Car deaths in B.C. take dive after drunk driving crackdown

According to the data found on BC's data catalogue, motor vehicle fatalities has dropped by 22.2% in British Columbia from 2010 to 2011.  After seeing the massive drop the decline of fatalities continues to persist through to 2014.   A motor vehicle fatality is characterized as a road user that was injured in a collision involving a motor vehicle on a public highway and died anytime within 30 days of the crash from their injuries.  This data excludes all deaths that occurred on forest service roads, industrial roads and private driveways as well as fatal victims of off-road snow mobile accidents, homicides and suicides.

The reason for this drop can be linked to the introduction of stricter impaired driving laws in B.C. in 2010. 


These changes were enacted by the Motor Vehicle Act (MVA) in 2010 and take drivers off the road if they fail a breathalyzer test where their sample is north of 0.08 per cent they will receive a 90-day driving ban and a $500 dollar fine.  Even more stringently so, drivers caught once between the level of 0.05 to 0.08 on a breathalyzer test will face bans and fines and an increase in the length of the ban and the cost of the fine for each additional offence.  This is known as the "warn" range.

These new tools for police officers and especially the new "warn" range is what Solicitor General Michael de Jong hoped would help curb repeat offenders from staying on the bottle while they drive.  According to de Jong there has been an increase in drinking and driving in British Columbia for some time and with the implementation of the Motor Vehicle Act he is setting a province wide goal of 35 per cent by the end of 2013.  These changes finally came about to honour the death of Alexa Middelaer, a 4-year old who was killed by a drunk driver. 

After seeing the first years results, Langley's RCMP "E" Division Traffic Services was awarded the 2011 National Police Award for Traffic Safety where they saw a decrease of 30 per cent decrease in roadside fatalities.  And now with just 4 years since these new laws and road side assistance programs being put in place there has been an estimated 52 percent decrease in impaired driving fatalities.  The average found of drinking-and-driving related deaths is thought to be now at 54 per year whereas before it was 112.  This drop in the average mimics this data found and portrayed on the above chart.

Interestingly enough, even though the motor vehicle act and immediate roadside prohibition program have undeniably yielded results in dropping the rate of fatalities for vehicles, the laws have been met with criticism from some saying that the new rules gives the police officer to much power.  An article done by the Huffington Post shows critics listing examples that the banning process that is enforced onto a driver under the police officer's discretion is immediate and in the cases of a breathalyzer malfunction or an officer's mistake, the plaintiff is left with no way to remove the ban during his or her's sentence.

While the program seems to have it's faults and inconveniences for people who toe the line with having a few drinks and then deciding to drive, the drop in impaired driving fatalities and total driving fatalities is enough to deem the new regulations as effective.    

Data Update #3

My chart shows the motor vehicle fatalities that occurred yearly on British Columbian highways by motor vehicles and especially accentuates the drop of fatalities from 2010 to 2012.

The information I wasn't able to obtain from my data that I wish I could have would have been more specific motor vehicle user types.  This could include different groups such as motorcycles, trucks, semis, cars, etc. Lumping all motor vehicles into one group is too vague in my opinion and would be better to contrast fluctuations in weight and size depending on the road user type.  Furthermore, this would be interesting and important to know whether laws and regulations in BC affected vehicles with these different attributes differently.  I would also be interested to know if some of the fatalities that occurred did so with the use of alcohol and other mind impairing stimulants.  I could investigate this further with a Freedom of Information (FOI) request to find more specific details to the fatalities in the tickets/fines issued by Police.  Also continue to search for more laws or regulations that could have brought about this decrease in fatalities.

Data Update #2

The most compelling information I found in my data set was the 20% decrease of motor vehicle fatalities between 2010 and 2011. The decrease comes specifically from the road user type "Motor Vehicle Driver" which saw a decrease of 22% in those same years. After the decrease, the fatalities stayed relatively consistent until 2014.

My spreadsheet shows the decrease of these motor vehicle fatalities in total and in the specific motor vehicle type of "Motor Vehicle Driver."  As well as my original data set can be found here.

My relevant news article can be found here.

This article done by the Canada Safety Council explains that in 2010 a program was implemented called Immediate Roadside Prohibition (IRP) Program which was aimed at reducing roadside fatalities. That a year after the program was put into action in BC it won an award for reducing fatalities drastically. This is relevant to my data because my data set contains all the road users that were injured in a collision involving a motor vehicle on a public highway and died anytime within 30 days of the crash from their injuries in British Columbia from 1996 to 2014. As this is a road side assistance program, it is relevant to my data because my fatality data includes this 30 day after the accident period. So this could be used as an example as to why we see a decrease in fatalities in these years. Even though this program doesn't cover all of my data collected. So there is other catalysts to find but this is a good start.

Data Update #1

1. What dataset will you use for your final report? 

The data set that I will be using for my final project is "Motor Vehicle Fatalities by Road User Type."
It can be found here.

2. Describe the dataset. What kind of data does it contain? 

This data set contains all the road users that were injured in a collision involving a motor vehicle on a public highway and died anytime within 30 days of the crash from their injuries in British Columbia from 1996 to 2014.  This data excludes all deaths that occurred on forest service roads, industrial roads and private driveways as well as fatal victims of off-road snow mobile accidents, homicides and suicides.

There are four categories in the data that include:

1. Cyclist
2. Motor Vehicle Driver
3. Other
4. Pedestrian
5. Motor Vehicle Passenger

3. Is there anything about your data that you don’t understand? (i.e. what a column heading means). How will you find this out? 

I'm not sure what the category of "other" stands for at the present time but should be able to look up what the "Motor Vehicle Act" constitutes it as.

4. What are some questions you hope to answer with your data? List at least three. (you don’t need the answers at this point)

Are the amount of deaths on public highways increasing or decreasing?  (Over the 18 years)
Is there a specific year that had more deaths than other years?  Why?
Which road users have more fatalities in British Columbia?

Data Viz Assignment 1

This article done by the Washington Post entitled "The Wizard Shooting Stars" is an interactive data visualization charged with helping Washington Wizard fans compare the type and success rate of the shot selection their team took in 2013.  

The article begins with showing us a rather stimulating graph that has 6,920 lines (1) heading towards a basketball hoop.  Each line illustrates where each shot was taken on the court by the Washington Wizards in 2013.  The use of these lines to correspond to each shot in this graph is both a strong way to show each piece of data/shot attempt in that it follows the same route aesthetically as a ball going towards a hoop as opposed to a chart that shows no similarity to a literal basketball shot.  These lines follow in line with the data visualization rule of simplicity in that no extra detail is put in to detract readers from the actual data.  What this chart lacks is being able to distinguish made field goals and attempts in this chart for there is no explanation as to what the varying colours of the lines mean.  We see a clutter of colour varying from white to orange and no way of distinguishing which is which.

Viewers are then able to further break down the shot selection into individual players' shots.  In doing so, using the same chart but now only showing the lines/shots that correspond to a specific player on the team (2).  This interactive option helps us break down the cluttered data we see in the first graph and see it broken down into 15 different players.

Once viewers have chosen a player, ((2) We picked John Wall in our case) we see a similar graph but now with just the shots John Wall has taken.  On top of the updated graph we saw before, we now have two extra graphs (4, 5) that give us a birds eye view of where the data/shots take place rather than just the length away from the hoop.  Specifically in chart (4), this is a very accurate way of showing exactly where the player is shooting from with each tiny red and white circle symbolising a shot. As well as little bagels to break down how the specific player was taking his shot ie. Jump shots, layups and/or dunks.  This is effective because we can definitely tell the difference between the 3 bagels in chart (4).  Chart (5) is helpful in comparing the specific player to the average player in the NBA and does a good job in showing the data's specificity; however, it brings in new information that isn't directly relevant to the team.  A good comparison but perhaps not one that needs to be there.

 The info-graph continues it's interactivity by selecting all shots (3) and by selecting the other options shots made (6) and shots missed (7)  helps us solve the mystery the different colours of white, orange and red.  As well as finding out what each line colour symbolises, the data visualization does a good job on further simplifying itself and un-cluttering the data.  It shows that it is using colour extremely well to highlight the differences in the data.  By clicking on either (6) or (7) readers change the original graph as well as the new (4) graph.





The static nature of graph (5) is not a failing per say, but probably would have been more effective if it was some data that changed with the different options like the two other graphs.

The truly impressive thing is how many ways they have made the data available to be viewed and while the colours seemed cluttered and complicated before, now very simplistic and easy to maneuver.  Overall the interactive part of the data visualization is very easy to use.

The article then goes into trying to visualize the trend of John Wall's shooting by trying to symbolize the writing "58 percent of Wall's shots came from the 15 to 29 feet range" (8) in the varying degrees of circles corresponding with where he's shooting from the floor.  This is a little too complicated in that the only way of connecting the information being portrayed in (8) by careful inspection.  As well as the failure of the cardinal rule of bubble graphs not needed, as the numbers are powerful enough statistics.  Or perhaps even if they put the numbers in the bubbles.  If they had highlighted (8) with a greyish background it would have been easier to connect.  

Lastly, all the labels of graphs are labeled in full words and not acronyms but here in (9) we see it used here.  For a basketball fan these acronyms would be easily discernible as FGM = "field goal made" and FGA = "field goals attempted" but may be too cryptic for the average sport fan.

URL: http://www.washingtonpost.com/wp-srv/special/sports/wizards-shooting-stars/