analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# wine-country-fires\n",
    "by Priya Krishnakumar\n",
    "\n",
    "This analysis was conducted for the Nov. 7 2019 [Los Angeles Times story](https://www.latimes.com/projects/wine-country-fires-california-sonoma-napa/) which looked at the growing size of fires in California's wine country. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "import altair as alt\n",
    "import geopandas as gpd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "gpd.pd.set_option('display.float_format', lambda x: '%.3f' % x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "warnings.simplefilter(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This file is a clipped shapefile which shows every fire perimeter in California's wine country after 1950. The data is from Cal Fire's FRAP database of all fire perimeters since the 1800s."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "fires = gpd.read_file(\"data/wine_country_fires_with_2019.shp\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'geopandas.geodataframe.GeoDataFrame'>\n",
      "RangeIndex: 879 entries, 0 to 878\n",
      "Data columns (total 9 columns):\n",
      "year          879 non-null int64\n",
      "is_large      874 non-null float64\n",
      "name          842 non-null object\n",
      "total_acre    879 non-null float64\n",
      "winecountr    879 non-null float64\n",
      "start_date    833 non-null object\n",
      "before_200    874 non-null float64\n",
      "dissolve      874 non-null object\n",
      "geometry      879 non-null geometry\n",
      "dtypes: float64(4), geometry(1), int64(1), object(3)\n",
      "memory usage: 61.9+ KB\n"
     ]
    }
   ],
   "source": [
    "fires.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>is_large</th>\n",
       "      <th>name</th>\n",
       "      <th>total_acre</th>\n",
       "      <th>winecountr</th>\n",
       "      <th>start_date</th>\n",
       "      <th>before_200</th>\n",
       "      <th>dissolve</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2007</td>\n",
       "      <td>0.000</td>\n",
       "      <td>PEAK</td>\n",
       "      <td>46.322</td>\n",
       "      <td>46.321</td>\n",
       "      <td>2007/07/11 00:00:00.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>dissolve</td>\n",
       "      <td>POLYGON ((-13608550.878 4632018.124, -13608563...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2007</td>\n",
       "      <td>0.000</td>\n",
       "      <td>MOUNTAIN</td>\n",
       "      <td>49.278</td>\n",
       "      <td>49.281</td>\n",
       "      <td>2007/06/24 00:00:00.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>dissolve</td>\n",
       "      <td>POLYGON ((-13652152.604 4660763.643, -13652163...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2007</td>\n",
       "      <td>0.000</td>\n",
       "      <td>SPRUCE</td>\n",
       "      <td>129.011</td>\n",
       "      <td>129.039</td>\n",
       "      <td>2007/07/15 00:00:00.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>dissolve</td>\n",
       "      <td>POLYGON ((-13641823.708 4699137.158, -13641805...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2007</td>\n",
       "      <td>0.000</td>\n",
       "      <td>ROBINSON</td>\n",
       "      <td>121.548</td>\n",
       "      <td>121.602</td>\n",
       "      <td>2007/07/28 00:00:00.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>dissolve</td>\n",
       "      <td>POLYGON ((-13676996.417 4742135.934, -13676989...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2007</td>\n",
       "      <td>0.000</td>\n",
       "      <td>WEST</td>\n",
       "      <td>36.995</td>\n",
       "      <td>37.003</td>\n",
       "      <td>2007/07/22 00:00:00.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>dissolve</td>\n",
       "      <td>POLYGON ((-13695317.575 4699277.743, -13695315...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   year  is_large      name  total_acre  winecountr               start_date  \\\n",
       "0  2007     0.000      PEAK      46.322      46.321  2007/07/11 00:00:00.000   \n",
       "1  2007     0.000  MOUNTAIN      49.278      49.281  2007/06/24 00:00:00.000   \n",
       "2  2007     0.000    SPRUCE     129.011     129.039  2007/07/15 00:00:00.000   \n",
       "3  2007     0.000  ROBINSON     121.548     121.602  2007/07/28 00:00:00.000   \n",
       "4  2007     0.000      WEST      36.995      37.003  2007/07/22 00:00:00.000   \n",
       "\n",
       "   before_200  dissolve                                           geometry  \n",
       "0       0.000  dissolve  POLYGON ((-13608550.878 4632018.124, -13608563...  \n",
       "1       0.000  dissolve  POLYGON ((-13652152.604 4660763.643, -13652163...  \n",
       "2       0.000  dissolve  POLYGON ((-13641823.708 4699137.158, -13641805...  \n",
       "3       0.000  dissolve  POLYGON ((-13676996.417 4742135.934, -13676989...  \n",
       "4       0.000  dissolve  POLYGON ((-13695317.575 4699277.743, -13695315...  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fires.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "fires[\"before_2000\"] = fires.year.apply(lambda x:x < 2000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True     492\n",
       "False    387\n",
       "Name: before_2000, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fires.before_2000.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "before_2000\n",
       "False   1101322.089\n",
       "True    1011749.927\n",
       "Name: winecountr, dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fires.groupby(\"before_2000\").winecountr.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>year</th>\n",
       "      <th>winecountr</th>\n",
       "      <th>before_2000</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>869</th>\n",
       "      <td>RANCH</td>\n",
       "      <td>2018</td>\n",
       "      <td>297848.625</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>479</th>\n",
       "      <td>FORK</td>\n",
       "      <td>1996</td>\n",
       "      <td>82654.686</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>875</th>\n",
       "      <td>KINCADE</td>\n",
       "      <td>2019</td>\n",
       "      <td>77752.000</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>786</th>\n",
       "      <td>VALLEY</td>\n",
       "      <td>2015</td>\n",
       "      <td>76098.719</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280</th>\n",
       "      <td>MENDENHALL</td>\n",
       "      <td>1987</td>\n",
       "      <td>65523.214</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>C. HANLY</td>\n",
       "      <td>1964</td>\n",
       "      <td>55964.451</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>834</th>\n",
       "      <td>NUNS</td>\n",
       "      <td>2017</td>\n",
       "      <td>55796.780</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>782</th>\n",
       "      <td>ROCKY</td>\n",
       "      <td>2015</td>\n",
       "      <td>53339.546</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>840</th>\n",
       "      <td>ATLAS</td>\n",
       "      <td>2017</td>\n",
       "      <td>51623.269</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868</th>\n",
       "      <td>RIVER</td>\n",
       "      <td>2018</td>\n",
       "      <td>48938.734</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           name  year  winecountr  before_2000\n",
       "869       RANCH  2018  297848.625        False\n",
       "479        FORK  1996   82654.686         True\n",
       "875     KINCADE  2019   77752.000        False\n",
       "786      VALLEY  2015   76098.719        False\n",
       "280  MENDENHALL  1987   65523.214         True\n",
       "247    C. HANLY  1964   55964.451         True\n",
       "834        NUNS  2017   55796.780        False\n",
       "782       ROCKY  2015   53339.546        False\n",
       "840       ATLAS  2017   51623.269        False\n",
       "868       RIVER  2018   48938.734        False"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fires.sort_values(\"winecountr\",ascending=False).head(10)[[\"name\",\"year\",\"winecountr\",\"before_2000\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This separates the fires into before and after 2000, and calculates the new areas that have burned in the region since 2000."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "before_2000 = fires[fires.before_2000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "after_2000 = fires[~fires.before_2000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "only_after_2000 = gpd.overlay(after_2000, before_2000, how='difference')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "sqft2acres = lambda sqft: sqft / 43560"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x100563dd0>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "ax = only_after_2000.plot(cmap='tab10')\n",
    "after_2000.plot(ax=ax, facecolor='none', edgecolor='k')\n",
    "before_2000.plot(ax=ax, facecolor='none', edgecolor='k')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "702487.2754703652"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sqft2acres(only_after_2000.to_crs(epsg=2227).geometry.area.sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1950"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fires.year.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "before_2000.to_file(\"before-2000.geojson\",driver=\"GeoJSON\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "after_2000.to_file(\"after-2000.geojson\",driver=\"GeoJSON\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "only_after_2000.to_file(\"only-after-2000.geojson\",driver=\"GeoJSON\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}