00_Accidentes_Madrid.ipynb

{
 "cells": [
  {
   "attachments": {},
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   "id": "98cf3baa",
   "metadata": {},
   "source": [
    "# Introducción"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2ae8f180",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/javascript": "IPython.OutputArea.prototype._should_scroll = function(lines) {\n    return false;\n}\n",
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%javascript\n",
    "IPython.OutputArea.prototype._should_scroll = function(lines) {\n",
    "    return false;\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "17d02656",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "import pandas as pd\n",
    "warnings.filterwarnings(\"ignore\", category=pd.core.common.SettingWithCopyWarning)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "bb273a4e",
   "metadata": {},
   "source": [
    "<div style=\"position: relative;\">\n",
    "    <img src=\"https://www.arcgis.com/sharing/rest/content/items/10bc2a493a974d5caf024ff13d7891b3/resources/images/widget_178/1622195050113.jpg\" width=\"1100\"/>\n",
    "    <p style=\"color:white; font-size:36px; font-weight:bold; position:absolute; top:50px; left:120px;\">Análisis de Siniestralidad |</p>\n",
    "    <p style=\"color:white; font-size:20px; font-weight:bold; position:absolute; top:70px; left:580px;\">Madrid</p>\n",
    "    <p style=\"color:white; font-size:24px; position:absolute; top:100px; left:120px;\">GIS & Spatial Data Science</p>    \n",
    "</div>\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "cde87ae6",
   "metadata": {},
   "source": [
    "<div style=\"position: relative;\">\n",
    "    <img src=\"https://media.licdn.com/dms/image/C5612AQHzwP87iXPCBA/article-inline_image-shrink_1500_2232/0/1595492940439?e=1692230400&v=beta&t=yaN2L0M58wNSNpAbyePhy0ok9ZPqHdzZVVuY8EY4Yr0\" width=\"1100\"/>\n",
    "    <p style=\"color:black; font-size:36px; font-weight:bold; position:absolute; top:50px; left:120px;\">Ciencia de datos ≈ Cocinar</p> \n",
    "</div>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "698f46a4",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "<div style=\"text-align:center\">\n",
    "    <span style=\"font-size: 30px;\"><strong>Análisis de Siniestralidad | Madrid</strong></span>\n",
    "    <br>\n",
    "    <span style=\"font-size: 20px;\">GIS & Spatial Data Science</span>\n",
    "</div>\n",
    " <br>\n",
    " <br>\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "406827a7",
   "metadata": {},
   "source": [
    "\n",
    "<div style=\"text-align:center\">\n",
    "    <img src=\"https://d35fo82fjcw0y8.cloudfront.net/2016/07/03210504/infographic-chef-vs-analyst-01-01-3.jpg\"/>\n",
    "</div>\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "3fc85c01",
   "metadata": {},
   "source": [
    "<div style=\"text-align:left\">\n",
    "    <span style=\"font-size: 20px;\"><strong>Guía de estudio</strong></span>\n",
    "</div>\n",
    "<ol>\n",
    "    <li>Procesamiento de datos (Ingesta, Limpieza, eliminar nulos, formatos correctos, duplicados,...)</li>\n",
    "    <li>Exploración y visualización del dato (Análisis descriptivo: Graficos y mapas)</li>\n",
    "    <li>Análisis Forense</li>\n",
    "    <li>Análisis predictivo (modelo)</li>\n",
    "    <li>Conclusión y comunicar los datos</li>\n",
    "</ol>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "c19128bb",
   "metadata": {},
   "source": [
    "# Procesamiento de datos"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "820739c8",
   "metadata": {
    "hide_input": true
   },
   "source": [
    "## Ingesta de datos\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "7c511e3e",
   "metadata": {},
   "source": [
    "<div style=\"text-align:center\">\n",
    "    <img src=\"https://datos.gob.es/sites/default/files/styles/aporta_teaser/public/madrid.jpg?itok=OLlU2-mS\" alt=\"drawing\" width=\"200\"/>\n",
    "</div>\n",
    "\n",
    "<div style=\"text-align:center\">\n",
    "    <a href=\"https://datos.madrid.es/sites/v/index.jsp?vgnextoid=7c2843010d9c3610VgnVCM2000001f4a900aRCRD&vgnextchannel=374512b9ace9f310VgnVCM100000171f5a0aRCRD\">VER PORTAL</a>\n",
    "</div>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "e9eb8f16",
   "metadata": {},
   "source": [
    "Links de descarga de los CSV:\n",
    "\n",
    "- https://datos.madrid.es/egob/catalogo/300228-26-accidentes-trafico-detalle.csv\n",
    "\n",
    "- https://datos.madrid.es/egob/catalogo/300228-24-accidentes-trafico-detalle.csv\n",
    "\n",
    "- https://datos.madrid.es/egob/catalogo/300228-22-accidentes-trafico-detalle.csv\n",
    "\n",
    "- https://datos.madrid.es/egob/catalogo/300228-21-accidentes-trafico-detalle.csv\n",
    "\n",
    "- https://datos.madrid.es/egob/catalogo/300228-19-accidentes-trafico-detalle.csv\n",
    "\n",
    "- https://datos.madrid.es/egob/catalogo/300228-28-accidentes-trafico-detalle.csv\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "eab61eb3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(51811, 19)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cargamos unos datos de muestra \n",
    "import pandas as pd\n",
    "\n",
    "csv_2019 = 'C:\\\\00_Accidentes_Madrid\\\\datos\\\\2019_Accidentalidad.csv'\n",
    "df_2019 = pd.read_csv(csv_2019, delimiter=\";\")\n",
    "df_2019.head(3)\n",
    "df_2019.shape"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "b6792df3",
   "metadata": {},
   "source": [
    "Impotamos todo los CSV de desde el año 2019 hasta el 2023 \n",
    "Comprobamos la dimensiones de cada uno "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "bf577439",
   "metadata": {
    "hide_input": false
   },
   "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>Año</th>\n",
       "      <th>Filas</th>\n",
       "      <th>Columnas</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019</td>\n",
       "      <td>51811</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020</td>\n",
       "      <td>32433</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021</td>\n",
       "      <td>41783</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022</td>\n",
       "      <td>47053</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2023</td>\n",
       "      <td>16217</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Año  Filas  Columnas\n",
       "0  2019  51811        19\n",
       "1  2020  32433        19\n",
       "2  2021  41783        19\n",
       "3  2022  47053        21\n",
       "4  2023  16217        21"
      ]
     },
     "execution_count": 11,
     "metadata": {},
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   ],
   "source": [
    "import os\n",
    "\n",
    "# Obtener la ruta absoluta del directorio actual\n",
    "dir_actual = os.getcwd()\n",
    "\n",
    "# Ruta ruta relativa al archivo en la carpeta \"datos\"\n",
    "mis_datos = \"datos/\"\n",
    "\n",
    "# Lista de años\n",
    "años = [2019, 2020, 2021, 2022, 2023]\n",
    "\n",
    "# Lista para almacenar las dimensiones\n",
    "dimensiones = []\n",
    "\n",
    "# Obtener la ruta absoluta del directorio actual\n",
    "current_dir = os.getcwd()\n",
    "\n",
    "# Iterar sobre los años\n",
    "for año in años:\n",
    "    # Construir la ruta del archivo CSV\n",
    "    ruta_csv = os.path.join(current_dir, mis_datos, f\"{año}_Accidentalidad.csv\")\n",
    "    \n",
    "    # Leer el archivo CSV para el año actual\n",
    "    df = pd.read_csv(ruta_csv, delimiter=\";\")\n",
    "    \n",
    "    # Obtener las dimensiones del DataFrame\n",
    "    filas, columnas = df.shape\n",
    "    \n",
    "    # Agregar las dimensiones a la lista\n",
    "    dimensiones.append({'Año': año, 'Filas': filas, 'Columnas': columnas})\n",
    "\n",
    "# Crear el DataFrame dimensiones_df usando pd.DataFrame()\n",
    "dimensiones_df = pd.DataFrame(dimensiones)\n",
    "\n",
    "# Imprimir el DataFrame con las dimensiones\n",
    "dimensiones_df\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "b8b30631",
   "metadata": {},
   "source": [
    "Vemos que no todos los dataframe tienen el mismo tamaño. \n",
    "Comprobanos el nombre de las columnas antes de unir las tablas."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "23c33ddb",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Limpieza de datos (Preparación, transformación)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "e332c937",
   "metadata": {
    "hidden": true
   },
   "source": [
    "<div style=\"text-align:center\">\n",
    "    <img src=\"./assets/Clean_data.png\" alt=\"drawing\" width=\"200\"/>\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "54433dcc",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>2019</th>\n",
       "      <th>2020</th>\n",
       "      <th>2021</th>\n",
       "      <th>2022</th>\n",
       "      <th>2023</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>num_expediente</td>\n",
       "      <td>num_expediente</td>\n",
       "      <td>num_expediente</td>\n",
       "      <td>num_expediente</td>\n",
       "      <td>num_expediente</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>fecha</td>\n",
       "      <td>fecha</td>\n",
       "      <td>fecha</td>\n",
       "      <td>fecha</td>\n",
       "      <td>fecha</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>hora</td>\n",
       "      <td>hora</td>\n",
       "      <td>hora</td>\n",
       "      <td>hora</td>\n",
       "      <td>hora</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>localizacion</td>\n",
       "      <td>localizacion</td>\n",
       "      <td>localizacion</td>\n",
       "      <td>localizacion</td>\n",
       "      <td>localizacion</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>numero</td>\n",
       "      <td>numero</td>\n",
       "      <td>numero</td>\n",
       "      <td>numero</td>\n",
       "      <td>numero</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>cod_distrito</td>\n",
       "      <td>cod_distrito</td>\n",
       "      <td>cod_distrito</td>\n",
       "      <td>cod_distrito</td>\n",
       "      <td>cod_distrito</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>distrito</td>\n",
       "      <td>distrito</td>\n",
       "      <td>distrito</td>\n",
       "      <td>distrito</td>\n",
       "      <td>distrito</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>tipo_accidente</td>\n",
       "      <td>tipo_accidente</td>\n",
       "      <td>tipo_accidente</td>\n",
       "      <td>tipo_accidente</td>\n",
       "      <td>tipo_accidente</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>estado_meteorológico</td>\n",
       "      <td>estado_meteorológico</td>\n",
       "      <td>estado_meteorológico</td>\n",
       "      <td>estado_meteorológico</td>\n",
       "      <td>estado_meteorológico</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>tipo_vehiculo</td>\n",
       "      <td>tipo_vehiculo</td>\n",
       "      <td>tipo_vehiculo</td>\n",
       "      <td>tipo_vehiculo</td>\n",
       "      <td>tipo_vehiculo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>tipo_persona</td>\n",
       "      <td>tipo_persona</td>\n",
       "      <td>tipo_persona</td>\n",
       "      <td>tipo_persona</td>\n",
       "      <td>tipo_persona</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>rango_edad</td>\n",
       "      <td>rango_edad</td>\n",
       "      <td>rango_edad</td>\n",
       "      <td>rango_edad</td>\n",
       "      <td>rango_edad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>sexo</td>\n",
       "      <td>sexo</td>\n",
       "      <td>sexo</td>\n",
       "      <td>sexo</td>\n",
       "      <td>sexo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>cod_lesividad</td>\n",
       "      <td>cod_lesividad</td>\n",
       "      <td>cod_lesividad</td>\n",
       "      <td>cod_lesividad</td>\n",
       "      <td>cod_lesividad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>lesividad</td>\n",
       "      <td>lesividad</td>\n",
       "      <td>lesividad</td>\n",
       "      <td>lesividad</td>\n",
       "      <td>lesividad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>coordenada_x_utm</td>\n",
       "      <td>coordenada_x_utm</td>\n",
       "      <td>coordenada_x_utm</td>\n",
       "      <td>coordenada_x_utm</td>\n",
       "      <td>coordenada_x_utm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>coordenada_y_utm</td>\n",
       "      <td>coordenada_y_utm</td>\n",
       "      <td>coordenada_y_utm</td>\n",
       "      <td>coordenada_y_utm</td>\n",
       "      <td>coordenada_y_utm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>positiva_alcohol</td>\n",
       "      <td>positiva_alcohol</td>\n",
       "      <td>positiva_alcohol</td>\n",
       "      <td>positiva_alcohol</td>\n",
       "      <td>positiva_alcohol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>positiva_droga</td>\n",
       "      <td>positiva_droga</td>\n",
       "      <td>positiva_droga</td>\n",
       "      <td>positiva_droga</td>\n",
       "      <td>positiva_droga</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Unnamed: 19</td>\n",
       "      <td>Unnamed: 19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Unnamed: 20</td>\n",
       "      <td>Unnamed: 20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    2019  ...                  2023\n",
       "0         num_expediente  ...        num_expediente\n",
       "1                  fecha  ...                 fecha\n",
       "2                   hora  ...                  hora\n",
       "3           localizacion  ...          localizacion\n",
       "4                 numero  ...                numero\n",
       "5           cod_distrito  ...          cod_distrito\n",
       "6               distrito  ...              distrito\n",
       "7         tipo_accidente  ...        tipo_accidente\n",
       "8   estado_meteorológico  ...  estado_meteorológico\n",
       "9          tipo_vehiculo  ...         tipo_vehiculo\n",
       "10          tipo_persona  ...          tipo_persona\n",
       "11            rango_edad  ...            rango_edad\n",
       "12                  sexo  ...                  sexo\n",
       "13         cod_lesividad  ...         cod_lesividad\n",
       "14             lesividad  ...             lesividad\n",
       "15      coordenada_x_utm  ...      coordenada_x_utm\n",
       "16      coordenada_y_utm  ...      coordenada_y_utm\n",
       "17      positiva_alcohol  ...      positiva_alcohol\n",
       "18        positiva_droga  ...        positiva_droga\n",
       "19                   NaN  ...           Unnamed: 19\n",
       "20                   NaN  ...           Unnamed: 20\n",
       "\n",
       "[21 rows x 5 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualizar todas las variables\n",
    "import numpy as np\n",
    "\n",
    "# Lista de años\n",
    "años = [2019, 2020, 2021, 2022, 2023]\n",
    "\n",
    "# Crear un diccionario para almacenar los nombres de variables de cada año\n",
    "nombres_variables = {}\n",
    "\n",
    "# Obtener la ruta absoluta del directorio actual\n",
    "current_dir = os.getcwd()\n",
    "\n",
    "# Iterar sobre los años\n",
    "for año in años:\n",
    "    # Construir la ruta del archivo CSV\n",
    "    ruta_csv = os.path.join(current_dir, mis_datos, f\"{año}_Accidentalidad.csv\")\n",
    "    \n",
    "    # Leer el archivo CSV para el año actual\n",
    "    df = pd.read_csv(ruta_csv, delimiter=\";\")\n",
    "    \n",
    "    # Obtener los nombres de las variables\n",
    "    variables = df.columns.tolist()\n",
    "    \n",
    "    # Agregar los nombres de variables al diccionario\n",
    "    nombres_variables[año] = variables\n",
    "\n",
    "# Obtener la longitud máxima de nombres de variables\n",
    "longitud_maxima = max(len(nombres) for nombres in nombres_variables.values())\n",
    "\n",
    "# Rellenar las listas de nombres de variables con valores nulos para igualar la longitud\n",
    "for año, nombres in nombres_variables.items():\n",
    "    diferencia = longitud_maxima - len(nombres)\n",
    "    nombres += [np.nan] * diferencia\n",
    "\n",
    "# Crear el DataFrame nombres_variables_df usando pd.DataFrame()\n",
    "nombres_variables_df = pd.DataFrame(nombres_variables)\n",
    "\n",
    "# Imprimir el DataFrame con los nombres de las variables\n",
    "nombres_variables_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e0924e19",
   "metadata": {
    "hidden": true,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Filas: 189297\n",
      "Variables: 19\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "    }\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>num_expediente</th>\n",
       "      <th>fecha</th>\n",
       "      <th>hora</th>\n",
       "      <th>localizacion</th>\n",
       "      <th>numero</th>\n",
       "      <th>cod_distrito</th>\n",
       "      <th>distrito</th>\n",
       "      <th>tipo_accidente</th>\n",
       "      <th>estado_meteorológico</th>\n",
       "      <th>tipo_vehiculo</th>\n",
       "      <th>tipo_persona</th>\n",
       "      <th>rango_edad</th>\n",
       "      <th>sexo</th>\n",
       "      <th>cod_lesividad</th>\n",
       "      <th>lesividad</th>\n",
       "      <th>coordenada_x_utm</th>\n",
       "      <th>coordenada_y_utm</th>\n",
       "      <th>positiva_alcohol</th>\n",
       "      <th>positiva_droga</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018S017842</td>\n",
       "      <td>04/02/2019</td>\n",
       "      <td>9:10:00</td>\n",
       "      <td>CALL. ALBERTO AGUILERA, 1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>CENTRO</td>\n",
       "      <td>Colisión lateral</td>\n",
       "      <td>Despejado</td>\n",
       "      <td>Motocicleta &gt; 125cc</td>\n",
       "      <td>Conductor</td>\n",
       "      <td>De 45 a 49 años</td>\n",
       "      <td>Hombre</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Asistencia sanitaria sólo en el lugar del acci...</td>\n",
       "      <td>440068,049</td>\n",
       "      <td>4475679,17</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018S017842</td>\n",
       "      <td>04/02/2019</td>\n",
       "      <td>9:10:00</td>\n",
       "      <td>CALL. ALBERTO AGUILERA, 1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>CENTRO</td>\n",
       "      <td>Colisión lateral</td>\n",
       "      <td>Despejado</td>\n",
       "      <td>Turismo</td>\n",
       "      <td>Conductor</td>\n",
       "      <td>De 30 a 34 años</td>\n",
       "      <td>Mujer</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Asistencia sanitaria sólo en el lugar del acci...</td>\n",
       "      <td>440068,049</td>\n",
       "      <td>4475679,17</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019S000001</td>\n",
       "      <td>01/01/2019</td>\n",
       "      <td>3:45:00</td>\n",
       "      <td>PASEO. SANTA MARIA DE LA CABEZA / PLAZA. ELIPTICA</td>\n",
       "      <td>168</td>\n",
       "      <td>11.0</td>\n",
       "      <td>CARABANCHEL</td>\n",
       "      <td>Alcance</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Furgoneta</td>\n",
       "      <td>Conductor</td>\n",
       "      <td>De 40 a 44 años</td>\n",
       "      <td>Hombre</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>439139,603</td>\n",
       "      <td>4470836,854</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  num_expediente       fecha  ... positiva_alcohol positiva_droga\n",
       "0    2018S017842  04/02/2019  ...                N            NaN\n",
       "1    2018S017842  04/02/2019  ...                N            NaN\n",
       "2    2019S000001  01/01/2019  ...                S            NaN\n",
       "\n",
       "[3 rows x 19 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lista de años\n",
    "años = [2019, 2020, 2021, 2022, 2023]\n",
    "\n",
    "# DataFrame para almacenar los datos seleccionados\n",
    "df_total = pd.DataFrame()\n",
    "\n",
    "# Obtener la ruta absoluta del directorio actual\n",
    "current_dir = os.getcwd()\n",
    "\n",
    "# Iterar sobre los años\n",
    "for año in años:\n",
    "    # Construir la ruta del archivo CSV\n",
    "    ruta_csv = os.path.join(current_dir, mis_datos, f\"{año}_Accidentalidad.csv\")\n",
    "    \n",
    "    # Leer el archivo CSV para el año actual\n",
    "    df = pd.read_csv(ruta_csv, delimiter=\";\")\n",
    "    \n",
    "    # Seleccionar las variables de índice 0 a 18\n",
    "    df_seleccionado_año = df.iloc[:, 0:19]\n",
    "    \n",
    "    # Agregar los datos seleccionados al DataFrame total\n",
    "    df_total = pd.concat([df_total, df_seleccionado_año], ignore_index=True)\n",
    "\n",
    "# Imprimir los primeros registros del DataFrame seleccionado\n",
    "print('Filas:', df_total.shape[0])\n",
    "print('Variables:', df_total.shape[1])\n",
    "df_total.head(3)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "beec5ce6",
   "metadata": {
    "hidden": true
   },
   "source": [
    "### Info variables Numéricas y categóricas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d472ba59",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>cod_distrito</th>\n",
       "      <th>cod_lesividad</th>\n",
       "      <th>positiva_droga</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>189291.000000</td>\n",
       "      <td>103840.000000</td>\n",
       "      <td>542.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>9.895151</td>\n",
       "      <td>9.985622</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5.639678</td>\n",
       "      <td>4.733800</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>14.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>21.000000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        cod_distrito  cod_lesividad  positiva_droga\n",
       "count  189291.000000  103840.000000           542.0\n",
       "mean        9.895151       9.985622             1.0\n",
       "std         5.639678       4.733800             0.0\n",
       "min         1.000000       1.000000             1.0\n",
       "25%         5.000000       7.000000             1.0\n",
       "50%        10.000000      14.000000             1.0\n",
       "75%        14.000000      14.000000             1.0\n",
       "max        21.000000      77.000000             1.0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# como se distribuyen las variables numéricas\n",
    "df_total.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "df1bcd74",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>num_expediente</th>\n",
       "      <th>fecha</th>\n",
       "      <th>hora</th>\n",
       "      <th>localizacion</th>\n",
       "      <th>numero</th>\n",
       "      <th>distrito</th>\n",
       "      <th>tipo_accidente</th>\n",
       "      <th>estado_meteorológico</th>\n",
       "      <th>tipo_vehiculo</th>\n",
       "      <th>tipo_persona</th>\n",
       "      <th>rango_edad</th>\n",
       "      <th>sexo</th>\n",
       "      <th>lesividad</th>\n",
       "      <th>coordenada_x_utm</th>\n",
       "      <th>coordenada_y_utm</th>\n",
       "      <th>positiva_alcohol</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>189297</td>\n",
       "      <td>189297</td>\n",
       "      <td>189297</td>\n",
       "      <td>189297</td>\n",
       "      <td>189291</td>\n",
       "      <td>189291</td>\n",
       "      <td>189293</td>\n",
       "      <td>169395</td>\n",
       "      <td>188600</td>\n",
       "      <td>189294</td>\n",
       "      <td>189297</td>\n",
       "      <td>189297</td>\n",
       "      <td>103840</td>\n",
       "      <td>189290</td>\n",
       "      <td>189290</td>\n",
       "      <td>188627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>81132</td>\n",
       "      <td>1581</td>\n",
       "      <td>1411</td>\n",
       "      <td>44935</td>\n",
       "      <td>1661</td>\n",
       "      <td>21</td>\n",
       "      <td>13</td>\n",
       "      <td>7</td>\n",
       "      <td>40</td>\n",
       "      <td>3</td>\n",
       "      <td>18</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>71342</td>\n",
       "      <td>76737</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>2022S002867</td>\n",
       "      <td>05/04/2019</td>\n",
       "      <td>18:00:00</td>\n",
       "      <td>AUTOV. M-30, +01100I</td>\n",
       "      <td>1</td>\n",
       "      <td>PUENTE DE VALLECAS</td>\n",
       "      <td>Colisión fronto-lateral</td>\n",
       "      <td>Despejado</td>\n",
       "      <td>Turismo</td>\n",
       "      <td>Conductor</td>\n",
       "      <td>Desconocido</td>\n",
       "      <td>Hombre</td>\n",
       "      <td>Sin asistencia sanitaria</td>\n",
       "      <td>438909,004</td>\n",
       "      <td>4476421,943</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>36</td>\n",
       "      <td>272</td>\n",
       "      <td>1691</td>\n",
       "      <td>378</td>\n",
       "      <td>18214</td>\n",
       "      <td>14955</td>\n",
       "      <td>47282</td>\n",
       "      <td>143488</td>\n",
       "      <td>131256</td>\n",
       "      <td>153155</td>\n",
       "      <td>20842</td>\n",
       "      <td>114516</td>\n",
       "      <td>56799</td>\n",
       "      <td>36</td>\n",
       "      <td>36</td>\n",
       "      <td>183127</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       num_expediente       fecha  ... coordenada_y_utm positiva_alcohol\n",
       "count          189297      189297  ...           189290           188627\n",
       "unique          81132        1581  ...            76737                2\n",
       "top       2022S002867  05/04/2019  ...      4476421,943                N\n",
       "freq               36         272  ...               36           183127\n",
       "\n",
       "[4 rows x 16 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# como se comportan las variables categóricas\n",
    "df_total.describe(include=['O'])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "bde27923",
   "metadata": {
    "hidden": true
   },
   "source": [
    "### Valores nulos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "269f10b3",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "num_expediente               0\n",
       "fecha                        0\n",
       "hora                         0\n",
       "localizacion                 0\n",
       "numero                       6\n",
       "cod_distrito                 6\n",
       "distrito                     6\n",
       "tipo_accidente               4\n",
       "estado_meteorológico     19902\n",
       "tipo_vehiculo              697\n",
       "tipo_persona                 3\n",
       "rango_edad                   0\n",
       "sexo                         0\n",
       "cod_lesividad            85457\n",
       "lesividad                85457\n",
       "coordenada_x_utm             7\n",
       "coordenada_y_utm             7\n",
       "positiva_alcohol           670\n",
       "positiva_droga          188755\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Verificar valores nulos\n",
    "df_total.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "5a38672f",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# Eliminamos nulos de las coordenadas\n",
    "df_total.dropna(subset=['coordenada_x_utm', 'coordenada_y_utm'], inplace=True)\n",
    "\n",
    "# Rellenamos las celdas vacillas con 0\n",
    "df_total.fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "49b03f40",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "num_expediente          0\n",
       "fecha                   0\n",
       "hora                    0\n",
       "localizacion            0\n",
       "numero                  0\n",
       "cod_distrito            0\n",
       "distrito                0\n",
       "tipo_accidente          0\n",
       "estado_meteorológico    0\n",
       "tipo_vehiculo           0\n",
       "tipo_persona            0\n",
       "rango_edad              0\n",
       "sexo                    0\n",
       "cod_lesividad           0\n",
       "lesividad               0\n",
       "coordenada_x_utm        0\n",
       "coordenada_y_utm        0\n",
       "positiva_alcohol        0\n",
       "positiva_droga          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Verificar valores nulos\n",
    "df_total.isnull().sum()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "73782817",
   "metadata": {
    "hidden": true
   },
   "source": [
    "### Formato de la variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9809dd82",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 189290 entries, 0 to 189296\n",
      "Data columns (total 19 columns):\n",
      " #   Column                Non-Null Count   Dtype  \n",
      "---  ------                --------------   -----  \n",
      " 0   num_expediente        189290 non-null  object \n",
      " 1   fecha                 189290 non-null  object \n",
      " 2   hora                  189290 non-null  object \n",
      " 3   localizacion          189290 non-null  object \n",
      " 4   numero                189290 non-null  object \n",
      " 5   cod_distrito          189290 non-null  float64\n",
      " 6   distrito              189290 non-null  object \n",
      " 7   tipo_accidente        189290 non-null  object \n",
      " 8   estado_meteorológico  189290 non-null  object \n",
      " 9   tipo_vehiculo         189290 non-null  object \n",
      " 10  tipo_persona          189290 non-null  object \n",
      " 11  rango_edad            189290 non-null  object \n",
      " 12  sexo                  189290 non-null  object \n",
      " 13  cod_lesividad         189290 non-null  float64\n",
      " 14  lesividad             189290 non-null  object \n",
      " 15  coordenada_x_utm      189290 non-null  object \n",
      " 16  coordenada_y_utm      189290 non-null  object \n",
      " 17  positiva_alcohol      189290 non-null  object \n",
      " 18  positiva_droga        189290 non-null  float64\n",
      "dtypes: float64(3), object(16)\n",
      "memory usage: 28.9+ MB\n"
     ]
    }
   ],
   "source": [
    "df_total.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "5981765d",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# Cambiar formato de la variable \"fecha\" a fecha\n",
    "df_total['fecha'] = pd.to_datetime(df_total['fecha'], format='%d/%m/%Y')\n",
    "#df_total['hora'] = pd.to_datetime(df_total['hora'], format='%H:%M:%S', errors='coerce').dt.time\n",
    "df_total['hora'] = pd.to_datetime(df_total['hora'])\n",
    "\n",
    "# Cambiar formato de las variables \"coordenada_x_utm\" y \"coordenada_y_utm\" a float\n",
    "df_total['coordenada_x_utm'] = pd.to_numeric(df_total['coordenada_x_utm'].str.replace(',', '.'), errors='coerce')\n",
    "df_total['coordenada_y_utm'] = pd.to_numeric(df_total['coordenada_y_utm'].str.replace(',', '.'), errors='coerce')\n",
    "df_total['coordenada_x_utm'] = df_total['coordenada_x_utm'].astype(float)\n",
    "df_total['coordenada_y_utm'] = df_total['coordenada_y_utm'].astype(float)\n",
    "\n",
    "# Cambiar formato de la variable \"cod_lesividad\" a nuemeric\n",
    "df_total['cod_lesividad'] = pd.to_numeric(df_total['cod_lesividad'], errors='coerce')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c4ee6f83",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 189290 entries, 0 to 189296\n",
      "Data columns (total 19 columns):\n",
      " #   Column                Non-Null Count   Dtype         \n",
      "---  ------                --------------   -----         \n",
      " 0   num_expediente        189290 non-null  object        \n",
      " 1   fecha                 189290 non-null  datetime64[ns]\n",
      " 2   hora                  189290 non-null  datetime64[ns]\n",
      " 3   localizacion          189290 non-null  object        \n",
      " 4   numero                189290 non-null  object        \n",
      " 5   cod_distrito          189290 non-null  float64       \n",
      " 6   distrito              189290 non-null  object        \n",
      " 7   tipo_accidente        189290 non-null  object        \n",
      " 8   estado_meteorológico  189290 non-null  object        \n",
      " 9   tipo_vehiculo         189290 non-null  object        \n",
      " 10  tipo_persona          189290 non-null  object        \n",
      " 11  rango_edad            189290 non-null  object        \n",
      " 12  sexo                  189290 non-null  object        \n",
      " 13  cod_lesividad         189290 non-null  float64       \n",
      " 14  lesividad             189290 non-null  object        \n",
      " 15  coordenada_x_utm      189260 non-null  float64       \n",
      " 16  coordenada_y_utm      189260 non-null  float64       \n",
      " 17  positiva_alcohol      189290 non-null  object        \n",
      " 18  positiva_droga        189290 non-null  float64       \n",
      "dtypes: datetime64[ns](2), float64(5), object(12)\n",
      "memory usage: 28.9+ MB\n"
     ]
    }
   ],
   "source": [
    "ruta_csv = os.path.join(current_dir, mis_datos, 'df_total.csv')\n",
    "df_total.to_csv(ruta_csv, sep=';', index=False, encoding='latin1', decimal='.')\n",
    "df_total.info()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "162664d9",
   "metadata": {
    "hidden": true
   },
   "source": [
    "### Eliminar Duplicados - Datos por número de expendiente"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "37519636",
   "metadata": {
    "hidden": true
   },
   "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>num_expediente</th>\n",
       "      <th>fecha</th>\n",
       "      <th>hora</th>\n",
       "      <th>localizacion</th>\n",
       "      <th>numero</th>\n",
       "      <th>cod_distrito</th>\n",
       "      <th>distrito</th>\n",
       "      <th>tipo_accidente</th>\n",
       "      <th>estado_meteorológico</th>\n",
       "      <th>tipo_vehiculo</th>\n",
       "      <th>tipo_persona</th>\n",
       "      <th>rango_edad</th>\n",
       "      <th>sexo</th>\n",
       "      <th>cod_lesividad</th>\n",
       "      <th>lesividad</th>\n",
       "      <th>coordenada_x_utm</th>\n",
       "      <th>coordenada_y_utm</th>\n",
       "      <th>positiva_alcohol</th>\n",
       "      <th>positiva_droga</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018S017842</td>\n",
       "      <td>2019-02-04</td>\n",
       "      <td>2023-06-19 09:10:00</td>\n",
       "      <td>CALL. ALBERTO AGUILERA, 1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>CENTRO</td>\n",
       "      <td>Colisión lateral</td>\n",
       "      <td>Despejado</td>\n",
       "      <td>Motocicleta &gt; 125cc</td>\n",
       "      <td>Conductor</td>\n",
       "      <td>De 45 a 49 años</td>\n",
       "      <td>Hombre</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Asistencia sanitaria sólo en el lugar del acci...</td>\n",
       "      <td>440068.049</td>\n",
       "      <td>4475679.170</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019S000001</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>2023-06-19 03:45:00</td>\n",
       "      <td>PASEO. SANTA MARIA DE LA CABEZA / PLAZA. ELIPTICA</td>\n",
       "      <td>168</td>\n",
       "      <td>11.0</td>\n",
       "      <td>CARABANCHEL</td>\n",
       "      <td>Alcance</td>\n",
       "      <td>0</td>\n",
       "      <td>Furgoneta</td>\n",
       "      <td>Conductor</td>\n",
       "      <td>De 40 a 44 años</td>\n",
       "      <td>Hombre</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>439139.603</td>\n",
       "      <td>4470836.854</td>\n",
       "      <td>S</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2019S000002</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>2023-06-19 03:50:00</td>\n",
       "      <td>CALL. CUART DE POBLET, 65</td>\n",
       "      <td>65</td>\n",
       "      <td>10.0</td>\n",
       "      <td>LATINA</td>\n",
       "      <td>Choque contra obstáculo fijo</td>\n",
       "      <td>Despejado</td>\n",
       "      <td>Furgoneta</td>\n",
       "      <td>Conductor</td>\n",
       "      <td>Desconocido</td>\n",
       "      <td>Desconocido</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>436473.789</td>\n",
       "      <td>4472030.489</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2019S000003</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>2023-06-19 04:00:00</td>\n",
       "      <td>CALL. MARCELO USERA, 30</td>\n",
       "      <td>30</td>\n",
       "      <td>12.0</td>\n",
       "      <td>USERA</td>\n",
       "      <td>Colisión fronto-lateral</td>\n",
       "      <td>Despejado</td>\n",
       "      <td>Turismo</td>\n",
       "      <td>Conductor</td>\n",
       "      <td>De 30 a 34 años</td>\n",
       "      <td>Hombre</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>440462.407</td>\n",
       "      <td>4470926.552</td>\n",
       "      <td>S</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2019S000004</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>2023-06-19 04:00:00</td>\n",
       "      <td>CALL. ISLA DE OZA, 15</td>\n",
       "      <td>15</td>\n",
       "      <td>9.0</td>\n",
       "      <td>MONCLOA-ARAVACA</td>\n",
       "      <td>Choque contra obstáculo fijo</td>\n",
       "      <td>Despejado</td>\n",
       "      <td>Turismo</td>\n",
       "      <td>Conductor</td>\n",
       "      <td>De 60 a 64 años</td>\n",
       "      <td>Hombre</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>438274.088</td>\n",
       "      <td>4479629.873</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</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",
       "      <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>189281</th>\n",
       "      <td>2023S017960</td>\n",
       "      <td>2023-04-30</td>\n",
       "      <td>2023-06-19 22:30:00</td>\n",
       "      <td>AVDA. ENSANCHE DE VALLECAS, 44</td>\n",
       "      <td>44</td>\n",
       "      <td>18.0</td>\n",
       "      <td>VILLA DE VALLECAS</td>\n",
       "      <td>Colisión fronto-lateral</td>\n",
       "      <td>Despejado</td>\n",
       "      <td>Motocicleta hasta 125cc</td>\n",
       "      <td>Conductor</td>\n",
       "      <td>De 30 a 34 años</td>\n",
       "      <td>Hombre</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Ingreso inferior o igual a 24 horas</td>\n",
       "      <td>448488.631</td>\n",
       "      <td>4468674.221</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189284</th>\n",
       "      <td>2023S017978</td>\n",
       "      <td>2023-04-30</td>\n",
       "      <td>2023-06-19 05:10:00</td>\n",
       "      <td>CALLE LOPEZ DE HOLLOS CON CALLE PPE VERGARA</td>\n",
       "      <td>120</td>\n",
       "      <td>5.0</td>\n",
       "      <td>CHAMARTÍN</td>\n",
       "      <td>Colisión fronto-lateral</td>\n",
       "      <td>Despejado</td>\n",
       "      <td>Turismo</td>\n",
       "      <td>Conductor</td>\n",
       "      <td>De 45 a 49 años</td>\n",
       "      <td>Hombre</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>442433.751</td>\n",
       "      <td>4476960.554</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189289</th>\n",
       "      <td>2023S018017</td>\n",
       "      <td>2023-04-28</td>\n",
       "      <td>2023-06-19 19:36:00</td>\n",
       "      <td>CALL. MONTANA / CALL. ADORA</td>\n",
       "      <td>2</td>\n",
       "      <td>12.0</td>\n",
       "      <td>USERA</td>\n",
       "      <td>Colisión lateral</td>\n",
       "      <td>Nublado</td>\n",
       "      <td>Turismo</td>\n",
       "      <td>Conductor</td>\n",
       "      <td>De 21 a 24 años</td>\n",
       "      <td>Hombre</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>441667.701</td>\n",
       "      <td>4468990.513</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189291</th>\n",
       "      <td>2023S018331</td>\n",
       "      <td>2023-04-23</td>\n",
       "      <td>2023-06-19 14:00:00</td>\n",
       "      <td>PLAZA. INDEPENDENCIA, 1</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>SALAMANCA</td>\n",
       "      <td>Otro</td>\n",
       "      <td>Despejado</td>\n",
       "      <td>Ciclomotor</td>\n",
       "      <td>Conductor</td>\n",
       "      <td>De 30 a 34 años</td>\n",
       "      <td>Hombre</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>441512.687</td>\n",
       "      <td>4474614.835</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189294</th>\n",
       "      <td>2023S018455</td>\n",
       "      <td>2023-04-18</td>\n",
       "      <td>2023-06-19 12:30:00</td>\n",
       "      <td>PASEO. CASTELLANOS, 45</td>\n",
       "      <td>45</td>\n",
       "      <td>11.0</td>\n",
       "      <td>CARABANCHEL</td>\n",
       "      <td>Alcance</td>\n",
       "      <td>Despejado</td>\n",
       "      <td>Todo terreno</td>\n",
       "      <td>Conductor</td>\n",
       "      <td>De 55 a 59 años</td>\n",
       "      <td>Hombre</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>436777.181</td>\n",
       "      <td>4469693.185</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>81129 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       num_expediente      fecha  ... positiva_alcohol positiva_droga\n",
       "0         2018S017842 2019-02-04  ...                N            0.0\n",
       "2         2019S000001 2019-01-01  ...                S            0.0\n",
       "8         2019S000002 2019-01-01  ...                N            0.0\n",
       "12        2019S000003 2019-01-01  ...                S            0.0\n",
       "16        2019S000004 2019-01-01  ...                N            0.0\n",
       "...               ...        ...  ...              ...            ...\n",
       "189281    2023S017960 2023-04-30  ...                N            0.0\n",
       "189284    2023S017978 2023-04-30  ...                N            0.0\n",
       "189289    2023S018017 2023-04-28  ...                N            0.0\n",
       "189291    2023S018331 2023-04-23  ...                N            0.0\n",
       "189294    2023S018455 2023-04-18  ...                N            0.0\n",
       "\n",
       "[81129 rows x 19 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Eliminar registros duplicados en la columna \"num_expediente\"\n",
    "df_total_unicos = df_total.drop_duplicates(subset='num_expediente', keep='first')\n",
    "\n",
    "# Guardar datos \n",
    "ruta_csv = os.path.join(current_dir, mis_datos, 'df_total_unicos.csv')\n",
    "df_total_unicos.to_csv(ruta_csv, sep=';', index=False, encoding='latin1', decimal='.')\n",
    "# Mostrar el DataFrame resultante\n",
    "df_total_unicos\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "7ad8ad87",
   "metadata": {},
   "source": [
    "# Visualización y Exploración de los datos"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "c6507989",
   "metadata": {},
   "source": [
    "<div style=\"display: flex; justify-content: center; align-items: center;\">\n",
    "    <img src=\"./assets/data_visualisation.png\" alt=\"drawing\" width=\"200\" style=\"margin-right: 10px;\">\n",
    "    <img src=\"./assets/Explore_data.png\" alt=\"drawing\" width=\"200\" style=\"margin-left: 10px;\">\n",
    "</div>\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "bd097cb8",
   "metadata": {},
   "source": [
    "## Granuralidad temporal (Gráfico)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "91b3a932",
   "metadata": {},
   "source": [
    "### Distribución de los accidente a lo largo del tiempo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "09f33f43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "# Configuración para mostrar el gráfico en el notebook\n",
    "%matplotlib inline\n",
    "\n",
    "# Crear el gráfico tipo histograma\n",
    "plt.figure(figsize=(12, 4))  # Ajustar el ancho del gráfico\n",
    "plt.hist(df_total_unicos['fecha'], bins=50, color='#35b6ff', rwidth=0.85)\n",
    "plt.suptitle('Dinámica temporal de accidentes en Madrid', y=1, fontsize=18)\n",
    "plt.title('Fecha del 01-01-2019 al 30-04-2023', fontsize=10)\n",
    "plt.xlabel('Fecha')\n",
    "plt.ylabel('Accidentes')\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "e82b11ca",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Tuning del gráfico"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "c6b3f5c2",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "import matplotlib.patches as patches\n",
    "\n",
    "def configurar_grafico():\n",
    "    # Estilo gráfico\n",
    "    plt.xticks(rotation=30, ha='right')  # Rotar y alinear las etiquetas del eje x para mejorar legibilidad\n",
    "    plt.tight_layout()  # Ajustar el espacio entre los elementos del gráfico\n",
    "    \n",
    "    # Añadir líneas finas horizontales\n",
    "    plt.grid(axis='y', color='gray', linestyle=':', linewidth=0.5)\n",
    "    \n",
    "    # Configurar los ejes de fecha\n",
    "    ax = plt.gca()  # Obtener los ejes actuales\n",
    "    ax.xaxis.set_major_locator(mdates.MonthLocator(bymonth=(1, 7)))  # Establecer localizador de los principales ticks de mes\n",
    "    ax.xaxis.set_minor_locator(mdates.MonthLocator())  # Establecer localizador de los ticks menores de mes\n",
    "    ax.xaxis.set_major_formatter(mdates.ConciseDateFormatter(ax.xaxis.get_major_locator()))  # Establecer formato conciso para los principales ticks de fecha\n",
    "    \n",
    "    # Quitar el marco\n",
    "    ax.spines['top'].set_visible(False)\n",
    "    ax.spines['right'].set_visible(False)\n",
    "    ax.spines['left'].set_visible(False)\n",
    "    ax.spines['bottom'].set_visible(False)\n",
    "\n",
    "\n",
    "plt.figure(figsize=(12, 4))  # Ajustar el ancho del gráfico\n",
    "plt.hist(df_total_unicos['fecha'], bins=50, color='#35b6ff', rwidth=0.85)\n",
    "plt.suptitle('Dinámica temporal de accidentes en Madrid', y=1, fontsize=18)\n",
    "plt.title('Fecha del 01-01-2019 al 30-04-2023', fontsize=10)\n",
    "plt.xlabel('Fecha', fontsize=9, fontweight='bold')\n",
    "plt.ylabel('Accidentes', fontsize=9, fontweight='bold')\n",
    "\n",
    "# Configurar los ejes de fecha\n",
    "ax = plt.gca()  # Obtener los ejes actuales\n",
    "ax.xaxis.set_major_locator(mdates.MonthLocator(bymonth=(1, 7)))  # Establecer localizador de los principales ticks de mes\n",
    "ax.xaxis.set_minor_locator(mdates.MonthLocator())  # Establecer localizador de los ticks menores de mes\n",
    "ax.xaxis.set_major_formatter(mdates.ConciseDateFormatter(ax.xaxis.get_major_locator()))  # Establecer formato conciso para los principales ticks de fecha\n",
    "\n",
    "# Agregar rectángulo de confinamiento\n",
    "start_date = '2020-03-15'\n",
    "end_date = '2020-06-21'\n",
    "rectangle = patches.Rectangle((mdates.datestr2num(start_date), 0), mdates.datestr2num(end_date) - mdates.datestr2num(start_date), plt.ylim()[1], facecolor='red', alpha=0.3)\n",
    "ax.add_patch(rectangle)\n",
    "\n",
    "# Agregar etiqueta \"confinamiento\" con flecha curva\n",
    "label_text = ax.annotate('Confinamiento', xy=(mdates.datestr2num('2020-05-15'), 1500), xytext=(mdates.datestr2num('2020-07-21'), 2000),\n",
    "                          arrowprops=dict(arrowstyle='->'), ha='center', va='center', rotation=0, fontsize=14)\n",
    "\n",
    "\n",
    "\n",
    "configurar_grafico()\n",
    "plt.show()\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "de1c72f6",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Accidentes por año"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "91e79fc7",
   "metadata": {
    "hidden": true,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Gráfico de barras para el número de accidentes por año\n",
    "ax = df_total_unicos['fecha'].groupby(df_total_unicos.fecha.dt.year).agg('count').plot(\n",
    "       kind='bar',  # Tipo de gráfico de barras\n",
    "       color='#35b6ff',  # Color de las barras\n",
    "       figsize=(12, 3) # Tamaño de la figura\n",
    ")\n",
    "\n",
    "\n",
    "# Estilo gráfico\n",
    "plt.xticks(rotation=30, ha='right')  # Rotar y alinear las etiquetas del eje x para mejorar legibilidad\n",
    "plt.tight_layout()  # Ajustar el espacio entre los elementos del gráfico\n",
    "plt.title(\"Número de accidentes por años\" , fontsize=12)\n",
    "plt.xlabel('Años', y=2, fontsize=9, fontweight='bold') # Etiqueta del eje x con salto de línea\n",
    "plt.ylabel('Nº Accidentes', fontsize=9, fontweight='bold') # Etiqueta del eje y\n",
    "\n",
    "# Añadir líneas finas horizontales\n",
    "plt.grid(axis='y', color='gray', linestyle=':', linewidth=0.5)\n",
    "\n",
    "\n",
    "    \n",
    "ax.tick_params(axis='x', which='both', bottom=False, top=False)  # Ocultar marcadores verticales en el eje x\n",
    "# Quitar el marco\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['left'].set_visible(False)\n",
    "\n",
    "# Agregar etiquetas con el número de accidentes dentro de las barras\n",
    "for rect in ax.containers[0]:\n",
    "    height = rect.get_height()\n",
    "    ax.annotate(str(height), xy=(rect.get_x() + rect.get_width() / 2, height),\n",
    "                xytext=(0, 3), textcoords=\"offset points\",\n",
    "                ha='center', va='bottom')\n",
    "    \n",
    "\n",
    "    \n",
    "    \n",
    "plt.show()  # Mostrar el gráfico\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "5450d4d6",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Accidentes por meses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "89e1572c",
   "metadata": {
    "hidden": true,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import calendar\n",
    "\n",
    "# Obtener las abreviaturas de los meses\n",
    "month_abbr = [calendar.month_abbr[i] for i in range(1, 13)]\n",
    "\n",
    "# Gráfico de barras para el número de accidentes por año\n",
    "ax = df_total_unicos['fecha'].groupby(df_total_unicos.fecha.dt.month).agg('count').plot(\n",
    "       kind='bar',  # Tipo de gráfico de barras\n",
    "       color='#35b6ff',  # Color de las barras\n",
    "       figsize=(12, 3) # Tamaño de la figura\n",
    ")\n",
    "\n",
    "# Calcula la media de los accidentes\n",
    "mean_accidents = df_total_unicos['fecha'].groupby(df_total_unicos.fecha.dt.month).agg('count').mean()\n",
    "\n",
    "# Dibuja la línea discontinua roja para representar la media\n",
    "ax.axhline(mean_accidents, color='red', linestyle='-.', label='Media', linewidth=1)\n",
    "\n",
    "# Estilo de la etiqueta de la media\n",
    "label_props = {'color': 'red', 'weight': 'bold', 'ha': 'center', 'va': 'bottom'}\n",
    "\n",
    "# Agregar etiqueta con el valor de la media\n",
    "ax.text(0, mean_accidents, f'{mean_accidents:.1f}', **label_props)\n",
    "\n",
    "# Estilo gráfico\n",
    "plt.xticks(range(12), month_abbr, rotation=30, ha='right')  # Usar las abreviaturas de los meses en el eje x\n",
    "plt.tight_layout()  # Ajustar el espacio entre los elementos del gráfico\n",
    "plt.title(\"Número de accidentes por meses\", fontsize=12)\n",
    "plt.xlabel('Meses', y=2, fontsize=9, fontweight='bold') # Etiqueta del eje x con salto de línea\n",
    "plt.ylabel('Nº Accidentes', fontsize=9, fontweight='bold') # Etiqueta del eje y\n",
    "\n",
    "# Añadir líneas finas horizontales\n",
    "plt.grid(axis='y', color='gray', linestyle=':', linewidth=0.5)\n",
    "\n",
    "ax.tick_params(axis='x', which='both', bottom=False, top=False)  # Ocultar marcadores verticales en el eje x\n",
    "# Quitar el marco\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['left'].set_visible(False)\n",
    "\n",
    "# Agregar etiquetas con el número de accidentes dentro de las barras\n",
    "for rect in ax.containers[0]:\n",
    "    height = rect.get_height()\n",
    "    ax.annotate(str(height), xy=(rect.get_x() + rect.get_width() / 2, height),\n",
    "                xytext=(0, 3), textcoords=\"offset points\",\n",
    "                ha='center', va='bottom')\n",
    "\n",
    "# Mostrar leyenda\n",
    "ax.legend()\n",
    "\n",
    "plt.show()  # Mostrar el gráfico\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "0944c25a",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Accidentes por días"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "8c69f41a",
   "metadata": {
    "hidden": true,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Gráfico de barras para el número de accidentes por días del año\n",
    "ax = df_total_unicos['fecha'].groupby(df_total_unicos.fecha.dt.day).agg('count').plot(\n",
    "       kind='bar',  # Tipo de gráfico de barras\n",
    "       color='#35b6ff',  # Color de las barras\n",
    "       figsize=(12, 3) # Tamaño de la figura\n",
    ")\n",
    "\n",
    "# Estilo gráfico\n",
    "plt.xticks(rotation=45, ha='right')  # Rotar y alinear las etiquetas del eje x para mejorar legibilidad\n",
    "plt.tight_layout()  # Ajustar el espacio entre los elementos del gráfico\n",
    "plt.title(\"Número de accidentes por días del mes\", fontsize=12)\n",
    "plt.xlabel('Días del mes', y=2, fontsize=9, fontweight='bold') # Etiqueta del eje x con salto de línea\n",
    "plt.ylabel('Nº Accidentes', fontsize=9, fontweight='bold') # Etiqueta del eje y\n",
    "\n",
    "# Añadir líneas finas horizontales\n",
    "plt.grid(axis='y', color='gray', linestyle=':', linewidth=0.5)\n",
    "\n",
    "ax.tick_params(axis='x', which='both', bottom=False, top=False)  # Ocultar marcadores verticales en el eje x\n",
    "# Quitar el marco\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['left'].set_visible(False)\n",
    "\n",
    "plt.show()  # Mostrar el gráfico\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "839f6d98",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Accidentes por horas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "29c23725",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Convertir la columna \"hora\" a cadena\n",
    "df_total_unicos['hora'] = df_total_unicos['hora'].astype(str)\n",
    "\n",
    "# Convertir la columna \"hora\" a tipo datetime\n",
    "df_total_unicos['hora'] = pd.to_datetime(df_total_unicos['hora'], format='%Y-%m-%d %H:%M:%S')\n",
    "\n",
    "# Agrupar por hora y contar el número de accidentes\n",
    "accidentes_por_hora = df_total_unicos.groupby(df_total_unicos['hora'].dt.hour)['hora'].count()\n",
    "\n",
    "# Crear el gráfico de barras\n",
    "ax = accidentes_por_hora.plot(kind='bar', color='#35b6ff', figsize=(12, 3))\n",
    "\n",
    "# Estilo gráfico\n",
    "plt.xticks(rotation=0)  # No rotar las etiquetas del eje x\n",
    "plt.tight_layout()\n",
    "plt.title(\"Número de accidentes por horas del día\", fontsize=12)\n",
    "plt.xlabel('Horas del día', fontsize=9, fontweight='bold')\n",
    "plt.ylabel('Nº Accidentes', fontsize=9, fontweight='bold')\n",
    "plt.grid(axis='y', color='gray', linestyle=':', linewidth=0.5)\n",
    "\n",
    "ax.tick_params(axis='x', which='both', bottom=False, top=False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['left'].set_visible(False)\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "8d5780e4",
   "metadata": {},
   "source": [
    "### Accidentes por minutos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "13852a59",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Convertir la columna \"hora\" a tipo datetime\n",
    "df_total_unicos['hora'] = pd.to_datetime(df_total_unicos['hora'], format='%Y-%m-%d %H:%M:%S', errors='coerce')\n",
    "\n",
    "# Obtener el componente de minutos de la columna \"hora\"\n",
    "df_total_unicos['minutos'] = df_total_unicos['hora'].dt.minute\n",
    "\n",
    "# Filtrar los valores nulos en la columna \"minutos\"\n",
    "df_filtered = df_total_unicos.dropna(subset=['minutos'])\n",
    "\n",
    "# Agrupar por minutos y contar el número de accidentes\n",
    "accidentes_por_minuto = df_filtered.groupby(df_filtered['minutos'])['minutos'].count()\n",
    "\n",
    "# Crear el gráfico de barras\n",
    "ax = accidentes_por_minuto.plot(kind='bar', color='#35b6ff', figsize=(12, 3))\n",
    "\n",
    "# Estilo gráfico\n",
    "plt.xticks(rotation=0)\n",
    "plt.tight_layout()\n",
    "plt.title(\"Número de accidentes por minutos\", fontsize=12)\n",
    "plt.xlabel('Minutos', fontsize=9, fontweight='bold')\n",
    "plt.ylabel('Nº Accidentes', fontsize=9, fontweight='bold')\n",
    "plt.grid(axis='y', color='gray', linestyle=':', linewidth=0.5)\n",
    "\n",
    "ax.tick_params(axis='x', which='both', bottom=False, top=False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['left'].set_visible(False)\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "0f648082",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Experience - Dashboard"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "6e868816",
   "metadata": {
    "hidden": true
   },
   "source": [
    "<div style=\"text-align:center;\">\n",
    "    <img src=\"./assets/dashboard.png\">\n",
    "    <a href=\"https://experience.arcgis.com/experience/10bc2a493a974d5caf024ff13d7891b3/page/Visualizaci%C3%B3n-espacial/?views=Cibeles\">Experience - Dashboard</a>\n",
    "</div>\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "9e00bc3c",
   "metadata": {},
   "source": [
    "## Granularidad espacial (Mapa)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "83a35140",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "#### De cordenadas UTM a geometría "
   ]
  },
  {
   "cell_type": "raw",
   "id": "3c147991",
   "metadata": {
    "hidden": true,
    "scrolled": true
   },
   "source": [
    "import os\n",
    "import arcpy\n",
    "current_dir = os.getcwd()\n",
    "mis_datos = \"datos/\"\n",
    "\n",
    "#-----------------------------Varaiables----------------------------------\n",
    "df_total_unicos_csv = os.path.join(current_dir, mis_datos, \"df_total_unicos.csv\")\n",
    "puntos_accidentes = '\\\\puntos_accidentes'\n",
    "path_gdb = r\"C:\\00_Documentos_ESRI\\99_Mis_proyectos_ArcGIS_Pro\\Análisis de Siniestralidad - Madrid\\Análisis de Siniestralidad - Madrid.gdb\"\n",
    "\n",
    "path_output = path_gdb + puntos_accidentes\n",
    "#-------------------------------------------------------------------------\n",
    "\n",
    "#-Convertir tabla en geometria\n",
    "arcpy.management.XYTableToPoint(\n",
    "    in_table = df_total_unicos_csv,\n",
    "    out_feature_class=path_output,\n",
    "    x_field=\"coordenada_x_utm\",\n",
    "    y_field=\"coordenada_y_utm\",\n",
    "    z_field=None,\n",
    "    coordinate_system = arcpy.SpatialReference(25830) # Coordenadas UTM\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "e3ac89f9",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "#puntos_accidentes\n",
    "puntos_accidentes = arcpy.management.CopyFeatures(r'C:\\00_Documentos_ESRI\\99_Mis_proyectos_ArcGIS_Pro\\Análisis de Siniestralidad - Madrid\\datos\\outputs\\puntos_accidentes.shp', 'puntos_accidentes')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "cdb4da20",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "#### Densidad accidentes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "7263436a",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "mapa_densidad = arcpy.sa.KernelDensity(\n",
    "    in_features=\"puntos_accidentes\",\n",
    "    population_field=\"NONE\",\n",
    "    cell_size=10,\n",
    "    search_radius=None,\n",
    "    area_unit_scale_factor=\"SQUARE_KILOMETERS\",\n",
    "    out_cell_values=\"DENSITIES\",\n",
    "    method=\"PLANAR\",\n",
    "    in_barriers=None\n",
    ")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d0ccd77d",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# Eliminar la capa\n",
    "#path_gdb = r\"C:\\00_Documentos_ESRI\\99_Mis_proyectos_ArcGIS_Pro\\Análisis de Siniestralidad - Madrid\\Análisis de Siniestralidad - Madrid.gdb\"\n",
    "#Densidad = '\\\\mapa_densidad'\n",
    "##########################\n",
    "#arcpy.Delete_management(path_gdb+ Densidad)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "c1a8aa7e",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "#### Accidentes por Distritos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "448442b5",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# Cargar Distritos y barrios\n",
    "Distritos = arcpy.management.CopyFeatures(r'C:\\00_Documentos_ESRI\\99_Mis_proyectos_ArcGIS_Pro\\Análisis de Siniestralidad - Madrid\\datos\\Shapefile\\Distritos_Madrid\\Distritos.shp', 'Distritos')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "96b34039",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Aplicamos una unión espacial (**Spatial Join**) con la capa de distritos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "de9db809",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class='gpresult'><h2>Messages</h2><div id='messages' data-messages='[\"Tiempo de Inicio: lunes, 19 de junio de 2023 12:55:09\",\"Completado correctamente el lunes, 19 de junio de 2023 12:55:09 (Tiempo transcurrido: 0,29 segundos)\"]' data-show='true'><div id = 'default' /></div></div>"
      ],
      "text/plain": [
       "<Result 'true'>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Unión Espacial\n",
    "################\n",
    "path_gdb = r\"C:\\00_Documentos_ESRI\\99_Mis_proyectos_ArcGIS_Pro\\Análisis de Siniestralidad - Madrid\\Análisis de Siniestralidad - Madrid.gdb\"\n",
    "accidentes_distritos = '\\\\Accidentes_Distritos'\n",
    "############################################\n",
    "\n",
    "arcpy.analysis.SpatialJoin(\n",
    "    target_features= Distritos,\n",
    "    join_features=\"puntos_accidentes\",\n",
    "    out_feature_class = path_gdb + accidentes_distritos,\n",
    "    join_operation=\"JOIN_ONE_TO_ONE\",\n",
    "    join_type=\"KEEP_ALL\",\n",
    "    field_mapping='CODDIS \"CODDIS\" true true false 2 Text 0 0,First,#,Barrios,CODDIS,0,2;NOMDIS \"NOMDIS\" true true false 50 Text 0 0,First,#,Barrios,NOMDIS,0,50;COD_BAR \"COD_BAR\" true true false 3 Text 0 0,First,#,Barrios,COD_BAR,0,3;NOMBRE \"NOMBRE\" true true false 50 Text 0 0,First,#,Barrios,NOMBRE,0,50;Shape_Leng \"Shape_Leng\" true true false 8 Double 0 0,First,#,Barrios,Shape_Leng,-1,-1;COD_DIS_TX \"COD_DIS_TX\" true true false 2 Text 0 0,First,#,Barrios,COD_DIS_TX,0,2;BARRIO_MAY \"BARRIO_MAY\" true true false 50 Text 0 0,First,#,Barrios,BARRIO_MAY,0,50;COD_DISBAR \"COD_DISBAR\" true true false 3 Text 0 0,First,#,Barrios,COD_DISBAR,0,3;NUM_BAR \"NUM_BAR\" true true false 1 Text 0 0,First,#,Barrios,NUM_BAR,0,1;BARRIO_MT \"BARRIO_MT\" true true false 50 Text 0 0,First,#,Barrios,BARRIO_MT,0,50;COD_DISB \"COD_DISB\" true true false 4 Text 0 0,First,#,Barrios,COD_DISB,0,4;Shape_Length \"Shape_Length\" false true true 8 Double 0 0,First,#,Barrios,Shape_Length,-1,-1;Shape_Area \"Shape_Area\" false true true 8 Double 0 0,First,#,Barrios,Shape_Area,-1,-1;num_expedi \"num_expedi\" true true false 254 Text 0 0,First,#,puntos_accidentes,num_expedi,0,254;fecha \"fecha\" true true false 8 Date 0 0,First,#,puntos_accidentes,fecha,-1,-1;hora \"hora\" true true false 8 Date 0 0,First,#,puntos_accidentes,hora,-1,-1;localizaci \"localizaci\" true true false 254 Text 0 0,First,#,puntos_accidentes,localizaci,0,254;numero \"numero\" true true false 254 Text 0 0,First,#,puntos_accidentes,numero,0,254;cod_distri \"cod_distri\" true true false 19 Double 0 0,First,#,puntos_accidentes,cod_distri,-1,-1;distrito \"distrito\" true true false 254 Text 0 0,First,#,puntos_accidentes,distrito,0,254;tipo_accid \"tipo_accid\" true true false 254 Text 0 0,First,#,puntos_accidentes,tipo_accid,0,254;estado_met \"estado_met\" true true false 254 Text 0 0,First,#,puntos_accidentes,estado_met,0,254;tipo_vehic \"tipo_vehic\" true true false 254 Text 0 0,First,#,puntos_accidentes,tipo_vehic,0,254;tipo_perso \"tipo_perso\" true true false 254 Text 0 0,First,#,puntos_accidentes,tipo_perso,0,254;rango_edad \"rango_edad\" true true false 254 Text 0 0,First,#,puntos_accidentes,rango_edad,0,254;sexo \"sexo\" true true false 254 Text 0 0,First,#,puntos_accidentes,sexo,0,254;cod_lesivi \"cod_lesivi\" true true false 19 Double 0 0,First,#,puntos_accidentes,cod_lesivi,-1,-1;lesividad \"lesividad\" true true false 254 Text 0 0,First,#,puntos_accidentes,lesividad,0,254;coordenada \"coordenada\" true true false 19 Double 0 0,First,#,puntos_accidentes,coordenada,-1,-1;coordena_1 \"coordena_1\" true true false 19 Double 0 0,First,#,puntos_accidentes,coordena_1,-1,-1;positiva_a \"positiva_a\" true true false 254 Text 0 0,First,#,puntos_accidentes,positiva_a,0,254;positiva_d \"positiva_d\" true true false 19 Double 0 0,First,#,puntos_accidentes,positiva_d,-1,-1',\n",
    "    match_option=\"INTERSECT\",\n",
    "    search_radius=None,\n",
    "    distance_field_name=\"\"\n",
    ")\n",
    "\n",
    "# Eliminar la capa\n",
    "Distritos = '\\\\Distritos'\n",
    "arcpy.Delete_management(path_gdb+ Distritos)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "96dc9a79",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "#### Accidentes por Barrios"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "a2d4b08d",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Aplicamos una unión espacial (**Spatial Join**) con la capa de barrios"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "44d91136",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class='gpresult'><h2>Messages</h2><div id='messages' data-messages='[\"Tiempo de Inicio: lunes, 19 de junio de 2023 12:54:22\",\"WARNING 000110: C:\\\\00_Documentos_ESRI\\\\99_Mis_proyectos_ArcGIS_Pro\\\\Análisis de Siniestralidad - Madrid\\\\Análisis de Siniestralidad - Madrid.gdb\\\\Barrios no existe\",\"Completado correctamente el lunes, 19 de junio de 2023 12:54:22 (Tiempo transcurrido: 0,01 segundos)\"]' data-show='true'><div id = 'default' /></div></div>"
      ],
      "text/plain": [
       "<Result 'true'>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Barrios = arcpy.management.CopyFeatures(r'C:\\00_Documentos_ESRI\\99_Mis_proyectos_ArcGIS_Pro\\Análisis de Siniestralidad - Madrid\\datos\\Shapefile\\Barrios_Madrid\\Barrios.shp', 'Barrios')\n",
    "Barrios = r'C:\\00_Documentos_ESRI\\99_Mis_proyectos_ArcGIS_Pro\\Análisis de Siniestralidad - Madrid\\datos\\Shapefile\\Barrios_Madrid\\Barrios.shp'\n",
    "accidentes_barrios = '\\\\Accidentes_Barrios'\n",
    "#########################################\n",
    "\n",
    "arcpy.analysis.SpatialJoin(\n",
    "    target_features= Barrios,\n",
    "    join_features=\"puntos_accidentes\",\n",
    "    out_feature_class = path_gdb + accidentes_barrios,\n",
    "    join_operation=\"JOIN_ONE_TO_ONE\",\n",
    "    join_type=\"KEEP_ALL\",\n",
    "    field_mapping='CODDIS \"CODDIS\" true true false 2 Text 0 0,First,#,Barrios,CODDIS,0,2;NOMDIS \"NOMDIS\" true true false 50 Text 0 0,First,#,Barrios,NOMDIS,0,50;COD_BAR \"COD_BAR\" true true false 3 Text 0 0,First,#,Barrios,COD_BAR,0,3;NOMBRE \"NOMBRE\" true true false 50 Text 0 0,First,#,Barrios,NOMBRE,0,50;Shape_Leng \"Shape_Leng\" true true false 8 Double 0 0,First,#,Barrios,Shape_Leng,-1,-1;COD_DIS_TX \"COD_DIS_TX\" true true false 2 Text 0 0,First,#,Barrios,COD_DIS_TX,0,2;BARRIO_MAY \"BARRIO_MAY\" true true false 50 Text 0 0,First,#,Barrios,BARRIO_MAY,0,50;COD_DISBAR \"COD_DISBAR\" true true false 3 Text 0 0,First,#,Barrios,COD_DISBAR,0,3;NUM_BAR \"NUM_BAR\" true true false 1 Text 0 0,First,#,Barrios,NUM_BAR,0,1;BARRIO_MT \"BARRIO_MT\" true true false 50 Text 0 0,First,#,Barrios,BARRIO_MT,0,50;COD_DISB \"COD_DISB\" true true false 4 Text 0 0,First,#,Barrios,COD_DISB,0,4;Shape_Length \"Shape_Length\" false true true 8 Double 0 0,First,#,Barrios,Shape_Length,-1,-1;Shape_Area \"Shape_Area\" false true true 8 Double 0 0,First,#,Barrios,Shape_Area,-1,-1;num_expedi \"num_expedi\" true true false 254 Text 0 0,First,#,puntos_accidentes,num_expedi,0,254;fecha \"fecha\" true true false 8 Date 0 0,First,#,puntos_accidentes,fecha,-1,-1;hora \"hora\" true true false 8 Date 0 0,First,#,puntos_accidentes,hora,-1,-1;localizaci \"localizaci\" true true false 254 Text 0 0,First,#,puntos_accidentes,localizaci,0,254;numero \"numero\" true true false 254 Text 0 0,First,#,puntos_accidentes,numero,0,254;cod_distri \"cod_distri\" true true false 19 Double 0 0,First,#,puntos_accidentes,cod_distri,-1,-1;distrito \"distrito\" true true false 254 Text 0 0,First,#,puntos_accidentes,distrito,0,254;tipo_accid \"tipo_accid\" true true false 254 Text 0 0,First,#,puntos_accidentes,tipo_accid,0,254;estado_met \"estado_met\" true true false 254 Text 0 0,First,#,puntos_accidentes,estado_met,0,254;tipo_vehic \"tipo_vehic\" true true false 254 Text 0 0,First,#,puntos_accidentes,tipo_vehic,0,254;tipo_perso \"tipo_perso\" true true false 254 Text 0 0,First,#,puntos_accidentes,tipo_perso,0,254;rango_edad \"rango_edad\" true true false 254 Text 0 0,First,#,puntos_accidentes,rango_edad,0,254;sexo \"sexo\" true true false 254 Text 0 0,First,#,puntos_accidentes,sexo,0,254;cod_lesivi \"cod_lesivi\" true true false 19 Double 0 0,First,#,puntos_accidentes,cod_lesivi,-1,-1;lesividad \"lesividad\" true true false 254 Text 0 0,First,#,puntos_accidentes,lesividad,0,254;coordenada \"coordenada\" true true false 19 Double 0 0,First,#,puntos_accidentes,coordenada,-1,-1;coordena_1 \"coordena_1\" true true false 19 Double 0 0,First,#,puntos_accidentes,coordena_1,-1,-1;positiva_a \"positiva_a\" true true false 254 Text 0 0,First,#,puntos_accidentes,positiva_a,0,254;positiva_d \"positiva_d\" true true false 19 Double 0 0,First,#,puntos_accidentes,positiva_d,-1,-1',\n",
    "    match_option=\"INTERSECT\",\n",
    "    search_radius=None,\n",
    "    distance_field_name=\"\"\n",
    ")\n",
    "\n",
    "# Eliminar la capa\n",
    "arcpy.Delete_management(path_gdb+ accidentes_distritos)\n",
    "Barrios = '\\\\Barrios'\n",
    "arcpy.Delete_management(path_gdb+ Barrios)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "a04d8c35",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class='gpresult'><h2>Messages</h2><div id='messages' data-messages='[\"Tiempo de Inicio: lunes, 19 de junio de 2023 12:03:18\",\"Completado correctamente el lunes, 19 de junio de 2023 12:03:18 (Tiempo transcurrido: 0,29 segundos)\"]' data-show='true'><div id = 'default' /></div></div>"
      ],
      "text/plain": [
       "<Result 'true'>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Eliminar la capa\n",
    "arcpy.Delete_management(path_gdb+ accidentes_barrios)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "b5be4c13",
   "metadata": {},
   "source": [
    "#### Accidentes en la red navegable (Carreteras)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "b9ebc938",
   "metadata": {},
   "source": [
    "Cargamos los datos la red de carreteras de **OSM**"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "1be369b0",
   "metadata": {},
   "source": [
    "<img src=\"./assets/accidentes_en_carretera.png\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "2389b560",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class='gpresult'><h2>Messages</h2><div id='messages' data-messages='[\"Tiempo de Inicio: lunes, 19 de junio de 2023 14:18:00\",\"Completado correctamente en lunes, 19 de junio de 2023 14:19:58 (Tiempo transcurrido: 1 minutos 57 segundos)\"]' data-show='true'><div id = 'default' /></div></div>"
      ],
      "text/plain": [
       "<Result 'Carreteras'>"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Carreteras = arcpy.management.CopyFeatures(r'C:\\00_Documentos_ESRI\\99_Mis_proyectos_ArcGIS_Pro\\Análisis de Siniestralidad - Madrid\\datos\\Shapefile\\OSM_MADRID\\gis_osm_roads_free_1.shp', 'Carreteras')\n",
    "#Carreteras = r'C:\\00_Documentos_ESRI\\99_Mis_proyectos_ArcGIS_Pro\\Análisis de Siniestralidad - Madrid\\datos\\Shapefile\\OSM_MADRID\\gis_osm_roads_free_1.shp'\n",
    "accidentes_Carreteras = 'Accidentes_Carreteras'\n",
    "###############################################\n",
    "\n",
    "\n",
    "# Puntos de accidente cerca de la carreter\n",
    "# Agregamos a la capa de Carreteras -> NEAR_FID NEAR_DIST\n",
    "arcpy.analysis.Near(\n",
    "    in_features=\"puntos_accidentes\",\n",
    "    near_features=\"Carreteras\",\n",
    "    search_radius=None,\n",
    "    location=\"NO_LOCATION\",\n",
    "    angle=\"NO_ANGLE\",\n",
    "    method=\"PLANAR\",\n",
    "    field_names=\"NEAR_FID NEAR_FID;NEAR_DIST NEAR_DIST\",\n",
    "    distance_unit=\"\"\n",
    ")\n",
    "\n",
    "# Agrupamos por campo NEAR_FID y contamos por NEAR_DIST\n",
    "conteo_puntos_carratera = '\\conteo_puntos_carratera'\n",
    "#####################################################\n",
    "\n",
    "arcpy.management.Dissolve(\n",
    "    in_features=\"puntos_accidentes\",\n",
    "    out_feature_class = path_gdb + conteo_puntos_carratera,\n",
    "    dissolve_field=\"NEAR_FID\",\n",
    "    statistics_fields=\"NEAR_DIST COUNT\",\n",
    "    multi_part=\"MULTI_PART\",\n",
    "    unsplit_lines=\"DISSOLVE_LINES\",\n",
    "    concatenation_separator=\"\"\n",
    ")\n",
    "\n",
    "# Unimos las dos tablas\n",
    "arcpy.management.JoinField(\n",
    "    in_data=\"Carreteras\",\n",
    "    in_field=\"OBJECTID\",\n",
    "    join_table=\"conteo_puntos_carratera\",\n",
    "    join_field=\"NEAR_FID\",\n",
    "    fields=None,\n",
    "    fm_option=\"NOT_USE_FM\",\n",
    "    field_mapping=None\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7d7c255c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Eliminar la capa\n",
    "#arcpy.Delete_management(path_gdb+ conteo_puntos_carratera)\n",
    "\n",
    "# Eliminar la capa\n",
    "#Carreteras = '\\\\Carreteras'\n",
    "#arcpy.Delete_management(path_gdb+ Carreteras)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "2b0db1b6",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# Análisis Forense"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "995091a8",
   "metadata": {
    "hidden": true
   },
   "source": [
    "## ¿Se producen más Atropellos cerca de colegios?\n",
    "\n",
    "Nuestro siguiente estudio trabajará sobre las áreas de influencia de los colegios de Madrid. Para el presente análisis hemos usado tanto los colegios públicos como privados de educación infantil, primaria y secundaria del Municipio de Madrid, analizando un total de **1.505 colegios**.\n",
    "\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "4a8ac6e7",
   "metadata": {
    "hidden": true
   },
   "source": [
    "### Nº de Atropellos en un área de 1 minuto andando "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "759936c1",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# Coloegios Publicos y Privados.\n",
    "colegios = arcpy.management.CopyFeatures(r'C:\\00_Documentos_ESRI\\99_Mis_proyectos_ArcGIS_Pro\\Análisis de Siniestralidad - Madrid\\datos\\Shapefile\\colegios_madrid_pu_pr_sf\\colegios_madrid_pu_pr_sf.shp', 'Colegios')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e7f74cf0",
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# Cargamos la capa con el total de accidentes\n",
    "#todos_accidentes = arcpy.management.CopyFeatures(r'C:\\00_Documentos_ESRI\\99_Mis_proyectos_ArcGIS_Pro\\Análisis de Siniestralidad - Madrid\\datos\\outputs\\todos_accidentes\\todos_accidentes.shp', 'todos_accidentes')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "0b3ba586",
   "metadata": {
    "hidden": true,
    "scrolled": true
   },
   "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>accidentes</th>\n",
       "      <th>Número</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Atropellos a persona Totals</td>\n",
       "      <td>12620</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    accidentes  Número\n",
       "0  Atropellos a persona Totals   12620"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Selecciona de la variable tiop de accidente \"Atropello a persona\"\n",
    "todos_accidentes = \"\\\\puntos_accidentes\"\n",
    "todos_accidentes  = path_gdb + todos_accidentes\n",
    "\n",
    "# Hacemos la selección de Atropello a persona sobre la capa de todos los accidentes\n",
    "seleccion = arcpy.management.SelectLayerByAttribute(todos_accidentes , \n",
    "                                        \"NEW_SELECTION\",  \n",
    "                                        # Definir la expresión de selección\n",
    "                                        \"tipo_accid = 'Atropello a persona'\")\n",
    "\n",
    "\n",
    "\n",
    "'''\n",
    "arcpy.conversion.ExportFeatures(\n",
    "    in_features=\"todos_accidentes_Layer1\",\n",
    "    out_features=r\"C:\\00_Documentos_ESRI\\99_Mis_proyectos_ArcGIS_Pro\\Análisis de Siniestralidad - Madrid\\Análisis de Siniestralidad - Madrid.gdb\\atropellos_persona\",\n",
    "    where_clause=\"\",\n",
    "    use_field_alias_as_name=\"NOT_USE_ALIAS\",\n",
    "    field_mapping='num_expediente \"num_expediente\" true true false 8000 Text 0 0,First,#,todos_accidentes_Layer1,num_expediente,0,8000;fecha \"fecha\" true true false 8 Date 0 0,First,#,todos_accidentes_Layer1,fecha,-1,-1;hora \"hora\" true true false 8 Date 0 0,First,#,todos_accidentes_Layer1,hora,-1,-1;localizacion \"localizacion\" true true false 8000 Text 0 0,First,#,todos_accidentes_Layer1,localizacion,0,8000;numero \"numero\" true true false 8000 Text 0 0,First,#,todos_accidentes_Layer1,numero,0,8000;cod_distrito \"cod_distrito\" true true false 8 Double 0 0,First,#,todos_accidentes_Layer1,cod_distrito,-1,-1;distrito \"distrito\" true true false 8000 Text 0 0,First,#,todos_accidentes_Layer1,distrito,0,8000;tipo_accidente \"tipo_accidente\" true true false 8000 Text 0 0,First,#,todos_accidentes_Layer1,tipo_accidente,0,8000;estado_meteorológico \"estado_meteorológico\" true true false 8000 Text 0 0,First,#,todos_accidentes_Layer1,estado_meteorológico,0,8000;tipo_vehiculo \"tipo_vehiculo\" true true false 8000 Text 0 0,First,#,todos_accidentes_Layer1,tipo_vehiculo,0,8000;tipo_persona \"tipo_persona\" true true false 8000 Text 0 0,First,#,todos_accidentes_Layer1,tipo_persona,0,8000;rango_edad \"rango_edad\" true true false 8000 Text 0 0,First,#,todos_accidentes_Layer1,rango_edad,0,8000;sexo \"sexo\" true true false 8000 Text 0 0,First,#,todos_accidentes_Layer1,sexo,0,8000;cod_lesividad \"cod_lesividad\" true true false 8 Double 0 0,First,#,todos_accidentes_Layer1,cod_lesividad,-1,-1;lesividad \"lesividad\" true true false 8000 Text 0 0,First,#,todos_accidentes_Layer1,lesividad,0,8000;coordenada_x_utm \"coordenada_x_utm\" true true false 8 Double 0 0,First,#,todos_accidentes_Layer1,coordenada_x_utm,-1,-1;coordenada_y_utm \"coordenada_y_utm\" true true false 8 Double 0 0,First,#,todos_accidentes_Layer1,coordenada_y_utm,-1,-1;positiva_alcohol \"positiva_alcohol\" true true false 8000 Text 0 0,First,#,todos_accidentes_Layer1,positiva_alcohol,0,8000;positiva_droga \"positiva_droga\" true true false 8 Double 0 0,First,#,todos_accidentes_Layer1,positiva_droga,-1,-1',\n",
    "    sort_field=None\n",
    ")\n",
    "\n",
    "'''\n",
    "\n",
    "# Deseleccionar todos los elementos\n",
    "#arcpy.management.SelectLayerByAttribute(capa_accidentes, \"CLEAR_SELECTION\")#SWITCH_SELECTION\n",
    "\n",
    "# Ruta de la geodatabase y nombre de la capa\n",
    "path_gdb = r\"C:\\00_Documentos_ESRI\\99_Mis_proyectos_ArcGIS_Pro\\Análisis de Siniestralidad - Madrid\\Análisis de Siniestralidad - Madrid.gdb\"\n",
    "# Obtener el número de filas de la capa\n",
    "registros_totales = int(arcpy.GetCount_management(\"atropellos_persona\").getOutput(0))\n",
    "\n",
    "\n",
    "        \n",
    "df = pd.DataFrame()\n",
    "df[\"accidentes\"] = [\"Atropellos a persona Totals\"]\n",
    "df[\"Número\"] = registros_totales\n",
    "df"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "0f16c9f4",
   "metadata": {
    "hidden": true
   },
   "source": [
    "#### Area influencia a 1 minuto andando"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "75b30b8d",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class='gpresult'><h2>Messages</h2><div id='messages' data-messages='[\"Tiempo de Inicio: lunes, 19 de junio de 2023 13:13:43\",\"Se ha eliminado el dataset de la entidad de origen de los datos de análisis \\\"C:\\\\00_Documentos_ESRI\\\\99_Mis_proyectos_ArcGIS_Pro\\\\Análisis de Siniestralidad - Madrid\\\\Análisis de Siniestralidad - Madrid.gdb\\\\ServiceAreaSolversg2zck\\\".\",\"Completado correctamente el lunes, 19 de junio de 2023 13:13:47 (Tiempo transcurrido: 4,29 segundos)\"]' data-show='true'><div id = 'default' /></div></div>"
      ],
      "text/plain": [
       "<Result 'true'>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Area influencia a 1 minuto andando\n",
    "\n",
    "Tiempo_andando = 1\n",
    "area_colegio = '\\\\Área_colegio_'+str(Tiempo_andando)+'_min'\n",
    "#####################################\n",
    "\n",
    "# Añadimos el servicio de Áreas\n",
    "Área_de_Influencia = arcpy.na.MakeServiceAreaAnalysisLayer(\n",
    "    network_data_source=r\"C:\\Users\\aaron.benchiheub\\OneDrive - ESRI ESPAÑA Soluciones Geoespaciales S.L\\Documentos\\ArcGIS\\Packages\\Spain_972783\\p14\\europe.geodatabase\\main.Routing\\main.Routing_ND\",\n",
    "    layer_name=\"Área_de_Influencia\",\n",
    "    travel_mode=\"Walking Time\", #Driving time, Driving Distance, Walking Time, Walking Distance, Rural Driving Distance, Rural Driving Time, Trucking Distance, Trucking Time\n",
    "    travel_direction=\"FROM_FACILITIES\",\n",
    "    cutoffs=[Tiempo_andando],# Timpo \n",
    "    time_of_day=None,\n",
    "    time_zone=\"LOCAL_TIME_AT_LOCATIONS\",\n",
    "    output_type=\"POLYGONS\",\n",
    "    polygon_detail=\"STANDARD\",\n",
    "    geometry_at_overlaps=\"OVERLAP\",\n",
    "    geometry_at_cutoffs=\"RINGS\",\n",
    "    polygon_trim_distance=\"100 Meters\",\n",
    "    exclude_sources_from_polygon_generation=None,\n",
    "    accumulate_attributes=None,\n",
    "    ignore_invalid_locations=\"SKIP\"\n",
    ")\n",
    "\n",
    "# Añadimos los colegios al servicio \n",
    "arcpy.na.AddLocations(\n",
    "    in_network_analysis_layer=\"Área_de_Influencia\",\n",
    "    sub_layer=\"Instalaciones\",\n",
    "    in_table=\"Colegios\",\n",
    "    field_mappings=\"Name # #;CurbApproach # 0;Attr_Minutes # 0;Attr_TravelTime # 0;Attr_Miles # 0;Attr_Kilometers # 0;Attr_TimeAt1KPH # 0;Attr_WalkTime # 0;Attr_TruckMinutes # 0;Attr_TruckTravelTime # 0;Breaks_Minutes # #;Breaks_TravelTime # #;Breaks_Miles # #;Breaks_Kilometers # #;Breaks_TimeAt1KPH # #;Breaks_WalkTime # #;Breaks_TruckMinutes # #;Breaks_TruckTravelTime # #\",\n",
    "    search_tolerance=\"5000 Meters\",\n",
    "    sort_field=None,\n",
    "    search_criteria=\"main.Routing_Streets SHAPE;main.Custom_Streets SHAPE;main.Routing_Streets_Override NONE;main.Custom_Streets_Override SHAPE;main.Routing_ND_Junctions NONE\",\n",
    "    match_type=\"MATCH_TO_CLOSEST\",\n",
    "    append=\"APPEND\",\n",
    "    snap_to_position_along_network=\"NO_SNAP\",\n",
    "    snap_offset=\"5 Meters\",\n",
    "    exclude_restricted_elements=\"EXCLUDE\",\n",
    "    search_query=None,\n",
    "    allow_auto_relocate=None\n",
    ")\n",
    "\n",
    "# Resolvemos \n",
    "arcpy.na.Solve(\n",
    "    in_network_analysis_layer=\"Área_de_Influencia\",\n",
    "    ignore_invalids=\"SKIP\",\n",
    "    terminate_on_solve_error=\"TERMINATE\",\n",
    "    simplification_tolerance=None,\n",
    "    overrides=\"\"\n",
    ")\n",
    "# Exportamos la cápa de polígonos (áreas) resultante\n",
    "arcpy.conversion.ExportFeatures(\n",
    "    in_features=r\"Área_de_Influencia\\Polígonos\",\n",
    "    out_features=path_gdb + area_colegio,\n",
    "    where_clause=\"\",\n",
    "    use_field_alias_as_name=\"NOT_USE_ALIAS\",\n",
    "    field_mapping=r'FacilityID \"FacilityID\" true true true 4 Long 0 0,First,#,Área_de_Influencia\\Polígonos,FacilityID,-1,-1;Name \"Name\" true true true 1024 Text 0 0,First,#,Área_de_Influencia\\Polígonos,Name,0,1024;FromBreak \"FromBreak\" true true true 8 Double 0 0,First,#,Área_de_Influencia\\Polígonos,FromBreak,-1,-1;ToBreak \"ToBreak\" true true true 8 Double 0 0,First,#,Área_de_Influencia\\Polígonos,ToBreak,-1,-1;Shape_Length \"Shape_Length\" false true true 8 Double 0 0,First,#,Área_de_Influencia\\Polígonos,Shape_Length,-1,-1;Shape_Area \"Shape_Area\" false true true 8 Double 0 0,First,#,Área_de_Influencia\\Polígonos,Shape_Area,-1,-1',\n",
    "    sort_field=None\n",
    ")\n",
    "# Borrar servicio de cálculo de áreas de influencia\n",
    "arcpy.na.DeleteNetworkAnalysisLayer('Área_de_Influencia')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "1f695b2a",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[54]:15: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n"
     ]
    },
    {
     "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>accidentes</th>\n",
       "      <th>Número</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Atropellos a persona Totals</td>\n",
       "      <td>12620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Atropellos_1min</td>\n",
       "      <td>4516</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    accidentes  Número\n",
       "0  Atropellos a persona Totals   12620\n",
       "1              Atropellos_1min    4516"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Área_colegio_1_min = arcpy.analysis.Clip(\n",
    "    in_features=\"atropellos_persona\",\n",
    "    clip_features=\"Área_colegio_1_min\",\n",
    "    out_feature_class=r\"C:\\00_Documentos_ESRI\\99_Mis_proyectos_ArcGIS_Pro\\Análisis de Siniestralidad - Madrid\\Análisis de Siniestralidad - Madrid.gdb\\Atropellos_1min\",\n",
    "    cluster_tolerance=None\n",
    ")\n",
    "# Obtener el número de filas de la capa\n",
    "registros_totales = int(arcpy.GetCount_management(\"Atropellos_1min\").getOutput(0))\n",
    "# Nuevo registro a agregar\n",
    "nuevo_registro = {\n",
    "    \"accidentes\": \"Atropellos_1min\",\n",
    "    \"Número\": registros_totales\n",
    "}\n",
    "# Añadir el nuevo registro al DataFrame\n",
    "df = df.append(nuevo_registro, ignore_index=True)\n",
    "df"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "1f5da0e3",
   "metadata": {
    "hidden": true
   },
   "source": [
    "#### Area de influencia 2 y 3 minutos andando"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "7860c1cb",
   "metadata": {
    "hidden": true,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[55]:77: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "[55]:157: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n"
     ]
    },
    {
     "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>accidentes</th>\n",
       "      <th>Número</th>\n",
       "      <th>porcentaje</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Atropellos a persona Totals</td>\n",
       "      <td>12620</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Atropellos_1min</td>\n",
       "      <td>4516</td>\n",
       "      <td>35.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Atropellos_2min</td>\n",
       "      <td>7064</td>\n",
       "      <td>55.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Atropellos_3min</td>\n",
       "      <td>9370</td>\n",
       "      <td>74.25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    accidentes  Número  porcentaje\n",
       "0  Atropellos a persona Totals   12620      100.00\n",
       "1              Atropellos_1min    4516       35.78\n",
       "2              Atropellos_2min    7064       55.97\n",
       "3              Atropellos_3min    9370       74.25"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Area de influencia 2 y 3 minutos andando\n",
    "\n",
    "Tiempo_andando = 2\n",
    "area_colegio = '\\\\Área_colegio_'+str(Tiempo_andando)+'_min'\n",
    "#####################################\n",
    "\n",
    "# Añadimos el servicio de Áreas\n",
    "Área_de_Influencia = arcpy.na.MakeServiceAreaAnalysisLayer(\n",
    "    network_data_source=r\"C:\\Users\\aaron.benchiheub\\OneDrive - ESRI ESPAÑA Soluciones Geoespaciales S.L\\Documentos\\ArcGIS\\Packages\\Spain_972783\\p14\\europe.geodatabase\\main.Routing\\main.Routing_ND\",\n",
    "    layer_name=\"Área_de_Influencia\",\n",
    "    travel_mode=\"Walking Time\", #Driving time, Driving Distance, Walking Time, Walking Distance, Rural Driving Distance, Rural Driving Time, Trucking Distance, Trucking Time\n",
    "    travel_direction=\"FROM_FACILITIES\",\n",
    "    cutoffs=[Tiempo_andando],# Timpo \n",
    "    time_of_day=None,\n",
    "    time_zone=\"LOCAL_TIME_AT_LOCATIONS\",\n",
    "    output_type=\"POLYGONS\",\n",
    "    polygon_detail=\"STANDARD\",\n",
    "    geometry_at_overlaps=\"OVERLAP\",\n",
    "    geometry_at_cutoffs=\"RINGS\",\n",
    "    polygon_trim_distance=\"100 Meters\",\n",
    "    exclude_sources_from_polygon_generation=None,\n",
    "    accumulate_attributes=None,\n",
    "    ignore_invalid_locations=\"SKIP\"\n",
    ")\n",
    "\n",
    "# Añadimos los colegios al servicio \n",
    "arcpy.na.AddLocations(\n",
    "    in_network_analysis_layer=\"Área_de_Influencia\",\n",
    "    sub_layer=\"Instalaciones\",\n",
    "    in_table=\"Colegios\",\n",
    "    field_mappings=\"Name # #;CurbApproach # 0;Attr_Minutes # 0;Attr_TravelTime # 0;Attr_Miles # 0;Attr_Kilometers # 0;Attr_TimeAt1KPH # 0;Attr_WalkTime # 0;Attr_TruckMinutes # 0;Attr_TruckTravelTime # 0;Breaks_Minutes # #;Breaks_TravelTime # #;Breaks_Miles # #;Breaks_Kilometers # #;Breaks_TimeAt1KPH # #;Breaks_WalkTime # #;Breaks_TruckMinutes # #;Breaks_TruckTravelTime # #\",\n",
    "    search_tolerance=\"5000 Meters\",\n",
    "    sort_field=None,\n",
    "    search_criteria=\"main.Routing_Streets SHAPE;main.Custom_Streets SHAPE;main.Routing_Streets_Override NONE;main.Custom_Streets_Override SHAPE;main.Routing_ND_Junctions NONE\",\n",
    "    match_type=\"MATCH_TO_CLOSEST\",\n",
    "    append=\"APPEND\",\n",
    "    snap_to_position_along_network=\"NO_SNAP\",\n",
    "    snap_offset=\"5 Meters\",\n",
    "    exclude_restricted_elements=\"EXCLUDE\",\n",
    "    search_query=None,\n",
    "    allow_auto_relocate=None\n",
    ")\n",
    "\n",
    "# Resolvemos \n",
    "arcpy.na.Solve(\n",
    "    in_network_analysis_layer=\"Área_de_Influencia\",\n",
    "    ignore_invalids=\"SKIP\",\n",
    "    terminate_on_solve_error=\"TERMINATE\",\n",
    "    simplification_tolerance=None,\n",
    "    overrides=\"\"\n",
    ")\n",
    "# Exportamos la cápa de polígonos (áreas) resultante\n",
    "arcpy.conversion.ExportFeatures(\n",
    "    in_features=r\"Área_de_Influencia\\Polígonos\",\n",
    "    out_features=path_gdb + area_colegio,\n",
    "    where_clause=\"\",\n",
    "    use_field_alias_as_name=\"NOT_USE_ALIAS\",\n",
    "    field_mapping=r'FacilityID \"FacilityID\" true true true 4 Long 0 0,First,#,Área_de_Influencia\\Polígonos,FacilityID,-1,-1;Name \"Name\" true true true 1024 Text 0 0,First,#,Área_de_Influencia\\Polígonos,Name,0,1024;FromBreak \"FromBreak\" true true true 8 Double 0 0,First,#,Área_de_Influencia\\Polígonos,FromBreak,-1,-1;ToBreak \"ToBreak\" true true true 8 Double 0 0,First,#,Área_de_Influencia\\Polígonos,ToBreak,-1,-1;Shape_Length \"Shape_Length\" false true true 8 Double 0 0,First,#,Área_de_Influencia\\Polígonos,Shape_Length,-1,-1;Shape_Area \"Shape_Area\" false true true 8 Double 0 0,First,#,Área_de_Influencia\\Polígonos,Shape_Area,-1,-1',\n",
    "    sort_field=None\n",
    ")\n",
    "# Borrar servicio de cálculo de áreas de influencia\n",
    "arcpy.na.DeleteNetworkAnalysisLayer('Área_de_Influencia')\n",
    "\n",
    "\n",
    "Área_colegio_2_min = arcpy.analysis.Clip(\n",
    "    in_features=\"atropellos_persona\",\n",
    "    clip_features=\"Área_colegio_2_min\",\n",
    "    out_feature_class=r\"C:\\00_Documentos_ESRI\\99_Mis_proyectos_ArcGIS_Pro\\Análisis de Siniestralidad - Madrid\\Análisis de Siniestralidad - Madrid.gdb\\Atropellos_2min\",\n",
    "    cluster_tolerance=None\n",
    ")\n",
    "# Obtener el número de filas de la capa\n",
    "registros_totales = int(arcpy.GetCount_management(\"Atropellos_2min\").getOutput(0))\n",
    "# Nuevo registro a agregar\n",
    "nuevo_registro = {\n",
    "    \"accidentes\": \"Atropellos_2min\",\n",
    "    \"Número\": registros_totales\n",
    "}\n",
    "# Añadir el nuevo registro al DataFrame\n",
    "df = df.append(nuevo_registro, ignore_index=True)\n",
    "\n",
    "##############################\n",
    "##############################\n",
    "\n",
    "Tiempo_andando = 3\n",
    "area_colegio = '\\\\Área_colegio_'+str(Tiempo_andando)+'_min'\n",
    "#####################################\n",
    "\n",
    "# Añadimos el servicio de Áreas\n",
    "Área_de_Influencia = arcpy.na.MakeServiceAreaAnalysisLayer(\n",
    "    network_data_source=r\"C:\\Users\\aaron.benchiheub\\OneDrive - ESRI ESPAÑA Soluciones Geoespaciales S.L\\Documentos\\ArcGIS\\Packages\\Spain_972783\\p14\\europe.geodatabase\\main.Routing\\main.Routing_ND\",\n",
    "    layer_name=\"Área_de_Influencia\",\n",
    "    travel_mode=\"Walking Time\", #Driving time, Driving Distance, Walking Time, Walking Distance, Rural Driving Distance, Rural Driving Time, Trucking Distance, Trucking Time\n",
    "    travel_direction=\"FROM_FACILITIES\",\n",
    "    cutoffs=[Tiempo_andando],# Timpo \n",
    "    time_of_day=None,\n",
    "    time_zone=\"LOCAL_TIME_AT_LOCATIONS\",\n",
    "    output_type=\"POLYGONS\",\n",
    "    polygon_detail=\"STANDARD\",\n",
    "    geometry_at_overlaps=\"OVERLAP\",\n",
    "    geometry_at_cutoffs=\"RINGS\",\n",
    "    polygon_trim_distance=\"100 Meters\",\n",
    "    exclude_sources_from_polygon_generation=None,\n",
    "    accumulate_attributes=None,\n",
    "    ignore_invalid_locations=\"SKIP\"\n",
    ")\n",
    "\n",
    "# Añadimos los colegios al servicio \n",
    "arcpy.na.AddLocations(\n",
    "    in_network_analysis_layer=\"Área_de_Influencia\",\n",
    "    sub_layer=\"Instalaciones\",\n",
    "    in_table=\"Colegios\",\n",
    "    field_mappings=\"Name # #;CurbApproach # 0;Attr_Minutes # 0;Attr_TravelTime # 0;Attr_Miles # 0;Attr_Kilometers # 0;Attr_TimeAt1KPH # 0;Attr_WalkTime # 0;Attr_TruckMinutes # 0;Attr_TruckTravelTime # 0;Breaks_Minutes # #;Breaks_TravelTime # #;Breaks_Miles # #;Breaks_Kilometers # #;Breaks_TimeAt1KPH # #;Breaks_WalkTime # #;Breaks_TruckMinutes # #;Breaks_TruckTravelTime # #\",\n",
    "    search_tolerance=\"5000 Meters\",\n",
    "    sort_field=None,\n",
    "    search_criteria=\"main.Routing_Streets SHAPE;main.Custom_Streets SHAPE;main.Routing_Streets_Override NONE;main.Custom_Streets_Override SHAPE;main.Routing_ND_Junctions NONE\",\n",
    "    match_type=\"MATCH_TO_CLOSEST\",\n",
    "    append=\"APPEND\",\n",
    "    snap_to_position_along_network=\"NO_SNAP\",\n",
    "    snap_offset=\"5 Meters\",\n",
    "    exclude_restricted_elements=\"EXCLUDE\",\n",
    "    search_query=None,\n",
    "    allow_auto_relocate=None\n",
    ")\n",
    "\n",
    "# Resolvemos \n",
    "arcpy.na.Solve(\n",
    "    in_network_analysis_layer=\"Área_de_Influencia\",\n",
    "    ignore_invalids=\"SKIP\",\n",
    "    terminate_on_solve_error=\"TERMINATE\",\n",
    "    simplification_tolerance=None,\n",
    "    overrides=\"\"\n",
    ")\n",
    "# Exportamos la cápa de polígonos (áreas) resultante\n",
    "arcpy.conversion.ExportFeatures(\n",
    "    in_features=r\"Área_de_Influencia\\Polígonos\",\n",
    "    out_features=path_gdb + area_colegio,\n",
    "    where_clause=\"\",\n",
    "    use_field_alias_as_name=\"NOT_USE_ALIAS\",\n",
    "    field_mapping=r'FacilityID \"FacilityID\" true true true 4 Long 0 0,First,#,Área_de_Influencia\\Polígonos,FacilityID,-1,-1;Name \"Name\" true true true 1024 Text 0 0,First,#,Área_de_Influencia\\Polígonos,Name,0,1024;FromBreak \"FromBreak\" true true true 8 Double 0 0,First,#,Área_de_Influencia\\Polígonos,FromBreak,-1,-1;ToBreak \"ToBreak\" true true true 8 Double 0 0,First,#,Área_de_Influencia\\Polígonos,ToBreak,-1,-1;Shape_Length \"Shape_Length\" false true true 8 Double 0 0,First,#,Área_de_Influencia\\Polígonos,Shape_Length,-1,-1;Shape_Area \"Shape_Area\" false true true 8 Double 0 0,First,#,Área_de_Influencia\\Polígonos,Shape_Area,-1,-1',\n",
    "    sort_field=None\n",
    ")\n",
    "# Borrar servicio de cálculo de áreas de influencia\n",
    "arcpy.na.DeleteNetworkAnalysisLayer('Área_de_Influencia')\n",
    "\n",
    "Área_colegio_3_min = arcpy.analysis.Clip(\n",
    "    in_features=\"atropellos_persona\",\n",
    "    clip_features=\"Área_colegio_3_min\",\n",
    "    out_feature_class=r\"C:\\00_Documentos_ESRI\\99_Mis_proyectos_ArcGIS_Pro\\Análisis de Siniestralidad - Madrid\\Análisis de Siniestralidad - Madrid.gdb\\Atropellos_3min\",\n",
    "    cluster_tolerance=None\n",
    ")\n",
    "# Obtener el número de filas de la capa\n",
    "registros_totales = int(arcpy.GetCount_management(\"Atropellos_3min\").getOutput(0))\n",
    "# Nuevo registro a agregar\n",
    "nuevo_registro = {\n",
    "    \"accidentes\": \"Atropellos_3min\",\n",
    "    \"Número\": registros_totales\n",
    "}\n",
    "# Añadir el nuevo registro al DataFrame\n",
    "df = df.append(nuevo_registro, ignore_index=True)\n",
    "# Calcular el porcentaje en relación a \"Atropellos a persona Totals\"\n",
    "df[\"porcentaje\"] = round(df[\"Número\"] / df.loc[0, \"Número\"] * 100, 2)\n",
    "df\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "817e28c8",
   "metadata": {
    "hidden": true
   },
   "source": [
    "#### Resulstaos Area de influencia 1, 2 y 3 minutos andando"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "id": "1636cace",
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Gráfico de barras\n",
    "ax = df.plot(x=\"accidentes\", y=\"porcentaje\", kind=\"bar\", color=\"#35b6ff\", figsize=(12, 5))\n",
    "\n",
    "# Añadir las etiquetas de porcentaje\n",
    "for p in ax.patches:\n",
    "    percentage = f\"{p.get_height():.1f}%\"\n",
    "    x = p.get_x() + p.get_width() / 2\n",
    "    y = p.get_height()\n",
    "    ax.annotate(percentage, (x, y), ha=\"center\", va=\"bottom\")\n",
    "\n",
    "# Estilo gráfico\n",
    "plt.xticks(rotation=0)  # No rotar las etiquetas del eje x\n",
    "plt.tight_layout()  # Ajustar el espacio entre los elementos del gráfico\n",
    "plt.title(\"Porcentaje de accidentes por tipo\", fontsize=12)\n",
    "plt.xlabel(\"Tipo de accidente\", fontsize=9, fontweight=\"bold\")\n",
    "plt.ylabel(\"Porcentaje de Accidentes\", fontsize=9, fontweight=\"bold\")\n",
    "plt.grid(axis=\"y\", color=\"gray\", linestyle=\":\", linewidth=0.5)\n",
    "\n",
    "ax.tick_params(axis=\"x\", which=\"both\", bottom=False, top=False)\n",
    "ax.spines[\"top\"].set_visible(False)\n",
    "ax.spines[\"right\"].set_visible(False)\n",
    "ax.spines[\"left\"].set_visible(False)\n",
    "\n",
    "plt.show()  # Mostrar el gráfico\n",
    "\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "e8b02acd",
   "metadata": {
    "hidden": true
   },
   "source": [
    "##### Borrar capas"
   ]
  },
  {
   "cell_type": "raw",
   "id": "ebc89fc9",
   "metadata": {
    "hidden": true
   },
   "source": [
    "# Ver todas las capas\n",
    "capas = arcpy.mp.ArcGISProject(\"CURRENT\").listMaps()[0].listLayers()\n",
    "nombres_capas_a_eliminar = []\n",
    "for capa in capas:\n",
    "    nombres_capas_a_eliminar.append(capa.name)\n",
    "    print(capa.name)\n",
    "\n",
    "# Lista de nombres de capas a eliminar\n",
    "nombres_capas_a_eliminar = [capa for capa in nombres_capas_a_eliminar if capa not in ['Referencia gris clara', \n",
    "                                                                                      'Base gris clara', \n",
    "                                                                                      'Routing_ND']]\n",
    "\n",
    "# Eliminar las capas\n",
    "for capa in capas:\n",
    "    if capa.name in nombres_capas_a_eliminar:\n",
    "        mapa.removeLayer(capa)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "a35c1e46",
   "metadata": {},
   "source": [
    "# Análisis predictivo - Modelo"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "1fd4727d",
   "metadata": {},
   "source": [
    "<div style=\"text-align:center\">\n",
    "    <img src=\"./assets/Modeling_data.png\" alt=\"drawing\" width=\"200\"/>\n",
    "</div>\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "e03b4770",
   "metadata": {},
   "source": [
    "## Cubos espacio-temporales | Análisis de puntos calientes emergentes con "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "ddb7fcc4",
   "metadata": {},
   "source": [
    "<div style=\"text-align:center\">\n",
    "<a href=\"https://pro.arcgis.com/en/pro-app/latest/tool-reference/space-time-pattern-mining/emerginghotspots.htm\">Emerging Hot Spot Analysis (Space Time Pattern Mining)</a>\n",
    "\n",
    "<img src=\"https://pro.arcgis.com/en/pro-app/latest/tool-reference/space-time-pattern-mining/GUID-3FE51525-E732-4FAB-80E2-71B866BECE61-web.png\" alt=\"drawing\" width=\"200\"/>\n",
    "<img src=\"https://pro.arcgis.com/en/pro-app/latest/tool-reference/space-time-pattern-mining/GUID-0FEECE1A-6B54-44B4-AE49-05E7EA849A8B-web.png\" alt=\"drawing\" width=\"200\"/>\n",
    "<img src=\"https://pro.arcgis.com/en/pro-app/latest/tool-reference/space-time-pattern-mining/GUID-42A31756-6518-41E9-A900-2C892AF4023A-web.png\" alt=\"drawing\" width=\"200\"/>\n",
    "</div>\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "94da1de9",
   "metadata": {},
   "source": [
    "Agregamos el códico de barrio a la capa de puntos **'COD_BAR'**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "b653874e",
   "metadata": {},
   "outputs": [],
   "source": [
    "Barrios = arcpy.management.CopyFeatures(r'C:\\00_Documentos_ESRI\\99_Mis_proyectos_ArcGIS_Pro\\Análisis de Siniestralidad - Madrid\\datos\\Shapefile\\Barrios_Madrid\\Barrios.shp', 'Barrios')\n",
    "#puntos_accidentes = arcpy.management.CopyFeatures(r'C:\\00_Documentos_ESRI\\99_Mis_proyectos_ArcGIS_Pro\\Análisis de Siniestralidad - Madrid\\datos\\outputs\\puntos_accidentes.shp', 'puntos_accidentes')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "355ec864",
   "metadata": {},
   "source": [
    "### Visialización en 2 D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "ac33c94f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class='gpresult'><h2>Messages</h2><div id='messages' data-messages='[\"Tiempo de Inicio: lunes, 19 de junio de 2023 13:21:41\",\"Completado correctamente el lunes, 19 de junio de 2023 13:22:05 (Tiempo transcurrido: 23,41 segundos)\"]' data-show='true'><div id = 'default' /></div></div>"
      ],
      "text/plain": [
       "<Result 'C:\\\\00_Documentos_ESRI\\\\99_Mis_proyectos_ArcGIS_Pro\\\\Análisis de Siniestralidad - Madrid\\\\datos\\\\outputs\\\\puntos_accidentes_COD_barrios.shp'>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Agregación de barrios en puntos\n",
    "arcpy.analysis.SpatialJoin(\n",
    "    target_features=\"puntos_accidentes\",\n",
    "    join_features=\"Barrios\",\n",
    "    out_feature_class=r\"C:\\00_Documentos_ESRI\\99_Mis_proyectos_ArcGIS_Pro\\Análisis de Siniestralidad - Madrid\\datos\\outputs\\puntos_accidentes_COD_barrios.shp\",\n",
    "    join_operation=\"JOIN_ONE_TO_ONE\",\n",
    "    join_type=\"KEEP_ALL\",\n",
    "    field_mapping=None,\n",
    "    match_option=\"INTERSECT\",\n",
    "    search_radius=None,\n",
    "    distance_field_name=\"\"\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "4ebc0bf1",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class='gpresult'><h2>Messages</h2><div id='messages' data-messages='[\"Tiempo de Inicio: lunes, 19 de junio de 2023 13:24:15\",\"WARNING 110020: La distancia de vecindad predeterminada es 1312,150442 metros.\",\"\",[{\"element\":\"table\",\"data\":[[\"Intervalo de distancia\",\"317 metros\"],[\"Intervalo de período de tiempo\",\"3 weeks\"],[\"\",\"\"],[\"Tipo de forma de agregación\",\"Hexagon Grid\"],[\"\",\"\"],[\"Sesgo temporal del primer período de tiempo\",\"76,19%\"],[{\"data\":\"Primer intervalo de período de tiempo\",\"prop\":{\"rowspan\":4}},\"después de\"],[{\"data\":\"2018-12-16 00:00:00\",\"prop\":{\"text-align\":\"right\"}}],[{\"data\":\"hasta o antes de\",\"prop\":{\"text-align\":\"right\"}}],[{\"data\":\"2019-01-06 00:00:00\",\"prop\":{\"text-align\":\"right\"}}],[\"\",\"\"],[\"Sesgo temporal del último período de tiempo\",\"0,00%\"],[{\"data\":\"Último intervalo de período de tiempo\",\"prop\":{\"rowspan\":4}},\"después de\"],[{\"data\":\"2023-04-09 00:00:00\",\"prop\":{\"text-align\":\"right\"}}],[{\"data\":\"hasta o antes de\",\"prop\":{\"text-align\":\"right\"}}],[{\"data\":\"2023-04-30 00:00:00\",\"prop\":{\"text-align\":\"right\"}}],[\"\",\"\"],[\"Número de períodos de tiempo\",\"76\"],[\"Número de ubicaciones analizadas\",\"3028\"],[\"Número de bins de espacio-tiempo analizados\",\"230128\"],[\"% distinto de cero\",\"23,51%\"]],\"elementProps\":{\"striped\":\"true\",\"noHeader\":true,\"title\":\"Detalles de cubo de espacio-tiempo de entrada\",\"0\":{\"align\":\"left\",\"pad\":\"0px\",\"wrap\":true},\"1\":{\"align\":\"right\",\"pad\":\"0px\",\"wrap\":true}}}],[{\"element\":\"table\",\"data\":[[\"Distancia de vecindad\",\"1312,150442 metros\"],[\"Intervalos de período de tiempo de vecindad\",\"1 (expandiendo 3 weeks)\"]],\"elementProps\":{\"striped\":\"true\",\"noHeader\":true,\"title\":\"Detalles del análisis\",\"0\":{\"align\":\"left\",\"pad\":\"0px\",\"wrap\":true},\"1\":{\"align\":\"right\",\"pad\":\"0px\",\"wrap\":true}}}],[{\"element\":\"table\",\"data\":[[\"\",\"CALIENTE\",\"FRÍO\"],[\"Nuevo\",\"27\",\"0\"],[\"Consecutiva\",\"0\",\"0\"],[\"En aumento\",\"51\",\"19\"],[\"Persistente\",\"300\",\"305\"],[\"En disminución\",\"0\",\"102\"],[\"Esporádico\",\"0\",\"382\"],[\"Oscilante\",\"589\",\"2\"],[\"Historical\",\"0\",\"35\"]],\"elementProps\":{\"striped\":\"true\",\"footnote\":[\"Todas las ubicaciones con tendencias de puntos calientes o fríos: 1812 de 3028\"],\"title\":\"Resumen de resultados\",\"0\":{\"align\":\"left\",\"pad\":\"0px\",\"wrap\":true},\"1\":{\"align\":\"right\",\"pad\":\"0px\",\"wrap\":true},\"2\":{\"align\":\"right\",\"pad\":\"0px\",\"wrap\":true}}}],{\"element\":\"h5\",\"data\":\"Definiciones de categoría\"},{\"element\":\"content\",\"data\":\"El último período de tiempo es caliente:\"},{\"element\":\"unorderedlist\",\"data\":[\"Nuevo: el intervalo de período de tiempo más reciente es caliente por primera vez\",\"Consecutiva: una única ejecución ininterrumpida de intervalos de períodos de tiempo calientes, con menos del 90% del total\",\"En aumento: al menos el 90% de los intervalos de períodos de tiempo son calientes y cada vez son más calientes a medida que pasa el tiempo\",\"Persistente: al menos el 90% de los intervalos de períodos de tiempo son calientes, sin tendencia a aumentar ni a disminuir\",\"En disminución: al menos el 90% de los intervalos de períodos de tiempo son calientes y cada vez son menos calientes a medida que pasa el tiempo\",\"Esporádico: algunos de los intervalos de períodos de tiempo son calientes\",\"Oscilante: algunos de los intervalos de períodos de tiempo son calientes y otros son fríos\"]},{\"element\":\"content\",\"data\":\"El último período de tiempo no es caliente:\"},{\"element\":\"unorderedlist\",\"data\":[\"Histórico: al menos el 90% de los intervalos de períodos de tiempo son calientes, pero no lo es el intervalo de períodos de tiempo más reciente\"]},{\"element\":\"content\",\"data\":\"El último período de tiempo es frío:\"},{\"element\":\"unorderedlist\",\"data\":[\"Nuevo: el intervalo de período de tiempo más reciente es frío por primera vez\",\"Consecutiva: una única ejecución ininterrumpida de intervalos de períodos de tiempo fríos, con menos del 90% del total\",\"En aumento: al menos el 90% de los intervalos de períodos de tiempo son fríos y cada vez son más fríos a medida que pasa el tiempo\",\"Persistente: al menos el 90% de los intervalos de períodos de tiempo son fríos, sin tendencia a aumentar ni a disminuir\",\"En disminución: al menos el 90% de los intervalos de períodos de tiempo son fríos y cada vez son menos fríos a medida que pasan los intervalos de tiempo\",\"Esporádico: algunos de los intervalos de períodos de tiempo son fríos\",\"Oscilante: algunos de los intervalos de períodos de tiempo son fríos y otros son calientes\"]},{\"element\":\"content\",\"data\":\"El último período de tiempo no es frío:\"},{\"element\":\"unorderedlist\",\"data\":[\"Histórico: al menos el 90% de los intervalos de períodos de tiempo son fríos, pero no lo es el intervalo de períodos de tiempo más reciente\"]},\"\\n\\n\\n\\n\\n\\n\\n\\n\\n\",\"Completado correctamente el lunes, 19 de junio de 2023 13:24:20 (Tiempo transcurrido: 4,64 segundos)\"]' data-show='true'><div id = 'default' /></div></div>"
      ],
      "text/plain": [
       "<Result 'C:\\\\00_Documentos_ESRI\\\\99_Mis_proyectos_ArcGIS_Pro\\\\Análisis de Siniestralidad - Madrid\\\\datos\\\\outputs\\\\cubos_espacio_temporales\\\\EmergingHotSpot_2d.shp'>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Configuración de la herramientea cubo espacio temporales\n",
    "cubo_accidentes = '\\\\cubo_accidentes.nc'\n",
    "ruta = '\\\\datos\\\\outputs\\\\cubos_espacio_temporales'\n",
    "path_gdb = r\"C:\\00_Documentos_ESRI\\99_Mis_proyectos_ArcGIS_Pro\\Análisis de Siniestralidad - Madrid\"\n",
    "\n",
    "############################################################\n",
    "\n",
    "arcpy.stpm.CreateSpaceTimeCube(\n",
    "    in_features=\"puntos_accidentes_COD_barrios\",\n",
    "    output_cube = path_gdb + ruta + cubo_accidentes,\n",
    "    time_field=\"fecha\",\n",
    "    template_cube=None,\n",
    "    time_step_interval=None,\n",
    "    time_step_alignment=\"END_TIME\",\n",
    "    reference_time=None,\n",
    "    distance_interval=None,\n",
    "    summary_fields=None,\n",
    "    aggregation_shape_type=\"HEXAGON_GRID\",\n",
    "    defined_polygon_locations=None,\n",
    "    location_id=None\n",
    ")\n",
    "\n",
    "EmergingHotSpot_2d = '\\\\EmergingHotSpot_2d'\n",
    "###########################################\n",
    "\n",
    "arcpy.stpm.EmergingHotSpotAnalysis(\n",
    "    in_cube = path_gdb + ruta + cubo_accidentes,\n",
    "    analysis_variable=\"COUNT\",\n",
    "    output_features=path_gdb + ruta + EmergingHotSpot_2d,\n",
    "    neighborhood_distance=None,\n",
    "    neighborhood_time_step=1,\n",
    "    polygon_mask=None,\n",
    "    conceptualization_of_spatial_relationships=\"FIXED_DISTANCE\",\n",
    "    number_of_neighbors=None,\n",
    "    define_global_window=\"ENTIRE_CUBE\"\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "c78a3fca",
   "metadata": {},
   "source": [
    "### Visialización en 3 D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "f07287b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class='gpresult'><h2>Messages</h2><div id='messages' data-messages='[\"Tiempo de Inicio: lunes, 19 de junio de 2023 13:25:20\",\"WARNING 110044: El tiempo que se tarda en obtener el cubo en tres dimensiones puede variar considerablemente según el número de entidades y la tarjeta gráfica asociada a su CPU.\",\"WARNING 110054: Si alguno de los datos no se dibuja, es posible que esté fuera de los límites del rango de visibilidad, que se pueden borrar. Si alguno de los datos queda bajo tierra, es posible que necesite desactivar las superficies de elevación.\",\"Completado correctamente el lunes, 19 de junio de 2023 13:26:03 (Tiempo transcurrido: 42,51 segundos)\"]' data-show='true'><div id = 'default' /></div></div>"
      ],
      "text/plain": [
       "<Result 'C:\\\\00_Documentos_ESRI\\\\99_Mis_proyectos_ArcGIS_Pro\\\\Análisis de Siniestralidad - Madrid\\\\datos\\\\outputs\\\\cubos_espacio_temporales\\\\EmergingHotSpot_3d.shp'>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "EmergingHotSpot_3d = '\\\\EmergingHotSpot_3d'\n",
    "###########################################\n",
    "\n",
    "arcpy.stpm.VisualizeSpaceTimeCube3D(\n",
    "    in_cube=path_gdb + ruta + cubo_accidentes,\n",
    "    cube_variable=\"COUNT\",\n",
    "    display_theme=\"HOT_AND_COLD_SPOT_RESULTS\",\n",
    "    output_features=path_gdb + ruta + EmergingHotSpot_3d\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "e95eddbc",
   "metadata": {},
   "source": [
    "## Serie temporal"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "c4576593",
   "metadata": {},
   "source": [
    "\n",
    "Descomponemos la agregación de accientes en barrios y de forma temporal. Basado en los cubos espacio temporales"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "42e72739",
   "metadata": {},
   "source": [
    "<div style=\"text-align:center\">\n",
    "<a href=\"https://pro.arcgis.com/en/pro-app/latest/tool-reference/space-time-pattern-mining/exponentialsmoothingforecast.htm\">Exponential Smoothing Forecast (Space Time Pattern Mining)</a>\n",
    "\n",
    "<img src=\"https://pro.arcgis.com/en/pro-app/latest/tool-reference/space-time-pattern-mining/GUID-DFD83A2B-94FB-4369-9485-5424D5351D60-web.png\" alt=\"drawing\" width=\"400\"/>\n",
    "</div>\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "31d71a6b",
   "metadata": {},
   "source": [
    "### Serie temporal por barrios"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "14c23d75",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class='gpresult'><h2>Messages</h2><div id='messages' data-messages='[\"Tiempo de Inicio: lunes, 19 de junio de 2023 13:27:29\",\"Completado correctamente el lunes, 19 de junio de 2023 13:27:31 (Tiempo transcurrido: 1,99 segundos)\"]' data-show='true'><div id = 'default' /></div></div>"
      ],
      "text/plain": [
       "<Result 'C:\\\\00_Documentos_ESRI\\\\99_Mis_proyectos_ArcGIS_Pro\\\\Análisis de Siniestralidad - Madrid\\\\datos\\\\outputs\\\\cubos_espacio_temporales\\\\serie_tiempo_por_barrios.gdb\\\\serie_tiempo_por_barrios'>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cubo espaciotemporal barrios\n",
    "arcpy.stpm.CreateSpaceTimeCubeDefinedLocations(\n",
    "    in_features=\"Barrios\",\n",
    "    output_cube=r\"C:\\00_Documentos_ESRI\\99_Mis_proyectos_ArcGIS_Pro\\Análisis de Siniestralidad - Madrid\\datos\\outputs\\cubos_espacio_temporales\\cubo_serie_barrios.nc\",\n",
    "    location_id=\"COD_BAR\",\n",
    "    temporal_aggregation=\"APPLY_TEMPORAL_AGGREGATION\",\n",
    "    time_field=\"fecha\",\n",
    "    time_step_interval=\"1 Months\",\n",
    "    time_step_alignment=\"END_TIME\",\n",
    "    reference_time=None,\n",
    "    variables=None,\n",
    "    summary_fields=\"Join_Count SUM ZEROS\",\n",
    "    in_related_table=\"puntos_accidentes_COD_barrios\",\n",
    "    related_location_id=\"COD_BAR\"\n",
    ")\n",
    "\n",
    "# Serie de tiempo por barrios\n",
    "arcpy.stpm.VisualizeSpaceTimeCube2D(\n",
    "    in_cube=r\"C:\\00_Documentos_ESRI\\99_Mis_proyectos_ArcGIS_Pro\\Análisis de Siniestralidad - Madrid\\datos\\outputs\\cubos_espacio_temporales\\cubo_serie_barrios.nc\",\n",
    "    cube_variable=\"JOIN_COUNT_SUM_ZEROS\",\n",
    "    display_theme=\"TRENDS\",\n",
    "    output_features=r\"C:\\00_Documentos_ESRI\\99_Mis_proyectos_ArcGIS_Pro\\Análisis de Siniestralidad - Madrid\\datos\\outputs\\cubos_espacio_temporales\\serie_tiempo_por_barrios.gdb\\serie_tiempo_por_barrios\",\n",
    "    enable_time_series_popups=\"CREATE_POPUP\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5fcc9389",
   "metadata": {},
   "source": [
    "![Serie temporal por barrios](./assets/serie_tiempo_por_barrios.gif)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "caf44e38",
   "metadata": {},
   "source": [
    "### Clustering serie temporal por barrios"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "fbdd5ec3",
   "metadata": {},
   "source": [
    "<div style=\"text-align:center\">\n",
    "<a href=\"https://pro.arcgis.com/en/pro-app/latest/tool-reference/space-time-pattern-mining/time-series-clustering.htm\">Time Series Clustering (Space Time Pattern Mining)</a>\n",
    "<br/>\n",
    "<img src=\"https://pro.arcgis.com/en/pro-app/latest/tool-reference/space-time-pattern-mining/GUID-78899E30-876B-410E-9592-F3A663E8B4CD-web.png\" alt=\"drawing\" width=\"400\"/>\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "b0226908",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class='gpresult'><h2>Messages</h2><div id='messages' data-messages='[\"Tiempo de Inicio: lunes, 19 de junio de 2023 14:09:47\",{\"element\":\"unorderedlist\",\"data\":[\"La seudo estadística F para el resultado del clúster es 119,990\",\"Valor de inicialización aleatorio: 4593\"]},\"\",[{\"element\":\"table\",\"data\":[[\"Intervalo de período de tiempo\",\"1 month\"],[\"\",\"\"],[\"Tipo de forma\",\"Polígono\"],[\"\",\"\"],[\"Sesgo temporal del primer período de tiempo\",\"6,45%\"],[{\"data\":\"Primer intervalo de período de tiempo\",\"prop\":{\"rowspan\":4}},\"después de\"],[{\"data\":\"2018-12-30 00:00:00\",\"prop\":{\"text-align\":\"right\"}}],[{\"data\":\"hasta o antes de\",\"prop\":{\"text-align\":\"right\"}}],[{\"data\":\"2019-01-30 00:00:00\",\"prop\":{\"text-align\":\"right\"}}],[\"\",\"\"],[\"Sesgo temporal del último período de tiempo\",\"0,00%\"],[{\"data\":\"Último intervalo de período de tiempo\",\"prop\":{\"rowspan\":4}},\"después de\"],[{\"data\":\"2023-03-30 00:00:00\",\"prop\":{\"text-align\":\"right\"}}],[{\"data\":\"hasta o antes de\",\"prop\":{\"text-align\":\"right\"}}],[{\"data\":\"2023-04-30 00:00:00\",\"prop\":{\"text-align\":\"right\"}}],[\"\",\"\"],[\"Número de períodos de tiempo\",\"52\"],[\"Número de ubicaciones analizadas\",\"131\"],[\"Número de bins de espacio-tiempo analizados\",\"6812\"]],\"elementProps\":{\"striped\":\"true\",\"noHeader\":true,\"title\":\"Detalles de cubo de espacio-tiempo de entrada\",\"0\":{\"align\":\"left\",\"pad\":\"0px\",\"wrap\":true},\"1\":{\"align\":\"right\",\"pad\":\"0px\",\"wrap\":true}}}],[{\"element\":\"table\",\"data\":[[\"Id. de clúster\",\"Dirección\",\"Estadística\",\"valor p\"],[\"1\",\"No significativo\",\"0,8523\",\"0,3941\"],[\"2\",\"No significativo\",\"0,7342\",\"0,4628\"],[\"3\",\"No significativo\",\"0,2289\",\"0,8190\"]],\"elementProps\":{\"striped\":\"true\",\"title\":\"Estadísticas de tendencia para series temporales medias\",\"0\":{\"align\":\"left\",\"pad\":\"0px\",\"wrap\":true},\"1\":{\"align\":\"left\",\"pad\":\"0px\",\"wrap\":true},\"2\":{\"align\":\"right\",\"pad\":\"0px\",\"wrap\":true},\"3\":{\"align\":\"right\",\"pad\":\"0px\",\"wrap\":true}}}],[{\"element\":\"table\",\"data\":[[\"Id. de clúster\",\"Número de ubicaciones\"],[\"1\",\"59\"],[\"2\",\"42\"],[\"3\",\"30\"]],\"elementProps\":{\"striped\":\"true\",\"className\":\"small\",\"title\":\"Número de ubicaciones por clúster\",\"0\":{\"align\":\"left\",\"pad\":\"0px\",\"wrap\":true},\"1\":{\"align\":\"right\",\"pad\":\"0px\",\"wrap\":true}}}],\"\\n\\n\",\"Completado correctamente el lunes, 19 de junio de 2023 14:09:53 (Tiempo transcurrido: 5,85 segundos)\"]' data-show='true'><div id = 'default' /></div></div>"
      ],
      "text/plain": [
       "<Result 'C:\\\\00_Documentos_ESRI\\\\99_Mis_proyectos_ArcGIS_Pro\\\\Análisis de Siniestralidad - Madrid\\\\Análisis de Siniestralidad - Madrid.gdb\\\\cubo_barrios_serie_tiempo_TimeSeriesClustering2'>"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arcpy.stpm.TimeSeriesClustering(\n",
    "    in_cube=r\"C:\\00_Documentos_ESRI\\99_Mis_proyectos_ArcGIS_Pro\\Análisis de Siniestralidad - Madrid\\datos\\outputs\\cubos_espacio_temporales\\cubo_serie_barrios.nc\",\n",
    "    analysis_variable=\"JOIN_COUNT_SUM_ZEROS\",\n",
    "    output_features=r\"C:\\00_Documentos_ESRI\\99_Mis_proyectos_ArcGIS_Pro\\Análisis de Siniestralidad - Madrid\\Análisis de Siniestralidad - Madrid.gdb\\cubo_barrios_serie_tiempo_TimeSeriesClustering2\",\n",
    "    characteristic_of_interest=\"VALUE\",\n",
    "    cluster_count=3,\n",
    "    output_table_for_charts=r\"C:\\00_Documentos_ESRI\\99_Mis_proyectos_ArcGIS_Pro\\Análisis de Siniestralidad - Madrid\\Análisis de Siniestralidad - Madrid.gdb\\tabla_cluster\",\n",
    "    shape_characteristic_to_ignore=None,\n",
    "    enable_time_series_popups=\"CREATE_POPUP\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c9cef73",
   "metadata": {},
   "source": [
    "![Clustering serie temporal por barrios](./assets/serie_tiempo_por_barrios_clustering.gif)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "4fc6b392",
   "metadata": {},
   "source": [
    "### Predicción serie temporal por barrios"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "eaa8795f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class='gpresult'><h2>Messages</h2><div id='messages' data-messages='[\"Tiempo de Inicio: lunes, 19 de junio de 2023 13:32:39\",\"\",[{\"element\":\"table\",\"data\":[[\"Intervalo de período de tiempo\",\"1 month\"],[\"\",\"\"],[\"Tipo de forma\",\"Polígono\"],[\"\",\"\"],[\"Sesgo temporal del primer período de tiempo\",\"6,45%\"],[{\"data\":\"Primer intervalo de período de tiempo\",\"prop\":{\"rowspan\":4}},\"después de\"],[{\"data\":\"2018-12-30 00:00:00\",\"prop\":{\"text-align\":\"right\"}}],[{\"data\":\"hasta o antes de\",\"prop\":{\"text-align\":\"right\"}}],[{\"data\":\"2019-01-30 00:00:00\",\"prop\":{\"text-align\":\"right\"}}],[\"\",\"\"],[\"Sesgo temporal del último período de tiempo\",\"0,00%\"],[{\"data\":\"Último intervalo de período de tiempo\",\"prop\":{\"rowspan\":4}},\"después de\"],[{\"data\":\"2023-03-30 00:00:00\",\"prop\":{\"text-align\":\"right\"}}],[{\"data\":\"hasta o antes de\",\"prop\":{\"text-align\":\"right\"}}],[{\"data\":\"2023-04-30 00:00:00\",\"prop\":{\"text-align\":\"right\"}}],[\"\",\"\"],[\"Número de períodos de tiempo\",\"52\"],[\"Número de ubicaciones analizadas\",\"131\"],[\"Número de bins de espacio-tiempo analizados\",\"6812\"]],\"elementProps\":{\"striped\":\"true\",\"noHeader\":true,\"title\":\"Detalles de cubo de espacio-tiempo de entrada\",\"0\":{\"align\":\"left\",\"pad\":\"0px\",\"wrap\":true},\"1\":{\"align\":\"right\",\"pad\":\"0px\",\"wrap\":true}}}],[{\"element\":\"table\",\"data\":[[\"Cubo de espacio-tiempo de entrada\",\"Método de predicción\"],[\"cubo_serie_barrios.nc\",\"Suavizado exponencial\"],[\"\",\"\"],[\"Número de periodos de tiempo de predicción\",\"4\"],[\"Periodos de tiempo excluidos para la validación\",\"4\"],[\"% de ubicaciones modeladas con estacionalidad\",\"100,00\"],[\"Duración de estación (definida por el usuario)\",\"4\"],[\"\",\"\"],[{\"data\":\"Primer intervalo de periodo de tiempo de predicción\",\"prop\":{\"rowspan\":4}},\"después de\"],[{\"data\":\"2023-04-30 00:00:00\",\"prop\":{\"text-align\":\"right\"}}],[{\"data\":\"hasta o antes de\",\"prop\":{\"text-align\":\"right\"}}],[{\"data\":\"2023-05-30 00:00:00\",\"prop\":{\"text-align\":\"right\"}}],[\"\",\"\"],[{\"data\":\"Último intervalo de periodo de tiempo de predicción\",\"prop\":{\"rowspan\":4}},\"después de\"],[{\"data\":\"2023-07-30 00:00:00\",\"prop\":{\"text-align\":\"right\"}}],[{\"data\":\"hasta o antes de\",\"prop\":{\"text-align\":\"right\"}}],[{\"data\":\"2023-08-30 00:00:00\",\"prop\":{\"text-align\":\"right\"}}]],\"elementProps\":{\"striped\":\"true\",\"title\":\"Detalles del análisis\",\"0\":{\"align\":\"left\",\"pad\":\"0px\",\"wrap\":true},\"1\":{\"align\":\"right\",\"pad\":\"0px\",\"wrap\":true}}}],[{\"element\":\"table\",\"data\":[[\"Categoría\",\"Mín.\",\"Máx.\",\"Valor medio\",\"Mediana\",\"Desv. est.\"],[\"RMSE de predicción\",\"1,21\",\"14,02\",\"4,42\",\"4,07\",\"1,99\"],[\"RMSE de validación\",\"0,47\",\"26,06\",\"4,10\",\"3,40\",\"2,95\"]],\"elementProps\":{\"striped\":\"true\",\"title\":\"Resumen de precisión entre ubicaciones\",\"0\":{\"align\":\"left\",\"pad\":\"0px\",\"wrap\":true},\"1\":{\"align\":\"right\",\"pad\":\"0px\",\"wrap\":true},\"2\":{\"align\":\"right\",\"pad\":\"0px\",\"wrap\":true},\"3\":{\"align\":\"right\",\"pad\":\"0px\",\"wrap\":true},\"4\":{\"align\":\"right\",\"pad\":\"0px\",\"wrap\":true},\"5\":{\"align\":\"right\",\"pad\":\"0px\",\"wrap\":true}}}],\"\\n\",\"Completado correctamente el lunes, 19 de junio de 2023 13:32:45 (Tiempo transcurrido: 5,90 segundos)\"]' data-show='true'><div id = 'default' /></div></div>"
      ],
      "text/plain": [
       "<Result 'C:\\\\00_Documentos_ESRI\\\\99_Mis_proyectos_ArcGIS_Pro\\\\Análisis de Siniestralidad - Madrid\\\\Análisis de Siniestralidad - Madrid.gdb\\\\cubo_barrios_ExponentialSmoothingForecast'>"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Previsón de suavizazo exponencial\n",
    "arcpy.stpm.ExponentialSmoothingForecast(\n",
    "    in_cube=r\"C:\\00_Documentos_ESRI\\99_Mis_proyectos_ArcGIS_Pro\\Análisis de Siniestralidad - Madrid\\datos\\outputs\\cubos_espacio_temporales\\cubo_serie_barrios.nc\",\n",
    "    analysis_variable=\"JOIN_COUNT_SUM_ZEROS\",\n",
    "    output_features=r\"C:\\00_Documentos_ESRI\\99_Mis_proyectos_ArcGIS_Pro\\Análisis de Siniestralidad - Madrid\\Análisis de Siniestralidad - Madrid.gdb\\cubo_barrios_ExponentialSmoothingForecast\",\n",
    "    output_cube=r\"C:\\00_Documentos_ESRI\\99_Mis_proyectos_ArcGIS_Pro\\Análisis de Siniestralidad - Madrid\\datos\\outputs\\cubos_espacio_temporales\\cubo_barrios_serie_tiempo.nc\",\n",
    "    number_of_time_steps_to_forecast=4,\n",
    "    season_length=4,\n",
    "    number_for_validation=4,\n",
    "    outlier_option=\"NONE\",\n",
    "    level_of_confidence=\"90%\",\n",
    "    maximum_number_of_outliers=2\n",
    ")"
   ]
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  {
   "cell_type": "markdown",
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   "source": [
    "![Predicción serie temporal por barrios](./assets/serie_tiempo_por_barrios_prediccion.gif)"
   ]
  },
  {
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   "source": [
    "# Conclusiones"
   ]
  },
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   "source": [
    "<div style=\"text-align:center\">\n",
    "    <img src=\"./assets/conclusiones.png\"/>\n",
    "</div>"
   ]
  },
  {
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    "<div style=\"position: relative;\">\n",
    "    <img src=\"https://www.arcgis.com/sharing/rest/content/items/10bc2a493a974d5caf024ff13d7891b3/resources/images/widget_178/1622195050113.jpg\" width=\"1100\"/>\n",
    "    <p style=\"color:white; font-size:36px; font-weight:bold; position:absolute; top:50px; left:120px;\">Análisis de Siniestralidad |</p>\n",
    "    <p style=\"color:white; font-size:20px; font-weight:bold; position:absolute; top:70px; left:580px;\">Madrid</p>\n",
    "    <p style=\"color:white; font-size:24px; position:absolute; top:100px; left:120px;\">GIS & Spatial Data Science</p>    \n",
    "</div>\n"
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