{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "ename": "OperationalError",
     "evalue": "(psycopg2.OperationalError) connection to server at \"192.168.0.74\", port 5432 failed: Connection refused\n\tIs the server running on that host and accepting TCP/IP connections?\n\n(Background on this error at: https://sqlalche.me/e/20/e3q8)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mOperationalError\u001b[0m                          Traceback (most recent call last)",
      "File \u001b[0;32m~/miniconda3/envs/spatial_env/lib/python3.12/site-packages/sqlalchemy/engine/base.py:146\u001b[0m, in \u001b[0;36mConnection.__init__\u001b[0;34m(self, engine, connection, _has_events, _allow_revalidate, _allow_autobegin)\u001b[0m\n\u001b[1;32m    145\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 146\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dbapi_connection \u001b[38;5;241m=\u001b[39m \u001b[43mengine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mraw_connection\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    147\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m dialect\u001b[38;5;241m.\u001b[39mloaded_dbapi\u001b[38;5;241m.\u001b[39mError \u001b[38;5;28;01mas\u001b[39;00m err:\n",
      "File \u001b[0;32m~/miniconda3/envs/spatial_env/lib/python3.12/site-packages/sqlalchemy/engine/base.py:3302\u001b[0m, in \u001b[0;36mEngine.raw_connection\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   3281\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Return a \"raw\" DBAPI connection from the connection pool.\u001b[39;00m\n\u001b[1;32m   3282\u001b[0m \n\u001b[1;32m   3283\u001b[0m \u001b[38;5;124;03mThe returned object is a proxied version of the DBAPI\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   3300\u001b[0m \n\u001b[1;32m   3301\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m-> 3302\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpool\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconnect\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/miniconda3/envs/spatial_env/lib/python3.12/site-packages/sqlalchemy/pool/base.py:449\u001b[0m, in \u001b[0;36mPool.connect\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    442\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Return a DBAPI connection from the pool.\u001b[39;00m\n\u001b[1;32m    443\u001b[0m \n\u001b[1;32m    444\u001b[0m \u001b[38;5;124;03mThe connection is instrumented such that when its\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    447\u001b[0m \n\u001b[1;32m    448\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m--> 449\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_ConnectionFairy\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_checkout\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/miniconda3/envs/spatial_env/lib/python3.12/site-packages/sqlalchemy/pool/base.py:1263\u001b[0m, in \u001b[0;36m_ConnectionFairy._checkout\u001b[0;34m(cls, pool, threadconns, fairy)\u001b[0m\n\u001b[1;32m   1262\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m fairy:\n\u001b[0;32m-> 1263\u001b[0m     fairy \u001b[38;5;241m=\u001b[39m \u001b[43m_ConnectionRecord\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcheckout\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpool\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1265\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m threadconns \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "File \u001b[0;32m~/miniconda3/envs/spatial_env/lib/python3.12/site-packages/sqlalchemy/pool/base.py:712\u001b[0m, in \u001b[0;36m_ConnectionRecord.checkout\u001b[0;34m(cls, pool)\u001b[0m\n\u001b[1;32m    711\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 712\u001b[0m     rec \u001b[38;5;241m=\u001b[39m \u001b[43mpool\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_do_get\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    714\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n",
      "File \u001b[0;32m~/miniconda3/envs/spatial_env/lib/python3.12/site-packages/sqlalchemy/pool/impl.py:179\u001b[0m, in \u001b[0;36mQueuePool._do_get\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    178\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m:\n\u001b[0;32m--> 179\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m \u001b[43mutil\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msafe_reraise\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[1;32m    180\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dec_overflow()\n",
      "File \u001b[0;32m~/miniconda3/envs/spatial_env/lib/python3.12/site-packages/sqlalchemy/util/langhelpers.py:146\u001b[0m, in \u001b[0;36msafe_reraise.__exit__\u001b[0;34m(self, type_, value, traceback)\u001b[0m\n\u001b[1;32m    145\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exc_info \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m  \u001b[38;5;66;03m# remove potential circular references\u001b[39;00m\n\u001b[0;32m--> 146\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m exc_value\u001b[38;5;241m.\u001b[39mwith_traceback(exc_tb)\n\u001b[1;32m    147\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
      "File \u001b[0;32m~/miniconda3/envs/spatial_env/lib/python3.12/site-packages/sqlalchemy/pool/impl.py:177\u001b[0m, in \u001b[0;36mQueuePool._do_get\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    176\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 177\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_create_connection\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    178\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m:\n",
      "File \u001b[0;32m~/miniconda3/envs/spatial_env/lib/python3.12/site-packages/sqlalchemy/pool/base.py:390\u001b[0m, in \u001b[0;36mPool._create_connection\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    388\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Called by subclasses to create a new ConnectionRecord.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 390\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_ConnectionRecord\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/miniconda3/envs/spatial_env/lib/python3.12/site-packages/sqlalchemy/pool/base.py:674\u001b[0m, in \u001b[0;36m_ConnectionRecord.__init__\u001b[0;34m(self, pool, connect)\u001b[0m\n\u001b[1;32m    673\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m connect:\n\u001b[0;32m--> 674\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__connect\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    675\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfinalize_callback \u001b[38;5;241m=\u001b[39m deque()\n",
      "File \u001b[0;32m~/miniconda3/envs/spatial_env/lib/python3.12/site-packages/sqlalchemy/pool/base.py:900\u001b[0m, in \u001b[0;36m_ConnectionRecord.__connect\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    899\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m--> 900\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m \u001b[43mutil\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msafe_reraise\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[1;32m    901\u001b[0m         pool\u001b[38;5;241m.\u001b[39mlogger\u001b[38;5;241m.\u001b[39mdebug(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mError on connect(): \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, e)\n",
      "File \u001b[0;32m~/miniconda3/envs/spatial_env/lib/python3.12/site-packages/sqlalchemy/util/langhelpers.py:146\u001b[0m, in \u001b[0;36msafe_reraise.__exit__\u001b[0;34m(self, type_, value, traceback)\u001b[0m\n\u001b[1;32m    145\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exc_info \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m  \u001b[38;5;66;03m# remove potential circular references\u001b[39;00m\n\u001b[0;32m--> 146\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m exc_value\u001b[38;5;241m.\u001b[39mwith_traceback(exc_tb)\n\u001b[1;32m    147\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
      "File \u001b[0;32m~/miniconda3/envs/spatial_env/lib/python3.12/site-packages/sqlalchemy/pool/base.py:896\u001b[0m, in \u001b[0;36m_ConnectionRecord.__connect\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    895\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstarttime \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime()\n\u001b[0;32m--> 896\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdbapi_connection \u001b[38;5;241m=\u001b[39m connection \u001b[38;5;241m=\u001b[39m \u001b[43mpool\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_invoke_creator\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m    897\u001b[0m pool\u001b[38;5;241m.\u001b[39mlogger\u001b[38;5;241m.\u001b[39mdebug(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCreated new connection \u001b[39m\u001b[38;5;132;01m%r\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, connection)\n",
      "File \u001b[0;32m~/miniconda3/envs/spatial_env/lib/python3.12/site-packages/sqlalchemy/engine/create.py:643\u001b[0m, in \u001b[0;36mcreate_engine.<locals>.connect\u001b[0;34m(connection_record)\u001b[0m\n\u001b[1;32m    641\u001b[0m             \u001b[38;5;28;01mreturn\u001b[39;00m connection\n\u001b[0;32m--> 643\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdialect\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconnect\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mcargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mcparams\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/miniconda3/envs/spatial_env/lib/python3.12/site-packages/sqlalchemy/engine/default.py:621\u001b[0m, in \u001b[0;36mDefaultDialect.connect\u001b[0;34m(self, *cargs, **cparams)\u001b[0m\n\u001b[1;32m    619\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mconnect\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39mcargs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mcparams):\n\u001b[1;32m    620\u001b[0m     \u001b[38;5;66;03m# inherits the docstring from interfaces.Dialect.connect\u001b[39;00m\n\u001b[0;32m--> 621\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mloaded_dbapi\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconnect\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mcargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mcparams\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/miniconda3/envs/spatial_env/lib/python3.12/site-packages/psycopg2/__init__.py:122\u001b[0m, in \u001b[0;36mconnect\u001b[0;34m(dsn, connection_factory, cursor_factory, **kwargs)\u001b[0m\n\u001b[1;32m    121\u001b[0m dsn \u001b[38;5;241m=\u001b[39m _ext\u001b[38;5;241m.\u001b[39mmake_dsn(dsn, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m--> 122\u001b[0m conn \u001b[38;5;241m=\u001b[39m \u001b[43m_connect\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdsn\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconnection_factory\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconnection_factory\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwasync\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    123\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m cursor_factory \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "\u001b[0;31mOperationalError\u001b[0m: connection to server at \"192.168.0.74\", port 5432 failed: Connection refused\n\tIs the server running on that host and accepting TCP/IP connections?\n",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mOperationalError\u001b[0m                          Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[3], line 19\u001b[0m\n\u001b[1;32m     16\u001b[0m engine \u001b[38;5;241m=\u001b[39m create_engine(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpostgresql+psycopg2://\u001b[39m\u001b[38;5;132;01m{\u001b[39;00muser\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m:\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpassword\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m@\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mhost\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m:\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mport\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m/\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mdb_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m     18\u001b[0m \u001b[38;5;66;03m# Read in the spills_with_demographics as spills\u001b[39;00m\n\u001b[0;32m---> 19\u001b[0m spills \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_sql_table\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mspills_with_demographics\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mengine\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/miniconda3/envs/spatial_env/lib/python3.12/site-packages/pandas/io/sql.py:384\u001b[0m, in \u001b[0;36mread_sql_table\u001b[0;34m(table_name, con, schema, index_col, coerce_float, parse_dates, columns, chunksize, dtype_backend)\u001b[0m\n\u001b[1;32m    381\u001b[0m     dtype_backend \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnumpy\u001b[39m\u001b[38;5;124m\"\u001b[39m  \u001b[38;5;66;03m# type: ignore[assignment]\u001b[39;00m\n\u001b[1;32m    382\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m dtype_backend \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m lib\u001b[38;5;241m.\u001b[39mno_default\n\u001b[0;32m--> 384\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[43mpandasSQL_builder\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcon\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mschema\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mschema\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mneed_transaction\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m pandas_sql:\n\u001b[1;32m    385\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m pandas_sql\u001b[38;5;241m.\u001b[39mhas_table(table_name):\n\u001b[1;32m    386\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTable \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mtable_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m not found\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "File \u001b[0;32m~/miniconda3/envs/spatial_env/lib/python3.12/site-packages/pandas/io/sql.py:906\u001b[0m, in \u001b[0;36mpandasSQL_builder\u001b[0;34m(con, schema, need_transaction)\u001b[0m\n\u001b[1;32m    903\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUsing URI string without sqlalchemy installed.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    905\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m sqlalchemy \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(con, (\u001b[38;5;28mstr\u001b[39m, sqlalchemy\u001b[38;5;241m.\u001b[39mengine\u001b[38;5;241m.\u001b[39mConnectable)):\n\u001b[0;32m--> 906\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mSQLDatabase\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcon\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mschema\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mneed_transaction\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    908\u001b[0m adbc \u001b[38;5;241m=\u001b[39m import_optional_dependency(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124madbc_driver_manager.dbapi\u001b[39m\u001b[38;5;124m\"\u001b[39m, errors\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mignore\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    909\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m adbc \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(con, adbc\u001b[38;5;241m.\u001b[39mConnection):\n",
      "File \u001b[0;32m~/miniconda3/envs/spatial_env/lib/python3.12/site-packages/pandas/io/sql.py:1636\u001b[0m, in \u001b[0;36mSQLDatabase.__init__\u001b[0;34m(self, con, schema, need_transaction)\u001b[0m\n\u001b[1;32m   1634\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mexit_stack\u001b[38;5;241m.\u001b[39mcallback(con\u001b[38;5;241m.\u001b[39mdispose)\n\u001b[1;32m   1635\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(con, Engine):\n\u001b[0;32m-> 1636\u001b[0m     con \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mexit_stack\u001b[38;5;241m.\u001b[39menter_context(\u001b[43mcon\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconnect\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m   1637\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m need_transaction \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m con\u001b[38;5;241m.\u001b[39min_transaction():\n\u001b[1;32m   1638\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mexit_stack\u001b[38;5;241m.\u001b[39menter_context(con\u001b[38;5;241m.\u001b[39mbegin())\n",
      "File \u001b[0;32m~/miniconda3/envs/spatial_env/lib/python3.12/site-packages/sqlalchemy/engine/base.py:3278\u001b[0m, in \u001b[0;36mEngine.connect\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   3255\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mconnect\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Connection:\n\u001b[1;32m   3256\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Return a new :class:`_engine.Connection` object.\u001b[39;00m\n\u001b[1;32m   3257\u001b[0m \n\u001b[1;32m   3258\u001b[0m \u001b[38;5;124;03m    The :class:`_engine.Connection` acts as a Python context manager, so\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   3275\u001b[0m \n\u001b[1;32m   3276\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m-> 3278\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_connection_cls\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/miniconda3/envs/spatial_env/lib/python3.12/site-packages/sqlalchemy/engine/base.py:148\u001b[0m, in \u001b[0;36mConnection.__init__\u001b[0;34m(self, engine, connection, _has_events, _allow_revalidate, _allow_autobegin)\u001b[0m\n\u001b[1;32m    146\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dbapi_connection \u001b[38;5;241m=\u001b[39m engine\u001b[38;5;241m.\u001b[39mraw_connection()\n\u001b[1;32m    147\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m dialect\u001b[38;5;241m.\u001b[39mloaded_dbapi\u001b[38;5;241m.\u001b[39mError \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[0;32m--> 148\u001b[0m         \u001b[43mConnection\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_handle_dbapi_exception_noconnection\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    149\u001b[0m \u001b[43m            \u001b[49m\u001b[43merr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdialect\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mengine\u001b[49m\n\u001b[1;32m    150\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    151\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m\n\u001b[1;32m    152\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
      "File \u001b[0;32m~/miniconda3/envs/spatial_env/lib/python3.12/site-packages/sqlalchemy/engine/base.py:2442\u001b[0m, in \u001b[0;36mConnection._handle_dbapi_exception_noconnection\u001b[0;34m(cls, e, dialect, engine, is_disconnect, invalidate_pool_on_disconnect, is_pre_ping)\u001b[0m\n\u001b[1;32m   2440\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m should_wrap:\n\u001b[1;32m   2441\u001b[0m     \u001b[38;5;28;01massert\u001b[39;00m sqlalchemy_exception \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m-> 2442\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m sqlalchemy_exception\u001b[38;5;241m.\u001b[39mwith_traceback(exc_info[\u001b[38;5;241m2\u001b[39m]) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01me\u001b[39;00m\n\u001b[1;32m   2443\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   2444\u001b[0m     \u001b[38;5;28;01massert\u001b[39;00m exc_info[\u001b[38;5;241m1\u001b[39m] \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n",
      "File \u001b[0;32m~/miniconda3/envs/spatial_env/lib/python3.12/site-packages/sqlalchemy/engine/base.py:146\u001b[0m, in \u001b[0;36mConnection.__init__\u001b[0;34m(self, engine, connection, _has_events, _allow_revalidate, _allow_autobegin)\u001b[0m\n\u001b[1;32m    144\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m connection \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    145\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 146\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dbapi_connection \u001b[38;5;241m=\u001b[39m \u001b[43mengine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mraw_connection\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    147\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m dialect\u001b[38;5;241m.\u001b[39mloaded_dbapi\u001b[38;5;241m.\u001b[39mError \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[1;32m    148\u001b[0m         Connection\u001b[38;5;241m.\u001b[39m_handle_dbapi_exception_noconnection(\n\u001b[1;32m    149\u001b[0m             err, dialect, engine\n\u001b[1;32m    150\u001b[0m         )\n",
      "File \u001b[0;32m~/miniconda3/envs/spatial_env/lib/python3.12/site-packages/sqlalchemy/engine/base.py:3302\u001b[0m, in \u001b[0;36mEngine.raw_connection\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   3280\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mraw_connection\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m PoolProxiedConnection:\n\u001b[1;32m   3281\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Return a \"raw\" DBAPI connection from the connection pool.\u001b[39;00m\n\u001b[1;32m   3282\u001b[0m \n\u001b[1;32m   3283\u001b[0m \u001b[38;5;124;03m    The returned object is a proxied version of the DBAPI\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   3300\u001b[0m \n\u001b[1;32m   3301\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m-> 3302\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpool\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconnect\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/miniconda3/envs/spatial_env/lib/python3.12/site-packages/sqlalchemy/pool/base.py:449\u001b[0m, in \u001b[0;36mPool.connect\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    441\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mconnect\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m PoolProxiedConnection:\n\u001b[1;32m    442\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Return a DBAPI connection from the pool.\u001b[39;00m\n\u001b[1;32m    443\u001b[0m \n\u001b[1;32m    444\u001b[0m \u001b[38;5;124;03m    The connection is instrumented such that when its\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    447\u001b[0m \n\u001b[1;32m    448\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 449\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_ConnectionFairy\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_checkout\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/miniconda3/envs/spatial_env/lib/python3.12/site-packages/sqlalchemy/pool/base.py:1263\u001b[0m, in \u001b[0;36m_ConnectionFairy._checkout\u001b[0;34m(cls, pool, threadconns, fairy)\u001b[0m\n\u001b[1;32m   1255\u001b[0m \u001b[38;5;129m@classmethod\u001b[39m\n\u001b[1;32m   1256\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_checkout\u001b[39m(\n\u001b[1;32m   1257\u001b[0m     \u001b[38;5;28mcls\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1260\u001b[0m     fairy: Optional[_ConnectionFairy] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m   1261\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m _ConnectionFairy:\n\u001b[1;32m   1262\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m fairy:\n\u001b[0;32m-> 1263\u001b[0m         fairy \u001b[38;5;241m=\u001b[39m \u001b[43m_ConnectionRecord\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcheckout\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpool\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1265\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m threadconns \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m   1266\u001b[0m             threadconns\u001b[38;5;241m.\u001b[39mcurrent \u001b[38;5;241m=\u001b[39m weakref\u001b[38;5;241m.\u001b[39mref(fairy)\n",
      "File \u001b[0;32m~/miniconda3/envs/spatial_env/lib/python3.12/site-packages/sqlalchemy/pool/base.py:712\u001b[0m, in \u001b[0;36m_ConnectionRecord.checkout\u001b[0;34m(cls, pool)\u001b[0m\n\u001b[1;32m    710\u001b[0m     rec \u001b[38;5;241m=\u001b[39m cast(_ConnectionRecord, pool\u001b[38;5;241m.\u001b[39m_do_get())\n\u001b[1;32m    711\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 712\u001b[0m     rec \u001b[38;5;241m=\u001b[39m \u001b[43mpool\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_do_get\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    714\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    715\u001b[0m     dbapi_connection \u001b[38;5;241m=\u001b[39m rec\u001b[38;5;241m.\u001b[39mget_connection()\n",
      "File \u001b[0;32m~/miniconda3/envs/spatial_env/lib/python3.12/site-packages/sqlalchemy/pool/impl.py:179\u001b[0m, in \u001b[0;36mQueuePool._do_get\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    177\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_create_connection()\n\u001b[1;32m    178\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m:\n\u001b[0;32m--> 179\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m \u001b[43mutil\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msafe_reraise\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[1;32m    180\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dec_overflow()\n\u001b[1;32m    181\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m\n",
      "File \u001b[0;32m~/miniconda3/envs/spatial_env/lib/python3.12/site-packages/sqlalchemy/util/langhelpers.py:146\u001b[0m, in \u001b[0;36msafe_reraise.__exit__\u001b[0;34m(self, type_, value, traceback)\u001b[0m\n\u001b[1;32m    144\u001b[0m     \u001b[38;5;28;01massert\u001b[39;00m exc_value \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    145\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exc_info \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m  \u001b[38;5;66;03m# remove potential circular references\u001b[39;00m\n\u001b[0;32m--> 146\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m exc_value\u001b[38;5;241m.\u001b[39mwith_traceback(exc_tb)\n\u001b[1;32m    147\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    148\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exc_info \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m  \u001b[38;5;66;03m# remove potential circular references\u001b[39;00m\n",
      "File \u001b[0;32m~/miniconda3/envs/spatial_env/lib/python3.12/site-packages/sqlalchemy/pool/impl.py:177\u001b[0m, in \u001b[0;36mQueuePool._do_get\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    175\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_inc_overflow():\n\u001b[1;32m    176\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 177\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_create_connection\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    178\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m:\n\u001b[1;32m    179\u001b[0m         \u001b[38;5;28;01mwith\u001b[39;00m util\u001b[38;5;241m.\u001b[39msafe_reraise():\n",
      "File \u001b[0;32m~/miniconda3/envs/spatial_env/lib/python3.12/site-packages/sqlalchemy/pool/base.py:390\u001b[0m, in \u001b[0;36mPool._create_connection\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    387\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_create_connection\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m ConnectionPoolEntry:\n\u001b[1;32m    388\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Called by subclasses to create a new ConnectionRecord.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 390\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_ConnectionRecord\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/miniconda3/envs/spatial_env/lib/python3.12/site-packages/sqlalchemy/pool/base.py:674\u001b[0m, in \u001b[0;36m_ConnectionRecord.__init__\u001b[0;34m(self, pool, connect)\u001b[0m\n\u001b[1;32m    672\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m__pool \u001b[38;5;241m=\u001b[39m pool\n\u001b[1;32m    673\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m connect:\n\u001b[0;32m--> 674\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__connect\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    675\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfinalize_callback \u001b[38;5;241m=\u001b[39m deque()\n",
      "File \u001b[0;32m~/miniconda3/envs/spatial_env/lib/python3.12/site-packages/sqlalchemy/pool/base.py:900\u001b[0m, in \u001b[0;36m_ConnectionRecord.__connect\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    898\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfresh \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m    899\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m--> 900\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m \u001b[43mutil\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msafe_reraise\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[1;32m    901\u001b[0m         pool\u001b[38;5;241m.\u001b[39mlogger\u001b[38;5;241m.\u001b[39mdebug(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mError on connect(): \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, e)\n\u001b[1;32m    902\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    903\u001b[0m     \u001b[38;5;66;03m# in SQLAlchemy 1.4 the first_connect event is not used by\u001b[39;00m\n\u001b[1;32m    904\u001b[0m     \u001b[38;5;66;03m# the engine, so this will usually not be set\u001b[39;00m\n",
      "File \u001b[0;32m~/miniconda3/envs/spatial_env/lib/python3.12/site-packages/sqlalchemy/util/langhelpers.py:146\u001b[0m, in \u001b[0;36msafe_reraise.__exit__\u001b[0;34m(self, type_, value, traceback)\u001b[0m\n\u001b[1;32m    144\u001b[0m     \u001b[38;5;28;01massert\u001b[39;00m exc_value \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    145\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exc_info \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m  \u001b[38;5;66;03m# remove potential circular references\u001b[39;00m\n\u001b[0;32m--> 146\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m exc_value\u001b[38;5;241m.\u001b[39mwith_traceback(exc_tb)\n\u001b[1;32m    147\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    148\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exc_info \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m  \u001b[38;5;66;03m# remove potential circular references\u001b[39;00m\n",
      "File \u001b[0;32m~/miniconda3/envs/spatial_env/lib/python3.12/site-packages/sqlalchemy/pool/base.py:896\u001b[0m, in \u001b[0;36m_ConnectionRecord.__connect\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    894\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    895\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstarttime \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime()\n\u001b[0;32m--> 896\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdbapi_connection \u001b[38;5;241m=\u001b[39m connection \u001b[38;5;241m=\u001b[39m \u001b[43mpool\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_invoke_creator\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m    897\u001b[0m     pool\u001b[38;5;241m.\u001b[39mlogger\u001b[38;5;241m.\u001b[39mdebug(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCreated new connection \u001b[39m\u001b[38;5;132;01m%r\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, connection)\n\u001b[1;32m    898\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfresh \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n",
      "File \u001b[0;32m~/miniconda3/envs/spatial_env/lib/python3.12/site-packages/sqlalchemy/engine/create.py:643\u001b[0m, in \u001b[0;36mcreate_engine.<locals>.connect\u001b[0;34m(connection_record)\u001b[0m\n\u001b[1;32m    640\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m connection \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    641\u001b[0m             \u001b[38;5;28;01mreturn\u001b[39;00m connection\n\u001b[0;32m--> 643\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdialect\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconnect\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mcargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mcparams\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/miniconda3/envs/spatial_env/lib/python3.12/site-packages/sqlalchemy/engine/default.py:621\u001b[0m, in \u001b[0;36mDefaultDialect.connect\u001b[0;34m(self, *cargs, **cparams)\u001b[0m\n\u001b[1;32m    619\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mconnect\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39mcargs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mcparams):\n\u001b[1;32m    620\u001b[0m     \u001b[38;5;66;03m# inherits the docstring from interfaces.Dialect.connect\u001b[39;00m\n\u001b[0;32m--> 621\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mloaded_dbapi\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconnect\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mcargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mcparams\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/miniconda3/envs/spatial_env/lib/python3.12/site-packages/psycopg2/__init__.py:122\u001b[0m, in \u001b[0;36mconnect\u001b[0;34m(dsn, connection_factory, cursor_factory, **kwargs)\u001b[0m\n\u001b[1;32m    119\u001b[0m     kwasync[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124masync_\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mpop(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124masync_\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m    121\u001b[0m dsn \u001b[38;5;241m=\u001b[39m _ext\u001b[38;5;241m.\u001b[39mmake_dsn(dsn, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m--> 122\u001b[0m conn \u001b[38;5;241m=\u001b[39m \u001b[43m_connect\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdsn\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconnection_factory\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconnection_factory\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwasync\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    123\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m cursor_factory \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    124\u001b[0m     conn\u001b[38;5;241m.\u001b[39mcursor_factory \u001b[38;5;241m=\u001b[39m cursor_factory\n",
      "\u001b[0;31mOperationalError\u001b[0m: (psycopg2.OperationalError) connection to server at \"192.168.0.74\", port 5432 failed: Connection refused\n\tIs the server running on that host and accepting TCP/IP connections?\n\n(Background on this error at: https://sqlalche.me/e/20/e3q8)"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sqlalchemy import create_engine\n",
    "import geopandas as gpd\n",
    "\n",
    "import os\n",
    "\n",
    "# Database connection details from zshrc environment variables\n",
    "db_name = 'colorado_spills'\n",
    "user = os.getenv('DB_USER')\n",
    "password = os.getenv('DB_PASSWORD')\n",
    "host = os.getenv('DB_HOST')\n",
    "port = os.getenv('DB_PORT')\n",
    "\n",
    "\n",
    "# Create an engine to connect to the PostgreSQL database\n",
    "engine = create_engine(f'postgresql+psycopg2://{user}:{password}@{host}:{port}/{db_name}')\n",
    "\n",
    "# Read in the spills_with_demographics as spills\n",
    "spills = pd.read_sql_table('spills_with_demographics', engine)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# use longitude and latitude to create a GeoDataFrame from spills data\n",
    "spills['geometry'] = gpd.points_from_xy(spills['Longitude'], spills['Latitude'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a GeoDataFrame from spills\n",
    "spills_gdf = gpd.GeoDataFrame(spills, crs='EPSG:4326') \n",
    "\n",
    "# Write the GeoDataFrame to a new table in the database\n",
    "spills_gdf.to_postgis('spills_with_demographics_geog', engine, if_exists='replace')\n",
    "\n",
    "# Close the database connection\n",
    "engine.dispose()\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date of Discovery</th>\n",
       "      <th>Initial Report Date</th>\n",
       "      <th>Report Delay (Days)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-06-11</td>\n",
       "      <td>2014-06-18</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-06-14</td>\n",
       "      <td>2014-06-19</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-06-14</td>\n",
       "      <td>2014-06-19</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-06-19</td>\n",
       "      <td>2014-06-20</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-06-20</td>\n",
       "      <td>2014-06-23</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Date of Discovery Initial Report Date  Report Delay (Days)\n",
       "0        2014-06-11          2014-06-18                    7\n",
       "1        2014-06-14          2014-06-19                    5\n",
       "2        2014-06-14          2014-06-19                    5\n",
       "3        2014-06-19          2014-06-20                    1\n",
       "4        2014-06-20          2014-06-23                    3"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Ensure the date columns are in datetime format\n",
    "spills_gdf['Date of Discovery'] = pd.to_datetime(spills_gdf['Date of Discovery'])\n",
    "spills_gdf['Initial Report Date'] = pd.to_datetime(spills_gdf['Initial Report Date'])\n",
    "\n",
    "# Calculate the time difference in days\n",
    "spills_gdf['Report Delay (Days)'] = (spills_gdf['Initial Report Date'] - spills_gdf['Date of Discovery']).dt.days\n",
    "\n",
    "# Display the first few rows to check the new column\n",
    "spills_gdf[['Date of Discovery', 'Initial Report Date', 'Report Delay (Days)']].head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Corrected 5889 negative report delays to zero.\n"
     ]
    }
   ],
   "source": [
    "# Set negative reporting delays to zero\n",
    "spills_gdf['Report Delay (Days)'] = spills_gdf['Report Delay (Days)'].apply(lambda x: max(x, 0))\n",
    "\n",
    "# Check how many negative values were corrected\n",
    "corrected_values = (spills_gdf['Report Delay (Days)'] == 0).sum()\n",
    "print(f'Corrected {corrected_values} negative report delays to zero.')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.neighbors import NearestNeighbors\n",
    "\n",
    "# Identify rows with negative or missing median household income\n",
    "negative_income_rows = spills_gdf[spills_gdf['median_household_income'] < 0].copy()\n",
    "\n",
    "# Identify rows with valid median household income\n",
    "valid_income_rows = spills_gdf[spills_gdf['median_household_income'] >= 0].copy()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use latitude and longitude for nearest neighbor search\n",
    "nbrs = NearestNeighbors(n_neighbors=1, algorithm='ball_tree').fit(valid_income_rows[['Latitude', 'Longitude']])\n",
    "distances, indices = nbrs.kneighbors(negative_income_rows[['Latitude', 'Longitude']])\n",
    "\n",
    "# Extract the nearest neighbor's median household income for each negative income row\n",
    "nearest_incomes = valid_income_rows.iloc[indices.flatten()]['median_household_income'].values\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Imputed 159 negative income values using nearest neighbors.\n"
     ]
    }
   ],
   "source": [
    "# Replace the negative income values with the nearest neighbor's income\n",
    "spills_gdf.loc[negative_income_rows.index, 'median_household_income'] = nearest_incomes\n",
    "\n",
    "# Check the result\n",
    "imputed_values = len(negative_income_rows)\n",
    "print(f'Imputed {imputed_values} negative income values using nearest neighbors.')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     16890.00000\n",
       "mean      79281.58958\n",
       "std       24119.48779\n",
       "min       29965.00000\n",
       "25%       64573.00000\n",
       "50%       72717.00000\n",
       "75%       85561.00000\n",
       "max      190417.00000\n",
       "Name: median_household_income, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# summarize median household income\n",
    "spills_gdf['median_household_income'].describe()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    16890.000000\n",
       "mean         9.911131\n",
       "std        126.932370\n",
       "min          0.000000\n",
       "25%          0.000000\n",
       "50%          1.000000\n",
       "75%          2.000000\n",
       "max       9261.000000\n",
       "Name: Report Delay (Days), dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Summary statistics for reporting delay\n",
    "report_delay_summary = spills_gdf['Report Delay (Days)'].describe()\n",
    "report_delay_summary\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Scatter plot of reporting delay vs. median household income\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.scatterplot(x='median_household_income', y='Report Delay (Days)', data=spills_gdf)\n",
    "plt.title('Reporting Delay vs. Median Household Income')\n",
    "plt.xlabel('Median Household Income')\n",
    "plt.ylabel('Reporting Delay (Days)')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                             OLS Regression Results                            \n",
      "===============================================================================\n",
      "Dep. Variable:     Report Delay (Days)   R-squared:                       0.000\n",
      "Model:                             OLS   Adj. R-squared:                 -0.000\n",
      "Method:                  Least Squares   F-statistic:                    0.2970\n",
      "Date:                 Mon, 12 Aug 2024   Prob (F-statistic):              0.586\n",
      "Time:                         22:35:51   Log-Likelihood:            -1.0577e+05\n",
      "No. Observations:                16890   AIC:                         2.116e+05\n",
      "Df Residuals:                    16888   BIC:                         2.116e+05\n",
      "Df Model:                            1                                         \n",
      "Covariance Type:             nonrobust                                         \n",
      "===========================================================================================\n",
      "                              coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------------------\n",
      "const                       8.1613      3.356      2.432      0.015       1.583      14.739\n",
      "median_household_income  2.207e-05   4.05e-05      0.545      0.586   -5.73e-05       0.000\n",
      "==============================================================================\n",
      "Omnibus:                    48059.926   Durbin-Watson:                   1.990\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):       3185509630.382\n",
      "Skew:                          37.898   Prob(JB):                         0.00\n",
      "Kurtosis:                    2129.202   Cond. No.                     2.85e+05\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 2.85e+05. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "\n",
    "# Define the independent variable (adding a constant for intercept)\n",
    "X = sm.add_constant(spills_gdf['median_household_income'])\n",
    "\n",
    "# Define the dependent variable\n",
    "y = spills_gdf['Report Delay (Days)']\n",
    "\n",
    "# Fit the regression model\n",
    "model = sm.OLS(y, X).fit()\n",
    "\n",
    "# Get the summary of the model\n",
    "model_summary = model.summary()\n",
    "print(model_summary)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Plot spill locations by median household income\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.histplot(data=spills_gdf, x='median_household_income', bins=30, kde=True)\n",
    "plt.title('Distribution of Spills by Median Household Income')\n",
    "plt.xlabel('Median Household Income')\n",
    "plt.ylabel('Number of Spills')\n",
    "plt.show()\n",
    "\n",
    "# Plot spill locations by percent poverty\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.histplot(data=spills_gdf, x='percent_poverty', bins=30, kde=True)\n",
    "plt.title('Distribution of Spills by Percent Poverty')\n",
    "plt.xlabel('Percent Poverty')\n",
    "plt.ylabel('Number of Spills')\n",
    "plt.show()\n",
    "\n",
    "# Plot spill locations by unemployment rate\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.histplot(data=spills_gdf, x='unemployment_rate', bins=30, kde=True)\n",
    "plt.title('Distribution of Spills by Unemployment Rate')\n",
    "plt.xlabel('Unemployment Rate')\n",
    "plt.ylabel('Number of Spills')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot spill locations by percent white\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.histplot(data=spills_gdf, x='percent_white', bins=30, kde=True)\n",
    "plt.title('Distribution of Spills by Percent White Population')\n",
    "plt.xlabel('Percent White')\n",
    "plt.ylabel('Number of Spills')\n",
    "plt.show()\n",
    "\n",
    "# Plot spill locations by percent hispanic\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.histplot(data=spills_gdf, x='percent_hispanic', bins=30, kde=True)\n",
    "plt.title('Distribution of Spills by Percent Hispanic Population')\n",
    "plt.xlabel('Percent Hispanic')\n",
    "plt.ylabel('Number of Spills')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scatter plot of reporting delay vs. median household income\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.scatterplot(x='median_household_income', y='Report Delay (Days)', data=spills_gdf)\n",
    "plt.title('Reporting Delay vs. Median Household Income')\n",
    "plt.xlabel('Median Household Income')\n",
    "plt.ylabel('Reporting Delay (Days)')\n",
    "plt.show()\n",
    "\n",
    "# Scatter plot of reporting delay vs. percent poverty\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.scatterplot(x='percent_poverty', y='Report Delay (Days)', data=spills_gdf)\n",
    "plt.title('Reporting Delay vs. Percent Poverty')\n",
    "plt.xlabel('Percent Poverty')\n",
    "plt.ylabel('Reporting Delay (Days)')\n",
    "plt.show()\n",
    "\n",
    "# Scatter plot of reporting delay vs. unemployment rate\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.scatterplot(x='unemployment_rate', y='Report Delay (Days)', data=spills_gdf)\n",
    "plt.title('Reporting Delay vs. Unemployment Rate')\n",
    "plt.xlabel('Unemployment Rate')\n",
    "plt.ylabel('Reporting Delay (Days)')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scatter plot of reporting delay vs. percent white\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.scatterplot(x='percent_white', y='Report Delay (Days)', data=spills_gdf)\n",
    "plt.title('Reporting Delay vs. Percent White Population')\n",
    "plt.xlabel('Percent White')\n",
    "plt.ylabel('Reporting Delay (Days)')\n",
    "plt.show()\n",
    "\n",
    "# Scatter plot of reporting delay vs. percent hispanic\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.scatterplot(x='percent_hispanic', y='Report Delay (Days)', data=spills_gdf)\n",
    "plt.title('Reporting Delay vs. Percent Hispanic Population')\n",
    "plt.xlabel('Percent Hispanic')\n",
    "plt.ylabel('Reporting Delay (Days)')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select relevant columns for correlation analysis\n",
    "correlation_columns = [\n",
    "    'Report Delay (Days)', \n",
    "    'median_household_income', \n",
    "    'percent_poverty', \n",
    "    'unemployment_rate', \n",
    "    'percent_white', \n",
    "    'percent_hispanic'\n",
    "]\n",
    "\n",
    "# Create a DataFrame with the selected columns\n",
    "correlation_df = spills_gdf[correlation_columns]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>Report Delay (Days)</th>\n",
       "      <th>median_household_income</th>\n",
       "      <th>percent_poverty</th>\n",
       "      <th>unemployment_rate</th>\n",
       "      <th>percent_white</th>\n",
       "      <th>percent_hispanic</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Report Delay (Days)</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.004194</td>\n",
       "      <td>-0.011415</td>\n",
       "      <td>-0.008741</td>\n",
       "      <td>-0.033242</td>\n",
       "      <td>-0.003342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median_household_income</th>\n",
       "      <td>0.004194</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.572311</td>\n",
       "      <td>-0.218962</td>\n",
       "      <td>0.070998</td>\n",
       "      <td>-0.224321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>percent_poverty</th>\n",
       "      <td>-0.011415</td>\n",
       "      <td>-0.572311</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.374435</td>\n",
       "      <td>-0.093304</td>\n",
       "      <td>-0.032890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unemployment_rate</th>\n",
       "      <td>-0.008741</td>\n",
       "      <td>-0.218962</td>\n",
       "      <td>0.374435</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.122596</td>\n",
       "      <td>-0.131676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>percent_white</th>\n",
       "      <td>-0.033242</td>\n",
       "      <td>0.070998</td>\n",
       "      <td>-0.093304</td>\n",
       "      <td>0.122596</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.643538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>percent_hispanic</th>\n",
       "      <td>-0.003342</td>\n",
       "      <td>-0.224321</td>\n",
       "      <td>-0.032890</td>\n",
       "      <td>-0.131676</td>\n",
       "      <td>-0.643538</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Report Delay (Days)  median_household_income  \\\n",
       "Report Delay (Days)                 1.000000                 0.004194   \n",
       "median_household_income             0.004194                 1.000000   \n",
       "percent_poverty                    -0.011415                -0.572311   \n",
       "unemployment_rate                  -0.008741                -0.218962   \n",
       "percent_white                      -0.033242                 0.070998   \n",
       "percent_hispanic                   -0.003342                -0.224321   \n",
       "\n",
       "                         percent_poverty  unemployment_rate  percent_white  \\\n",
       "Report Delay (Days)            -0.011415          -0.008741      -0.033242   \n",
       "median_household_income        -0.572311          -0.218962       0.070998   \n",
       "percent_poverty                 1.000000           0.374435      -0.093304   \n",
       "unemployment_rate               0.374435           1.000000       0.122596   \n",
       "percent_white                  -0.093304           0.122596       1.000000   \n",
       "percent_hispanic               -0.032890          -0.131676      -0.643538   \n",
       "\n",
       "                         percent_hispanic  \n",
       "Report Delay (Days)             -0.003342  \n",
       "median_household_income         -0.224321  \n",
       "percent_poverty                 -0.032890  \n",
       "unemployment_rate               -0.131676  \n",
       "percent_white                   -0.643538  \n",
       "percent_hispanic                 1.000000  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the correlation matrix\n",
    "correlation_matrix = correlation_df.corr()\n",
    "\n",
    "# Display the correlation matrix\n",
    "correlation_matrix\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Set up the matplotlib figure\n",
    "plt.figure(figsize=(10, 8))\n",
    "\n",
    "# Draw the heatmap\n",
    "sns.heatmap(correlation_matrix, annot=True, fmt=\".2f\", cmap='coolwarm', vmin=-1, vmax=1)\n",
    "\n",
    "# Add title and labels\n",
    "plt.title('Correlation Matrix of Spill-Related Variables and EJ Characteristics')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Operator\n",
       "NOBLE ENERGY INC                              2402\n",
       "KERR MCGEE OIL & GAS ONSHORE LP               2243\n",
       "PDC ENERGY INC                                1182\n",
       "CAERUS PICEANCE LLC                           1051\n",
       "BONANZA CREEK ENERGY OPERATING COMPANY LLC     643\n",
       "KP KAUFFMAN COMPANY INC                        468\n",
       "CHEVRON USA INC                                467\n",
       "HIGHPOINT OPERATING CORPORATION                411\n",
       "CRESTONE PEAK RESOURCES OPERATING LLC          394\n",
       "LARAMIE ENERGY LLC                             376\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Count the number of spills by operator\n",
    "operator_spill_count = spills_gdf['Operator'].value_counts()\n",
    "\n",
    "# Display the top operators with the most spills\n",
    "operator_spill_count.head(10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "county\n",
       "WELD               9850\n",
       "GARFIELD           1886\n",
       "RIO BLANCO         1034\n",
       "LAS ANIMAS          614\n",
       "ADAMS               583\n",
       "LA PLATA            496\n",
       "MESA                362\n",
       "JACKSON             271\n",
       "YUMA                270\n",
       "WASHINGTON          226\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Count the number of spills by county\n",
    "county_spill_count = spills_gdf['county'].value_counts()\n",
    "\n",
    "# Display the top counties with the most spills\n",
    "county_spill_count.head(10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Operator\n",
       "NOBLE ENERGY INC                              2402\n",
       "KERR MCGEE OIL & GAS ONSHORE LP               2243\n",
       "PDC ENERGY INC                                1182\n",
       "CAERUS PICEANCE LLC                           1051\n",
       "BONANZA CREEK ENERGY OPERATING COMPANY LLC     643\n",
       "                                              ... \n",
       "WOLVERINE RESOURCES LLC                          2\n",
       "DAN A HUGHES COMPANY LP                          2\n",
       "SUMMIT MIDSTREAM NIOBRARA LLC                    2\n",
       "LYSTER OIL COMPANY INC                           2\n",
       "SOVEREIGN OPERATING COMPANY LLC                  2\n",
       "Name: count, Length: 275, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Identify operators with more than one spill\n",
    "recurring_operator_spills = operator_spill_count[operator_spill_count > 1]\n",
    "\n",
    "# Display the operators with recurring spills\n",
    "recurring_operator_spills\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "county\n",
       "WELD               9850\n",
       "GARFIELD           1886\n",
       "RIO BLANCO         1034\n",
       "LAS ANIMAS          614\n",
       "ADAMS               583\n",
       "LA PLATA            496\n",
       "MESA                362\n",
       "JACKSON             271\n",
       "YUMA                270\n",
       "WASHINGTON          226\n",
       "MOFFAT              172\n",
       "ARAPAHOE            153\n",
       "LARIMER             122\n",
       "BOULDER             117\n",
       "CHEYENNE            106\n",
       "MONTEZUMA            97\n",
       "LOGAN                85\n",
       "LINCOLN              60\n",
       "ARCHULETA            56\n",
       "BROOMFIELD           52\n",
       "ELBERT               50\n",
       "GUNNISON             45\n",
       "KIOWA                39\n",
       "HUERFANO             32\n",
       "MORGAN               31\n",
       "BACA                 29\n",
       "PROWERS              10\n",
       "DOLORES               9\n",
       "ROUTT                 9\n",
       "KIT CARSON            8\n",
       "PITKIN                7\n",
       "DENVER                5\n",
       "FREMONT               4\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Identify counties with more than one spill\n",
    "recurring_county_spills = county_spill_count[county_spill_count > 1]\n",
    "\n",
    "# Display the counties with recurring spills\n",
    "recurring_county_spills\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>Number of Spills</th>\n",
       "      <th>median_household_income</th>\n",
       "      <th>percent_poverty</th>\n",
       "      <th>percent_white</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GEOID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>08001008354</th>\n",
       "      <td>31</td>\n",
       "      <td>101250.0</td>\n",
       "      <td>10.757540</td>\n",
       "      <td>28.591783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08001008401</th>\n",
       "      <td>122</td>\n",
       "      <td>88286.0</td>\n",
       "      <td>5.918776</td>\n",
       "      <td>86.814788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08001008402</th>\n",
       "      <td>95</td>\n",
       "      <td>114276.0</td>\n",
       "      <td>4.121431</td>\n",
       "      <td>82.046643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08001008526</th>\n",
       "      <td>1</td>\n",
       "      <td>98283.0</td>\n",
       "      <td>3.801249</td>\n",
       "      <td>79.011675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08001008538</th>\n",
       "      <td>9</td>\n",
       "      <td>131231.0</td>\n",
       "      <td>4.150727</td>\n",
       "      <td>75.181714</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Number of Spills  median_household_income  percent_poverty  \\\n",
       "GEOID                                                                     \n",
       "08001008354                31                 101250.0        10.757540   \n",
       "08001008401               122                  88286.0         5.918776   \n",
       "08001008402                95                 114276.0         4.121431   \n",
       "08001008526                 1                  98283.0         3.801249   \n",
       "08001008538                 9                 131231.0         4.150727   \n",
       "\n",
       "             percent_white  \n",
       "GEOID                       \n",
       "08001008354      28.591783  \n",
       "08001008401      86.814788  \n",
       "08001008402      82.046643  \n",
       "08001008526      79.011675  \n",
       "08001008538      75.181714  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group by GEOID and aggregate the number of spills and demographic characteristics\n",
    "tract_spill_summary = spills_gdf.groupby('GEOID').agg({\n",
    "    'Document #': 'count',\n",
    "    'median_household_income': 'mean',\n",
    "    'percent_poverty': 'mean',\n",
    "    'percent_white': 'mean'\n",
    "}).rename(columns={'Document #': 'Number of Spills'})\n",
    "\n",
    "# Display the summary\n",
    "tract_spill_summary.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>Number of Spills</th>\n",
       "      <th>median_household_income</th>\n",
       "      <th>percent_poverty</th>\n",
       "      <th>percent_white</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Number of Spills</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.157514</td>\n",
       "      <td>0.133947</td>\n",
       "      <td>-0.008777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median_household_income</th>\n",
       "      <td>-0.157514</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.625752</td>\n",
       "      <td>-0.001162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>percent_poverty</th>\n",
       "      <td>0.133947</td>\n",
       "      <td>-0.625752</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.067320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>percent_white</th>\n",
       "      <td>-0.008777</td>\n",
       "      <td>-0.001162</td>\n",
       "      <td>-0.067320</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Number of Spills  median_household_income  \\\n",
       "Number of Spills                 1.000000                -0.157514   \n",
       "median_household_income         -0.157514                 1.000000   \n",
       "percent_poverty                  0.133947                -0.625752   \n",
       "percent_white                   -0.008777                -0.001162   \n",
       "\n",
       "                         percent_poverty  percent_white  \n",
       "Number of Spills                0.133947      -0.008777  \n",
       "median_household_income        -0.625752      -0.001162  \n",
       "percent_poverty                 1.000000      -0.067320  \n",
       "percent_white                  -0.067320       1.000000  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the correlation matrix for the tract-level data\n",
    "correlation_matrix_tract = tract_spill_summary.corr()\n",
    "\n",
    "# Display the correlation matrix\n",
    "correlation_matrix_tract\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Scatter plot of Number of Spills vs. Median Household Income\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.scatterplot(x='median_household_income', y='Number of Spills', data=tract_spill_summary)\n",
    "plt.title('Number of Spills vs. Median Household Income (Census Tract Level)')\n",
    "plt.xlabel('Median Household Income')\n",
    "plt.ylabel('Number of Spills')\n",
    "plt.show()\n",
    "\n",
    "# Scatter plot of Number of Spills vs. Percent Poverty\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.scatterplot(x='percent_poverty', y='Number of Spills', data=tract_spill_summary)\n",
    "plt.title('Number of Spills vs. Percent Poverty (Census Tract Level)')\n",
    "plt.xlabel('Percent Poverty')\n",
    "plt.ylabel('Number of Spills')\n",
    "plt.show()\n",
    "\n",
    "# Scatter plot of Number of Spills vs. Percent White Population\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.scatterplot(x='percent_white', y='Number of Spills', data=tract_spill_summary)\n",
    "plt.title('Number of Spills vs. Percent White Population (Census Tract Level)')\n",
    "plt.xlabel('Percent White Population')\n",
    "plt.ylabel('Number of Spills')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Number of Spills           0\n",
       "median_household_income    0\n",
       "percent_poverty            0\n",
       "percent_white              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove rows with NaN or infinite values\n",
    "tract_spill_summary_cleaned = tract_spill_summary.replace([np.inf, -np.inf], np.nan).dropna()\n",
    "\n",
    "# Check that the data is clean\n",
    "tract_spill_summary_cleaned.isna().sum()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:       Number of Spills   R-squared:                       0.027\n",
      "Model:                            OLS   Adj. R-squared:                  0.008\n",
      "Method:                 Least Squares   F-statistic:                     1.403\n",
      "Date:                Mon, 12 Aug 2024   Prob (F-statistic):              0.244\n",
      "Time:                        22:36:52   Log-Likelihood:                -1091.6\n",
      "No. Observations:                 157   AIC:                             2191.\n",
      "Df Residuals:                     153   BIC:                             2203.\n",
      "Df Model:                           3                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "===========================================================================================\n",
      "                              coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------------------\n",
      "const                     185.0017    197.832      0.935      0.351    -205.834     575.837\n",
      "median_household_income    -0.0010      0.001     -1.178      0.241      -0.003       0.001\n",
      "percent_poverty             2.5445      4.494      0.566      0.572      -6.335      11.424\n",
      "percent_white              -0.1193      1.909     -0.063      0.950      -3.890       3.652\n",
      "==============================================================================\n",
      "Omnibus:                      196.289   Durbin-Watson:                   1.335\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             6950.948\n",
      "Skew:                           4.985   Prob(JB):                         0.00\n",
      "Kurtosis:                      34.035   Cond. No.                     9.33e+05\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 9.33e+05. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "# Define the independent variables (with a constant for intercept)\n",
    "X = tract_spill_summary_cleaned[['median_household_income', 'percent_poverty', 'percent_white']]\n",
    "X = sm.add_constant(X)\n",
    "\n",
    "# Define the dependent variable\n",
    "y = tract_spill_summary_cleaned['Number of Spills']\n",
    "\n",
    "# Fit the regression model\n",
    "model = sm.OLS(y, X).fit()\n",
    "\n",
    "# Get the summary of the model\n",
    "model_summary = model.summary()\n",
    "print(model_summary)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>Number of Spills</th>\n",
       "      <th>median_household_income</th>\n",
       "      <th>percent_poverty</th>\n",
       "      <th>percent_white</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Number of Spills</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.157012</td>\n",
       "      <td>0.133947</td>\n",
       "      <td>-0.008777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median_household_income</th>\n",
       "      <td>-0.157012</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.625752</td>\n",
       "      <td>-0.001162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>percent_poverty</th>\n",
       "      <td>0.133947</td>\n",
       "      <td>-0.625752</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.067320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>percent_white</th>\n",
       "      <td>-0.008777</td>\n",
       "      <td>-0.001162</td>\n",
       "      <td>-0.067320</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Number of Spills  median_household_income  \\\n",
       "Number of Spills                 1.000000                -0.157012   \n",
       "median_household_income         -0.157012                 1.000000   \n",
       "percent_poverty                  0.133947                -0.625752   \n",
       "percent_white                   -0.008777                -0.001162   \n",
       "\n",
       "                         percent_poverty  percent_white  \n",
       "Number of Spills                0.133947      -0.008777  \n",
       "median_household_income        -0.625752      -0.001162  \n",
       "percent_poverty                 1.000000      -0.067320  \n",
       "percent_white                  -0.067320       1.000000  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Re-calculate the correlation matrix\n",
    "correlation_matrix_cleaned = tract_spill_summary_cleaned.corr()\n",
    "\n",
    "# Display the cleaned correlation matrix\n",
    "correlation_matrix_cleaned\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>Initial Report Date</th>\n",
       "      <th>Report Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-06-18</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-06-19</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-06-19</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-06-20</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-06-23</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Initial Report Date  Report Year\n",
       "0          2014-06-18         2014\n",
       "1          2014-06-19         2014\n",
       "2          2014-06-19         2014\n",
       "3          2014-06-20         2014\n",
       "4          2014-06-23         2014"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Ensure the 'Initial Report Date' is in datetime format\n",
    "spills_gdf['Initial Report Date'] = pd.to_datetime(spills_gdf['Initial Report Date'])\n",
    "\n",
    "# Extract the year from the 'Initial Report Date'\n",
    "spills_gdf['Report Year'] = spills_gdf['Initial Report Date'].dt.year\n",
    "\n",
    "# Display the first few rows to verify the new column\n",
    "spills_gdf[['Initial Report Date', 'Report Year']].head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>Report Year</th>\n",
       "      <th>Number of Spills</th>\n",
       "      <th>Average Spills (Jan-Dec)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015</td>\n",
       "      <td>1630</td>\n",
       "      <td>135.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016</td>\n",
       "      <td>1376</td>\n",
       "      <td>114.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017</td>\n",
       "      <td>1611</td>\n",
       "      <td>134.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018</td>\n",
       "      <td>1583</td>\n",
       "      <td>131.916667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019</td>\n",
       "      <td>1556</td>\n",
       "      <td>129.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2020</td>\n",
       "      <td>1174</td>\n",
       "      <td>97.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2021</td>\n",
       "      <td>1780</td>\n",
       "      <td>148.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2022</td>\n",
       "      <td>2146</td>\n",
       "      <td>178.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2023</td>\n",
       "      <td>2077</td>\n",
       "      <td>173.083333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Report Year  Number of Spills  Average Spills (Jan-Dec)\n",
       "0         2015              1630                135.833333\n",
       "1         2016              1376                114.666667\n",
       "2         2017              1611                134.250000\n",
       "3         2018              1583                131.916667\n",
       "4         2019              1556                129.666667\n",
       "5         2020              1174                 97.833333\n",
       "6         2021              1780                148.333333\n",
       "7         2022              2146                178.833333\n",
       "8         2023              2077                173.083333"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filter the data for years 2015 to 2023\n",
    "spills_gdf_filtered = spills_gdf[(spills_gdf['Report Year'] >= 2015) & (spills_gdf['Report Year'] <= 2023)]\n",
    "\n",
    "\n",
    "# Group by year and count the number of spills for each year up to August\n",
    "spills_by_year_filtered = spills_gdf_filtered.groupby('Report Year').size().reset_index(name='Number of Spills')\n",
    "\n",
    "# Calculate the average number of spills per month for each year (January-August)\n",
    "spills_by_year_filtered['Average Spills (Jan-Dec)'] = spills_by_year_filtered['Number of Spills'] / 12\n",
    "\n",
    "# Display the result\n",
    "spills_by_year_filtered\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reporting Delay by Year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>Report Year</th>\n",
       "      <th>Average Report Delay (Days)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014</td>\n",
       "      <td>5.991485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015</td>\n",
       "      <td>31.561963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016</td>\n",
       "      <td>8.813953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017</td>\n",
       "      <td>4.448169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018</td>\n",
       "      <td>2.939987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2019</td>\n",
       "      <td>16.108612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2020</td>\n",
       "      <td>12.212947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2021</td>\n",
       "      <td>3.074157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2022</td>\n",
       "      <td>7.804287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2023</td>\n",
       "      <td>6.325469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2024</td>\n",
       "      <td>12.123333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Report Year  Average Report Delay (Days)\n",
       "0          2014                     5.991485\n",
       "1          2015                    31.561963\n",
       "2          2016                     8.813953\n",
       "3          2017                     4.448169\n",
       "4          2018                     2.939987\n",
       "5          2019                    16.108612\n",
       "6          2020                    12.212947\n",
       "7          2021                     3.074157\n",
       "8          2022                     7.804287\n",
       "9          2023                     6.325469\n",
       "10         2024                    12.123333"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group by 'Report Year' and calculate the average reporting delay\n",
    "report_delay_by_year = spills_gdf.groupby('Report Year')['Report Delay (Days)'].mean().reset_index(name='Average Report Delay (Days)')\n",
    "\n",
    "# Display the average reporting delay by year\n",
    "report_delay_by_year\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the average reporting delay by year\n",
    "plt.figure(figsize=(12, 6))\n",
    "sns.lineplot(x='Report Year', y='Average Report Delay (Days)', data=report_delay_by_year, marker='o')\n",
    "plt.title('Average Reporting Delay by Year')\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Average Report Delay (Days)')\n",
    "plt.grid(True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filter the data for years 2015 to 2023\n",
    "spill_gdf_filtered = spills_gdf[(spills_gdf['Report Year'] >= 2015) & (spills_gdf['Report Year'] <= 2023)]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/dadams/miniconda3/envs/funkyfunk/lib/python3.10/site-packages/geopandas/geodataframe.py:1528: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  super().__setitem__(key, value)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Report Year</th>\n",
       "      <th>Average Spills per Month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015</td>\n",
       "      <td>135.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016</td>\n",
       "      <td>114.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017</td>\n",
       "      <td>134.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018</td>\n",
       "      <td>131.916667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019</td>\n",
       "      <td>129.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2020</td>\n",
       "      <td>97.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2021</td>\n",
       "      <td>148.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2022</td>\n",
       "      <td>178.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2023</td>\n",
       "      <td>173.083333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Report Year  Average Spills per Month\n",
       "0         2015                135.833333\n",
       "1         2016                114.666667\n",
       "2         2017                134.250000\n",
       "3         2018                131.916667\n",
       "4         2019                129.666667\n",
       "5         2020                 97.833333\n",
       "6         2021                148.333333\n",
       "7         2022                178.833333\n",
       "8         2023                173.083333"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group by year and month, then count the number of spills\n",
    "spills_gdf_filtered['Report Month'] = spills_gdf_filtered['Initial Report Date'].dt.month\n",
    "spills_by_month = spills_gdf_filtered.groupby(['Report Year', 'Report Month']).size().reset_index(name='Number of Spills')\n",
    "\n",
    "# Calculate the average number of spills per month for each year\n",
    "monthly_average_spills = spills_by_month.groupby('Report Year')['Number of Spills'].mean().reset_index(name='Average Spills per Month')\n",
    "\n",
    "# Display the result\n",
    "monthly_average_spills\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Plot the average number of spills per month for each year (2015-2023)\n",
    "plt.figure(figsize=(12, 6))\n",
    "sns.lineplot(x='Report Year', y='Average Spills per Month', data=monthly_average_spills, marker='o')\n",
    "plt.title('Average Monthly Spill Reporting (January-December, 2015-2023)')\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Average Number of Spills per Month')\n",
    "plt.grid(True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>Document #</th>\n",
       "      <th>Operator #</th>\n",
       "      <th>Tracking #</th>\n",
       "      <th>Initial Report Date</th>\n",
       "      <th>Date of Discovery</th>\n",
       "      <th>Section</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Facility ID</th>\n",
       "      <th>Waters of the State</th>\n",
       "      <th>...</th>\n",
       "      <th>hispanic_population</th>\n",
       "      <th>median_household_income</th>\n",
       "      <th>poverty_population</th>\n",
       "      <th>unemployed_population</th>\n",
       "      <th>percent_white</th>\n",
       "      <th>percent_hispanic</th>\n",
       "      <th>percent_poverty</th>\n",
       "      <th>unemployment_rate</th>\n",
       "      <th>Report Delay (Days)</th>\n",
       "      <th>Report Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.689000e+04</td>\n",
       "      <td>16890.000000</td>\n",
       "      <td>1.689000e+04</td>\n",
       "      <td>16890</td>\n",
       "      <td>16890</td>\n",
       "      <td>16890.000000</td>\n",
       "      <td>16890.000000</td>\n",
       "      <td>16890.000000</td>\n",
       "      <td>9.348000e+03</td>\n",
       "      <td>10450.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>16890.000000</td>\n",
       "      <td>16890.00000</td>\n",
       "      <td>16890.000000</td>\n",
       "      <td>16890.000000</td>\n",
       "      <td>16886.000000</td>\n",
       "      <td>16886.000000</td>\n",
       "      <td>16886.000000</td>\n",
       "      <td>16886.000000</td>\n",
       "      <td>16890.000000</td>\n",
       "      <td>16890.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.022262e+08</td>\n",
       "      <td>42616.314032</td>\n",
       "      <td>4.019028e+08</td>\n",
       "      <td>2019-09-07 19:15:24.618117376</td>\n",
       "      <td>2019-08-28 21:23:22.912966144</td>\n",
       "      <td>18.915157</td>\n",
       "      <td>39.905069</td>\n",
       "      <td>-105.446183</td>\n",
       "      <td>3.778717e+05</td>\n",
       "      <td>0.146411</td>\n",
       "      <td>...</td>\n",
       "      <td>1029.143813</td>\n",
       "      <td>79281.58958</td>\n",
       "      <td>474.496152</td>\n",
       "      <td>129.642274</td>\n",
       "      <td>83.509353</td>\n",
       "      <td>22.542174</td>\n",
       "      <td>10.344773</td>\n",
       "      <td>2.652712</td>\n",
       "      <td>9.911131</td>\n",
       "      <td>2019.191948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.006288e+08</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.125370e+05</td>\n",
       "      <td>2014-06-18 00:00:00</td>\n",
       "      <td>1994-11-14 00:00:00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>36.994626</td>\n",
       "      <td>-109.048729</td>\n",
       "      <td>1.071900e+04</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>29965.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>28.591783</td>\n",
       "      <td>0.958248</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2014.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.012788e+08</td>\n",
       "      <td>10456.000000</td>\n",
       "      <td>4.012024e+08</td>\n",
       "      <td>2017-02-08 00:00:00</td>\n",
       "      <td>2017-02-01 00:00:00</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>39.762535</td>\n",
       "      <td>-106.417460</td>\n",
       "      <td>3.268140e+05</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>475.000000</td>\n",
       "      <td>64573.00000</td>\n",
       "      <td>234.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>78.829310</td>\n",
       "      <td>12.506417</td>\n",
       "      <td>5.832087</td>\n",
       "      <td>1.541138</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2017.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.022739e+08</td>\n",
       "      <td>35080.000000</td>\n",
       "      <td>4.021915e+08</td>\n",
       "      <td>2019-09-30 00:00:00</td>\n",
       "      <td>2019-09-25 00:00:00</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>40.161668</td>\n",
       "      <td>-104.794379</td>\n",
       "      <td>4.158920e+05</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>72717.00000</td>\n",
       "      <td>344.000000</td>\n",
       "      <td>110.000000</td>\n",
       "      <td>85.916837</td>\n",
       "      <td>18.302199</td>\n",
       "      <td>9.571823</td>\n",
       "      <td>2.433281</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2019.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.031000e+08</td>\n",
       "      <td>69175.000000</td>\n",
       "      <td>4.030576e+08</td>\n",
       "      <td>2022-05-24 00:00:00</td>\n",
       "      <td>2022-05-17 00:00:00</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>40.372890</td>\n",
       "      <td>-104.536800</td>\n",
       "      <td>4.400930e+05</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1658.000000</td>\n",
       "      <td>85561.00000</td>\n",
       "      <td>693.000000</td>\n",
       "      <td>225.000000</td>\n",
       "      <td>87.848339</td>\n",
       "      <td>35.419782</td>\n",
       "      <td>13.510880</td>\n",
       "      <td>3.973510</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2022.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.038263e+08</td>\n",
       "      <td>200565.000000</td>\n",
       "      <td>4.038263e+08</td>\n",
       "      <td>2024-06-15 00:00:00</td>\n",
       "      <td>2024-06-15 00:00:00</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>41.001058</td>\n",
       "      <td>-102.052878</td>\n",
       "      <td>1.663827e+06</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>4661.000000</td>\n",
       "      <td>190417.00000</td>\n",
       "      <td>1842.000000</td>\n",
       "      <td>398.000000</td>\n",
       "      <td>99.377593</td>\n",
       "      <td>79.060266</td>\n",
       "      <td>28.060166</td>\n",
       "      <td>6.580830</td>\n",
       "      <td>9261.000000</td>\n",
       "      <td>2024.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.811532e+05</td>\n",
       "      <td>36494.807268</td>\n",
       "      <td>1.030487e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.407988</td>\n",
       "      <td>0.888095</td>\n",
       "      <td>1.644985</td>\n",
       "      <td>8.116550e+04</td>\n",
       "      <td>0.353535</td>\n",
       "      <td>...</td>\n",
       "      <td>715.639127</td>\n",
       "      <td>24119.48779</td>\n",
       "      <td>361.781419</td>\n",
       "      <td>87.822415</td>\n",
       "      <td>8.492630</td>\n",
       "      <td>13.006549</td>\n",
       "      <td>6.393041</td>\n",
       "      <td>1.352330</td>\n",
       "      <td>126.932370</td>\n",
       "      <td>3.035637</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Document #     Operator #    Tracking #  \\\n",
       "count  1.689000e+04   16890.000000  1.689000e+04   \n",
       "mean   4.022262e+08   42616.314032  4.019028e+08   \n",
       "min    4.006288e+08       5.000000  1.125370e+05   \n",
       "25%    4.012788e+08   10456.000000  4.012024e+08   \n",
       "50%    4.022739e+08   35080.000000  4.021915e+08   \n",
       "75%    4.031000e+08   69175.000000  4.030576e+08   \n",
       "max    4.038263e+08  200565.000000  4.038263e+08   \n",
       "std    9.811532e+05   36494.807268  1.030487e+07   \n",
       "\n",
       "                 Initial Report Date              Date of Discovery  \\\n",
       "count                          16890                          16890   \n",
       "mean   2019-09-07 19:15:24.618117376  2019-08-28 21:23:22.912966144   \n",
       "min              2014-06-18 00:00:00            1994-11-14 00:00:00   \n",
       "25%              2017-02-08 00:00:00            2017-02-01 00:00:00   \n",
       "50%              2019-09-30 00:00:00            2019-09-25 00:00:00   \n",
       "75%              2022-05-24 00:00:00            2022-05-17 00:00:00   \n",
       "max              2024-06-15 00:00:00            2024-06-15 00:00:00   \n",
       "std                              NaN                            NaN   \n",
       "\n",
       "            Section      Latitude     Longitude   Facility ID  \\\n",
       "count  16890.000000  16890.000000  16890.000000  9.348000e+03   \n",
       "mean      18.915157     39.905069   -105.446183  3.778717e+05   \n",
       "min        1.000000     36.994626   -109.048729  1.071900e+04   \n",
       "25%       10.000000     39.762535   -106.417460  3.268140e+05   \n",
       "50%       20.000000     40.161668   -104.794379  4.158920e+05   \n",
       "75%       28.000000     40.372890   -104.536800  4.400930e+05   \n",
       "max       36.000000     41.001058   -102.052878  1.663827e+06   \n",
       "std       10.407988      0.888095      1.644985  8.116550e+04   \n",
       "\n",
       "       Waters of the State  ...  hispanic_population  median_household_income  \\\n",
       "count         10450.000000  ...         16890.000000              16890.00000   \n",
       "mean              0.146411  ...          1029.143813              79281.58958   \n",
       "min               0.000000  ...             0.000000              29965.00000   \n",
       "25%               0.000000  ...           475.000000              64573.00000   \n",
       "50%               0.000000  ...           846.000000              72717.00000   \n",
       "75%               0.000000  ...          1658.000000              85561.00000   \n",
       "max               1.000000  ...          4661.000000             190417.00000   \n",
       "std               0.353535  ...           715.639127              24119.48779   \n",
       "\n",
       "       poverty_population  unemployed_population  percent_white  \\\n",
       "count        16890.000000           16890.000000   16886.000000   \n",
       "mean           474.496152             129.642274      83.509353   \n",
       "min              0.000000               0.000000      28.591783   \n",
       "25%            234.000000              57.000000      78.829310   \n",
       "50%            344.000000             110.000000      85.916837   \n",
       "75%            693.000000             225.000000      87.848339   \n",
       "max           1842.000000             398.000000      99.377593   \n",
       "std            361.781419              87.822415       8.492630   \n",
       "\n",
       "       percent_hispanic  percent_poverty  unemployment_rate  \\\n",
       "count      16886.000000     16886.000000       16886.000000   \n",
       "mean          22.542174        10.344773           2.652712   \n",
       "min            0.958248         0.000000           0.000000   \n",
       "25%           12.506417         5.832087           1.541138   \n",
       "50%           18.302199         9.571823           2.433281   \n",
       "75%           35.419782        13.510880           3.973510   \n",
       "max           79.060266        28.060166           6.580830   \n",
       "std           13.006549         6.393041           1.352330   \n",
       "\n",
       "       Report Delay (Days)   Report Year  \n",
       "count         16890.000000  16890.000000  \n",
       "mean              9.911131   2019.191948  \n",
       "min               0.000000   2014.000000  \n",
       "25%               0.000000   2017.000000  \n",
       "50%               1.000000   2019.000000  \n",
       "75%               2.000000   2022.000000  \n",
       "max            9261.000000   2024.000000  \n",
       "std             126.932370      3.035637  \n",
       "\n",
       "[8 rows x 81 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# spills_gdf summary statistics\n",
    "spills_gdf.describe()\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# update spills_with_demographics_geog table with spills_gdf data\n",
    "engine = create_engine(f'postgresql+psycopg2://{user}:{password}@{host}:{port}/{db_name}')\n",
    "\n",
    "# Write the GeoDataFrame to the existing table in the database\n",
    "spills_gdf.to_postgis('spills_with_demographics_geog', engine, if_exists='replace')\n",
    "\n",
    "# Close the database connection\n",
    "engine.dispose()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data exported to CSV and GeoJSON files successfully.\n"
     ]
    }
   ],
   "source": [
    "# create csv and geojson files from spills_gdf\n",
    "spills_gdf.to_csv('spills_with_demographics.csv', index=False)\n",
    "spills_gdf.to_file('spills_with_demographics.geojson', driver='GeoJSON')\n",
    "\n",
    "print('Data exported to CSV and GeoJSON files successfully.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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