Population movement under extreme events.

T he last century has witnessed the fastest population growth in human history, with the result that, for the first time, more than 50% of the world population lives in cities. This process is still advancing. According to United Nations predictions, urban population will exceed 75% of the world’s population by 2070. Furthermore, estimates are that if the current trends continue, by the end of the 21st century the entire world will be urbanized (1). Beyond this statistical–demographic fact, this prediction implies that the effects of extreme events (ExEvs), such as blackout, flood, tsunami, or terrorist attack, will have increasingly dramatic and intimate affects on the specific structure and dynamics of cities and urbanism. Thus, there is a critical need to understand the ways in which ExEvs will affect urban life and population dynamics. This new understanding can be achieved through the exponentially growing field of computational social sciences, as illustrated by a study in PNAS (2). In this work, the authors use mobile phone use data in Haiti to investigate the population dynamics and displacement as a result of the tragic 2010 earthquake. This unique work allows a study of how population concentrations, which are established in cities, respond to and are affected by ExEvs. ExEvs have concerned human societies since their early history (e.g., the biblical flood). However, it is only recently that the issue entered the core of public discourse. This new attention reflects the fact that catastrophes in modern society tend to be less localized, with a significant impact on a larger hazard zone. The mere movement of people inside cities depends on the integration of the electricity grid, the railway network, the roads, the control and communication of traffic lights, and many more infrastructures. Furthermore, a significant amount of people uses the same infrastructure extensively. These two elements (interdependencies between different infrastructures and massive use of infrastructures) yield new vulnerabilities that did not exist a few decades ago (see for example ref. 3). A scenario of cascading failures that rapidly diffuse throughout the entire urban structure is thus a relatively new threat. As such, new methods are necessary to mitigate such threats, with insights such as those presented by Lu et al. (2). Cities are not merely collections of people. They are interdependent multilevel network systems shaped by interacting and multidimensional driving forces, such as the various urban agents (e.g., individuals, households, firms, planning agencies, and other entities that are acting in the city), infrastructures (e.g., power grids, transportation), economy (e.g., trade, innovation), dependency on resources, sustainability, demography, culture, and values. The distinct role of urbanism in creating new vulnerabilities is strikingly demonstrated in the recent tsunami in Southeast Asia, the September 11, 2001 attack on New York City, Hurricane Katrina in New Orleans, and the 2011 earthquake followed by tsunami leading to nuclear reactor meltdowns and radioactive air pollution at the Fukushima Daiichi Nuclear Power Station in Japan. These events testify that even the most technologically advanced societies are in general ill prepared for ExEvs, but particularly in cities. The last 30 years have witnessed the emergence of complexity theories of cities (CTC)—a domain of research that applies the various complexity theories to the study of cities. CTC portray cities as complex, self-organizing systemic networks. They suggest that cities have originally emerged and are still developing out of the space–time interactions between the many urban agents, that is to say, the individuals, families, households, firms, and other entities that act and interact in the city. The activities and interactions between these urban agents give rise to the global urban multilevel network and structure that in turn affects the agent’s cognition, behavior, movement, and action in the city in a circular causality. CTC have demonstrated a whole set of resemblances between cities on the one hand, and natural, material, and organic networks on the other (4–7). However, these studies have also exposed important differences that are a consequence of, first, the fact that cities are large-scale collective artifacts, and second, that cities are dualcomplex systems; that is, the city as a whole is a complex system/network, and each of its components (the urban agents) is a complex system/network by itself. Their behavior depends on many factors, and when facing a danger, people react in different ways. The new science of big data is now allowing researchers such as Lu et al. to quantify the interplay between micro and macro hierarchical levels in these multilevel complex systems (see for example ref. 8). Crucial questions that entail ExEvs in cities have not as yet been given sufficient answers: (i) How are the many urban networks of which a city is composed related to each other in space and time, and how might the complex interdependencies between them influence the effects of different kinds of ExEv? (ii) How can the vulnerability and resilience of geographic locations be evaluated with respect to the interdependencies between spatial networks, activity of the residents, and users of cities and their possible reactions to ExEv? (iii) How does a cascade of ExEvs emerge (i.e., the earthquake, tsunami, and nuclear crisis in Japan in 2011), and how can ExEvs be monitored before and after they erupt? (iv) How can ExEvs be planned and prepared for to mitigate the catastrophic? Answers to each of these questions will require the effective Fig. 1. Hägerstrand’s web model of time geography. The path represents the daily movement in space–time of an individual; the bundle is the space–time place/cylinder where the individual congregates with other individuals (e.g., home, work, school, etc.), whereas the domain describes the space–time area/cylinder within which the individual’s daily movement takes place. Reprinted with kind permission from Springer Science + Business Media: Cognition, Complexity and the City, 2011, Portugali, J., Figure 3.3. (7).