pnc/informes_pnc.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data analysis of police reports\n",
    "\n",
    "Based on the dataset of police reports from 2016, this model aims to predict the time and days with highets chance of police reports."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scikit-learn will be used to create the prediction models; `pandas` and `numpy` will be used for data manipulation and `matplotlib` for visualization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dependencias\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import gridspec\n",
    "from sklearn import (\n",
    "    svm, linear_model, neighbors, discriminant_analysis,\n",
    "    neural_network, naive_bayes, tree\n",
    ")\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def split_objective(input_df, dims):\n",
    "    features = input_df.loc[:, input_df.columns != list(dims.keys())[-1]]\n",
    "    objective = input_df[input_df.columns[-1:]]\n",
    "    return (features, objective)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We load the victims' dataset into a `DataFrame` and process the date."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": 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>núm_corre_f</th>\n",
       "      <th>núm_corre</th>\n",
       "      <th>año_ocu</th>\n",
       "      <th>mes_ocu</th>\n",
       "      <th>día_ocu</th>\n",
       "      <th>día_sem_ocu</th>\n",
       "      <th>hora_ocu</th>\n",
       "      <th>g_hora</th>\n",
       "      <th>g_hora_mañ.tar.noch</th>\n",
       "      <th>área_geo_ocu</th>\n",
       "      <th>...</th>\n",
       "      <th>zona_ocu</th>\n",
       "      <th>sexo_per</th>\n",
       "      <th>edad_per</th>\n",
       "      <th>g_edad_60ymás</th>\n",
       "      <th>g_edad_80ymás</th>\n",
       "      <th>edad_quinquenales</th>\n",
       "      <th>menor_mayor</th>\n",
       "      <th>delito_com</th>\n",
       "      <th>g_delitos</th>\n",
       "      <th>fecha</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>56</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>69</td>\n",
       "      <td>3</td>\n",
       "      <td>2016-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>99</td>\n",
       "      <td>1</td>\n",
       "      <td>42</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>69</td>\n",
       "      <td>3</td>\n",
       "      <td>2016-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>99</td>\n",
       "      <td>1</td>\n",
       "      <td>38</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>69</td>\n",
       "      <td>3</td>\n",
       "      <td>2016-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>99</td>\n",
       "      <td>1</td>\n",
       "      <td>69</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>69</td>\n",
       "      <td>3</td>\n",
       "      <td>2016-01-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>69</td>\n",
       "      <td>3</td>\n",
       "      <td>2016-01-02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   núm_corre_f  núm_corre  año_ocu  mes_ocu  día_ocu  día_sem_ocu  hora_ocu  \\\n",
       "0            1          1     2016        1        1            5         0   \n",
       "1            2          2     2016        1        1            5         8   \n",
       "2            3          3     2016        1        1            5         8   \n",
       "3            4          4     2016        1        2            6        15   \n",
       "4            5          5     2016        1        2            6        19   \n",
       "\n",
       "   g_hora  g_hora_mañ.tar.noch  área_geo_ocu    ...      zona_ocu  sexo_per  \\\n",
       "0       1                    1             1    ...            12         1   \n",
       "1       2                    1             2    ...            99         1   \n",
       "2       2                    1             2    ...            99         1   \n",
       "3       3                    2             2    ...            99         1   \n",
       "4       4                    3             1    ...             6         1   \n",
       "\n",
       "   edad_per  g_edad_60ymás  g_edad_80ymás  edad_quinquenales  menor_mayor  \\\n",
       "0        56             10             10                 12            1   \n",
       "1        42              7              7                  9            1   \n",
       "2        38              6              6                  8            1   \n",
       "3        69             11             12                 14            1   \n",
       "4        27              4              4                  6            1   \n",
       "\n",
       "   delito_com  g_delitos      fecha  \n",
       "0          69          3 2016-01-01  \n",
       "1          69          3 2016-01-01  \n",
       "2          69          3 2016-01-01  \n",
       "3          69          3 2016-01-02  \n",
       "4          69          3 2016-01-02  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vic_df = pd.read_excel('pnc_victimas.xlsx')\n",
    "vic_df['fecha'] = pd.to_datetime(\n",
    "    dict(year=vic_df['año_ocu'], month=vic_df['mes_ocu'],day=vic_df['día_ocu']), \n",
    "    format='%Y-%m-%d')\n",
    "\n",
    "vic_df = vic_df[vic_df.hora_ocu != 99]\n",
    "vic_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We aim to create a new `DataFrame` with the ammount of police reports by month, day, hour of the day and municipality."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    ano  mes  dia  hora  muni  total\n",
      "0  2016    1    1     0   101      1\n",
      "1  2016    1    1     0   108      2\n",
      "2  2016    1    1     0   110      2\n",
      "3  2016    1    1     0   501      1\n",
      "4  2016    1    1     0  1201      1\n",
      "(30914, 6)\n"
     ]
    }
   ],
   "source": [
    "grupos = vic_df.groupby(by=('año_ocu', 'mes_ocu', 'día_ocu', 'hora_ocu', 'mupio_ocu'))\n",
    "convertido = pd.DataFrame({'total': grupos.size()}).reset_index()\n",
    "convertido.columns = ['ano', 'mes', 'dia', 'hora', 'muni', 'total']\n",
    "convertido.sort_values(['ano', 'mes', 'dia', 'hora'])\n",
    "\n",
    "print(convertido.head())\n",
    "print(convertido.shape)\n",
    "\n",
    "# Export the new dataframe\n",
    "convertido.to_csv('convertido.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear model by time of the day\n",
    "\n",
    "This model aims to find the hours with highest amount of reports."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd6a8f419b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "grupos_hora = convertido.groupby(by='hora')\n",
    "convertido_hora = pd.DataFrame({'total': grupos_hora.size()}).reset_index()\n",
    "convertido_hora.to_csv('horas.csv', index=False)\n",
    "\n",
    "lr = linear_model.LinearRegression()\n",
    "lr.fit(convertido_hora[['hora']], convertido_hora[['total']])\n",
    "convertido_hora['predict'] = lr.predict(convertido_hora[['hora']])\n",
    "\n",
    "plt.scatter(convertido_hora.hora, convertido_hora.total)\n",
    "plt.plot(convertido_hora.hora, convertido_hora.predict, lw=2, label='Modelo lineal')\n",
    "plt.xlabel('Hora')\n",
    "plt.ylabel('Víctimas')\n",
    "plt.xticks(np.arange(len(convertido_hora[['hora']])))\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Busiest date and times\n",
    "\n",
    "Now, let's find out what days and times have the highest amount of police reports."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd6a0704198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_limpio = vic_df.groupby(['día_sem_ocu', 'hora_ocu']).size().unstack('hora_ocu', fill_value=0)\n",
    "\n",
    "# print(df_limpio.head())\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(15,10))\n",
    "\n",
    "im = ax.imshow(df_limpio, cmap='Blues')\n",
    "fig.colorbar(im, ax=ax)\n",
    "xticks = list(range(0, 24))\n",
    "yticks = ['Lunes', 'Mar', 'Mie', 'Jue', 'Vie', 'Sáb', 'Dom']\n",
    "ax.set_xticks(range(len(xticks)))\n",
    "ax.set_yticks(range(len(yticks)))\n",
    "\n",
    "ax.set_xticklabels(xticks, rotation=90)\n",
    "ax.set_yticklabels(yticks, rotation=0)\n",
    "\n",
    "#plt.pcolor(df_limpio)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}