diff --git a/analysis/analysis5.ipynb b/analysis/analysis5.ipynb
index 497e7a4..8a830ed 100644
--- a/analysis/analysis5.ipynb
+++ b/analysis/analysis5.ipynb
@@ -6,11 +6,14 @@
    "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"
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'geopandas'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[1], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mpd\u001b[39;00m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msqlalchemy\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m create_engine\n\u001b[0;32m----> 3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mgeopandas\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mgpd\u001b[39;00m\n\u001b[1;32m      4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mdotenv\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m load_dotenv\n\u001b[1;32m      5\u001b[0m load_dotenv()\n",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'geopandas'"
      ]
     }
    ],
@@ -44,7 +47,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -54,7 +57,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -63,7 +66,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -76,7 +79,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -87,7 +90,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -109,7 +112,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -137,7 +140,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -183,7 +186,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -214,7 +217,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -263,7 +266,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -278,8 +281,8 @@
     }
    ],
    "source": [
-    "# Add a column to indicate whether the spill occurred before or after 2019\n",
-    "spills_gdf['Period'] = spills_gdf['Report Year'].apply(lambda x: 'Before 2019' if x < 2019 else '2019 and After')\n",
+    "# Add a column to indicate whether the spill occurred before or after 2021\n",
+    "spills_gdf['Period'] = spills_gdf['Report Year'].apply(lambda x: 'Before 2021' if x < 2021 else '2021 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",
@@ -288,7 +291,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -322,13 +325,13 @@
     }
    ],
    "source": [
-    "# t-test for the difference in response time before and after 2019\n",
+    "# t-test for the difference in response time before and after 2021\n",
     "from scipy.stats import ttest_ind\n",
     "\n",
-    "before_2019 = spills_gdf[spills_gdf['Period'] == 'Before 2019']['Report Delay (Days)']\n",
-    "after_2019 = spills_gdf[spills_gdf['Period'] == '2019 and After']['Report Delay (Days)']\n",
+    "before_2021 = spills_gdf[spills_gdf['Period'] == 'Before 2021']['Report Delay (Days)']\n",
+    "after_2021 = spills_gdf[spills_gdf['Period'] == '2021 and After']['Report Delay (Days)']\n",
     "\n",
-    "t_stat, p_value = ttest_ind(before_2019, after_2019, equal_var=False)\n",
+    "t_stat, p_value = ttest_ind(before_2021, after_2021, equal_var=False)\n",
     "print(f'T-Statistic: {t_stat:.4f}')\n",
     "print(f'P-Value: {p_value:.4f}')\n",
     "\n",
@@ -338,7 +341,7 @@
     "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 2019\n",
+    "# Create a plot for the response time distribution before and after 2021\n",
     "fig, ax = plt.subplots(figsize=(10, 6))\n",
     "\n",
     "# Plot the response time distribution for each period\n",
@@ -349,7 +352,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -411,9 +414,9 @@
     }
    ],
    "source": [
-    "# Add a column to indicate whether the spill occurred before or after 2019 for historical and recent spills\n",
-    "historical_spills['Period'] = historical_spills['Report Year'].apply(lambda x: 'Before 2019' if x < 2019 else '2019 and After')\n",
-    "recent_spills['Period'] = recent_spills['Report Year'].apply(lambda x: 'Before 2019' if x < 2019 else '2019 and After')\n",
+    "# Add a column to indicate whether the spill occurred before or after 2021 for historical and recent spills\n",
+    "historical_spills['Period'] = historical_spills['Report Year'].apply(lambda x: 'Before 2021' if x < 2021 else '2021 and After')\n",
+    "recent_spills['Period'] = recent_spills['Report Year'].apply(lambda x: 'Before 2021' if x < 2021 else '2021 and After')\n",
     "\n",
     "# Historical Spills Analysis\n",
     "historical_report_delay_summary = historical_spills['Report Delay (Days)'].describe()\n",
@@ -446,7 +449,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "spatial_env2",
+   "display_name": "dadams",
    "language": "python",
    "name": "python3"
   },
@@ -460,7 +463,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.15"
+   "version": "3.11.8"
   }
  },
  "nbformat": 4,