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Data: The First, Critical Step Towards The Solution

The fictional character of Sherlock Holmes is cantankerous, intolerant, self-opinionated and above all, arrogant.

Perhaps this seems inevitable when his observations seem supernatural, but he knew that his brilliant explanations had to be based on data; Sir Arthur Conan Doyle, the writer of Sherlock Holmes, ensured that Holmes knew that to start drawing a conclusion before you had the data would be a monumental blunder.

Data: the first, critical step towards the solution

It is a capital mistake to theorise before one has data. Insensibly one begins to twist the facts to suit theories, instead of theories to suit facts"    -A Scandal in Bohemia

The character of Sherlock Holmes is loosely based on Dr Joseph Bell, a Scottish surgeon and lecturer at the medical school of the University of Edinburgh in the 19th century. He served as personal surgeon to Queen Victoria whenever she visited Scotland. (1)

Conan Doyle first met Dr. Joseph Bell in 1877 while he was a medical student and served as a clerk for him for a time.

Bell emphasized the importance of close observation in making a diagnosis. To illustrate this, he would often pick a stranger and deduce his occupation and recent activities by observing him.

Arthur Conan Doyle wrote an account of Bell picking a man from the audience and concluding that he had served in the army and had not long been discharged. Was from a Highland Regiment and a non-commissioned officer who had been Stationed at Barbados.

Bell told how he made these conclusions “The man was a respectful man but did not remove his hat. They do not in the army, but he would have learned civilian’s ways had he been long discharged. He has an air of authority, and he is obviously Scottish. As to Barbados, his complaint is elephantiasis, which is West Indian and not British, and the Scottish regiments are at present in that particular island.” (2)

Clearly, he needed to have a wide understanding of many aspects of life and current events, but this could only be used once some data had been collected.

The most obvious is not the whole story

Like Bell, Holmes’ observations were brilliant, not because he saw the obvious but because he saw all the aspects that made the whole story.

“There is nothing more deceptive than an obvious fact” -The Boscombe Valley Mystery – a Sherlock Holmes short story

Transport planning has been plagued with the promotion and discussion often centred around promoting “obvious facts”.

When we see a lot of traffic on a section of road heading in the general direction of the city centre, then “obviously” it must all be going to the city centre. But this is very far from the truth.

AITPM fellow Chris Stapleton once challenged this perception but simply saying that we should all look at the amount of traffic that is making turning movements on a major urban arterial.

If there is a lot of traffic on one section of an arterial and a similar amount on the next section, we need to measure whether it is in every way the same traffic if we are to understand the nature of the task.

We see things from our own limited experience. If we travel along a major road, it seems obvious that the traffic lights should be coordinated, but what happens to traffic flows in the other direction. How can you coordinate in one direction on a road with a given capacity, if turning movements add significantly to the volume at various intersections?

Much of the early modelling was based on the journey-to-work but we now understand that there are many reasons for travel even in the peak period that we need to consider. People going to work is an obvious fact of life but it is not the only fact.

Understanding the variations from the “norm”

“It has long been an axiom of mine that the little things re infinitely the most important” -Sir Arthur Conan Doyle, The Memoirs of Sherlock Holmes

The advent of big data has compounded the danger of blurring the complex nature of our transport task with a huge sample size that is then compressed into only one or several average figures.

Such averages, which can include erroneous readings, obscure the nature of non-typical events. Indeed, closer analysis can show that in some ways, all trips are non-typical.

I have written in the past about the American Airforce in the early 1950s that developed a pilot’s cockpit seating based on an extensive measurement of ten key physiological factors of current pilots. They then averaged the figures and ended up with the ergonomics of the cockpits which suited nobody and resulted in an appalling crash rate with loss of life and expensive equipment. A huge improvement came about with adjustable seats.

Transport planning has long focused on long journeys. Yet many journeys in our cities are only of short length, with many different origins and destinations. Many trips are made outside peak periods and, peak trip generations are occurring at weekends, yet we have been consumed by long trips in the peak, weekday periods.

Don’t just collect numbers; observe the true meaning

“You see, but you do not observe”  -A Scandal in Bohemia

The need to observe is more than a quick glance for a quick opinion.

We need to see deeper than just what is happening at the moment: we need to see what the reasons are that results in the trip being taken.

In a video presentation Austraffic did for the last AITPM conference,  one of the experts who spoke, Liz Ampt, said

If you just look at patterns of people's trips, you have no idea why they're doing it. And therefore, you can't use any of that data really for planning. I was just the other day I was talking to a colleague of mine in Chile and he was talking about looking at some data where he found a mother and father were both going to this very odd place about 10 kilometres out of the city, they were measuring the data and it turned out that the reason was because they were trying to minimise their car use and they were dropping a child off at the mother in-laws place. One person would drop it off there and the next person would pick it up, pick the child up. So when he just looked at the data before he had understood, it looked completely crazy. And there are many such instances where planners and decision makers simply cannot work if they don't understand why people are doing the trips that they seeing in their survey.

We should not shun literature as an area to gain insight.

Sherlock might well be an exaggerated hero, vain and difficult in his behaviour, but this fictional character is undoubtedly a hero of data.

(1) Joseph Bell Wikipedia

(2) Past Medical History


John Reid

Managing Director, Austraffic

From the beginning of his career in local government and then when he established Austraffic in 1983, John realised that data collection is not just about numbers but about understanding people and the activities that serve the community's needs.  Poor or even bad data is counter-productive.  Even if results fit our preconceived ideas that doesn’t mean it is accurate. John has seen how good data expands our perceptions and thinking and can be surprising in its results. Connect with John on LinkedIn.

John Reid