arXiv subjects

Riccardo Di Clemente

Publications and source records attributed to Riccardo Di Clemente.

At least 19 recordsLinked to original sources

arXiv2026

Workplace dependence in urban economies

Remote work has fundamentally reshaped urban economic life, and the spatial organisation of activity across cities. However, access to flexible work is distributed unevenly across industries, income groups, and genders, creating disparities in health risks, social mixing, and economic opportunity. Understanding where workplace dependence (WPD) is concentrated is therefore important, yet its distribution across urban areas remains poorly understood. Here we pair fine-grained hourly population data with detailed company records in a large European city to examine how location, industry composition, and socio-economic characteristics shape physical workplace attendance. By comparing workplace activity during periods of low versus high COVID-19 restrictions, we identify the determinants of WPD. We find that while industry and firm productivity are key drivers, the relationship between WPD, income, and gender is highly contingent on distance from the city center. Near the centre, female-majority and income-diverse locations show the highest WPD, consistent with a residual, place-bound service workforce. These findings reveal a spatially contingent 'service trap' at the urban core, extending remote-work inequalities beyond individuals to the urban ecosystem as a whole.

econ.GN↗

arXiv2026

Evolution and determinants of firm-level systemic risk in local production networks

Recent crises like the Covid-19 pandemic and geopolitical tensions have exposed vulnerabilities and caused disruptions of supply chains, leading to product shortages, increased costs, and economic instability. This has prompted growing efforts to assess systemic risk, namely the effects of firm disruptions on entire economies. However, the ability of firms to react to crises by rewiring their supply links has been largely overlooked, limiting our understanding of production networks resilience. Here, we study dynamics and determinants of firm-level systemic risk in the Hungarian economy from 2015 to 2022. We benchmark our results to a heuristic maximum entropy null model that generates randomized production networks while preserving the total input (demand) and output (supply) of each firm at the sector level. We show that the fairly stable set of firms with highest systemic risk undergoes a structural change during Covid-19, as those enabling economic exchanges become key players in the economy -- a pattern not reproduced by the null model. Although empirical systemic risk closely matches the null value prior to the pandemic, it becomes significantly lower afterwards, reflecting the emergence of a more resilient economy driven by firms' adaptive behavior. Furthermore, firms' international trade volume (being itself a channel of potential disruption) becomes a significant predictor of their systemic risk. However, international linkages alone cannot fully explain the observed trends, as imports and exports exert opposing effects on local systemic risk through the supply and demand channels.

physics.soc-ph↗

arXiv2026

Polarization and Integration in Global AI Research

The AI race amplifies security risks and international tensions. While the US restricts mobility and knowledge flows, challenges regulatory efforts to protect its advantage, China leads initiatives of global governance. Both strategies depend on cross-country relationships in AI innovation; yet, how this system evolves is unclear. Here, we measure the processes of polarization and integration in the global AI research over three decades by using large-scale data of scientific publications. Comparing cross-country collaboration and citation links to their random realizations, we find that the US and China have long diverged in both dimensions, forming two poles around which global AI research increasingly revolves. While the United Kingdom and Germany have integrated exclusively with the US, many European countries have converged with both poles. Developing and further developed countries, however, only integrate with China, signaling its expanding influence over the international AI research landscape. Our results inform national science policies and efforts toward global AI regulations.

physics.soc-ph↗

arXiv2025

Understanding Urban-Rural Disparities in Mobility Inefficiency for Colombia, Mexico, and India

Rural and urban areas exhibit distinct mobility patterns, yet a systematic understanding of how these trends differ across regions and contexts remains underexplored. By using origin-destination matrices from Location-Based Services data in Colombia, India, and Mexico, we delineate urban and rural boundaries through network percolation, reducing reliance on conventional urbanisation metrics tied to the built environment. We gauge mobility dynamics across regions developing a measure for routing inefficiency, which measures how much longer empirical trips are than their optimal shortest path. Our findings reveal that rural areas experience greater inefficiencies, particularly for longer trips made later in the day. At the urban level, we determine the misalignment between urban mobility efficiency and public transit accessibility, by measuring the difference between their respective vector fields. We observe that most cities experience misalignment during regular commuting hours, with Colombian cities exhibiting particularly high alignment. Meanwhile, mobility inefficiency in rural areas are associated with their orientation around their most proximate city. City-level analyses uncover disparities in the functions of rural and urban areas, with significant variations between weekdays and weekends, reflecting distinct roles in commuting and access to services. These findings highlight the importance of tailored, context-sensitive approaches to improving connectivity and reducing disparities. This study offers new insights into the spatial and temporal dynamics of mobility inefficiency, contributing to equitable regional planning and sustainable mobility solutions.

physics.soc-ph↗

arXiv2025

Highly engaging events reveal semantic and temporal compression in online community discourse

People nowadays express their opinions in online spaces, using different forms of interactions such as posting, sharing and discussing with one another. How do these digital traces change in response to events happening in the real world? We leverage Reddit conversation data, exploiting its community-based structure, to elucidate how offline events influence online user interactions and behavior. Online conversations, as posts and comments, are analysed along their temporal and semantic dimensions. Conversations tend to become repetitive with a more limited vocabulary, develop at a faster pace, and feature heightened emotions. As the event approaches, the shifts occurring in conversations are reflected in the users' dynamics. Users become more active and they exchange information with a growing audience, despite using a less rich vocabulary and repetitive messages. The recurring patterns we discovered are persistent across a wide range of events and several contexts, representing a fingerprint of how online dynamics change in response to real-world occurrences.

physics.soc-ph↗

arXiv2025

Garbage in Garbage out: Impacts of data quality on criminal network intervention

Criminal networks such as human trafficking rings are threats to the rule of law, democracy and public safety in our global society. Network science provides invaluable tools to identify key players and design interventions for Law Enforcement Agencies (LEAs), e.g., to dismantle their organisation. However, poor data quality and the adaptiveness of criminal networks through self-organization make effective disruption extremely challenging. Although there exists a large body of work building and applying network scientific tools to attack criminal networks, these work often implicitly assume that the network measurements are accurate and complete. Moreover, there is thus far no comprehensive understanding of the impacts of data quality on the downstream effectiveness of interventions. This work investigates the relationship between data quality and intervention effectiveness based on classical graph theoretic and machine learning-based approaches. Decentralization emerges as a major factor in network robustness, particularly under conditions of incomplete data, which renders attack strategies largely ineffective. Moreover, the robustness of centralized networks can be boosted using simple heuristics, making targeted attack more infeasible. Consequently, we advocate for a more cautious application of network science in disrupting criminal networks, the continuous development of an interoperable intelligence ecosystem, and the creation of novel network inference techniques to address data quality challenges.

physics.soc-ph↗

arXiv2024

Unveiling social vibrancy in urban spaces with app usage

Urban vibrancy is an important measure of the energetic nature of a city that is related to why and how people use urban spaces, and it is inherently connected with our social behaviour. Increasingly, people use a wide range of mobile phone apps in their daily lives to connect socially, search for information, make decisions, and arrange travel, amongst many other reasons. However, the relationship between online app usage and urban vibrancy remains unclear, particularly regarding how sociospatial behaviours interact with urban features. Here, we use app-usage data as a digital signature to investigate this question. To do this, we use a high-resolution data source of mobile service-level traffic volumes across eighteen cities in France. We investigate the social component of cities using socially relevant urban features constructed from OpenStreetMap 'Points of Interest'. We developed a methodology for identifying and classifying multidimensional app usage time series based on similarity. We used these in predictive models to interpret the results for each city and across France. Across cities, there were spatial behavioural archetypes, characterised by multidimensional properties. We found patterns between the week and the weekend, and across cities, and the country. These archetypes correspond to changes in socially relevant urban features that impact urban vibrancy. Our results add further evidence for the importance of using computational approaches to understand urban environments, the use of sociological concepts in computational science, and urban vibrancy in cities.

cs.CY↗

arXiv2024

The rise and fall of WallStreetBets: social roles and opinion leaders across the GameStop saga

Nowadays human interactions largely take place on social networks, with online users' behavior often falling into a few general typologies or "social roles". Among these, opinion leaders are of crucial importance as they have the ability to spread an idea or opinion on a large scale across the network, with possible tangible consequences in the real world. In this work we extract and characterize the different social roles of users within the Reddit WallStreetBets community, around the time of the GameStop short squeeze of January 2021 -- when a handful of committed users led the whole community to engage in a large and risky financial operation. We identify the profiles of both average users and of relevant outliers, including opinion leaders, using an iterative, semi-supervised classification algorithm, which allows us to discern the characteristics needed to play a particular social role. The key features of opinion leaders are large risky investments and constant updates on a single stock, which allowed them to attract a large following and, in the case of GameStop, ignite the interest of the community. Finally, we observe a substantial change in the behavior and attitude of users after the short squeeze event: no new opinion leaders are found and the community becomes less focused on investments. Overall, this work sheds light on the users' roles and dynamics that led to the GameStop short squeeze, while also suggesting why WallStreetBets no longer wielded such large influence on financial markets, in the aftermath of this event.

physics.soc-ph↗

arXiv2024

Time-space dynamics of income segregation: a case study of Milan's neighbourhoods

Traditional approaches to urban income segregation focus on static residential patterns, often failing to capture the dynamic nature of social mixing at the neighborhood level. Leveraging high-resolution location-based data from mobile phones, we capture the interplay of three different income groups (high, medium, low) based on their daily routines. We propose a three-dimensional space to analyze social mixing, which is embedded in the temporal dynamics of urban activities. This framework offers a more detailed perspective on social interactions, closely linked to the geographical features of each neighborhood. While residential areas fail to encourage social mixing in the nighttime, the working hours foster inclusion, with the city center showing a heightened level of interaction. As evening sets in, leisure areas emerge as potential facilitators for social interactions, depending on urban features such as public transport and a variety of Points Of Interest. These characteristics significantly modulate the magnitude and type of social stratification involved in social mixing, also underscoring the significance of urban design in either bridging or widening socio-economic divides.

physics.soc-ph↗

arXiv2023

Understanding vehicular routing behavior with location-based service data

Properly extracting patterns of individual mobility with high resolution data sources such as the one extracted from smartphone applications offers important opportunities. Potential opportunities not offered by call detailed records (CDRs), which offer resolutions triangulated from antennas, are route choices, travel modes detection and close encounters. Nowadays, there is not a standard and large scale data set collected over long periods that allows us to characterize these. In this work we thoroughly examine the use of data from smartphone applications, also referred to as location-based services (LBS) data, to extract and understand the vehicular route choice behavior. Taking the Dallas-Fort Worth metroplex as an example, we first extract the vehicular trips with simple rules and reconstruct the origin-destination matrix by coupling the extracted vehicular trips of the active LBS users and the United States census data. We then present a method to derive the commonly used routes by individuals from the LBS traces with varying sample rate intervals. We further inspect the relation between the number of routes and the trip characteristics, including the departure time, trip length and travel time. Specifically, we consider the travel time index and buffer index for the LBS users taking different number of routes. Empirical results demonstrate that during the peak hours, travelers tend to reduce the impact of traffic congestion by taking alternative routes. Overall, the proposed data analysis framework is cost-effective to treat sparse data generated from the use of smartphones to inform routing behavior. The potential in practice is to inform demand management strategies, by targeting individual users while generating large scale estimates of congestion mitigation.

physics.soc-ph↗

arXiv2023

Spontaneous Opinion Swings in the Voter Model with Latency

The cognitive process of opinion formation is often characterized by stubbornness or resistance of agents to changes of opinion. To capture such a feature we introduce a constant latency time in the standard voter model of opinion dynamics: after switching opinion, an agent must keep it for a while. This seemingly simple modification drastically changes the stochastic diffusive behavior of the original model, leading to deterministic dynamical oscillations in the average opinion of the agents. We explain the origin of the oscillations and develop a mathematical formulation of the dynamics that is confirmed by extensive numerical simulations. We further characterize the rich phase space of the model and its asymptotic behavior. Our work offers insights into understanding and modeling opinion swings in diverse social contexts.

physics.soc-ph↗

arXiv2023

Spatiotemporal gender differences in urban vibrancy

Urban vibrancy is the dynamic activity of humans in urban locations. It can vary with urban features and the opportunities for human interactions, but it might also differ according to the underlying social conditions of city inhabitants across and within social surroundings. Such heterogeneity in how different demographic groups may experience cities has the potential to cause gender segregation because of differences in the preferences of inhabitants, their accessibility and opportunities, and large-scale mobility behaviours. However, traditional studies have failed to capture fully a high-frequency understanding of how urban vibrancy is linked to urban features, how this might differ for different genders, and how this might affect segregation in cities. Our results show that (1) there are differences between males and females in terms of urban vibrancy, (2) the differences relate to `Points of Interest` as well as transportation networks, and (3) that there are both positive and negative `spatial spillovers` existing across each city. To do this, we use a quantitative approach using Call Detail Record data--taking advantage of the near-ubiquitous use of mobile phones--to gain high-frequency observations of spatial behaviours across the seven most prominent cities of Italy. We use a spatial model comparison approach of the direct and `spillover` effects from urban features on male-female differences. Our results increase our understanding of inequality in cities and how we can make future cities fairer.

physics.soc-ph↗

arXiv2023

COVID-19 is linked to changes in the time-space dimension of human mobility

Socio-economic constructs and urban topology are crucial drivers of human mobility patterns. During the coronavirus disease 2019 pandemic, these patterns were reshaped in their components: the spatial dimension represented by the daily travelled distance, and the temporal dimension expressed as the synchronization time of commuting routines. Here, leveraging location-based data from de-identified mobile phone users, we observed that, during lockdowns restrictions, the decrease of spatial mobility is interwoven with the emergence of asynchronous mobility dynamics. The lifting of restriction in urban mobility allowed a faster recovery of the spatial dimension compared with the temporal one. Moreover, the recovery in mobility was different depending on urbanization levels and economic stratification. In rural and low-income areas, the spatial mobility dimension suffered a more considerable disruption when compared with urbanized and high-income areas. In contrast, the temporal dimension was more affected in urbanized and high-income areas than in rural and low-income areas.

physics.soc-ph↗

arXiv2022

Understanding European Integration with Bipartite Networks of Comparative Advantage

Core objectives of European common market integration are convergence and economic growth, but these are hampered by redundancy, and value chain asymmetries. The challenge is how to harmonize labor division to reach global competitiveness, meanwhile bridging productivity differences across the EU. We develop a bipartite network approach to trace pairwise co-specialization, by applying the Revealed Comparative Advantage method, within and between EU15 and Central and Eastern European (CEE). This approach assesses redundancies and division of labor in the EU at the level of industries and countries. We find significant co-specialization among CEE countries but a diverging specialization between EU15 and CEE. Productivity increases in those CEE industries that have co-specialized with other CEE countries after EU accession, while co-specialization across CEE and EU15 countries is less related to productivity growth. These results show that a division of sectoral specialization can lead to productivity convergence between EU15 and CEE countries.

econ.GN↗

arXiv2022

Self-induced consensus of Reddit users to characterise the GameStop short squeeze

The short squeeze of GameStop (GME) shares in mid-January 2021 has been primarily orchestrated by retail investors of the Reddit r/wallstreetbets community. As such, it represents a paramount example of collective coordination action on social media, resulting in large-scale consensus formation and significant market impact. In this work we characterise the structure and time evolution of Reddit conversation data, showing that the occurrence and sentiment of GME-related comments (representing how much users are engaged with GME) increased significantly much before the short squeeze actually took place. Taking inspiration from these early warnings as well as evidence from previous literature, we introduce a model of opinion dynamics where user engagement can trigger a self-reinforcing mechanism leading to the emergence of consensus, which in this particular case is associated to the success of the short squeeze operation. Analytical solutions and model simulations on interaction networks of Reddit users feature a phase transition from heterogeneous to homogeneous opinions as engagement grows, which we qualitatively compare to the sudden hike of GME stock price. Although the model cannot be validated with available data, it offers a possible and minimal interpretation for the increasingly important phenomenon of self-organized collective actions taking place on social networks.

physics.soc-ph↗

arXiv2021

Mobilkit: A Python Toolkit for Urban Resilience and Disaster Risk Management Analytics using High Frequency Human Mobility Data

Increasingly available high-frequency location datasets derived from smartphones provide unprecedented insight into trajectories of human mobility. These datasets can play a significant and growing role in informing preparedness and response to natural disasters. However, limited tools exist to enable rapid analytics using mobility data, and tend not to be tailored specifically for disaster risk management. We present an open-source, Python-based toolkit designed to conduct replicable and scalable post-disaster analytics using GPS location data. Privacy, system capabilities, and potential expansions of \textit{Mobilkit} are discussed.

cs.CY↗

arXiv2021

Urbanization and Economic Complexity

Urbanization plays a crucial role in the economic development of every country. The mutual relationship between the urbanization of any country and its economic productive structure is far from being understood. We analyzed the historical evolution of product exports for all countries using the World Trade Web (WTW) with respect to patterns of urbanization from 1995-2010. Using the evolving framework of economic complexity, we reveal that a country's economic development in terms of its production and export of goods, is interwoven with the urbanization process during the early stages of its economic development and growth. Meanwhile in urbanized countries, the reciprocal relation between economic growth and urbanization fades away with respect to its later stages, becoming negligible for countries highly dependent on the export of resources where urbanization is not linked to any structural economic transformation.

physics.soc-ph↗

arXiv2020

The role of geography in the complex diffusion of innovations

The urban-rural divide is increasing in modern societies calling for geographical extensions of social influence modelling. Improved understanding of innovation diffusion across locations and through social connections can provide us with new insights into the spread of information, technological progress and economic development. In this work, we analyze the spatial adoption dynamics of iWiW, an Online Social Network (OSN) in Hungary and uncover empirical features about the spatial adoption in social networks. During its entire life cycle from 2002 to 2012, iWiW reached up to 300 million friendship ties of 3 million users. We find that the number of adopters as a function of town population follows a scaling law that reveals a strongly concentrated early adoption in large towns and a less concentrated late adoption. We also discover a strengthening distance decay of spread over the life-cycle indicating high fraction of distant diffusion in early stages but the dominance of local diffusion in late stages. The spreading process is modelled within the Bass diffusion framework that enables us to compare the differential equation version with an agent-based version of the model run on the empirical network. Although both models can capture the macro trend of adoption, they have limited capacity to describe the observed trends of urban scaling and distance decay. We find, however that incorporating adoption thresholds, defined by the fraction of social connections that adopt a technology before the individual adopts, improves the network model fit to the urban scaling of early adopters. Controlling for the threshold distribution enables us to eliminate the bias induced by local network structure on predicting local adoption peaks. Finally, we show that geographical features such as distance from the innovation origin and town size influence prediction of adoption peak at local scales.

physics.soc-ph↗