The 20th edition of the international Conference in Complex Systems (CCS2024) took place in the first week of September at the University of Exeter, preceded by a Warm-Up event for young researchers hosted at the network science institute at Northeastern University London. CCS is the flagship conference of the Complex Systems Society, and is a major international conference in the area of complex systems. The conference saw the participation of nearly 500 participants in person, and was hailed as a great success by all.
We had a number of keynote speakers from around the world, including one of the world’s most cited computers and data scientists, Professor Sandy Pentland (MIT & Stanford), and, over the course of a week, we had in excess of 300 presentations across over 50 sessions. The presentation by Giovanni Mauro was chosen as the PeerJ Award winner for Best Oral Presentation.
Federico Botta, CCS’24 Organizing Committee
Giovanni Mauro PhD Candidate at University of Pisa and IMT Lucca, Italy.
Can you tell us a bit about yourself and your research interests?
I hold a BSc in Computer Science from University of Pisa (during which heI participated in a one-year Erasmus+ grant at Universidad Autónoma de Madrid) and a MSc in Data Science from Universitat Politècnica de Catalunya – BarcelonaTech. Currently, I am a PhD Candidate of the Italian National PhD in AI, at University of Pisa and IMT Lucca. Also I am a Research Associate at the Institute of Information Science and Technologies of the National Research Council of Italy (ISTI-CNR).
Can you briefly explain the research you presented at CCS’24?
At the CCS2024 conference, I presented my work on gentrification, a phenomenon where higher-income residents move into neighborhoods, pricing out long-term, lower-income residents due to rising costs. Our research used simulations to study the relocation patterns of people with different income levels within a virtual city. We found that middle-income residents often trigger gentrification, but the process is driven by high-income residents—without their involvement, gentrification does not occur.
We introduced two new ways to measure gentrification: one based on changes in the population’s class composition over time, and another that tracks the movement of different income groups within the city. Interestingly, the second method proved to be an early warning signal for gentrification.
Finally, our simulations indicated that denser cities are more prone to gentrification than those with lower population densities.
How will you continue to build on this research?
Our plan moving forward is to refine and finalize this research, integrating and comparing our findings with real-world housing relocation data. This will allow us to provide a more accurate understanding of gentrification dynamics in actual urban environments.
