AQI_Predictions.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1f551cb9",
   "metadata": {},
   "source": [
    "# Project: California Air Quality Analysis\n",
    "## _Analyzing and Predicting AQI Data through Modeling_\n",
    "### by Sushant Harischandra Vema | sushant.h.vema@berkeley.edu | [github.com](github.com/sushantvema)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4a565e2",
   "metadata": {},
   "source": [
    "In this project, I will be investigating AQI data for the year 2020 from **USA EPA** data. All the data used for this project can be accessed from the [EPA Website](https://aqs.epa.gov/aqsweb/airdata/download_files.html), from which I will pull from directly in this notebook. This dataset contains geographical and time-series data on various factors which contribute to AQI from all government sites. My main end goal for this project is to understand how AQI varies both geographically and over time, and to use Exploratory Data Analysis techniques to predict AQI at a certain point in time for various locations in California.\n",
    "\n",
    "Through this final project, I will demonstrate my experience with the following :\n",
    "* EDA and merging on location using Pandas\n",
    "* Unsupervised and supervised learning techniques\n",
    "* Visualization and interpolation\n",
    "\n",
    "This is **part 1** of the project, which includes the data cleaning, guided EDA and open-ended EDA components. In **part 2** (later in the notebook), I will be completing the modeling component. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6cdc9036",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import re\n",
    "import geopandas as gpd\n",
    "\n",
    "import os\n",
    "import requests, zipfile, io\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "028786c6",
   "metadata": {},
   "source": [
    "<br/><br/>\n",
    "\n",
    "---\n",
    "## Section 1: Data Cleaning\n",
    "\n",
    "As mentioned, I will be using the **US EPA** data from the EPA website. Below is a dataframe of the files needed the project. The following two cells will download the data and put it into a dictionary called `epa_data`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8366dda3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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>name</th>\n",
       "      <th>epa_filename</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>annual_county_aqi</td>\n",
       "      <td>annual_aqi_by_county_2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>daily_county_aqi</td>\n",
       "      <td>daily_aqi_by_county_2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>daily_ozone</td>\n",
       "      <td>daily_44201_2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>daily_so2</td>\n",
       "      <td>daily_42401_2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>daily_co</td>\n",
       "      <td>daily_42101_2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>daily_no2</td>\n",
       "      <td>daily_42602_2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>daily_temp</td>\n",
       "      <td>daily_WIND_2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>daily_wind</td>\n",
       "      <td>daily_TEMP_2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>aqs_sites</td>\n",
       "      <td>aqs_sites</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                name               epa_filename\n",
       "0  annual_county_aqi  annual_aqi_by_county_2020\n",
       "1   daily_county_aqi   daily_aqi_by_county_2020\n",
       "2        daily_ozone           daily_44201_2020\n",
       "3          daily_so2           daily_42401_2020\n",
       "4           daily_co           daily_42101_2020\n",
       "5          daily_no2           daily_42602_2020\n",
       "6         daily_temp            daily_WIND_2020\n",
       "7         daily_wind            daily_TEMP_2020\n",
       "8          aqs_sites                  aqs_sites"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "current_directory = os.getcwd()\n",
    "final_directory = os.path.join(current_directory, r'data')\n",
    "if not os.path.exists(final_directory):\n",
    "    os.makedirs(final_directory)\n",
    "    \n",
    "epa_filenames_data = [['annual_county_aqi', 'annual_aqi_by_county_2020'], \n",
    "                      ['daily_county_aqi', \"daily_aqi_by_county_2020\"], \n",
    "                      ['daily_ozone', 'daily_44201_2020'],\n",
    "                      ['daily_so2', 'daily_42401_2020'],\n",
    "                      ['daily_co', 'daily_42101_2020'],\n",
    "                      ['daily_no2', 'daily_42602_2020'],\n",
    "                      ['daily_temp', 'daily_WIND_2020'],\n",
    "                      ['daily_wind', 'daily_TEMP_2020'],\n",
    "                      ['aqs_sites', 'aqs_sites']]\n",
    " \n",
    "epa_filenames = pd.DataFrame(epa_filenames_data, columns = ['name', 'epa_filename'])\n",
    "\n",
    "epa_filenames.to_csv(final_directory + \"/epa_filenames.csv\")\n",
    "\n",
    "epa_filenames"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "552962d7",
   "metadata": {},
   "source": [
    "Below is code that I used to extract datasets from the AQI website! This will pull directly from the website urls and put it into the `data/` folder. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "81769990",
   "metadata": {},
   "outputs": [],
   "source": [
    "epa_weburl = \"https://web.archive.org/web/20211118232504/https://aqs.epa.gov/aqsweb/airdata/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "194c7290",
   "metadata": {},
   "outputs": [],
   "source": [
    "epa_data = {}\n",
    "for name, filename in zip(epa_filenames['name'], epa_filenames['epa_filename']):\n",
    "    path_name = 'data/{}'.format(name)\n",
    "    # if not os.path.isdir(path_name): \n",
    "    data_url = '{}{}.zip'.format(epa_weburl, filename)\n",
    "    req = requests.get(data_url)\n",
    "    z = zipfile.ZipFile(io.BytesIO(req.content))\n",
    "    z.extractall(path_name)\n",
    "    data = pd.read_csv(f'data/{name}/{filename}.csv')\n",
    "    epa_data[name] = data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47a9c379",
   "metadata": {},
   "source": [
    "<br/>\n",
    "\n",
    "### Creating Month and Day Columns\n",
    "\n",
    "In the `daily_county_aqi` table in `epa_data`, I add two new columns called `Day` and `Month` which denote the day and month, respectively, of the AQI reading. The day and month are both reported as an **integer** as opposed to a string (`Jan`, `Feb`, etc.), using pd.datetime functions.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "87e358c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State Name</th>\n",
       "      <th>county Name</th>\n",
       "      <th>State Code</th>\n",
       "      <th>County Code</th>\n",
       "      <th>Date</th>\n",
       "      <th>AQI</th>\n",
       "      <th>Category</th>\n",
       "      <th>Defining Parameter</th>\n",
       "      <th>Defining Site</th>\n",
       "      <th>Number of Sites Reporting</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Baldwin</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>48</td>\n",
       "      <td>Good</td>\n",
       "      <td>PM2.5</td>\n",
       "      <td>01-003-0010</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Baldwin</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2020-01-04</td>\n",
       "      <td>13</td>\n",
       "      <td>Good</td>\n",
       "      <td>PM2.5</td>\n",
       "      <td>01-003-0010</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Baldwin</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2020-01-07</td>\n",
       "      <td>14</td>\n",
       "      <td>Good</td>\n",
       "      <td>PM2.5</td>\n",
       "      <td>01-003-0010</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Baldwin</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2020-01-10</td>\n",
       "      <td>39</td>\n",
       "      <td>Good</td>\n",
       "      <td>PM2.5</td>\n",
       "      <td>01-003-0010</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Baldwin</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2020-01-13</td>\n",
       "      <td>29</td>\n",
       "      <td>Good</td>\n",
       "      <td>PM2.5</td>\n",
       "      <td>01-003-0010</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  State Name county Name  State Code  County Code        Date  AQI Category  \\\n",
       "0    Alabama     Baldwin           1            3  2020-01-01   48     Good   \n",
       "1    Alabama     Baldwin           1            3  2020-01-04   13     Good   \n",
       "2    Alabama     Baldwin           1            3  2020-01-07   14     Good   \n",
       "3    Alabama     Baldwin           1            3  2020-01-10   39     Good   \n",
       "4    Alabama     Baldwin           1            3  2020-01-13   29     Good   \n",
       "\n",
       "  Defining Parameter Defining Site  Number of Sites Reporting  Month  Day  \n",
       "0              PM2.5   01-003-0010                          1      1    1  \n",
       "1              PM2.5   01-003-0010                          1      1    1  \n",
       "2              PM2.5   01-003-0010                          1      1    1  \n",
       "3              PM2.5   01-003-0010                          1      1    1  \n",
       "4              PM2.5   01-003-0010                          1      1    1  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily_county = epa_data.get('daily_county_aqi')\n",
    "daily_county['Month'] = pd.to_datetime(daily_county['Date']).dt.month\n",
    "daily_county['Day'] = pd.to_datetime(daily_county['Date']).dt.month\n",
    "\n",
    "\n",
    "daily_county.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20c78cd7",
   "metadata": {},
   "source": [
    "<br/>\n",
    "\n",
    "### California Data\n",
    "\n",
    "Currently, `epa_data` contains data for **all** counties in the United States. For this part of the project, I am specifically going to be focusing on AQI data for counties in California only. I assign `epa_data_CA` a dictionary mapping table names to dataframes. This mapping has the same contents as `epa_data` but only tables which contain **daily data** in the state of `California`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9adcc685",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State Name</th>\n",
       "      <th>county Name</th>\n",
       "      <th>State Code</th>\n",
       "      <th>County Code</th>\n",
       "      <th>Date</th>\n",
       "      <th>AQI</th>\n",
       "      <th>Category</th>\n",
       "      <th>Defining Parameter</th>\n",
       "      <th>Defining Site</th>\n",
       "      <th>Number of Sites Reporting</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14003</th>\n",
       "      <td>California</td>\n",
       "      <td>Alameda</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>53</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>PM2.5</td>\n",
       "      <td>06-001-0009</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14004</th>\n",
       "      <td>California</td>\n",
       "      <td>Alameda</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-01-02</td>\n",
       "      <td>43</td>\n",
       "      <td>Good</td>\n",
       "      <td>PM2.5</td>\n",
       "      <td>06-001-0013</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14005</th>\n",
       "      <td>California</td>\n",
       "      <td>Alameda</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-01-03</td>\n",
       "      <td>74</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>PM2.5</td>\n",
       "      <td>06-001-0013</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14006</th>\n",
       "      <td>California</td>\n",
       "      <td>Alameda</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-01-04</td>\n",
       "      <td>45</td>\n",
       "      <td>Good</td>\n",
       "      <td>PM2.5</td>\n",
       "      <td>06-001-0007</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14007</th>\n",
       "      <td>California</td>\n",
       "      <td>Alameda</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-01-05</td>\n",
       "      <td>33</td>\n",
       "      <td>Good</td>\n",
       "      <td>PM2.5</td>\n",
       "      <td>06-001-0007</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33223</th>\n",
       "      <td>California</td>\n",
       "      <td>Yolo</td>\n",
       "      <td>6</td>\n",
       "      <td>113</td>\n",
       "      <td>2020-12-27</td>\n",
       "      <td>20</td>\n",
       "      <td>Good</td>\n",
       "      <td>Ozone</td>\n",
       "      <td>06-113-0004</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33224</th>\n",
       "      <td>California</td>\n",
       "      <td>Yolo</td>\n",
       "      <td>6</td>\n",
       "      <td>113</td>\n",
       "      <td>2020-12-28</td>\n",
       "      <td>33</td>\n",
       "      <td>Good</td>\n",
       "      <td>Ozone</td>\n",
       "      <td>06-113-0004</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33225</th>\n",
       "      <td>California</td>\n",
       "      <td>Yolo</td>\n",
       "      <td>6</td>\n",
       "      <td>113</td>\n",
       "      <td>2020-12-29</td>\n",
       "      <td>28</td>\n",
       "      <td>Good</td>\n",
       "      <td>Ozone</td>\n",
       "      <td>06-113-0004</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33226</th>\n",
       "      <td>California</td>\n",
       "      <td>Yolo</td>\n",
       "      <td>6</td>\n",
       "      <td>113</td>\n",
       "      <td>2020-12-30</td>\n",
       "      <td>39</td>\n",
       "      <td>Good</td>\n",
       "      <td>PM2.5</td>\n",
       "      <td>06-113-0004</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33227</th>\n",
       "      <td>California</td>\n",
       "      <td>Yolo</td>\n",
       "      <td>6</td>\n",
       "      <td>113</td>\n",
       "      <td>2020-12-31</td>\n",
       "      <td>35</td>\n",
       "      <td>Good</td>\n",
       "      <td>PM2.5</td>\n",
       "      <td>06-113-0004</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>19225 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       State Name county Name  State Code  County Code        Date  AQI  \\\n",
       "14003  California     Alameda           6            1  2020-01-01   53   \n",
       "14004  California     Alameda           6            1  2020-01-02   43   \n",
       "14005  California     Alameda           6            1  2020-01-03   74   \n",
       "14006  California     Alameda           6            1  2020-01-04   45   \n",
       "14007  California     Alameda           6            1  2020-01-05   33   \n",
       "...           ...         ...         ...          ...         ...  ...   \n",
       "33223  California        Yolo           6          113  2020-12-27   20   \n",
       "33224  California        Yolo           6          113  2020-12-28   33   \n",
       "33225  California        Yolo           6          113  2020-12-29   28   \n",
       "33226  California        Yolo           6          113  2020-12-30   39   \n",
       "33227  California        Yolo           6          113  2020-12-31   35   \n",
       "\n",
       "       Category Defining Parameter Defining Site  Number of Sites Reporting  \\\n",
       "14003  Moderate              PM2.5   06-001-0009                          7   \n",
       "14004      Good              PM2.5   06-001-0013                          7   \n",
       "14005  Moderate              PM2.5   06-001-0013                          7   \n",
       "14006      Good              PM2.5   06-001-0007                          7   \n",
       "14007      Good              PM2.5   06-001-0007                          7   \n",
       "...         ...                ...           ...                        ...   \n",
       "33223      Good              Ozone   06-113-0004                          2   \n",
       "33224      Good              Ozone   06-113-0004                          2   \n",
       "33225      Good              Ozone   06-113-0004                          3   \n",
       "33226      Good              PM2.5   06-113-0004                          2   \n",
       "33227      Good              PM2.5   06-113-0004                          2   \n",
       "\n",
       "       Month  Day  \n",
       "14003      1    1  \n",
       "14004      1    1  \n",
       "14005      1    1  \n",
       "14006      1    1  \n",
       "14007      1    1  \n",
       "...      ...  ...  \n",
       "33223     12   12  \n",
       "33224     12   12  \n",
       "33225     12   12  \n",
       "33226     12   12  \n",
       "33227     12   12  \n",
       "\n",
       "[19225 rows x 12 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = epa_data.get('daily_county_aqi')\n",
    "b[b['State Name'] == 'California']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "71fce6ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2       52\n",
       "1       52\n",
       "1001    46\n",
       "3       44\n",
       "4       41\n",
       "        ..\n",
       "9007     1\n",
       "242      1\n",
       "243      1\n",
       "244      1\n",
       "4000     1\n",
       "Name: Site Number, Length: 220, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = epa_data.get('aqs_sites')\n",
    "a[a['State Name'] == 'California']['Site Number'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "14f67170",
   "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>State Name</th>\n",
       "      <th>county Name</th>\n",
       "      <th>State Code</th>\n",
       "      <th>County Code</th>\n",
       "      <th>Date</th>\n",
       "      <th>AQI</th>\n",
       "      <th>Category</th>\n",
       "      <th>Defining Parameter</th>\n",
       "      <th>Defining Site</th>\n",
       "      <th>Number of Sites Reporting</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14003</th>\n",
       "      <td>California</td>\n",
       "      <td>Alameda</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>53</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>PM2.5</td>\n",
       "      <td>06-001-0009</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14004</th>\n",
       "      <td>California</td>\n",
       "      <td>Alameda</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-01-02</td>\n",
       "      <td>43</td>\n",
       "      <td>Good</td>\n",
       "      <td>PM2.5</td>\n",
       "      <td>06-001-0013</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14005</th>\n",
       "      <td>California</td>\n",
       "      <td>Alameda</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-01-03</td>\n",
       "      <td>74</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>PM2.5</td>\n",
       "      <td>06-001-0013</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14006</th>\n",
       "      <td>California</td>\n",
       "      <td>Alameda</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-01-04</td>\n",
       "      <td>45</td>\n",
       "      <td>Good</td>\n",
       "      <td>PM2.5</td>\n",
       "      <td>06-001-0007</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14007</th>\n",
       "      <td>California</td>\n",
       "      <td>Alameda</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-01-05</td>\n",
       "      <td>33</td>\n",
       "      <td>Good</td>\n",
       "      <td>PM2.5</td>\n",
       "      <td>06-001-0007</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       State Name county Name  State Code  County Code        Date  AQI  \\\n",
       "14003  California     Alameda           6            1  2020-01-01   53   \n",
       "14004  California     Alameda           6            1  2020-01-02   43   \n",
       "14005  California     Alameda           6            1  2020-01-03   74   \n",
       "14006  California     Alameda           6            1  2020-01-04   45   \n",
       "14007  California     Alameda           6            1  2020-01-05   33   \n",
       "\n",
       "       Category Defining Parameter Defining Site  Number of Sites Reporting  \\\n",
       "14003  Moderate              PM2.5   06-001-0009                          7   \n",
       "14004      Good              PM2.5   06-001-0013                          7   \n",
       "14005  Moderate              PM2.5   06-001-0013                          7   \n",
       "14006      Good              PM2.5   06-001-0007                          7   \n",
       "14007      Good              PM2.5   06-001-0007                          7   \n",
       "\n",
       "       Month  Day  \n",
       "14003      1    1  \n",
       "14004      1    1  \n",
       "14005      1    1  \n",
       "14006      1    1  \n",
       "14007      1    1  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "epa_data_CA = epa_data.copy()\n",
    "epa_data_CA.pop('annual_county_aqi')\n",
    "epa_data_CA.pop('aqs_sites')\n",
    "for key,value in epa_data_CA.items():\n",
    "    newdata = epa_data_CA.get(key)\n",
    "    newdata = newdata[newdata['State Name'] == 'California']\n",
    "    epa_data_CA[key] = newdata\n",
    "    \n",
    "epa_data_CA.get('daily_county_aqi').head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7530c97f",
   "metadata": {},
   "source": [
    "<br/>\n",
    "\n",
    "### Merging Site Information\n",
    "\n",
    "Refer to [link](https://www.epa.gov/outdoor-air-quality-data/about-air-data-reports) and look under \"Site ID\". For later analysis, I want to first get the latitude and longitudes of each of the measurements in the `daily_county_aqi` table by merging aqs_sites and other tables in `epa_data_CA` (one of the tables is `daily_county_aqi`).\n",
    "\n",
    "The final merged table should be assigned to `epa_data_CA_merged` and the result contains the following columns: `State Name`, `county Name`, `Month`, `Day`, `AQI`, `Category`, `Defining Site`, `Latitude`, and `Longitude`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c9173e4a",
   "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>State Name</th>\n",
       "      <th>county Name</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>AQI</th>\n",
       "      <th>Category</th>\n",
       "      <th>Defining Site</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>California</td>\n",
       "      <td>Alameda</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>53</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>06-001-0009</td>\n",
       "      <td>37.743065</td>\n",
       "      <td>-122.169935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>California</td>\n",
       "      <td>Alameda</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>40</td>\n",
       "      <td>Good</td>\n",
       "      <td>06-001-0009</td>\n",
       "      <td>37.743065</td>\n",
       "      <td>-122.169935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>California</td>\n",
       "      <td>Alameda</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>48</td>\n",
       "      <td>Good</td>\n",
       "      <td>06-001-0009</td>\n",
       "      <td>37.743065</td>\n",
       "      <td>-122.169935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>California</td>\n",
       "      <td>Alameda</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>115</td>\n",
       "      <td>Unhealthy for Sensitive Groups</td>\n",
       "      <td>06-001-0009</td>\n",
       "      <td>37.743065</td>\n",
       "      <td>-122.169935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>California</td>\n",
       "      <td>Alameda</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>78</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>06-001-0009</td>\n",
       "      <td>37.743065</td>\n",
       "      <td>-122.169935</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   State Name county Name  Month  Day  AQI                        Category  \\\n",
       "0  California     Alameda      1    1   53                        Moderate   \n",
       "1  California     Alameda      1    1   40                            Good   \n",
       "2  California     Alameda      7    7   48                            Good   \n",
       "3  California     Alameda      7    7  115  Unhealthy for Sensitive Groups   \n",
       "4  California     Alameda      7    7   78                        Moderate   \n",
       "\n",
       "  Defining Site   Latitude   Longitude  \n",
       "0   06-001-0009  37.743065 -122.169935  \n",
       "1   06-001-0009  37.743065 -122.169935  \n",
       "2   06-001-0009  37.743065 -122.169935  \n",
       "3   06-001-0009  37.743065 -122.169935  \n",
       "4   06-001-0009  37.743065 -122.169935  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = ['State Name', 'county Name', 'Month', 'Day', 'AQI', 'Category', 'Defining Site', 'Latitude', 'Longitude']\n",
    "county_aqis = epa_data_CA.get('daily_county_aqi')\n",
    "\n",
    "def merge_site_information(county_aqis):\n",
    "    arr = []\n",
    "    iterate_array = county_aqis['Defining Site']\n",
    "    for i in iterate_array:\n",
    "        arr.append(int(i[7:]))\n",
    "    county_aqis['Site Number'] = arr\n",
    "    sites = epa_data.get('aqs_sites')\n",
    "    sites = sites[sites['State Name'] == 'California']\n",
    "    epa_data_CA_merged = county_aqis.merge(sites, on = ['County Code', 'Site Number'])\n",
    "    epa_data_CA_merged = epa_data_CA_merged.rename(columns = {'State Name_x': 'State Name'})\n",
    "    epa_data_CA_merged = epa_data_CA_merged[cols]\n",
    "    return epa_data_CA_merged\n",
    "\n",
    "epa_data_CA_merged = merge_site_information(county_aqis)\n",
    "epa_data_CA_merged.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88f0e9ee",
   "metadata": {},
   "source": [
    "<br/>\n",
    "\n",
    "### Cleaning Traffic Data \n",
    "\n",
    "Throughout this project, I will be using other datasets to assist with analysis and predictions. Traditionally, to join dataframes, one would need to join on a specific column with shared values (a primary key). However, when it comes to locations, exact latitudes and longitudes are hard to come by since these variables are in a continuous space. Below is a dataset that we want to merge on with `epa_data_CA_merged`. \n",
    "\n",
    "In the below cell, I have loaded in the `traffic_data` dataset, which contains traffic data for various locations in California. I will clean this table so that it includes only the following columns: `District`, `Route`, `County`, `Descriptn`, `AADT`, `Latitude`, `Longitude`, where `AADT` (Annual Average Daily Traffic) is found by taking the sum of the back and ahead `AADT`s. The AADT metric is calculated as the sum of the traffic north of the route (ahead AADT) and south of the route (back AADT). I will also clean and remove any illegal values from the dataframe, mostly in `Latitude` and `Longitude`."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1dd32716",
   "metadata": {},
   "source": [
    "#### NOTE: In order to reproduce my work from here on forward, you must do the following steps:\n",
    "1. Go to https://gis.data.ca.gov/datasets/f71f49fb87b3426e9688fe66039170bc/explore \n",
    "2. Click download, and download the dataset as a .csv file. The version of data I am using is from December 27th, 2021. The download was 996kb for me.\n",
    "3. **Save this dataset to the /data subdirectory in the working directory of this notebook.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "33a41d59",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\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>District</th>\n",
       "      <th>Route</th>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
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       "      <td>SONOMA/MENDOCINO COUNTY LINE</td>\n",
       "      <td>4000</td>\n",
       "      <td>38.75984264</td>\n",
       "      <td>-123.5185026</td>\n",
       "    </tr>\n",
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       "      <th>1</th>\n",
       "      <td>1</td>\n",
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       "      <td>MEN</td>\n",
       "      <td>NORTH LIMITS GUALALA</td>\n",
       "      <td>7100</td>\n",
       "      <td>38.77004592</td>\n",
       "      <td>-123.53189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>MEN</td>\n",
       "      <td>FISH ROCK ROAD</td>\n",
       "      <td>6200</td>\n",
       "      <td>38.80354931</td>\n",
       "      <td>-123.585411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>MEN</td>\n",
       "      <td>POINT ARENA, SOUTH CITY LIMITS</td>\n",
       "      <td>4600</td>\n",
       "      <td>38.90397338</td>\n",
       "      <td>-123.6915134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>MEN</td>\n",
       "      <td>POINT ARENA, RIVERSIDE DRIVE</td>\n",
       "      <td>5000</td>\n",
       "      <td>38.91091252</td>\n",
       "      <td>-123.6924099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <th>7115</th>\n",
       "      <td>12</td>\n",
       "      <td>605</td>\n",
       "      <td>ORA</td>\n",
       "      <td>SEAL BEACH, JCT RTE 22</td>\n",
       "      <td>46100</td>\n",
       "      <td>33.7786335</td>\n",
       "      <td>-118.0914735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7116</th>\n",
       "      <td>12</td>\n",
       "      <td>605</td>\n",
       "      <td>ORA</td>\n",
       "      <td>JCT. RTE. 405</td>\n",
       "      <td>212200</td>\n",
       "      <td>33.78441396</td>\n",
       "      <td>-118.0917676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7117</th>\n",
       "      <td>12</td>\n",
       "      <td>605</td>\n",
       "      <td>ORA</td>\n",
       "      <td>LOS ALAMITOS, KATELLA AVENUE</td>\n",
       "      <td>326800</td>\n",
       "      <td>33.80279867</td>\n",
       "      <td>-118.0820302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7118</th>\n",
       "      <td>12</td>\n",
       "      <td>605</td>\n",
       "      <td>ORA</td>\n",
       "      <td>ORANGE/LOS ANGELES COUNTY LINE</td>\n",
       "      <td>170000</td>\n",
       "      <td>33.80613969</td>\n",
       "      <td>-118.081547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7119</th>\n",
       "      <td>3</td>\n",
       "      <td>99</td>\n",
       "      <td>SAC</td>\n",
       "      <td>BREAK IN ROUTE</td>\n",
       "      <td>0</td>\n",
       "      <td>38.55883754</td>\n",
       "      <td>-121.4736488</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6913 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      District  Route County                       Descriptn    AADT  \\\n",
       "0            1      1    MEN    SONOMA/MENDOCINO COUNTY LINE    4000   \n",
       "1            1      1    MEN            NORTH LIMITS GUALALA    7100   \n",
       "2            1      1    MEN                  FISH ROCK ROAD    6200   \n",
       "3            1      1    MEN  POINT ARENA, SOUTH CITY LIMITS    4600   \n",
       "4            1      1    MEN    POINT ARENA, RIVERSIDE DRIVE    5000   \n",
       "...        ...    ...    ...                             ...     ...   \n",
       "7115        12    605    ORA          SEAL BEACH, JCT RTE 22   46100   \n",
       "7116        12    605    ORA                   JCT. RTE. 405  212200   \n",
       "7117        12    605    ORA    LOS ALAMITOS, KATELLA AVENUE  326800   \n",
       "7118        12    605    ORA  ORANGE/LOS ANGELES COUNTY LINE  170000   \n",
       "7119         3     99    SAC                  BREAK IN ROUTE       0   \n",
       "\n",
       "         Latitude     Longitude  \n",
       "0     38.75984264  -123.5185026  \n",
       "1     38.77004592    -123.53189  \n",
       "2     38.80354931   -123.585411  \n",
       "3     38.90397338  -123.6915134  \n",
       "4     38.91091252  -123.6924099  \n",
       "...           ...           ...  \n",
       "7115   33.7786335  -118.0914735  \n",
       "7116  33.78441396  -118.0917676  \n",
       "7117  33.80279867  -118.0820302  \n",
       "7118  33.80613969   -118.081547  \n",
       "7119  38.55883754  -121.4736488  \n",
       "\n",
       "[6913 rows x 7 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "traffic_data = pd.read_csv(\"data/Traffic_Volumes_AADT.csv\")\n",
    "\n",
    "def clean_traffic_data(traffic_data):\n",
    "\n",
    "    cols = [\"District\", \"Route\", \"County\", \"Descriptn\", \"AADT\", \"Latitude\", \"Longitude\"]\n",
    "    renaming = {\"Lat_S_or_W\": \"Latitude\", \"Lon_S_or_W\":  \"Longitude\"}\n",
    "    traffic_data = traffic_data.rename(columns = renaming)\n",
    "    traffic_data[\"Latitude\"] = traffic_data[\"Latitude\"].str.strip()\n",
    "    traffic_data[\"Latitude\"] = traffic_data[\"Latitude\"].str.replace(r'[a-zA-Z]', '')\n",
    "    traffic_data = traffic_data[~traffic_data[\"Latitude\"].str.contains(\" - \")]\n",
    "    traffic_data = traffic_data.replace(\" \", 0)\n",
    "    traffic_data[\"AADT\"] = traffic_data[\"Back_AADT\"].astype(int) + traffic_data[\"Ahead_AADT\"].astype(int)\n",
    "    traffic_data_cleaned = traffic_data[cols]\n",
    "    return traffic_data_cleaned\n",
    "\n",
    "traffic_data_cleaned = clean_traffic_data(traffic_data)\n",
    "traffic_data_cleaned"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75e175a6",
   "metadata": {},
   "source": [
    "### Merging on Traffic Data \n",
    "\n",
    "Traditionally, one could employ some sort of join in which where we join `epa_data_CA_merged` rows with the row in `traffic_data` that it is the \"closest\" to, as measured by euclidean distance. Since this is quite tedious, I will use a special type of join called a **spatial join**, which can be done using the package `geopandas`, which is imported as `gpd`. The documentation for `geopandas` is linked [here](https://geopandas.org/docs/reference/api/geopandas.sjoin_nearest.html). I will proceed to: \n",
    "\n",
    " - turn `traffic_data_cleaned` and `epa_data_CA_merged` into a geopandas dataframe using the latitude and longitude.\n",
    " - Use a spatial join to match the correct traffic row information to each entry in `epa_data_CA_merged`. \n",
    "\n",
    "I will create a dataframe named `gpd_epa_traffic` with the following columns: `State Name`, `county Name`, `Month`, `Day`, `AQI`, `Category`, `Defining Site`, `Site Lat`, `Site Long`, `Traffic Lat`, `Traffic Long`, `Descriptn`, and `AADT`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d57618f0",
   "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>State Name</th>\n",
       "      <th>county Name</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>AQI</th>\n",
       "      <th>Category</th>\n",
       "      <th>Defining Site</th>\n",
       "      <th>Site Lat</th>\n",
       "      <th>Site Long</th>\n",
       "      <th>Traffic Lat</th>\n",
       "      <th>Traffic Long</th>\n",
       "      <th>Descriptn</th>\n",
       "      <th>AADT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>California</td>\n",
       "      <td>Alameda</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>53</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>06-001-0009</td>\n",
       "      <td>37.743065</td>\n",
       "      <td>-122.169935</td>\n",
       "      <td>37.74435189</td>\n",
       "      <td>-122.170586</td>\n",
       "      <td>OAKLAND, 98TH AVENUE</td>\n",
       "      <td>48300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>California</td>\n",
       "      <td>Alameda</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>40</td>\n",
       "      <td>Good</td>\n",
       "      <td>06-001-0009</td>\n",
       "      <td>37.743065</td>\n",
       "      <td>-122.169935</td>\n",
       "      <td>37.74435189</td>\n",
       "      <td>-122.170586</td>\n",
       "      <td>OAKLAND, 98TH AVENUE</td>\n",
       "      <td>48300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>California</td>\n",
       "      <td>Alameda</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>48</td>\n",
       "      <td>Good</td>\n",
       "      <td>06-001-0009</td>\n",
       "      <td>37.743065</td>\n",
       "      <td>-122.169935</td>\n",
       "      <td>37.74435189</td>\n",
       "      <td>-122.170586</td>\n",
       "      <td>OAKLAND, 98TH AVENUE</td>\n",
       "      <td>48300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>California</td>\n",
       "      <td>Alameda</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>115</td>\n",
       "      <td>Unhealthy for Sensitive Groups</td>\n",
       "      <td>06-001-0009</td>\n",
       "      <td>37.743065</td>\n",
       "      <td>-122.169935</td>\n",
       "      <td>37.74435189</td>\n",
       "      <td>-122.170586</td>\n",
       "      <td>OAKLAND, 98TH AVENUE</td>\n",
       "      <td>48300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>California</td>\n",
       "      <td>Alameda</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>78</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>06-001-0009</td>\n",
       "      <td>37.743065</td>\n",
       "      <td>-122.169935</td>\n",
       "      <td>37.74435189</td>\n",
       "      <td>-122.170586</td>\n",
       "      <td>OAKLAND, 98TH AVENUE</td>\n",
       "      <td>48300</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   State Name county Name  Month  Day  AQI                        Category  \\\n",
       "0  California     Alameda      1    1   53                        Moderate   \n",
       "1  California     Alameda      1    1   40                            Good   \n",
       "2  California     Alameda      7    7   48                            Good   \n",
       "3  California     Alameda      7    7  115  Unhealthy for Sensitive Groups   \n",
       "4  California     Alameda      7    7   78                        Moderate   \n",
       "\n",
       "  Defining Site   Site Lat   Site Long  Traffic Lat  Traffic Long  \\\n",
       "0   06-001-0009  37.743065 -122.169935  37.74435189   -122.170586   \n",
       "1   06-001-0009  37.743065 -122.169935  37.74435189   -122.170586   \n",
       "2   06-001-0009  37.743065 -122.169935  37.74435189   -122.170586   \n",
       "3   06-001-0009  37.743065 -122.169935  37.74435189   -122.170586   \n",
       "4   06-001-0009  37.743065 -122.169935  37.74435189   -122.170586   \n",
       "\n",
       "              Descriptn   AADT  \n",
       "0  OAKLAND, 98TH AVENUE  48300  \n",
       "1  OAKLAND, 98TH AVENUE  48300  \n",
       "2  OAKLAND, 98TH AVENUE  48300  \n",
       "3  OAKLAND, 98TH AVENUE  48300  \n",
       "4  OAKLAND, 98TH AVENUE  48300  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def merge_on_traffic_data(traffic_data_cleaned):\n",
    "\n",
    "    geom_traffic = gpd.points_from_xy(traffic_data_cleaned.Longitude, traffic_data_cleaned.Latitude)\n",
    "    geom_merged = gpd.points_from_xy(epa_data_CA_merged.Longitude, epa_data_CA_merged.Latitude)\n",
    "    gpd_traffic = gpd.GeoDataFrame(traffic_data_cleaned, geometry = geom_traffic)\n",
    "    gpd_CA_merged = gpd.GeoDataFrame(epa_data_CA_merged, geometry = geom_merged)\n",
    "    joined = gpd.sjoin_nearest(left_df = gpd_CA_merged, right_df = gpd_traffic, how = \"inner\") \n",
    "    renaming = {\"Latitude_left\": \"Site Lat\", \"Longitude_left\": \"Site Long\", \"Latitude_right\": \"Traffic Lat\", \n",
    "                \"Longitude_right\": \"Traffic Long\"}\n",
    "    joined = joined.rename(columns = renaming)\n",
    "    joined[\"Traffic Long\"] = joined[\"Traffic Long\"].astype(str).astype(float)\n",
    "    cols = [\"State Name\", \"county Name\", \"Month\", \"Day\", \"AQI\", \"Category\", \"Defining Site\", \"Site Lat\", \"Site Long\", \n",
    "            \"Traffic Lat\", \"Traffic Long\", \"Descriptn\", \"AADT\"]\n",
    "    gpd_epa_traffic = joined[cols]\n",
    "    \n",
    "    return gpd_epa_traffic\n",
    "\n",
    "gpd_epa_traffic = merge_on_traffic_data(traffic_data_cleaned)\n",
    "gpd_epa_traffic.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9300af20",
   "metadata": {},
   "source": [
    "<br/><br/>\n",
    "\n",
    "---\n",
    "## Section 2: Preliminary EDA\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb4264b7",
   "metadata": {},
   "source": [
    "### Initial AQI Analysis\n",
    "\n",
    "I will create a Series called `worst_median_aqis` which contains the states with the top 10 worst median AQIs throughout the year 2020, as measured by the average median AQIs across all counties for a single state. \n",
    "\n",
    "Next, I will create a Series called `worst_max_aqis`, except instead of aggregating the average median AQIs across all counties, I will aggregate the average **max AQIs** across all counties. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5765acab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Worst Median AQI : \n",
      "State\n",
      "California       48.018868\n",
      "Arizona          47.307692\n",
      "Utah             41.066667\n",
      "Connecticut      39.125000\n",
      "Delaware         38.000000\n",
      "Mississippi      37.200000\n",
      "New Jersey       36.937500\n",
      "Massachusetts    36.538462\n",
      "Nevada           36.222222\n",
      "Pennsylvania     35.756098\n",
      "Name: Average Median AQI, dtype: float64\n",
      "\n",
      "Worst Max AQI : \n",
      "State\n",
      "Oregon          430.347826\n",
      "Washington      334.419355\n",
      "California      286.981132\n",
      "Arizona         238.230769\n",
      "Idaho           197.857143\n",
      "Wyoming         196.666667\n",
      "Nevada          196.666667\n",
      "Montana         137.421053\n",
      "Rhode Island    133.000000\n",
      "Connecticut     124.750000\n",
      "Name: Average Max AQI, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "annual_data = epa_data.get('annual_county_aqi')\n",
    "\n",
    "states = [\"California\", \"Texas\", \"Florida\", \"Washington\", \"Hawaii\", \"Virginia\", \"Alaska\", \"Massachusetts\", \n",
    "         \"New Jersey\", \"Pennsylvania\", \"Montana\", \"North Carolina\", \"Arizona\", \"Georgia\", \"Ohio\", \"Colorado\",\n",
    "         \"Illinois\", \"Michigan\", \"Oregon\", \"New York\", \"Minnesota\", \"Maryland\", \"Louisiana\", \"Connecticut\", \n",
    "         \"Tennessee\", \"Alabama\", \"Indiana\", \"Utah\", \"Maine\", \"Nevada\", \"New Mexico\", \"Missouri\", \"Wisconsin\", \n",
    "         \"Oklahoma\", \"South Carolina\", \"Kentucky\", \"Mississippi\", \"Wyoming\", \"Iowa\", \"Nebraska\", \"Idaho\", \"Kansas\", \n",
    "         \"Delaware\", \"Arkansas\", \"New Hampshire\", \"Vermont\", \"Rhode Island\", \"West Virginia\", \"South Dakota\", \n",
    "         \"North Dakota\"]\n",
    "\n",
    "annual_data = annual_data[annual_data[\"State\"].isin(states)]\n",
    "\n",
    "def create_worst_median_aqis(annual_data, states):\n",
    "    grouped_avg = annual_data.groupby(\"State\").mean(\"Median AQI\")\n",
    "    grouped_avg = grouped_avg.rename(columns = {\"Median AQI\": \"Average Median AQI\"})\n",
    "    grouped_avg = grouped_avg.sort_values(\"Average Median AQI\", ascending = False)\n",
    "    worst_median_aqis = grouped_avg.head(10)[\"Average Median AQI\"]\n",
    "    return worst_median_aqis\n",
    "\n",
    "def create_worst_max_aqis(annual_data, states):    \n",
    "    grouped_max = annual_data.groupby(\"State\").mean(\"Max AQI\")\n",
    "    grouped_max = grouped_max.rename(columns = {\"Max AQI\": \"Average Max AQI\"})\n",
    "    grouped_max = grouped_max.sort_values(\"Average Max AQI\", ascending = False)\n",
    "    worst_max_aqis = grouped_max.head(10)[\"Average Max AQI\"]\n",
    "    return worst_max_aqis\n",
    "\n",
    "\n",
    "\n",
    "print(\"Worst Median AQI : \\n{}\\n\".format(create_worst_median_aqis(annual_data, states)))\n",
    "print(\"Worst Max AQI : \\n{}\".format(create_worst_max_aqis(annual_data, states)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66ad6769",
   "metadata": {},
   "source": [
    "### Worst AQI States - Observations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9fa0e15",
   "metadata": {},
   "source": [
    "California, Arizona, and Connecticut are countries that make both top 10 lists. California had a devastating fire season that both peaked the charts in terms of poor air quality while also sustaining that level for a long period of time across the state. \n",
    "\n",
    "Moreover, large valleys and river basins in California such as the San Fernando Valley and Riverside County allow air to settle for long periods of time. This sustains poor air quality for longer, and thereby pushing up the median AQI. \n",
    "\n",
    "As for Arizona, the state also had an intense wildfire season in the year 2020 proving to be the largest season since 2011 (as per Wikipedia). Arizona is also a neighboring state of California which may have experienced a settling of atmospheric particulates stemming from the California wildfires. Moreover, Arizona is a desert which does not experience much rain-like weather patterns which would clear up atmospheric pollutants. Hence, Arizona experienced a similarly elevated median."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc96f68d",
   "metadata": {},
   "source": [
    "### Missing AQI Data\n",
    "\n",
    "Before continuing on with my analysis, I would like to see how accessible AQI data is across states. In the following cell, I will assign `days_with_AQI` to a series which contains the state as the index and the average number of days with AQI entries across all counties in that particular state. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "1b426990",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "State\n",
       "Alaska        235.222222\n",
       "Arkansas      251.545455\n",
       "New Mexico    264.062500\n",
       "Virginia      265.303030\n",
       "Colorado      278.892857\n",
       "Name: Days with AQI, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "days_with_AQI = annual_data.groupby('State').mean()\n",
    "days_with_AQI = days_with_AQI.sort_values(by = 'Days with AQI', \n",
    "                                          ascending = True)\n",
    "\n",
    "days_with_AQI['Days with AQI'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6dd4da4f",
   "metadata": {},
   "source": [
    "### What are the missing dates?\n",
    "\n",
    "I will create a series called `ca_aqi_days` which contains each county in California mapped to the number of days that they have AQI data. Observe that a handful of counties (10 of them to be precise) apparently DO NOT have a full year's worth of data, which is what I will investigate subsequently."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d595ffe8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "54    274\n",
       "96    331\n",
       "63    351\n",
       "98    353\n",
       "49    359\n",
       "76    360\n",
       "51    364\n",
       "57    364\n",
       "72    365\n",
       "79    366\n",
       "Name: Days with AQI, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ca_annual_data = epa_data.get('annual_county_aqi')[epa_data.get('annual_county_aqi')['State'] == 'California']\n",
    "ca_aqi_days = ca_annual_data['Days with AQI'].sort_values()\n",
    "ca_aqi_days.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec49ee97",
   "metadata": {},
   "source": [
    "#### Missing Days\n",
    "\n",
    "I will assign `county_to_missing_dates` to a dictionary which maps each county having less than the full year of data to the dates of missing AQI data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "1619b197",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Del Norte': ['2020-01-15',\n",
       "  '2020-01-16',\n",
       "  '2020-01-17',\n",
       "  '2020-01-18',\n",
       "  '2020-01-20',\n",
       "  '2020-01-21',\n",
       "  '2020-01-23',\n",
       "  '2020-03-14',\n",
       "  '2020-03-15',\n",
       "  '2020-04-22',\n",
       "  '2020-04-23',\n",
       "  '2020-04-25',\n",
       "  '2020-04-26',\n",
       "  '2020-04-28',\n",
       "  '2020-04-29',\n",
       "  '2020-05-01',\n",
       "  '2020-05-02',\n",
       "  '2020-05-04',\n",
       "  '2020-05-05',\n",
       "  '2020-05-07',\n",
       "  '2020-05-08',\n",
       "  '2020-05-10',\n",
       "  '2020-05-11',\n",
       "  '2020-05-13',\n",
       "  '2020-05-14',\n",
       "  '2020-05-16',\n",
       "  '2020-05-17',\n",
       "  '2020-05-19',\n",
       "  '2020-05-20',\n",
       "  '2020-05-22',\n",
       "  '2020-05-23',\n",
       "  '2020-05-25',\n",
       "  '2020-05-26',\n",
       "  '2020-05-28',\n",
       "  '2020-05-29',\n",
       "  '2020-05-31',\n",
       "  '2020-06-01',\n",
       "  '2020-06-02',\n",
       "  '2020-06-03',\n",
       "  '2020-06-04',\n",
       "  '2020-06-05',\n",
       "  '2020-06-06',\n",
       "  '2020-06-07',\n",
       "  '2020-06-08',\n",
       "  '2020-06-09',\n",
       "  '2020-06-10',\n",
       "  '2020-06-11',\n",
       "  '2020-06-12',\n",
       "  '2020-06-13',\n",
       "  '2020-06-14',\n",
       "  '2020-06-15',\n",
       "  '2020-06-16',\n",
       "  '2020-06-17',\n",
       "  '2020-06-18',\n",
       "  '2020-06-19',\n",
       "  '2020-06-20',\n",
       "  '2020-06-21',\n",
       "  '2020-06-22',\n",
       "  '2020-06-23',\n",
       "  '2020-06-24',\n",
       "  '2020-06-25',\n",
       "  '2020-06-26',\n",
       "  '2020-06-27',\n",
       "  '2020-06-28',\n",
       "  '2020-06-29',\n",
       "  '2020-06-30',\n",
       "  '2020-07-01',\n",
       "  '2020-07-02',\n",
       "  '2020-07-03',\n",
       "  '2020-07-04',\n",
       "  '2020-07-05',\n",
       "  '2020-07-06',\n",
       "  '2020-07-07',\n",
       "  '2020-07-08',\n",
       "  '2020-07-09',\n",
       "  '2020-07-10',\n",
       "  '2020-07-11',\n",
       "  '2020-07-12',\n",
       "  '2020-07-13',\n",
       "  '2020-07-14',\n",
       "  '2020-07-15',\n",
       "  '2020-07-16',\n",
       "  '2020-07-17',\n",
       "  '2020-07-18',\n",
       "  '2020-07-19',\n",
       "  '2020-07-20',\n",
       "  '2020-07-21',\n",
       "  '2020-09-13',\n",
       "  '2020-09-14',\n",
       "  '2020-09-15',\n",
       "  '2020-12-02',\n",
       "  '2020-12-03'],\n",
       " 'Trinity': ['2020-01-29',\n",
       "  '2020-02-28',\n",
       "  '2020-02-29',\n",
       "  '2020-03-02',\n",
       "  '2020-03-03',\n",
       "  '2020-07-01',\n",
       "  '2020-07-02',\n",
       "  '2020-07-03',\n",
       "  '2020-07-04',\n",
       "  '2020-07-05',\n",
       "  '2020-07-06',\n",
       "  '2020-07-08',\n",
       "  '2020-07-09',\n",
       "  '2020-07-10',\n",
       "  '2020-07-11',\n",
       "  '2020-07-12',\n",
       "  '2020-07-13',\n",
       "  '2020-07-14',\n",
       "  '2020-07-23',\n",
       "  '2020-07-24',\n",
       "  '2020-07-25',\n",
       "  '2020-07-26',\n",
       "  '2020-07-27',\n",
       "  '2020-07-28',\n",
       "  '2020-07-29',\n",
       "  '2020-07-30',\n",
       "  '2020-07-31',\n",
       "  '2020-08-01',\n",
       "  '2020-08-02',\n",
       "  '2020-08-03',\n",
       "  '2020-08-13',\n",
       "  '2020-09-27',\n",
       "  '2020-09-28',\n",
       "  '2020-12-01',\n",
       "  '2020-12-02'],\n",
       " 'Lake': ['2020-02-04',\n",
       "  '2020-02-05',\n",
       "  '2020-02-29',\n",
       "  '2020-04-24',\n",
       "  '2020-05-04',\n",
       "  '2020-05-05',\n",
       "  '2020-05-06',\n",
       "  '2020-05-07',\n",
       "  '2020-06-16',\n",
       "  '2020-06-17',\n",
       "  '2020-08-17',\n",
       "  '2020-08-22',\n",
       "  '2020-08-23',\n",
       "  '2020-08-24',\n",
       "  '2020-10-21'],\n",
       " 'Tuolumne': ['2020-01-08',\n",
       "  '2020-01-26',\n",
       "  '2020-01-27',\n",
       "  '2020-01-28',\n",
       "  '2020-01-29',\n",
       "  '2020-01-30',\n",
       "  '2020-01-31',\n",
       "  '2020-02-01',\n",
       "  '2020-02-02',\n",
       "  '2020-02-03',\n",
       "  '2020-09-07',\n",
       "  '2020-09-08',\n",
       "  '2020-10-25'],\n",
       " 'Amador': ['2020-01-04',\n",
       "  '2020-01-05',\n",
       "  '2020-01-06',\n",
       "  '2020-01-07',\n",
       "  '2020-08-24',\n",
       "  '2020-10-17',\n",
       "  '2020-10-18'],\n",
       " 'Plumas': ['2020-01-06',\n",
       "  '2020-02-16',\n",
       "  '2020-02-25',\n",
       "  '2020-02-26',\n",
       "  '2020-02-28',\n",
       "  '2020-02-29'],\n",
       " 'Calaveras': ['2020-06-04', '2020-09-21'],\n",
       " 'Glenn': ['2020-11-08', '2020-11-11'],\n",
       " 'Napa': ['2020-11-04']}"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "county_to_missing_dates = {}\n",
    "\n",
    "missing_date_counties = ['Del Norte', 'Trinity', 'Lake', 'Tuolumne', 'Amador', 'Plumas', 'Calaveras', 'Glenn', 'Napa']\n",
    "d1 = epa_data_CA.get('daily_county_aqi')\n",
    "for county in missing_date_counties:\n",
    "    curr_d1 = d1[d1['county Name'] == county]\n",
    "    date_series = curr_d1['Date'].value_counts()\n",
    "    idx = pd.date_range('01-01-2020', '12-31-2020')\n",
    "    s = pd.Series(date_series)\n",
    "    s.index = pd.DatetimeIndex(s.index)\n",
    "    s = s.reindex(idx, fill_value=0)\n",
    "    for index, value in s.items():\n",
    "        if value == 0 and county not in county_to_missing_dates:\n",
    "            county_to_missing_dates[county] = [str(index)[:10]]\n",
    "        elif value == 0 and county in county_to_missing_dates:\n",
    "            county_to_missing_dates[county].append(str(index)[:10])\n",
    "\n",
    "county_to_missing_dates"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33fc5842",
   "metadata": {},
   "source": [
    "#### Missing Days - Observations\n",
    "\n",
    "Important questions about the data which will inform my later analyses:\n",
    "\n",
    "1. Are there any key missing dates in common between the counties that have missing AQI data? \n",
    "2. What two counties have the most missing days and why do you think they do?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70a94641",
   "metadata": {},
   "source": [
    "1. There do not appear to be any missing dates in common between the counties that have missing AQI data; when inspecting the dictionary we see very few dates that are in more than one county. This implies that there likely isn't a systemic reason why certain dates are unaccounted for.\n",
    "2. The two counties which contain the most missing dates are Del Norte County and Trinity County.  These two counties are actually very close in proximity to one another (Northwest corner of California) and also have extremely low populations.  Together these two counties have a combined population of approximately 40,000 residents; thus, we infer that there may be a lack of AQI monitoring in this specific region due to the low number of people inhabiting this area."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c76d3d0d",
   "metadata": {},
   "source": [
    "### AQI over Time "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20753443",
   "metadata": {},
   "source": [
    "I will assign \n",
    "- `aqi_per_month` to a series of the average AQI per month across all US states and \n",
    "- `aqi_per_month_CA` to a series of the average AQI per month across California."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "5d7fe189",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AQI per Month: \n",
      "Month\n",
      "1     31.032050\n",
      "2     32.258621\n",
      "3     34.509181\n",
      "4     37.287264\n",
      "5     36.273464\n",
      "6     40.533681\n",
      "7     40.070404\n",
      "8     41.252281\n",
      "9     43.290611\n",
      "10    35.285558\n",
      "11    34.184020\n",
      "12    34.990632\n",
      "Name: AQI, dtype: float64\n",
      "\n",
      "AQI per Month California : \n",
      "Month\n",
      "1      46.346888\n",
      "2      47.110236\n",
      "3      40.114094\n",
      "4      41.443462\n",
      "5      49.538319\n",
      "6      47.996146\n",
      "7      56.069375\n",
      "8      79.960220\n",
      "9     107.020228\n",
      "10     75.491763\n",
      "11     52.070573\n",
      "12     53.645516\n",
      "Name: AQI, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "def create_aqi_per_month():\n",
    "    daily_county_aqi = epa_data.get('daily_county_aqi')\n",
    "    daily_county_aqi = pd.DataFrame(daily_county_aqi\n",
    "                                    .groupby('Month')\n",
    "                                    .agg(np.mean))\n",
    "    aqi_per_month = daily_county_aqi['AQI']\n",
    "    daily_county_aqi_CA = epa_data_CA.get('daily_county_aqi')\n",
    "    daily_county_aqi_CA = pd.DataFrame(daily_county_aqi_CA\n",
    "                                       .groupby('Month')\n",
    "                                       .agg(np.mean))\n",
    "    aqi_per_month_CA = daily_county_aqi_CA['AQI']\n",
    "    return (aqi_per_month, aqi_per_month_CA)\n",
    "\n",
    "aqi_per_month = create_aqi_per_month()[0]\n",
    "aqi_per_month_CA = create_aqi_per_month()[1]\n",
    "\n",
    "print(\"AQI per Month: \\n{}\\n\".format(aqi_per_month))\n",
    "print(\"AQI per Month California : \\n{}\".format(aqi_per_month_CA))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0219bfab",
   "metadata": {},
   "source": [
    "### AQI over Time - Observations "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2425e74",
   "metadata": {},
   "source": [
    "I noticed that there is an exceedingly high AQI level in September (107). Also, I observe higher than usual AQI levels in the neighboring months of August (79) and October (75); on the other hand, most other months hover around an AQI level of 40-50. \n",
    "\n",
    "A possible explanation for this is that California experiences very severe wildfires during this time of the year, which is a direct cause of heightened AQI levels."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "873e7991",
   "metadata": {},
   "source": [
    "### Create Heatmap Buckets\n",
    "\n",
    "Now I will create a function called `bucket_data`, which takes in the following parameters: `table`, `resolution`. It outputs a pivot table with the latitude bucket (smallest latitude for that grid point) on the index and the longitude bucket (smallest longitude for that grid point) on the columns. The values in the pivot table will be the average AQI of the monitoring sites inside that respective rectangle grid of latitudes and longitudes."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e63738fa",
   "metadata": {},
   "source": [
    "NOTE: The `resolution` parameter describes the number of buckets that the latitudes and longitudes are divided into on the heatmap."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d52ea395",
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"5\" halign=\"left\">AQI</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>long_bucket</th>\n",
       "      <th>-124.20</th>\n",
       "      <th>-122.28</th>\n",
       "      <th>-120.36</th>\n",
       "      <th>-118.44</th>\n",
       "      <th>-116.52</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lat_bucket</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>32.58</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>65.317391</td>\n",
       "      <td>87.059645</td>\n",
       "      <td>67.955508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34.41</th>\n",
       "      <td>NaN</td>\n",
       "      <td>50.221983</td>\n",
       "      <td>67.597970</td>\n",
       "      <td>74.442308</td>\n",
       "      <td>58.294118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36.25</th>\n",
       "      <td>42.792276</td>\n",
       "      <td>51.489882</td>\n",
       "      <td>81.094254</td>\n",
       "      <td>74.704918</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38.08</th>\n",
       "      <td>38.934803</td>\n",
       "      <td>53.699484</td>\n",
       "      <td>48.977778</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39.92</th>\n",
       "      <td>38.790792</td>\n",
       "      <td>55.207065</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   AQI                                            \n",
       "long_bucket    -124.20    -122.28    -120.36    -118.44    -116.52\n",
       "lat_bucket                                                        \n",
       "32.58              NaN        NaN  65.317391  87.059645  67.955508\n",
       "34.41              NaN  50.221983  67.597970  74.442308  58.294118\n",
       "36.25        42.792276  51.489882  81.094254  74.704918        NaN\n",
       "38.08        38.934803  53.699484  48.977778        NaN        NaN\n",
       "39.92        38.790792  55.207065        NaN        NaN        NaN"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def bucket_data(table, aggfunc, resolution):\n",
    "    long_bucket = np.sort(np.linspace(table['Longitude'].min(), table['Longitude'].max(), num=resolution, endpoint = False))\n",
    "    lat_bucket = np.sort(np.linspace(table['Latitude'].min(), table['Latitude'].max(), num=resolution, endpoint = False))\n",
    "\n",
    "    long_bucket_map = dict(list(zip(long_bucket, np.around(long_bucket, decimals=2))))\n",
    "    lat_bucket_map = dict(list(zip(lat_bucket, np.around(lat_bucket, decimals=2))))\n",
    "\n",
    "    get_long_bucket_num = lambda loc: long_bucket_map.get(long_bucket[long_bucket <= loc].max())\n",
    "    get_lat_bucket_num = lambda loc: lat_bucket_map.get(lat_bucket[lat_bucket <= loc].max())\n",
    "\n",
    "    table['long_bucket'] = table['Longitude'].apply(get_long_bucket_num)\n",
    "    table['lat_bucket'] = table['Latitude'].apply(get_lat_bucket_num)\n",
    "\n",
    "    pivot_cols = ['lat_bucket', 'long_bucket', 'AQI']\n",
    "    return pd.pivot_table(table[pivot_cols], index= 'lat_bucket', columns = 'long_bucket', aggfunc=aggfunc)\n",
    "\n",
    "\n",
    "bucket_data(epa_data_CA_merged, np.mean, 5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b21b0f4b",
   "metadata": {},
   "source": [
    "### Visualizing the Heatmap\n",
    "\n",
    "I will assign `heatmap_data` to a heatmap bucket pivot table aggregated by median with resolution 30 for California AQI for the month of September."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "d6677550",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = epa_data_CA_merged\n",
    "data = data[data['Month'] == 9]\n",
    "\n",
    "heatmap_data = bucket_data(data, np.median, 30)\n",
    "\n",
    "\n",
    "# Create Visualization\n",
    "plt.figure(figsize=(15, 8))\n",
    "ax = sns.heatmap(heatmap_data, vmin=0, vmax=230, cmap = sns.cm.rocket_r)\n",
    "ax.invert_yaxis()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c7424f4",
   "metadata": {},
   "source": [
    "### Heatmap Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67298d37",
   "metadata": {},
   "source": [
    "This heat map is both informative and intuitive because it corresponds to the shape of California. The coastlines, naturally, have superior air quality since ocean weather fronts neutralize atmospheric pollutants and thus correspond to lighter colors in the heatmap. \n",
    "\n",
    "However, once we get to industrial and heavily populated regions like Los Angeles, despite being near the coast, these areas have poor AQI and hence very dark squares on the heatmap. \n",
    "\n",
    "Lastly, as we move inland to the Valleys such as the San Fernando Valley and Death Valley as well as the heavily forested regions in the Sierra Nevada, we start to see AQI levels rise."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84073cf5",
   "metadata": {},
   "source": [
    "---\n",
    "## Part 3: Further EDA\n",
    "\n",
    "Now that I have explored the data from spatial and temporal perspectives, I want to investigate what other indicators there are of air quality in California. \n",
    "\n",
    "In the previous couple sections, I have concluded that that wildfire data as well as temperature may be good indicators. In order to verify my hypothesis, I can look at various correlations with respect to the temperature. Like temperature, there are other data features such as particulate matter, chemical concentrations, wind data, and more.  \n",
    "\n",
    "In this section, I will try to find relationships between AQI and other features in the current datasets, across time and space. My exploration will include, but will not be limited to: \n",
    "- Looking at correlations between AQI and various columns of interest in `epa_data_CA`.\n",
    "- Performing clustering and/or other unsupervised learning methods such as PCA to discover clusters or useful (combinations of) features in the data.\n",
    "- Merging and exploring other external datasets that I may think are useful."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4c7b2d5",
   "metadata": {},
   "source": [
    "### EDA Pipeline and Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "b24d2bad",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline, make_pipeline\n",
    "\n",
    "# Helper functions for our merging and cleaning pipeline\n",
    "def split_dates(df):\n",
    "    df['Month'] = pd.to_datetime(df['Date Local']).dt.month  \n",
    "    df['Day'] = pd.to_datetime(df['Date Local']).dt.day\n",
    "    return df\n",
    "\n",
    "def is_city(df):\n",
    "    array = []\n",
    "    for i in df[\"City Name\"]:\n",
    "        if i == \"Not in a city\":\n",
    "            array.append(1)\n",
    "        else:\n",
    "            array.append(0)\n",
    "    return df\n",
    "\n",
    "def get_features(df):\n",
    "    wanted = [\"County Code\", \"Site Num\", \n",
    "          \"Month\", \"Day\", \"Arithmetic Mean\"]\n",
    "    return df[wanted]\n",
    "\n",
    "cols = [\"County Code\", \"Site Number\", \"Month\", \"Day\", \"AQI\"]\n",
    "cleaned_AQI = epa_data_CA.get(\"daily_county_aqi\")[cols].rename(columns = {\"Site Number\": \"Site Num\"})\n",
    "\n",
    "def data_creation():\n",
    "    array = [\"daily_wind\", \"daily_no2\", \"daily_ozone\", \"daily_temp\", \"daily_so2\"]#, \"daily_co\"]\n",
    "    names = [\"Mean Wind Speed (Knots)\", \"Mean Nitrogen Dioxide (ppb)\", \"Mean Ozone (ppm)\", \"Mean Temp (F)\"]\n",
    "    storage = {}\n",
    "    for i in range(len(array)):\n",
    "        data = epa_data_CA.get(array[i])\n",
    "        data = data.pipe(split_dates).pipe(is_city).pipe(get_features)\n",
    "        storage[array[i]] = data   \n",
    "    return storage\n",
    "\n",
    "\n",
    "def merge_data():\n",
    "    start = data_creation()\n",
    "    names = [\"Mean Wind Speed (Knots)\", \"Mean Nitrogen Dioxide (ppb)\", \"Mean Ozone (ppm)\", \"Mean Temp (F)\", \n",
    "             \"Mean Sulphur Dioxide (ppb)\"]\n",
    "    values = []\n",
    "    for key in start:\n",
    "        values.append(key)\n",
    "    index = 0\n",
    "    merged = cleaned_AQI.merge(start[values[index]], on = [\"County Code\", \"Site Num\", \"Month\", \"Day\"])\n",
    "    merged = merged.rename(columns = {\"Arithmetic Mean\": names[index]})\n",
    "    index += 1\n",
    "    \n",
    "    while index < len(start):\n",
    "        merged = merged.merge(start[values[index]], on = [\"County Code\", \"Site Num\", \"Month\", \"Day\"])\n",
    "        merged = merged.rename(columns = {\"Arithmetic Mean\": names[index]})\n",
    "        index += 1\n",
    "    \n",
    "    data_cleaned = merged.groupby([\"County Code\", \"Month\"], as_index = False).mean()\n",
    "    return data_cleaned\n",
    "       \n",
    "data = merge_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f554c29",
   "metadata": {},
   "source": [
    "### Visualization\n",
    "\n",
    "I will create visualizations to summarize my analysis above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "386e28f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x180 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x900 with 30 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(data=data, x='Month', y='AQI', hue='County Code')\n",
    "plt.legend(loc='upper left')\n",
    "plt.show()\n",
    "\n",
    "piv = data.pivot(\"Month\", \"County Code\", \"AQI\")\n",
    "ax = sns.heatmap(piv)\n",
    "plt.show()\n",
    "\n",
    "pp = sns.pairplot(data=data,\n",
    "                  x_vars=['Mean Wind Speed (Knots)', 'Mean Nitrogen Dioxide (ppb)', 'Mean Ozone (ppm)', 'Mean Temp (F)', 'Mean Sulphur Dioxide (ppb)'],\n",
    "                  y_vars=['AQI'])\n",
    "plt.show() \n",
    "\n",
    "pp2 = sns.pairplot(data=data,\n",
    "                  x_vars=['Mean Wind Speed (Knots)', 'Mean Nitrogen Dioxide (ppb)', 'Mean Ozone (ppm)', 'Mean Temp (F)', 'Mean Sulphur Dioxide (ppb)'],\n",
    "                  y_vars=['Mean Wind Speed (Knots)', 'Mean Nitrogen Dioxide (ppb)', 'Mean Ozone (ppm)', 'Mean Temp (F)', 'Mean Sulphur Dioxide (ppb)'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59129793",
   "metadata": {},
   "source": [
    "### EDA Summary and Observations\n",
    "\n",
    "_How will this EDA be useful for predicting AQI?_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b9dc269",
   "metadata": {},
   "source": [
    "A. The first lineplot show how the average AQI changes per month in the year of 2020 for counties for which we have the following data: Mean Wind Speed (Knots), Mean Nitrogen Dioxide (ppb), Mean Ozone (ppm), Mean Temp (F), Mean Sulphur Dioxide (ppb). The lineplot shows a clear temporality to the data, specifically that average monthly AQI uniformly increases depending on the seasons; hotter summer months yield higher average AQIs, and vice versa.\n",
    "\n",
    "B. The heat map shows roughly the same idea, however it also reveals that there are large chunks of monthly data missing for certain counties. We will have to map county codes to county names later on, as well as do some geographical mapping to see spatial trends.\n",
    "\n",
    "C. The first of the set of two pairplots shows scatterplots of AQI and each of 'Mean Wind Speed (Knots)', 'Mean Nitrogen Dioxide (ppb)', 'Mean Ozone (ppm)', 'Mean Temp (F)', 'Mean Sulphur Dioxide (ppb)'. These scatterplots reveal that there is generally a very significant correlation between these variables and AQI, with the exception of temperature (which is a little more tenuous). This is very heartening to see and poses a great deal of avenues to investigate in the future.\n",
    "\n",
    "D. The last pairplot set shows relationships between 'Mean Wind Speed (Knots)', 'Mean Nitrogen Dioxide (ppb)', 'Mean Ozone (ppm)', 'Mean Temp (F)', 'Mean Sulphur Dioxide (ppb)' and 'Mean Wind Speed (Knots)', 'Mean Nitrogen Dioxide (ppb)', 'Mean Ozone (ppm)', 'Mean Temp (F)', 'Mean Sulphur Dioxide (ppb)'. There are some interesting observations here as well, such as the fact that wind speed and ozone are very highly correlated. We plan to do PCA and see whether we can find redundant variables in situations like these."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23b4f472",
   "metadata": {},
   "source": [
    "---\n",
    "## Part 4: Preliminary Modeling\n",
    "\n",
    "In this part, I will look at various open-ended modeling approaches in order to answer the question of how to predict AQI given a location and a date. \n",
    "\n",
    "### Data Interpolation\n",
    "\n",
    "I will be using interpolation techniques to evaluate the missing grid values for AQI on the heatmap visualization that I produced in part 1. Specifically I will be using Simple Linear Interpolation:\n",
    "\n",
    "Simple linear interpolation takes the locations' values and averages them to produce an estimate of the current location's metric. Although this is not as predictive –we are not predicting based on features about the location itself – it will give me a sense of spatial AQI trends which will inform the rest of my project's work. \n",
    "\n",
    "For reference, here is the original heatmap from before:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "0abba38f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "table_sep = epa_data_CA_merged[epa_data_CA_merged['Month'] == 9]\n",
    "heatmap_data = bucket_data(table_sep, np.median, 25)\n",
    "\n",
    "plt.figure(figsize=(15, 8))\n",
    "ax = sns.heatmap(heatmap_data, vmin=0, vmax=230, cmap = sns.cm.rocket_r)\n",
    "ax.invert_yaxis()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63cc95a6",
   "metadata": {},
   "source": [
    "<br/>\n",
    "\n",
    "### Simple Linear Interpolation\n",
    "As I mentioned previously, interpolation is a technique that is used to predict labels in a dataset by forming a model out of already labelled data. In this case, I have a pivot table from which I created a heatmap, however there exists many `NaN` values which I want to fill in, via interpolation. \n",
    "\n",
    "- I will create a function called `fill_bucket` which takes in the following parameters:\n",
    "    - `pivot_table`: a pivot table from which to aggregate data. \n",
    "    - `lat_bucket`: the bucket number which the latitude is in, indexed by zero. eg, if there are 25 buckets, they are numbered $ 0, 2, \\dots 24 $, from lowest to highest value latitudes. \n",
    "    - `lon_bucket`: the bucket number which the longitude is in, indexed by zero. eg, if there are 25 buckets, they are numbered $ 0, 2, \\dots 24 $. from lowest to highest value longitudes.\n",
    " \n",
    "#### Interpolation Algorithm \n",
    " \n",
    "- In the pivot table, every value has cells above (A cells), cells below (B cells), cells to the left (L cells), and cells to the right (R cells). A direction (R for example) is valid if and only if there exists a cell **anywhere** to its right that is not `NaN`. The closest such cell will be called the \"closest R cell\". The same goes for the rest of the directions. For the cases below, assuming that our current cell is called cell K. \n",
    "    - If cell K is not `NaN`, then I will simply return the AQI at that given cell.\n",
    "    - **Only** if there are **at least** three valid directional cells (ex. has A, B, and L valid but not R valid), I will call K *interpolable*. If K is *interpolable*, then interpolate K by assigning it an AQI value equal to the average of the closest cell AQIs in each of the valid directions. \n",
    "    - If K is *not interpolable*, then do not do anything and simply return `NaN`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "618ff532",
   "metadata": {},
   "outputs": [],
   "source": [
    "def fill_bucket(pivot_table, lat_bucket, lon_bucket):\n",
    "    # if k is null\n",
    "    if(pd.isnull(pivot_table.iloc[lat_bucket, lon_bucket])):\n",
    "        # above\n",
    "        a = pivot_table.iloc[lat_bucket, lon_bucket]\n",
    "        latb = lat_bucket\n",
    "        while pd.isnull(a) and latb >= 0:\n",
    "            a = pivot_table.iloc[latb, lon_bucket]\n",
    "            latb -= 1\n",
    "\n",
    "        # below\n",
    "        b = pivot_table.iloc[lat_bucket, lon_bucket]\n",
    "        latb = lat_bucket\n",
    "        while pd.isnull(b) and latb < pivot_table.shape[0]:\n",
    "            b = pivot_table.iloc[latb, lon_bucket]\n",
    "            latb += 1\n",
    "\n",
    "        # right\n",
    "        r = pivot_table.iloc[lat_bucket, lon_bucket]\n",
    "        lonb = lon_bucket\n",
    "        while pd.isnull(r) and lonb < pivot_table.shape[1]:\n",
    "            r = pivot_table.iloc[lat_bucket, lonb]\n",
    "            lonb += 1\n",
    "\n",
    "        # left\n",
    "        l = pivot_table.iloc[lat_bucket, lon_bucket]\n",
    "        lonb = lon_bucket\n",
    "        while pd.isnull(l) and lonb >= 0:\n",
    "            l = pivot_table.iloc[lat_bucket, lonb]\n",
    "            lonb -= 1\n",
    "\n",
    "        count = 0\n",
    "        interpol = 0\n",
    "        if not pd.isnull(a):\n",
    "            count +=1\n",
    "            interpol += a\n",
    "        if not pd.isnull(b):\n",
    "            count+=1\n",
    "            interpol += b\n",
    "        if not pd.isnull(r):\n",
    "            count +=1\n",
    "            interpol += r\n",
    "        if not pd.isnull(l):\n",
    "            count+=1\n",
    "            interpol += l\n",
    "        if count >= 3:\n",
    "            k = interpol/count\n",
    "            return k\n",
    "        else:\n",
    "            return np.nan\n",
    "\n",
    "    else:\n",
    "        return pivot_table.iloc[lat_bucket, lon_bucket]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e42e0a4",
   "metadata": {},
   "source": [
    "### Create Filled Heatmap\n",
    "\n",
    "Now that I have created the `fill_bucket` function, I will use it to interpolate the values in `heatmap_data` via the function `fill_all` which takes in the pivot table and fills in all the values, returning a pivot table with the updated values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "3947dbba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def fill_all(pivot_table):\n",
    "    new_pt = pivot_table.copy()\n",
    "    for lat in range(pivot_table.shape[0]):\n",
    "        for lon in range(pivot_table.shape[1]):\n",
    "            k = fill_bucket(new_pt, lat, lon)\n",
    "            new_pt.iloc[lat,lon] = k\n",
    "\n",
    "    return new_pt\n",
    "\n",
    "filled_heatmap_data = fill_all(heatmap_data)\n",
    "\n",
    "plt.figure(figsize=(15, 8))\n",
    "ax = sns.heatmap(filled_heatmap_data, vmin=0, vmax=230, cmap = sns.cm.rocket_r)\n",
    "ax.invert_yaxis()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e99c373",
   "metadata": {},
   "source": [
    "### Other Interpolation Ideas\n",
    "\n",
    "Instead of just interpolating in a simple fashion as I did above, there are plenty of other ways to interpolate. For example, I can take into account of the distance of the surrounding cells, the number of cells I used, and many other parameters. I'll describe one more idea for interpolation I considered implementing in this project:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3c06250",
   "metadata": {},
   "source": [
    "Another way to interpolate would be to incorporate the distance of the nearby cells (above, below, left, and right).  More specifically, I could use an approach that puts lower weight on cells that are farther away compared to cells that are closer. For instance, if the above closest cell is 2 cells away and the right closest cell is 10 cells away; I would fill that bucket with a value much closer to the above cell than the right because it is significantly closer.  To implement this I could simply weight the value of the neighboring cell based upon its proximity to the bucket I am filling.  By exploiting a more elegant method of interpolation (in this example using proximity as a heuristic) I could possibly gain a more accurate estimate of the bucket I am attemping to fill."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "986de78f",
   "metadata": {},
   "source": [
    "### Choosing my Loss Function"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa43b39e",
   "metadata": {},
   "source": [
    "Sparse categorical cross entropy seems like the best suited loss function for this particular model.  The reason this loss function is my choice is because it is used for many multi-class classification models, and in this case I am trying to determine what constitutes good, moderate, unhealthy, etc (multi-class classification). The equation of this loss function is $-\\sum{i=1}^nt_{i}log(p_{i})$ where $t_{i}$ is the truth or actual label, $p_{i}$ is the probability of the ith class, and n denotes the number of classes in our model (thus n equals 6 for our model).  This loss function penalizes each output probability based upon how far off the probability is from the actual value (truth value)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89ec3c6e",
   "metadata": {},
   "source": [
    "### Creating my Model\n",
    "\n",
    "Now that I have an idea of how to interpolate values based on existing data, I will try to use more predictive methods such as supervised modeling. In this part, my final goal is to create a model using a variety of features I will select from the data I have and possibly from external data. Here is how I plan to functionalize my code:\n",
    "\n",
    "**`features`**: This is a `pd.DataFrame` object that represents the design matrix that will be fed in as input to myy model. Each row represents a data point and each column represents a feature. Essentially my $X$ matrix. \n",
    "\n",
    "**`targets`**: This will be a numpy array where each value corresponds to the AQI value or AQI category for each of the data points in `features`. Essentially my $y$ vector. \n",
    "\n",
    "**`build_model`**: This function will have two parameters: `features` that will be used as input into my model (as a `pd.DataFrame` object), and `targets` which will be a numpy array of AQI values OR AQI categories. It returns an *object* that represents your model.\n",
    "\n",
    "**`predict`**: This function will have two parameters: `model`, the model that I will have built from the previous function `build_model`, and `features` that represent the design matrix for the test values that I want to predict. It will return the **AQI category** (not a value) that the model predicts for these inputs."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f675b042",
   "metadata": {},
   "source": [
    "### Choose Features and Model\n",
    "\n",
    "For my first iteration, I will decide on the features that I will be using for my model, as well as considerations about:\n",
    "- feature predictivity\n",
    "- model design"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b90a5c4",
   "metadata": {},
   "source": [
    "The features that I plan to use for my model include: Temperature, Elevation (from AQS Sites dataset), Location Setting (from AQS Sites dataset), and Annual Average Daily Traffic from an external traffic dataset.\n",
    "\n",
    "Temperature should be very predictive of AQI, since according to my part 1 analysis, higher temperatures correlated to higher AQIs. Elevation is more of a wild card requiring some EDA, but I think it might correlate to AQIs in that lower elevations might have higher AQIs. Location Setting (which we will one hot encode) should be predictive of AQIs; urban and suburban locations might be predictive of higher AQIs and rural locations might be predictive of lower AQIs. And finally, the average annual daily traffic might be a proxy for AQI in that traffic density might positively correlate with AQI.\n",
    "\n",
    "I plan to use decision trees to classify the AQI levels of our data as good, moderate, unhealthy for sensitive groups, unhealthy, very unhealthy, or hazardous. I might even use ensemble methods such as random forests since I am less concerned with interpretability and more concerned with performance on large datasets."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3638429a",
   "metadata": {},
   "source": [
    "### Feature Engineering\n",
    "\n",
    "I will create the `build_features` function as described at the beginning of this part. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "6fc70e8c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Log AADT</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Month</th>\n",
       "      <th>NO2</th>\n",
       "      <th>setting_URBAN AND CENTER CITY</th>\n",
       "      <th>Elevation</th>\n",
       "      <th>AQI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11.634258</td>\n",
       "      <td>87.472916</td>\n",
       "      <td>1</td>\n",
       "      <td>10.334783</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>62.875000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11.634258</td>\n",
       "      <td>154.500000</td>\n",
       "      <td>2</td>\n",
       "      <td>2.769565</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>58.000000</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11.634258</td>\n",
       "      <td>71.727083</td>\n",
       "      <td>4</td>\n",
       "      <td>2.400000</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>36.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.634258</td>\n",
       "      <td>76.529167</td>\n",
       "      <td>5</td>\n",
       "      <td>3.756522</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>35.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11.634258</td>\n",
       "      <td>112.606250</td>\n",
       "      <td>6</td>\n",
       "      <td>1.208696</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>67.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>445</th>\n",
       "      <td>11.332602</td>\n",
       "      <td>82.527083</td>\n",
       "      <td>8</td>\n",
       "      <td>3.621739</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>92.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>446</th>\n",
       "      <td>11.332602</td>\n",
       "      <td>119.472916</td>\n",
       "      <td>9</td>\n",
       "      <td>6.020833</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.703704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>447</th>\n",
       "      <td>11.332602</td>\n",
       "      <td>100.439583</td>\n",
       "      <td>10</td>\n",
       "      <td>3.221739</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>74.071429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>448</th>\n",
       "      <td>11.332602</td>\n",
       "      <td>140.393750</td>\n",
       "      <td>11</td>\n",
       "      <td>4.800000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>49.695652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>449</th>\n",
       "      <td>11.332602</td>\n",
       "      <td>116.762500</td>\n",
       "      <td>12</td>\n",
       "      <td>4.878261</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>43.576923</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>450 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Log AADT  Temperature  Month        NO2  setting_URBAN AND CENTER CITY  \\\n",
       "0    11.634258    87.472916      1  10.334783                              0   \n",
       "1    11.634258   154.500000      2   2.769565                              0   \n",
       "2    11.634258    71.727083      4   2.400000                              0   \n",
       "3    11.634258    76.529167      5   3.756522                              0   \n",
       "4    11.634258   112.606250      6   1.208696                              0   \n",
       "..         ...          ...    ...        ...                            ...   \n",
       "445  11.332602    82.527083      8   3.621739                              0   \n",
       "446  11.332602   119.472916      9   6.020833                              0   \n",
       "447  11.332602   100.439583     10   3.221739                              0   \n",
       "448  11.332602   140.393750     11   4.800000                              0   \n",
       "449  11.332602   116.762500     12   4.878261                              0   \n",
       "\n",
       "     Elevation         AQI  \n",
       "0          1.0   62.875000  \n",
       "1          1.0   58.000000  \n",
       "2          1.0   36.500000  \n",
       "3          1.0   35.000000  \n",
       "4          1.0   67.000000  \n",
       "..         ...         ...  \n",
       "445        0.0   92.500000  \n",
       "446        0.0  100.703704  \n",
       "447        0.0   74.071429  \n",
       "448        0.0   49.695652  \n",
       "449        0.0   43.576923  \n",
       "\n",
       "[450 rows x 7 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def split_dates(df):\n",
    "    df['Month'] = pd.to_datetime(df['Date Local']).dt.month  \n",
    "    df['Day'] = pd.to_datetime(df['Date Local']).dt.day\n",
    "    return df\n",
    "\n",
    "def normalize(df):\n",
    "    result = df.copy()\n",
    "    for feature_name in df.columns:\n",
    "        max_value = df[feature_name].max()\n",
    "        min_value = df[feature_name].min()\n",
    "        result[feature_name] = (df[feature_name] - min_value) / (max_value - min_value)\n",
    "    return result\n",
    "\n",
    "def build_features(q11=False):\n",
    "    # Features: Temperature (daily_temp), Elevation (AQS Sites), \n",
    "    # Location Setting (AQS Sites), AADT (Traffic)\n",
    "    selected_features = ['State Name', 'county Name', 'County Code', 'Month', 'Day', 'AQI', 'Category', 'Defining Site', 'Latitude', 'Longitude', \n",
    "                     'Elevation', 'Location Setting']\n",
    "    data1 = epa_data_CA.get('daily_county_aqi')\n",
    "    arr = []\n",
    "    iterate_array = data1['Defining Site']\n",
    "    for i in iterate_array:\n",
    "        arr.append(int(i[7:]))\n",
    "    data1['Site Number'] = arr\n",
    "    data2 = epa_data.get('aqs_sites')\n",
    "    data2 = data2[data2['State Name'] == 'California']\n",
    "    features = data1.merge(data2, on = ['County Code', 'Site Number'])\n",
    "    features = features.rename(columns = {'State Name_x': 'State Name'})\n",
    "    features = features[selected_features]\n",
    "    geom_traffic = gpd.points_from_xy(traffic_data_cleaned.Longitude, traffic_data_cleaned.Latitude)\n",
    "    geom_merged = gpd.points_from_xy(features.Longitude, features.Latitude)\n",
    "    gpd_traffic = gpd.GeoDataFrame(traffic_data_cleaned, geometry = geom_traffic)\n",
    "    gpd_CA_merged = gpd.GeoDataFrame(features, geometry = geom_merged)\n",
    "    joined = gpd.sjoin_nearest(left_df = gpd_CA_merged, right_df = gpd_traffic, how = \"inner\") \n",
    "    renaming = {\"Latitude_left\": \"Site Lat\", \"Longitude_left\": \"Site Long\", \"Latitude_right\": \"Traffic Lat\", \n",
    "                \"Longitude_right\": \"Traffic Long\"}\n",
    "    joined = joined.rename(columns = renaming)\n",
    "    joined[\"Traffic Long\"] = joined[\"Traffic Long\"].astype(str).astype(float)\n",
    "    cols = [\"State Name\", \"county Name\", 'County Code', \"Month\", \"Day\", \"AQI\", \"Category\", \"Defining Site\", \"Site Lat\", \"Site Long\", \n",
    "            \"Traffic Lat\", \"Traffic Long\", \"Descriptn\", \"AADT\", 'Elevation', 'Location Setting']\n",
    "    gpd_epa_traffic = joined[cols]\n",
    "    arr2 = []\n",
    "    for i in gpd_epa_traffic['Defining Site']:\n",
    "        arr2.append(int(i[7:]))\n",
    "    gpd_epa_traffic['Site Num'] = arr2\n",
    "    temp = epa_data_CA.get('daily_temp')\n",
    "    temp = temp[['County Code', 'Site Num', 'Month', 'Day', 'Arithmetic Mean']]\n",
    "    \n",
    "    inter = gpd_epa_traffic.merge(temp, on = [\"County Code\", \"Site Num\", \"Month\", \"Day\"])\n",
    "    inter = inter[['county Name', 'County Code', 'Site Num', 'Month', 'Day', 'AADT', 'Elevation', 'Location Setting', 'Arithmetic Mean', 'AQI']]\n",
    "    inter = inter.rename(columns = {'Arithmetic Mean':'Temperature', 'AQI_x':'AQI'})\n",
    "    \n",
    "    wind = epa_data_CA.get('daily_wind')\n",
    "    wind = wind[['County Code', 'Site Num', 'Month', 'Day', 'Arithmetic Mean']]\n",
    "    \n",
    "    inter2 = inter.merge(wind, on = [\"County Code\", \"Site Num\", \"Month\", \"Day\"])\n",
    "    inter2 = inter2.rename(columns = {'Arithmetic Mean':'Wind'})#, 'AQI_x':'AQI'})\n",
    "\n",
    "    no2 = epa_data_CA.get('daily_no2')\n",
    "    no2 = split_dates(no2)\n",
    "    no2 = no2[['County Code', 'Site Num', 'Month', 'Day', 'Arithmetic Mean']]\n",
    "    \n",
    "    final = inter2.merge(no2, on = [\"County Code\", \"Site Num\", \"Month\", \"Day\"])\n",
    "    final = final.rename(columns = {'Arithmetic Mean':'NO2'})#, 'AQI_x':'AQI'})\n",
    "    \n",
    "    final = pd.concat([final[['county Name', 'County Code', 'Site Num', 'Month', 'Day', 'AADT', 'Elevation', 'Wind', 'NO2', 'Temperature', 'AQI']], \n",
    "                                      pd.get_dummies(final[['Location Setting']], prefix='setting')], axis=1)\n",
    "    grouped_multiple = final.groupby(['County Code', 'Site Num', 'Month']).agg({ \n",
    "                                                                                'Day': 'mean', \n",
    "                                                                                'AADT': 'mean', \n",
    "                                                                                'Elevation': 'mean', \n",
    "                                                                                'Wind': 'mean', \n",
    "                                                                                'NO2': 'mean', \n",
    "                                                                                'Temperature': 'mean', \n",
    "                                                                                'AQI': 'mean', \n",
    "                                                                                'setting_RURAL': 'mean', \n",
    "                                                                                'setting_SUBURBAN': 'mean',\n",
    "                                                                                'setting_URBAN AND CENTER CITY': 'mean',\n",
    "                                                                                'county Name': 'first'})                                                                \n",
    "    grouped_multiple['County Code'] = grouped_multiple.index.get_level_values(0)\n",
    "    grouped_multiple['Site Num'] = grouped_multiple.index.get_level_values(1)\n",
    "    grouped_multiple['Month'] = grouped_multiple.index.get_level_values(2)\n",
    "    grouped_multiple.index = [i for i in range(len(grouped_multiple))]\n",
    "    grouped_multiple = grouped_multiple[[c for c in grouped_multiple if c not in ['Day', 'AQI']] \n",
    "       + ['AQI']]\n",
    "    grouped_multiple['AADT'] = np.log(grouped_multiple['AADT'])\n",
    "    grouped_multiple = grouped_multiple.rename(columns={'AADT' : 'Log AADT'})\n",
    "    \n",
    "    if q11 == True:\n",
    "        grouped_multiple = grouped_multiple.drop(['Site Num', 'setting_RURAL', 'setting_SUBURBAN'], axis=1)\n",
    "    else:\n",
    "        grouped_multiple = grouped_multiple.drop(['Site Num', 'County Code', 'setting_RURAL', 'setting_SUBURBAN'], axis=1)\n",
    "    if q11 == True:\n",
    "        grouped_multiple = grouped_multiple[['Log AADT', 'county Name', 'Temperature', 'NO2', 'setting_URBAN AND CENTER CITY', 'Elevation', 'AQI']]\n",
    "    else:\n",
    "        grouped_multiple = grouped_multiple[['Log AADT', 'Temperature', 'Month', 'NO2', 'setting_URBAN AND CENTER CITY', 'Elevation', 'AQI']]\n",
    "    \n",
    "    return grouped_multiple\n",
    "    \n",
    "features = build_features().iloc[:, :-1]\n",
    "targets = build_features().iloc[:, -1]\n",
    "\n",
    "build_features()\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "786fed36",
   "metadata": {},
   "source": [
    "### Building my Model\n",
    "\n",
    "I will create the `build_model` function as described at the beginning of this part. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "45fba006",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.datasets import make_regression\n",
    "\n",
    "def build_model(features, targets):\n",
    "    model = RandomForestRegressor(max_depth=20, random_state=0)\n",
    "    model.fit(features, targets)\n",
    "    return model\n",
    "\n",
    "model = build_model(features, targets)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4bde7c8",
   "metadata": {},
   "source": [
    "### Predict Points\n",
    "\n",
    "I will create the `predict` function as described at the beginning of this part. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "6d2e1207",
   "metadata": {},
   "outputs": [],
   "source": [
    "categories = [\"good\", \"moderate\", \"unhealthy sensitive groups\", \"unhealthy\", \"very unhealthy\", \"hazardous\"]\n",
    "\n",
    "def predict(model, features):\n",
    "    predictions = model.predict(features)\n",
    "    classified = []\n",
    "    for pred in predictions:\n",
    "        if pred >= 301 and pred <= 500:\n",
    "            classified = np.append(classified, \"hazardous\")\n",
    "        elif pred >= 201:\n",
    "            classified = np.append(classified, \"very unhealthy\")\n",
    "        elif pred >= 151:\n",
    "            classified = np.append(classified, \"unhealthy\")\n",
    "        elif pred >= 101:\n",
    "            classified = np.append(classified, \"unhealthy sensitive groups\")\n",
    "        elif pred >= 51:\n",
    "            classified = np.append(classified, \"moderate\")\n",
    "        else:\n",
    "            classified = np.append(classified, \"good\")\n",
    "    return classified"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "2d6d0128",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "moderate                      236\n",
       "good                          124\n",
       "unhealthy sensitive groups     74\n",
       "unhealthy                      16\n",
       "dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(predict(model, features)).value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c26bc23b",
   "metadata": {},
   "source": [
    "### Model Validation and Performance\n",
    "\n",
    "Now that I have finished making my model, I want to see how well it performs on the data. I will use the following cell to split my data into training and validation sets. I will partition 70\\% of your data to be used as the training set, and the remaining to be used as the validation set. \n",
    "\n",
    "I will assign `binary_error` to be the **fraction of inputs on your validation set that the the `predict` function classifies incorrectly.** This is essentially binary loss in some sense as it assigns a loss of 1 to those points predicted incorrectly, and a loss of 0 to those points predicted correctly.\n",
    "\n",
    "I will assign `cv_error` to be the error on the validation set produced by the loss function $ L $ that I designed previously."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "74d85fe8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.2222222222222222\n",
      "40.072455282866734\n"
     ]
    }
   ],
   "source": [
    "import sklearn as sk\n",
    "from sklearn.model_selection import KFold\n",
    "\n",
    "X_train, X_validation, y_train, y_validation = sk.model_selection.train_test_split(features, targets, test_size = 0.3, random_state = 42)\n",
    "\n",
    "def ordinal_rep(arr):\n",
    "    new = []\n",
    "    for i in range(len(arr)):\n",
    "        if arr[i] == \"hazardous\":\n",
    "            new = np.append(new, 6)\n",
    "        elif arr[i] == \"very unhealthy\":\n",
    "            new = np.append(new, 5)\n",
    "        elif arr[i] == \"unhealthy\":\n",
    "            new = np.append(new, 4)\n",
    "        elif arr[i] == \"unhealthy sensitive groups\":\n",
    "            new = np.append(new, 3)\n",
    "        elif arr[i] == \"moderate\":\n",
    "            new = np.append(new, 2)\n",
    "        else:\n",
    "            new = np.append(new, 1)\n",
    "    return new\n",
    "    \n",
    "def classifier(targets):\n",
    "    classified = []\n",
    "    for aqi in targets:\n",
    "        if aqi >= 301 and aqi <= 500:\n",
    "            classified = np.append(classified, \"hazardous\")\n",
    "        elif aqi >= 201:\n",
    "            classified = np.append(classified, \"very unhealthy\")\n",
    "        elif aqi >= 151:\n",
    "            classified = np.append(classified, \"unhealthy\")\n",
    "        elif aqi >= 101:\n",
    "            classified = np.append(classified, \"unhealthy sensitive groups\")\n",
    "        elif aqi >= 51:\n",
    "            classified = np.append(classified, \"moderate\")\n",
    "        else:\n",
    "            classified = np.append(classified, \"good\")\n",
    "    targets = classified\n",
    "    targets = pd.DataFrame(targets)\n",
    "    return targets\n",
    "    \n",
    "def get_binary_error(y_hat, y_actual):\n",
    "    compared = y_hat != y_actual\n",
    "    return sum(compared) / len(compared)\n",
    "\n",
    "def categorical_crossentropy(y_pred, y_true):\n",
    "    # - SUM(target * log(pred))\n",
    "    return -np.sum(y_true * np.log(y_pred))\n",
    "\n",
    "def loss_function(y_pred, y_true):\n",
    "    y_predicted = []\n",
    "    for aqi in y_pred:\n",
    "        if aqi >= 301 and aqi <= 500:\n",
    "            y_predicted = np.append(y_pred, 6)\n",
    "        elif aqi >= 201:\n",
    "            y_predicted = np.append(y_pred, 5)\n",
    "        elif aqi >= 151:\n",
    "            y_predicted = np.append(y_pred, 4)\n",
    "        elif aqi >= 101:\n",
    "            y_predicted = np.append(y_pred, 3)\n",
    "        elif aqi >= 51:\n",
    "            y_predicted = np.append(y_pred, 2)\n",
    "        else:\n",
    "            y_predicted = np.append(y_pred, 1)\n",
    "    cverror = np.sum(np.square(y_predicted - y_true)) / len(y_true)\n",
    "    return cverror\n",
    "\n",
    "def compute_CV_error(model, X_train, Y_train):\n",
    "    '''\n",
    "    Split the training data into 5 subsets.\n",
    "    For each subset, \n",
    "        fit a model holding out that subset\n",
    "        compute the MSE on that subset (the validation set)\n",
    "    You should be fitting 5 models total.\n",
    "    Return the average MSE of these 5 folds.\n",
    "\n",
    "    Args:\n",
    "        model: an sklearn model with fit and predict functions \n",
    "        X_train (data_frame): Training data\n",
    "        Y_train (data_frame): Label \n",
    "\n",
    "    Return:\n",
    "        the average validation MSE for the 5 splits.\n",
    "    '''\n",
    "    kf = KFold(n_splits=5, shuffle=False)\n",
    "    validation_errors = []\n",
    "    for train_idx, valid_idx in kf.split(X_train):\n",
    "        # split the data\n",
    "        split_X_train, split_X_valid = X_train.iloc[train_idx], X_train.iloc[valid_idx]\n",
    "        split_Y_train, split_Y_valid = Y_train.iloc[train_idx], Y_train.iloc[valid_idx]\n",
    "        \n",
    "        split_Y_train_unclassified = split_Y_train\n",
    "        split_Y_valid_unclassified = split_Y_valid\n",
    "        \n",
    "        split_Y_train = classifier(split_Y_train)\n",
    "        split_Y_valid = classifier(split_Y_valid)\n",
    "\n",
    "        tempArr = []\n",
    "        for i in split_Y_train.values:\n",
    "            tempArr.append(i[0])\n",
    "        \n",
    "        \n",
    "        tempArrChanged = ordinal_rep(tempArr)\n",
    "        \n",
    "        # Fit the model on the training split\n",
    "        unvalidated_lm = model.fit(split_X_train, split_Y_train_unclassified) #creates the model fitted onto the training set\n",
    "        Y_hat_validation = predict(unvalidated_lm, split_X_valid) #test the model against the validation set\n",
    "        \n",
    "        numerical_y_hat = ordinal_rep(Y_hat_validation)\n",
    "        \n",
    "        tempArr1 = []\n",
    "        for i in split_Y_valid.values:\n",
    "            tempArr1 = np.append(tempArr1, i[0])\n",
    "        \n",
    "        tempArrChanged1 = ordinal_rep(tempArr1)\n",
    "        numerical_y_hat = [int(x) for x in numerical_y_hat]\n",
    "        error = categorical_crossentropy(numerical_y_hat, tempArrChanged1)\n",
    "        validation_errors.append(error)\n",
    "        \n",
    "    return np.mean(validation_errors)\n",
    "    \n",
    "cv_error = np.abs(compute_CV_error(model, X_validation, y_validation))\n",
    "y_validation = classifier(y_validation)[0]\n",
    "binary_error = sum((predict(model, X_validation) != y_validation)) / len(X_validation)\n",
    "\n",
    "print(binary_error)\n",
    "print(cv_error)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef20a2e6",
   "metadata": {},
   "source": [
    "---\n",
    "## Part 5: Further Modeling\n",
    "\n",
    "In this section, I will use the tools that I developed in the previous parts to answer a different hypothesis as described in the README. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fce0b52e",
   "metadata": {},
   "source": [
    "### Model Creation\n",
    "\n",
    "I will train a variety of baseline models to attempt to answer my hypothesis and predict something related to AQI."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56afcf2d",
   "metadata": {},
   "source": [
    "#### NOTE: In order to make this last section reproducible, you must do the following:\n",
    "1. Go to https://data-nifc.opendata.arcgis.com/datasets/wfigs-wildland-fire-locations-full-history/explore?location=39.186204%2C-106.715618%2C11.62\n",
    "2. Download the dataset as a .csv\n",
    "3. **Save the dataset to the /data subdirectory in the current working directory of this notebook**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "2a0acb0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "wildfire_data1 = pd.read_csv(\"data/WFIGS_-_Wildland_Fire_Locations_Full_History.csv\")\n",
    "annual_county_aqi1 = pd.read_csv(\"data/annual_county_aqi/annual_aqi_by_county_2020.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "3d5cf024",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The RandomForestClassifier model's performance on testing set\n",
      "--------------------------------------\n",
      "Confusion Matrix\n",
      "\n",
      "[[0 0 2 1]\n",
      " [0 2 0 0]\n",
      " [0 1 0 0]\n",
      " [0 1 0 0]]\n",
      "\n",
      "Accuracy: 0.29\n",
      "\n",
      "\n",
      "Classification Report\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.00      0.00      0.00         3\n",
      "           1       0.50      1.00      0.67         2\n",
      "           3       0.00      0.00      0.00         1\n",
      "           4       0.00      0.00      0.00         1\n",
      "\n",
      "    accuracy                           0.29         7\n",
      "   macro avg       0.12      0.25      0.17         7\n",
      "weighted avg       0.14      0.29      0.19         7\n",
      "\n",
      "\n",
      "5-Fold Cross Validation Error: [0.66666667 0.5        0.5       ]\n",
      "\n",
      "5-Fold Cross Validation Error: [-1.02205184e+00 -2.73478847e+02 -2.13510245e+03]\n",
      "The LinearRegression model's performance on testing set\n",
      "--------------------------------------\n",
      "MAE is 11.130827462658422\n",
      "MSE is 208.06021042648558\n",
      "R2 score is -1.6114114525865246\n"
     ]
    },
    {
     "data": {
      "image/png": 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i0ZTE38XdHwC+dPeX3P1cYFDEcYmkvwkT4Mgj4d//TrYVFoahnksuCXX0RWLQlMSfOAPFMjM72czygR4RxiSSlkpKnN59q2mTE26nVx0L220XNnbuDBMnwowZ0KdPvIFK1mvKPP5xZrYDcClwB9AZGBNpVCJppqTEGT2mio6Fc+k5dBXrl+Rx5vh8Xv7B/zDgyzlw993wzW/GHaYIAOa+xan6KaOgoMDLyjS7VFKUO7/Y7THa9XybaUcf+lXzhvIudJqVz38Wtgtz80VamZnNdveCjdubMqvnQTZzIVdirF8kuy1bBuefz12f/I11K3IpO2BPPtpxVwBye6zig6lK+pJ6mjLGPx14OvFTShjqWRNlUCIpzx0mTw7LIP7tbwB0rK7k0pcf+uoplUvy6NWnJqYARbZsqz1+d/9L/cdm9gjw98giEkl1ixeH0skzZjRontx+BL/d53S8porKJXmsK81n/ATN3JHU05wibX2B3Vs6EJGUV1sLd94JV18Na9cm2/v0gUmTyF16JNXFNXz0txx69alh/IQcioo0zCOppylj/F/QcIz/E8KVvCLZY8ECGDkyFFSr06ZNmI9/ww3QsSNFQFFR3a+UCt9K6mrKUM/2rRGISMqqrYUf/jCsc1unX78wxl+/qqZImtjqyV0zK21Km0jGatMG7rgj3G/bFq69FubMUdKXtNVYPf72QEdgZzPbCagbrOwM7NYKsYnE48svk1fc1jn6aLj5Zjj++FB7RySNNdbj/xkwG9gncVv38wRwV/ShicTgtddCYv/LXzbddtllSvqSEbaY+N39dnfvDfzK3fu4e+/EzwB3v7MVYxSJ3po1cNFFcNhh8N57Yb3bVavijkokEk25gKvWzHase2BmO5nZ+U39ADPLMbO5ZjY98bjYzD42szcTPydte9giLWjGDOjfP4zj15UwWbcO3nwz1rBEotKUxH+eu39W98DdVwPnbcNnXEyo51/fBHcfmPh5ZhveS6TlrF4d1r097jgoL0+2n3hiWBP3aK01JJmpKYm/jVmy2IiZ5QDtmvLmZtYDOBmY1LzwRCIybVqYkvngg8m2vDx46CF4+mno2TO+2EQi1pTE/xww1cwKzexo4BHg/5r4/rcBlwO1G7VfYGZvm9nkxIyhTZjZKDMrM7OyioqKJn6cyFZUVMCwYXDqqfDJJ8n2YcPCRVpnnaWiapLxmpL4ryAUZxsN/AJ4G+iwtReZ2feA5e4+e6NN9wB7AgOBZcD4zb3e3e939wJ3L+jatWsTwhRpgpoa+Hu9UlO77hp6/489BrvsEl9cIq1oq4nf3WuBfwKLgAKgkE3H7DfnUOAHZrYYeBQ42sz+5O6funtN4n0nAgc2N3iRbbbrrmFJRAjj+/Pnw9ChsYYk0toau4Brb+B04AxgJfAYgLsf1ZQ3dvergKsS73UkYVroWWbW3d2XJZ52CjCvucGLNKq2Fv7xDzj00IbtP/lJGN//7nfjiUskZo31+N8j9O6/7+6HufsdQEsUF7/ZzN4xs7eBo9AyjhKFhQvhqKPg8MPhn/9suM1MSV+yWmNF2k4j9Pj/n5k9SxiuadZZL3d/EXgxcf/s5ryHSJNUV4ehnGuvhQ0bQtuIEaG2Tm5uvLGJpIgtJn53nwZMM7NOwFBCz7ybmd0DTHP351snRJEmevvtkOTrr8+ck6MxfJGNNOXk7lp3L3H37wE9gDeBK6MOTKTJKith7Fj4zncaJv2BA2HWLLjxRvX2RerZptUi3H0VcF/iRyR+r78eevnvvptsy80NXwS/+tWmVTZFRMsESRq7555QTM3rLRB3yCHwwAOwzz7xxSWS4ppyAZdIajryyGSPvlMn+MMfYOZMJX2RrVDil60qKXF6962mTU64LSnxrb+oNey7b5i9c8wxMG8eXHhhOJkrIo0y9xT5JW5EQUGBl9U/aSetpqTEGT2mio6Fc8ntsYrKJXmsK83nngntKCpqxZo206fD8uXhatv6amrC0oiqryOyCTOb7e4Fm7Qr8UtjevetZn1BGe17rfyqbUN5FzqUFfDBwlY4RVRRAZdcAg8/DB06wDvvwJ57Rv+5IhlgS4lfQz3SqPJFOeT2aLgSVW6PVZQvinhIxR0efTSUVnj44dC2fj1cdVW0nyuSBZT4pVG9+tRQuSSvQVvlkjx69WmJ6h1b8PHH4aKrM86AFSuS7WefHWbyiMjXosQvjRpXnMO60nw2lHfBa4wN5V1YV5rPuOIIevzuMGkS7LcfPPlksr1nT3jmGfjjH6FLl5b/XJEso3n80qhwArcd1xQXUD41h159ahg/IaflT+wuWgTnnQcvvNCwffRouOkm6Ny5ZT9PJIupxy9bVVRkfLCwLbU14bbFk747nHJKw6S/117w0ktw991pn/RTdjqsZC0lfomfGdx+e7jfpg1cdlkouHbEEfHG1QLqpsOuLyij5y//j/UFZYweU6XkL7HSdE5pfdXV4UKrjefe/+53cPTRGVUrP/bpsJLVNJ1TUsPs2aGK5uOPb7rtiisyKulDjNNhRRqhxC+tY/16uPJKOOigMIxzwQWwcuXWX5fmYpkOK7IVSvwSvZdfhgEDwlBOTSLhffFFqJWf4Vp1OqxIE2mQUaLzxRehl3/33Q3bjzwSJk4MM3cyXKtNhxXZBkr8Eo1nn4VRo+Cjj5Jt228Pt9wCI0eG2TtZoqjIKCqq+1XTr5zET/8LpWWtXh2Kqv3xjw3bTz4Z7r0XevSIJSwRScqebpe0jqqqUEK5TpcuUFICTz2lpC+SIpT4pWV165a8GOv002HBAjjzTNXLF0khGuqR5nMPM3MOPLBhe1ER9O4Nhx4aT1wi0ij1+KV5ysvhxBNh0CB47bWG28yU9EVSWOSJ38xyzGyumU1PPM4zsxlmtjBxu1PUMUgLqq2FO+8MpZOfey70+keMgA0b4o5MRJqoNXr8FwML6j2+Eih1975AaeKxpIN//QsGDw6Lmq9dG9rMQs8/DWo+iUgQaeI3sx7AycCkes1DgCmJ+1OAoVHGIC2gujrUxB8wAF55Jdner18Y5rn11rAeroikhah7/LcBlwO19dq6ufsygMTtLpt7oZmNMrMyMyurqKiIOEzZojffDPV1rroKKitDW9u28Otfw5w5YYxfRNJKZInfzL4HLHf32c15vbvf7+4F7l7QtWvXFo5OmuTBB0O1zDlzkm3f+Q6UlcH110NubnyxiUizRTmd81DgB2Z2EtAe6GxmfwI+NbPu7r7MzLoDyyOMQb6Ogw8OdfOrq6F9e7juOvjlL0OPX0TSVmQ9fne/yt17uPsewOnAC+5+FvAkMDzxtOHAE1HFIF/TPvvA2LFw+OHw1ltw+eVK+iIZII55/DcBx5rZQuDYxGOJ29//DpMnb9p+2WXw4ouw996tHpKIRKNVum/u/iLwYuL+SqCwNT5XmuCzz+DSS0PSb98+9O779k1uVw9fJOPoyt1s9sQTYUpmXU9/w4bQwxeRjKbEn42WL4cf/xiGDoVly5Ltp50WSieLSEbT3/HZxB0efhguvrjherfdusFdd4XELyIZTz3+bPHRR/D978NZZzVM+j/9Kcyfr6QvkkXU488G7nDKKTC73rV0u+8O998Pxx8fX1wiEgv1+LOBGYwfn3x8wQUwb56SvkiWUo8/E9XUhMXM6696NXgw3HgjHHEEHHZYfLGJSOzU48808+bBIYfA1Kmbbrv6aiV9EVHizxhVVaGWzgEHwBtvhJr5K1bEHZWIpCAl/kwwa1aomllcDF9+Gdo+/xxefTXWsEQkNSnxp7N168KVtoMGhSGeOoMGwdy5MGRIfLGJSMrSyd109dJLMHIk/PvfybaOHeE3vwmzdnJy4otNRFKaEn+6+e9/4YorNi2tUFgIEydC797xxCUiaUNDPemmshL+/Ofk4x12gAcegBkzlPRFpEmU+NNN167whz+E+0OGhHIL557bcM6+iEgjNNSTytzDSdoDDmjYfsYZsNtu4aIsJXwR2Ubq8aeqpUtDfZ3vfAdeeaXhNjM48kglfRFpFiX+VOMexuz79QsLpQCMGBEWSRERaQEa6kklH3wAo0aF9W/rO+ooqK6OJyYRyTjq8aeCmhq4/Xbo379h0t9zT3jhhTB18xvfiC8+EWl1JSVO777VtMkJtyUl3mLvrR5/3BYsCEM5//hHsq1NGxgzBq6/PlyUJSJZpaTEGT2mio6Fc+k5dBXrl+Qxekw+0I6ioq9/bs/cW+5bJCoFBQVeVlYWdxgt7+GH4ZxzQoG1OvvtFxY/P/DA+OISkVj17lvN+oIy2vdKrpa3obwLHcoK+GBh0/vrZjbb3Qs2btdQT5wKCkLvHqBtWxg7FubMUdIXyXLli3LI7bGqQVtuj1WUL2qZUixK/HHae+9QSrmgICT84mJo1y7uqEQkZr361FC5JK9BW+WSPHr1qWmR91fiby2vvgr/+7+btv/yl2F8/9vfbvWQRCQ1jSvOYV1pPhvKu+A1xobyLqwrzWdcccv0+CM7uWtm7YGZQG7icx5397FmVgycB1Qknnq1uz8TVRyxW7MmrHx1552QmxtWx9p77+T2tjq/LiINhRO47bimuIDyqTn06lPD+Ak5LXJiFyI8uWtmBnRy9zVmth3wCnAxcAKwxt1vaep7pe3J3eefD/Pyy8uTbSedBE8/HV9MIpI1Wv3krgdrEg+3S/yk/hSilrB6dZitc/zxmyb9jcspi4i0skjH+M0sx8zeBJYDM9z99cSmC8zsbTObbGY7beG1o8yszMzKKioqNveU1PTXv4ZyC/XH87t0gT/9CaZPh549YwtNRAQiTvzuXuPuA4EewIFm1h+4B9gTGAgsA8Zv4bX3u3uBuxd07do1yjBbxiefwI9+BKedFu7XGTYslE4uKlJRNRFJCa0yq8fdPwNeBE5w908TXwi1wEQgMyatn3oqPP548nH37jBtGjz2GOyyS3xxiYhsJLLEb2ZdzWzHxP0OwDHAe2bWvd7TTgHmbebl6ed3v0veHzEi9PKHDo0tHBGRLYlyLmF3YIqZ5RC+YKa6+3Qze8jMBhJO9C4GfhZhDNGorQ3DNvWHbg4/HMaNg4MOgmOOiS82EZGtUK2ebfX++zByJIweHVbCEhFJUarV83VVV8PNN8OAAfDyy3DRRZBOs41ERBKU+Jvirbdg0CC44orkSlirV8OLL8YalohIcyjxN6ayEn7961BEbfbsZHt+PpSVhembIiJpRoVituSf/0zOzqmTmxsqaF56KWy3XWyhiYh8HUr8G1u7NvTyb7stLHxe59BDYdIk2Gef2EITEWkJGurZ2Pr1obxCXdLv1AnuuANmzlTSF5GMoMS/sZ13Doke4NhjYd48uOCC5EpZIiJpTkM98+ZB//4N24YNg512Colf9XVEJMNkbze2ogLOPDOsfDVzZsNtZnDccUr6IpKRsi/xu8Mjj4TSyY88EtpGjgxj+yIiWSC7Ev/HH8OQIaGnv2JFsv3gg6GqKr64RERaUXYkfneYODH08p96Ktnesyc88wxMmQI77BBffCIirSjzE/9//gOFhWHt2//+N9l+/vnw7rtw4onxxSaxKClxevetpk1OuC0pSf1ChSItKbNn9fzlL3D22Q3H7/v2DRdiHXFEfHFJbEpKnNFjquhYOJeeQ1exfkkeo8fkA+0oKtLJfMkOmd3j33//5IVYbdrA5ZeHgmtK+lnrmuIaOhbOpX2vlViO077XSjoWzuWa4pq4QxNpNZmd+Pv2hRtuCF8Ar78eVsnq0CHuqCRG5YtyyO2xqkFbbo9VlC/KiSkikdaX2YkfYMwYmDUrVNiUrNerTw2VS/IatFUuyaNXH/X4JXtkfuLPyYF27eKOQlLEuOIc1pXms6G8C15jbCjvwrrSfMYVq8cv2SOzT+6KbCScwG3HNcUFlE/NoVefGsZPyNGJXckqSvySdYqKjKKiuv/6+hWQ7JP5Qz0iItKAEr+ISJZR4hcRyTJK/CIiWUaJX0Qky5h76heoMrMKoDzuOJppZ2DFVp+VPrQ/qS/T9kn703y93L3rxo1pkfjTmZmVuXvGXDas/Ul9mbZP2p+Wp6EeEZEso8QvIpJllPijd3/cAbQw7U/qy7R90v60MI3xi4hkGfX4RUSyjBK/iEiWUeJvIWbW3szeMLO3zOxdM7su0V5sZh+b2ZuJn5PijnVbmFmOmc01s+mJx3lmNsPMFiZud4o7xm2xmf1J9+Oz2MzeScRelmhL22O0hf1J92O0o5k9bmbvmdkCMzs47mOkxN9yKoGj3X0AMBA4wcwGJbZNcPeBiZ9nYouweS4GFtR7fCVQ6u59gdLE43Sy8f5Aeh8fgKMSsdfNDU/3Y7Tx/kB6H6PbgWfdfR9gAOH/X6zHSIm/hXiwJvFwu8RPWp85N7MewMnApHrNQ4ApiftTgKGtHFazbWF/MlHaHqNMY2adgSOABwDcvcrdPyPmY6TE34ISwwhvAsuBGe7+emLTBWb2tplNTqc/u4HbgMuB2npt3dx9GUDidpcY4mqu29h0fyB9jw+EzsXzZjbbzEYl2tL5GG1ufyB9j1EfoAJ4MDHEOMnMOhHzMVLib0HuXuPuA4EewIFm1h+4B9iTMPyzDBgfW4DbwMy+Byx399lxx9ISGtmftDw+9Rzq7gcAJwK/MLMj4g7oa9rc/qTzMWoLHADc4+75wFpSYOhNiT8CiT/lXgROcPdPE18ItcBE4MA4Y9sGhwI/MLPFwKPA0Wb2J+BTM+sOkLhdHl+I22Sz+5PGxwcAd1+auF0OTCPEn67HaLP7k+bHaAmwpN5f/48TvghiPUZK/C3EzLqa2Y6J+x2AY4D36g5uwinAvBjC22bufpW793D3PYDTgRfc/SzgSWB44mnDgSdiCnGbbGl/0vX4AJhZJzPbvu4+cBwh/rQ8Rlvan3Q+Ru7+CfCRmX0r0VQIzCfmY6SVpltOd2CKmeUQvlCnuvt0M3vIzAYSxi4XAz+LL8QWcRMw1cxGAB8CP4o5nq/r5jQ+Pt2AaWYG4Xf5YXd/1sxmkZ7HaEv7k+6/QxcCJWbWDlgEnEMiR8R1jFSyQUQky2ioR0Qkyyjxi4hkGSV+EZEso8QvIpJllPhFRLKMEr9kBTOrSVR2nGdmfzazjl/jvf7XzH6YuD/JzPo18twjzeyQZnzGYjPbubkxijRGiV+yxfpEZcf+QBXw8/obE9dfbDN3H+nu8xt5ypHANid+kSgp8Us2ehnYK9Eb/39m9jDwTqLI3u/NbFaiINjPACy408zmm9nT1CuoZWYvmllB4v4JZjbHwpoMpWa2B+ELZkzir43DE1d4/yXxGbPM7NDEa7uY2fOJQl73AdbK/yaSRXTlrmQVM2tLKAD2bKLpQKC/u3+QqAb5ubt/18xygVfN7HkgH/gW8G3C1aXzgckbvW9XQh2ZIxLvlefuq8zsXmCNu9+SeN7DhNryr5jZ7sBzwL7AWOAVd7/ezE4G6lemFGlRSvySLTokSmZD6PE/QBiCecPdP0i0HwfsXzd+D+wA9CXUU3/E3WuApWb2wmbefxAws+693H3VFuI4BuiXKEsA0DlRn+YI4NTEa582s9XN202RrVPil2yxPlEy+yuJ5Lu2fhNwobs/t9HzTmLri+pYE54DYXj1YHdfv5lYVD9FWoXG+EWSngNGm9l2AGa2d6JK5Ezg9MQ5gO7AUZt57T+AwWbWO/HavET7F8D29Z73PHBB3YNE8TESn1GUaDsRSKfFRiTNKPGLJE0ijN/PMbN5wH2Ev4qnAQuBdwiLgry08QvdvYIwLv9XM3sLeCyx6SnglLqTu8BFQEHi5PF8krOLrgOOMLM5hCGnDyPaRxFV5xQRyTbq8YuIZBklfhGRLKPELyKSZZT4RUSyjBK/iEiWUeIXEckySvwiIlnm/wPlB6+wMsVWNgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "5-Fold Cross Validation Error: [ -1.66722849 -25.64984898  -0.38851177]\n",
      "The RandomForestRegressor model's performance on testing set\n",
      "--------------------------------------\n",
      "MAE is 23.447142857142858\n",
      "MSE is 651.4888142857143\n",
      "R2 score is -0.32967976924358533\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import datetime as dt\n",
    "import sklearn as skl\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.model_selection import KFold, cross_validate, train_test_split, cross_val_score\n",
    "from sklearn.metrics import confusion_matrix, mean_absolute_error, mean_squared_error, r2_score\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, classification_report\n",
    "\n",
    "\n",
    "wildfire_data = wildfire_data1\n",
    "annual_county_aqi = annual_county_aqi1\n",
    "\n",
    "columns = [\"POOCounty\", \"DailyAcres\", 'FireBehaviorGeneral', \"FireDiscoveryDateTime\", \"FireOutDateTime\", \"POOState\"]\n",
    "def wildfire_setup(wildfire_data, cols):\n",
    "    wildfire_data = wildfire_data[cols]\n",
    "    wildfire_data = wildfire_data[wildfire_data['POOState'] == 'US-CA'].drop('POOState', axis=1)\n",
    "    wildfire_data = wildfire_data[wildfire_data['DailyAcres'].notnull()]\n",
    "    one_hot = pd.get_dummies(wildfire_data['FireBehaviorGeneral'], prefix='behavior')\n",
    "    # Drop column  as it is now encoded\n",
    "    wildfire_data = wildfire_data.drop('FireBehaviorGeneral', axis = 1)\n",
    "    # Join the encoded df\n",
    "    wildfire_data = wildfire_data.join(one_hot)\n",
    "    return wildfire_data\n",
    "\n",
    "def create_datetime_difference(df, date1, date2, duration_title):\n",
    "    df = df[df[date1].notnull()][df[date2].notnull()]\n",
    "    col1 = pd.to_datetime(df[date1], errors = 'coerce')\n",
    "    col2 = pd.to_datetime(df[date2], errors = 'coerce')\n",
    "    duration = (col2 - col1).dt.days\n",
    "    df[duration_title] = duration\n",
    "    df = df[df[duration_title].notnull()]\n",
    "    df = df[df[duration_title] >= 0]\n",
    "    df = df.drop([date1, date2], axis=1)\n",
    "    return df\n",
    "    \n",
    "def group_by_county(df, minimum_threshold):\n",
    "    v = df.POOCounty.value_counts()\n",
    "    df = df[df.POOCounty.isin(v.index[v.gt(minimum_threshold)])]\n",
    "    df = df.drop(['behavior_Moderate'], axis=1)\n",
    "    df = df.groupby('POOCounty').agg(\n",
    "            median_dailyacres=('DailyAcres', np.median),\n",
    "            count_ActiveBehavior=('behavior_Active', np.sum),\n",
    "            count_ExtremeBehavior=('behavior_Extreme', np.sum),\n",
    "            mean_FireDurationDays=('FireDurationDays', np.mean)\n",
    "        )\n",
    "    df['count_ExtremeOrActiveBehavior'] = df['count_ActiveBehavior'] + df['count_ExtremeBehavior']\n",
    "    df = df.drop(['count_ActiveBehavior', 'count_ExtremeBehavior'], axis=1)\n",
    "    df = df.reset_index()\n",
    "    return df\n",
    "\n",
    "def merge_with_annualcountyaqi(df, annual_county_aqi):\n",
    "    annual_county_aqi = annual_county_aqi[annual_county_aqi['State'] == 'California']\n",
    "    annual_county_aqi = annual_county_aqi[['County', 'Median AQI']]\n",
    "    df = df.rename(columns = {'POOCounty': 'County'})\n",
    "    df = df.merge(annual_county_aqi, on=['County'])\n",
    "    return df\n",
    "\n",
    "def create_initial_model(df, rf=False, rfr=False, lr=False, binned=False):\n",
    "    if binned == True:\n",
    "        df = df.copy()\n",
    "        df['Median AQI Bins'] = pd.qcut(x=df['Median AQI'],\n",
    "                                        q=5,\n",
    "                                        labels=False)\n",
    "        df = df.drop('Median AQI', axis=1)\n",
    "    df = df.drop('County', axis=1)\n",
    "    features = df.iloc[:, :-1]\n",
    "    targets = df.iloc[:, -1]\n",
    "    X_train, X_rem, y_train, y_rem = train_test_split(features, targets, train_size=0.6)\n",
    "    X_validation, X_test, y_validation, y_test = train_test_split(X_rem, y_rem, train_size=0.5)\n",
    "    if rf == True:\n",
    "        model = RandomForestClassifier()\n",
    "        model.fit(X_train, y_train)\n",
    "        evaluate_model(model, X_test, y_test, X_val=X_validation, y_val=y_validation, is_classifier=True, initial_nsplits=3)\n",
    "        return model\n",
    "        \n",
    "    if rfr == True:\n",
    "        model = RandomForestRegressor(n_estimators=100, random_state=0)\n",
    "        model.fit(X_train, y_train)\n",
    "        evaluate_model(model, X_test, y_test, X_val=X_validation, y_val=y_validation, initial_nsplits=3)\n",
    "        return model\n",
    "    if lr == True:\n",
    "        model = LinearRegression(normalize=True)\n",
    "        model.fit(X_train, y_train)\n",
    "        evaluate_model(model, X_test, y_test, X_val=X_validation, y_val=y_validation, initial_nsplits=3)\n",
    "        return model\n",
    "    \n",
    "def cross_validation(model, X, y, n_splits=5, is_classifier=False, initial_nsplits=None):\n",
    "    if initial_nsplits != None:\n",
    "        n_splits = initial_nsplits\n",
    "    scores = cross_val_score(model, X, y, cv=n_splits)\n",
    "    return scores\n",
    "\n",
    "def merge_with_q10data(wildfire_data):\n",
    "    wildfire_data = wildfire_data.iloc[:, :-1].rename(columns={'County':'county Name'})\n",
    "    df = build_features(q11=True)\n",
    "    df = df.merge(wildfire_data, on='county Name')\n",
    "    df = df[[c for c in df if c not in ['AQI']] \n",
    "       + ['AQI']]\n",
    "    return df\n",
    "\n",
    "def create_second_model(df, rf=False, rfr=False, lr=False, binned=False):\n",
    "    df = merge_with_q10data(df).drop(['county Name'], axis=1)\n",
    "    if binned == True:\n",
    "        df = df.copy()\n",
    "        df['AQI Bins'] = pd.qcut(x=df['AQI'],\n",
    "                                        q=5,\n",
    "                                        labels=False)\n",
    "        df = df.drop('AQI', axis=1)\n",
    "    features = df.iloc[:, :-1]\n",
    "    targets = df.iloc[:, -1]\n",
    "    X_train, X_rem, y_train, y_rem = train_test_split(features, targets, train_size=0.65)\n",
    "    X_validation, X_test, y_validation, y_test = train_test_split(X_rem, y_rem, train_size=0.5)\n",
    "    if rf == True:\n",
    "        model = RandomForestClassifier()\n",
    "        model.fit(X_train, y_train)\n",
    "        evaluate_model(model, X_test, y_test, X_val=X_validation, y_val=y_validation, is_classifier=True)\n",
    "    if rfr == True:\n",
    "        model = RandomForestRegressor(n_estimators=100, random_state=0)\n",
    "        model.fit(X_train, y_train)\n",
    "        evaluate_model(model, X_test, y_test, X_val=X_validation, y_val=y_validation)\n",
    "        return model\n",
    "    if lr == True:\n",
    "        model = LinearRegression()\n",
    "        model.fit(X_train, y_train)\n",
    "        evaluate_model(model, X_test, y_test, X_val=X_validation, y_val=y_validation)\n",
    "        return model\n",
    "    \n",
    "def evaluate_model(model, X_test, y_test, X_val=None, y_val=None, is_classifier=False, initial_nsplits=None):\n",
    "    y_predicted = model.predict(X_test)\n",
    "    if is_classifier==True:\n",
    "        print(\"The {0} model's performance on testing set\".format(type(model).__name__))\n",
    "        print(\"--------------------------------------\")\n",
    "        confusion = confusion_matrix(y_test, y_predicted)\n",
    "        print('Confusion Matrix\\n')\n",
    "        print(confusion)\n",
    "\n",
    "        #importing accuracy_score, precision_score, recall_score, f1_score\n",
    "        print('\\nAccuracy: {:.2f}\\n'.format(accuracy_score(y_test, y_predicted)))\n",
    "        print('\\nClassification Report\\n')\n",
    "        print(classification_report(y_test, y_predicted))\n",
    "        print('\\n5-Fold Cross Validation Error: {0}'.format(cross_validation(model, X_val, y_val, initial_nsplits)))\n",
    "    else:\n",
    "        # model evaluation for testing set\n",
    "        mae = skl.metrics.mean_absolute_error(y_test, y_predicted)\n",
    "        mse = skl.metrics.mean_squared_error(y_test, y_predicted)\n",
    "        r2 = skl.metrics.r2_score(y_test, y_predicted)\n",
    "        print('\\n5-Fold Cross Validation Error: {0}'.format(cross_validation(model, X_val, y_val, initial_nsplits)))\n",
    "        print(\"The {0} model's performance on testing set\".format(type(model).__name__))\n",
    "        print(\"--------------------------------------\")\n",
    "        print('MAE is {}'.format(mae))\n",
    "        print('MSE is {}'.format(mse))\n",
    "        print('R2 score is {}'.format(r2))\n",
    "        fig, ax = plt.subplots()\n",
    "        ax.scatter(y_predicted, y_test, edgecolors=(0, 0, 1))\n",
    "        ax.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'r--', lw=3)\n",
    "        ax.set_xlabel('Predicted')\n",
    "        ax.set_ylabel('Actual')\n",
    "        plt.show()\n",
    "    \n",
    "    \n",
    "    \n",
    "wildfire_data = wildfire_setup(wildfire_data, columns)\n",
    "wildfire_data = create_datetime_difference(wildfire_data, 'FireDiscoveryDateTime', 'FireOutDateTime', 'FireDurationDays')\n",
    "wildfire_data = group_by_county(wildfire_data, 10)\n",
    "wildfire_data = merge_with_annualcountyaqi(wildfire_data, annual_county_aqi)\n",
    "\n",
    "initial_rf_model = create_initial_model(wildfire_data, rf=True, binned=True)\n",
    "initial_lr_model = create_initial_model(wildfire_data,lr=True)\n",
    "initial_rfr_model = create_initial_model(wildfire_data, rfr=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28da22b1",
   "metadata": {},
   "source": [
    "### Baseline Model Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f014e993",
   "metadata": {},
   "source": [
    "The models I used are random forests, linear regression, and random forest regressor. I used these three models as baseline models to work with my sparse county-wise wildfire data to check whether there are any generalizable trends between counties' wildfire histories and annual median AQIs. Since the data was sprase, validation errors were mostly meaningless. Changes to my baseline models are addressed in the following section and the README."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a510b4e",
   "metadata": {},
   "source": [
    "### Improving the Baseline Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "61130990",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The RandomForestClassifier model's performance on testing set\n",
      "--------------------------------------\n",
      "Confusion Matrix\n",
      "\n",
      "[[9 2 1 0 1]\n",
      " [2 1 2 3 3]\n",
      " [2 1 3 0 3]\n",
      " [1 3 2 2 3]\n",
      " [2 1 3 2 5]]\n",
      "\n",
      "Accuracy: 0.35\n",
      "\n",
      "\n",
      "Classification Report\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.56      0.69      0.62        13\n",
      "           1       0.12      0.09      0.11        11\n",
      "           2       0.27      0.33      0.30         9\n",
      "           3       0.29      0.18      0.22        11\n",
      "           4       0.33      0.38      0.36        13\n",
      "\n",
      "    accuracy                           0.35        57\n",
      "   macro avg       0.32      0.34      0.32        57\n",
      "weighted avg       0.33      0.35      0.33        57\n",
      "\n",
      "\n",
      "5-Fold Cross Validation Error: [0.25       0.16666667 0.18181818 0.27272727 0.18181818]\n",
      "\n",
      "5-Fold Cross Validation Error: [ 0.04669794 -0.69183464 -5.00054639 -0.00868842  0.25332366]\n",
      "The LinearRegression model's performance on testing set\n",
      "--------------------------------------\n",
      "MAE is 27.480077467780184\n",
      "MSE is 1132.8017149278016\n",
      "R2 score is 0.1487277308256646\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "5-Fold Cross Validation Error: [-3.10237123 -0.49104442  0.55256466 -1.14808632 -1.69689511]\n",
      "The RandomForestRegressor model's performance on testing set\n",
      "--------------------------------------\n",
      "MAE is 31.17820292825147\n",
      "MSE is 1656.670314219916\n",
      "R2 score is 0.18646366635051892\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "second_rf_model = create_second_model(wildfire_data, rf=True, binned=True)\n",
    "second_lr_model = create_second_model(wildfire_data, lr=True)\n",
    "second_rfr_model = create_second_model(wildfire_data, rfr=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8504e87",
   "metadata": {},
   "source": [
    "### Comparing Baseline and Improved Models"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab0bec98",
   "metadata": {},
   "source": [
    "My baseline model(s) [random forest, linear regression, random forest regressor] were clearly deficient since the data was so sparse. The reason I modelled with the baseline data is to see if I can glean any hidden generalizable trends between the wildfire data of various different counties. Since the data was obviously sparse in terms of datapoints, the validation error is high and volatile. Our target variable for the baseline model was the annual median AQI for Californian counties in the year of 2020. Because I quickly saw via modelling that I couldn't generalize wildfire --> AQI predictivity between counties, I moved on to using counties' wildfire data features as reinforcements to my previous feature engineering to predict monthly average AQI for all sites in all Californian counties.\n",
    "\n",
    "The improved model as can be observed above is superior in terms of predictivity, as one can clearly see that I have gotten roughly the correct trend in the regression models. Thus, the validation errors are lower. Overall, with the limited features I conducted from wildfire data, amending it to the EPA features led me in the right direction in terms of prediction. The research question was partially answered since wildfire data seems to be somewhat predictive of AQI."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
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 },
 "nbformat": 4,
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