2014_2016_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Austin 311 Call Analysis from 2014 - 2017\n",
    "-----\n",
    "- The cleaned file csv file of Austin 311 calls, \"All_Austin_311.csv\" was loaded and used to do this analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from datetime import datetime\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.basemap import Basemap\n",
    "from matplotlib.patches import Polygon"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The first analysis was to do the number of calls by Department and Complaint Description"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>incident_zip</th>\n",
       "      <th>owning_department</th>\n",
       "      <th>complaint_description</th>\n",
       "      <th>complaint_type</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>78741</td>\n",
       "      <td>Animal Services Office</td>\n",
       "      <td>Loose Dog</td>\n",
       "      <td>ACLONAG</td>\n",
       "      <td>30.224549</td>\n",
       "      <td>-97.690675</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>78757</td>\n",
       "      <td>Animal Services Office</td>\n",
       "      <td>Loose Dog</td>\n",
       "      <td>ACLONAG</td>\n",
       "      <td>30.350881</td>\n",
       "      <td>-97.747492</td>\n",
       "      <td>2016</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>78744</td>\n",
       "      <td>Animal Services Office</td>\n",
       "      <td>Loose Animal (not dog)</td>\n",
       "      <td>ACLOANIM</td>\n",
       "      <td>30.199263</td>\n",
       "      <td>-97.711366</td>\n",
       "      <td>2014</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>78727</td>\n",
       "      <td>Austin Code Department</td>\n",
       "      <td>Austin Code - Request Code Officer</td>\n",
       "      <td>CODECOMP</td>\n",
       "      <td>30.425112</td>\n",
       "      <td>-97.707188</td>\n",
       "      <td>2014</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>78723</td>\n",
       "      <td>Animal Services Office</td>\n",
       "      <td>Animal - Proper Care</td>\n",
       "      <td>ACPROPER</td>\n",
       "      <td>30.311821</td>\n",
       "      <td>-97.669302</td>\n",
       "      <td>2014</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   incident_zip       owning_department               complaint_description  \\\n",
       "0         78741  Animal Services Office                           Loose Dog   \n",
       "1         78757  Animal Services Office                           Loose Dog   \n",
       "2         78744  Animal Services Office              Loose Animal (not dog)   \n",
       "3         78727  Austin Code Department  Austin Code - Request Code Officer   \n",
       "4         78723  Animal Services Office                Animal - Proper Care   \n",
       "\n",
       "  complaint_type   latitude  longitude  year  month  \n",
       "0        ACLONAG  30.224549 -97.690675  2015      1  \n",
       "1        ACLONAG  30.350881 -97.747492  2016      5  \n",
       "2       ACLOANIM  30.199263 -97.711366  2014      1  \n",
       "3       CODECOMP  30.425112 -97.707188  2014     11  \n",
       "4       ACPROPER  30.311821 -97.669302  2014      7  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Read the csv file that was downloaded from the City of Austin: \"https://data.austintexas.gov/\"\n",
    "Austin_311_df = pd.read_csv('data/All_Austin_311.csv',low_memory=False)\n",
    "\n",
    "Austin_311_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tom Berton analyzed the number of 311 calls over 3 years, from 2014-2016."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>owning_department</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Animal Services Office</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Animal Services Office</td>\n",
       "      <td>2016</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Animal Services Office</td>\n",
       "      <td>2014</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Austin Code Department</td>\n",
       "      <td>2014</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Animal Services Office</td>\n",
       "      <td>2014</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        owning_department  year  month\n",
       "0  Animal Services Office  2015      1\n",
       "1  Animal Services Office  2016      5\n",
       "2  Animal Services Office  2014      1\n",
       "3  Austin Code Department  2014     11\n",
       "4  Animal Services Office  2014      7"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Drop the Type of Complaint, Complaint Description, and Date since they are not needed.\n",
    "datetime_austin311 = Austin_311_df.drop([\"complaint_description\",\n",
    "                                         \"complaint_type\", \"incident_zip\",\n",
    "                                        \"latitude\", \"longitude\"], axis=1)\n",
    "datetime_austin311.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Number of Complaints</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <th>12</th>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">2014</th>\n",
       "      <th>1</th>\n",
       "      <td>8192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10751</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Number of Complaints\n",
       "Year Month                      \n",
       "2013 12                       44\n",
       "2014 1                      8192\n",
       "     2                      7466\n",
       "     3                      9253\n",
       "     4                     10751"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Pivot the table to make 'Year' the index and get the counts for Department \n",
    "# and change the name of Department to Number of Complaints.\n",
    "\n",
    "datetime_austin311 = datetime_austin311.rename(columns={\"owning_department\":\"Number of Complaints\",\n",
    "                                                     \"year\":\"Year\", \"month\":\"Month\"})\n",
    "austin311_by_year = datetime_austin311.pivot_table(datetime_austin311, index=['Year','Month'], aggfunc='count')\n",
    "\n",
    "austin311_by_year.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Number of Complaints</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"12\" valign=\"top\">2014</th>\n",
       "      <th>1</th>\n",
       "      <td>8192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>10124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>11943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>11591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>11620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>11649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>8407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>8417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"12\" valign=\"top\">2015</th>\n",
       "      <th>1</th>\n",
       "      <td>8356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>13462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>14552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>12991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>11763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>9273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>8007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"12\" valign=\"top\">2016</th>\n",
       "      <th>1</th>\n",
       "      <td>8379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>11843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>10828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>9082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>11141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>11616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>9749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>8387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>7658</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Number of Complaints\n",
       "Year Month                      \n",
       "2014 1                      8192\n",
       "     2                      7466\n",
       "     3                      9253\n",
       "     4                     10751\n",
       "     5                     10124\n",
       "     6                     11943\n",
       "     7                     11591\n",
       "     8                     11620\n",
       "     9                     11649\n",
       "     10                    10951\n",
       "     11                     8407\n",
       "     12                     8417\n",
       "2015 1                      8356\n",
       "     2                      7745\n",
       "     3                      9929\n",
       "     4                     11021\n",
       "     5                     13462\n",
       "     6                     14552\n",
       "     7                     12991\n",
       "     8                     11763\n",
       "     9                     10778\n",
       "     10                    11487\n",
       "     11                     9273\n",
       "     12                     8007\n",
       "2016 1                      8379\n",
       "     2                      8069\n",
       "     3                      9618\n",
       "     4                     10702\n",
       "     5                     11843\n",
       "     6                     10828\n",
       "     7                      9082\n",
       "     8                     11141\n",
       "     9                     11616\n",
       "     10                     9749\n",
       "     11                     8387\n",
       "     12                     7658"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Drop the year 2013 since it has only one month, December and 2107 since it only has 8 months, Jan - Aug.\n",
    "\n",
    "austin311_by_year.drop(austin311_by_year.index[0],inplace=True)\n",
    "austin311_by_year.drop(austin311_by_year.index[36:],inplace=True)\n",
    "austin311_by_year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "    </tr>\n",
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       "      <td>8192</td>\n",
       "      <td>8356</td>\n",
       "      <td>8379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>7466</td>\n",
       "      <td>7745</td>\n",
       "      <td>8069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>9253</td>\n",
       "      <td>9929</td>\n",
       "      <td>9618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>10751</td>\n",
       "      <td>11021</td>\n",
       "      <td>10702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>10124</td>\n",
       "      <td>13462</td>\n",
       "      <td>11843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>11943</td>\n",
       "      <td>14552</td>\n",
       "      <td>10828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>11591</td>\n",
       "      <td>12991</td>\n",
       "      <td>9082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>11620</td>\n",
       "      <td>11763</td>\n",
       "      <td>11141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>11649</td>\n",
       "      <td>10778</td>\n",
       "      <td>11616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>10951</td>\n",
       "      <td>11487</td>\n",
       "      <td>9749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>8407</td>\n",
       "      <td>9273</td>\n",
       "      <td>8387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>8417</td>\n",
       "      <td>8007</td>\n",
       "      <td>7658</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Year  Month   2014   2015   2016\n",
       "0         1   8192   8356   8379\n",
       "1         2   7466   7745   8069\n",
       "2         3   9253   9929   9618\n",
       "3         4  10751  11021  10702\n",
       "4         5  10124  13462  11843\n",
       "5         6  11943  14552  10828\n",
       "6         7  11591  12991   9082\n",
       "7         8  11620  11763  11141\n",
       "8         9  11649  10778  11616\n",
       "9        10  10951  11487   9749\n",
       "10       11   8407   9273   8387\n",
       "11       12   8417   8007   7658"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Pivot the df so 'Month' is the index \n",
    "\n",
    "austin311_by_month = austin311_by_year.pivot_table('Number of Complaints', ['Month'],'Year' )\n",
    "\n",
    "#Reset the index to and save to .cvs file.\n",
    "austin311_reset = austin311_by_month.reset_index()\n",
    "\n",
    "#Save the file as a .csv\n",
    "austin311_reset.to_csv('data/austin311_year_month.csv', encoding='utf-8', index=False)\n",
    "\n",
    "austin311_reset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([2014, 2015, 2016], dtype='int64', name='Year')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Plot the number of calls by year with the months on the x-axis. Find out what if the year is the index or not. \n",
    "years = austin311_by_month.keys()\n",
    "years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118755550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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vj9RoHkFE9iql6qSmjQMHDpytWbNmtnK8PXDgQJGaNWv6xXfM0SHrbcAEEXkf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\npZQX0ISslFJKeYH/AwBY3XRjCLbnAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11873ca58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the number of calls by year.\n",
    "plt.figure(figsize=(10,10))\n",
    "\n",
    "plot = austin311_by_month.plot(kind='line')\n",
    "plt.xticks(austin311_by_month.index)\n",
    "\n",
    "plt.xlabel(\"Months\", fontsize=14)\n",
    "plt.ylabel(\"Number of Calls\", fontsize=14)\n",
    "plt.title(\"Number of 311 Calls 2014-2016\", fontsize=16)\n",
    "plt.legend(bbox_to_anchor=(1.05,1),loc= 2, borderaxespad = 0.,title=\"Year\")\n",
    "\n",
    "fig1 = plt.gcf()\n",
    "plt.show()\n",
    "fig1.savefig('images/Number_311_calls.png', bbox_inches='tight', dpi=200)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tom Berton mapped the density of 311 calls in Travis count using the Basemap module in Matplotlib. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>incident_zip</th>\n",
       "      <th>owning_department</th>\n",
       "      <th>complaint_description</th>\n",
       "      <th>complaint_type</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>78741</td>\n",
       "      <td>Animal Services Office</td>\n",
       "      <td>Loose Dog</td>\n",
       "      <td>ACLONAG</td>\n",
       "      <td>30.224549</td>\n",
       "      <td>-97.690675</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>78757</td>\n",
       "      <td>Animal Services Office</td>\n",
       "      <td>Loose Dog</td>\n",
       "      <td>ACLONAG</td>\n",
       "      <td>30.350881</td>\n",
       "      <td>-97.747492</td>\n",
       "      <td>2016</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>78744</td>\n",
       "      <td>Animal Services Office</td>\n",
       "      <td>Loose Animal (not dog)</td>\n",
       "      <td>ACLOANIM</td>\n",
       "      <td>30.199263</td>\n",
       "      <td>-97.711366</td>\n",
       "      <td>2014</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>78727</td>\n",
       "      <td>Austin Code Department</td>\n",
       "      <td>Austin Code - Request Code Officer</td>\n",
       "      <td>CODECOMP</td>\n",
       "      <td>30.425112</td>\n",
       "      <td>-97.707188</td>\n",
       "      <td>2014</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>78723</td>\n",
       "      <td>Animal Services Office</td>\n",
       "      <td>Animal - Proper Care</td>\n",
       "      <td>ACPROPER</td>\n",
       "      <td>30.311821</td>\n",
       "      <td>-97.669302</td>\n",
       "      <td>2014</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   incident_zip       owning_department               complaint_description  \\\n",
       "0         78741  Animal Services Office                           Loose Dog   \n",
       "1         78757  Animal Services Office                           Loose Dog   \n",
       "2         78744  Animal Services Office              Loose Animal (not dog)   \n",
       "3         78727  Austin Code Department  Austin Code - Request Code Officer   \n",
       "4         78723  Animal Services Office                Animal - Proper Care   \n",
       "\n",
       "  complaint_type   latitude  longitude  year  month  \n",
       "0        ACLONAG  30.224549 -97.690675  2015      1  \n",
       "1        ACLONAG  30.350881 -97.747492  2016      5  \n",
       "2       ACLOANIM  30.199263 -97.711366  2014      1  \n",
       "3       CODECOMP  30.425112 -97.707188  2014     11  \n",
       "4       ACPROPER  30.311821 -97.669302  2014      7  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Austin_311_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/tom/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Number of Complaints</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Lon</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zip Code</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>78610</th>\n",
       "      <td>31</td>\n",
       "      <td>30.104558</td>\n",
       "      <td>-97.776716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78613</th>\n",
       "      <td>10</td>\n",
       "      <td>30.459545</td>\n",
       "      <td>-97.829108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78617</th>\n",
       "      <td>3276</td>\n",
       "      <td>30.178763</td>\n",
       "      <td>-97.633562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78620</th>\n",
       "      <td>1</td>\n",
       "      <td>30.284697</td>\n",
       "      <td>-98.067527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78621</th>\n",
       "      <td>1</td>\n",
       "      <td>30.382937</td>\n",
       "      <td>-97.392721</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Number of Complaints        Lat        Lon\n",
       "Zip Code                                            \n",
       "78610                       31  30.104558 -97.776716\n",
       "78613                       10  30.459545 -97.829108\n",
       "78617                     3276  30.178763 -97.633562\n",
       "78620                        1  30.284697 -98.067527\n",
       "78621                        1  30.382937 -97.392721"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Use the 'clean_Austin_311_df' and keep the approriate columns for this analysis.\n",
    "\n",
    "austin_lat_lon = Austin_311_df[[\"incident_zip\", \"owning_department\",\"latitude\", \"longitude\"]]\n",
    "\n",
    "# Change the zip to an integer\n",
    "austin_lat_lon[\"incident_zip\"] = austin_lat_lon[\"incident_zip\"].astype(int)\n",
    "austin_lat_lon = austin_lat_lon.rename(columns={\"incident_zip\":\"Zip Code\",\"owning_department\":\"Number of Complaints\",\n",
    "                                              \"latitude\": \"Lat\", \"longitude\":\"Lon\"})\n",
    "\n",
    "# Get the number of complaints from the \"Department\" and means of the Lat and Lon.\n",
    "atx_zip_by_latlon = austin_lat_lon.groupby(\"Zip Code\").agg({\"Number of Complaints\": 'count', \"Lat\": 'mean', \"Lon\": 'mean'})\n",
    "atx_zip_by_latlon.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "422827\n"
     ]
    }
   ],
   "source": [
    "total = atx_zip_by_latlon['Number of Complaints'].sum()\n",
    "print(total)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        text-align: right;\n",
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       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Number of Complaints</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Lon</th>\n",
       "      <th>Coordinates</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zip Code</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>78610</th>\n",
       "      <td>31</td>\n",
       "      <td>30.104558</td>\n",
       "      <td>-97.776716</td>\n",
       "      <td>(30.1045575026, -97.7767162003)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78613</th>\n",
       "      <td>10</td>\n",
       "      <td>30.459545</td>\n",
       "      <td>-97.829108</td>\n",
       "      <td>(30.459545485, -97.829108118)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78617</th>\n",
       "      <td>3276</td>\n",
       "      <td>30.178763</td>\n",
       "      <td>-97.633562</td>\n",
       "      <td>(30.1787626893, -97.6335617213)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78620</th>\n",
       "      <td>1</td>\n",
       "      <td>30.284697</td>\n",
       "      <td>-98.067527</td>\n",
       "      <td>(30.28469734, -98.06752687)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78621</th>\n",
       "      <td>1</td>\n",
       "      <td>30.382937</td>\n",
       "      <td>-97.392721</td>\n",
       "      <td>(30.38293725, -97.39272105)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Number of Complaints        Lat        Lon  \\\n",
       "Zip Code                                               \n",
       "78610                       31  30.104558 -97.776716   \n",
       "78613                       10  30.459545 -97.829108   \n",
       "78617                     3276  30.178763 -97.633562   \n",
       "78620                        1  30.284697 -98.067527   \n",
       "78621                        1  30.382937 -97.392721   \n",
       "\n",
       "                              Coordinates  \n",
       "Zip Code                                   \n",
       "78610     (30.1045575026, -97.7767162003)  \n",
       "78613       (30.459545485, -97.829108118)  \n",
       "78617     (30.1787626893, -97.6335617213)  \n",
       "78620         (30.28469734, -98.06752687)  \n",
       "78621         (30.38293725, -97.39272105)  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "atx_zip_by_latlon[\"Coordinates\"] = atx_zip_by_latlon[['Lat', 'Lon']].apply(tuple, axis=1)\n",
    "atx_zip_by_latlon.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/tom/anaconda/lib/python3.6/site-packages/mpl_toolkits/basemap/__init__.py:3260: MatplotlibDeprecationWarning: The ishold function was deprecated in version 2.0.\n",
      "  b = ax.ishold()\n",
      "/Users/tom/anaconda/lib/python3.6/site-packages/mpl_toolkits/basemap/__init__.py:3269: MatplotlibDeprecationWarning: axes.hold is deprecated.\n",
      "    See the API Changes document (http://matplotlib.org/api/api_changes.html)\n",
      "    for more details.\n",
      "  ax.hold(b)\n"
     ]
    },
    {
     "data": {
      "image/png": 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MNZSJl5eXR05OTmRsbEy3b9+uc5w4u3JNjcOHD5OHhwfTMlg+orllPNrb2ytWzNDHKCkB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\n1WgyRFEURVFUr0aTIYqiKIqierX/Aw9DWcOPP2EmAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118755160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting the 311 call density on Travis county zip codes marklines.\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10,10))\n",
    "map = Basemap(resolution='f', # c, l, i, h, f or None\n",
    "            projection='merc',\n",
    "            lat_0=30.3, lon_0=-97.8,\n",
    "            llcrnrlon=-98.2, llcrnrlat=30.02, urcrnrlon=-97.2, urcrnrlat=30.6) \n",
    "\n",
    "map.drawmapboundary(fill_color=\"#FFFFFF\")\n",
    "map.drawrivers(color=\"#0000FF\")\n",
    "\n",
    "map.readshapefile('USzip_codes/cb_2016_us_zcta510_500k', 'cb_2016_us_zcta510_500k')\n",
    "\n",
    "def plot_area(Coordinates):\n",
    "    count = atx_zip_by_latlon.loc[atx_zip_by_latlon.Coordinates == Coordinates][\"Number of Complaints\"]\n",
    "    x, y = map(Coordinates[1], Coordinates[0])\n",
    "    #The count number is too high to plot. So reduce it and plot the area of a circle.\n",
    "    size = (count/6000) ** 2 + 3.14\n",
    "    map.plot(x, y, 'o', markersize=size, color='#CC5500', alpha=0.8)\n",
    "\n",
    "\n",
    "atx_zip_by_latlon.Coordinates.apply(plot_area)\n",
    "fig1 = plt.gcf()\n",
    "plt.show()\n",
    "plt.draw()\n",
    "fig1.savefig('images/311_call_density_zip.png', dpi=200)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tom Berton mapped the density of 311 calls as a heatmap on a Google map using the 'gmaps' dependency. And an Google Api Key. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Import necessary modules\n",
    "import gmaps\n",
    "import json\n",
    "import gmaps.geojson_geometries\n",
    "import gmaps.datasets\n",
    "from config import g_key\n",
    "gmaps.configure(g_key)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "74931ec6600f4cc78e344f82a7a6ad3d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/html": [
       "<p>Failed to display Jupyter Widget of type <code>Figure</code>.</p>\n",
       "<p>\n",
       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
       "  that the widgets JavaScript is still loading. If this message persists, it\n",
       "  likely means that the widgets JavaScript library is either not installed or\n",
       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
       "  Widgets Documentation</a> for setup instructions.\n",
       "</p>\n",
       "<p>\n",
       "  If you're reading this message in another frontend (for example, a static\n",
       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
       "  it may mean that your frontend doesn't currently support widgets.\n",
       "</p>\n"
      ],
      "text/plain": [
       "Figure()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Draw the Google heatmap using Lat and Lon from 'atx_zip_by_latlon'\n",
    "with open(\"travis.geojson\") as f:\n",
    "    geometry = json.load(f)\n",
    "    \n",
    "fig = gmaps.figure()\n",
    "heatmap_layer = gmaps.heatmap_layer(\n",
    "    atx_zip_by_latlon[[\"Lat\", \"Lon\"]], weights=atx_zip_by_latlon[\"Number of Complaints\"],\n",
    "    max_intensity=50, point_radius=7.5)\n",
    "\n",
    "geojson_layer = gmaps.geojson_layer(geometry, fill_color=None , fill_opacity=0.0)\n",
    "fig.add_layer(heatmap_layer)\n",
    "fig.add_layer(geojson_layer)\n",
    "fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Zip Code</th>\n",
       "      <th>Number of Complaints</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Lon</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>78741</td>\n",
       "      <td>Animal Services Office</td>\n",
       "      <td>30.224549</td>\n",
       "      <td>-97.690675</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>78757</td>\n",
       "      <td>Animal Services Office</td>\n",
       "      <td>30.350881</td>\n",
       "      <td>-97.747492</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>78744</td>\n",
       "      <td>Animal Services Office</td>\n",
       "      <td>30.199263</td>\n",
       "      <td>-97.711366</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>78727</td>\n",
       "      <td>Austin Code Department</td>\n",
       "      <td>30.425112</td>\n",
       "      <td>-97.707188</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>78723</td>\n",
       "      <td>Animal Services Office</td>\n",
       "      <td>30.311821</td>\n",
       "      <td>-97.669302</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Zip Code    Number of Complaints        Lat        Lon  Count\n",
       "0     78741  Animal Services Office  30.224549 -97.690675      1\n",
       "1     78757  Animal Services Office  30.350881 -97.747492      1\n",
       "2     78744  Animal Services Office  30.199263 -97.711366      1\n",
       "3     78727  Austin Code Department  30.425112 -97.707188      1\n",
       "4     78723  Animal Services Office  30.311821 -97.669302      1"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "austin_lat_lon['Count'] = (austin_lat_lon['Number of Complaints'] !='').astype(int)\n",
    "    \n",
    "austin_lat_lon.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
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      "application/vnd.jupyter.widget-view+json": {
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      "text/html": [
       "<p>Failed to display Jupyter Widget of type <code>Figure</code>.</p>\n",
       "<p>\n",
       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
       "  that the widgets JavaScript is still loading. If this message persists, it\n",
       "  likely means that the widgets JavaScript library is either not installed or\n",
       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
       "  Widgets Documentation</a> for setup instructions.\n",
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       "  If you're reading this message in another frontend (for example, a static\n",
       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
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       "</p>\n"
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      "text/plain": [
       "Figure()"
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     },
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   ],
   "source": [
    "#Draw the Google heatmap using Lat and Lon from 'atx_zip_by_latlon'\n",
    "with open(\"travis.geojson\") as f:\n",
    "    geometry = json.load(f)\n",
    "    \n",
    "fig = gmaps.figure()\n",
    "heatmap_layer = gmaps.heatmap_layer(\n",
    "    austin_lat_lon[[\"Lat\", \"Lon\"]], weights=austin_lat_lon[\"Count\"],\n",
    "    max_intensity=10, point_radius=0.5)\n",
    "\n",
    "geojson_layer = gmaps.geojson_layer(geometry, fill_color=None , fill_opacity=0.0)\n",
    "fig.add_layer(heatmap_layer)\n",
    "fig.add_layer(geojson_layer)\n",
    "fig"
   ]
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
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