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Welcome to today’s What is The Future For Cities podcast and its Research episode; my name is Fanni, and today I will introduce a research paper by summarising it. The episode really is just a short summary of the original paper, and, in case it is interesting enough, I would encourage everyone to check out the whole paper.

Our summary today works with the commentary article titled Big data, smart cities and city planning from 2013 by Michael Batty published in the Dialogues in Human Geography journal. Since we are investigating the future of cities, I thought it would be interesting to see sea of available data changes urban areas. This article investigates how the big data shifts the emphasis from longer term strategic planning to short term thinking about urban functions and management.

Batty starts with a definition for big data: any data that cannot fit into an excel spreadsheet – giving an idea about size, dimensions and different purposes. Big data is not a new concept and exists in every era where the data processing tools are always stretched by the increasing data size. Currently, big data could be used to better understand the world around us. Batty argued that although stating that ‘this time is different’ is always tricky, today’s analytical approaches are very different beside the increasing size of the questions asked from the data sets.

Big data is produced automatically by sensors most of the time. This was the case since the beginning of industrial evolution in monitoring, but the miniaturisation was what really changed the game. Computers are being embedded into every object and even humans producing unprecedented amounts of data. These technologies became so ubiquitous that anyone can monitor and collect data with these devices.

Beside the widespread of computers and the communication enabled by them, the big data twists the logic further with computers for the first time being used to sense change and collect data while located remotely and send the data elsewhere – all of this still remote from the final data users. Batty highlighted this as a very new prospect as it is both a new step in the miniaturisation and fragmentation of computation. There is also the temptation to see big data as the current buzz word and just waiting to pass while something else will emerge in the digital world to take its place.

Batty’s main interest in big data was to develop a new understanding of how cities function but on a shorter timeframe than usual in urban geography. This, however, immediately generates a concern about the use to derive new theories for urban functioning and especially focused on the urban mobility part than land use and long-term planning. This is city planning in a new disguise – thinking of cities as being plannable in some sense over minutes, hours and days, rather than years, decades and generations.

Although majority of the data is produced by sensing technologies, there are many big data sets generated by human responses. Big data possibly will become associated with the routinely sensed data sets, especially as traditional data sets tend to be increasingly complemented with routine sensing and crowdsourcing.

Smartness and smart cities are involved with big data primarily due to the fact that sensors generate new real-time data with geo-positioning and how those data sets can be used to generate new value. This new value hope however seems a bit challenging because of the linking difficulties. Regardless, the data from mobility, energy use and utility flows in time may well extend into the spatial financial market to influence sales based on consumption for example – as the stuff for smart cities. Smart cities can also be synonymous with intelligent cities, information cities, virtual cities among others, but Batty restricted the understanding to data and theory that brings much more immediacy to the current urban understanding.

Interestingly, urban planning has been focused on the long term time horizons in cities without concrete idea about what happens in minutes or hours. Although many aspects and groups work with the shorter time frame, like transportation tries to solve peak hours or urban operators try to manage urban functions daily, but this has need been considered part for urban planning, only for emergency services. Urban management is rather ad hoc, not necessarily without data or science, but certainly without the kind of comprehensive theory and modelling that characterises the longer term.

According to Batty, smart cities belie a shift in this emphasis to a deeper understanding of how urban systems function in the short term. Big data and the tools for it can help to respond and plan for very short-term crises, like the beset of the transport system or issues pertaining on the housing market or the provision of social services. Big data and, consequently, smart city can change the traditional ad hoc management way in these areas. But to really get a grip on these issues, a new theory is needed. To find proper and real correlations in data sets, they need to be examined through the lens of theory, otherwise, everything and anything is provable. Additionally, with the increasing and continuously in-flowing data, the time horizons are enriched over many times. Therefore, the new theory should address the different time horizons as well. However hard big data pushes for short-termism, this must be resisted and the data must be viewed with the time horizons kept in mind.

Big data is certainly enriching our experiences of how cities function and it is offering many new opportunities for social interaction and more informed decision-making with respect to our knowledge of how best to interact in cities. Whether big data is for the advantage or disadvantage of humanity, is still a question. There are obviously the questions of privacy and confidentiality.

Batty used the example of the Oyster, London’s smart card to analyse transport patterns of passengers. Unfortunately, there are shortcomings in the system as people can change lines, forget to tap off, among others. In short, matching the demand data to the network is possible and is being attempted, but matching it with supply data is almost impossible. Diversion behaviour of travellers is also tricky as people can walk between stations and bus stops and there is considerable analysis needed to indicate how people might change mode of travel from one network to another.

These are massive challenges that will require new theories about how people behave in such situations at a very fine spatial scale. Big data provides the context for the study of this kind of short-term behaviour but we are at the beginning of such explorations and the many pitfalls that were indicated here are likely to persist for some time to come.

As the most important things, I would like to highlight 3 aspects:

1. Big data can be understood as any data that cannot fit into an excel sheet due to connections, complexion and size
2. Big data can be used to enhance our understanding about how cities work and enhance our urban management – regardless of talking about smart cities or not.
3. There are still major challenges in big data and analysist and users need to be careful about these shortcomings and the data set’s attributes themselves – where and how it was collected, what were the limitations on the collection and so on.

Additionally, it would be great to talk about the following questions:

1. What are the greatest challenges in big data and analysis of big data currently? Since the article, almost 10 years have passed – have we become better analysing such data sets?
2. How people fit into the big data picture? Are we only data points? Can we somehow influence the data? Are we accounted as living sensors providing data?
3. What are the biggest opportunities for big data in your opinion for cities and your life?

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