Embedded Files
The chart above displays a time series plotting the hours in a day along the x axis legend against the number of records on the y axis. By combining the data and time into a calculated field, it was possible to create the time stamp field and plot the time series graphs displayed on this page. The trend lines show the number of records per age band as they happen through the day. Null data has been hidden in the above chart. The colour detail of this chart shows the age band of the casualties.
It is clear that the 26-35 age band has the highest count of casualties with the lowest being the 0-5 age band.
It seems that between the ages of 16-55 the 2 peak times of day accidents have happened are 08.00 am and 05.00 pm, aka rush hour.
The 0-5, 6-10 and 11-15 age bands have a slight peak at 08.00 am, but the second peak crops up at the 03.00 pm mark. This peak is likely to be present due to parents driving on the school run. It is interesting that this peak occurs before school finishing time rather than after school finishes. It is likely this is because parents are rushing to pick up their children and drive with less caution so as not to be late. Once the children are collected, parents are likely to be more cautious drivers for their children's safety.
The second peak shifts from being at 15.00, to 17.00 from the age band of 16-20. From this age band onward, the peaks at 08.00 and 17.00 become progressively more prominent. From the previous analysis, it is known that the 26-35 age range appears to be the most at risk of road accidents and slight casualties. This is likely because most individuals 16 and over are working and are commuting in these hours of the day.
Beyond the age of 35, the peaks begin to steadily decline and once the age band of 56-65 is reached, the peaks disappear, likely due to retirement no longer requiring them to drive during rush hour each day. The hours between 7 am and 7 pm are are where most of the accidents occur.
Post 65, the most number of accidents happen midday at 12.00 which is when most retired people are likely to be our running their errands of the day.
The above time series shows trends lines of the number of casualties per journey purpose of the driver.
The commuting to and from work does not vary very much through out the year, however, there is a dip in August were the number of casualties drops from 4,459 to 3,464. This sudden but slight decrease is most likely due to drivers going on holiday during the summer. There is also a bit of a peak during November were the number of casualties increased which is likely to be due to Christmas and more people shopping on their way home from work and so more drivers on the road during that time.
The peak for number of casualties which appears for journey as part of work during July stands out from the rest of the data. It is interesting why casualties increase so drastically during July for this journey purpose. Perhaps due to it being summer time and more working age people are likely to go out socialising and are inclined to drink around July time opposed to other colder months where people are more inclined to go home and stay in. The graphs looked at previously of road surface/weather conditions support that a majority of accidents occur on dry roads when driving straight in day light. These are the conditions during July and so this could be the reason for the sudden peak. Perhaps the pleasant weather conditions is what brings out more drivers onto the road compared to other months, or an influx of people driving long distances while on holiday.
The third chart here displays trend lines for the casualty_IMD_decile for the number of records per hour.
By using bright colours to display the trend lines, the reader’s attention was drawn to the categories of information, telling them where to focus their attention on the page (Alexander & Wiley and Sons, Inc. Staff, 2014).
IMD_decile is a measure of deprivation in an area set by the department for transport. This measure can be used for identifying where resources need to be prioritised or where intervention may be needed. The ares are divided into 10 categories and ranked. In the above chart, IMD decile of 1 are the most deprived areas and moving up to a score of 10 which are the least deprived areas.
It is clear that the with every boundary of the IMD decile score decreasing (from 10 to 1) the number of records are increasing. The number of records within the IMD decile of 1 are the highest.
A common pattern featured in each of the trend lines is that there are peaks at the time before and after work which are 08.00 am and 17.00 pm.
The most number of accidents recorded have occurred in the lowest IMD decile neighbourhoods.
The IMD decile 3 does not follow the trends of the other trend lines. It has 4 peaks unlike the other lines and does not appear very stable. An assumption to the cause of this could be that once you move away from the lowest deprivation areas, you move to categories where individuals are able to afford being car owners but lack the finances to be able to afford cars with increased levels of safety features.
It is a possibility that less accidents occur in the higher IMD Decile categories as safer cars are more expensive and can be afforded by people in the less deprived locations. In the poorer locations there would be increased poverty, lower levels of education, creating a society of people with less awareness and so individuals are less likely to prioritise owning a car with passenger air bags or thinking about road safety conditions.
If there is a hazard, are the individuals in the lower IMD deciles going to push for change?
Are they going to act by contacting the local council and see an issue through?
Would they act if they knew the additional risk they are in compared to those less deprived than them?
Are the vehicle manufacturers responsible for the minimal requirements of vehicles not being safe enough?
There could be many answers to the questions raised here but to have firm conclusions on the changes that need to happen to ensure all individuals can expect the same levels of safety from their vehicles, more analysis needs to be done from these areas.
It is important to realise that in all of the analysis, where there is correlation between 2 values, it is not clear which factor is the cause and which is effect. There could be other factors causing the effects which can be seen in the data visualisations created but are not evident from the charts.
References:
- Alexander, M. & Wiley and Sons, Inc. Staff (2014) Data Visualization for Dummies. Somerset: John Wiley & Sons, Incorporated.
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