{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# IS608 Project -- Sreejaya Vasudevannair\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Project Title: US Motor Vehicle Fatality Analysis\n",
"The goal of this project is to create choropleth map of US Motor Vehicle Fatality on US State level. \n",
"The National Highway Traffic Safety Administration(NHTSA) has a dataset called **[Fatality Reporting System(FARS)](ftp://ftp.nhtsa.dot.gov/fars) ** to record every fatal traffic crash in the US. FARS is a nationwide census providing NHTSA, Congress and the American public yearly data regarding fatal injuries suffered in motor vehicle traffic crashes. \n",
"Collected data include \n",
"1.\tCrash characteristics\n",
"2.\tVehicle characteristics\n",
"3.\tPerson characteristics\n",
"4.\tWeather and time of the day.\n",
"\n",
"### Data Source #### \n",
"**[Download Raw Data from FTP Site](ftp://ftp.nhtsa.dot.gov/fars) ** \n",
"and http://www-fars.nhtsa.dot.gov/Trends/TrendsGeneral.aspx.\n",
" \n",
"The files are saved as .dbf. I converted them to comma delimited vectors(CSV). \n",
"\n",
"The project provide a general idea on the Fatal Traffic crashes in US. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"%matplotlib inline\n",
"import numpy as np\n",
"from numpy import *\n",
"import random\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"\n",
"import plotly\n",
"from plotly.graph_objs import Scatter, Layout\n",
"import plotly.plotly as py\n",
"import plotly.graph_objs as go"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"** Fatality trend from 1994 to 2014 **"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"\n",
"#df=pd.read_csv('C:/DataAnalytics/MSDASemester3/608/Project/Data_proj/CSV/USAFatalCrashes1994_2014.csv')\n",
"df=pd.read_csv('https://raw.githubusercontent.com/srajeev1/IS608Project/master/data/USAFatalCrashes1994_2014.csv')\n",
"plotly.offline.init_notebook_mode() # run at the start of every notebook\n",
"plotly.offline.iplot({\n",
"\"data\": [{\n",
" \"x\": df.Year,\n",
" \"y\": df.Total\n",
"}],\n",
" \n",
"\"layout\": go.Layout(\n",
" title='Total Crashes By Year',\n",
" xaxis=dict(\n",
" title='Year',\n",
" titlefont=dict(\n",
" family='Courier New, monospace',\n",
" size=18,\n",
" color='#7f7f7f'\n",
" )\n",
" ),\n",
" yaxis=dict(\n",
" title='Count',\n",
" titlefont=dict(\n",
" family='Courier New, monospace',\n",
" size=18,\n",
" color='#7f7f7f'\n",
" )\n",
" )\n",
")\n",
"})"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The Fatality Rate has declining from 2005."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Top 5 US States based of Traffic fatality Rates.###"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Anaconda2\\lib\\site-packages\\ipykernel\\__main__.py:8: FutureWarning:\n",
"\n",
"sort(columns=....) is deprecated, use sort_values(by=.....)\n",
"\n"
]
},
{
"data": {
"text/html": [
"\n",
"
\n",
" \n",
" \n",
" | \n",
" California | \n",
" Texas | \n",
" Florida | \n",
" Georgia | \n",
" North Carolina | \n",
"
\n",
" \n",
" \n",
" \n",
" | 1994 | \n",
" 4232 | \n",
" 3187 | \n",
" 2687 | \n",
" 1425 | \n",
" 1431 | \n",
"
\n",
" \n",
" | 1995 | \n",
" 4192 | \n",
" 3183 | \n",
" 2805 | \n",
" 1488 | \n",
" 1448 | \n",
"
\n",
" \n",
" | 1996 | \n",
" 3989 | \n",
" 3742 | \n",
" 2753 | \n",
" 1573 | \n",
" 1494 | \n",
"
\n",
" \n",
" | 1997 | \n",
" 3688 | \n",
" 3513 | \n",
" 2785 | \n",
" 1577 | \n",
" 1483 | \n",
"
\n",
" \n",
" | 1998 | \n",
" 3494 | \n",
" 3586 | \n",
" 2825 | \n",
" 1568 | \n",
" 1596 | \n",
"
\n",
" \n",
"
\n",
"