Global supply shortages of antibiotics are currently on the rise. A study by the newly founded Supply Chain Intelligence Institute Austria (ASCII) in collaboration with the Complexity Science Hub now deciphers the causes of the shortage: First and foremost, the increasing concentration of production in two countries – China and India. Investing in a well-developed data infrastructure can help remedy the situation.

In the interactive visualization, created by Liuhuaying Yang of the Complexity Science Hub, you can explore the connections and interdependencies for yourself. This video shows how it works.

“Our data shows a clear trend of increasing concentration of production on a few countries, namely China and India,” explains Peter Klimek of the Complexity Science Hub and the Supply Chain Intelligence Institute Austria (ASCII). A trend that accelerated even further during the pandemic.


These dependencies are not apparent in direct imports but become clear as one traces the value chain further back. Intermediaries and active pharmaceutical ingredients (APIs) are more concentrated than unpackaged and packaged products. 76% of intermediaries manufacturing sites and 59% of API producers are located in China and India. “If a shortage occurs here, potential substitute products are also more likely to be affected by shortages,” Klimek said. In line with this observation, the estimated number of shortages that could be addressed through substitution has halved from 2018 to 2020.


The measures taken to combat the SARS-CoV-2 pandemic also reduced the circulation of other pathogens. As a result, both demand and consumption of antibiotics in the general population and in hospitals fell dramatically – by about 20% – during the pandemic. In 2022, when many countries stopped taking measures to contain SARS-CoV-2, antibiotic consumption increased accordingly. Combined with geographically concentrated production systems, shortages occurred in large parts of the world.


One of the few European production facilities for antibiotics is located in Kundl, Austria. Despite this (according to the Austrian Federal Office for Safety in Health Care, as of March 9, 2023), several products are on the list of antibiotics not available in Austria. “Our data show that Austria is most closely linked to Spain in terms of supply chains,” Klimek said. However, due to indirect dependencies, China is right behind it in second place, he added.


To better quantify and predict drug shortages in the future, both short-term and long-term measures are needed. “One of the most important things will be to invest in data, planning and forecasting infrastructure to measure, understand and forecast the demand for antibiotics,” Klimek explains. The focus should be on those drugs for which substitutes are also lacking.


Furthermore, greater emphasis should be placed on security of supply and appropriate measures taken, he adds. After all, a well-designed market should internalize the risk of disruption. “Today’s antibiotics market structures are highly internationalised. This ensures low prices, which is desirable. Unfortunately, security of supply has repeatedly proved to be a weak point. Consideration should be given to a market design in which health policy does not need to intervene and which still guarantees security of supply”, says co-author Klaus Friesenbichler (ASCII and WIFO). Providing market incentives for the development of new antibiotics will be particularly important as resistance to existing products increases. A failure to develop new and improved antibiotics therefore poses a risk to society.

Why does the world seem to suddenly be fighting over antibiotics?

A new study around Peter Klimek of the Complexity Science Hub and the ASCII provides insights.

In the interactive visualization, created by Liuhuaying Yang of the Complexity Science Hub, you can explore the connections and interdependencies for yourself. This video shows how it works.

Antibiotics Shortage © Complexity Science Hub/Yang

The concentration of the market on China and India

1. There is a clear trend of increasing concentration of production in a few countries, namely China and India: The development of production concentration accelerated during the pandemic. Systemic trade risk indicators for China and India show sharp increases after 2018. These dependencies are not directly visible in import relationships but can be unravelled by tracing the production processes backwards along the antibiotics value chain.


2. The production concentration is more pronounced in upstream stages of the value chain (intermediaries and active pharmaceutical ingredients, APIs) rather than in downstream stages like unpackaged and packaged products. 76% of the manufacturing sites of intermediaries are located in China and India. 59% of API (active pharmaceutical ingredients) producers are situated in these countries.


3. Due to a higher production concentration in intermediaries and APIs, shocks affect APIs more strongly than packaged products. Hence, it becomes harder to find suitable substitutes when confronted with a shortage. In line with this observation, the estimated number of shortages that could be resolved by substitution halved in 2020. Negative impacts on patient care increased.


4. The data suggests a tendency towards market segmentation in which firms in European and North American countries developed an increasing dependence on Chinese suppliers. Indian producers trade mostly with local neighbours in Asia, Oceania and African countries.

SARS-CoV-2 led to reduced need for antibiotics

1. When the pandemic hit, non-pharmacological interventions aimed at curbing the spread of SARS-CoV-2 also reduced the circulation of other pathogens. Consequently, both community and hospital demand and consumption of antibiotics dropped sharply during the pandemic (by approximately 20%).


2. Reducing contagion risks, hospitals restricted their services to non-COVID patients. Drug shortages in hospitals nearly halved during the pandemic compared to the frequency of shortages in 2018. This uncovered improvement potential in inventory management and demand forecasting.


3. In 2022, in an increasing number of countries, SARS-CoV-2 related containment measures ceased. As societies by and large “returned to normal” so did antibiotic consumption. Volatile demand and geographically concentrated production systems led to simultaneous shortages of antibiotics across many parts of the world.


1. Supply disruption of medications in general, and antibiotics in particular, are not a new phenomenon. Since 2014, they have steadily increased in frequency and severity. In the vast majority of cases, it was possible to resolve these shortages by finding suitable substitutes and thereby reduce negative impacts on patients.


2. The consumption of antibiotics declined in many European countries between 2011 and 2018. This is welcome as it reduces the build-up of antimicrobial resistance.


3. These trends in antibiotics demand coincided with structural transformations in the antibiotics production system. Overall, there is a clear trend of increasing geographic concentration of production. Production sites in China and India have benefitted from this trend.

Policy considerations to avoid antibiotic shortages in the future

1. There are short-term and long-term remedies. Short-term remedies include improvements in the data, planning and forecasting infrastructure. This will require additional investments. Supply chain disruptions can reflect market structure problems. Long-term policies address the market structure and the international division of labour.


2. Existing supply networks arise from market processes that reflect a competitive combination of qualities and prices. Deviating from market results comes at a cost which can be interpreted as an insurance premium that health agencies need to incur to avoid impacts on patients. Additional costs require appropriate financing. Ideally, a well-designed market should internalise the risk of disruption, e.g., through appropriate contracts with the adequate incentive structures.


3. Antibiotics shortages need to be considered against the backdrop of the global risk of antimicrobial resistance. Ideally, policy remedies should address both issues.


1. Understand the scope of the problem. Data availability is an issue. Efforts need to be undertaken to not only track and forecast drug shortages, but to focus on shortages of non-substitutable drugs.


2. Demand forecasting and stable supplier relationships. Health authorities need to better understand the demand developments for antibiotics in the population. Evidence-driven demand planning could form the basis for building stable supply relationships, e.g., through multiyear contracts with producers that contain robust provisions in case of non-delivery.


3. Capacity markets and excess inventory. In case of emergencies, add-on production capacities that timely provide the drugs in question may address arising shortages from the outset.


4. Single Market. The European Union provides a powerful tool to mitigate supply risks across multiple players through the Single Market. Coordinated and more centralised EU inventories can also help to reduce overall safety stock and thereby avoid inefficiencies.


5. Bargaining power. Countries, regions, or health agencies might consider deeper cooperation and pursue joint forecasting and joint procurement strategies. Adverse effects on the market structure of suppliers and possibly anti-competitive behaviour of suppliers need to be monitored.


6. Diversification of supply. Diversification requires internationally competitive producers. Hence, the debate about broadening the supplier base is embedded in a wider discussion about competitiveness and structural change.


7. Subsidised procurement prices. Reimbursement models that delink development costs from unit sales have been proposed to tackle drug shortages.

After assessing how corporate investment can help certain climate technologies grow, researchers at the Complexity Science Hub, University of WisconsinMadison and University of Maryland find evidence in support of policymakers and researchers looking beyond government innovation and more comprehensively considering the role of corporations in energy and climate innovation.

Corporate investments in climate-tech start-ups are a growing but overlooked aspect of innovation that should be more fully considered in efforts to advance climate technology, according to a new report in the academic journal Joule.


“Corporations and the choices they make investing in climate-tech start-ups are particularly important as they tend to focus on technologies closer to reaching widespread adoption compared to public or other private investors. However, their role in climate change innovation has been overlooked to this point in our efforts to mitigate the effects of climate change,” says lead co-author Kavita Surana, an associate faculty member at the Complexity Science Hub Vienna and a senior fellow at the Center for Global Sustainability at the University of Maryland.

Strategic investors

Corporations are often strategic investors motivated by profits as well as by other interests including expanding existing business models, gaining innovation insights, or meeting environmental, social, and governance (ESG) commitments. Well-resourced corporations investing in start-ups that can rapidly commercialize innovation can have an outsized influence on which start-ups succeed and scale, therefore shaping climate technology trajectories.


Corporate investments in climate technology totaled over $11 billion in 2021, flowing to more than 460 start-ups, representing a quarter of all public and private investment dollars. This number has grown considerably since the Paris Agreement began in 2016 but still leaves a sizeable gap for governments to step in and incentivize investment in climate-tech that aligns with long-term climate and societal goals. This needs to be a larger point of emphasis moving forward to make necessary advances in climate technology, according to the new paper.

North America, Europe and Israel

The authors investigated a dataset of 6,996 climate-tech start-ups from North America, Europe, and Israel that were founded between 2005 and 2021. They also looked at 9,749 investors who participated in 33,698 investment deals.


Among the paper’s findings, the research team observed that corporate investors are most active in later investment stages when technologies are closer to market deployment. They also found that corporate investment in climate-tech start-ups is highly concentrated, with a few large corporations like Shell, Alphabet, and Samsung playing an outsized role. Between 2016 and 2021, these large companies each invested in over 25 climate-tech start-ups. A handful of companies, including Amazon, Ford, and Alphabet, each invested over $1 billion.


Investments were also concentrated in certain technologies. For example, fuel cell and hydrogen technologies received a much higher percentage of corporate investment than other sectors like marine and hydropower, nuclear, and biomass generations. These sectors also receive little funding from other private sources, suggesting that public investment may be necessary to fill the gap.

The team’s policy recommendations include:


  • Using data-driven insights on corporate climate-tech investments and their outcomes to anticipate technological change and identify policy and regulatory gaps for emerging sectors and industries.


  • Incentivizing investments that support long-term climate solutions over short-term workarounds. This could help policymakers target the technologies that reduce emissions most efficiently.


  • Identifying and filling in gaps in corporate and private investment in key technologies and infrastructure. Policymakers need a more complete picture on the full investment landscape to keep balanced the portfolio of technologies needed for decarbonization.


  • Mobilizing and rewarding additional corporate and private finance to support climate-tech start-ups. Designing new public-private models that mobilize capital from corporations through rewards or accountability nudges can help advance corporate efforts to invest in climate and energy innovation.

“We will need a whole host of new technologies to transition to a net-zero or net-negative emissions economy. Many innovations are currently in development but not yet mature,” says lead co-author Morgan Edwards, an assistant professor at the La Follette School of Public Affairs at the University of Wisconsin-Madison. “Finding the right mix of corporate, private, and public investments will be critical to getting these technologies to market quickly and encouraging new innovations.”


The article “The role of corporate investment in start-ups for climate-tech” is available online at Joule. All data and code to fully reproduce the analyses are available on Zenodo.

Model created by CSH scientist Samuel Martín-Gutiérrez and colleagues captures social division in multiparty democracies. The US could also benefit from it.  

Austria, India, Mexico, and Spain. As multiparty democracies, these countries present a more complex picture of what brings people together – or divides them. Using social media data, a group of researchers proposes a novel approach to measuring polarization in nations with multiple political parties.


Complexity Science Hub scientist Samuel Martín-Gutiérrez and his colleagues developed a model that infers opinions from social networks, and measures the left-right political division, as well as other more nuanced sources of tension. They analyzed Twitter data from the Spanish elections of 2015 and 2019.


“It’s important to understand polarization in our society, and our findings shed new light on how political beliefs are shaped,” says Martín-Gutiérrez. “Our approach can lead to useful insights when applied to real-world debates happening on social media.”


The findings of the study were recently published online in the journal Chaos, Solitons & Fractals.


The approach could be used in a variety of situations to identify the main points of disagreement. It could also be used in countries with two-party systems, such as the US. The primaries, a key part of the American voting system, are an excellent example, points out Martín-Gutiérrez. “There are several candidates in the presidential state primaries. Our model could be helpful in tracking and understanding how party members’ opinions are distributed” says the CSH researcher.

Neutral and unbiased

A key characteristic of the model, according to Martín-Gutiérrez, is its neutrality and unbiasedness. “We assumed we didn’t know anything about the [main Spanish] political parties in the study. And our findings demonstrate that a rigid classification of parties into pre-defined dimensions may not paint the most complete and accurate picture.”


Using Twitter data from two elections, Martín-Gutiérrez and his colleagues from the Universidad Politécnica de Madrid tested the proposed approach in practice. First, they explored the Spanish general elections of December 2015. In a second moment, they dissected the Spanish general elections of April 2019.


“In 2015, we had four opinion poles corresponding to the four main political parties: People’s Party (PP), Spanish Socialist Workers’ Party (PSOE), Podemos, and Citizens (Cs). In 2019, we have five opinion poles, with the addition of Vox,” explains Martín-Gutiérrez. 


As a first step, the team identified the opinion leaders, and their ideological positions, on Twitter. “The second step was to analyze the listeners, the interesting part of the network, and assign an opinion to a user according to the average opinion of the people they retweet”, says Martín-Gutiérrez.


Since the social spectrum is more complex and colorful than black and white, the researchers determined the same distance between every pair of poles to avoid bias. “In some contexts, it may seem more appropriate to place certain poles close together. For example, in an election certain political parties may be more ideologically aligned with each other than with the rest. However, parties that share ideology also compete for the same electoral base, often leading to even greater antagonism between them,” explain the authors of the study.

New knowledge

According to them, the opinion patterns of both analyses were consistent with the underlying Spanish social reality and could be interpreted taking into account their context. “There is a clear picture of the Spanish political spectrum, and we can see, for instance, where the opinion distribution is most stretched or where the average opinion is located,” says Martín-Gutiérrez.


“Our findings seem to be in line with what we intuitively know about Spanish politics. From this we can start building new knowledge, for instance, about citizens’ perceptions of politics and their behavior.”


In the visualization created by Liuhuaying Yang, CSH visualization expert, you can explore the opinion distribution of the 2015 Spanish elections: 

(c) CSH
Opinion distribution of the 2015 Spanish elections: a cloud of users’ opinions with its center of mass marked with a red square and the principal components as arrows with lengths proportional to their explained variances

The proposed method could also assist policymakers in dealing with polarization and healing social rifts. “It could help policymakers devise de-escalation interventions by addressing the issues that cause the strongest tensions,” argue the authors of the study. 


Furthermore, it could be used to combat online misinformation spread by bots and trolls. The model could be used to evaluate the influence automated bots have on social media as compared to real human accounts.


The study “Multipolar social systems: Measuring polarization beyond dichotomous contexts” is available online at Chaos, Solitons & Fractals

A recent study makes it clear: Countries like Sweden with the ability of linking data from different areas – such as the labor market and health care – have a decisive advantage when it comes to setting targeted actions.

A research team from the Complexity Science Hub, together with scientists from Sweden, Denmark and the Netherlands, investigated the extent to which mental and somatic illnesses influence integration into the labor market and whether there is a difference here between refugee and Swedish-born young adults. “In total, we analyzed data from 41,516 refugees and 207,729 Swedish-born people aged 20 to 25 from 2012 to 2016,” explains Jiaying Chen from the Complexity Science Hub and the Medical University of Vienna.


In both groups, multimorbidity (the simultaneous presence of several disorders) has a negative impact on labor market opportunities. Both mental and somatic disorders pose a risk that labor market integration will not succeed. However, this effect is more pronounced among young refugees. They have a higher risk of being and remaining unemployed than those born in Sweden. However, the strongest risk factor for long-term unemployment is refugee status per se, the study finds.


In addition, refugees have a lower chance of receiving disability pensions. While 7.2 percent of young Swedes with mental disorders receive disability pension, only 5.5 percent of those who have had to flee benefit from it. 


“This is a very robust result that has already been supported in many studies,” explains Peter Klimek of the Complexity Science Hub. “It suggests that refugees, who have often experienced massive trauma, are at risk of poorer access to the health care system. This implies that they often need to have a higher level of severity before a disease is diagnosed,” Klimek says. 


Poorer labor market integration can therefore also be exacerbated by the fact that poorer health already exists at the time of diagnosis, he adds. “With targeted public health actions to specifically address this group and to sensitize treating physicians, Sweden could bring about an improvement in the situation,” explains Ellenor Mittendorfer-Rutz from the Swedish Karolinska Institutet, who coordinated the project.


In other countries like Austria similar findings could not be obtained at all. Due to different social and health care systems, as well as different origin areas of refugees, the results of this study describe the situation in Sweden. However, they can only be transferred to other countries to a limited extent.


A good data infrastructure for research purposes is crucial. In Sweden, Finland and Denmark, there are already developed registry landscapes that allow, for example, de-identified information on health, labor market integration and refugee status to be centrally linked and securely evaluated. This ability of linking data allows researchers to analyze the interplay of these three factors.


The findings support decision makers to take action to improve the situation where appropriate – such as implementing strategies to promote the inclusion and participation of young refugees in the labor market, reducing social disadvantage within the refugee population, and potentially improving the economic stability of these countries.


“In Austria, a similar study could not be conducted. On the one hand, there is a lack of complete data. For example, there is no diagnosis recording in the private practice sector. And on the other hand, data are pseudonymized differently in various areas, which is why linking the data is not possible here.,” Klimek explains.

Word lists are the basis of so much research in so many fields. Researchers at the Complexity Science Hub have now developed an algorithm that can be applied to different languages and can expand word lists significantly better than others.

Many projects start with the creation of word lists. Not only in companies when mind maps are created, but also in all areas of research. Imagine you want to find out on which days people are in a particularly good mood by analyzing Twitter postings. Just looking for the word “happy” wouldn’t be enough. 


Instead, you would have to use an algorithm that detects all tweets that indicate that someone is happy. “So the first step is to create a list of all the words that indicate just that. The whole research stands or falls on doing so,” explains Anna Di Natale, a researcher at the Complexity Science Hub in Vienna. But how to come up with the most accurate, complete word lists possible? 

A problem that concerns many

This widespread problem not only concerns opinion researchers who want to find out how politicians’ statements are received by the public. Companies, too, want to find out how their products are perceived through sentiment analyses.


To improve things, Di Natale has now developed a new method, called LEXpander, that outperforms previous algorithms. And this even in two different languages – German and English. Moreover, for the very first time ever, she has developed a way through which it is possible to compare different tools at all.

Improved performance

In comparison with four other algorithms for wordlist expansion (WordNet, Empath 2.0, FastText and GloVe), LEXpander performed significantly better, especially in German. For example, the researchers found that LEXpander guesses 43% of words right when expanding an English word list for positive meaning. A very popular model, FastText, in comparison, is right only 28% of the time.

Complexity Science Hub researcher Anna Di Natale finds a new and better way to create word lists

Independence from the language itself

The reason is that this tool works language-independently. It is not based on one language, but on a so-called colexification network. This recognized linguistic concept resides on homonyms and polysemies, single words that have two or more distinct meanings. For example: the ancient Greek word φάρμακον (pharmacon) can mean medicine or poison. Two different things, but thematically close. But there are others that don’t suggest kinship – such as “bank” as a financial institution or the land alongside a river. 


“If you collect them across many languages – and here we analyzed about 19 different languages – you can see connections between them,” Di Natale says. The network is formed when these colexifications occur in several languages across different language families, creating connections.


This independence from the language itself allows LEXpander to achieve better results in different languages. “There are many methods developed for English. They work very well and quickly and everyone uses them. Trying to apply them to other languages works, but not as well as it might work if you had started developing a method for German or Italian,” Di Natale explains. 

Especially important for new topics

For many topics there are already good word lists. But for new topicslike when COVID came up – new ones have to be created. Until now, they were usually created by hand during brainstorming with colleagues and several tools were used to help. But until now there was no way to compare them. Anna Di Natale and her team have now created this possibility and have also developed a new tool that performs better than the others. This can be an important cornerstone for many future research projects in various fields.


The study “LEXpander: Applying colexification networks to automated lexicon expansion” has been published in Behavior Research Methods.

A new machine learning model can predict city traffic activity in different urban zones. To do so, a Complexity Science Hub researcher used data from a main car-sharing company in Italy as a proxy for overall city traffic. Understanding how different urban zones interact can help avoid traffic jams, for example, and enable targeted responses of policy makers – such as local expansion of public transportation.

Understanding people’s mobility patterns will be central to improving urban traffic flow. “As populations grow in urban areas, this knowledge can help policymakers design and implement effective transportation policies and inclusive urban planning”, says Simone Daniotti of the Complexity Science Hub. 


For example, if the model shows that there is a nontrivial connection between two zones, i.e., that people commute from one zone to another for certain reasons, services could be provided that compensate for this interaction. If, on the flip side, the model shows that there is little activity in a particular location, policymakers could use that knowledge to invest in structures to change that.


For this study a major car-sharing company provided the data: the location of all cars in their fleet in four Italian cities (Rome, Turin, Milan, and Florence) in 2017. The data was obtained by constantly querying the service provider’s web APIs, recording the parking location of each car, as well as the start and end timestamps. “This information allows us to identify the origin and destination of each trip,” Daniotti explains. 


Daniotti used that as a proxy for all city traffic and created a model that not only allows accurate spatio-temporal forecasting in different urban areas, but also accurate anomaly detection. Anomalies such as strikes and bad weather conditions, both of which are related to traffic. 


The model could also make predictions about traffic patterns for other cities such as Vienna. “However, this would require appropriate data,” Daniotti points out.

Predict traffic in cities © Complexity Science Hub, Sony CS


While there are already many models designed to predict traffic behavior in cities, “the vast majority of prediction models on aggregated data are not fully interpretable. Even though some structure of the model connects two zones, they cannot be interpreted as an interaction” explains Daniotti. This limits understanding of the underlying mechanisms that govern citizens’ daily routines.


Since only a minimal number of constraints are considered and all parameters represent actual interactions, the new model is fully interpretable.

Predict traffic in cities © Complexity Science Hub, Sony CS


“Of course it is important to make predictions,” Daniotti explains, “but you can make very accurate predictions, and if you don’t interpret the results correctly, you sometimes run the risk of drawing very wrong conclusions.” 


Without knowing the reason why the model is showing a particular result, it is difficult to control for events where the model was not showing what you expected. “Inspecting the model and understanding it, helps us, and also policy makers, to not draw wrong conclusions,” Daniotti points out.


The study “A maximum entropy approach for the modelling of car-sharing parking dynamics” has been published in Scientific Reports.

Using a new dataset, Rafael Prieto-Curiel of the Complexity Science Hub and colleagues analyzed the coordinates and surface of 183 million buildings in nearly 6,000 African cities across all 52 countries of the continent. With their model, they quantify the shape of cities. Thus, they show that if a city’s population doubles, the energy demand associated with commuting triples. These results clearly show how important it will be to plan fast-growing cities in a sustainable way.

“Our model allows us to estimate transport requirements and energy needs of African cities with a never before seen accuracy,” Prieto-Curiel, researcher at Complexity Science Hub, says. 


Together with Jorge E. Patino from Universidad EAFIT and Brilé Anderson from OECD, he studied a recently published dataset from Google AI. With this unprecedented data the researchers measured the mean distance between buildings in 6.000 African cities and used it as a proxy for energy demand related to mobility. Africa is an exceptional place for this research. By comparison, there are less than 400 metropolitan areas in the US. In addition, African cities will grow faster than ever before.


Based on the distance between buildings, we calculated the expected distance and time to travel for every single person within a city,” Prieto-Curiel explains. Their result: double the population means triple the energy costs. After all, more people are traveling longer distances.  



Measuring 6,000 African cities at Complexity Science Hub © Complexity Science Hub
Sprawl and elongation of a selection of African cities © Complexity Science Hub

This tripling effect is due in part to urban morphology and how cities grow. “Constructing bigger buildings (area and height) near the city center reduces the commuting distance, as well as the energy consumption of the city and helps preserve green spaces,” Prieto-Curiel explains one of their findings. 


The team also learned that big cities tend to be shaped differently than smaller cities. As they grow, larger cities tend to become slightly rounder and more compact. Smaller cities tend to have more of a sausage shape. 




Imagine two cities that both have the same number of inhabitants. If one is round and compact, then objects and people are relatively close to each other. If the other one is shaped like a sausage, then some people must spend more time and energy to get from one side to the other. The problem with this is not only time, but also pollution. So, a sausage-like city causes more pollution because of its shape.


“Frequently, cities grow on the periphery as more housing is built in areas where the city is growing. This type of urban growth increases commuting distances and makes it more difficult to provide enough services for the new houses, such as sewage and electricity,” Prieto-Curiel explains.  

Brilé Anderson from OECD presented the results of this research at COP 27 in Egypt



In many parts of the world extensive urbanization has already taken place. In Africa, the next three decades will bring profound changes, forecasts indicate. Currently, cities like Cairo, Lagos, Luanda, Dar es Salaam, Nairobi and Addis Ababa are home to millions of people, and they are expected to increase their size considerably within the next decades. 


“By 2050, Africa will have an additional 950 million people living in cities, up from 574 million people in 2015,” Prieto-Curiel states. More buildings – apartments, schools, hospitals, etc. – will have to be constructed. 


Where and how these new buildings are built matters greatly because today’s decisions will be with us for decades to come. And the resulting morphology of cities impacts permanently on a city’s energy needs. “We should plan especially those cities that will grow extremely fast in the next twenty years with special consideration. They must be extremely resilient to many challenges,” Prieto-Curiel emphasizes.




To conduct this study, the research team brought together massive amounts of data: A new dataset of Google AI provided coordinates of every single building in Africa’s cities. In combination with data from Africapolis, the German Aerospace Center and OpenStreetMap the researchers measured the shape of nearly 6000 cities across Africa.


They then constructed a set of indicators. Indicators like the number of buildings, their constructed surface, the amount of space used by the city center, how elongated or compact a city is – to name just a few. These indicators make all cities comparable for the first time – large and small.


Asking different people you would probably get different answers. Africapolis, for the first time, managed to map every single city in Africa by using the same definition of what a city is. They looked at census data first and combined it with aerial photos then, to see how close to each other the buildings are. If a building is within 200 meters of another building, they belong to the same urban area.

“Until now, we only had satellite images, but it was very difficult to manipulate these photos. Especially in smaller cities, it’s not so easy to identify what a building is,” Prieto-Curiel explains. Google AI’s new open access data set has changed that. Using a machine learning process, it can define the vertices of each building as coordinates. Like a city made of Lego bricks.




Much of the collective knowledge of urban forms in African cities and future energy needs was based on samples of only a few cities. Advancements like the data used for this study and its findings are critical to the future of cities. Only in this way we can put urbanization on a path of resilience and sustainability in the coming decades.


Our results show that future energy needs for transport could be incredibly cumbersome if trends continue,” Prieto-Curiel states. Designing compact, dense, and better-connected urban forms will help cities be more sustainable and liveable. Particularly in the case of African cities that will experience rapid growth within the next few decades.


If large cities were just a scaled version of small cities, the mean distance between buildings should grow with the square root of their population. Which is not the case. Instead, the mean distance grows faster. This is due sublinear growth in the number of buildings and a sublinear increase in building size and sprawl. 

The study Scaling of the morphology of African citieshas been published in the journal Proceedings of the National Academy of Sciences (PNAS).

At first, it was just an urge to understand in what ways a complex system can synchronize. “I’m curious about how things work,” says physicist Jan Fialkowski, who recently joined the Complexity Science Hub. 


However, his findings about complex dynamical networks, which have just been published in Physical Review Letters, can help us better understand the dynamics of social and biological systems.


“Having my paper accepted was a pleasant surprise,” says Fialkowski, whose paper was the result of his master’s thesis.


“In this work, we show that the synchronization of complex dynamical networks can be achieved following two very distinct paths,” explains Fialkowski. The precise description of these phenomena can help understand, for instance, the dynamics of opinions in society. 


Synchronization can be interpreted as a consensus between individuals,” points out Fialkowski. “When an idea is well accepted by the population, it can recruit more and more people until consensus is reached. If two ideas are sufficiently different, you will observe the emergence of two distinct opinions that coexist for a long time. Then it is much more difficult to reach a consensus.”


From disorder to order


In the study, Fialkowski and his colleagues found that systems of coupled oscillators exhibit little to no synchronization when weak interactions are present, since each oscillator follows its own rhythm. When the coupling strength increases, oscillators tend to behave similarly and synchronize their movements.


“Our simulations and advanced analytical methods show that the transition to synchronization can occur in two different ways: either one group of oscillators dominates and forces successively the others into synchronization, or two clusters of equal strength emerge and synchronize with each other in a sudden event,” says Fialkowski, who’s now pursuing his PhD on supply networks.  




The article Heterogeneous Nucleation in Finite-Size Adaptive Dynamical Networks was published in Physical Review Letters

A comprehensive study of the city’s potential to supply fresh vegetables is conducted for the first time


Berlin has enough space for urban gardening, and up to 82 percent of Berlin’s vegetable consumption could be produced locally, a new study finds. “The amount of vegetables represents a significant share of the annual consumption,” highlights Diego Rybski, an external faculty member from the Complexity Science Hub and a co-author of the paper that will appear in the April issue of Sustainable Cities and Society journal.


Rybski and his team were interested in finding out how much vegetables could be produced in Berlin. A total of five urban spaces were evaluated for agriculture — non-built residential areas, allotment gardens, rooftops, supermarket parking lots, and closed cemeteries.


A local culture


Urban gardening has been a tradition in Berlin for many years, and there are more than 200 community gardens and more than 73,000 allotment gardens in the city. But rooftops and non-built residential areas — green spaces between large housing complexes — provide a great opportunity for urban gardening because they are underutilized, according to Rybski.


“With Berlin’s plans to shift to fewer cars, parking lots are also good candidates for growing vegetables,” adds Rybski, who is also a researcher at the Potsdam Institute for Climate Impact Research and the Wuppertal Institute.


The study found that a total of 4,154 hectares of Berlin could be used to grow vegetables. It accounts for almost 5 percent of the city’s total area. And 82 percent of Berlin’s vegetable demand could be met locally if all this land was used for urban gardening, according to the paper. Investing in water, human resources, and investment would be crucial for this production to be possible. For instance, a total investment cost of 753 million euros would be required. It is equivalent to slightly less than 0.5 percent of Berlin’s 2020 Gross Domestic Product (GDP).

(c) CSH
The researchers mapped the geographical distribution of Berlin's potential areas for urban gardening

Great challenges


As Rybski emphasizes, promoting urban gardening would pose great challenges due to the high resource usage. “There is space, but we need to consider a number of factors. For instance, who is going to do the gardening? Can it be private gardeners or do we need a business model? What’s necessary to increase the production in allotment gardens? How do we create the conditions to promote urban agriculture within the city?,” points out the researcher. 


“In principle, however, I believe this would be a positive development. The locally grown vegetable would probably be more expensive, but we could create a kind of label for it — like we have for organic,” says Marion De Simone,  the lead author of the study from Potsdam Institute. 


The benefits of local gardening are numerous as well. “Just to name a few: community gardens bring people together. Green spaces are beneficial to people’s health, as well as the environment and biodiversity. And local food production also reduces carbon emissions from transportation,” adds Prajal Pradhan, another Potsdam Institute co-author. 

The study A large share of Berlin’s vegetable consumption can be produced within the city is available online and will appear in the April printed issue of Sustainable Cities and Society journal.