Amidst Singapore’s transformation into a Smart Nation, we speak to data visualiser Yin Shanyang and data scientist Alvin Chua to learn how data can be meaningfully used to better different facets of life in the city. 

How many different ways can you order coffee at the kopitiam? Between a regular “kopi” to a bespoke “kopi c peng gao ga dai”, Yin Shanyang has mapped out 132 variations in an online data visualisation.

It all started when a good friend experimenting with constraint programming—a technique in computer science—created an application to combine suffixes and prefixes in language. “The program he built was essentially a ‘kopi lingo’ constructor,” he says. “From there, I thought it would be interesting to calculate and explore all variants of kopi that one can get from the humble coffeeshop.”

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132 Ways to Kopi” is a fun example of how data can be used to explore the city. Over the years, the Shanyang has tapped into public information to generate a variety of projects, from mapping out the flow of taxi traffic in a day to the connections amongst board members of Singapore’s top 50 corporations. Besides theses self-initiated projects, the founder of the data visualisation firm Swarm also helps corporations and government agencies design interfaces to access and understand data—a service that is increasingly in demand with Singapore’s push to build a data-driven nation.


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132 Ways to Kopi maps out the number of ways one can order a cup of coffee at the kopitiam, be it varying in type of milk used, amount of sugar added and the temperature of the drink.


But this wasn’t the case when Shanyang first returned home in 2011 from Melbourne, where the Singaporean first encountered data visualisation as a design student and then at work. Drawn to how it operates at the intersection of design, statistics and software engineering, he sought out such jobs in Singapore only to realise few people here had even heard of this term then.

“Even before I could peddle my wares, I had to explain what I was doing. I soon got sick of saying I’m doing data visualisation and I would just say I’m in IT,” he recalls.

Undeterred, Shanyang started his own data visualisation company in 2011. He sized upon the general elections that year to create his first visualisation project about Singapore using data provided by social media intelligence company JamieQ. The result was “Singapore GE2011 Tracker”, a website that maps out the “true national agenda” based on what netizens were sharing—news articles, blog posts and tweets about the elections—during the campaign. “That got a lot of coverage… and that’s how people got to know about us,” he says.


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The Singapore GE2011 Tracker provided a snapshot of what netizens were discussing about the general elections via Twitter, Facebook and news websites. At any point of time, one could see the messages that were trending and trace the source.



BUILDING A BETTER CITY WITH DATA

Five years on, Swarm is known in Singapore’s fledging data services industry for its bespoke interface designs. As companies and cities rely increasing on data, visualisers like Shanyang play an important role in helping those who are not statistically trained to understand the information, he says. In the same way that a clock tell the time or a speedometer shows how fast a car is moving, Swarm’s work is all about creating displays that can easily explain data to its users. 

But even before a data visualiser can begin work, someone has to figure out what data to use and how to collect them. This is the job of a data scientist such as Alvin Chua. Over the last four years, the Singaporean has been pursuing a PhD at Belgium’s University of Leuven looking at the role of data science in urban studies. He recently returned and is now interning at the Singapore-office of Teralytics, a Zurich-based data science company. In his own words: “I do math from which I make pictures that are useful for decision makers.” 

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The end users of Alvin’s works are often urban planners. One project he has worked on was analysing how people moved about in an urban area. Typically, researchers literally spend hours on-site observing the flow of human traffic. A data scientist, however, can find this out using big data gleamed from cellular networks and social media instead—especially so in Singapore which has a high mobile penetration rate and good cellular network coverage. Not only does data science make research more convenient, the huge quantity of data also allows the analysis to be conducted on a larger and more granular scale. This allows users to see patterns over an entire day and also zoom into specific times.

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In a study of tourist flows in southern Italy, Alvin showed how tourists travelled differently depending on where they came from. While foreign tourists travelled along the coastline, domestic and locals did so mainly inland.

This is an instance of how use of data greatly enhances the process of planning a city. As a result, traditional ways of drawing up 5, 10, or 15 year plans for huge parcels of land in the city would give way to planning methods that are more specific and intense, says Alvin. “In the past, planning would be done on zones which may or may not be arbitrary. Today, it’s even possible to plan for buildings or plots and these may change over time. At 6pm it could be a particular policy, and at 7pm we can apply a different policy to that building or plot,” he explains. For instance, with data on noise levels in a residential area, policy makers can propose when exactly to restrict activities that generate noise. “This is currently in practice, but due to the widespread availability of sensors, we are now able to be more specific.”

But even as he extols the benefits of big data, Alvin is also cautious about its limitations. Data is after all just numbers, and what matters more are the interpretations made from it. This is why subject experts and their small studies remain important. For instance, data-driven urban planning tools are still based on urban planners’ traditional methods, and what data scientists often do are to create computer processes that mimic this. “So small data complements big data by providing interpretation,” he says. “What is required is to try to merge them.”

Find out more about their dreams of a data-driven city in Part 2

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