Visualizing Fatal Car Accidents in America

Transcription

1 Kenneth Thieu CMPS161 Visualizing Fatal Car Accidents in America Abstract The goal of this paper is to examine and analyze car accidents in America with fatal consequences, the rate of accidents, when they occur, and how much they contribute to America's Fatality Rate. Introduction Cars are a convenient asset of our everyday lives; where public transit cannot do cars can. However, the roads of our country are not a child s playground. The cars that we bought and cherish so much can just as much rob us of our greatest treasure: our lives. The question here is how safe should we be driving on the road; this is a question that this paper strives to answer through data analysis and visualization. After visualizing fatal car accident data with different measurements and techniques can we see the details that surround when and how frequent these events occur. Related Work There are a few methods of analyzing fatal car accidents in America. An article written by the National Motorists Association provides an insight on the accuracy of certain measurements, particularly the fatality rate and the fatality accident rate. I use such measurements to analyze the fatality of car accidents per state of the country. A few of the ideas of this paper came from the visualizations present in Nathan Yau's book Data Points: Visualization That Means Something. Particularly, the analysis of car accidents based on time of day, what day of the week, and a map of America with points that refer to incidents; this paper presents a variation based off these techniques. Data For this project, I used Javascript and D3 as a visualization tool for my data. I collected the data in various formats, such as comma or tab separated values (.csv or.tsv) as well as a Javascript Object Notation file (JSON) for the Geometric representation of America. The Map of the United States was taken from the Census Bureau in the form of a shape file which I converted into a JSON file through a library called TopoJSON by Mike Bostock, who is also the creator of the D3 Library. Population data also came from the US Census Bureau and Traffic related information came from the National Highway Traffic Safety Administration (NHTSA) and the Federal Highway Administration (FHWA). Approach The visualization techniques present in this project are somewhat simple yet effective in conveying the data. I divided the data collected into separate data documents, all of which were used in different.html results or shared. For the maps of America I decided to do a variation of Yau's map with a heat mapping of America divided by the 50 states; part of this idea is also influenced by Bostock's Choropleth Map of America's Unemployment Rate per county, per state. This same map is used a number of times to represent 2 groups of data. The maps are represented through D3 and TopoJSON, which renders out the geometric shapes of the states into a readable format that coincides with the data file. The remaining visualizations are simple bar charts and one bubble chart, all of which provide information on time and conditions of the accidents.

2 Death Rate Death Rate is a measurement of how many fatalities that result from a car accident; data found includes into the count the driver, occupants, pedestrians, other motorists, and bicyclists. For this set of data, I chose to divide the Death Rate in a percentage of the total population per state. Each state's population is taken from the US Census Data, and since population numbers tend to reach the thousands, if not, millions, I divided the population by 100,000 in order to split the population into groups. In other words, Death Rate is measured by the total number of fatalities divided by the population per 100,000 people. This creates a set of data that is not only easier for the program to read but for the user to understand. I took this measurement and applied it to two years of fatal car accident data. The reason I decided for two years in favor of the past decade is to mainly see how much a year can change in terms of fatality rates. Thus, for the years 2011 and 2012, which are the years that have the most up-to-date data, I created heat maps of the 50 States of America and colored them based on a quantize scale of a the death rates; the higher the rate, the darker the color, with yellow being the color for the lower rate values. Colors are taken from Colorbrewer, which is a specialized style sheet of colors created by Cynthia Brewer. Figure 1: Death Rate in In order to understand the rates for each state, we must understand the circumstances that influence them. The size of the state itself influences not only the geographic level but also the population total. If we take these two factors into account when looking at the given figure, we can see how certain states we could expect would have high death rates would not. California, shown in the figure as somewhat lighter in the color spectrum, is one of America's most populous states; the amount of crashes in California and the fatalities as a result, when taken as a percentage of the overall total, may be miniscule. This idea applies to other states that house America's most populous cities, including New York and Illinois. Whereas states like Wyoming are darker due to lower population and high fatal

3 accident rates. Judging from this data, we can define the south-eastern and middle northern areas to have the higher death rates from fatal accidents. These states, aside from a smaller total of population, may have limitations or obstacles that somehow influence fatal accidents. Figure 2: Death Rates in Comparing this figure to 2011's, we can see that some states' death rate has increased while others decreased. The regions where death rate is high seems to have expanded to a bit of south-west, which means that more car accidents are becoming fatal. States that we defined as having high population countering the death rate seems to remain. It is important to note that when comparing maps with the same color scheme, the quantize scale used to classify each state's color is determined overall by the minimum and maximum values of rates of all 50 states combined. Maps can still demonstrate a clear increase by a darker shade of color; a lighter color can represent a decrease or a new minimum in terms of data. Keep this in mind when observing the maps present throughout this project. Measuring fatality through population and death rate, however, isn't an effective way to analyze highway and traffic safety. We can't learn much from this because pure numbers of deaths is ambiguous in the context of accidents. Fatality Rate Fatality Rate is one of the two important measures of Fatal Car Accident analysis. Fatality Rate is measured by taking the amount of deaths from fatal car accidents and dividing by the total vehicle miles driven in the state. Total Vehicle Miles Driven is usually measured in the millions, and the data is given by the Federal Highway Administration per State. When we measure Fatality Rate, we base the total vehicle miles traveled as per 100 million miles traveled; this provides a nice measurement of the rate in terms of percentage. One reason why vehicle miles traveled is used is because of driver exposure, or how much experience does an individual driver have on the highway. The more a driver drives, the greater the risk of an accident; the result is cumulative to represent all the drivers of the

4 state. Once again, I compare two heat maps of the Fatality Rate of America per state between 2011 and Figure 3: Fatality Rate in The distribution of colors in the map, following the same color spectrum as the Death Rate Maps, is shown to be similar in terms of certain states but also different for others. For Fatality Rate, one of the important circumstances behind the rate values lies in the geographic location of the states. States that are geographically challenged may prove to have higher fatality rates due to frequent accidents. By geographically challenged we can assume the roads could be narrow, in the mountains, or perhaps too congested. States like Montana, South Carolina, Arkansas, and West Virginia have the highest fatality rates; more people die on a per 100 million vehicle miles traveled basis. While states like California and Washington prove lower due to straight roads and less congestion in the long drive.

5 Figure 4: Fatality Rate in It seems that between 2011 and 2012, the fatality rates remain consistent in some states while others increased and decreased. The spread in 2012 shows that the overall fatality rate has increased compared to 2011; a sign that highway safety isn't showing signs of improvement. Fatal Accident Rate Fatal Accident Rates are the second of the two important measures of Fatal Car Accidents. As the term name implies, it is measured by taking the total number of fatal car accidents and dividing by total vehicle miles traveled on a per 100 million mile basis. Fatal Accident Rates are considered measurements based on driver exposure, hence we once again measure it by total vehicle miles. What is interesting about measuring fatal accidents is that it doesn't take into account individual fatalities but rather the accidents that result in such fatalities. Fatal Accident Total is measured per accident; accidents involving multiple vehicles or pedestrians is considered one accident. As such, measuring these rates eliminates extra recording of data as well as fluctuations in the data per state. This is also why the National Motorists Association argue that Fatal Accident Rates are the best measure of exploring highway safety.

6 Figure 5: Fatal Accident Rate in Similar to Fatality Rate, circumstances behind the Fatal Accident Rate include the geographic nature of the state. This spacing of data bears similarity to the 2011 data of Fatality Rate. However, this data eliminates individual person fatality. This leads me to assume that fatalities come from accidents involving multiple vehicles or bikes, as some states are actually safer in terms of fatal accident rate. Figure 6: Fatal Accident Rate in 2012

7 Comparing 2011 and 2012 Fatal Accident Rates, just like we've seen with the maps on Fatality Rate, it is becoming dangerous to drive on the highways of America; states like North Dakota are rising in terms of fatal accidents. This means that certain states need to make improvements with their driving system to decrease the rate; this includes but not limited to training improvements or road reconstruction. Bar Graph Representations Let us shift focus from state percentage data and focus on America as a whole. Aside from looking at individual states in terms of how much they each contribute to America's overall rates, we can also analyze fatal accidents through time and type. For this section, I'll visualize through bar graphs the percentage of when fatal accidents occur; these graphs span for a decade, from 2001 to 2011, which is the latest compiled data given by the NHTSA. For comparison, I'll also visualize similar graphs with crash totals instead of percentages. Figure 7: Time Crashes in Percentages This bar graph represents, for 2001 to 2011, the frequency of fatal car accidents in certain time frames of the day. The legend in the top right generalizes the time zones into 5 categories. Night is from 12AM to 6AM, Morning is from 6AM to 12PM, Afternoon is from 12PM to 6PM, and Evening is from 6PM to 12AM. Unknown represents accidents whose time of incident was not recorded. The data itself was already given in a table format on the NHTSA FARS Database, but I formatted it so that D3 can read it. Color set was once again from Cynthia's Colorbrewer. From the data, we can observe that most accidents happen during the afternoon and evening hours; a trend that does show some fluctuation but overall remains within a certain percentile range; around 30%. This confirms my hypothesis that during the afternoon lies one of the more congested hours of the day to drive, whereas night time limits your field of vision while driving.

8 Figure 8: Time Crashes with Crash Total The above figure describes the same set of Time related crashes in terms of crash totals per time frame instead of percentages. While percentages allows us to see a trend of frequent occurrences, looking at crash totals allows us to view changes in the number of fatal car accidents; this analysis serves as a better insight in how safety and regulation on highways have been improving throughout the decade with less car accidents. While this data alone serves to be informative of when driver should be careful when driving, I felt that it wasn't enough. So I took the same table from the NHTSA and used it to find alcohol related crashes that happen within the time frames.

9 Figure 9: Alcohol Crashes in Percentages The above figure is similar to the previous figure that visualized crashes in time frames from 2001 to 2011; this condenses it further down into alcohol related accidents, or D.U.I. violations. This data counts D.U.I as a concentration level of 0.08 or higher, which is the actual law given to us by the DMV. This visualization confirmed my hypothesis about when alcohol plays the biggest factor in fatal car accidents. Indeed, the time frame of 6PM to 6AM serves as the most recorded incidents, with a huge value of 35% - 40%. This can relate to parties where people drink, but then make the foolish decision to drive while intoxicated. Figure 10: Alcohol Crashes with Crash Total Similar to the Time Crashes variation, the above figure shows empirical crash totals instead of percentages. This, again, shows a trend of less alcohol related accidents due to less fatal car accidents throughout the decade, which also backs up the notion of increasing safety and regulations in America's highway system.

10 Figure 11: Day Crashes in Percentages The above figure, once again, follows the format of the previous two graphs, but the data is divided between the seven days of the week instead. The results show that fatal car accidents happen mostly during the weekend, with the most frequent day being Saturday at 18%. This trend remains consistent throughout the decade I took the data from.

11 Figure 12: Day Crashes with Crash Total I'm becoming confident that the trend is now noticeable in terms of how many fatal car accidents have occurred nationally throughout the decade. While we still see the weekend as accident heavy, the overall change in accidents has been a negative one, which demonstrates increasing safety and awareness of the road. Another way to visualize fatal car accidents in terms of time frames is to add another dimension to the analysis. Weather conditions play a good contribution to these accidents; based on the weather and time of day, accidents are more prone to happen. With tutorials by Nathan Yau, I didn't use Javascript or D3 for this graph but instead opted for R, which is a free source visualization tool. I used R to plot a bubble chart, which allows me to compare and find correlations between three variables. In this graph, I have the variables of weather conditions, light conditions, and crash total. Weather lies on the x-axis, light lies on the y-axis, and the size of the individual bubbles represent the number of crashes given the conditions. This was, again, using data for the decade of 2001 to The results proved to be half expected. There is a huge difference between conditions and crash totals. It seems that fatal car accidents occur mostly during dawn or dusk, which serves as the darkest times of the day, and under normal weather conditions. Even so, those light conditions prove to be the greatest threat to highway safety given the distribution of data. However, I honestly expected crashes to occur more during the dark instead of daylight, when vision is limited. This can be attributed to congestion on the roads during the day as opposed to night.

12 Figure 10: Weather/Light Condition crashes in Conclusion We discovered how certain states have unusually high rates of fatality, but this can be attributed to driving style or geographic restrictions. Fatality and Fatal Accident Rates prove to be effective measurements of analyzing and improving highway and traffic safety because they gives representation of driver exposure to the roads; how much experience does each have driving on the road. Accidents in the context of time frames, alcohol relevance, and days prove to be expected results based on reality's context; the trend in terms of empirical data on crash total is a negative one, which is positive in terms of society becoming aware of the dangers of the highway traffic. Weather and Light Conditions also play a role in how frequent accidents happen. Analysis of Fatal Car Accidents are always contributing to Highway and Traffic Safety. The maps demonstrate a sense of where accidents the most in terms of states, whereas the graphs visualize nationally the percentages of when and under what conditions do accidents occur. These visualizations serve as a guide to determine what should we do to lower fatality and fatal car accident rates throughout America. They also serve in knowing when and how accidents are prone to happen and help develop methods to reduce the rate. References National Motorists Association. Understanding Highway Crash Data. < Yau, Nathan. Data Points: Visualization That Means Something. Somerset, New Jersey : Wiley, Print. pg Yau, Nathan. Visualize This : The Flowing Data Guide to Design, Visualization, and Statistics. Hoboken, New Jersey : Wiley, Print. pg Bostock, Mike. D3: Data-Driven Documents Web.