{ "cells": [ { "cell_type": "markdown", "metadata": { "execution": { "iopub.execute_input": "2025-07-15T17:54:57.930578Z", "iopub.status.busy": "2025-07-15T17:54:57.926902Z", "iopub.status.idle": "2025-07-15T17:54:57.937203Z", "shell.execute_reply": "2025-07-15T17:54:57.936416Z", "shell.execute_reply.started": "2025-07-15T17:54:57.930422Z" } }, "source": [ "# Temperature and Sunscreen Search Trends in California (2004–2024)\n", "\n", "In this notebook, we explore the relationship between temperature trends and public interest in sunscreen in California from 2004 to 2024.\n", "\n", "We use **`varunayan`** to easily extract monthly average temperature data for California from the ERA5 climate reanalysis dataset.\n", "\n", "We aim to:\n", "- Compare temperature data with Google Trends search queries for \"sunscreen.\"\n", "- Visualize time series of both temperature and search interest and identify potential lead-lag relationships.\n", "- Explore regional patterns in temperature using raw high-resolution climate data (latitude-longitude based), which was obtained with the processed data from `varunayan`.\n", "\n", "This study can help identify when and where sunscreen demand may rise, aiding in health awareness and regional sales planning.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Step 1: Extract Monthly Temperature Data for California\n", "\n", "We use the `varunayan` package to download ERA5 temperature data for the California region using a GeoJSON boundary. The data spans from January 2004 to December 2024, with monthly frequency and 0.1° spatial resolution.\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "execution": { "iopub.execute_input": "2025-07-15T18:03:49.340292Z", "iopub.status.busy": "2025-07-15T18:03:49.339724Z", "iopub.status.idle": "2025-07-15T18:09:27.454747Z", "shell.execute_reply": "2025-07-15T18:09:27.454301Z", "shell.execute_reply.started": "2025-07-15T18:03:49.340264Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[0m\n", "============================================================\u001b[0m\n", "\u001b[0m\u001b[0;34mSTARTING ERA5 SINGLE LEVEL PROCESSING\u001b[0m\u001b[0m\n", "\u001b[0m============================================================\u001b[0m\n", "\u001b[0mRequest ID: temp_california_2004_2024\u001b[0m\n", "\u001b[0mVariables: ['2m_temperature']\u001b[0m\n", "\u001b[0mDate Range: 2004-01-01 to 2024-12-31\u001b[0m\n", "\u001b[0mFrequency: monthly\u001b[0m\n", "\u001b[0mResolution: 0.1°\u001b[0m\n", "\u001b[0mGeoJSON File: C:\\Users\\ATHARV~1\\AppData\\Local\\Temp\\temp_california_2004_2024_temp_geojson.json\u001b[0m\n", "\n", "\n", "--- GeoJSON Mini Map ---\n", "\n", "\u001b[0;34mMINI MAP (-124.45°W to -114.14°E, 32.53°S to 42.00°N):\u001b[0m\n", "┌─────────────────────────────────────────┐\n", 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shape\n", " \u001b[0;31m·\u001b[0m = Outside the shape\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "5a1ebfa607aa4fee91d5638b94b9e956", "version_major": 2, "version_minor": 0 }, "text/plain": [ "dbc527cd904c59dd9eb75ed248619a56.zip: 0%| | 0.00/1.84M [00:00" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "setup_matplotlib()\n", "\n", "fig, ax1 = plt.subplots(figsize=(12, 5))\n", "\n", "# Plot t2m on left y-axis\n", "ax1.plot(df_merged['date'], df_merged['t2m'], color='tab:blue', marker='o', label='Temperature (t2m)')\n", "ax1.set_ylabel('Temperature (K)', color='tab:blue')\n", "ax1.tick_params(axis='y', labelcolor='tab:blue')\n", "\n", "# Apply log transform to sunscreen\n", "sunscreen_log = np.log(df_merged['sunscreen'] + 1) # Add 1 to avoid log(0)\n", "\n", "# Plot on right y-axis\n", "ax2 = ax1.twinx()\n", "ax2.plot(df_merged['date'], sunscreen_log, color='tab:orange', marker='s', label='Sunscreen (log-scaled)')\n", "ax2.set_ylabel('Log of Sunscreen Trend', color='tab:orange')\n", "ax2.tick_params(axis='y', labelcolor='tab:orange')\n", "\n", "# Title and grid\n", "plt.title('Temperature vs Google Trends for Sunscreen (Log-Transformed)')\n", "ax1.set_xlabel('Date')\n", "ax1.grid(True)\n", "fig.tight_layout()\n", "\n", "plt.show()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "> The above plot shows that there is close relation between the Google trend for the search \"sunscreen\" and temperature in California." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Step 8: Cross-Correlation Analysis (CCF)\n", "\n", "We standardize both variables and compute the cross-correlation function (CCF) over lags from -12 to +12 months. This helps identify if temperature changes lead or lag public interest in sunscreen.\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "\n", "t2m = (df_merged['t2m'] - df_merged['t2m'].mean()) / df_merged['t2m'].std()\n", "sunscreen = (df_merged['sunscreen'] - df_merged['sunscreen'].mean()) / df_merged['sunscreen'].std()\n", "\n", "# Full cross-correlation\n", "corr = np.correlate(sunscreen - sunscreen.mean(), t2m - t2m.mean(), mode='full')\n", "lags = np.arange(-len(sunscreen)+1, len(sunscreen))\n", "corr = corr / (len(sunscreen) * sunscreen.std() * t2m.std()) # Normalize\n", "\n", "lag_limit = 12\n", "mask = (lags >= -lag_limit) & (lags <= lag_limit)\n", "lags_limited = lags[mask]\n", "corr_limited = corr[mask]\n", "\n", "# Plot\n", "plt.figure(figsize=(10, 5))\n", "plt.stem(lags_limited, corr_limited)\n", "plt.xlabel('Lag (months)')\n", "plt.ylabel('Cross-correlation')\n", "plt.title('CCF: Sunscreen vs Temperature')\n", "plt.axvline(0, color='gray', linestyle='--')\n", "plt.grid(True)\n", "plt.tight_layout()\n", "plt.show()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "> The cross-correlation plot (CCF) shows that **public interest in sunscreen tends to peak about one month before** surface temperatures reach their maximum. \n", "> This suggests that people **anticipate the heat** and start searching or preparing for summer **in advance** (a month before the peak to be exact)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Step 9: Load Raw ERA5 Temperature Data (Hourly, Grid-based)\n", "\n", "We load the raw gridded data (hourly values at individual lat-long points) extracted from the `temp_california_2004_2024_raw_data.csv` file for spatial analysis.\n" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "raw_df = pd.read_csv(\"temp_california_2004_2024_output/temp_california_2004_2024_raw_data.csv\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Step 10: Filter June and July\n", "\n", "To study spatial temperature distribution during peak sunscreen usage months, we isolate June and July data from the full hourly dataset.\n" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "raw_df['date'] = pd.to_datetime(raw_df['date'])\n", "\n", "# Filter June (6) and July (7)\n", "raw_df_summer = raw_df[raw_df['date'].dt.month.isin([6, 7])]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Step 11: Calculate Mean Temperature at Each Location\n", "\n", "We group the summer data by latitude and longitude to compute the average temperature for June and July across all years.\n" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "raw_df_avg = raw_df_summer.groupby(['latitude', 'longitude'])['t2m'].mean().reset_index()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Step 12: Visualize Spatial Distribution of Summer Temperatures\n", "\n", "We create a heatmap of average June–July temperature (t2m) across California, revealing spatial patterns in heat intensity that may affect sunscreen demand.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "\n", "setup_matplotlib()\n", "\n", "# Pivot data for heatmap\n", "heatmap_data = raw_df_avg.pivot(index='latitude', columns='longitude', values='t2m')\n", "\n", "# Plot heatmap\n", "plt.figure(figsize=(6, 5))\n", "ax = sns.heatmap(\n", " heatmap_data,\n", " cmap='coolwarm',\n", " cbar_kws={'label': 'Avg Temperature (K)'}\n", ")\n", "\n", "# Format tick labels to 3 decimal places\n", "ax.set_xticklabels([f\"{float(label.get_text()):.3f}\" for label in ax.get_xticklabels()])\n", "ax.set_yticklabels([f\"{float(label.get_text()):.3f}\" for label in ax.get_yticklabels()])\n", "\n", "plt.title('Average Temperature (t2m) for June-July Across Years')\n", "plt.xlabel('Longitude')\n", "plt.ylabel('Latitude')\n", "plt.gca().invert_yaxis()\n", "plt.tight_layout()\n", "plt.show()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [] } ], "metadata": { "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.12" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "state": {}, "version_major": 2, "version_minor": 0 } } }, "nbformat": 4, "nbformat_minor": 4 }