Forget the ***city

In 2009, IMB launched a global marketing campaign around the previously little-known concept of ‘smart city’ with the aim of making city governments receptive to ICT applications in the public sector. The initial emphasis was on process control. Emerging countries were interested in the first place: Many made plans to build smart cities ‘from scratch’, in the first place to attract foreign investors. The Korean city of Songdo, developed by Cisco and Gale International, is a well-known example. 

The emphasis soon shifted from process control to using data from the residents themselves. Google wanted to supplement its already rich collection of data with data that city dwellers provide with their mobile phones to create a range of new commercial applications. Its sister company Sidewalk Labs, which was set up for that purpose, started developing a pilot project in Toronto. That failed, partly due to the growing resistance to the prospective violation of privacy. This opposition has had global repercussions and resulted in many countries in legislation to protect privacy.

The rapid development of digital technologies, such as artificial intelligence, gave further impetus to discussion about the ethical implications of technology. Especially in the US, applications in facial recognition and predictive police were heavily criticized.

This current situation – particularly in the Netherlands – can be characterized on the one hand by the development of regulations to safeguard ethical principles and on the other by the search for responsible applications of digital technology.

The question is therefore how useful the term ‘smart city’ is. Touria Meliani, alderman of Amsterdam, prefers to speak of ‘wise city’ than of ‘smart city’ to emphasize that she is serious about putting people first. But instead of introducing other adjectives, skipping them all is better.

The best way to understand human life in the city is respecting the complexity of the city and life within it. Exactly because of the city’s complexity, the use of reductionist adjectives such as ‘smart’, ‘sharing’, circular, climate-neutral’, ‘resilient’ is better omitted. The doughnut-principle is the best way to analyze the city from different perspectives and to define the way people can live in a social and ecological sustainable way, the use of digital technology included.

This post based on by the new e-book Better cities, the contribution of digital technology.  Interested? Download the book here for free

Collect meaningful data and stay away from dataism.

I am a happy user of a Sonos sound system. Nevertheless, the helpdesk must be involved occasionally. Recently, it knew within five minutes that my problem was the result of a faulty connection cable between the modem and the amplifier. As it turned out, the helpdesk was able to remotely generate a digital image of the components of my sound system and their connections and saw that the cable in question was not transmitting any signal. A simple example of a digital twin. I was happy with it. But where is the line between the sense and nonsense of collecting masses of data?

What is a digital twin

A digital twin is a digital model of an object, product, or process. In my training as a social geographer, I had a lot to do with maps, the oldest form of ‘twinning’. Maps have laid the foundation for GIS technology, which in turn is the foundation of digital twins. Geographical information systems relate data based on geographical location and provide insight into their coherence in the form of a model. If this model is permanently connected to reality with the help of sensors, then the dynamics in the real world and those in the model correspond and we speak of a ‘digital twin’. Such a dynamic model can be used for simulation purposes, monitoring and maintenance of machines, processes, buildings, but also for much larger-scale entities, for example the electricity grid.

From data to insight

Every scientist knows that data is indispensable, but also that there is a long way to go before data leads to knowledge and insight. That road starts even before data is collected. The first step is assumptions about the essence of reality and thus the possibility of knowing it. There has been a lot of discussion about this within the philosophy of science, from which two points of view have been briefly crystallized, a systems approach and a complexity approach.

The systems approach assumes that reality consists of a stable series of actions and reactions in which law-like connections can be sought. Today, almost everyone assumes that this only applies to physical and biological phenomena. Yet there is also talk of social systems. This is not a question of law-like relationships, but of generalizing assumptions about human behavior at a high level of aggregation. The homo economicus is a good example. Based on such assumptions, conclusions can be drawn about how behavior can be influenced.

The complexity approach sees (social) reality as the result of a complex adaptive process that arises from countless interactions, which – when it comes to human actions – are fed by diverse motives. In that case it will be much more difficult to make generic statements at a high level of aggregation and interventions will have a less predictable result.

Traffic models

Traffic policy is a good example to illustrate the distinction between a process and a complexity approach. Simulation using a digital twin in Chattanooga of the use of flexible lane assignment and traffic light phasing showed that congestion could be reduced by 30%. Had this experiment been carried out, the result would probably have been very different. Traffic experts note time and again that every newly opened road becomes full after a short time, while the traffic picture on other roads hardly changes. In econometrics this phenomenon is called induced demand. In a study of urban traffic patterns between 1983 and 2003, economists Gilles Duranton and Matthew Turner found that car use increases proportionally with the growth of road capacity. The cause only becomes visible to those who use a complexity approach: Every road user reacts differently to the opening or closing of a road. That reaction can be to move the ride to another time, to use a different road, to ride with someone else, to use public transport or to cancel the ride.

Carlos Gershenson, a Mexican computer specialist, has examined traffic behavior from a complexity approach and he concludes that self-regulation is the best way to tackle congestion and to maximize the capacity of roads. If the simulated traffic changes in Chattanooga had taken place in the real world, thousands of travelers would have changed their driving behavior in a short time. They had started trying out the smart highway, and due to induced demand, congestion there would increase to old levels in no time. Someone who wants to make the effect of traffic measures visible with a digital twin should feed it with results of research into the induced demand effect, instead of just manipulating historical traffic data.

The value of digital twins

Digital twins prove their worth when simulating physical systems, i.e. processes with a parametric progression. This concerns, for example, the operation of a machine, or in an urban context, the relationship between the amount of UV light, the temperature, the wind (speed) and the number of trees per unit area. In Singapore, for example, digital twins are being used to investigate how heat islands arise in the city and how their effect can be reduced. Schiphol Airporthas a digital twin that shows all moving parts at the airport, such as roller conveyors and stairs. This enables technicians to get to work immediately in the event of a malfunction. It is impossible to say in advance whether the costs of building such a model outweigh the benefits. Digital twins often develop from small to large, driven by proven needs.

Boston also developed a digital twin of part of the city in 2017, with technical support from Esri. A limited number of processes have been merged into a virtual 3D model. One is the shadowing caused by the height of buildings. One of the much-loved green spaces in the city is the Boston Common. For decades, it has been possible to limit the development of high-rise buildings along the edges of the park and thus to limit shade. Time and again, project developers came up with new proposals for high-rise buildings. With the digital twin, the effect of the shadowing of these buildings can be simulated in different weather conditions and in different seasons (see title image). The digital twin can be consulted online, so that everyone can view these and other effects of urban planning interventions at home.

Questions in advance

Three questions precede the construction of a digital twin. In the first place, what the user wants to achieve with it, then which processes will be involved and thirdly, which knowledge is available of these processes and their impact. Chris Andrews, an urban planner working on the ESRI ArcGIS platform, emphasizes the need to limit the number of elements in a digital twin and to pre-calculate the relationship between them: To help limit complexity, the number of systems modeled in a digital twin should likely be focused on the problems the twin will be used to solve.

Both the example of traffic forecasts in Chattanooga, the formation of heat islands in Singapore and the shadowing of the Boston Common show that raw data is insufficient to feed a digital twin. Instead, data are used that are the result of scientific research, after the researcher has decided whether a systems approach or a complexity approach is appropriate. In the words of Nigel Jacob, former Chief Technology Officer in Boston: For many years now, we’ve been talking about the need to become data-driven… But there’s a step beyond that. We need to make the transition to being science-driven in …… It’s not enough to be data mining to look for patterns. We need to understand root causes of issues and develop policies to address these issues.

Digital twins are valuable tools. But if they are fed with raw data, they provide at best insight into statistical connections and every scientist knows how dangerous it is to draw conclusions from that: Trash in, trash out.

3. Ten years of smart city technology marketing

This post is the third episode in the series Better cities: The contribution of digital technologies. It deals with the rise of the smart city movement, the different forms it has taken and what its future can be.

The term smart cities shows up in the last decade of the 20th century. Most definitions  refer to the use of (digital) technology as a tool for empowering cities and citizens, and a key to fuel economic growth and to attract investments. Some observants will add as an instrument to generate large profits.

Barcelona, Ottawa, Brisbane, Amsterdam, Kyoto, and Bangalore belong to the forerunners of cities that flagged themselves as ‘smart’. In 2013 approximately 143 ‘self-appointed’ smart cities existed worldwide. To date, this number has exploded over more than 1000.

Five smart city tales

In their article Smart Cities as Company Story telling Ola Söderström et al. document how technology companies crafted the smart city as a fictional story that framed the problems of world cities in a way these companies can offer to solve. Over time, the story has multiplied, resulting in what I have called the Smart city tales, a series of narratives used by companies and city representatives. I will address with five dominant ones below: The connected city, the entrepreneurial city, the data-driven city, the digital services city and the consumers’ city. 

The connected city

On November 4th 2011, the trademark smarter cities was officially registered as belonging to IBM. It marked a period in which the company became the leader of the smart city technology market. Other companies followed fast, attracted by an expected growth of this market by 20% per year from over $300bn in 2015 to over $750bn to date.  In the IBM vision cities are systems of systems: Planning and management services, infrastructural services and human services, each to be differentiated further, to be oversighted and controlled from one cenral point, such as the iconic control center that IBM has build in Rio de Janeiro.  All systems can be characterized by three ‘I’s, which are the hard core of any smart city: Being instrumented, interconnected and intelligent.

The corporate smart city

In many cities in the world, emerging and developing countries in the first place, administrators were dreaming about building smart towns from scratch.  They envisioned being ‘connected’ as a major marketing tool for new business development. 

Cisco and Gale, an international property development company, became the developers of New Songdo in South Korea. New Songdo was in the first place meant to become a giant business park and it is set out to enable a decent corporate lifestyle and business experience for people from abroad, offering houses full of technical gadgets, attractive parks, full-featured office space, outstanding connectivity and accessibility. 

Quite some other countries took comparable initiatives in order to attract foreign capital and experts to boost economic growth. For example, India, that has planned to build 100 smart cities.

The data driven city

The third narrative is fueled by the collection and refined analyses of data that technology companies ‘tap’ for commercial reasons from citizens’ Internet and mobile phones communication. Google was the first to discover the unlimited opportunities of integrating its huge knowledge of consumer behavior with city data. 

Sidewalk Labs – legally operating under the umbrella of Alphabet – responded to an open call for a proposal for redevelopment of Quayside, brownfield land around Toronto’s old port, and  won the competition. Its plans were on par with contemporary urbanist thinking. However, that was not Sidewalk Labs’ first motive. Instead, its interest was ‘ubiquitous sensing’ of city life’, to expand Google’s already massive collection of personalized profiles with real-time geotagged knowledge of where people are, what they are whishing or doing in order to provide them with commercial information. 

As could be expected, privacy issues dominated the discussion over the urbanist merits of the plan and most observers believe that therefore the company put the plug out of the project in May 2020. The official reason was investors’ restraint, due to Covid-19.

The consumers’ smart city

The fourth narrative is focusing on rise of urban tech targeted on consumers. Amazon, Uber and Airbnb are forerunners disrupting traditional sectors like retail, taxi and hotel business. They introduced a platform approach that decimated the middleclass in in the US. Others followed, such as bike- and scooter-sharing companies Bird and Lyme, co-working companies like We Work and meal delivery services like Delivero.

City tech embodies the influence of entrepreneurship backed by venture capitalists and at the same time the necessity for city governments to establish a democratic legitimized framework to manage these initiatives.

The smart services city

Thanks to numerous ‘apps’, cities started to offer a wealth of information and services to citizens concerning employment, housing, administration, mobility, health, security and utilities. These apps enable city administrators, transit authorities, utility services and many others to inform citizens better than before. With these apps, citizens also can raise questions or make a request to repair broken street furniture.

Some cities, for instance Barcelona and Madrid, started to use digital technologies to increase public engagement, or to give people a voice in decision making or budgeting. 

All aforementioned narratives suggest a tight link between technology and the wellbeing of citizens, symbolizing a new kind of technology-led urban utopia. In essence, each narrative puts available technology in the center and looks for a good-looking rationale to put it into the market. Probably, the fifth one witnesses an upcoming change into a more human-centric direction.

An upcoming techlash or a second wave of smart cities

It is unmistakably that business leaders, having in mind a multi-billion smart city technologies market overstate the proven benefits of technology. Garbage containers with built-in sensors and adaptive street lighting are not that great after all, and the sensors appearing everywhere raise many questions. According to The Economist, it is not surprising that a techlash is underway. As I accentuated in last week’s post, politicians are becoming more critical regarding behemoths like Google, Amazon and Facebook, because of their treatment of sensitive data, their lack of transparency of algorithm-based decision making, their profits and tax evasion and the gig economy in general. Skepticism within the general public is increasing too. 

Nevertheless, a second wave of smart cities is upcoming. The first wave lacked openess for the ethics of urban technology and the governance of urban development. The second wave excels in ethical considerations and intentions to preserve privacy. Intentions alone are insufficient, politics will also have to break the monopolies of Big Tech

Besides, in order to gain trust in the general public, city governors must discuss the city’s real challenges with residents, (knowledge) institutions, and other stakeholder before praising the role of technologies of all kind.  Governance comes prior to technology. As Francesca Bria, former chief technology officer of Barcelona said: We are reversing the smart city paradigm. Instead of starting from technology and extracting all the data we can before thinking about how to use it, we started aligning the tech agenda with the agenda of the city

Apart from Barcelona, this also happens in cities such as Amsterdam, Boston, Portland and the Polish city of Lublin. The question is no longer which problems technology is going to solve, but which exactly are these problems, who is trusted to define them, which are their causes, whose intersts are involved, who is most affected, and which ones must be solved most urgently. Only after answering these questions, the discussion can be extended to the contribution of (digital) technology. In a next contribution, I explore digital social innovation, as a contribution to a revised smart city concept.

This post is a brief summary of my article Humane by choice. Smart by default: 39 building blocks for cities in the future. Published in the Journal of the American Institution of Engineers and Technology, June 2020. You will fine a copy of this article below:

https://www.dropbox.com/s/3rmrwnzdoph114w/SMC-2020-0030-FINAL.pdf?dl=1