Data is not the new oil

I suggest that anybody who is talking about ‘big data’ and ‘data driven policy’ or using grotesque statements like data is the new oil to revisit the foundations of scientific research and the embedded vision on data. 

Without elementary insight in the way scientists arrive at their conclusions ‘data driven policy’ can have disastrous consequences. The city of Chattanooga has build a digital twin. That is a digital model that is connected to reality with the help of sensors. Such a dynamic model can be used for simulation purposes if the connections between the variables have been established. Here things can go wrong. In Chattanooga the model was used to simulate the impact of flexible lane assignment and traffic light phasing. It turned out that this could result in a 30% decrease of congestion.

Had this experiment been carried out in the real world, the result would probably have been disastrous. Traffic experts note time and again that every newly opened road gets satiated after a short time, while the traffic on other roads hardly decreases. 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: Every road user reacts differently to the opening or closing of a road. Those reactions 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. To understand this pattern data must be collected from e sufficient large sample of road behavior of individual drivers. 

What the computer scientist in Chattanooga did wrong is assuming that only the adding of a single lane and changing the intervals of the traffic lights would cause all drivers’ behavior change into the same direction, as if they were metal balls, reacting upon a change in the magnetic field. If the ICT-experts had collaborated with traffic experts, the digital twin might have been fed with an empirical justifiable model, that incorporates the assumption of induced demand. 

In essence, data is useless without a theory, based on already established insights or views. 

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

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

Beyond smart cities: Digital innovation for the Good of citizens[1]

Next months, these posts focus on the challenges of Earthlings of to bring humane cities closer. These posts represent the main findings of my e-book Humane cities. Always humane. Smart if helpful, updates and supplements included. The English version of this book can be downloaded for free here and the Dutch version here.

Citizens involved in a participative policy formulation process

About ten years ago, technology companies started to provide cities with technological tools, luring them with the predicate ‘smart(er)’, now a registered trademark of IBM.  At that time Cisco’s vice-president of strategy Inder Sidhu described the company’s ‘smart city play’ as its biggest opportunity, a 39,5 billion dollar-market. During the years, that followed, the prospects rocketed: The consultancy firm Frost and Sullivan estimated the global smart city technology market to be worth $1.56 trillion by 2020. 

The persistent policy of technology companies to suggest a tight link between technology and the wellbeing of the citizens, angers me. Every euro these companies are chasing at, is citizens’ tax money. What has been accomplished until now is disappointing, as I documented in the IET Journal.  According to The Economist it is not surprising that a ‘techlash’ is underway: Many have had it with the monopolistic dominance of behemoths like Google, Amazon, Facebook and the like, because of their treatment of sensitive data, the lack of transparency and accountability of algorithm-based decision making and the huge profits they make from it. 

Regaining public control

However, let’s not throw out the baby with the bathwater and see how digital innovation can be harnessed for the Good of all citizens. Regaining public control demands four institutional actions at city level.

1. Practicing governance

Before even thinking about digitalization, a city must convert into best practices of governance. Governance goes beyond elections and enforcing the law. An essential characteristic is that all citizens can trust that government represents their will and protects their interests. Therefore, it is necessary to go beyond formal democratic procedures and contact stakeholders directly, enable forms of participatory budgeting and deploy deliberative polling. 

Aligning views of political parties and needs and wants of citizens takes time and a lot of effort. The outcome might be a common vision on the solution of a city’s problems and the realisation of its ambitions, and a consecutive political agenda including the use of tools, digital ones included. 

2. Strengthening executive governmental power

Lack of cooperation within the departmental urban organizations prevents not only an adequate diagnosis of urban problems but also the establishment of a comprehensive package of policy instruments, including legislation, infrastructure, communication, finance and technology. Instead, decisions are made from within individual silos, resulting in fragmented and ineffective policies. Required is a problem-oriented organization instead of a departmental one and a mayor that oversees the internal coherence of the policy.

3. Level playing field with technology companies

Cities must increase their knowledge in the field of digitization, artificial intelligence in particular. Besides,  but they should only work with companies that comply with ethical codes as formulated in the comprehensivemanual, Ethically Aligned Design: A Vision for Prioritizing Human Well-being with Autonomous and Intelligent Systems, drafted by the influential Institute of Electric and Electronic Engineers (IEEE)

Expertise at city level must come from a Chief Technology Officer who aligns technological knowledge with insight in urban problems and will discuss with company representatives on equal foot. Digitalisation must be part of all policy areas, therefore delegating responsibility to one alderman is a bad idea. Moreover, an alderman is not an adequate discussion partner for tech companies.

4. Approving and supporting local initiatives

Decentralization of decision-making and delegating responsibility for the execution of parts of the policy to citizen’s groups or other stakeholders helps to become a thriving city. Groups of citizens, start-ups or other local companies can invoke the right of challenge and might compete with established companies or organizations.

Steps towards seamless integration of digitalization in citizen-orientated policy

  1. Define together with citizens a vision on the development of the city, based on a few central goals such as sustainable prosperity, inclusive growth, humanity or – simply – happiness.
  2. Make an inventory of what citizens and other stakeholders feel as the most urgent issues (problems and ambitions).
  3. Find out how these issues are related and rephrase them if desirable.
  4. Deepen insight in these issues, based on available data and data to be collected by experts or citizens themselves.
  5. Assess ways to address these issues, their pros and cons and how they align with the already formulated vision.
  6. Make sure that digital technology has been explored as part of the collected solutions.
  7. Investigate which legal, organizational, personnel and financial barriers may arise in the application of potential solutions and how to address them.
  8. Investigate undesired effects of digital techniques, in particular long-term dependence (‘lock-in’) on commercial parties.
  9. Formulate clear actions within the defined directions for dealing with the issues to be addressed. Involve as many expert fellow citizens as possible in this.
  10. Make a timetable, calculate costs, and indicate when realization of the stated goals should be observable.
  11. Involve citizens, non-governmental and other organizations in the implementation of the actions and make agreements about this.
  12. At all stages of the process, seek support from those who are directly involved and the elected democratic bodies.
  13. Act with full openness to all citizens.

I can’t agree more than with the words of Léan Doody (smart city expert Arup Group): I don’t necessarily think ‘smart’ is something to strive for in itself. Unlike sustainability or resilience, ‘smart’ is not a normative concept…. The technology must be a tool to deliver a sustainable city. As a result, you can only talk about technological solutions if you understand which problems must be solved, whether these problems are rooted in the perceptions of stakeholders and how they relate to other policy instruments.


[1] This article was posted before at the Amsterdam Smart City website

Resilience and prediction of hazards

Next months, these posts focus on the challenges of Earthlings of to bring humane cities closer. These posts represent the main findings of my e-book Humane cities. Always humane. Smart if helpful, updates and supplements included. The English version of this book can be downloaded for free here and the Dutch version here

In my last post, I elaborated on resilience. Resilience has two sides. At the one hand it has to do with policy aimed at anticipation and mitigation hazards. At the other hand, it refers to the capacity of both government and citizens to deal with their impact.

Anticipating hazards

The most difficult problem in anticipating hazards is knowing what hazard to anticipate. This is difficult, given the long list of chronic stressors and acute shocks that can affect a city. Emergency plans should focus not only on the most likely disasters, but on all conceivable ones. Listing possible threats is not that difficult: plane crashes, terrorists blowing up a dam or shooting visitors during a football match, previously unknown massive and violent protests, outbreak of a hitherto unknown deadly disease, an attack by a foreign power or, if you want, aliens, et cetera.

It is impossible to make separate plans for all these threats. The preparation should take place on a more abstract level. For example, what to do if roads are impassable, many people have died, there is no electricity, water and gas, an evacuation must take place within a few hours, et cetera. Agreements must be made in advance about outside assistance, and which means of communication can be used permanently.  

Citizens should be involved in these activities. Otherwise, they will become dependent on government initiatives, which will not come as the command center is destroyed.  Citizens should be trained in self-management complementary – or in case of emergency – to replace official actions.

Anticipating hazards is easier if some types of hazards are a recurring phenomenon, such as flooding. Activities include installing early warning systems, preparing emergency services, providing scenarios for the evacuation of the elderly and the sick, allocating places for temporary housing, gathering tents, organizing access to food, drinking water and to medical care. The faster and more accurate the prediction is, the better the preparations can be.

Flood Concern creates map-based visualizations of places where floods can hit hardest, up to five days before an approaching storm using artificial intelligence. These are simulations in the form of time-lapses of how the water will rise, at what speed and in which direction.  These maps also indicate which parts of the infrastructure will flooded or wash away, and how mitigation efforts – from sand backs to opening locks – will turn out. With this data, emergency services can determine which roads are still accessible, and plan evacuation routes accordingly. 

Dealing with impact

If accurate forecasts are available, the government, together with citizens, can implement previously designed and trained plans to mitigate the effects of the flooding. However, anybody must stay vigilant to respond to unexpected changes in the anticipated course of events. 

One of the most dramatic cases to discuss is the massive earthquake that devasted all of Haiti on January 12, 2010, claiming 316,000 lives, injuring another 300.000 and displacing more than 1.5 million people. The earthquake was just the beginning:  In the following years other devasting natural disasters caused thousands of new deaths, engraved famine, and a deadly cholera epidemic, wiping out ongoing efforts to rebuild the country. Until now, millions of Haitians are still in need of humanitarian aid and many still live in camps without proper sanitation and drinking water. To date, the international community has raised € 8 billion in aid. What it was used for is unclear, in spite of a large number of helping hands. It seems that the rebuilding of the country is mainly due to the inhabitants themselves, who started rebuilding their primitive huts again and again by using the remains of their previous emergency shelters. The government infrastructure was destroyed by the dictatorial regimes of father and son Duvalier and led, among other things, to the depart of most residents with some education. So the country had done nothing to prepare for a possible disaster, and there was no policy to cope with its consequences.

It is evident that dealing with the impact of hazards depends from te degree of anticipation. Otherwise, full reliance on social capital is the only hope.