arXiv subjects

Laura Alessandretti

Publications and source records attributed to Laura Alessandretti.

At least 19 recordsLinked to original sources

arXiv2026

The limits of visitation entropy as a summary of mobility patterns

Visitation entropy, the Shannon entropy of an individual's distribution of visits across locations, is a widely used metric in the human mobility literature. Yet, its widespread use rests on assumptions that are rarely made explicit: entropy is defined over a fixed set of states, and estimating it empirically requires abundant, well-sampled observations. The limitations that arise when entropy is used outside this setting have been documented and explored in fields such as statistical physics, ecology, and cryptography. The implications for mobility studies, however, remain unclear. Here, we leverage synthetic and empirical trajectories to systematically examine the strengths and weaknesses of visitation entropy as a measure for characterizing human mobility. We show that, for a sequence of locations visited by an individual, the visitation entropy primarily reflects the number of unique locations visited and sequence length, which together explain 90.7% of its variance in empirical data. We also show that shorter sequences systematically lead to an underestimation of entropy, showing finite-sample bias to be a key limitation. As a result, comparisons of groups based on visitation entropy should be treated with caution. We find that apparent entropy gaps between genders, commuters and non-commuters, and urban and rural residents are reduced, or reversed, once sequence length and the number of unique locations are taken into account. Finally, we show that these issues can be addressed by controlling for sequence length and the number of unique locations, and by complementing entropy with structural network measures, which provide more nuanced insight into how mobility is organized beyond the aspects that entropy alone captures.

physics.soc-ph↗

arXiv2026

The Effect of Mobility Trajectory Sparsity on Epidemic Modeling Outcomes

GPS mobility data are increasingly used in epidemic modeling, allowing the construction of co-location networks or population flows. These trajectories typically exhibit high temporal sparsity because data collection is opportunistic and tied to phone use. Despite growing awareness of this limitation, the analysis and treatment of biases derived from it have been largely overlooked in existing epidemic modeling studies, raising concerns about the robustness of downstream inferences. We introduce a principled framework to quantify the impact of trajectory sparsity on key epidemic modeling outcomes across different levels of missingness. Our approach leverages a highly-complete dataset that exhibits both near-complete and sparse GPS trajectories. Near-complete trajectories provide baseline epidemic outcomes, while sparse trajectories provide realistic missingness patterns that we impose on the baseline to measure bias. In this way, we show how missing records can result in substantial underestimation of key measures of epidemic intensity, explained not only by the amount of missing data, but by more complex features of data missingness that should be taken into account when designing correction methods. Finally, we propose and evaluate a correction based on inverse probability weighting of network edges before epidemic model calibration, which is shown to reduce bias and parameter misspecification. We also demonstrate this correction on a separate anonymized sample from a commercial GPS mobility dataset and report on its effect. Together, our findings provide a first rigorous quantification of trajectory-sparsity bias in epidemic modeling, offering initial guidance on the treatment of this issue.

cs.SI↗

arXiv2026

Space-time accessibility supports participation in after-work leisure activities

Understanding how accessibility shapes participation in leisure activities is central to promoting inclusive and vibrant urban life. Conventional accessibility measures often focus on potential access from fixed home locations, overlooking the constraints and opportunities embedded in daily routines. In this study, we apply a space-time accessibility (STA) metric rooted in the capability approach, capturing feasible leisure opportunities between home and work given a certain time budget, individual transport modes, and urban infrastructure. Using high-resolution GPS data from 2,415 working residents in the Paris region, we assess how STA influences leisure participation during weekdays, measured as the diversity of leisure locations visited and activity duration. Observed destination choices confirm that most individuals select leisure locations within their STA-defined opportunity sets, validating the metric as a proxy for capability sets. Structural equation modeling shows that STA exerts a significant positive total effect on leisure participation ($β= 0.14$, $p < .001$), driven by a significant direct effect ($β= 0.18$, $p < .001$) that is only modestly offset by an indirect pathway through reduced travel time ($β= -0.04$, $p < .01$). Individual attributes also directly shape participation: active mode use and higher education promote leisure engagement, while local poverty and caregiving responsibilities constrain it. These findings highlight the value of person-centered, capability-informed accessibility metrics for understanding inequalities in urban mobility and informing transport planning strategies that expand real freedoms to participate in social life across diverse population groups.

cs.SI↗

arXiv2026

Women's mobility networks enable more efficient travel

Our understanding of gender differences in mobility is marked by a clear tension: surveys portray women's movements as more complex than men's, while digital traces suggest less diverse travel. Here, we resolve the contradiction by modeling trajectories as networks of sequential visits, using smartphone traces linked to self-reported gender for 543,155 individuals across 10 countries. We show that the apparent conflict in the literature arises because women's mobility networks are simultaneously more clustered and more home-anchored -- a nuance obscured by aggregate metrics. This pattern arises because women tend to link multiple destinations within single trips, for trips spanning up to 150 km and multiple days. This organization yields systematically higher travel efficiency, measured as distance saved through destination chaining over monthly sequences.

physics.soc-ph↗

arXiv2026

Urban mobility enables deprivation bubble breaking in Indian and Mexican cities

Urban deprivation is traditionally measured using static, residence-based indicators, capturing the socioeconomic, demographic, and spatial conditions of neighborhoods. However, this approach overlooks how daily movement allows residents to navigate the city, potentially exposing them to opportunities that differ significantly from their residential environments. To bridge this gap, we quantify the extent of bubble breaking - travel to less deprived areas - by analyzing mobile phone mobility networks combined with satellite-derived deprivation indices across 64 cities in India and Mexico. We find that residents of deprived areas systematically travel to better-off locations to meet daily needs, exhibiting a compensatory mobility pattern that significantly exceeds expectations derived from gravity models based on population and road networks. This residual bubble breaking (the part gravity models can not explain) is associated with a tension in the built environment: while high local amenity diversity allows residents to satisfy needs locally, high amenity density and positive spillovers from neighboring areas is associated with movement across socioeconomic boundaries. Overall, residual bubble breaking reflects the extent to which residents rely on cross-neighborhood mobility to overcome local amenity deficits, a dimension of spatial inequality that residence-based measures leave unobserved.

physics.soc-ph↗

arXiv2025

HoWDe: a validated algorithm for Home and Work location Detection

Smartphone location data have become a key resource for understanding urban mobility, yet extracting actionable insights requires robust and reproducible preprocessing pipelines. A central step is the identification of individuals' home and work locations, which underpins analyses of commuting, employment, accessibility, and socioeconomic patterns. However, existing approaches are often ad hoc, data-specific, and difficult to reproduce, limiting comparability across studies and datasets. We introduce HoWDe, an open-source software library for detecting home and work locations from large-scale mobility data. HoWDe implements a transparent, modular pipeline explicitly designed to handle missing data, heterogeneous sampling rates, and differences in data sparsity across individuals. The code allows users to tune a small set of interpretable parameters, enabling to adapt the algorithm to diverse applications and datasets. Using two unique ground truth datasets comprising 5,099 individuals across 68 countries, we show that HoWDe achieves home and work detection accuracies of up to 97% and 88%, respectively, with consistent performance across demographic groups and geographic contexts. We further demonstrate how parameter settings propagate to downstream metrics such as employment estimates and commuting flows, highlighting the importance of transparent methodological choices. By providing a validated, documented, and easily deployable pipeline, HoWDe supports scalable in-house preprocessing and facilitates the sharing of privacy-preserving mobility datasets. Our software and evaluation benchmarks establish methodological standards that enhance the robustness and reproducibility of human mobility research at urban and national scales.

cs.SI↗

arXiv2025

The dynamics of the Reddit collective action leading to the GameStop short squeeze

In early 2021, the stock prices of GameStop, AMC, Nokia and BlackBerry experienced dramatic increases, triggered by short-squeeze operations that have been largely attributed to Reddit's retail investors. Here, we shed light on the extent and timing of Reddit users' influence on the GameStop short squeeze. Using statistical analysis tools with high temporal resolution, we find that increasing Reddit discussions anticipated high trading volumes. This effect emerged abruptly a few weeks before the event but waned once the community gained widespread visibility through Twitter. Meanwhile, the collective investment of the community quantified through posts of individual positions, closely mirrored the market capitalization of the stock. This evidence suggests a coordinated action of users in developing a shared financial strategy through social media--targeting GameStop first and other stocks afterward. Overall, our results provide novel insights into the role of Reddit users in the dynamics of the GameStop short squeeze.

physics.soc-ph↗

arXiv2025

The Effect of Limited Mobility on the Experienced Segregation of Foreign-born Minorities

Segregation is a key challenge in promoting more diverse and inclusive cities. Research based on large-scale mobility data indicates that segregation between majority and minority groups persists in daily activities beyond residential areas, like visiting shops and restaurants. Aspects including lifestyle differences, homophily, and mobility constraints have been proposed as drivers of this phenomenon, but their contributions remain poorly quantified. Here, we elucidate how different mechanisms influence segregation outside home, looking at the distinctive segregation experienced by native and foreign-born individuals. Our study is based on the movement of ~320,000 individual smartphone devices collected in Sweden, where immigration creates profound divides. We find that while day-to-day activities lead to mixing for native-born individuals, foreign-born individuals remain segregated in their out-of-home activities. Using counterfactual simulations, we show that this heterogeneous effect of mobility on experienced segregation results mainly from two mechanisms: homophily and limited travel, i.e. foreign-born individuals (i) tend to visit destinations visited by similar individuals, and (ii) have limited mobility ranges. We show that homophily, as represented by destination preference, plays a minor role, while limited mobility, associated with reduced transport access, limits opportunities for foreign-born minorities to diversify their encounters. Our findings suggest that enhancing transport accessibility in foreign-born concentrated areas could reduce social segregation.

cs.SI↗

arXiv2024

Socio-spatial segregation and human mobility: A review of empirical evidence

Socio-spatial segregation is the physical separation of different social, economic, or demographic groups within a geographic space, often resulting in unequal access to resources, services, and opportunities. The literature has traditionally focused on residential segregation, examining how individuals' residential locations are distributed differently across neighborhoods based on various social attributes, e.g., race, ethnicity, and income. However, this approach overlooks the complexity of spatial segregation in people's daily activities, which often extend far beyond residential areas. Since the 2010s, emerging mobility data sources have enabled a new understanding of socio-spatial segregation by considering daily activities such as work, school, shopping, and leisure visits. From traditional surveys to GPS trajectories, diverse data sources reveal that daily mobility can result in spatial segregation levels that differ from those observed in residential segregation. This literature review focuses on three critical questions: (a) What are the strengths and limitations of segregation research incorporating extensive mobility data? (b) How do human mobility patterns relate to individuals' residential vs. experienced segregation levels? and (c) What key factors explain the relationship between one's mobility patterns and experienced segregation? Our literature review enhances the understanding of socio-spatial segregation at the individual level and clarifies core concepts and methodological challenges in the field. Our review explores studies of key themes: segregation, activity space, co-presence, and the built environment. By synthesizing their findings, we aim to offer actionable insights for reducing segregation.

cs.SI↗

arXiv2024

Future Directions in Human Mobility Science

We provide a brief review of human mobility science and present three key areas where we expect to see substantial advancements. We start from the mind and discuss the need to better understand how spatial cognition shapes mobility patterns. We then move to societies and argue the importance of better understanding new forms of transportation. We conclude by discussing how algorithms shape mobility behaviour and provide useful tools for modellers. Finally, we discuss how progress in these research directions may help us address some of the challenges our society faces today.

physics.soc-ph↗

arXiv2023

Cryptocurrency co-investment network: token returns reflect investment patterns

Since the introduction of Bitcoin in 2009, the dramatic and unsteady evolution of the cryptocurrency market has also been driven by large investments by traditional and cryptocurrency-focused hedge funds. Notwithstanding their critical role, our understanding of the relationship between institutional investments and the evolution of the cryptocurrency market has remained limited, also due to the lack of comprehensive data describing investments over time. In this study, we present a quantitative study of cryptocurrency institutional investments based on a dataset collected for 1324 currencies in the period between 2014 and 2022 from Crunchbase, one of the largest platforms gathering business information. We show that the evolution of the cryptocurrency market capitalization is highly correlated with the size of institutional investments, thus confirming their important role. Further, we find that the market is dominated by the presence of a group of prominent investors who tend to specialise by focusing on particular technologies. Finally, studying the co-investment network of currencies that share common investors, we show that assets with shared investors tend to be characterized by similar market behavior. Our work sheds light on the role played by institutional investors and provides a basis for further research on their influence in the cryptocurrency ecosystem.

q-fin.ST↗

arXiv2022

Urban Mobility

In this chapter, we discuss urban mobility from a complexity science perspective. First, we give an overview of the datasets that enable this approach, such as mobile phone records, location-based social network traces, or GPS trajectories from sensors installed on vehicles. We then review the empirical and theoretical understanding of the properties of human movements, including the distribution of travel distances and times, the entropy of trajectories, and the interplay between exploration and exploitation of locations. Next, we explain generative and predictive models of individual mobility, and their limitations due to intrinsic limits of predictability. Finally, we discuss urban transport from a systemic perspective, including system-wide challenges like ridesharing, multimodality, and sustainable transport.

physics.soc-ph↗

arXiv2022

Heterogeneous rarity patterns drive price dynamics in NFT collections

We quantify Non Fungible Token (NFT) rarity and investigate how it impacts market behaviour by analysing a dataset of 3.7M transactions collected between January 2018 and June 2022, involving 1.4M NFTs distributed across 410 collections. First, we consider the rarity of an NFT based on the set of human-readable attributes it possesses and show that most collections present heterogeneous rarity patterns, with few rare NFTs and a large number of more common ones. Then, we analyze market performance and show that, on average, rarer NFTs: (i) sell for higher prices, (ii) are traded less frequently, (iii) guarantee higher returns on investment (ROIs), and (iv) are less risky, i.e., less prone to yield negative returns. We anticipate that these findings will be of interest to researchers as well as NFT creators, collectors, and traders.

q-fin.ST↗

arXiv2021

Multimodal urban mobility and multilayer transport networks

Transportation networks, from bicycle paths to buses and railways, are the backbone of urban mobility. In large metropolitan areas, the integration of different transport modes has become crucial to guarantee the fast and sustainable flow of people. Using a network science approach, multimodal transport systems can be described as multilayer networks, where the networks associated to different transport modes are not considered in isolation, but as a set of interconnected layers. Despite the importance of multimodality in modern cities, a unified view of the topic is currently missing. Here, we provide a comprehensive overview of the emerging research areas of multilayer transport networks and multimodal urban mobility, focusing on contributions from the interdisciplinary fields of complex systems, urban data science, and science of cities. First, we present an introduction to the mathematical framework of multilayer networks. We apply it to survey models of multimodal infrastructures, as well as measures used for quantifying multimodality, and related empirical findings. We review modelling approaches and observational evidence in multimodal mobility and public transport system dynamics, focusing on integrated real-world mobility patterns, where individuals navigate urban systems using different transport modes. We then provide a survey of freely available datasets on multimodal infrastructure and mobility, and a list of open source tools for their analyses. Finally, we conclude with an outlook on open research questions and promising directions for future research.

physics.soc-ph↗

arXiv2021

Mapping the NFT revolution: market trends, trade networks and visual features

Non Fungible Tokens (NFTs) are digital assets that represent objects like art, collectible, and in-game items. They are traded online, often with cryptocurrency, and are generally encoded within smart contracts on a blockchain. Public attention towards NFTs has exploded in 2021, when their market has experienced record sales, but little is known about the overall structure and evolution of its market. Here, we analyse data concerning 6.1 million trades of 4.7 million NFTs between June 23, 2017 and April 27, 2021, obtained primarily from Ethereum and WAX blockchains. First, we characterize statistical properties of the market. Second, we build the network of interactions, show that traders typically specialize on NFTs associated with similar objects and form tight clusters with other traders that exchange the same kind of objects. Third, we cluster objects associated to NFTs according to their visual features and show that collections contain visually homogeneous objects. Finally, we investigate the predictability of NFT sales using simple machine learning algorithms and find that sale history and, secondarily, visual features are good predictors for price. We anticipate that these findings will stimulate further research on NFT production, adoption, and trading in different contexts.

q-fin.ST↗

arXiv2021

The Scales of Human Mobility

There is a contradiction at the heart of our current understanding of individual and collective mobility patterns. On one hand, a highly influential stream of literature on human mobility driven by analyses of massive empirical datasets finds that human movements show no evidence of characteristic spatial scales. There, human mobility is described as scale-free. On the other hand, in geography, the concept of scale, referring to meaningful levels of description from individual buildings through neighborhoods, cities, regions, and countries, is central for the description of various aspects of human behavior such as socio-economic interactions, or political and cultural dynamics. Here, we resolve this apparent paradox by showing that day-to-day human mobility does indeed contain meaningful scales, corresponding to spatial containers restricting mobility behavior. The scale-free results arise from aggregating displacements across containers. We present a simple model, which given a person's trajectory, infers their neighborhoods, cities, and so on, as well as the sizes of these geographical containers. We find that the containers characterizing the trajectories of more than 700,000 individuals do indeed have typical sizes. We show that our model generates highly realistic trajectories without overfitting and provides a new lens through which to understand the differences in mobility behaviour across countries, gender groups, and urban-rural areas.

physics.soc-ph↗

arXiv2021

From Reddit to Wall Street: The role of committed minorities in financial collective action

In January 2021, retail investors coordinated on Reddit to target short selling activity by hedge funds on GameStop shares, causing a surge in the share price and triggering significant losses for the funds involved. Such an effective collective action was unprecedented in finance, and its dynamics remain unclear. Here, we analyse Reddit and financial data and rationalise the events based on recent findings describing how a small fraction of committed individuals may trigger behavioural cascades. First, we operationalise the concept of individual commitment in financial discussions. Second, we show that the increase of commitment within Reddit predated the initial surge in price. Third, we reveal that initial committed users occupied a central position in the network of Reddit conversations. Finally, we show that the social identity of the broader Reddit community grew as the collective action unfolded. These findings shed light on financial collective action, as several observers anticipate it will grow in importance.

physics.soc-ph↗

arXiv2021

From centre to centres: polycentric structures in individual mobility

The availability of large-scale datasets collected via mobile phones has opened up opportunities to study human mobility at an individual level. The granular nature of these datasets calls for the design of summary statistics that can be used to describe succinctly mobility patterns. In this work, we show that the radius of gyration, a popular summary statistic to quantify the extent of an individual's whereabouts, suffers from a sensitivity to outliers, and is incapable of capturing mobility organised around multiple centres. We propose a natural generalisation of the radius of gyration to a polycentric setting, as well as a novel metric to assess the quality of its description. With these notions, we propose a method to identify the centres in an individual's mobility and apply it to two large mobility datasets with socio-demographic features, showing that a polycentric description can capture features that a monocentric model is incapable of.

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