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Given the global imperative to meet ‘net zero’, and growing interest in the potential for green jobs growth, there is an urgent need to better understand the drivers and processes underlying green structural economic transitions. How should we in fact define ‘green’ products, jobs and technologies? How do local economies transition into greener jobs – is this generally an incremental process or does it require more radical innovation? Building on nascent green definitions, recent work emerging from the literature in Evolutionary Economic Geography suggests that there is a degree of path dependency to green transitions, with regions benefiting from existing capabilities which are somehow related to newer green tasks and technologies. On the other hand, having diverse, frequently unrelated, skills and competencies also helps local economies to make the recombinations necessary for the emergence of new green activities. These drivers are moderated by factors such as the local institutional environment, IT skills and the degree of maturity of the local industrial base. This article summarises the recent literature in order to provide an overview of emerging findings of relevance to local policy delivery, while also highlighting future research directions.

One of the driving rationales behind plans for major transport investment between Northern UK cities is that of connecting labour pools. Integrated labour pools, the argument goes, will give rise to agglomeration economies and subsequent growth. Behind this logic lies the idea that access to complementary skills in neighbouring cities will yield benefits for firms. Yet, at what travel time radius is access to these skills beneficial? Can a better understanding of this inform our assessment of the likely economic impact of proposed transport investments and subsequent reductions in travel times? Here we develop a method to estimate this radius based on the relationship between industry employment growth in a city and the size of employment in ‘skill-related’ sectors. Assuming car or rail travel, we find that for Northern cities the radius is around 45 min while for the South it is around 110 min. Manufacturing peaks around 45 min for the whole UK, including both North and South. Services, however, peak around 110 min in the South but 70 min in the North. A comparison between the UK’s Integrated Rail Plan and earlier proposals to invest in High Speed 2 rail and Northern rail connections uncovers lost opportunities for employment growth in Manchester, Leeds and Sheffield. This study provides a road-map for the deployment of methods from Evolutionary Economic Geography, originally developed to predict regional and urban growth and diversification processes as a function of the local skill mix, to evaluate the potential gains from transport infrastructure schemes.

Boko Haram has caused nearly 40,000 casualties in Nigeria, Niger, Cameroon and Chad, becoming one of the deadliest Jihadist organisations in recent history. At its current rate, Boko Haram takes part in more than two events each day, taking the lives of nearly 11 people daily. Yet, little is known concerning Boko Haram’s internal structure, organisation, and its mobility.Here, we propose a novel technique to uncover the internal structure of Boko Haram based on the sequence of events in which the terrorist group takes part. Data from the Armed Conflict Location & Event Data Project (ACLED) gives the location and time of nearly 3,800 events in which Boko Haram has been involved since the organisation became violent 10 years ago. Using this dataset, we build an algorithm to detect the fragmentation of Boko Haram into multiple cells, assuming that travel costs and reduced familiarity with unknown locations limit the mobility of individual cells.Our results suggest that the terrorist group has a very high level of fragmentation and consists of at least 50–60 separate cells. Our methodology enables us to detect periods of time during which Boko Haram exhibits exceptionally high levels of fragmentation, and identify a number of key routes frequently travelled by separate cells of Boko Haram where military interventions could be concentrated.

Labour mobility within a large city or metropolitan area is a necessary condition for the optimal exploitation of agglomeration economies. We propose a method to establish which municipalities should be considered part of a metropolitan area based on labour market integration. In order to aggregate geographically proximate urban municipalities, we develop a network-based model that makes industry productivity (manifesting in formal employment creation) dependent on firms’ ability to find, within city limits, the diversity of skills they need to move to new complex industries. In this way, we uncover the natural city scale at which firms optimally exploit the range of skills available to them within a broad catchment area. Considering Colombian cities, we find that commuting times between 45 and 75 min, corresponding to between 43 and 62 distinct cities or integrated labour markets, allow firms to maximise formal employment creation (between 2008 and 2013). This result supports the development of passenger transport limiting commuting times within cities, connecting small- and mid-size cities to nearby large cities, and coordinating transportation investments across traditional administrative boundaries.

Cities are home to a vast array of amenities, from local barbers to science museums and shopping malls. But these are unequally distributed across urban space. Using Google Places data combined with trip-based mobility data for Bogotá, Colombia, we shed light on the impact of neighbourhood amenities on urban mobility patterns. By deriving a new accessibility metric that explicitly takes into account spatial range, we find that a higher density of local amenities is associated with a higher likelihood of walking as well as shorter bus and car trips. Digging deeper, we use an effect modification framework to show that this relationship varies by socioeconomic status. Our main focus is walking and driving, finding that amenities within about a 1-km radius from home are robustly associated with a higher propensity to walk and shorter driving time only for the wealthiest group. These results suggest that wealthier groups may weigh the proximity of local amenities more heavily into travel decisions, perhaps based on differentiated time-money trade-offs. As cities globally aim to boost public transport and green travel, these findings enable us to better understand how commercial structure shapes urban mobility in highly income-segregated settings.

As early as the 1920's Marshall suggested that firms co-locate in cities to reduce the costs of moving goods, people, and ideas. These 'forces of agglomeration' have given rise, for example, to the high tech clusters of San Francisco and Boston, and the automobile cluster in Detroit. Yet, despite its importance for city planners and industrial policy-makers, until recently there has been little success in estimating the relative importance of each Marshallian channel to the location decisions of firms. Here we explore a burgeoning literature that aims to exploit the co-location patterns of industries in cities in order to disentangle the relationship between industry co-agglomeration and customer/supplier, labour and idea sharing. Building on previous approaches that focus on across- and between-industry estimates, we propose a network-based method to estimate the relative importance of each Marshallian channel at a meso scale. Specifically, we use a community detection technique to construct a hierarchical decomposition of the full set of industries into clusters based on co-agglomeration patterns, and show that these industry clusters exhibit distinct patterns in terms of their relative reliance on individual Marshallian channels.

People changing residence within their own country is the most common type of migration and is one of the main driving forces of a country's demography. Yet, due to a lack of data and tools, some key characteristics of internal migration such as the propensity to remain in the same city or to return to previous locations are frequently ignored. Here, a network-based model of city-to-city migration is constructed, where the movement of individuals is modelled using the frequency of distinct network motifs. We fit this model to longitudinal data on 3.3 million workers in Colombia, including 1.4 million migrations, and compare the motif frequency based on migration and return rates between men and women, and between distinct age and income groups. Results show that the majority of people do not move in general, but that there is a small group which exhibits frequent migration, particularly the young and male. Nearly three out of four times that a person moves, they are going back to a previous city, although women and mature people are less likely to move and more likely to return if they actually move. At a city level, different onward and return migration patterns are observed, whereby people from small secondary towns are more likely to leave and not return than people from large metropolitan areas like Bogotá or Medellín.

Economic complexity offers a potentially powerful paradigm to understand key societal issues and challenges of our time. The underlying idea is that growth, development, technological change, income inequality, spatial disparities, and resilience are the visible outcomes of hidden systemic interactions. The study of economic complexity seeks to understand the structure of these interactions and how they shape various socioeconomic processes. This emerging field relies heavily on big data and machine learning techniques. This brief introduction to economic complexity has three aims. The first is to summarize key theoretical foundations and principles of economic complexity. The second is to briefly review the tools and metrics developed in the economic complexity literature that exploit information encoded in the structure of the economy to find new empirical patterns. The final aim is to highlight the insights from economic complexity to improve prediction and political decision-making. Institutions including the World Bank, the European Commission, the World Economic Forum, the OECD, and a range of national and regional organizations have begun to embrace the principles of economic complexity and its analytical framework. We discuss policy implications of this field, in particular the usefulness of building recommendation systems for major public investment decisions in a complex world.

Despite the key role of multinational enterprises (MNEs) in both international markets and domestic economies, there is no consensus on their impact on their host economy. In particular, do MNEs stimulate new domestic firms through knowledge spillovers? Here, we look at the impact of MNEs on the entry and exit of domestic industries in Irish regions before, during, and after the 2008 Financial Crisis. Specifically, we are interested in whether the presence of MNEs in a region results in knowledge spillovers and the creation of new domestic industries in related sectors. To quantify how related an industry is to a region’s industry basket we propose two cohesion measures, weighted closeness and strategic closeness which capture direct linkages and dense inter-industry links between local industries respectively. We use a dataset of government-supported firms in Ireland (covering 90% of manufacturing and exporting) between 2006 and 2019. We find that domestic industries are both more likely to enter and less likely to leave a region if they are related to so-called ‘overlapping’ industries containing both domestic and MNE firms. In contrast, we find a negative impact on domestic entry and survival from cohesion to ‘exclusive MNE’ industries, suggesting that domestic firms are unable to ‘leap’ and thrive in MNE-proximate industries likely due to a technology or know-how gap. Finally, the type of cohesion matters. During the economic recovery (2015–2019), it is strategic rather than weighted closeness to overlapping industries that is associated with both domestic industry entry and survival.

Countries and cities around the world have resorted to unprecedented mobility restrictions to combat COVID-19 transmission. Here we exploit a natural experiment whereby Colombian cities implemented varied lockdown policies based on ID number and gender to analyse the impact of these policies on urban mobility. Using mobile phone data, we find that the restrictiveness of cities’ mobility quotas (the share of residents allowed out daily according to policy advice) does not correlate with mobility reduction. Instead, we find that larger, wealthier cities with more formalized and complex industrial structure experienced greater reductions in mobility. Within cities, wealthier residents are more likely to reduce mobility, and commuters are especially more likely to stay at home when their work is located in wealthy or commercially/industrially formalized neighbourhoods. Hence, our results indicate that cities’ employment characteristics and work-from-home capabilities are the primary determinants of mobility reduction. This finding underscores the need for mitigations aimed at lower income/informal workers, and sheds light on critical dependencies between socio-economic classes in Latin American cities.

The Employment Equity Act No. 55 of 1998 was introduced by the South African government to address the legacy of apartheid and ensure equitable representation of black people and women in the South African labour market. Although the impacts of the Act are highly controversial, its widespread adoption among firms opens up questions on its impact on the structure of the South African labour market. This study primarily focuses on determining the impact of the Act on female inter-industry labour mobility and the gender wage gap.

Since the outbreak of COVID-19 in early March 2020, supermarkets around the world have implemented different policies to reduce the virus transmission in stores to protect both customers and staff, such as restricting the maximum number of customers in a store, changes to the store layout, or enforcing a mandatory face covering policy. To quantitatively assess these mitigation methods, we formulate an agent-based model of customer movement in a supermarket (which we represent by a network) with a simple virus transmission model based on the amount of time a customer spends in close proximity to infectious customers (which we call the exposure time). We apply our model to synthetic store and shopping data to show how one can use our model to estimate exposure time and thereby the number of infections due to human-to-human contact in stores and how to model different store interventions. The source code is openly available under https://github.com/fabianying/covid19-supermarket-abm. We encourage retailers to use the model to find the most effective store policies that reduce virus transmission in stores and thereby protect both customers and staff.

Here we propose a new method to compare the modular structure of a pair of node-aligned networks. The majority of current methods, such as normalized mutual information, compare two node partitions derived from a community detection algorithm yet ignore the respective underlying network topologies. Addressing this gap, our method deploys a community detection quality function to assess the fit of each node partition with respect to the other network’s connectivity structure. Specifically, for two networks A and B, we project the node partition of B onto the connectivity structure of A. By evaluating the fit of B’s partition relative to A’s own partition on network A (using a standard quality function), we quantify how well network A describes the modular structure of B. Repeating this in the other direction, we obtain a two-dimensional distance measure, the bi-directional (BiDir) distance. The advantages of our methodology are three-fold. First, it is adaptable to a wide class of community detection algorithms that seek to optimize an objective function. Second, it takes into account the network structure, specifically the strength of the connections within and between communities, and can thus capture differences between networks with similar partitions but where one of them might have a more defined or robust community structure. Third, it can also identify cases in which dissimilar optimal partitions hide the fact that the underlying community structure of both networks is relatively similar. We illustrate our method for a variety of community detection algorithms, including multi-resolution approaches, and a range of both simulated and real world networks.

Human activity is organised around daily and weekly cycles, which should, in turn, dominate all types of social interactions, such as transactions, communications, gatherings and so on. Yet, despite their strategic importance for policing and security, cyclical weekly patterns in crime and road incidents have been unexplored at the city and neighbourhood level. Here we construct a novel method to capture the weekly trace, or “heartbeat” of events and use geotagged data capturing the time and location of more than 200,000 violent crimes and nearly one million crashes in Mexico City. On aggregate, our findings show that the heartbeats of crime and crashes follow a similar pattern. We observe valleys during the night and peaks in the evening, where the intensity during a peak is 7.5 times the intensity of valleys in terms of crime and 12.3 times in terms of road accidents. Although distinct types of events, crimes and crashes reach their respective intensity peak on Friday night and valley on Tuesday morning, the result of a hyper-synchronised society. Next, heartbeats are computed for city neighbourhood ‘tiles’, a division of space within the city based on the distance to Metro and other public transport stations. We find that heartbeats are spatially heterogeneous with some diffusion, so that nearby tiles have similar heartbeats. Tiles are then clustered based on the shape of their heartbeat, e.g., tiles within groups suffer peaks and valleys of crime or crashes at similar times during the week. The clusters found are similar to those based on economic activities. This enables us to anticipate temporal traces of crime and crashes based on local amenities.

In many domains we see a proliferation of claims made about how we can predict and measure the future city, how we make visible its form and shape its settlement. This paper synthesises contemporary debates in data analytics, anthropology, geography and the history of urban thought to consider the context of such claims making around urban futures and the promise (and promises) of attempts to make visible the urban as a ‘lab’ or ‘observatory’ through which we might ‘see like a city’. Building on a ‘systems of systems approach’ the paper develops an original PEAK Urban conceptual framing of this new subdiscipline and addresses the potential for academic research to inform the capacity of cities to anticipate and reshape the challenges that characterise 21st century urban life through interdisciplinary and transdisciplinary engaged scholarship that situates the new urban sciences within a context of an experimental urbanism that makes visible the trade offs and the ethical dilemmas of the future city.

Economic growth is associated with the diversification of economic activities, which can be observed via the evolution of product export baskets. Exporting a new product is dependent on having, and acquiring, a specific set of capabilities, making the diversification process path-dependent. Taking an agnostic view on the identity of the capabilities, here we derive a probabilistic model for the directed dynamical process of capability accumulation and product diversification of countries. Using international trade data, we identify the set of pre-existing products, the product Ecosystem, that enables a product to be exported competitively. We construct a directed network of products, the Eco Space, where the edge weight corresponds to capability overlap. We uncover a modular structure, and show that low- and middle-income countries move from product communities dominated by small Ecosystem products to advanced (large Ecosystem) product clusters over time. Finally, we show that our network model is predictive of product appearances., European Union (EU); Horizon 2020; Research and Innovation Programme; Marie Skłodowska-Curie Grant; PEAK Urban Programme; UKRI’s Global Challenge Research Fund

The fact that firms benefit from close proximity to other firms with which they can exchange inputs, skilled labor or know-how helps explain why many industrial clusters are so successful. Studying the evolution of coagglomeration patterns, we show that the type of agglomeration that benefits firms has drastically changed over the course of a century and differs markedly across industries. Whereas, at the beginning of the twentieth century, industries tended to colocate with their value chain partners, in more recent decades the importance of this channel has declined and colocation seems to be driven more by similarities in industries’ skill requirements. By calculating industry-specific Marshallian agglomeration forces, we are able to show that, today, skill-sharing is the most salient motive behind the location choices of services, whereas value chain linkages still explain much of the colocation patterns in manufacturing. Moreover, the estimated degrees to which labor and input-output linkages are reflected in an industry’s coagglomeration patterns help improve predictions of city-industry employment growth.

Using the intrinsic relationship between the external equitable partition (EEP) and the spectral properties of the graph Laplacian, we characterize convergence and observability properties of consensus dynamics on networks. In particular, we establish the relationship between the original consensus dynamics and the associated consensus of the quotient graph under varied initial conditions, and characterize the asymptotic convergence to the synchronization manifold under nonuniform input signals. We also show that the EEP with respect to a node can reveal nodes in the graph with an increased rate of asymptotic convergence to the consensus value, as characterized by the second smallest eigenvalue of the quotient Laplacian. Finally, we show that the quotient graph preserves the observability properties of the full graph and how the inheritance by the quotient graph of particular aspects of the eigenstructure of the full Laplacian underpins the observability and convergence properties of the system.