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2025
Journal article
Open Access
Dynamic models of gentrification
Mauro G., Pedreschi N., Lambiotte R., Pappalardo L.
The phenomenon of gentrification of an urban area is characterized by the displacement of lower-income residents due to rising living costs and an influx of wealthier individuals. This study presents an agent-based model that simulates urban gentrification through the relocation of three income groups — low, middle, and high — driven by living costs. The model incorporates economic and sociological theories to generate realistic neighborhood transition patterns. We introduce a temporal network-based measure to track the outflow of low-income residents and the inflow of middle- and high-income residents over time. Our experiments reveal that high-income residents trigger gentrification and that our network-based measure consistently detects gentrification patterns earlier than traditional count-based methods, potentially serving as an early detection tool in real-world scenarios. Moreover, the analysis highlights how city density promotes gentrification. This framework offers valuable insights for understanding gentrification dynamics and informing urban planning and policy decisions.Source: ADVANCES IN COMPLEX SYSTEM, vol. 28 (issue 06)
DOI: 10.1142/s0219525925400065
DOI: 10.48550/arxiv.2410.18004
Project(s): Community Detection And Dynamics in Temporal Networks
, SoBigData-PlusPlus
Metrics:
Mauro G., Pedreschi N., Lambiotte R., Pappalardo L.
The phenomenon of gentrification of an urban area is characterized by the displacement of lower-income residents due to rising living costs and an influx of wealthier individuals. This study presents an agent-based model that simulates urban gentrification through the relocation of three income groups — low, middle, and high — driven by living costs. The model incorporates economic and sociological theories to generate realistic neighborhood transition patterns. We introduce a temporal network-based measure to track the outflow of low-income residents and the inflow of middle- and high-income residents over time. Our experiments reveal that high-income residents trigger gentrification and that our network-based measure consistently detects gentrification patterns earlier than traditional count-based methods, potentially serving as an early detection tool in real-world scenarios. Moreover, the analysis highlights how city density promotes gentrification. This framework offers valuable insights for understanding gentrification dynamics and informing urban planning and policy decisions.Source: ADVANCES IN COMPLEX SYSTEM, vol. 28 (issue 06)
DOI: 10.1142/s0219525925400065
DOI: 10.48550/arxiv.2410.18004
Project(s): Community Detection And Dynamics in Temporal Networks
, SoBigData-PlusPlus Metrics:
See at:
arXiv.org e-Print Archive 
| Archivio istituzionale della Ricerca - Scuola Normale Superiore | Archivio istituzionale della Ricerca - Scuola Normale Superiore | CNR IRIS | Oxford University Research Archive | www.worldscientific.com | Advances in Complex Systems 
| doi.org | CNR IRIS
2025
Conference article
Open Access
Burstiness in emotions: a case study on collective affective responses in Italian soccer fandoms
Citraro S., Mauro G., Ferragina E.
The bursty nature of emotions is rarely investigated outside cognitive and psychological studies. Therefore this work addresses a gap in the literature, investigating the phenomenon of emotional burstiness using tools from the analysis of complex systems, and considering as case-study soccer fans’ affective responses on social media. We reconstruct collective reactions on Instagram posts from official accounts of 40 Italian football teams during the first round of the 2023–2024 season – 20 teams from Serie B (the second tier of Italian Football) and the 20 most followed teams in Serie C (the third tier). With this data, we build sequences of emotional signals for four types of emotions: joy, anger, sadness, and fear. Our analysis reveals trends of anti-burstiness in expressions of joy among users, reflecting fans’ consistent support for teams, occasionally interspersed by bursts of anger and sadness, with no signals of fear. This preliminary investigation provides insights for the understanding of emotional dynamics in online discussions and team supporting in soccer leagues.Source: LECTURE NOTES IN COMPUTER SCIENCE, vol. 15211, pp. 56-69. Rende, Cosenza, Italy, 2–5/09/2024
DOI: 10.1007/978-3-031-78541-2_4
Metrics:
Citraro S., Mauro G., Ferragina E.
The bursty nature of emotions is rarely investigated outside cognitive and psychological studies. Therefore this work addresses a gap in the literature, investigating the phenomenon of emotional burstiness using tools from the analysis of complex systems, and considering as case-study soccer fans’ affective responses on social media. We reconstruct collective reactions on Instagram posts from official accounts of 40 Italian football teams during the first round of the 2023–2024 season – 20 teams from Serie B (the second tier of Italian Football) and the 20 most followed teams in Serie C (the third tier). With this data, we build sequences of emotional signals for four types of emotions: joy, anger, sadness, and fear. Our analysis reveals trends of anti-burstiness in expressions of joy among users, reflecting fans’ consistent support for teams, occasionally interspersed by bursts of anger and sadness, with no signals of fear. This preliminary investigation provides insights for the understanding of emotional dynamics in online discussions and team supporting in soccer leagues.Source: LECTURE NOTES IN COMPUTER SCIENCE, vol. 15211, pp. 56-69. Rende, Cosenza, Italy, 2–5/09/2024
DOI: 10.1007/978-3-031-78541-2_4
Metrics:
See at:
Hyper Article en Ligne - Sciences de l'Homme et de la Société | CNR IRIS | link.springer.com | Hyper Article en Ligne - Sciences de l'Homme et de la Société | Hyper Article en Ligne - Sciences de l'Homme et de la Société | Archivio della Ricerca - Università di Pisa | Archivio della Ricerca - Università di Pisa | CNR IRIS | CNR IRIS
2025
Journal article
Open Access
Dynamic models of gentrification
Mauro G., Pedreschi N., Lambiotte R., Pappalardo L.
The phenomenon of gentrification of an urban area is characterized by the displacement of lower-income residents due to rising living costs and an influx of wealthier individuals. This study presents an agent-based model that simulates urban gentrification through the relocation of three income groups — low, middle, and high — driven by living costs. The model incorporates economic and sociological theories to generate realistic neighborhood transition patterns. We introduce a temporal network-based measure to track the outflow of low-income residents and the inflow of middle- and high-income residents over time. Our experiments reveal that high-income residents trigger gentrification and that our network-based measure consistently detects gentrification patterns earlier than traditional count-based methods, potentially serving as an early detection tool in real-world scenarios. Moreover, the analysis highlights how city density promotes gentrification. This framework offers valuable insights for understanding gentrification dynamics and informing urban planning and policy decisions.Source: ADVANCES IN COMPLEX SYSTEM, vol. 28 (issue 06)
DOI: 10.1142/s0219525925400065
DOI: 10.48550/arxiv.2410.18004
Project(s): Community Detection And Dynamics in Temporal Networks, SoBigData-PlusPlus , Urban Artificial Intelligence
Metrics:
Mauro G., Pedreschi N., Lambiotte R., Pappalardo L.
The phenomenon of gentrification of an urban area is characterized by the displacement of lower-income residents due to rising living costs and an influx of wealthier individuals. This study presents an agent-based model that simulates urban gentrification through the relocation of three income groups — low, middle, and high — driven by living costs. The model incorporates economic and sociological theories to generate realistic neighborhood transition patterns. We introduce a temporal network-based measure to track the outflow of low-income residents and the inflow of middle- and high-income residents over time. Our experiments reveal that high-income residents trigger gentrification and that our network-based measure consistently detects gentrification patterns earlier than traditional count-based methods, potentially serving as an early detection tool in real-world scenarios. Moreover, the analysis highlights how city density promotes gentrification. This framework offers valuable insights for understanding gentrification dynamics and informing urban planning and policy decisions.Source: ADVANCES IN COMPLEX SYSTEM, vol. 28 (issue 06)
DOI: 10.1142/s0219525925400065
DOI: 10.48550/arxiv.2410.18004
Project(s): Community Detection And Dynamics in Temporal Networks
, SoBigData-PlusPlus , Urban Artificial IntelligenceMetrics:
See at:
arXiv.org e-Print Archive | Archivio istituzionale della Ricerca - Scuola Normale Superiore | Archivio istituzionale della Ricerca - Scuola Normale Superiore | CNR IRIS | Oxford University Research Archive | www.worldscientific.com | Advances in Complex Systems | doi.org | CNR IRIS
2024
Conference article
Restricted
A preliminary investigation of user- and item-centered bias in POI recommendation
Mauro G., Minici M., Pugliese C.
This study investigates the application of Recom-mender Systems (RS) to predict future Point of Interest (POI) visits based on check-in data, with a particular focus on biases related to individual mobility patterns and POI popularity. We conduct a comprehensive analysis by training and evaluating three RS models based on different architectures: a Convo-lutional Neural Network, an Attention-based Neural Network, and a Markov-based predictor. Our analysis reveals that POI recommenders: do not show bias in terms of the typical distance traveled by users but tend to favor less exploratory users, and are biased towards more popular POIs. Our findings highlight the potential of RS in capturing and forecasting user behavior, while also underscoring the need to mitigate these biases, thereby advancing the understanding of RS and their broader social impact.DOI: 10.1109/mdm61037.2024.00058
Project(s): SERICS
Metrics:
Mauro G., Minici M., Pugliese C.
This study investigates the application of Recom-mender Systems (RS) to predict future Point of Interest (POI) visits based on check-in data, with a particular focus on biases related to individual mobility patterns and POI popularity. We conduct a comprehensive analysis by training and evaluating three RS models based on different architectures: a Convo-lutional Neural Network, an Attention-based Neural Network, and a Markov-based predictor. Our analysis reveals that POI recommenders: do not show bias in terms of the typical distance traveled by users but tend to favor less exploratory users, and are biased towards more popular POIs. Our findings highlight the potential of RS in capturing and forecasting user behavior, while also underscoring the need to mitigate these biases, thereby advancing the understanding of RS and their broader social impact.DOI: 10.1109/mdm61037.2024.00058
Project(s): SERICS
Metrics:
See at:
Archivio della Ricerca - Università di Pisa | Archivio della Ricerca - Università di Pisa | IRIS Cnr | Archivio della Ricerca - Università di Pisa | ieeexplore.ieee.org | CNR IRIS
2024
Conference article
Open Access
The role of relocation policies in urban segregation dynamics
Mauro G., Pappalardo L.
This study addresses a gap in the existing literature on the Schelling segregation model by conducting a comprehensive qualitative assessment of various relocation policies. We introduce novel Schelling models driven by different relocation policies and analyse their impact on the convergence time and final segregation levels. Our findings demonstrate that all policies result in segregation levels within bounds established by policies where agents relocate to maximize their happiness. Notably, a policy ensuring the minimum improvement in agent segregation significantly reduces the model’s convergence time. These results underscore the potential influence of relocation policies, such as those employed by online recommenders in real estate platforms, on societal segregation dynamics. The study provides valuable insights into potential strategies for mitigating and decelerating segregation through tailored recommendations.Source: CEUR WORKSHOP PROCEEDINGS, vol. 3651. Paestum, Italy, 25/03/2024
Project(s): Urban Artificial Intelligence
Mauro G., Pappalardo L.
This study addresses a gap in the existing literature on the Schelling segregation model by conducting a comprehensive qualitative assessment of various relocation policies. We introduce novel Schelling models driven by different relocation policies and analyse their impact on the convergence time and final segregation levels. Our findings demonstrate that all policies result in segregation levels within bounds established by policies where agents relocate to maximize their happiness. Notably, a policy ensuring the minimum improvement in agent segregation significantly reduces the model’s convergence time. These results underscore the potential influence of relocation policies, such as those employed by online recommenders in real estate platforms, on societal segregation dynamics. The study provides valuable insights into potential strategies for mitigating and decelerating segregation through tailored recommendations.Source: CEUR WORKSHOP PROCEEDINGS, vol. 3651. Paestum, Italy, 25/03/2024
Project(s): Urban Artificial Intelligence
See at:
ceur-ws.org | CNR IRIS | CNR IRIS
2024
Contribution to book
Open Access
Message from the MAURO 2024 Workshop Chairs
Chondrodima E., Cornacchia G., Mauro G., Nanni M., Pappalardo L., Pugliese C.
An abstract is not availableDOI: 10.1109/mdm61037.2024.00011
Metrics:
Chondrodima E., Cornacchia G., Mauro G., Nanni M., Pappalardo L., Pugliese C.
An abstract is not availableDOI: 10.1109/mdm61037.2024.00011
Metrics:
See at:
CNR IRIS | www.computer.org | doi.org | CNR IRIS
2023
Journal article
Open Access
Mobility constraints in segregation models
Gambetta D, Mauro G, Pappalardo L
Since the development of the original Schelling model of urban segregation, several enhancements have been proposed, but none have considered the impact of mobility constraints on model dynamics. Recent studies have shown that human mobility follows specific patterns, such as a preference for short distances and dense locations. This paper proposes a segregation model incorporating mobility constraints to make agents select their location based on distance and location relevance. Our findings indicate that the mobility-constrained model produces lower segregation levels but takes longer to converge than the original Schelling model. We identified a few persistently unhappy agents from the minority group who cause this prolonged convergence time and lower segregation level as they move around the grid centre. Our study presents a more realistic representation of how agents move in urban areas and provides a novel and insightful approach to analyzing the impact of mobility constraints on segregation models. We highlight the significance of incorporating mobility constraints when policymakers design interventions to address urban segregation.Source: SCIENTIFIC REPORTS, vol. 13
DOI: 10.1038/s41598-023-38519-6
DOI: 10.48550/arxiv.2305.10170
Project(s): HumanE-AI-Net, SoBigData-PlusPlus
Metrics:
Gambetta D, Mauro G, Pappalardo L
Since the development of the original Schelling model of urban segregation, several enhancements have been proposed, but none have considered the impact of mobility constraints on model dynamics. Recent studies have shown that human mobility follows specific patterns, such as a preference for short distances and dense locations. This paper proposes a segregation model incorporating mobility constraints to make agents select their location based on distance and location relevance. Our findings indicate that the mobility-constrained model produces lower segregation levels but takes longer to converge than the original Schelling model. We identified a few persistently unhappy agents from the minority group who cause this prolonged convergence time and lower segregation level as they move around the grid centre. Our study presents a more realistic representation of how agents move in urban areas and provides a novel and insightful approach to analyzing the impact of mobility constraints on segregation models. We highlight the significance of incorporating mobility constraints when policymakers design interventions to address urban segregation.Source: SCIENTIFIC REPORTS, vol. 13
DOI: 10.1038/s41598-023-38519-6
DOI: 10.48550/arxiv.2305.10170
Project(s): HumanE-AI-Net
, SoBigData-PlusPlus Metrics:
See at:
Scientific Reports | CNR IRIS | ISTI Repository | www.nature.com | doi.org | CNR IRIS
2023
Conference article
Open Access
Human mobility, AI assistants, and urban emissions: an insidious triangle
Pappalardo L, Bohm M, Cornacchia G, Mauro G, Pedreschi D, Nanni M
Transportation remains a significant contributor to greenhouse gas emissions, with a substantial proportion originating from road transport and passenger travel in particular. Today, the relationship between transportation and urban emissions is even more complex, given the increasingly prevalent role and the pervasiveness of AI-based GPS navigation systems such as Google Maps and TomTom. While these services offer benefits to individual drivers, they can also exacerbate congestion and increase pollution if too many drivers are directed onto the same route. In this article, we provide two examples from our research group that explore the impact of vehicular transportation and mobility-AI-based applications on urban emissions. By conducting realistic simulations and studying the impact of GPS navigation systems on emissions, we provide insights into the potential for mitigating transportation emissions and developing policies that promote sustainable urban mobility. Our examples demonstrate how vehicle-generated emissions can be reduced and how studying the impact of GPS navigation systems on emissions can lead to unexpected findings. Overall, our analysis suggests that it is crucial to consider the impact of emerging technologies on transportation and emissions, and to develop strategies that promote sustainable mobility while ensuring the optimal use of these tools.Source: CEUR WORKSHOP PROCEEDINGS, pp. 585-589. Pisa, Italy, 29-31/05/2023
Project(s): HumanE-AI-Net, SoBigData-PlusPlus
Pappalardo L, Bohm M, Cornacchia G, Mauro G, Pedreschi D, Nanni M
Transportation remains a significant contributor to greenhouse gas emissions, with a substantial proportion originating from road transport and passenger travel in particular. Today, the relationship between transportation and urban emissions is even more complex, given the increasingly prevalent role and the pervasiveness of AI-based GPS navigation systems such as Google Maps and TomTom. While these services offer benefits to individual drivers, they can also exacerbate congestion and increase pollution if too many drivers are directed onto the same route. In this article, we provide two examples from our research group that explore the impact of vehicular transportation and mobility-AI-based applications on urban emissions. By conducting realistic simulations and studying the impact of GPS navigation systems on emissions, we provide insights into the potential for mitigating transportation emissions and developing policies that promote sustainable urban mobility. Our examples demonstrate how vehicle-generated emissions can be reduced and how studying the impact of GPS navigation systems on emissions can lead to unexpected findings. Overall, our analysis suggests that it is crucial to consider the impact of emerging technologies on transportation and emissions, and to develop strategies that promote sustainable mobility while ensuring the optimal use of these tools.Source: CEUR WORKSHOP PROCEEDINGS, pp. 585-589. Pisa, Italy, 29-31/05/2023
Project(s): HumanE-AI-Net
, SoBigData-PlusPlus
See at:
ceur-ws.org | CNR IRIS | ISTI Repository | CNR IRIS
2022
Journal article
Open Access
Generating mobility networks with generative adversarial networks
Mauro G, Luca M, Longa A, Lepri B, Pappalardo L
The increasingly crucial role of human displacements in complex societal phenomena, such as traffic congestion, segregation, and the diffusion of epidemics, is attracting the interest of scientists from several disciplines. In this article, we address mobility network generation, i.e., generating a city's entire mobility network, a weighted directed graph in which nodes are geographic locations and weighted edges represent people's movements between those locations, thus describing the entire mobility set flows within a city. Our solution is MoGAN, a model based on Generative Adversarial Networks (GANs) to generate realistic mobility networks. We conduct extensive experiments on public datasets of bike and taxi rides to show that MoGAN outperforms the classical Gravity and Radiation models regarding the realism of the generated networks. Our model can be used for data augmentation and performing simulations and what-if analysis.Source: EPJ DATA SCIENCE, vol. 11 (issue 1)
DOI: 10.1140/epjds/s13688-022-00372-4
Project(s): SoBigData-PlusPlus
Metrics:
Mauro G, Luca M, Longa A, Lepri B, Pappalardo L
The increasingly crucial role of human displacements in complex societal phenomena, such as traffic congestion, segregation, and the diffusion of epidemics, is attracting the interest of scientists from several disciplines. In this article, we address mobility network generation, i.e., generating a city's entire mobility network, a weighted directed graph in which nodes are geographic locations and weighted edges represent people's movements between those locations, thus describing the entire mobility set flows within a city. Our solution is MoGAN, a model based on Generative Adversarial Networks (GANs) to generate realistic mobility networks. We conduct extensive experiments on public datasets of bike and taxi rides to show that MoGAN outperforms the classical Gravity and Radiation models regarding the realism of the generated networks. Our model can be used for data augmentation and performing simulations and what-if analysis.Source: EPJ DATA SCIENCE, vol. 11 (issue 1)
DOI: 10.1140/epjds/s13688-022-00372-4
Project(s): SoBigData-PlusPlus
Metrics:
See at:
EPJ Data Science | epjdatascience.springeropen.com | CNR IRIS | ISTI Repository | CNR IRIS