{
 "cells": [
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   "cell_type": "markdown",
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   "source": [
    "# US Military & Veteran Analysis by State\n",
    "\n",
    "State-level integration of veteran demographics, firearm ownership, mental health indicators, and VA healthcare from 6 sources."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Load the dataset\n",
    "df = pd.read_csv('military_firearm_merged_analysis.csv')\n",
    "print(f'Total states/territories: {len(df)}')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# States with highest veteran populations\n",
    "top_veteran_states = df.nlargest(10, 'veteran_population')[['NAME', 'veteran_population', 'veteran_percentage']]\n",
    "print('Top 10 States by Veteran Population:')\n",
    "print(top_veteran_states.to_string(index=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Veteran suicide risk ratio analysis\n",
    "if 'veteran_risk_ratio' in df.columns:\n",
    "    print(f\"\\nVeteran Suicide Risk Ratio (vs civilians):\")\n",
    "    print(f\"  Mean: {df['veteran_risk_ratio'].mean():.2f}x\")\n",
    "    print(f\"  Max: {df['veteran_risk_ratio'].max():.2f}x\")\n",
    "    \n",
    "    high_risk = df.nlargest(10, 'veteran_risk_ratio')[['NAME', 'veteran_risk_ratio', 'veteran_suicide_rate', 'civilian_suicide_rate']]\n",
    "    print('\\nStates with Highest Veteran Risk Ratio:')\n",
    "    print(high_risk.to_string(index=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Firearm ownership vs veteran percentage correlation\n",
    "if 'ownership_percentage' in df.columns and 'veteran_percentage' in df.columns:\n",
    "    correlation = df['ownership_percentage'].corr(df['veteran_percentage'])\n",
    "    print(f\"Correlation: Firearm ownership vs Veteran percentage: {correlation:.3f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# VA healthcare utilization\n",
    "if 'va_utilization_pct' in df.columns:\n",
    "    print(f\"\\nVA Healthcare Utilization:\")\n",
    "    print(f\"  Mean: {df['va_utilization_pct'].mean():.1f}%\")\n",
    "    print(f\"  Range: {df['va_utilization_pct'].min():.1f}% - {df['va_utilization_pct'].max():.1f}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Sources\n",
    "\n",
    "| Data | Source |\n",
    "|------|--------|\n",
    "| Veteran population | Census ACS |\n",
    "| Firearm ownership | RAND Corporation (2016) |\n",
    "| FFL counts | ATF |\n",
    "| Suicide rates | CDC WONDER |\n",
    "| PTSD, VA utilization | VA |\n",
    "| Active duty, economic impact | DoD DMDC |"
   ]
  }
 ],
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