analise_bioma.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Algumas constantes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "217029"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "area_cerrado_maranhense = 217029\n",
    "area_cerrado_maranhense"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "329651"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "area_estado_ma = 329651\n",
    "area_estado_ma"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6583599018355776"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "area_cerrado_maranhense/ area_estado_ma"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Carregando os dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>areakm</th>\n",
       "      <th>municipality</th>\n",
       "      <th>geocode_ibge</th>\n",
       "      <th>state</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2000</td>\n",
       "      <td>42.515084</td>\n",
       "      <td>Afonso Cunha</td>\n",
       "      <td>2100105</td>\n",
       "      <td>Maranhão</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2001</td>\n",
       "      <td>1.687509</td>\n",
       "      <td>Afonso Cunha</td>\n",
       "      <td>2100105</td>\n",
       "      <td>Maranhão</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2002</td>\n",
       "      <td>1.687509</td>\n",
       "      <td>Afonso Cunha</td>\n",
       "      <td>2100105</td>\n",
       "      <td>Maranhão</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2003</td>\n",
       "      <td>3.922876</td>\n",
       "      <td>Afonso Cunha</td>\n",
       "      <td>2100105</td>\n",
       "      <td>Maranhão</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2004</td>\n",
       "      <td>3.922876</td>\n",
       "      <td>Afonso Cunha</td>\n",
       "      <td>2100105</td>\n",
       "      <td>Maranhão</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year     areakm  municipality  geocode_ibge     state\n",
       "0  2000  42.515084  Afonso Cunha       2100105  Maranhão\n",
       "1  2001   1.687509  Afonso Cunha       2100105  Maranhão\n",
       "2  2002   1.687509  Afonso Cunha       2100105  Maranhão\n",
       "3  2003   3.922876  Afonso Cunha       2100105  Maranhão\n",
       "4  2004   3.922876  Afonso Cunha       2100105  Maranhão"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('dados/csv/terrabrasilis_cerrado_5_18_2024,_11_26_39 AM.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Para quais anos temos dados ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010,\n",
       "       2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021,\n",
       "       2022, 2023])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"year\"].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Algumas perguntas inicias, sobre o desmatamento absoluto e relativo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Qual o total de desmatamento no bioma ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "89880.3660872508 0.4141398895412632\n"
     ]
    }
   ],
   "source": [
    "total_desmatamento = df[\"areakm\"].sum()\n",
    "print( total_desmatamento, total_desmatamento / area_cerrado_maranhense)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Qual o total de desmatamento no bioma, na mascara de 2000 ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "41356.95237303544 0.19055956749114378\n"
     ]
    }
   ],
   "source": [
    "total_desmatamento_00 = df[df[\"year\"] == 2000][\"areakm\"].sum()\n",
    "print( total_desmatamento_00, total_desmatamento_00 / area_cerrado_maranhense)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Qual o total de desmatamento no bioma, de 2001 a 2023 ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "48523.41371421536 0.2235803220501194\n"
     ]
    }
   ],
   "source": [
    "total_desmatamento_01_23 = df[df[\"year\"] != 2000][\"areakm\"].sum()\n",
    "print( total_desmatamento_01_23, total_desmatamento_01_23 / area_cerrado_maranhense)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizando a evolução do desmatamento"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>areakm</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>41356.952373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>2338.595392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>2338.595392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>2908.182797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>2908.182797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>2431.024448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>2431.024448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>2961.520519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>2961.520519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>1733.885848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>1733.885848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>1439.871703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>1439.871703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>1703.487512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>1883.071290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>1673.220149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>1281.110614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>1501.902384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>1570.840733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>1341.287813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>1898.819961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>2282.037005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>2833.951749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023</th>\n",
       "      <td>2927.523091</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            areakm\n",
       "year              \n",
       "2000  41356.952373\n",
       "2001   2338.595392\n",
       "2002   2338.595392\n",
       "2003   2908.182797\n",
       "2004   2908.182797\n",
       "2005   2431.024448\n",
       "2006   2431.024448\n",
       "2007   2961.520519\n",
       "2008   2961.520519\n",
       "2009   1733.885848\n",
       "2010   1733.885848\n",
       "2011   1439.871703\n",
       "2012   1439.871703\n",
       "2013   1703.487512\n",
       "2014   1883.071290\n",
       "2015   1673.220149\n",
       "2016   1281.110614\n",
       "2017   1501.902384\n",
       "2018   1570.840733\n",
       "2019   1341.287813\n",
       "2020   1898.819961\n",
       "2021   2282.037005\n",
       "2022   2833.951749\n",
       "2023   2927.523091"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_por_ano = df.groupby(\"year\")[\"areakm\"].sum().reset_index().set_index(\"year\")\n",
    "df_por_ano"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>areakm</th>\n",
       "      <th>Percentual do Bioma</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>41356.952373</td>\n",
       "      <td>0.190560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>2338.595392</td>\n",
       "      <td>0.010775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>2338.595392</td>\n",
       "      <td>0.010775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>2908.182797</td>\n",
       "      <td>0.013400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>2908.182797</td>\n",
       "      <td>0.013400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>2431.024448</td>\n",
       "      <td>0.011201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>2431.024448</td>\n",
       "      <td>0.011201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>2961.520519</td>\n",
       "      <td>0.013646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>2961.520519</td>\n",
       "      <td>0.013646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>1733.885848</td>\n",
       "      <td>0.007989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>1733.885848</td>\n",
       "      <td>0.007989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>1439.871703</td>\n",
       "      <td>0.006634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>1439.871703</td>\n",
       "      <td>0.006634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>1703.487512</td>\n",
       "      <td>0.007849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>1883.071290</td>\n",
       "      <td>0.008677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>1673.220149</td>\n",
       "      <td>0.007710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>1281.110614</td>\n",
       "      <td>0.005903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>1501.902384</td>\n",
       "      <td>0.006920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>1570.840733</td>\n",
       "      <td>0.007238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>1341.287813</td>\n",
       "      <td>0.006180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>1898.819961</td>\n",
       "      <td>0.008749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>2282.037005</td>\n",
       "      <td>0.010515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>2833.951749</td>\n",
       "      <td>0.013058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023</th>\n",
       "      <td>2927.523091</td>\n",
       "      <td>0.013489</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            areakm  Percentual do Bioma\n",
       "year                                   \n",
       "2000  41356.952373             0.190560\n",
       "2001   2338.595392             0.010775\n",
       "2002   2338.595392             0.010775\n",
       "2003   2908.182797             0.013400\n",
       "2004   2908.182797             0.013400\n",
       "2005   2431.024448             0.011201\n",
       "2006   2431.024448             0.011201\n",
       "2007   2961.520519             0.013646\n",
       "2008   2961.520519             0.013646\n",
       "2009   1733.885848             0.007989\n",
       "2010   1733.885848             0.007989\n",
       "2011   1439.871703             0.006634\n",
       "2012   1439.871703             0.006634\n",
       "2013   1703.487512             0.007849\n",
       "2014   1883.071290             0.008677\n",
       "2015   1673.220149             0.007710\n",
       "2016   1281.110614             0.005903\n",
       "2017   1501.902384             0.006920\n",
       "2018   1570.840733             0.007238\n",
       "2019   1341.287813             0.006180\n",
       "2020   1898.819961             0.008749\n",
       "2021   2282.037005             0.010515\n",
       "2022   2833.951749             0.013058\n",
       "2023   2927.523091             0.013489"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_por_ano[\"Percentual do Bioma\"] = df_por_ano[\"areakm\"] / area_cerrado_maranhense\n",
    "df_por_ano"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* variação, ou aceleração"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>areakm</th>\n",
       "      <th>Percentual do Bioma</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>24.355962</td>\n",
       "      <td>24.355962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>-16.407440</td>\n",
       "      <td>-16.407440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         areakm  Percentual do Bioma\n",
       "year                                \n",
       "2002   0.000000             0.000000\n",
       "2003  24.355962            24.355962\n",
       "2004   0.000000             0.000000\n",
       "2005 -16.407440           -16.407440\n",
       "2006   0.000000             0.000000"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aceleracao_desmatamento = df_por_ano.pct_change().drop([2000, 2001]) * 100\n",
    "aceleracao_desmatamento.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_theme(style=\"darkgrid\")\n",
    "fig, axs = plt.subplots(1, 2, figsize=(14, 6))\n",
    "sns.lineplot(ax=axs[0], x='year', y='areakm', data=df_por_ano.drop(2000), marker='o', legend=False)\n",
    "\n",
    "axs[0].set_title('Incremento anual de desmatamento (km²)')\n",
    "axs[0].set_xlabel('')\n",
    "axs[0].set_ylabel('Área em km²')\n",
    "\n",
    "\n",
    "sns.barplot(ax=axs[1], x='year', y='areakm', data=aceleracao_desmatamento, )\n",
    "axs[1].xaxis.set_major_locator(plt.AutoLocator())\n",
    "\n",
    "axs[1].set(title='Variação em percentagem do incremento anual', xlabel='', ylabel='Percentual de variação')\n",
    "\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "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": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}