diff --git a/Projeto_Final.ipynb b/Projeto_Final.ipynb new file mode 100644 index 0000000..0bc17da --- /dev/null +++ b/Projeto_Final.ipynb @@ -0,0 +1,4732 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [], + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "source": [ + "# **Análise de Dados do Mês de Setembro sobre as Queimadas no Distrito Federal e em São Paulo**\n", + "\n", + "As queimadas têm se tornado um problema crescente no Brasil, especialmente as regiões estudadas neste projeto, no Distrito Federal e em São Paulo, afetando a saúde pública e a biodiversidade. Este projeto visa investigar as ocorrências de queimadas nessas regiões, analisando dados sobre Número de dias sem chuva, Risco de fogo e Precipitação.\n", + "\n", + "Através de análises estatísticas e visualizações feitas com base nos dados do mês de setembro, buscamos identificar padrões que esclareçam a relação entre o número de dias sem chuva, o risco de fogo e as precipitações com as queimadas nessas regiões. Nosso objetivo é contribuir para a conscientização e oferecer subsídios para políticas públicas que visem a mitigação desse problema." + ], + "metadata": { + "id": "WECcPpEyZaAa" + } + }, + { + "cell_type": "markdown", + "source": [ + "# Base de Dados utilizado\n" + ], + "metadata": { + "id": "eiBf2GYMBcfB" + } + }, + { + "cell_type": "markdown", + "source": [ + "Dados de Queimadas: https://drive.google.com/file/d/1S16NonMM5GmUH_aPeJT-vCc31hcuST_b/view?usp=drive_link\n", + "\n", + "\n", + "\n", + "\n", + "---\n", + "\n" + ], + "metadata": { + "id": "AaEcnaPsB87B" + } + }, + { + "cell_type": "markdown", + "source": [ + "# Importação inicial\n", + "\n", + "* As bibliotecas NumPy (np) e Pandas (pd) são importadas para facilitar a manipulação de arrays e a análise de dados em formato tabular.\n" + ], + "metadata": { + "id": "-XufaDcaT-ft" + } + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "GMW2vrbFxYo7" + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n" + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Banco de dados sobre Queimadas\n", + "\n", + "* O comando pd.read_csv é utilizado para ler o arquivo CSV localizado em \"/content/focos_mensal_br_202409.csv\" e armazenar os dados em um DataFrame chamado dfquei.\n", + "\n", + "* O comando dfquei exibe a estrutura e as primeiras linhas do DataFrame, permitindo uma visualização inicial dos dados importados." + ], + "metadata": { + "id": "337wULLxMoNl" + } + }, + { + "cell_type": "code", + "source": [ + "# Comando para leitura do Banco de Dados selecionado\n", + "\n", + "dfquei = pd.read_csv(\"/content/focos_mensal_br_202409.csv\")" + ], + "metadata": { + "collapsed": true, + "id": "yizWOGUq9nT2" + }, + "execution_count": 2, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Visualização inicial da base\n", + "\n", + "dfquei" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 617 + }, + "id": "RV9UO9H8KMuk", + "outputId": "9e9776aa-6abe-416f-9e43-9afda40c824f" + }, + "execution_count": 3, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " id lat lon \\\n", + "0 4d9dd1a4-b0a7-348b-9eac-481ad6728002 -14.9600 -58.8200 \n", + "1 8e84b5cb-8393-36e8-b2f3-7d20cd72cf7b -14.9900 -58.8400 \n", + "2 04af82ca-c4d8-37ee-bace-961312afebfa -10.6400 -57.8200 \n", + "3 cd695f4e-657b-3f09-bc88-9068cdbc79fb -10.6300 -57.7400 \n", + "4 d0478035-87d6-353a-833f-83b8deeba46b -10.6700 -57.8300 \n", + "... ... ... ... \n", + "111026 1ab31c25-52a1-3dab-91a9-98a566240b2f -9.2307 -54.8726 \n", + "111027 fb3609ce-2594-35f9-820c-5cfb68d3bead -9.2397 -60.4063 \n", + "111028 6d0f9a44-e492-3d88-b5bd-b011f6a244b7 -9.2176 -60.9073 \n", + "111029 ff650feb-05f7-3675-86b5-a528ae0c5e1d -9.2398 -60.3871 \n", + "111030 94da8d2a-3095-367c-ae36-391159ccedf7 -9.2124 -54.8335 \n", + "\n", + " data_hora_gmt satelite municipio estado pais \\\n", + "0 2024-09-01 00:04:01 MSG-03 BARRA DO BUGRES MATO GROSSO Brasil \n", + "1 2024-09-01 00:04:01 MSG-03 BARRA DO BUGRES MATO GROSSO Brasil \n", + "2 2024-09-01 00:04:26 MSG-03 JUARA MATO GROSSO Brasil \n", + "3 2024-09-01 00:04:26 MSG-03 JUARA MATO GROSSO Brasil \n", + "4 2024-09-01 00:04:26 MSG-03 JUARA MATO GROSSO Brasil \n", + "... ... ... ... ... ... \n", + "111026 2024-09-01 18:55:59 GOES-16 NOVO PROGRESSO PARÁ Brasil \n", + "111027 2024-09-01 18:55:59 GOES-16 COLNIZA MATO GROSSO Brasil \n", + "111028 2024-09-01 18:55:59 GOES-16 COLNIZA MATO GROSSO Brasil \n", + "111029 2024-09-01 18:55:59 GOES-16 COLNIZA MATO GROSSO Brasil \n", + "111030 2024-09-01 18:55:59 GOES-16 NOVO PROGRESSO PARÁ Brasil \n", + "\n", + " municipio_id estado_id pais_id numero_dias_sem_chuva precipitacao \\\n", + "0 5101704 51.0 33.0 14.0 0.0 \n", + "1 5101704 51.0 33.0 14.0 0.0 \n", + "2 5105101 51.0 33.0 89.0 0.0 \n", + "3 5105101 51.0 33.0 89.0 0.0 \n", + "4 5105101 51.0 33.0 89.0 0.0 \n", + "... ... ... ... ... ... \n", + "111026 1505031 15.0 33.0 117.0 0.0 \n", + "111027 5103254 51.0 33.0 100.0 0.0 \n", + "111028 5103254 51.0 33.0 47.0 0.0 \n", + "111029 5103254 51.0 33.0 100.0 0.0 \n", + "111030 1505031 NaN NaN NaN NaN \n", + "\n", + " risco_fogo bioma frp \n", + "0 1.0 Cerrado NaN \n", + "1 1.0 Cerrado NaN \n", + "2 1.0 Amazônia NaN \n", + "3 1.0 Amazônia NaN \n", + "4 1.0 Amazônia NaN \n", + "... ... ... ... \n", + "111026 1.0 Amazônia 115.9 \n", + "111027 1.0 Amazônia 81.5 \n", + "111028 1.0 Amazônia 119.0 \n", + "111029 1.0 Amazônia 144.2 \n", + "111030 NaN NaN NaN \n", + "\n", + "[111031 rows x 16 columns]" + ], + "text/html": [ + "\n", + "
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" + ] + }, + "metadata": {}, + "execution_count": 5 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Exclusão de Valores NaN\n", + "\n", + "* O comando dfquei.dropna() é utilizado para remover todas as linhas do DataFrame dfquei que contêm valores ausentes (NaN). Essa operação resulta em um novo DataFrame sem dados faltantes, facilitando análises posteriores com informações completas. Note que, para que a alteração tenha efeito, o resultado deve ser atribuído de volta a dfquei ou a um novo DataFrame, já que dropna() não modifica o DataFrame original por padrão" + ], + "metadata": { + "id": "gs4mgRGhQdSU" + } + }, + { + "cell_type": "code", + "source": [ + "# Excluindo os Nan de toda a tabela\n", + "\n", + "dfquei.dropna()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 617 + }, + "id": "BnyswqCou0zg", + "outputId": "ae5b8ced-744b-4e27-abde-fd89a732d4d0" + }, + "execution_count": 6, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " id lat lon \\\n", + "14 b4cef1ac-95bb-37b1-ae86-b30573f92400 -2.6340 -48.0465 \n", + "15 e0e8b551-b0ca-37fe-a04c-bbdd2f1da255 -2.6526 -48.0244 \n", + "16 c4d983cc-07ff-3e4c-9f93-da13eed1187b -2.6525 -48.0460 \n", + "17 a469570c-8104-3a9c-9ec2-5cf6b99b1ddc -3.8717 -52.3999 \n", + "18 afcff067-c202-3a94-9c85-a961b7316731 -4.0480 -46.9475 \n", + "... ... ... ... \n", + "111025 1c0008bc-2ceb-3f1f-8042-8ede88b6305b -9.3352 -57.7349 \n", + "111026 1ab31c25-52a1-3dab-91a9-98a566240b2f -9.2307 -54.8726 \n", + "111027 fb3609ce-2594-35f9-820c-5cfb68d3bead -9.2397 -60.4063 \n", + "111028 6d0f9a44-e492-3d88-b5bd-b011f6a244b7 -9.2176 -60.9073 \n", + "111029 ff650feb-05f7-3675-86b5-a528ae0c5e1d -9.2398 -60.3871 \n", + "\n", + " data_hora_gmt satelite municipio estado pais \\\n", + "14 2024-09-01 00:05:21 GOES-16 TOMÉ-AÇU PARÁ Brasil \n", + "15 2024-09-01 00:05:21 GOES-16 IPIXUNA DO PARÁ PARÁ Brasil \n", + "16 2024-09-01 00:05:21 GOES-16 IPIXUNA DO PARÁ PARÁ Brasil \n", + "17 2024-09-01 00:05:28 GOES-16 ALTAMIRA PARÁ Brasil \n", + "18 2024-09-01 00:05:29 GOES-16 ITINGA DO MARANHÃO MARANHÃO Brasil \n", + "... ... ... ... ... ... \n", + "111025 2024-09-01 18:55:59 GOES-16 APIACÁS MATO GROSSO Brasil \n", + "111026 2024-09-01 18:55:59 GOES-16 NOVO PROGRESSO PARÁ Brasil \n", + "111027 2024-09-01 18:55:59 GOES-16 COLNIZA MATO GROSSO Brasil \n", + "111028 2024-09-01 18:55:59 GOES-16 COLNIZA MATO GROSSO Brasil \n", + "111029 2024-09-01 18:55:59 GOES-16 COLNIZA MATO GROSSO Brasil \n", + "\n", + " municipio_id estado_id pais_id numero_dias_sem_chuva precipitacao \\\n", + "14 1508001 15.0 33.0 5.0 0.0 \n", + "15 1503457 15.0 33.0 5.0 0.0 \n", + "16 1503457 15.0 33.0 5.0 0.0 \n", + "17 1500602 15.0 33.0 4.0 0.0 \n", + "18 2105427 21.0 33.0 20.0 0.0 \n", + "... ... ... ... ... ... \n", + "111025 5100805 51.0 33.0 26.0 0.0 \n", + "111026 1505031 15.0 33.0 117.0 0.0 \n", + "111027 5103254 51.0 33.0 100.0 0.0 \n", + "111028 5103254 51.0 33.0 47.0 0.0 \n", + "111029 5103254 51.0 33.0 100.0 0.0 \n", + "\n", + " risco_fogo bioma frp \n", + "14 0.89 Amazônia 70.0 \n", + "15 0.95 Amazônia 100.1 \n", + "16 0.90 Amazônia 120.7 \n", + "17 0.97 Amazônia 57.6 \n", + "18 1.00 Amazônia 71.2 \n", + "... ... ... ... \n", + "111025 1.00 Amazônia 88.6 \n", + "111026 1.00 Amazônia 115.9 \n", + "111027 1.00 Amazônia 81.5 \n", + "111028 1.00 Amazônia 119.0 \n", + "111029 1.00 Amazônia 144.2 \n", + "\n", + "[105500 rows x 16 columns]" + ], + "text/html": [ + "\n", + "
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\n" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "dataframe", + "summary": "{\n \"name\": \"dados_sp\",\n \"rows\": 8,\n \"fields\": [\n {\n \"column\": \"numero_dias_sem_chuva\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 735.4835207478386,\n \"min\": -999.0,\n \"max\": 1696.0,\n \"num_unique_values\": 6,\n \"samples\": [\n 1696.0,\n 31.440448113207548,\n 120.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"risco_fogo\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 740.1201817778053,\n \"min\": -999.0,\n \"max\": 1696.0,\n \"num_unique_values\": 6,\n \"samples\": [\n 1696.0,\n -55.052199292452826,\n 1.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"precipitacao\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 599.6212794580932,\n \"min\": 0.0,\n \"max\": 1696.0,\n \"num_unique_values\": 5,\n \"samples\": [\n 0.0001768867924528302,\n 0.1,\n 0.004203309130596187\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" + } + }, + "metadata": {}, + "execution_count": 8 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Removendo Números Negativos\n", + "\n", + "* Este trecho identifica linhas no DataFrame dados_sp com valores negativos nas colunas risco_fogo, numero_dias_sem_chuva e precipitacao. As expressões dados_sp[(dfquei['risco_fogo'] < 0)], dados_sp[(dfquei['numero_dias_sem_chuva'] < 0)] e dados_sp[(dfquei['precipitacao'] < 0)] retornam essas linhas, mas não as removem.\n" + ], + "metadata": { + "id": "__qh8tdKRljq" + } + }, + { + "cell_type": "code", + "source": [ + "# Removendo os números negativos encontrado em cada coluna\n", + "\n", + "dados_sp[(dfquei['risco_fogo'] < 0)]\n", + "dados_sp[(dfquei['numero_dias_sem_chuva'] < 0)]\n", + "dados_sp[(dfquei['precipitacao'] < 0)]" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 157 + }, + "id": "J3fdRd4sBg1o", + "outputId": "7c42d01a-aba1-41aa-ecad-b478d1d6b151" + }, + "execution_count": 9, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + ":3: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " dados_sp[(dfquei['risco_fogo'] < 0)]\n", + ":4: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " dados_sp[(dfquei['numero_dias_sem_chuva'] < 0)]\n", + ":5: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " dados_sp[(dfquei['precipitacao'] < 0)]\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Empty DataFrame\n", + "Columns: [estado, numero_dias_sem_chuva, risco_fogo, precipitacao]\n", + "Index: []" + ], + "text/html": [ + "\n", + "
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estadonumero_dias_sem_chuvarisco_fogoprecipitacao
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numero_dias_sem_chuvarisco_fogoprecipitacao
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estadonumero_dias_sem_chuvarisco_fogoprecipitacao
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estadonumero_dias_sem_chuvarisco_fogoprecipitacao
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estadonumero_dias_sem_chuvarisco_fogoprecipitacao
2529DISTRITO FEDERAL113.01.00.0
14311DISTRITO FEDERAL120.01.00.0
29115DISTRITO FEDERAL120.01.00.0
29444DISTRITO FEDERAL120.01.00.0
56416DISTRITO FEDERAL120.01.00.0
56417DISTRITO FEDERAL120.01.00.0
56422DISTRITO FEDERAL120.01.00.0
56423DISTRITO FEDERAL120.01.00.0
56424DISTRITO FEDERAL120.01.00.0
56425DISTRITO FEDERAL120.01.00.0
56426DISTRITO FEDERAL119.01.00.0
56427DISTRITO FEDERAL105.01.00.0
63039DISTRITO FEDERAL120.01.00.0
63040DISTRITO FEDERAL120.01.00.0
63041DISTRITO FEDERAL120.01.00.0
63044DISTRITO FEDERAL120.01.00.0
63045DISTRITO FEDERAL120.01.00.0
63046DISTRITO FEDERAL120.01.00.0
63047DISTRITO FEDERAL120.01.00.0
63048DISTRITO FEDERAL120.01.00.0
63049DISTRITO FEDERAL120.01.00.0
79054DISTRITO FEDERAL120.01.00.0
79055DISTRITO FEDERAL120.01.00.0
79061DISTRITO FEDERAL120.01.00.0
79062DISTRITO FEDERAL120.01.00.0
79063DISTRITO FEDERAL120.01.00.0
79064DISTRITO FEDERAL120.01.00.0
79355DISTRITO FEDERAL119.01.00.0
79356DISTRITO FEDERAL120.01.00.0
93554DISTRITO FEDERAL120.01.00.0
93556DISTRITO FEDERAL120.01.00.0
93561DISTRITO FEDERAL120.01.00.0
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numero_dias_sem_chuvarisco_fogoprecipitacao
count32.00000032.032.0
mean119.2500001.00.0
std2.8848880.00.0
min105.0000001.00.0
25%120.0000001.00.0
50%120.0000001.00.0
75%120.0000001.00.0
max120.0000001.00.0
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\"description\": \"\"\n }\n }\n ]\n}" + } + }, + "metadata": {}, + "execution_count": 16 + } + ] + }, + { + "cell_type": "code", + "source": [ + "dados_sp.to_csv('dados_sp_tratados.csv', index=False)" + ], + "metadata": { + "id": "XlxgwiH4LqKz" + }, + "execution_count": 17, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "dado_distf.to_csv('dados_df_tratados.csv', index=False)" + ], + "metadata": { + "id": "0x-Y8ZfkNX44" + }, + "execution_count": 18, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "from google.colab import files\n", + "\n", + "# Fazendo o download do CSV de SP\n", + "files.download('dados_sp_tratados.csv')\n", + "\n", + "# Fazendo o download do CSV de DF\n", + "files.download('dados_df_tratados.csv')\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 17 + }, + "id": "8XJeDkaSNfuH", + "outputId": "2e64ed6b-bf0e-4b4e-c8da-53993b509a3f" + }, + "execution_count": 19, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "application/javascript": [ + "\n", + " async function download(id, filename, size) {\n", + " if (!google.colab.kernel.accessAllowed) {\n", + " return;\n", + " }\n", + " const div = document.createElement('div');\n", + " const label = document.createElement('label');\n", + " label.textContent = `Downloading \"${filename}\": `;\n", + " div.appendChild(label);\n", + " const progress = document.createElement('progress');\n", + " progress.max = size;\n", + " div.appendChild(progress);\n", + " document.body.appendChild(div);\n", + "\n", + " const buffers = [];\n", + " let downloaded = 0;\n", + "\n", + " const channel = await google.colab.kernel.comms.open(id);\n", + " // Send a message to notify the kernel that we're ready.\n", + " channel.send({})\n", + "\n", + " for await (const message of channel.messages) {\n", + " // Send a message to notify the kernel that we're ready.\n", + " channel.send({})\n", + " if (message.buffers) {\n", + " for (const buffer of message.buffers) {\n", + " buffers.push(buffer);\n", + " downloaded += buffer.byteLength;\n", + " progress.value = downloaded;\n", + " }\n", + " }\n", + " }\n", + " const blob = new Blob(buffers, {type: 'application/binary'});\n", + " const a = document.createElement('a');\n", + " a.href = window.URL.createObjectURL(blob);\n", + " a.download = filename;\n", + " div.appendChild(a);\n", + " a.click();\n", + " div.remove();\n", + " }\n", + " " + ] + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "application/javascript": [ + "download(\"download_02891f1c-fa8e-4683-bc83-eda7e458e2aa\", \"dados_sp_tratados.csv\", 37915)" + ] + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "application/javascript": [ + "\n", + " async function download(id, filename, size) {\n", + " if (!google.colab.kernel.accessAllowed) {\n", + " return;\n", + " }\n", + " const div = document.createElement('div');\n", + " const label = document.createElement('label');\n", + " label.textContent = `Downloading \"${filename}\": `;\n", + " div.appendChild(label);\n", + " const progress = document.createElement('progress');\n", + " progress.max = size;\n", + " div.appendChild(progress);\n", + " document.body.appendChild(div);\n", + "\n", + " const buffers = [];\n", + " let downloaded = 0;\n", + "\n", + " const channel = await google.colab.kernel.comms.open(id);\n", + " // Send a message to notify the kernel that we're ready.\n", + " channel.send({})\n", + "\n", + " for await (const message of channel.messages) {\n", + " // Send a message to notify the kernel that we're ready.\n", + " channel.send({})\n", + " if (message.buffers) {\n", + " for (const buffer of message.buffers) {\n", + " buffers.push(buffer);\n", + " downloaded += buffer.byteLength;\n", + " progress.value = downloaded;\n", + " }\n", + " }\n", + " }\n", + " const blob = new Blob(buffers, {type: 'application/binary'});\n", + " const a = document.createElement('a');\n", + " a.href = window.URL.createObjectURL(blob);\n", + " a.download = filename;\n", + " div.appendChild(a);\n", + " a.click();\n", + " div.remove();\n", + " }\n", + " " + ] + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "application/javascript": [ + "download(\"download_66bc0ad1-5b17-46dd-bb92-f7f773ff87ef\", \"dados_df_tratados.csv\", 1045)" + ] + }, + "metadata": {} + } + ] + } + ] +} \ No newline at end of file diff --git a/material/Analise_Queimadas.md b/material/Analise_Queimadas.md new file mode 100644 index 0000000..c50d079 --- /dev/null +++ b/material/Analise_Queimadas.md @@ -0,0 +1,76 @@ + +# ANÁLISE DE DADOS DO MÊS DE SETEMBRO SOBRE AS QUEIMADAS NO DISTRITO FEDERAL E EM SÃO PAULO + +Reprograma Turma On34 + +Integrantes: Maria Eduarda Quaresma de Andrade e Fernanda da Silva Bazanini + +# Materiais + +Atualizamos recentemente nosso [dashboard do Tableau], que contém as análises mais recentes do projeto. Acesse o link para visualizar os dados detalhados: https://public.tableau.com/views/AnlisedeQueimadas-Projeto/ComparaodaSecuraeRiscodeFogoentreosEstados?:language=pt-BR&:sid=&:redirect=auth&:display_count=n&:origin=viz_share_link + +Disponibilizamos também os [slides de apresentação] atualizados, onde você pode conferir os principais pontos e conclusões do projeto. Veja o link para acessar: https://www.canva.com/design/DAGTOYf96Gg/Rc6ubGolKdAGacsVHv5ghA/edit?utm_content=DAGTOYf96Gg&utm_campaign=designshare&utm_medium=link2&utm_source=sharebutton + +# Contexto + +Este projeto foi desenvolvido para analisar as ocorrências de queimadas nas regiões do Distrito Federal e São Paulo. A proposta é identificar padrões e tendências relacionadas ao risco de fogo, número de dias sem chuva e precipitação a partir de dados coletados, visando contribuir para a formulação de políticas públicas que ajudem a mitigar as queimadas. + +# Sobre Queimadas no Brasil + +As queimadas são um fenômeno recorrente no Brasil, especialmente em períodos de seca. Elas têm um impacto significativo no meio ambiente, na saúde pública e na economia. Analisar as condições que favorecem essas queimadas é crucial para o desenvolvimento de estratégias de prevenção e resposta a desastres. + +# Objetivo + +Realizar uma análise estatística dos dados de queimadas nas regiões de São Paulo e Distrito Federal, visando responder às seguintes perguntas estratégicas: + +1. Quais são os padrões históricos de queimadas nas regiões analisadas? + +* Justificativa: Compreender os padrões pode auxiliar na previsão de queimadas futuras e na implementação de ações preventivas. + +2. Qual é a relação entre dias sem chuva e o aumento do risco de fogo? + +* Justificativa: Identificar como a falta de chuva influencia o risco de queimadas pode ajudar na formulação de alertas e campanhas de conscientização. + +3. Como a precipitação afeta as ocorrências de queimadas? + +* Justificativa: Entender a relação entre precipitação e queimadas é essencial para a gestão de recursos naturais e a proteção de áreas suscetíveis a incêndios. + +# Metodologia # + +* Banco de Dados + +Os dados utilizados foram extraídos de um conjunto de dados contendo informações sobre queimadas no Brasil. As seguintes etapas foram realizadas: + +1. Importação de Bibliotecas: + +* Utilização de bibliotecas como pandas e numpy para manipulação de dados. + +2. Leitura e Visualização: + +* Carregamento dos dados em formato CSV e visualização inicial. + +3. Tratamento de Dados: + +* Verificação de valores ausentes e remoção de linhas com dados inválidos, como números negativos. +* Filtragem dos dados por estado (São Paulo e Distrito Federal) e limpeza dos conjuntos de dados. + +4. Análise Estatística: + +* Geração de estatísticas descritivas para compreender a distribuição dos dados. +* Criação de visualizações gráficas para ilustrar os padrões identificados. + +# Entregáveis Esperados + +* Relatório contendo insights e visualizações que ajudem a responder às perguntas acima. + +* Gráficos que demonstrem os padrões de queimadas em relação às variáveis analisadas. + +* Conclusões baseadas nas análises, incluindo recomendações para a mitigação de queimadas. + +# Ferramentas Utilizadas + + Python (Pandas, Matplotlib, Seaborn) + Jupyter Notebook + Google Sheets para compartilhamento e documentação do progresso. + + diff --git a/material/datasets/dados_df_tratados.csv b/material/datasets/dados_df_tratados.csv new file mode 100644 index 0000000..f06ff8f --- /dev/null +++ b/material/datasets/dados_df_tratados.csv @@ -0,0 +1,33 @@ +estado,numero_dias_sem_chuva,risco_fogo,precipitacao +DISTRITO FEDERAL,113.0,1.0,0.0 +DISTRITO FEDERAL,120.0,1.0,0.0 +DISTRITO FEDERAL,120.0,1.0,0.0 +DISTRITO FEDERAL,120.0,1.0,0.0 +DISTRITO FEDERAL,120.0,1.0,0.0 +DISTRITO FEDERAL,120.0,1.0,0.0 +DISTRITO FEDERAL,120.0,1.0,0.0 +DISTRITO FEDERAL,120.0,1.0,0.0 +DISTRITO FEDERAL,120.0,1.0,0.0 +DISTRITO FEDERAL,120.0,1.0,0.0 +DISTRITO FEDERAL,119.0,1.0,0.0 +DISTRITO FEDERAL,105.0,1.0,0.0 +DISTRITO FEDERAL,120.0,1.0,0.0 +DISTRITO FEDERAL,120.0,1.0,0.0 +DISTRITO FEDERAL,120.0,1.0,0.0 +DISTRITO FEDERAL,120.0,1.0,0.0 +DISTRITO FEDERAL,120.0,1.0,0.0 +DISTRITO FEDERAL,120.0,1.0,0.0 +DISTRITO FEDERAL,120.0,1.0,0.0 +DISTRITO FEDERAL,120.0,1.0,0.0 +DISTRITO FEDERAL,120.0,1.0,0.0 +DISTRITO FEDERAL,120.0,1.0,0.0 +DISTRITO FEDERAL,120.0,1.0,0.0 +DISTRITO FEDERAL,120.0,1.0,0.0 +DISTRITO FEDERAL,120.0,1.0,0.0 +DISTRITO FEDERAL,120.0,1.0,0.0 +DISTRITO FEDERAL,120.0,1.0,0.0 +DISTRITO FEDERAL,119.0,1.0,0.0 +DISTRITO FEDERAL,120.0,1.0,0.0 +DISTRITO FEDERAL,120.0,1.0,0.0 +DISTRITO FEDERAL,120.0,1.0,0.0 +DISTRITO FEDERAL,120.0,1.0,0.0 diff --git a/material/datasets/dados_sp_tratados.csv b/material/datasets/dados_sp_tratados.csv new file mode 100644 index 0000000..743d8f9 --- /dev/null +++ b/material/datasets/dados_sp_tratados.csv @@ -0,0 +1,1601 @@ +estado,numero_dias_sem_chuva,risco_fogo,precipitacao +SÃO PAULO,7.0,1.0,0.0 +SÃO PAULO,7.0,1.0,0.0 +SÃO PAULO,7.0,1.0,0.0 +SÃO PAULO,7.0,0.84,0.0 +SÃO PAULO,7.0,0.84,0.0 +SÃO PAULO,7.0,0.96,0.0 +SÃO PAULO,7.0,0.93,0.0 +SÃO PAULO,7.0,1.0,0.0 +SÃO PAULO,7.0,1.0,0.0 +SÃO PAULO,7.0,1.0,0.0 +SÃO PAULO,7.0,1.0,0.0 +SÃO PAULO,7.0,0.84,0.0 +SÃO PAULO,7.0,0.99,0.0 +SÃO PAULO,7.0,0.93,0.0 +SÃO PAULO,7.0,1.0,0.0 +SÃO PAULO,7.0,0.96,0.0 +SÃO PAULO,7.0,1.0,0.0 +SÃO PAULO,7.0,1.0,0.0 +SÃO PAULO,7.0,1.0,0.0 +SÃO PAULO,7.0,1.0,0.0 +SÃO PAULO,7.0,1.0,0.0 +SÃO PAULO,7.0,1.0,0.0 +SÃO PAULO,111.0,1.0,0.0 +SÃO PAULO,115.0,1.0,0.0 +SÃO PAULO,117.0,1.0,0.0 +SÃO PAULO,105.0,1.0,0.0 +SÃO PAULO,109.0,1.0,0.0 +SÃO PAULO,7.0,1.0,0.0 +SÃO PAULO,7.0,1.0,0.0 +SÃO PAULO,7.0,1.0,0.0 +SÃO PAULO,120.0,1.0,0.0 +SÃO 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O objetivo desse projeto é xxxxxxxxx. -Para desenvolver esse projeto, desenvolvemos uma análise exploratória de dados xxxxxxx e utilizamos o Tableau para gerar a visualização das nossas análises. - -### Objetivos gerais e específicos do projeto - -### Bases escolhidas - -- Base 1 (fonte) -- Base 2 (fonte) - -## Ferramentas utilizadas \ No newline at end of file