",
       "      <td>0.296556</td>\n",
       "      <td>-0.331448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>bus_ratio_total</td>\n",
       "      <td>0.334046</td>\n",
       "      <td>0.163674</td>\n",
       "      <td>0.023222</td>\n",
       "      <td>0.083267</td>\n",
       "      <td>0.202671</td>\n",
       "      <td>0.214775</td>\n",
       "      <td>0.158934</td>\n",
       "      <td>0.180828</td>\n",
       "      <td>0.605613</td>\n",
       "      <td>-0.326774</td>\n",
       "      <td>0.543040</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.836948</td>\n",
       "      <td>-0.509929</td>\n",
       "      <td>0.499361</td>\n",
       "      <td>0.165687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>public_ratio</td>\n",
       "      <td>0.457452</td>\n",
       "      <td>0.120726</td>\n",
       "      <td>0.000665</td>\n",
       "      <td>0.079433</td>\n",
       "      <td>0.230903</td>\n",
       "      <td>0.171703</td>\n",
       "      <td>0.203291</td>\n",
       "      <td>0.265682</td>\n",
       "      <td>0.653642</td>\n",
       "      <td>-0.235582</td>\n",
       "      <td>0.082200</td>\n",
       "      <td>0.836948</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.679726</td>\n",
       "      <td>0.405810</td>\n",
       "      <td>0.455047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>car_ratio</td>\n",
       "      <td>-0.472984</td>\n",
       "      <td>0.116117</td>\n",
       "      <td>0.297726</td>\n",
       "      <td>0.193788</td>\n",
       "      <td>-0.337277</td>\n",
       "      <td>-0.259858</td>\n",
       "      <td>-0.013414</td>\n",
       "      <td>-0.032680</td>\n",
       "      <td>-0.607124</td>\n",
       "      <td>0.265058</td>\n",
       "      <td>0.091746</td>\n",
       "      <td>-0.509929</td>\n",
       "      <td>-0.679726</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.602317</td>\n",
       "      <td>-0.796013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>bicy_ratio</td>\n",
       "      <td>0.135422</td>\n",
       "      <td>0.000465</td>\n",
       "      <td>-0.245764</td>\n",
       "      <td>-0.132271</td>\n",
       "      <td>0.367961</td>\n",
       "      <td>0.380765</td>\n",
       "      <td>0.063217</td>\n",
       "      <td>-0.072336</td>\n",
       "      <td>0.679478</td>\n",
       "      <td>-0.361301</td>\n",
       "      <td>0.296556</td>\n",
       "      <td>0.499361</td>\n",
       "      <td>0.405810</td>\n",
       "      <td>-0.602317</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.106777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>walked_ratio</td>\n",
       "      <td>0.507925</td>\n",
       "      <td>-0.137638</td>\n",
       "      <td>-0.190328</td>\n",
       "      <td>-0.154698</td>\n",
       "      <td>0.181080</td>\n",
       "      <td>0.063212</td>\n",
       "      <td>0.016178</td>\n",
       "      <td>0.095124</td>\n",
       "      <td>0.308324</td>\n",
       "      <td>-0.058144</td>\n",
       "      <td>-0.331448</td>\n",
       "      <td>0.165687</td>\n",
       "      <td>0.455047</td>\n",
       "      <td>-0.796013</td>\n",
       "      <td>0.106777</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 EstablishmentS  total_tran       pop      edut  collegemale  \\\n",
       "EstablishmentS         1.000000   -0.010765 -0.058005  0.019406     0.159578   \n",
       "total_tran            -0.010765    1.000000  0.912688  0.946363     0.615589   \n",
       "pop                   -0.058005    0.912688  1.000000  0.958546     0.345582   \n",
       "edut                   0.019406    0.946363  0.958546  1.000000     0.529231   \n",
       "collegemale            0.159578    0.615589  0.345582  0.529231     1.000000   \n",
       "collegefemale          0.069694    0.645998  0.370063  0.544621     0.902181   \n",
       "households             0.049947    0.651593  0.571089  0.633180     0.600162   \n",
       "income                 0.197374    0.903007  0.857712  0.934828     0.641574   \n",
       "ratio_65               0.362523    0.089370 -0.167666 -0.026685     0.358966   \n",
       "ratio_22_50           -0.030987   -0.114917 -0.004248  0.045693    -0.005564   \n",
       "bus_ratio             -0.088874    0.106394  0.031435  0.036980     0.086926   \n",
       "bus_ratio_total        0.334046    0.163674  0.023222  0.083267     0.202671   \n",
       "public_ratio           0.457452    0.120726  0.000665  0.079433     0.230903   \n",
       "car_ratio             -0.472984    0.116117  0.297726  0.193788    -0.337277   \n",
       "bicy_ratio             0.135422    0.000465 -0.245764 -0.132271     0.367961   \n",
       "walked_ratio           0.507925   -0.137638 -0.190328 -0.154698     0.181080   \n",
       "\n",
       "                 collegefemale  households    income  ratio_65  ratio_22_50  \\\n",
       "EstablishmentS        0.069694    0.049947  0.197374  0.362523    -0.030987   \n",
       "total_tran            0.645998    0.651593  0.903007  0.089370    -0.114917   \n",
       "pop                   0.370063    0.571089  0.857712 -0.167666    -0.004248   \n",
       "edut                  0.544621    0.633180  0.934828 -0.026685     0.045693   \n",
       "collegemale           0.902181    0.600162  0.641574  0.358966    -0.005564   \n",
       "collegefemale         1.000000    0.554723  0.607945  0.327157    -0.021633   \n",
       "households            0.554723    1.000000  0.679171  0.053121     0.109397   \n",
       "income                0.607945    0.679171  1.000000  0.108103     0.076131   \n",
       "ratio_65              0.327157    0.053121  0.108103  1.000000    -0.484744   \n",
       "ratio_22_50          -0.021633    0.109397  0.076131 -0.484744     1.000000   \n",
       "bus_ratio             0.186325    0.104192 -0.022193  0.150948    -0.115243   \n",
       "bus_ratio_total       0.214775    0.158934  0.180828  0.605613    -0.326774   \n",
       "public_ratio          0.171703    0.203291  0.265682  0.653642    -0.235582   \n",
       "car_ratio            -0.259858   -0.013414 -0.032680 -0.607124     0.265058   \n",
       "bicy_ratio            0.380765    0.063217 -0.072336  0.679478    -0.361301   \n",
       "walked_ratio          0.063212    0.016178  0.095124  0.308324    -0.058144   \n",
       "\n",
       "                 bus_ratio  bus_ratio_total  public_ratio  car_ratio  \\\n",
       "EstablishmentS   -0.088874         0.334046      0.457452  -0.472984   \n",
       "total_tran        0.106394         0.163674      0.120726   0.116117   \n",
       "pop               0.031435         0.023222      0.000665   0.297726   \n",
       "edut              0.036980         0.083267      0.079433   0.193788   \n",
       "collegemale       0.086926         0.202671      0.230903  -0.337277   \n",
       "collegefemale     0.186325         0.214775      0.171703  -0.259858   \n",
       "households        0.104192         0.158934      0.203291  -0.013414   \n",
       "income           -0.022193         0.180828      0.265682  -0.032680   \n",
       "ratio_65          0.150948         0.605613      0.653642  -0.607124   \n",
       "ratio_22_50      -0.115243        -0.326774     -0.235582   0.265058   \n",
       "bus_ratio         1.000000         0.543040      0.082200   0.091746   \n",
       "bus_ratio_total   0.543040         1.000000      0.836948  -0.509929   \n",
       "public_ratio      0.082200         0.836948      1.000000  -0.679726   \n",
       "car_ratio         0.091746        -0.509929     -0.679726   1.000000   \n",
       "bicy_ratio        0.296556         0.499361      0.405810  -0.602317   \n",
       "walked_ratio     -0.331448         0.165687      0.455047  -0.796013   \n",
       "\n",
       "                 bicy_ratio  walked_ratio  \n",
       "EstablishmentS     0.135422      0.507925  \n",
       "total_tran         0.000465     -0.137638  \n",
       "pop               -0.245764     -0.190328  \n",
       "edut              -0.132271     -0.154698  \n",
       "collegemale        0.367961      0.181080  \n",
       "collegefemale      0.380765      0.063212  \n",
       "households         0.063217      0.016178  \n",
       "income            -0.072336      0.095124  \n",
       "ratio_65           0.679478      0.308324  \n",
       "ratio_22_50       -0.361301     -0.058144  \n",
       "bus_ratio          0.296556     -0.331448  \n",
       "bus_ratio_total    0.499361      0.165687  \n",
       "public_ratio       0.405810      0.455047  \n",
       "car_ratio         -0.602317     -0.796013  \n",
       "bicy_ratio         1.000000      0.106777  \n",
       "walked_ratio       0.106777      1.000000  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "YVar='EstablishmentS'\n",
    "XVars=['total_tran',\n",
    "       'pop', 'edut', 'collegemale', 'collegefemale', 'households', 'income',\n",
    "    'ratio_65', 'ratio_22_50', 'bus_ratio', 'bus_ratio_total',\n",
    "       'public_ratio', 'car_ratio', 'bicy_ratio', 'walked_ratio']\n",
    "gdf2[[YVar]+XVars].corr()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pysal.model import spreg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "gdf2[YVar] = np.log((gdf2[YVar])+ 0.000001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jianq\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n",
      "C:\\Users\\jianq\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "Y=gdf2[YVar].as_matrix().reshape((len(gdf2),1))\n",
    "X=gdf2[XVars].as_matrix()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jianq\\Anaconda3\\lib\\site-packages\\scipy\\optimize\\_minimize.py:770: RuntimeWarning: Method 'bounded' does not support relative tolerance in x; defaulting to absolute tolerance.\n",
      "  \"defaulting to absolute tolerance.\", RuntimeWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "REGRESSION\n",
      "----------\n",
      "SUMMARY OF OUTPUT: MAXIMUM LIKELIHOOD SPATIAL LAG (METHOD = FULL)\n",
      "-----------------------------------------------------------------\n",
      "Data set            :     unknown\n",
      "Weights matrix      :     unknown\n",
      "Dependent Variable  :EstablishmentS                Number of Observations:         171\n",
      "Mean dependent var  :      3.6900                Number of Variables   :          17\n",
      "S.D. dependent var  :      1.0672                Degrees of Freedom    :         154\n",
      "Pseudo R-squared    :      0.4546\n",
      "Spatial Pseudo R-squared:  0.4533\n",
      "Sigma-square ML     :       0.617                Log likelihood        :    -201.483\n",
      "S.E of regression   :       0.786                Akaike info criterion :     436.965\n",
      "                                                 Schwarz criterion     :     490.374\n",
      "\n",
      "------------------------------------------------------------------------------------\n",
      "            Variable     Coefficient       Std.Error     z-Statistic     Probability\n",
      "------------------------------------------------------------------------------------\n",
      "            CONSTANT       4.8297822       1.5636179       3.0888507       0.0020093\n",
      "          total_tran      -0.0001883       0.0004227      -0.4456126       0.6558771\n",
      "                 pop      -0.0000285       0.0002948      -0.0967034       0.9229619\n",
      "                edut      -0.0003618       0.0004646      -0.7787453       0.4361298\n",
      "         collegemale      -0.0013037       0.0008797      -1.4819762       0.1383466\n",
      "       collegefemale      -0.0008253       0.0007915      -1.0427336       0.2970717\n",
      "          households      -0.0020756       0.0030984      -0.6698853       0.5029309\n",
      "              income       0.0015251       0.0004557       3.3466408       0.0008180\n",
      "            ratio_65       5.5177041       1.5468857       3.5669760       0.0003611\n",
      "         ratio_22_50       4.0722981       2.3753282       1.7144149       0.0864526\n",
      "           bus_ratio      -0.8968189       0.6651658      -1.3482636       0.1775736\n",
      "     bus_ratio_total      11.6825104       5.9352264       1.9683344       0.0490296\n",
      "        public_ratio      -9.9644608       4.5879864      -2.1718593       0.0298663\n",
      "           car_ratio      -3.8784009       2.1053843      -1.8421344       0.0654555\n",
      "          bicy_ratio      -4.4018336       3.1993467      -1.3758539       0.1688669\n",
      "        walked_ratio      -2.0025622       3.1200314      -0.6418404       0.5209768\n",
      "    W_EstablishmentS      -0.0705928       0.1155591      -0.6108804       0.5412788\n",
      "------------------------------------------------------------------------------------\n",
      "================================ END OF REPORT =====================================\n"
     ]
    }
   ],
   "source": [
    "lag=spreg.ML_Lag(Y, X, qW, name_y=YVar, name_x=XVars)\n",
    "print(lag.summary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jianq\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:5: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
      "  \"\"\"\n"
     ]
    }
   ],
   "source": [
    "XVars2=['total_tran',\n",
    "       'pop', 'edut', 'collegemale', 'collegefemale', 'households', 'income',\n",
    "       'ratio_65', 'ratio_22_50', 'bus_ratio', 'bus_ratio_total',\n",
    "       'public_ratio', 'car_ratio', 'bicy_ratio', 'walked_ratio']\n",
    "X=gdf2[XVars2].as_matrix()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "mi =esda.moran.Moran(lag.u, qW)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<esda.moran.Moran at 0x1c81a75d248>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Morans I   -0.001642\n",
       "Z-Score     0.097538\n",
       "P-Value     0.461150\n",
       "dtype: float64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mi = esda.Moran(lag.u, qW, two_tailed=False)\n",
    "pd.Series(index=['Morans I','Z-Score','P-Value'],data=[mi.I, mi.z_norm, mi.p_norm])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "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>Direct</th>\n",
       "      <th>Indirect</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Constant</td>\n",
       "      <td>4.829782</td>\n",
       "      <td>-0.318466</td>\n",
       "      <td>4.511316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>total_tran</td>\n",
       "      <td>-0.000188</td>\n",
       "      <td>0.000012</td>\n",
       "      <td>-0.000176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>pop</td>\n",
       "      <td>-0.000029</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>-0.000027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>edut</td>\n",
       "      <td>-0.000362</td>\n",
       "      <td>0.000024</td>\n",
       "      <td>-0.000338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>collegemale</td>\n",
       "      <td>-0.001304</td>\n",
       "      <td>0.000086</td>\n",
       "      <td>-0.001218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>collegefemale</td>\n",
       "      <td>-0.000825</td>\n",
       "      <td>0.000054</td>\n",
       "      <td>-0.000771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>households</td>\n",
       "      <td>-0.002076</td>\n",
       "      <td>0.000137</td>\n",
       "      <td>-0.001939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>income</td>\n",
       "      <td>0.001525</td>\n",
       "      <td>-0.000101</td>\n",
       "      <td>0.001424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ratio_65</td>\n",
       "      <td>5.517704</td>\n",
       "      <td>-0.363827</td>\n",
       "      <td>5.153878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ratio_22_50</td>\n",
       "      <td>4.072298</td>\n",
       "      <td>-0.268519</td>\n",
       "      <td>3.803779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>bus_ratio</td>\n",
       "      <td>-0.896819</td>\n",
       "      <td>0.059134</td>\n",
       "      <td>-0.837684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>bus_ratio_total</td>\n",
       "      <td>11.682510</td>\n",
       "      <td>-0.770322</td>\n",
       "      <td>10.912189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>public_ratio</td>\n",
       "      <td>-9.964461</td>\n",
       "      <td>0.657037</td>\n",
       "      <td>-9.307424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>car_ratio</td>\n",
       "      <td>-3.878401</td>\n",
       "      <td>0.255734</td>\n",
       "      <td>-3.622667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>bicy_ratio</td>\n",
       "      <td>-4.401834</td>\n",
       "      <td>0.290248</td>\n",
       "      <td>-4.111585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>walked_ratio</td>\n",
       "      <td>-2.002562</td>\n",
       "      <td>0.132045</td>\n",
       "      <td>-1.870517</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Direct  Indirect      Total\n",
       "Constant          4.829782 -0.318466   4.511316\n",
       "total_tran       -0.000188  0.000012  -0.000176\n",
       "pop              -0.000029  0.000002  -0.000027\n",
       "edut             -0.000362  0.000024  -0.000338\n",
       "collegemale      -0.001304  0.000086  -0.001218\n",
       "collegefemale    -0.000825  0.000054  -0.000771\n",
       "households       -0.002076  0.000137  -0.001939\n",
       "income            0.001525 -0.000101   0.001424\n",
       "ratio_65          5.517704 -0.363827   5.153878\n",
       "ratio_22_50       4.072298 -0.268519   3.803779\n",
       "bus_ratio        -0.896819  0.059134  -0.837684\n",
       "bus_ratio_total  11.682510 -0.770322  10.912189\n",
       "public_ratio     -9.964461  0.657037  -9.307424\n",
       "car_ratio        -3.878401  0.255734  -3.622667\n",
       "bicy_ratio       -4.401834  0.290248  -4.111585\n",
       "walked_ratio     -2.002562  0.132045  -1.870517"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b=lag.betas[:-1]\n",
    "rho=lag.betas[-1]\n",
    "btot=b/(float(1)-rho)\n",
    "bind=btot-b\n",
    "\n",
    "full_eff=pd.DataFrame(np.hstack([b,bind,btot]),index=['Constant']+XVars, columns=['Direct','Indirect','Total'])\n",
    "full_eff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'data' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-73-10c86cb42b2e>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdensity_increase\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'BarPSqMi'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;36m.1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mcoefficient\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfull_eff\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mvar\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'Total'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mchange_in_output\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdensity_increase\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mcoefficient\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mchange_in_out_pct\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mchange_in_output\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDrunkP1k\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mchange_in_out_pct\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'data' is not defined"
     ]
    }
   ],
   "source": [
    "density_increase=(data['BarPSqMi']*.1).mean()\n",
    "coefficient=full_eff.loc[var,'Total']\n",
    "change_in_output=density_increase*coefficient\n",
    "change_in_out_pct=change_in_output/data.DrunkP1k.mean()\n",
    "change_in_out_pct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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