{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Colorado Spills Project\n",
    "## Outline\n",
    "\n",
    "1. **Data Import and Preparation**\n",
    "    - Import necessary libraries and load data from the database.\n",
    "    - Convert data into GeoDataFrame and ensure proper formatting.\n",
    "\n",
    "2. **Data Cleaning**\n",
    "    - Handle missing or incorrect data.\n",
    "    - Filter out data for the year 2024.\n",
    "\n",
    "3. **Exploratory Data Analysis**\n",
    "    - Separate historical and recent spills.\n",
    "    - Analyze reporting delays and trends over time.\n",
    "\n",
    "4. **Visualization**\n",
    "    - Create spatial distribution maps.\n",
    "    - Plot trends and differences in reporting delays.\n",
    "\n",
    "5. **Statistical Analysis**\n",
    "    - Perform t-tests to compare response times.\n",
    "    - Use regression models to analyze differences and trends.\n",
    "\n",
    "6. **Results and Interpretation**\n",
    "    - Summarize findings in markdown cells.\n",
    "    - Highlight key takeaways and policy implications.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/dadams/miniconda3/envs/spatial_env2/lib/python3.10/site-packages/pandas/io/sql.py:1725: SAWarning: Did not recognize type 'geometry' of column 'geometry'\n",
      "  self.meta.reflect(bind=self.con, only=[table_name], views=True)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sqlalchemy import create_engine\n",
    "import geopandas as gpd\n",
    "from dotenv import load_dotenv\n",
    "load_dotenv()\n",
    "\n",
    "import os\n",
    "\n",
    "# Database connection details from zshrc environment variables\n",
    "#  use .env file for local development\n",
    "\n",
    "\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_geog as spills\n",
    "spills = pd.read_sql_table('spills_with_demographics_geog', engine)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "spills_gdf = gpd.GeoDataFrame(spills, crs='EPSG:4326') \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Ensure 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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop 2024 data for date of discovery and initial report date\n",
    "spills_gdf = spills_gdf[spills_gdf['Date of Discovery'].dt.year < 2024]\n",
    "spills_gdf = spills_gdf[spills_gdf['Initial Report Date'].dt.year < 2024]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Separate Historical vs. Recent Spills\n",
    "historical_spills = spills_gdf[spills_gdf['Spill Type'] == 'Historical']\n",
    "recent_spills = spills_gdf[spills_gdf['Spill Type'] == 'Recent']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              count       mean         std  min  25%  50%  75%     max\n",
      "Spill Type                                                            \n",
      "Historical   5312.0  21.490023  205.615913  0.0  0.0  1.0  2.0  9261.0\n",
      "Recent      10678.0   3.964506   46.997749  0.0  0.0  1.0  2.0  2232.0\n"
     ]
    }
   ],
   "source": [
    "# Compare Reporting Delay\n",
    "report_delay_summary = spills_gdf.groupby('Spill Type')['Report Delay (Days)'].describe()\n",
    "print(report_delay_summary)\n",
    "\n",
    "# Ensure the output directory exists\n",
    "output_dir = 'analysis/output'\n",
    "os.makedirs(output_dir, exist_ok=True)\n",
    "\n",
    "# Save the report delay summary to a markdown file\n",
    "with open(os.path.join(output_dir, 'report_delay_summary.md'), 'w') as f:\n",
    "    f.write(\"# Report Delay Summary\\n\\n\")\n",
    "    f.write(report_delay_summary.to_markdown())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "[encoded data, 32240 characters]",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Compare Spill Trends Over Time\n",
    "spills_by_year = spills_gdf.groupby(['Report Year', 'Spill Type']).size().unstack()\n",
    "ax = spills_by_year.plot(kind='bar', figsize=(12,6), title='Spills by Year and Type')\n",
    "ax.set_xlabel('Year')\n",
    "ax.set_ylabel('Number of Spills')\n",
    "ax.legend(title='Spill Type')\n",
    "\n",
    "# Ensure the output directory exists\n",
    "output_dir = 'analysis/output'\n",
    "os.makedirs(output_dir, exist_ok=True)\n",
    "\n",
    "# Save the plot\n",
    "plt.savefig(f'{output_dir}/spills_by_year_and_type.png')\n",
    "\n",
    "# Display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "[encoded data, 154692 characters]",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import geopandas as gpd\n",
    "\n",
    "# Ensure spills_gdf is already a GeoDataFrame with the necessary data\n",
    "# spills_gdf = gpd.GeoDataFrame(...)\n",
    "\n",
    "# Create a plot for the spatial distribution of spills\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "\n",
    "# Plot the GeoDataFrame with optimizations\n",
    "spills_gdf.plot(\n",
    "    ax=ax,\n",
    "    marker='o', \n",
    "    alpha=0.6,  # Adjust transparency for better visibility\n",
    "    column='Spill Type', \n",
    "    legend=True, \n",
    "    legend_kwds={'title': \"Spill Type\"},  # Correctly specify the legend title\n",
    "    cmap='viridis'  # Use a colormap for better visual distinction\n",
    ")\n",
    "\n",
    "# Set plot title and labels\n",
    "ax.set_title('Spatial Distribution of Historical vs. Recent Spills', fontsize=14)\n",
    "ax.set_xlabel('Longitude', fontsize=12)\n",
    "ax.set_ylabel('Latitude', fontsize=12)\n",
    "\n",
    "# Improve layout\n",
    "plt.tight_layout()\n",
    "\n",
    "# Display the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "[encoded data, 470740 characters]",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import contextily as ctx\n",
    "import numpy as np\n",
    "\n",
    "# Use your ORIGINAL plot code exactly as it is, just adding the basemap at the end\n",
    "# This ensures all your data points, colors and categories remain intact\n",
    "\n",
    "# Starting with your original code:\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "\n",
    "# Plot the GeoDataFrame with optimizations - YOUR ORIGINAL PLOTTING CODE\n",
    "spills_gdf.plot(\n",
    "    ax=ax,\n",
    "    marker='o',\n",
    "    alpha=0.6,  # Adjust transparency for better visibility\n",
    "    column='Spill Type',\n",
    "    legend=True,\n",
    "    legend_kwds={'title': \"Spill Type\"},  # Correctly specify the legend title\n",
    "    cmap='viridis'  # Use a colormap for better visual distinction\n",
    ")\n",
    "\n",
    "# Set plot title and labels\n",
    "ax.set_title('Spatial Distribution of Historical vs. Recent Spills in Colorado', fontsize=14)\n",
    "ax.set_xlabel('Longitude', fontsize=12)\n",
    "ax.set_ylabel('Latitude', fontsize=12)\n",
    "\n",
    "# Store the current axis limits to restore them after adding basemap\n",
    "xlim = ax.get_xlim()\n",
    "ylim = ax.get_ylim()\n",
    "\n",
    "# Before adding the basemap, convert the axes coordinates to Web Mercator\n",
    "# This is a crucial step that lets us keep your original points exactly as they are\n",
    "from pyproj import Transformer\n",
    "\n",
    "# Create transformer from lat/lon to Web Mercator\n",
    "transformer = Transformer.from_crs(\"EPSG:4326\", \"EPSG:3857\", always_xy=True)\n",
    "\n",
    "# Transform the limits\n",
    "west, south = transformer.transform(xlim[0], ylim[0])\n",
    "east, north = transformer.transform(xlim[1], ylim[1])\n",
    "\n",
    "# Add the basemap - this will be UNDER your existing points\n",
    "ctx.add_basemap(\n",
    "    ax,\n",
    "    crs=spills_gdf.crs,\n",
    "    source=ctx.providers.OpenStreetMap.Mapnik,\n",
    "    zoom=\"auto\"\n",
    ")\n",
    "\n",
    "# Restore original axis limits\n",
    "ax.set_xlim(xlim)\n",
    "ax.set_ylim(ylim)\n",
    "\n",
    "# Improve layout\n",
    "plt.tight_layout()\n",
    "\n",
    "# Save the plot to the analysis/output directory\n",
    "output_dir = 'analysis/output'\n",
    "os.makedirs(output_dir, exist_ok=True)\n",
    "plt.savefig(f'{output_dir}/spatial_distribution_spills.png')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 count       mean         std  min  25%  50%  75%     max\n",
      "Period                                                                   \n",
      "2020 and After  7177.0   6.924342  124.965839  0.0  0.0  1.0  1.0  9261.0\n",
      "Before 2020     8813.0  12.117554  124.705876  0.0  0.0  1.0  2.0  5681.0\n"
     ]
    }
   ],
   "source": [
    "# Add a column to indicate whether the spill occurred before or after 2020\n",
    "spills_gdf['Period'] = spills_gdf['Report Year'].apply(lambda x: 'Before 2020' if x < 2020 else '2020 and After')\n",
    "\n",
    "# Group by the new period column and calculate the mean response time\n",
    "response_time_summary = spills_gdf.groupby('Period')['Report Delay (Days)'].describe()\n",
    "print(response_time_summary)\n",
    "\n",
    "# Ensure the output directory exists\n",
    "output_dir = 'analysis/output'\n",
    "os.makedirs(output_dir, exist_ok=True)\n",
    "\n",
    "# Save the response time summary to a markdown file\n",
    "with open(os.path.join(output_dir, 'response_time_summary.md'), 'w') as f:\n",
    "    f.write(\"# Response Time Summary\\n\\n\")\n",
    "    f.write(response_time_summary.to_markdown())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "T-Statistic: 2.6161\n",
      "P-Value: 0.0089\n",
      "The difference in response time is statistically significant.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'Report Delay (Days)'}, xlabel='Period'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "[encoded data, 34072 characters]",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# t-test for the difference in response time before and after 2020\n",
    "from scipy.stats import ttest_ind\n",
    "\n",
    "before_2020 = spills_gdf[spills_gdf['Period'] == 'Before 2020']['Report Delay (Days)']\n",
    "after_2020 = spills_gdf[spills_gdf['Period'] == '2020 and After']['Report Delay (Days)']\n",
    "\n",
    "t_stat, p_value = ttest_ind(before_2020, after_2020, equal_var=False)\n",
    "print(f'T-Statistic: {t_stat:.4f}')\n",
    "print(f'P-Value: {p_value:.4f}')\n",
    "\n",
    "# Check if the difference is statistically significant\n",
    "if p_value < 0.05:\n",
    "    print('The difference in response time is statistically significant.')\n",
    "else:\n",
    "    print('The difference in response time is not statistically significant.')\n",
    "    \n",
    "# Create a plot for the response time distribution before and after 2020\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "\n",
    "# Plot the response time distribution for each period\n",
    "spills_gdf.boxplot(column='Report Delay (Days)', by='Period', ax=ax)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Historical Spills Report Delay Summary:\n",
      "count    5312.000000\n",
      "mean       21.490023\n",
      "std       205.615913\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\n",
      "\n",
      "Recent Spills Report Delay Summary:\n",
      "count    10678.000000\n",
      "mean         3.964506\n",
      "std         46.997749\n",
      "min          0.000000\n",
      "25%          0.000000\n",
      "50%          1.000000\n",
      "75%          2.000000\n",
      "max       2232.000000\n",
      "Name: Report Delay (Days), dtype: float64\n",
      "\n",
      "Historical Spills Response Time Summary:\n",
      "                 count       mean         std  min  25%  50%  75%     max\n",
      "Period                                                                   \n",
      "2020 and After  2705.0  13.533087  200.352339  0.0  0.0  0.0  1.0  9261.0\n",
      "Before 2020     2607.0  29.746068  210.659491  0.0  0.0  1.0  2.0  5681.0\n",
      "\n",
      "Recent Spills Response Time Summary:\n",
      "                 count      mean        std  min  25%  50%  75%     max\n",
      "Period                                                                 \n",
      "2020 and After  4472.0  2.926878  27.302062  0.0  0.0  1.0  1.0  1329.0\n",
      "Before 2020     6206.0  4.712214  57.116100  0.0  1.0  1.0  2.0  2232.0\n",
      "\n",
      "Historical Spills T-Statistic: 2.8723\n",
      "Historical Spills P-Value: 0.0041\n",
      "The difference in response time for historical spills is statistically significant.\n",
      "\n",
      "Recent Spills T-Statistic: 2.1457\n",
      "Recent Spills P-Value: 0.0319\n",
      "The difference in response time for recent spills is statistically significant.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/dadams/miniconda3/envs/spatial_env2/lib/python3.10/site-packages/geopandas/geodataframe.py:1819: 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",
      "/home/dadams/miniconda3/envs/spatial_env2/lib/python3.10/site-packages/geopandas/geodataframe.py:1819: 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"
     ]
    }
   ],
   "source": [
    "import tabulate\n",
    "from scipy.stats import ttest_ind\n",
    "\n",
    "# Ensure the output directory exists\n",
    "output_dir = 'analysis/output'\n",
    "os.makedirs(output_dir, exist_ok=True)\n",
    "\n",
    "# Add a column to indicate whether the spill occurred before or after 2020 for historical and recent spills\n",
    "historical_spills['Period'] = historical_spills['Report Year'].apply(lambda x: 'Before 2020' if x < 2020 else '2020 and After')\n",
    "recent_spills['Period'] = recent_spills['Report Year'].apply(lambda x: 'Before 2020' if x < 2020 else '2020 and After')\n",
    "\n",
    "# Historical Spills Analysis\n",
    "historical_report_delay_summary = historical_spills['Report Delay (Days)'].describe()\n",
    "print(\"Historical Spills Report Delay Summary:\")\n",
    "print(historical_report_delay_summary)\n",
    "\n",
    "# Recent Spills Analysis\n",
    "recent_report_delay_summary = recent_spills['Report Delay (Days)'].describe()\n",
    "print(\"\\nRecent Spills Report Delay Summary:\")\n",
    "print(recent_report_delay_summary)\n",
    "\n",
    "# Historical Spills Response Time Summary\n",
    "historical_response_time_summary = historical_spills.groupby('Period')['Report Delay (Days)'].describe()\n",
    "print(\"\\nHistorical Spills Response Time Summary:\")\n",
    "print(historical_response_time_summary)\n",
    "\n",
    "# Recent Spills Response Time Summary\n",
    "recent_response_time_summary = recent_spills.groupby('Period')['Report Delay (Days)'].describe()\n",
    "print(\"\\nRecent Spills Response Time Summary:\")\n",
    "print(recent_response_time_summary)\n",
    "\n",
    "# t-test for the difference in response time before and after 2020 for historical spills\n",
    "historical_before_2020 = historical_spills[historical_spills['Period'] == 'Before 2020']['Report Delay (Days)']\n",
    "historical_after_2020 = historical_spills[historical_spills['Period'] == '2020 and After']['Report Delay (Days)']\n",
    "\n",
    "t_stat_historical, p_value_historical = ttest_ind(historical_before_2020, historical_after_2020, equal_var=False)\n",
    "print(f'\\nHistorical Spills T-Statistic: {t_stat_historical:.4f}')\n",
    "print(f'Historical Spills P-Value: {p_value_historical:.4f}')\n",
    "\n",
    "# Check if the difference is statistically significant for historical spills\n",
    "if p_value_historical < 0.05:\n",
    "    print('The difference in response time for historical spills is statistically significant.')\n",
    "else:\n",
    "    print('The difference in response time for historical spills is not statistically significant.')\n",
    "    \n",
    "# t-test for the difference in response time before and after 2020 for recent spills\n",
    "recent_before_2020 = recent_spills[recent_spills['Period'] == 'Before 2020']['Report Delay (Days)']\n",
    "recent_after_2020 = recent_spills[recent_spills['Period'] == '2020 and After']['Report Delay (Days)']\n",
    "\n",
    "t_stat_recent, p_value_recent = ttest_ind(recent_before_2020, recent_after_2020, equal_var=False)\n",
    "print(f'\\nRecent Spills T-Statistic: {t_stat_recent:.4f}')\n",
    "print(f'Recent Spills P-Value: {p_value_recent:.4f}')\n",
    "\n",
    "# Check if the difference is statistically significant for recent spills\n",
    "if p_value_recent < 0.05:\n",
    "    print('The difference in response time for recent spills is statistically significant.')\n",
    "else:\n",
    "    print('The difference in response time for recent spills is not statistically significant.')\n",
    "\n",
    "# Save the summaries and t-test results to a markdown file\n",
    "with open(os.path.join(output_dir, 'analysis_results.md'), 'w') as f:\n",
    "    f.write(\"# Analysis Results\\n\\n\")\n",
    "    \n",
    "    f.write(\"## Historical Spills Report Delay Summary:\\n\")\n",
    "    f.write(historical_report_delay_summary.to_markdown() + \"\\n\\n\")\n",
    "    \n",
    "    f.write(\"## Recent Spills Report Delay Summary:\\n\")\n",
    "    f.write(recent_report_delay_summary.to_markdown() + \"\\n\\n\")\n",
    "    \n",
    "    f.write(\"## Historical Spills Response Time Summary:\\n\")\n",
    "    f.write(historical_response_time_summary.to_markdown() + \"\\n\\n\")\n",
    "    \n",
    "    f.write(\"## Recent Spills Response Time Summary:\\n\")\n",
    "    f.write(recent_response_time_summary.to_markdown() + \"\\n\\n\")\n",
    "    \n",
    "    f.write(\"## T-Test Results for Historical Spills\\n\")\n",
    "    f.write(f\"T-Statistic: {t_stat_historical:.4f}\\n\")\n",
    "    f.write(f\"P-Value: {p_value_historical:.4f}\\n\")\n",
    "    if p_value_historical < 0.05:\n",
    "        f.write('The difference in response time for historical spills is statistically significant.\\n')\n",
    "    else:\n",
    "        f.write('The difference in response time for historical spills is not statistically significant.\\n')\n",
    "    \n",
    "    f.write(\"\\n## T-Test Results for Recent Spills\\n\")\n",
    "    f.write(f\"T-Statistic: {t_stat_recent:.4f}\\n\")\n",
    "    f.write(f\"P-Value: {p_value_recent:.4f}\\n\")\n",
    "    if p_value_recent < 0.05:\n",
    "        f.write('The difference in response time for recent spills is statistically significant.\\n')\n",
    "    else:\n",
    "        f.write('The difference in response time for recent spills is not statistically significant.\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "[encoded data, 45116 characters]",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "[encoded data, 44908 characters]",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "# Ensure the output directory exists\n",
    "output_dir = 'analysis/output'\n",
    "os.makedirs(output_dir, exist_ok=True)\n",
    "\n",
    "# Save the t-test results to a markdown file\n",
    "with open(os.path.join(output_dir, 't_test_results.md'), 'w') as f:\n",
    "    f.write(f\"## T-Test Results for Historical Spills\\n\")\n",
    "    f.write(f\"T-Statistic: {t_stat_historical:.4f}\\n\")\n",
    "    f.write(f\"P-Value: {p_value_historical:.4f}\\n\")\n",
    "    if p_value_historical < 0.05:\n",
    "        f.write('The difference in response time for historical spills is statistically significant.\\n')\n",
    "    else:\n",
    "        f.write('The difference in response time for historical spills is not statistically significant.\\n')\n",
    "    \n",
    "    f.write(f\"\\n## T-Test Results for Recent Spills\\n\")\n",
    "    f.write(f\"T-Statistic: {t_stat_recent:.4f}\\n\")\n",
    "    f.write(f\"P-Value: {p_value_recent:.4f}\\n\")\n",
    "    if p_value_recent < 0.05:\n",
    "        f.write('The difference in response time for recent spills is statistically significant.\\n')\n",
    "    else:\n",
    "        f.write('The difference in response time for recent spills is not statistically significant.\\n')\n",
    "\n",
    "# Create and save the plots\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "historical_spills.boxplot(column='Report Delay (Days)', by='Period', ax=ax)\n",
    "ax.set_title('Historical Spills Response Time Distribution Before and After 2020', fontsize=14)\n",
    "ax.set_xlabel('Period', fontsize=12)\n",
    "ax.set_ylabel('Report Delay (Days)', fontsize=12)\n",
    "plt.suptitle('')\n",
    "plt.tight_layout()\n",
    "plt.savefig(os.path.join(output_dir, 'historical_spills_response_time_distribution.png'))\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "recent_spills.boxplot(column='Report Delay (Days)', by='Period', ax=ax)\n",
    "ax.set_title('Recent Spills Response Time Distribution Before and After 2020', fontsize=14)\n",
    "ax.set_xlabel('Period', fontsize=12)\n",
    "ax.set_ylabel('Report Delay (Days)', fontsize=12)\n",
    "plt.suptitle('')\n",
    "plt.tight_layout()\n",
    "plt.savefig(os.path.join(output_dir, 'recent_spills_response_time_distribution.png'))\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Historical Spills T-Statistic: 2.8723\n",
      "Historical Spills P-Value: 0.0041\n",
      "The difference in response time for historical spills is statistically significant.\n",
      "\n",
      "Recent Spills T-Statistic: 2.1457\n",
      "Recent Spills P-Value: 0.0319\n",
      "The difference in response time for recent spills is statistically significant.\n"
     ]
    },
    {
     "data": {
      "image/png": "[encoded data, 37736 characters]",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# t-test for the difference in response time before and after 2020 for historical spills\n",
    "historical_before_2020 = historical_spills[historical_spills['Period'] == 'Before 2020']['Report Delay (Days)']\n",
    "historical_after_2020 = historical_spills[historical_spills['Period'] == '2020 and After']['Report Delay (Days)']\n",
    "\n",
    "t_stat, p_value = ttest_ind(historical_before_2020, historical_after_2020, equal_var=False)\n",
    "print(f'\\nHistorical Spills T-Statistic: {t_stat:.4f}')\n",
    "print(f'Historical Spills P-Value: {p_value:.4f}')\n",
    "\n",
    "# Check if the difference is statistically significant for historical spills\n",
    "if p_value < 0.05:\n",
    "    print('The difference in response time for historical spills is statistically significant.')\n",
    "else:\n",
    "    print('The difference in response time for historical spills is not statistically significant.')\n",
    "    \n",
    "# t-test for the difference in response time before and after 2020 for recent spills\n",
    "recent_before_2020 = recent_spills[recent_spills['Period'] == 'Before 2020']['Report Delay (Days)']\n",
    "recent_after_2020 = recent_spills[recent_spills['Period'] == '2020 and After']['Report Delay (Days)']\n",
    "\n",
    "t_stat, p_value = ttest_ind(recent_before_2020, recent_after_2020, equal_var=False)\n",
    "print(f'\\nRecent Spills T-Statistic: {t_stat:.4f}')\n",
    "print(f'Recent Spills P-Value: {p_value:.4f}')\n",
    "\n",
    "# Check if the difference is statistically significant for recent spills\n",
    "if p_value < 0.05:\n",
    "    print('The difference in response time for recent spills is statistically significant.')\n",
    "else:\n",
    "    print('The difference in response time for recent spills is not statistically significant.')\n",
    "    \n",
    "# Create a plot for the response time distribution before and after 2020 for historical and recent spills\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "\n",
    "# Plot the response time distribution for historical spills\n",
    "historical_spills.boxplot(column='Report Delay (Days)', by='Period', ax=ax, positions=[1, 2])\n",
    "\n",
    "# Plot the response time distribution for recent spills\n",
    "recent_spills.boxplot(column='Report Delay (Days)', by='Period', ax=ax, positions=[4, 5])\n",
    "\n",
    "# Save the plot to the /output directory\n",
    "plt.savefig('analysis/output/response_time_distribution.png')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                               OLS Regression Results                               \n",
      "====================================================================================\n",
      "Dep. Variable:     Q(\"Report Delay (Days)\")   R-squared:                       0.006\n",
      "Model:                                  OLS   Adj. R-squared:                  0.006\n",
      "Method:                       Least Squares   F-statistic:                     31.12\n",
      "Date:                      Mon, 31 Mar 2025   Prob (F-statistic):           4.78e-20\n",
      "Time:                              17:54:14   Log-Likelihood:                -99827.\n",
      "No. Observations:                     15990   AIC:                         1.997e+05\n",
      "Df Residuals:                         15986   BIC:                         1.997e+05\n",
      "Df Model:                                 3                                         \n",
      "Covariance Type:                  nonrobust                                         \n",
      "======================================================================================\n",
      "                         coef    std err          t      P>|t|      [0.025      0.975]\n",
      "--------------------------------------------------------------------------------------\n",
      "Intercept             29.7461      2.438     12.200      0.000      24.967      34.525\n",
      "Post2020             -16.2130      3.417     -4.745      0.000     -22.910      -9.516\n",
      "SpillType            -25.0339      2.906     -8.616      0.000     -30.729     -19.339\n",
      "Post2020:SpillType    14.4276      4.200      3.435      0.001       6.196      22.660\n",
      "==============================================================================\n",
      "Omnibus:                    45995.174   Durbin-Watson:                   1.915\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):       3525625525.882\n",
      "Skew:                          38.959   Prob(JB):                         0.00\n",
      "Kurtosis:                    2302.059   Cond. No.                         8.21\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/dadams/miniconda3/envs/spatial_env2/lib/python3.10/site-packages/geopandas/geodataframe.py:1819: 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"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import statsmodels.formula.api as smf\n",
    "\n",
    "# Combine the datasets and label the groups\n",
    "historical_spills['SpillType'] = 0  # historical = 0\n",
    "recent_spills['SpillType'] = 1      # recent = 1\n",
    "\n",
    "combined = pd.concat([historical_spills, recent_spills], ignore_index=True)\n",
    "\n",
    "# Convert 'Period' to binary: 0 = Before 2020, 1 = 2020 and After\n",
    "combined['Post2020'] = combined['Period'].apply(lambda x: 1 if x == '2020 and After' else 0)\n",
    "\n",
    "# Interaction term is created implicitly in the regression\n",
    "model = smf.ols('Q(\"Report Delay (Days)\") ~ Post2020 + SpillType + Post2020:SpillType', data=combined).fit()\n",
    "\n",
    "print(model.summary())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Historical Spills:\n",
      "                                OLS Regression Results                               \n",
      "====================================================================================\n",
      "Dep. Variable:     Q(\"Report Delay (Days)\")   R-squared:                       0.002\n",
      "Model:                                  OLS   Adj. R-squared:                  0.001\n",
      "Method:                       Least Squares   F-statistic:                     8.265\n",
      "Date:                      Mon, 31 Mar 2025   Prob (F-statistic):            0.00406\n",
      "Time:                              17:54:40   Log-Likelihood:                -35825.\n",
      "No. Observations:                      5312   AIC:                         7.165e+04\n",
      "Df Residuals:                          5310   BIC:                         7.167e+04\n",
      "Df Model:                                 1                                         \n",
      "Covariance Type:                  nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept     29.7461      4.024      7.392      0.000      21.857      37.635\n",
      "Post2020     -16.2130      5.639     -2.875      0.004     -27.269      -5.157\n",
      "==============================================================================\n",
      "Omnibus:                    12943.048   Durbin-Watson:                   1.944\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):        189644913.057\n",
      "Skew:                          25.310   Prob(JB):                         0.00\n",
      "Kurtosis:                     927.266   Cond. No.                         2.64\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "\n",
      "Recent Spills:\n",
      "                                OLS Regression Results                               \n",
      "====================================================================================\n",
      "Dep. Variable:     Q(\"Report Delay (Days)\")   R-squared:                       0.000\n",
      "Model:                                  OLS   Adj. R-squared:                  0.000\n",
      "Method:                       Least Squares   F-statistic:                     3.752\n",
      "Date:                      Mon, 31 Mar 2025   Prob (F-statistic):             0.0528\n",
      "Time:                              17:54:40   Log-Likelihood:                -56260.\n",
      "No. Observations:                     10678   AIC:                         1.125e+05\n",
      "Df Residuals:                         10676   BIC:                         1.125e+05\n",
      "Df Model:                                 1                                         \n",
      "Covariance Type:                  nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      4.7122      0.597      7.900      0.000       3.543       5.881\n",
      "Post2020      -1.7853      0.922     -1.937      0.053      -3.592       0.021\n",
      "==============================================================================\n",
      "Omnibus:                    28541.449   Durbin-Watson:                   1.643\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):        676851501.928\n",
      "Skew:                          32.390   Prob(JB):                         0.00\n",
      "Kurtosis:                    1234.707   Cond. No.                         2.47\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "import statsmodels.formula.api as smf\n",
    "\n",
    "# Historical only\n",
    "historical_spills = historical_spills.copy()\n",
    "historical_spills['Post2020'] = historical_spills['Period'].apply(lambda x: 1 if x == '2020 and After' else 0)\n",
    "model_hist = smf.ols('Q(\"Report Delay (Days)\") ~ Post2020', data=historical_spills).fit()\n",
    "print(\"Historical Spills:\\n\", model_hist.summary())\n",
    "\n",
    "# Recent only\n",
    "recent_spills = recent_spills.copy()\n",
    "recent_spills['Post2020'] = recent_spills['Period'].apply(lambda x: 1 if x == '2020 and After' else 0)\n",
    "model_recent = smf.ols('Q(\"Report Delay (Days)\") ~ Post2020', data=recent_spills).fit()\n",
    "print(\"\\nRecent Spills:\\n\", model_recent.summary())\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                               OLS Regression Results                               \n",
      "====================================================================================\n",
      "Dep. Variable:     Q(\"Report Delay (Days)\")   R-squared:                       0.006\n",
      "Model:                                  OLS   Adj. R-squared:                  0.006\n",
      "Method:                       Least Squares   F-statistic:                     31.12\n",
      "Date:                      Mon, 31 Mar 2025   Prob (F-statistic):           4.78e-20\n",
      "Time:                              17:54:47   Log-Likelihood:                -99827.\n",
      "No. Observations:                     15990   AIC:                         1.997e+05\n",
      "Df Residuals:                         15986   BIC:                         1.997e+05\n",
      "Df Model:                                 3                                         \n",
      "Covariance Type:                  nonrobust                                         \n",
      "============================================================================================\n",
      "                               coef    std err          t      P>|t|      [0.025      0.975]\n",
      "--------------------------------------------------------------------------------------------\n",
      "Intercept                   29.7461      2.438     12.200      0.000      24.967      34.525\n",
      "Group[T.Recent]            -25.0339      2.906     -8.616      0.000     -30.729     -19.339\n",
      "Post2020                   -16.2130      3.417     -4.745      0.000     -22.910      -9.516\n",
      "Post2020:Group[T.Recent]    14.4276      4.200      3.435      0.001       6.196      22.660\n",
      "==============================================================================\n",
      "Omnibus:                    45995.174   Durbin-Watson:                   1.915\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):       3525625525.882\n",
      "Skew:                          38.959   Prob(JB):                         0.00\n",
      "Kurtosis:                    2302.059   Cond. No.                         8.21\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "# Label both groups, combine\n",
    "historical_spills['Group'] = 'Historical'\n",
    "recent_spills['Group'] = 'Recent'\n",
    "combined = pd.concat([historical_spills, recent_spills], ignore_index=True)\n",
    "\n",
    "combined['Post2020'] = combined['Period'].apply(lambda x: 1 if x == '2020 and After' else 0)\n",
    "\n",
    "# Run regression with interactions by group\n",
    "model = smf.ols('Q(\"Report Delay (Days)\") ~ Post2020 * Group', data=combined).fit()\n",
    "print(model.summary())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "[encoded data, 34068 characters]",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Data\n",
    "pre_means = [29.75, 4.71]\n",
    "post_means = [29.75 - 16.21, 4.71 - 1.79]\n",
    "labels = ['Historical Spills', 'Recent Spills']\n",
    "\n",
    "x = range(len(labels))\n",
    "width = 0.35\n",
    "\n",
    "# Plot\n",
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "ax.bar([i - width / 2 for i in x], pre_means, width, label='Pre-2020', color='skyblue')\n",
    "ax.bar([i + width / 2 for i in x], post_means, width, label='Post-2020', color='salmon')\n",
    "\n",
    "# Labels and aesthetics\n",
    "ax.set_ylabel('Mean Report Delay (Days)')\n",
    "ax.set_title('Mean Report Delay Before and After 2020 by Spill Type')\n",
    "ax.set_xticks(x)\n",
    "ax.set_xticklabels(labels)\n",
    "ax.legend()\n",
    "\n",
    "# Save and show\n",
    "plt.tight_layout()\n",
    "plt.savefig('report_delay_barplot.png')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| Group             | Pre-2020 Mean | Post-2020 Change (coef) | p-value | Significant (p < 0.05) |\n",
    "|------------------|---------------|--------------------------|---------|-------------------------|\n",
    "| Historical Spills| 29.75         | -16.21                   | 0.004   | Yes                     |\n",
    "| Recent Spills    | 4.71          | -1.79                    | 0.053   | No                      |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analysis of Reporting Delays for Historical and Recent Spills\n",
    "We analyzed reporting delays for historical and recent spills before and after 2020 using simple OLS regressions. For historical spills, the average delay decreased significantly by approximately 16.2 days after 2020 (p = 0.004), dropping from a pre-2020 mean of 29.75 days. In contrast, the reduction for recent spills was smaller—about 1.8 days—and only marginally significant (p = 0.053), starting from a much lower pre-2020 average of 4.71 days. These results suggest that the most substantial improvements in timeliness occurred within the historical spill reporting process."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Differences in Reporting Delays by Spill Type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                               OLS Regression Results                               \n",
      "====================================================================================\n",
      "Dep. Variable:     Q(\"Report Delay (Days)\")   R-squared:                       0.006\n",
      "Model:                                  OLS   Adj. R-squared:                  0.006\n",
      "Method:                       Least Squares   F-statistic:                     31.12\n",
      "Date:                      Mon, 31 Mar 2025   Prob (F-statistic):           4.78e-20\n",
      "Time:                              17:54:59   Log-Likelihood:                -99827.\n",
      "No. Observations:                     15990   AIC:                         1.997e+05\n",
      "Df Residuals:                         15986   BIC:                         1.997e+05\n",
      "Df Model:                                 3                                         \n",
      "Covariance Type:                  nonrobust                                         \n",
      "======================================================================================\n",
      "                         coef    std err          t      P>|t|      [0.025      0.975]\n",
      "--------------------------------------------------------------------------------------\n",
      "Intercept             29.7461      2.438     12.200      0.000      24.967      34.525\n",
      "Post2020             -16.2130      3.417     -4.745      0.000     -22.910      -9.516\n",
      "SpillType            -25.0339      2.906     -8.616      0.000     -30.729     -19.339\n",
      "Post2020:SpillType    14.4276      4.200      3.435      0.001       6.196      22.660\n",
      "==============================================================================\n",
      "Omnibus:                    45995.174   Durbin-Watson:                   1.915\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):       3525625525.882\n",
      "Skew:                          38.959   Prob(JB):                         0.00\n",
      "Kurtosis:                    2302.059   Cond. No.                         8.21\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import statsmodels.formula.api as smf\n",
    "\n",
    "# Combine the datasets and label the groups\n",
    "historical_spills['SpillType'] = 0  # historical = 0\n",
    "recent_spills['SpillType'] = 1      # recent = 1\n",
    "\n",
    "combined = pd.concat([historical_spills, recent_spills], ignore_index=True)\n",
    "\n",
    "# Convert 'Period' to binary: 0 = Before 2020, 1 = 2020 and After\n",
    "combined['Post2020'] = combined['Period'].apply(lambda x: 1 if x == '2020 and After' else 0)\n",
    "\n",
    "# Interaction term is created implicitly in the regression\n",
    "model = smf.ols('Q(\"Report Delay (Days)\") ~ Post2020 + SpillType + Post2020:SpillType', data=combined).fit()\n",
    "\n",
    "print(model.summary())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| Term                          | Coefficient | Std. Error | p-value | Interpretation                                                                 |\n",
    "|-------------------------------|-------------|------------|---------|---------------------------------------------------------------------------------|\n",
    "| Intercept                     | 29.75       | 2.44       | <0.001  | Historical spills, pre-2020 average report delay                               |\n",
    "| Post2020                      | -16.21      | 3.42       | <0.001  | Historical spills saw a 16.2-day reduction in delay after 2020                 |\n",
    "| Group: Recent Spills          | -25.03      | 2.91       | <0.001  | Recent spills had a 25-day shorter delay than historical spills pre-2020       |\n",
    "| Post2020 × Recent Spills      | +14.43      | 4.20       | 0.001   | Recent spills improved **14.4 days less** than historical spills post-2020     |\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "[encoded data, 45616 characters]",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# X-axis positions: 0 = Before 2020, 1 = 2020 and After\n",
    "x = [0, 1]\n",
    "\n",
    "# Y-values for each group\n",
    "historical = [29.75, 29.75 - 16.21]  # Before and after for historical\n",
    "recent = [4.71, 4.71 - 1.79]         # Before and after for recent\n",
    "\n",
    "# Plot\n",
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "\n",
    "ax.plot(x, historical, marker='o', label='Historical Spills', linewidth=2)\n",
    "ax.plot(x, recent, marker='o', label='Recent Spills', linewidth=2)\n",
    "\n",
    "# Aesthetics\n",
    "ax.set_xticks(x)\n",
    "ax.set_xticklabels(['Before 2020', '2020 and After'])\n",
    "ax.set_ylabel('Mean Report Delay (Days)')\n",
    "ax.set_title('Difference-in-Differences: Report Delay by Spill Type')\n",
    "ax.legend()\n",
    "ax.grid(True)\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "[encoded data, 27468 characters]",
      "text/plain": [
       "<Figure size 700x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "improvements = [-16.21, -1.79]\n",
    "labels = ['Historical Spills', 'Recent Spills']\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(7, 5))\n",
    "ax.bar(labels, improvements, color=['steelblue', 'orange'])\n",
    "ax.axhline(0, color='black', linewidth=0.8)\n",
    "ax.set_ylabel('Change in Report Delay (Days)')\n",
    "ax.set_title('Change in Report Delay After 2020')\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Interpretation of Difference-in-Differences Model**\n",
    "\n",
    "This Difference-in-Differences model estimates how report delays changed after 2020 for two groups—historical and recent spills—and compares the *magnitude of change* between them. The pre-2020 average delay for historical spills was approximately 29.75 days. After 2020, historical spills saw a statistically significant reduction of about 16.2 days (p < 0.001).\n",
    "\n",
    "Recent spills, by contrast, already had much shorter average delays (about 25 days less than historical spills before 2020). The DiD interaction term (+14.4 days, p = 0.001) shows that the improvement in timeliness for recent spills was significantly smaller than that for historical spills. In other words, while both groups saw reductions in report delays after 2020, the gains were **substantially greater** for historical spills.\n",
    "\n",
    "This suggests that any policy or process changes implemented around 2020 had a **disproportionately stronger effect** on addressing delays in the historical spill category."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Only keep pre-2020 years to check the parallel trends assumption\n",
    "pre_2020 = spills_gdf[spills_gdf['Report Year'] < 2020]\n",
    "\n",
    "# Group by year and spill type\n",
    "trend_df = pre_2020.groupby(['Report Year', 'Spill Type'])['Report Delay (Days)'].mean().reset_index()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "[encoded data, 72528 characters]",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "\n",
    "for spill_type in trend_df['Spill Type'].unique():\n",
    "    subset = trend_df[trend_df['Spill Type'] == spill_type]\n",
    "    ax.plot(subset['Report Year'], subset['Report Delay (Days)'], marker='o', label=spill_type)\n",
    "\n",
    "ax.set_title('Pre-2020 Trends in Report Delay by Spill Type')\n",
    "ax.set_xlabel('Report Year')\n",
    "ax.set_ylabel('Mean Report Delay (Days)')\n",
    "ax.legend()\n",
    "ax.grid(True)\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "spills_gdf['Report Year'] = pd.to_datetime(spills_gdf['Initial Report Date']).dt.year\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                               OLS Regression Results                               \n",
      "====================================================================================\n",
      "Dep. Variable:     Q(\"Report Delay (Days)\")   R-squared:                       0.010\n",
      "Model:                                  OLS   Adj. R-squared:                  0.009\n",
      "Method:                       Least Squares   F-statistic:                     14.56\n",
      "Date:                      Mon, 31 Mar 2025   Prob (F-statistic):           1.73e-28\n",
      "Time:                              18:04:09   Log-Likelihood:                -99794.\n",
      "No. Observations:                     15990   AIC:                         1.996e+05\n",
      "Df Residuals:                         15978   BIC:                         1.997e+05\n",
      "Df Model:                                11                                         \n",
      "Covariance Type:                  nonrobust                                         \n",
      "===============================================================================================\n",
      "                                  coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-----------------------------------------------------------------------------------------------\n",
      "Intercept                      24.2312      4.880      4.965      0.000      14.665      33.797\n",
      "Group[T.Recent]               -25.6229      3.295     -7.777      0.000     -32.081     -19.165\n",
      "C(Q(\"Report Year\"))[T.2015]    24.6495      5.413      4.554      0.000      14.040      35.260\n",
      "C(Q(\"Report Year\"))[T.2016]     2.0193      5.195      0.389      0.698      -8.164      12.203\n",
      "C(Q(\"Report Year\"))[T.2017]    -0.9935      4.921     -0.202      0.840     -10.640       8.653\n",
      "C(Q(\"Report Year\"))[T.2018]    -4.1817      5.083     -0.823      0.411     -14.145       5.781\n",
      "C(Q(\"Report Year\"))[T.2019]    11.6665      4.963      2.351      0.019       1.939      21.394\n",
      "C(Q(\"Report Year\"))[T.2020] -3.392e+13   1.55e+14     -0.218      0.827   -3.39e+14    2.71e+14\n",
      "C(Q(\"Report Year\"))[T.2021] -3.392e+13   1.55e+14     -0.218      0.827   -3.39e+14    2.71e+14\n",
      "C(Q(\"Report Year\"))[T.2022] -3.392e+13   1.55e+14     -0.218      0.827   -3.39e+14    2.71e+14\n",
      "C(Q(\"Report Year\"))[T.2023] -3.392e+13   1.55e+14     -0.218      0.827   -3.39e+14    2.71e+14\n",
      "Post2020                     3.392e+13   1.55e+14      0.218      0.827   -2.71e+14    3.39e+14\n",
      "Post2020:Group[T.Recent]       13.8374      4.229      3.272      0.001       5.547      22.127\n",
      "==============================================================================\n",
      "Omnibus:                    46016.971   Durbin-Watson:                   1.922\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):       3554990757.140\n",
      "Skew:                          39.006   Prob(JB):                         0.00\n",
      "Kurtosis:                    2311.622   Cond. No.                     4.97e+14\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The smallest eigenvalue is 1.28e-25. This might indicate that there are\n",
      "strong multicollinearity problems or that the design matrix is singular.\n"
     ]
    }
   ],
   "source": [
    "import statsmodels.formula.api as smf\n",
    "\n",
    "model_fe = smf.ols(\n",
    "    'Q(\"Report Delay (Days)\") ~ Post2020 * Group + C(Q(\"Report Year\"))',\n",
    "    data=combined\n",
    ").fit()\n",
    "\n",
    "print(model_fe.summary())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                               OLS Regression Results                               \n",
      "====================================================================================\n",
      "Dep. Variable:     Q(\"Report Delay (Days)\")   R-squared:                       0.016\n",
      "Model:                                  OLS   Adj. R-squared:                  0.015\n",
      "Method:                       Least Squares   F-statistic:                     13.78\n",
      "Date:                      Mon, 31 Mar 2025   Prob (F-statistic):           3.21e-44\n",
      "Time:                              18:04:52   Log-Likelihood:                -99743.\n",
      "No. Observations:                     15990   AIC:                         1.995e+05\n",
      "Df Residuals:                         15970   BIC:                         1.997e+05\n",
      "Df Model:                                19                                         \n",
      "Covariance Type:                  nonrobust                                         \n",
      "===============================================================================================================\n",
      "                                                  coef    std err          t      P>|t|      [0.025      0.975]\n",
      "---------------------------------------------------------------------------------------------------------------\n",
      "Intercept                                      12.6921      6.981      1.818      0.069      -0.992      26.376\n",
      "Group[T.Recent]                                -9.5452      8.333     -1.146      0.252     -25.878       6.788\n",
      "C(Q(\"Report Year\"))[T.2015]                    70.1325      8.870      7.907      0.000      52.747      87.518\n",
      "C(Q(\"Report Year\"))[T.2016]                     8.0517      9.141      0.881      0.378      -9.865      25.969\n",
      "C(Q(\"Report Year\"))[T.2017]                    -2.4258      9.115     -0.266      0.790     -20.293      15.441\n",
      "C(Q(\"Report Year\"))[T.2018]                    -8.4734      8.828     -0.960      0.337     -25.777       8.830\n",
      "C(Q(\"Report Year\"))[T.2019]                    28.6915      9.529      3.011      0.003      10.013      47.370\n",
      "C(Q(\"Report Year\"))[T.2020]                    33.1820     10.449      3.176      0.001      12.700      53.664\n",
      "C(Q(\"Report Year\"))[T.2021]                   -10.6444      8.550     -1.245      0.213     -27.404       6.115\n",
      "C(Q(\"Report Year\"))[T.2022]                     5.0125      8.157      0.615      0.539     -10.976      21.000\n",
      "C(Q(\"Report Year\"))[T.2023]                    -3.9384      8.048     -0.489      0.625     -19.713      11.836\n",
      "Group[T.Recent]:C(Q(\"Report Year\"))[T.2015]   -65.2606     10.635     -6.136      0.000     -86.107     -44.415\n",
      "Group[T.Recent]:C(Q(\"Report Year\"))[T.2016]    -8.0114     10.985     -0.729      0.466     -29.543      13.520\n",
      "Group[T.Recent]:C(Q(\"Report Year\"))[T.2017]     1.4929     10.815      0.138      0.890     -19.706      22.692\n",
      "Group[T.Recent]:C(Q(\"Report Year\"))[T.2018]     7.6302     10.637      0.717      0.473     -13.220      28.480\n",
      "Group[T.Recent]:C(Q(\"Report Year\"))[T.2019]   -23.4757     11.153     -2.105      0.035     -45.337      -1.615\n",
      "Group[T.Recent]:C(Q(\"Report Year\"))[T.2020]   -33.4093     12.106     -2.760      0.006     -57.139      -9.679\n",
      "Group[T.Recent]:C(Q(\"Report Year\"))[T.2021]    11.1341     10.351      1.076      0.282      -9.156      31.424\n",
      "Group[T.Recent]:C(Q(\"Report Year\"))[T.2022]    -7.0144      9.959     -0.704      0.481     -26.536      12.507\n",
      "Group[T.Recent]:C(Q(\"Report Year\"))[T.2023]     5.0382      9.959      0.506      0.613     -14.482      24.559\n",
      "==============================================================================\n",
      "Omnibus:                    46016.250   Durbin-Watson:                   1.932\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):       3571135236.213\n",
      "Skew:                          39.001   Prob(JB):                         0.00\n",
      "Kurtosis:                    2316.864   Cond. No.                         44.9\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "model_fe_fixed = smf.ols(\n",
    "    'Q(\"Report Delay (Days)\") ~ Group * C(Q(\"Report Year\"))',\n",
    "    data=combined\n",
    ").fit()\n",
    "\n",
    "print(model_fe_fixed.summary())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| Year | Historical Delay Change | Recent Relative Change | Significant? |\n",
    "|------|--------------------------|-------------------------|--------------|\n",
    "| 2015 | +70.13 days              | −65.26 days             | ✅ p < 0.001 |\n",
    "| 2019 | +28.69 days              | −23.48 days             | ✅ p = 0.035 |\n",
    "| 2020 | +33.18 days              | −33.41 days             | ✅ p = 0.006 |\n",
    "| 2023 | −3.94 days               | +5.04 days              | ❌           |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To test the robustness of our DiD findings, we specified a model with year fixed effects and group-year interactions. This approach allows us to control for year-specific shocks and identify how recent and historical spills responded differently across time. We found that historical spills experienced sharp spikes in reporting delays in 2015, 2019, and 2020—rising by 70, 29, and 33 days respectively—while recent spills did not mirror these trends. In fact, the interaction terms show that recent spills experienced significantly less delay than historical ones in each of those years. These results reinforce the observation that recent spills followed a more stable reporting pattern, and that 2020 disruptions disproportionately affected the historical spill category."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "[encoded data, 91160 characters]",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import statsmodels.formula.api as smf\n",
    "\n",
    "# Re-run the fixed model to get fitted values\n",
    "model_fe_fixed = smf.ols(\n",
    "    'Q(\"Report Delay (Days)\") ~ Group * C(Q(\"Report Year\"))',\n",
    "    data=combined\n",
    ").fit()\n",
    "\n",
    "# Get predicted means for each year and group\n",
    "combined['Fitted'] = model_fe_fixed.fittedvalues\n",
    "\n",
    "# Calculate average predicted values by year and group\n",
    "predicted_means = combined.groupby(['Group', 'Report Year'])['Fitted'].mean().reset_index()\n",
    "\n",
    "# Plot\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "\n",
    "for group in predicted_means['Group'].unique():\n",
    "    subset = predicted_means[predicted_means['Group'] == group]\n",
    "    ax.plot(subset['Report Year'], subset['Fitted'], marker='o', label=f'{group} Spills')\n",
    "\n",
    "ax.axvline(2020, color='gray', linestyle='--', linewidth=1, label='2020 Threshold')\n",
    "ax.set_title('Model-Predicted Report Delays by Spill Type and Year')\n",
    "ax.set_xlabel('Report Year')\n",
    "ax.set_ylabel('Predicted Report Delay (Days)')\n",
    "ax.legend()\n",
    "ax.grid(True)\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Figure X displays average report delays for historical and recent spills before and after 2020. The figure highlights a substantial reduction in reporting delay for historical spills after 2020, dropping from approximately 29.8 days to 13.5 days. Recent spills, by contrast, show a smaller decline—from 4.7 days to 2.9 days—over the same period. While both groups improved in reporting timeliness, the steeper drop for historical spills suggests that policy or administrative changes implemented around 2020 had a disproportionately greater effect on that category. This visual representation aligns with the Difference-in-Differences regression results, which estimate a significant 14.4-day larger improvement in historical spills relative to recent ones."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Results**\n",
    "\n",
    "We evaluated the effect of Colorado's 2021 spill reporting rule on reporting timeliness using a combination of descriptive, inferential, and causal-inference models. Our analysis focuses on changes in **report delays**—defined as the number of days between the date of spill discovery and the date the spill was officially reported—for two types of spills: **historical** (discovered long after occurrence) and **recent** (discovered and reported in near real-time).\n",
    "\n",
    "### **Descriptive and t-test Findings**\n",
    "\n",
    "Initial descriptive statistics showed notable improvements in reporting timeliness following the policy change. For **historical spills**, the mean report delay dropped from 31.17 days before 2021 to 10.18 days after 2020, with a significant reduction in variance (standard deviation dropped from 265.34 to 96.01). For **recent spills**, the average delay decreased from 4.48 to 2.92 days, with the standard deviation falling from 53.79 to 28.86.\n",
    "\n",
    "Two-sample t-tests confirmed that the decrease in report delay was **statistically significant for historical spills** (t = 3.94, p < 0.001) and **marginally significant for recent spills** (t = 1.94, p = 0.053). These results suggest meaningful improvement in spill reporting behavior following the regulatory change, with stronger effects observed for more complex, harder-to-detect spill types.\n",
    "\n",
    "### **Difference-in-Differences (DiD) Estimates**\n",
    "\n",
    "To evaluate whether recent and historical spills improved at different rates, we employed a Difference-in-Differences (DiD) model using a binary post-2020 indicator and a spill-type interaction term. The DiD model estimated that historical spills improved by 16.2 days on average, while recent spills improved by only 1.8 days. The interaction term was positive and significant (β = 14.4, p = 0.001), indicating that **historical spills experienced significantly greater improvements in reporting delay compared to recent spills**.\n",
    "\n",
    "### **Parallel Trends and Fixed Effects Models**\n",
    "\n",
    "Visual inspection of pre-2020 trends raised concerns about the parallel trends assumption. Historical spills showed sharp spikes in certain years (notably 2015 and 2019), while recent spill delays remained relatively flat. To address this, we estimated a fixed-effects regression model with year and group-by-year interactions. This model relaxed the parallel trends assumption and allowed us to account for time-specific shocks.\n",
    "\n",
    "The fixed-effects model confirmed our core finding: **recent spills diverged significantly from historical trends in key policy-relevant years**. In 2020, historical spills experienced a 33-day increase in reporting delay, while recent spills showed a 33-day smaller delay—resulting in a significant 66-day relative difference (p = 0.006). Significant differences were also observed in 2015 and 2019, further reinforcing the conclusion that recent spills were less affected by structural or reporting challenges.\n",
    "\n",
    "### **Reasons DiD Model Is Inappropriate**\n",
    "\n",
    "A difference-in-differences (DiD) design assumes parallel pre-policy trends between treated and control groups. In our case, *historical* and *recent* spills are fundamentally different: recent spills are reported shortly after discovery, while historical spills reflect delayed discoveries. Because their reporting dynamics are structurally distinct and not plausibly parallel over time, DiD is not an appropriate design.\n",
    "\n",
    "### **Key Takeaways**\n",
    "- **Historical spills**: Showed significant improvement in reporting delays post-2020, with a 16.2-day reduction.\n",
    "- **Recent spills**: Experienced a smaller, marginally significant reduction of 1.8 days.\n",
    "- **DiD model**: Indicated that historical spills improved significantly more than recent spills, but the parallel trends assumption is violated.\n",
    "- **Fixed-effects model**: Confirmed that recent spills diverged from historical trends, particularly in key years (2015, 2019, 2020), reinforcing the idea that recent spills are less affected by structural challenges.\n",
    "- **Policy implications**: The 2021 policy change appears to have standardized reporting practices and improved timeliness, particularly for historical spills, suggesting that regulatory changes can effectively address reporting delays.\n",
    "\n"
   ]
  }
 ],
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