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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 article by summarising it. The episode really is just a short summary of the original article, and, in case it is interesting enough, I would encourage everyone to check out the whole article.
So, this research episode is working with a 2021 article about smart cities, big data and urban policy. Since smart cities are often associated with technology and big data, I thought it would be beneficial to look at one of the freshest articles in this area. The authors highlight the importance and setbacks of using big data technologies while supporting their findings with real-life applications.
Our summary today works with the article titled Smart cities, big data and urban policy: Towards urban analytics for the long run from 2021 by Jens Kandt and Michael Batty. The article was published in the Cities journal to discuss the practical conditions for using big data in long-term urban planning and policy.
The article highlights that the evolution of big data analytics promises benefits in terms of real-time prediction, adaptation, higher energy efficiency and quality of life. The authors wanted to discuss how the real-time data could inform the casual insights needed in urban policy and planning. They aimed to highlight the conditions under which urban analytics of high-frequency data could realistically and productively inform long-term urban policy.
Kandt and Batty defined urban analytics as big data analytics applied for urban governance and planning, which is said to be crucial in shaping and running smart cities. The available real-time data could increase the detection of patterns in everyday urban systems. It could help develop applications to respond to those patterns, among others, in urban transport and energy. They assumed that urban data analytics could contribute to the long-standing urban challenges, such as pollution and congestion. However, the authors recognised a temporal tension between the short-term scale of fast dynamics of data and the long-term slower dynamics of urban structure and policy. Thus, the question emerged how big data could productively contribute to strategic urban policy and planning.
How the smart city fits into this picture? For Kandt and Batty, smart cities were the emerging future version of the city that runs in part on continuous data flow among physical objects, actors and institutions that define, inhabit and govern cities. Particularly, smart cities are collections of numerous sentient and connected built environments learning from daily activity patterns and adapting automatically to changes in such behaviours. And while smart city itself had been debated, urban analytics had been handled the same connecting to the different strands of smart city believers and critics, in their opinion.
Urban analytics can be understood as a set of technical and scientific methods applied to the digital infrastructure and it includes algorithm developments. Or, urban analytics can be seen as a political tool of control through software-sorting, profiling people and places with their characteristics and potentials. In this case, the best scenario is the naïve application lacking transparency and unacknowledged social bias coming from the political aspects. Finally, smart urbanism had become a manifestation of new planning and governance in cities with technological progress to confront the current and future urban challenges.
According to them, mobilities offered a contemporary and practice-based perspective through which the embedding of digital technologies in cities can be conceptualised. Mobilities were defined as the co-evolution of Transportation, Information and Communication Technology, or TICT for short, and the socio-spatial relations with the social interactions. Although they acknowledged the possibility of the face-to-face interactions’ decline, they highlighted that physical travel seemed necessary to advance social links and sustain society itself. Thus, the so-called mobility systems, from mobility networks to even smart city itself, are becoming indispensable in maintaining the communication and flow of a functioning society. The mobility systems are also becoming increasingly digital; therefore, urban analytics, processing the data of physico-digital mobility systems, is expanding the mobilities characterising the 21st-century society.
For the relationship between urban analytics and policy, temporality poses two practical questions: what kind of ‘understanding’ of the city is promoted by the analysis of fast dynamics and to which degree do insights apply to general and deep-seated causal dynamics urban policy is typically concerned with? The second problem is around the causality alongside the bias inherent in big data being critical for the long-term urban policy and planning.
One of the most used data derives from smart cards, such as the Oyster in London, used by 5 million passenger journeys on a typical weekday. Many studies present how the coverage and precision of smart card records enable novel characterisation of travel demand, however, with only one snapshot of time. Kandt and Batty made findings over a longer period, such as most passengers do not have regular routines, which results would have remained hidden in aggregate and low sample data, the usual way of analysing the smart card data. From this example, they concluded that pattern recognition and interpretation is a circular process relying on external data, information and theory about the functions of the urban systems, which increases the analysis of high-frequency data for long-term application.
The analysis over a longer period is quite rare in research. An example of such was again smart card data analysis over six years in the UK, where causality was also the main question. In this example, the patronage of older people for bus rides declined, which opposed the governing body’s aims with sustainability, social inclusion, and alike. This example showed that the derivation of any potential cause insights from high-frequency data for long-term policy requires several elements, such as theory-informed interpretation and contextualisation. This contradicts the purely computational approach taking the patterns as they are and creating partial solutions for them. This automated response, often identified with smart urbanism, would only create solutions for current symptoms rather than curing the causes.
Based on the examples, urban analytics and its practical elements, like pattern detection and interpretation, offer potential to create new and novel strategically relevant hypotheses that would not be derived without processing big data, but in none of the studies did big data deliver clear answers for the causal questions. Therefore, urban analytics contributes to the understanding of urban systems as it supports the fast generation of novel hypotheses that can be theoretically grounded and contextualised using small low-frequency data.
What can urban analytics provide for urban policy and planning? Kandt and Batty identified the following characteristics of urban analytics: a focus on real-time, fast dynamics captured in high-frequency data contrasting the slow dynamics of cities’ structural changes; the greater degree and role of subjectivity in interpreting data patterns found in big data; the political character of the deployment of sensing and computing technologies; and the compound nature of captured data including the technological signals, activities of data subjects and their effect on each other.
Additionally, urban analytics can create value for urban policy and planning with these characteristics: 1. Big urban data can help generate new hypotheses based on data from subjects and sentient elements of the environment. Still, urban analytics can rarely generate direct answers to urban policy problems. 2. Theory becomes more and not less important in interpreting the data sets and emerging patterns. 3. Small data becomes more and not less important, as small data is crucial for contextualisation to a specific situation and thus for hypothesis generation besides the use of big data, which is better to create generalisation.
4. Strategic insights depend on long-term evidence for urban policy and planning and its causal relations. When seeing the fast dynamics over a longer period, big data undoubtedly make a powerful resource to uncover and characterise deep-seated challenges in contemporary urban systems. 5. Insights from big data rely on contextual analysis, and technology deployment and operation modes need to become integral to the quantitative data analysis. 6. Urban analytics should embrace alternative rationalities rather than cementing in current but undesirable situations.
Finally, Kandt and Batty acknowledged that data-driven thinking is unavoidable in urban policymaking, but this can create social and political backdrops if not handled carefully, as they presented in the article. However, with contextualisation and hypotheses, the importance of long-established, low-frequency knowledge assets will increase not despite but because of the rise of big data. The identified six propositions for urban analytics are worth investigating further, focusing on the cognitive processes for pattern interpretation. They also suggest further research on urban analytics without its behaviourist orientation, giving more space to broader characteristics. They finished with the warning: the need for hypothesis and interpretation and contextualisation increases with the amount of available big data for promising applications in urban policy and planning.
As the most important things, I would like to highlight 3 aspects: 1. Big data, its urban analysis, and data-driven decision-making are essential parts of our future cities or, rather, current cities. 2. To utilise the information offered by big data, it is not enough to purely rely on automatic analytical tools. 3. The greatest insights are created with proper hypothesis, conceptualisation, and pattern interpretation in collaboration with analytical tools and human intervention, and the collaboration is a circular process.
Additionally, it would be great to talk about the following questions: 1. Whether Kandt and Batty considered the difference between data about people and data from people and how it would or could change the data and the results? 2. What are other examples of human interpretation and data analytics’ collaboration in the urban environment, and how can we operationalise this kind of collaboration to other parts of the city? 3. Connecting to the previous one – is it imaginable that the automatic urban analytics tool will reach the human capability of theorising and interpreting?
What was the most interesting part for you? What questions did arise for you? Do you have any follow up questions? Let me know on Twitter @WTF4Cities or on the website where the transcripts and show notes are available! I hope this was an interesting research for you as well, and thanks for tuning in!
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