The world is becoming more urban every day. This process presents enormous social, economic and environmental challenges, particularly in the developed world, where most of the growth in urban population will take place. This highlights the importance of improving our understanding of cities in the context of developing economies, in order to provide evidence-based tools to policy makers across the globe.
Cities in developing countries show a much higher of unemployment and informality than their counterparts in the developed world. Ensuring that cities are resilient and capable of providing decent job opportunities to everyone is one of the greatest challenges in the global south. By modelling the skills of a city using a network of labour flows, we aim to provide new ways to measure the economic resilience of a city.
In this project, we will take advantage of recent advances in remote sensing and computer vision to develop an alternative approach to obtaining useful data for urban researchers and policy makers alike. By showcasing the viability and usability of these new techniques, we hope to open the field to further innovation.
Furthermore, we propose a truly interdisciplinary approach to urban research, drawing from networks modelling, evolutionary economics and urban geography. On one hand, we will use the data set we will generate through remote sensing to explore the possible links between mobility, urban form and informality. On the other hand, we will develop analytical tools to measure the labour market resilience of cities, a critical issue given the transformative potential of the automation that is currently happening worldwide.
In this manner, we hope to address two of the Sustainable Development Goals (SDG) put forward by the UN: promote sustained, inclusive and sustainable economic growth, and make cities more inclusive, safe and resilient.
The project is built around two questions that address some of the economic challenges contained in the Sustainable Development Goals (SDG):
How can we use new sources of data to improve our understanding of the informal sector?
Can we develop novel methodologies to model the labour market resilience of cities?