By 2050, 80% of the world’s population will be living in cities. Globally, cities are being transformed redeveloped to continue providing citizens opportunities for sustainable and proposerous livelihoods. Historically, humans have always migrated to dense spatial agglomerations for intense social interactions, exchange of goods, services, information and ideas. The process of urbanisation has naturally fostered socioeconomic, technological and institutional transformations that are also neccessary for sustaining this growth. But the unprecendented development is tightly linked to the most pressing functional and environmental challenges of our time.
Multiple cities in the world are at the brink of collapse, suffering from poverty and segregation, excessive consumption, pollution and associated changes in climate, depleting agricultural utility and in exceptional cases, submergence of land. Lacking resources to solve such problems, some cities are directing further development to satellite centres like Accra in Ghana or Cairo in Egypt. Jakarta, home to 60% of the Indonesian population, is considering relocating its capital city to the rainforests of Borneo because of rising sea levels. Rapidly urbanising cities are also making an tremendous efforts to become smarter, sutainable, resilient and inclusive. But how?
In the last decade, technological advancements have inevitably led us to embed large-scale networked systems, sensors and computers into the built environment. Urban data has emerged as an excellent stream of constant, real-time and accurate information about all urban activities. The big data revolution, coupled with the capacity of infrastructure to be “smart” has enticed cities and urban managers worldwide to participate in machine learning based decision making for improving the course of humanity. But city planning has largely been instituted around loosely coupled organisations within municipal and regional governments, project developers, companies and investors, transport, water and energy operators. Citizens, who are both the cause and effect of the socioeconomic dynamics of our society are missing from such planning efforts. Who do we want to be our agents of changes: citizens, governments, organisations or a combination of all? Without a systems’ theory of cities, enormous data makes each discipline smart in its own respect, but fails to forecast for urban growth or even consider sustainable solutions for the future. How we develop urban solutions for the betterment of humans is largely determined by how we integrate urban data to interrogate it for advancing a transdisciplinary science of cities.
Taking inspiration from Meerow et al. (2016), we describe an urban system as a complex, adaptive and emerging environment that, for simplicity, is organised in 4 different layers of function. The layer of infrastructure form consists of public assets like buildings, service infrastructure and natural capital. Networks of energy, material and information flows enable the movement of resources that address our collective needs. The layer of socioeconomic dynamics express how demographics is interspersed with socially constructed values such as sustinability and equity. The layer of governance includes organisations, institutions and businesses that shape urban systems through new development, policies or innovation.
We study the fundamental social, economic, environmental and politcal processes formed by our needs that drive, shape and sustain cities in the context of an urban system.
We focus on three broad challenges:
Our constantly connected and digitally enhanced lives produce enormous amounts of data that has changed the way we observe and forecast for urban growth. Our work in this area focusses on measuring and mapping key urban variables about socioeconomic dynamics, urban form, and material, information and energy flows, globally. We design new methods for collecting and fusing existing datasets in time and space so the insights are generalisable over different scales of urban areas. We research how high-performance computing systems can translate models from case-based studies of smaller urban areas to applications wordwide. In the long term, we want to develop frameworks that enable city governance to generate and track data that is useful for long-term sustainable planning. This also includes analysing, validating and translating the needs of local communities through their own data collection intiatives to design policy at all levels of our society, for example by facilitating citizen cafés representing participatory democracy.
Different aspects of the four-layered urban system discussed above have been the subject of many discplines for over a century: sociologists, transportation engineers, economists and anthropologists. Perhaps, physicists and geographers have played a huge part in integrating much of these disparate sciences by studying the spatial size and form of cities through relationships of population and city size, showing how universal scaling relationships exist all over the world. Addressing global problems in cities requires a transdisciplinary perspective that integrate insights from an urban science into planning and policy. Methods from different fields reaveal much about the human condition in its inequalities, segregation, access and opportunities, and behavior in large societies. To develop and advance the common language of urban sciences, we need to integrate and advance methodologies from multiple disciplines by leveraging the overlap of data science, complex adaptive systems and artifical intelligence. We develop such frameworks to understand and forecast for urban growth where findings are applicable to both developed and developing nations.
Our overarching goal is to bring forth this knowledge of a city science into the foreground to facilitate productive interaction between discplines and action between urban stakeholders - including citizens - who are ultimately responsible for decisionmaking and development. How can distributed and complex modelling techniques provide simple and action-oritented insights to decisionmakers? To that effect, we believe an urban science requires combining new methods in computation, particpatory modelling, knowledge utilisation workshops and serious games to translate insights from different fields and strive to integrate them into a holistic decision-making framework for cities.
The lab is built by the efforts of students, scientists and scholars in the fields of urban science, machine learning, complex network theory, modelling and simulation, and policy analysis. Our group is led by Trivik Verma, Assistant Professor of Urban Science & Policy. To tackle these challenges, we design and use several methods and techniques from diverse areas such as,
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Introduction Many of us have experienced it: you wander about a city, and some streets are vibrant and abuzz with people and activity, while others are sleepy and deserted. What exactly is it that lends a place (or robs it of) its liveliness and candour?
At present an active societal debate is going on in the Netherlands whether consumer fireworks should be banned. This debate follows from increasing violence directed at first aid responders, annually increasing arson and damage to public and private property.
Trivik Verma is attending the Technology, Management, and Policy Consortium @ The George Washington University in Washington, DC between 16-18 June, 2019 representing the faculty of TPM, TU Delft. He will be chairing one of the sessions.