Urban design for complexity 

Throughout history, cities have faced many problems: War and violence, disease, disasters, housing, utilities, traffic, crime, inequality, poverty, and greed. Moreover, the pace of population growth in cities is frightening. Every day, urban population increases by almost 150,000 – mostly poor – people, due to migration or births. Between to date and 2050, the world’s urban population is projected to rise from 3.6 billion to 6.3 billion residents.

The litany of problems affects all cities in the world, but not in the same degree. To cope with these problems, each city must make a diagnose of its own challenges and define solutions. 

City-life is complex and most afore-mentioned problems are related and often at odds, think about struggling poverty and reversing global heating. Therefore, these problems cannot be solved in separated silos. This is the reason reason that I reject reductionist approaches like ‘smart city’, ‘sharing city’, ‘circular city’ and the like. 

Instead, framing the challenges that cities face must start from the complexity of the city as such and the interrelations of people causing these problems. In this respect, I found the concept of a doughnut economy particularly helpful. It is elaborated by the British economist Kate Raworth in a report entitled A Safe and Just Space for Humanity. The report takes the simultaneous application of social and environmental sustainability as the point of department for humane behavior.

In essence, Raworth says that people have a great deal of freedom in the choice of activities in their city, if they stay within two types of boundaries:

The first limit is set by ecosystems; which make life on earth possible. However, we can also frustrate their operation, which has a direct impact on our living conditions. 

Something similar applies to society. Here you can also distinguish several aspects and each of them has a level that people should not fall below, the second limit. If this does happen, it will jeopardize the survival of society.

If you look at a donut, you will see a small circle in the center and a large circle on the outside. The small circle represents the social foundation, the lower limit of the quality of society. The large circle refers to the ecological ceiling. Between the two circles lies the space within which people can act as they please. Kate Raworth calls this space a safe and just space for humanity.

On the way to a city for humanity , what we need to do is, first of all, to define human actions that comply with or are threatening the ecological ceiling and social foundation of our own city. What follows is the formulation of targets to correct and subsequently enforce all actual violations of ecological and social boundaries. This applies to the city itself and the global effects of its activities.

As an exercise, I created a table of principles for 10 clusters of activities to address the challenges that many cities in developed countries share, combined with one target for each principle. You may want to download this table here.

I recommend this procedure to any city that intends to develop an integral vision starting from the complexity of city life and the interdependency of its activities. Amsterdam went through this process, together with Kate Raworth. The Amsterdam city donut is worth exploring closely.

This post based on by the new e-book Better cities, the contribution of digital technology.  Interested? Download the book here for free (90 pages)

Content:

Hardcore: Technology-centered approaches

1. Ten years of smart city technology marketing

2. Scare off the monster behind the curtain: Big Tech’s monopoly

Towards a humancentric approach

3. A smart city, this is how you do it

4. Digital social innovation: For the social good

Misunderstanding the use of data

5. Digital twins

6. Artificial intelligence

Embedding digitization in urban policy

7. The steps to urban governance

8. Guidelines for a responsible digitization policy

9. A closer look at the digitization agenda of Amsterdam

10. Forging beneficial cooperation with technology companies

Applications

11. Government: How digital tools help residents regaining power?

12. Mobility: Will MaaS reduce the use of cars?

13. Energy: Smart grids – where social and digital innovation meet

14. Healthcare: Opportunities and risks of digitization

Wrapping up: Better cities and technology

15. Two 100 city missions: India and Europe

Epilogue: Beyond the Smart City

The main shortcoming of AI is its lack of intelligence

Artificial intelligence is the ‘independent construction of pattern in large datasets by computers’. Still, people hold an important role in this. This role consists in the first place in writing an instruction – an algorithm – and then in the composition of a training set, a selection of many examples, for example of animals that are labeled as dog or cat and if necessary, lion or tiger and more. In essence, the computer looks for statistically significant similarities in whatever data, provided by the operator, to predict the probability that some relation will exist or phenomenon will occur.

If computers are learned to make judgement about people, things can go terribly wrong. The St. George Hospital Medical School in London has employed disproportionately many white males for at least a decade because the ‘learning set’ reflected the incumbent staff. The learning set itself represented the bias of those who selected it. 

The fight against crime in the United States, has been the scene of artificial intelligence’s abuse for years. The two most used techniques that resulted are predictive policing (PredPol) and facial recognition. In the case of predictive policing, patrols are given directions in which neighborhood or even street they should patrol at a given moment because computers have calculated that the risk of crimes (vandalism, burglary, violence) is highest then.

Predictive policing and facial recognition are based on a “learning set” of thousands of “suspicious” individuals. At one point, New York police had a database of 48,000 individuals. 66% of those were black, 31.7% were Latino and only 1% were white. The composition of this dataset was completely biases, and therefore the computerized ‘decisions’ ware biased too. Even worse is that the final ‘decisions’ made by the computer cannot be explained, and the underlying process is a blackbox. This is a serious ethical issue and the reason why many demand to forbit the application of artificial intelligence. 

Bias is not the only thing. In so-called fight against crime the computer calculates on request the chances that crime would happen at a certain time and in a certain place.  But if the client exchanged the dominant paradigm of identifying, prosecuting and incarcerating criminals for that of finding potential offenders in a timely manner and giving them the help, they need?  A large proportion of those arrested by the police in the US are addicted to drugs or alcohol and severely mentally disturbed.  The University of Chicago Data Science for Social Good Program used artificial intelligence to analyze a database of 127,000 people. The aim was to find out, based on historical data, which of those involved was most likely to be arrested within a month. This is not with the intention of hastening an arrest with predictive techniques, but instead to offer them targeted medical assistance. This program was picked up in several cities and in Miami it resulted in a 40% reduction in arrests and the closing of an entire prison.

AI means computer power. Intelligence resides in those who are using this power. Less biased application of artificial intelligence depends on the through-out choice of the connections in the learning sets. These connections must be scientifically validated and approved by scholars with different backgrounds instead of police officers or computer scientists.

This post is based on the new e-book Better cities, the contribution of digital technology.  Interested? Download the book here for free (90 pages)

Content:

Hardcore: Technology-centered approaches

1. Ten years of smart city technology marketing

2. Scare off the monster behind the curtain: Big Tech’s monopoly

Towards a humancentric approach

3. A smart city, this is how you do it

4. Digital social innovation: For the social good

Misunderstanding the use of data

5. Digital twins

6. Artificial intelligence

Embedding digitization in urban policy

7. The steps to urban governance

8. Guidelines for a responsible digitization policy

9. A closer look at the digitization agenda of Amsterdam

10. Forging beneficial cooperation with technology companies

Applications

11. Government: How digital tools help residents regaining power?

12. Mobility: Will MaaS reduce the use of cars?

13. Energy: Smart grids – where social and digital innovation meet

14. Healthcare: Opportunities and risks of digitization

Wrapping up: Better cities and technology

15. Two 100 city missions: India and Europe

Epilogue: Beyond the Smart City