susanli2016/Vancouver_Auto_Crime.ipynb

## Vancouver_Auto_Crime.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's have a quick look what the data looks like."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Category</th>\n",
       "      <th>Year_</th>\n",
       "      <th>City</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Count_</th>\n",
       "      <th>CENSUS_FSA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Stolen Veh</td>\n",
       "      <td>2003</td>\n",
       "      <td>LOWER POST</td>\n",
       "      <td>59.930201</td>\n",
       "      <td>-128.475298</td>\n",
       "      <td>1</td>\n",
       "      <td>V0C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Break-in</td>\n",
       "      <td>2003</td>\n",
       "      <td>FORT NELSON</td>\n",
       "      <td>58.827521</td>\n",
       "      <td>-122.808157</td>\n",
       "      <td>1</td>\n",
       "      <td>V0C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Vandalism</td>\n",
       "      <td>2003</td>\n",
       "      <td>FORT NELSON</td>\n",
       "      <td>58.825548</td>\n",
       "      <td>-122.643124</td>\n",
       "      <td>1</td>\n",
       "      <td>V0C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Vandalism</td>\n",
       "      <td>2003</td>\n",
       "      <td>FORT NELSON</td>\n",
       "      <td>58.825138</td>\n",
       "      <td>-122.803747</td>\n",
       "      <td>1</td>\n",
       "      <td>V0C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Stolen Veh</td>\n",
       "      <td>2007</td>\n",
       "      <td>FORT NELSON</td>\n",
       "      <td>58.823206</td>\n",
       "      <td>-122.758232</td>\n",
       "      <td>1</td>\n",
       "      <td>V0C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Category  Year_         City   Latitude   Longitude  Count_ CENSUS_FSA\n",
       "0  Stolen Veh   2003   LOWER POST  59.930201 -128.475298       1        V0C\n",
       "1    Break-in   2003  FORT NELSON  58.827521 -122.808157       1        V0C\n",
       "2   Vandalism   2003  FORT NELSON  58.825548 -122.643124       1        V0C\n",
       "3   Vandalism   2003  FORT NELSON  58.825138 -122.803747       1        V0C\n",
       "4  Stolen Veh   2007  FORT NELSON  58.823206 -122.758232       1        V0C"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "auto_crime=pd.read_csv(\"Auto_crime_location.csv\")\n",
    "auto_crime.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 310787 entries, 0 to 310786\n",
      "Data columns (total 7 columns):\n",
      "Category      310787 non-null object\n",
      "Year_         310787 non-null int64\n",
      "City          310787 non-null object\n",
      "Latitude      310787 non-null float64\n",
      "Longitude     310787 non-null float64\n",
      "Count_        310787 non-null int64\n",
      "CENSUS_FSA    310787 non-null object\n",
      "dtypes: float64(2), int64(2), object(3)\n",
      "memory usage: 16.6+ MB\n"
     ]
    }
   ],
   "source": [
    "auto_crime.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### In general, looks like auto crime has been declining over the past 10 years. Good for them!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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7FKNHj5bVavU6595779VDDz10wz/87bffltvtVklJiUpKSiR9GoL8/Px05swZ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\ncubMGa/j9PR0paenD3h+Wlq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tbW2KjIz0Oj8sLEwtLS2SpNbW1j718PBwo37N2rVrlZGR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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x26e85c3db70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with sns.axes_style(\"white\"):\n",
    "    g=sns.factorplot(\"Year_\", data=auto_crime, aspect=2, kind=\"count\", color=\"steelblue\")\n",
    "    g.set_xticklabels(step=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How about different categories? As you can see, not many people would take high risks to steal vehicles anymore. They steal things inside of the vehicles or simply damage it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x26e86badba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with sns.axes_style(\"white\"):\n",
    "    g=sns.factorplot(\"Year_\", data=auto_crime, aspect=4.0, kind=\"count\", hue=\"Category\", order=range(2003, 2013))\n",
    "    g.set_ylabels(\"Number of Crimes in Categories\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let me make a scatter plot to see what I can find. Not much."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x26e88a2a0b8>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WQVGRwz33ZPHppzYffQRm7HxDNn/4g8OoUXDUUeY5In260udcGJ53tmmt92EC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      "text/plain": [
       "<matplotlib.figure.Figure at 0x26e8bbdce48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lat, lon=auto_crime[\"Latitude\"], auto_crime[\"Longitude\"]\n",
    "plt.scatter(lon, lat, label=None, cmap=\"viridis\", linewidth=0, alpha=0.5)\n",
    "plt.axis(aspect=\"equal\")\n",
    "plt.xlabel(\"longitude\")\n",
    "plt.ylabel(\"latitude\")\n",
    "plt.clim(3,7)\n",
    "plt.title(\"Vancouver Auto Crime\")"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [default]",
   "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": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}