Tuesday, September 13, 2016

The Tube Heartbeat




This article published by Forbes describes systems created by Oliver O’Brien, a data visualization specialist. This post will focus on his system called “The Tube Heartbeat”, that in essence visualizes the movement of people around London using public transportation. The map shows the direction and volume of people traveling in the tube and historical data summarizing favorable and unfavorable times to travel. The tube in London manages up to 4.8 million passengers every day and this data visualization is powered by the information gathered across the stations.

I am a fan of this system. I think it was a clever idea that was executed in a way that is manageable to update and maintain. However, when dealing with data at this magnitude, it can be tough to decipher and making it manageable is not a simple task.

One thing I found particularly interesting was how the data was sourced and the inputs into the map. One area that surprised me was that they actually used surveys to help create the map. I am not a large believer in the efficiency of surveys as I prefer raw uninfluenced data. Oliver explains that the smaller amount of cleaned data helps the map become more accurate. However, similar to a straw poll, if you ask two different groups of people the same question the answer could vary significantly.

I thought this was a great advancement in not only using big data but creating a system where it would be maintained and used by a layman. After clicking around the site and looking at certain stations I was familiar with I was able to see mass amounts of data condensed in a readable way. Through a combination of geography and data, Oliver’s system sourced mass amounts of data and displayed an interactive visualization that could potentially make a more efficient public transportation system.

One thought I had while reading this article was the connection of the data to people using the “Oystercards”. In my home city we have a similar rechargeable subway card and I was curious on how they used the data of my travels. The article mentioned that the data is sourced from the TFL (Transport for London) and is updated every 15 minutes. This idea concerned me purely because of the possible “big brother” capabilities. After reading the article and the personal blog of the creator, I found that the data is completely anonymous and cannot be traced to a single individual.

I picked this article because I studied abroad in England last year and the system used had everyone swipe on and off. This article gave me insight into what data they use and how they manage the mass amount of data they receive daily.

This visualization of data is the future of transportation technology and I can see maps such as these used for a variety of purposes. The Tube Heartbeat is a step forward visualizing big data and will help create a more efficient public transportation system.



Winkless, Laurie. "Using Big Data To Map A City's 'Heartbeat'" Forbes. Forbes Magazine, 12 Sept. 2016. Web. 12 Sept. 2016.

O'Brien, Oliver. "Suprageography." Suprageography. N.p., 24 Aug. 2016. Web. 13 Sept. 2016.


1 comment:

  1. Kevin chose an interesting article for his blog post, especially for those of us who went abroad last year and traveled to London while there. The tube is the main form of public transportation in London. While we were on tight budgets abroad, myself, as well as many other Loyola students utilized the tube.

    Kevin stated, “..when dealing with data at this magnitude, it can be tough to decipher and making it manageable is not a simple task”. I agree with this, but expanding on this point, what impressed me the most was the level of detail O’Brien was able to achieve while compiling this big data. The data was taken in fifteen minute intervals throughout a weekday. The entire tube networks includes 762 links across 11 different lines. The data collected was of the volume of tube passengers traveling through these links. That is an incredible feat in my opinion, encompassing that large amount of data into one place, the Tube Heartbeat.

    One question that Kevin did not pose that I still have is regarding the functionality of the Tube Heartbeat. While is it interesting to see that 1.35 billion journeys were taken on the tube network last year, I am not sure how useful it is to the everyday commuter who utilizes this public transportation method. The Tube Heartbeat says, “Find out when your local station is busiest and whether your commute indeed is busier than it used to be.” While knowing this information certainly doesn’t hurt anyone, I do not see the functionality. If you are working, you still need to get to work on time every morning, so even if you see that it is busiest from 8:00am-8:30am for example, you will still need to travel via the tube during this time.

    Kevin stated, “The Tube Heartbeat is a step forward visualizing big data and will help create a more efficient public transportation system.” I agree with him when he talks about visualizing big data. O’Brien did an excellent job organizing and then outputting the data in a way that is easy for anyone to follow. However, I question Kevin when he says it will make the tube become more efficient. I do not see evidence from his blog post nor O’Brien’s article regarding how organizing the big data collected by the tube in this manner will benefit the everyday commuter in the city of London.

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