In the last three posts, we have been looking at the statistics of road accidents in India. It would be interesting at this stage to compare that with the US.

In the final episode of accident data analysis, we will go into the remaining key stats – injuries and fatalities – and postulate a potential problem with the interpretation, i.e. data registration. But first, a plot of the number of injuries per population.

Kerala is now 33% more than the nearest rival, almost suggesting it is the most dangerous state for a passenger. But is that entirely true? Let’s see the following statistic – the fatalities per 100,000 population.

Strangely, it moves down to the 16th. Puducherry, which is third in injuries, also goes down. To understand this better, let’s define survival rate = the number of injured / (number of injured + number of dead).

Yes, Kerala has a > 90% survival chance after an accident. It may indicate a few things:
1) Kerala has better accident care for the injured (that prevents them from dying)
2) Kerala has more proportion of low-intensity accidents compared to other states
3) Kerala’s registration system is more thorough in recording incidents. And higher survival rate is an artefact of having a higher reporting rate of all incidents, however minor it could be.

Not so fast

When you are about to conclude data collection, here is another one: the proportion of grievously injured people among the total Injured.

Almost 75% of the injured are seriously injured. So to conclude, Kerala remains one the most dangerous for road safety, but most of the injured are somehow saved, despite the severity.

One of the rather unfortunate aspects of statistics is that it doesn’t say why something has happened. They also can’t reveal data quality, making it difficult to compare different entities. Therefore, it leaves the burden of interpretation in the hands of the (responsible) reporter. Not always a desirable combination! With this introduction, let’s continue with the road safety data. This time we go deeper into state-level statistics.

Number of Accidents

Does this make Goa the most accident-prone region? Not necessarily. It is one of the smaller states in India with about 1.4 mln population. The same goes for Puducherry, at number four, with a quarter of a million. If you want to know the difficulties of interpreting data from a smaller population, read this post. Another factor is the incident reporting system. It may not be a coincidence that the top four regions are also known for better data recording, with the three among the four (Kerala, Goa and Puducherry) at the top-5 of the human development index. We’ll come back to this a bit later.

The same statistics on a different basis – the number of accidents per 10,000 vehicles – are below:

Before we move on: let’s try and understand if we can explain the top candidates based on their vehicle per population density. For that, we divide accidents per 100,000 population with accidents per 10,000 vehicles and divide by 10.

Yes, the top regions (Sikkim, Madhya Pradesh and Jammu & Kashmir) of the previous plot are way down in this plot. Again the statistics of smaller samples. That leaves one curious entity that we haven’t addressed so far – Kerala, which is among the top so far, not so small in population (33 million) or in vehicle density (~ 0.5). More about this coming up next.

The R code used for building the plots is below:

state_data %>% 
  ggplot(aes(x=reorder(State, Acci_per_Pop), y=Acci_per_Pop, fill = State)) + 
  geom_bar(stat = "identity") +
  geom_col() +
  coord_flip()

_{HDI of Indian States: Wiki}