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The Moving Target of Urban Equity: Spatiotemporal Demand and Double Disadvantage in Hefei, China
Authors:
Shirui Zhou,
Matteo Bruno,
Mattia Mazzoli,
Junfang Tian,
Rui Jiang,
Enwan Zhang,
Zheng Li,
Vittorio Loreto
Abstract:
Equitable access to essential urban services is a pillar of modern planning, yet most accessibility models rely strictly on static residential locations, ignoring how demand shifts throughout the daily loop. This study introduces a population-based, temporally differentiated framework to examine the resulting "moving target" of urban equity, focusing on medical facilities and green spaces in Hefei…
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Equitable access to essential urban services is a pillar of modern planning, yet most accessibility models rely strictly on static residential locations, ignoring how demand shifts throughout the daily loop. This study introduces a population-based, temporally differentiated framework to examine the resulting "moving target" of urban equity, focusing on medical facilities and green spaces in Hefei, China. Utilising large-scale mobile phone GPS data, we construct dynamic residential and workplace population exposure surfaces to capture shifting hourly demand. We then evaluate accessibility via network-based travel times paired with a novel per-capita provision metric that accounts for real-time demand competition. We define \textit{double disadvantage} as the co-occurrence of poor spatial accessibility and insufficient per-capita service availability. Counterintuitively, the results reveal that double-disadvantaged areas cluster primarily along the inner suburban belt rather than the remote periphery, where per-capita service provision remains relatively sufficient. Furthermore, temporal shifts drastically alter equity landscapes: daytime workplace concentrations intensely exacerbate demand competition in urban job centres. These findings demonstrate that urban inequality depends heavily on spatiotemporal population flows rather than just the fixed location of services. Ultimately, achieving true urban equity requires dynamic planning interventions that address time-varying demand rather than focusing solely on static, home-based metrics.
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Submitted 18 June, 2026;
originally announced June 2026.
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Mobility shapes heat exposure inequalities in cities
Authors:
Marc Duran-Sala,
Mattia Mazzoli,
Martin Hendrick,
Gabriele Manoli
Abstract:
Segregation has long been recognized as a driver of environmental inequalities, with disadvantaged groups often living in neighborhoods where heat-related risks are highest. Yet, it remains unclear how daily mobility patterns, embedded within heterogeneous urban heat fields, shape heat exposure inequalities across sociodemographic groups. Using mobile phone records of daily mobility flows and urba…
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Segregation has long been recognized as a driver of environmental inequalities, with disadvantaged groups often living in neighborhoods where heat-related risks are highest. Yet, it remains unclear how daily mobility patterns, embedded within heterogeneous urban heat fields, shape heat exposure inequalities across sociodemographic groups. Using mobile phone records of daily mobility flows and urban temperature fields across 23 Spanish cities, we develop a network-based framework to quantify how different sociodemographic groups experience heat through their daily movements. We further apply the framework to tract-level commuting networks across 30 major US cities as an external validation, yielding qualitatively comparable patterns. We find systematic income-related inequalities, with low-income groups consistently experiencing higher exposure than high-income groups, while age-related disparities are smaller in magnitude. These inequalities intensify during commuting trips, indicating that routine mobility amplifies spatial heat gradients more than non-routine movements. Finally, we show that parsimonious population-based mobility models with group-agnostic mobility rules reproduce an important component of the observed exposure disparities, suggesting that these inequalities emerge from the interplay between the unequal spatial organization of daily activities across sociodemographic groups and urban heat gradients. Our findings provide a generalizable framework to characterize inequalities in mobility-based heat exposure across cities and inform climate-resilient urban planning and public health strategies under intensifying climate-related risks.
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Submitted 8 June, 2026; v1 submitted 25 March, 2026;
originally announced March 2026.
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Disentangling individual-level from location-based income uncovers socioeconomic preferential mobility and impacts segregation estimates
Authors:
Marc Duran-Sala,
Anandu Koikkalethu Balachandran,
Marta Morandini,
Timur Naushirvanov,
Adarsh Prabhakaran,
Andrew Renninger,
Mattia Mazzoli
Abstract:
Segregation encodes information about society, such as social cohesion, mixing, and inequality. However, most past and current studies tackled socioeconomic (SE) segregation by analyzing static aggregated mobility networks, often without considering further individual features beyond income and, most importantly, without distinguishing individual-level from location-based income. Accessing individ…
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Segregation encodes information about society, such as social cohesion, mixing, and inequality. However, most past and current studies tackled socioeconomic (SE) segregation by analyzing static aggregated mobility networks, often without considering further individual features beyond income and, most importantly, without distinguishing individual-level from location-based income. Accessing individual-level income may help mapping macroscopic behavior into more granular mobility patterns, hence impacting segregation estimates. Here we combine a mobile phone dataset of daily mobility flows across Spanish districts stratified and adjusted by age, gender and income with census data of districts median income. We build mobility-based SE assortativity matrices for multiple demographics and observe mobility patterns of three income groups with respect to location-based SE classes. We find that SE assortativity differs when isolating the mobility of specific income groups: we observe that groups prefer to visit areas with higher average income than their own, which we call preferential mobility. Our analysis suggests substantial differences between weekdays and weekends SE assortativity by age class, with weekends characterized by higher SE assortativity. Our modeling approach shows that the radiation model, which typically performs best at reproducing inter-municipal population mobility, best fits middle income and middle-aged flows, while performing worse on young and low income groups. Our double-sided approach, focusing on assortativity patterns and mobility modeling, suggests that state of the art mobility models fail at capturing preferential mobility behavior. Overall, our work indicates that mobility models considering the interplay of SE preferential behavior, age and gender gaps may sensibly improve the state of the art models performance.
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Submitted 1 July, 2024;
originally announced July 2024.
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Resilience of mobility network to dynamic population response across COVID-19 interventions: evidences from Chile
Authors:
Pasquale Casaburi,
Lorenzo Dall'Amico,
Nicolò Gozzi,
Kyriaki Kalimeri,
Anna Sapienza,
Rossano Schifanella,
T. Di Matteo,
Leo Ferres,
Mattia Mazzoli
Abstract:
The COVID19 pandemic highlighted the importance of non-traditional data sources, such as mobile phone data, to inform effective public health interventions and monitor adherence to such measures. Previous studies showed how socioeconomic characteristics shaped population response during restrictions and how repeated interventions eroded adherence over time. Less is known about how different popula…
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The COVID19 pandemic highlighted the importance of non-traditional data sources, such as mobile phone data, to inform effective public health interventions and monitor adherence to such measures. Previous studies showed how socioeconomic characteristics shaped population response during restrictions and how repeated interventions eroded adherence over time. Less is known about how different population strata changed their response to repeated interventions and how this impacted the resulting mobility network. We study population response during the first and second infection waves of the COVID-19 pandemic in Chile and Spain. Via spatial lag and regression models, we investigate the adherence to mobility interventions at the municipality level in Chile, highlighting the significant role of wealth, labor structure, COVID-19 incidence, and network metrics characterizing business-as-usual municipality connectivity in shaping mobility changes during the two waves. We assess network structural similarities in the two periods by defining mobility hotspots and traveling probabilities in the two countries. As a proof of concept, we simulate and compare outcomes of an epidemic diffusion occurring in the two waves. Our analysis reveals the resilience of the mobility network across waves. We test the robustness of our findings recovering similar results for Spain. Finally, epidemic modeling suggests that historical mobility data from past waves can be leveraged to inform future disease spatial invasion models in repeated interventions. This study highlights the value of historical mobile phone data for building pandemic preparedness and lessens the need for real-time data streams for risk assessment and outbreak response. Our work provides valuable insights into the complex interplay of factors driving mobility across repeated interventions, aiding in developing targeted mitigation strategies.
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Submitted 29 May, 2024;
originally announced May 2024.
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Exploring the spatial segmentation of housing markets from online listings
Authors:
David Abella,
Johann H. Martínez,
Mattia Mazzoli,
Thibault Le Corre,
Julien Migozzi,
Eduard Alonso-Paulí,
Rafel Crespí-Cladera,
Thomas Louail,
José J. Ramasco
Abstract:
The real estate market shows an inherent connection to space. Real estate agencies unevenly operate and specialize across space, price and type of properties, thereby segmenting the market into submarkets. We introduce here a methodology based on multipartite networks to detect the spatial segmentation emerging from data on housing online listings. Considering the spatial information of the listin…
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The real estate market shows an inherent connection to space. Real estate agencies unevenly operate and specialize across space, price and type of properties, thereby segmenting the market into submarkets. We introduce here a methodology based on multipartite networks to detect the spatial segmentation emerging from data on housing online listings. Considering the spatial information of the listings, we build a bipartite network that connects agencies and spatial units. This bipartite network is projected into a network of spatial units, whose connections account for similarities in the agency ecosystem. We then apply clustering methods to this network to segment markets into spatially-coherent regions, which are found to be robust across different clustering detection algorithms, discretization of space and spatial scales, and across countries with case studies in France and Spain. This methodology addresses the long-standing issue of housing market segmentation, relevant in disciplines such as urban studies and spatial economics, and with implications for policymaking.
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Submitted 15 May, 2024; v1 submitted 14 May, 2024;
originally announced May 2024.
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A generalized vector-field framework for mobility
Authors:
Erjian Liu,
Mattia Mazzoli,
Xiao-Yong Yan,
Jose J. Ramasco
Abstract:
Trip flow between areas is a fundamental metric for human mobility research. Given its identification with travel demand and its relevance for transportation and urban planning, many models have been developed for its estimation. These models focus on flow intensity, disregarding the information provided by the local mobility orientation. A field-theoretic approach can overcome this issue and hand…
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Trip flow between areas is a fundamental metric for human mobility research. Given its identification with travel demand and its relevance for transportation and urban planning, many models have been developed for its estimation. These models focus on flow intensity, disregarding the information provided by the local mobility orientation. A field-theoretic approach can overcome this issue and handling both intensity and direction at once. Here we propose a general vector-field representation starting from individuals' trajectories valid for any type of mobility. By introducing four models of spatial exploration, we show how individuals' elections determine the mesoscopic properties of the mobility field. Distance optimization in long displacements and random-like local exploration are necessary to reproduce empirical field features observed in Chinese logistic data and in New York City Foursquare check-ins. Our framework is an essential tool to capture hidden symmetries in mesoscopic urban mobility, it establishes a benchmark to test the validity of mobility models and opens the doors to the use of field theory in a wide spectrum of applications.
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Submitted 4 September, 2023;
originally announced September 2023.
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Impact of urban structure on infectious disease spreading
Authors:
Javier Aguilar,
Aleix Bassolas,
Gourab Ghoshal,
Surendra Hazarie,
Alec Kirkley,
Mattia Mazzoli,
Sandro Meloni,
Sayat Mimar,
Vincenzo Nicosia,
Jose J. Ramasco,
Adam Sadilek
Abstract:
The ongoing SARS-CoV-2 pandemic has been holding the world hostage for more than a year now. Mobility is key to viral spreading and its restriction is the main non-pharmaceutical interventions to fight the virus expansion. Previous works have shown a connection between the structural organization of cities and the movement patterns of their residents. This puts urban centers in the focus of epidem…
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The ongoing SARS-CoV-2 pandemic has been holding the world hostage for more than a year now. Mobility is key to viral spreading and its restriction is the main non-pharmaceutical interventions to fight the virus expansion. Previous works have shown a connection between the structural organization of cities and the movement patterns of their residents. This puts urban centers in the focus of epidemic surveillance and interventions. Here we show that the organization of urban flows has a tremendous impact on disease spreading and on the amenability of different mitigation strategies. By studying anonymous and aggregated intra-urban flows in a variety of cities in the United States and other countries, and a combination of empirical analysis and analytical methods, we demonstrate that the response of cities to epidemic spreading can be roughly classified in two major types according to the overall organization of those flows. Hierarchical cities, where flows are concentrated primarily between mobility hotspots, are particularly vulnerable to the rapid spread of epidemics. Nevertheless, mobility restrictions in such types of cities are very effective in mitigating the spread of a virus. Conversely, in sprawled cities which present many centers of activity, the spread of an epidemic is much slower, but the response to mobility restrictions is much weaker and less effective. Investing resources on early monitoring and prompt ad-hoc interventions in more vulnerable cities may prove helpful in containing and reducing the impact of future pandemics.
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Submitted 10 March, 2022; v1 submitted 30 July, 2020;
originally announced July 2020.
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Field theory for recurrent mobility
Authors:
Mattia Mazzoli,
Alex Molas,
Aleix Bassolas,
Maxime Lenormand,
Pere Colet,
Jose J. Ramasco
Abstract:
Understanding human mobility is crucial for applications such as forecasting epidemic spreading, planning transport infrastructure and urbanism in general. While, traditionally, mobility information has been collected via surveys, the pervasive adoption of mobile technologies has brought a wealth of (real time) data. The easy access to this information opens the door to study theoretical questions…
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Understanding human mobility is crucial for applications such as forecasting epidemic spreading, planning transport infrastructure and urbanism in general. While, traditionally, mobility information has been collected via surveys, the pervasive adoption of mobile technologies has brought a wealth of (real time) data. The easy access to this information opens the door to study theoretical questions so far unexplored. In this work, we show for a series of worldwide cities that commuting daily flows can be mapped into a well behaved vector field, fulfilling the divergence theorem and which is, besides, irrotational. This property allows us to define a potential for the field that can become a major instrument to determine separate mobility basins and discern contiguous urban areas. We also show that empirical fluxes and potentials can be well reproduced and analytically characterized using the so-called gravity model, while other models based on intervening opportunities have serious difficulties.
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Submitted 30 August, 2019;
originally announced August 2019.
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Migrant mobility flows characterized with digital data
Authors:
Mattia Mazzoli,
Boris Diechtiareff,
Antonia Tugores,
Willian Wives,
Natalia Adler,
Pere Colet,
Jose J. Ramasco
Abstract:
Monitoring migration flows is crucial to respond to humanitarian crisis and to design efficient policies. This information usually comes from surveys and border controls, but timely accessibility and methodological concerns reduce its usefulness. Here, we propose a method to detect migration flows worldwide using geolocated Twitter data. We focus on the migration crisis in Venezuela and show that…
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Monitoring migration flows is crucial to respond to humanitarian crisis and to design efficient policies. This information usually comes from surveys and border controls, but timely accessibility and methodological concerns reduce its usefulness. Here, we propose a method to detect migration flows worldwide using geolocated Twitter data. We focus on the migration crisis in Venezuela and show that the calculated flows are consistent with official statistics at country level. Our method is versatile and far-reaching, as it can be used to study different features of migration as preferred routes, settlement areas, mobility through several countries, spatial integration in cities, etc. It provides finer geographical and temporal resolutions, allowing the exploration of issues not contemplated in official records. It is our hope that these new sources of information can complement official ones, helping authorities and humanitarian organizations to better assess when and where to intervene on the ground.
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Submitted 7 August, 2019;
originally announced August 2019.
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Equilibria, information and frustration in heterogeneous network games with conflicting preferences
Authors:
Mattia Mazzoli,
Angel Sanchez
Abstract:
Interactions between people are the basis on which the structure of our society arises as a complex system and, at the same time, are the starting point of any physical description of it. In the last few years, much theoretical research has addressed this issue by combining the physics of complex networks with a description of interactions in terms of evolutionary game theory. We here take this re…
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Interactions between people are the basis on which the structure of our society arises as a complex system and, at the same time, are the starting point of any physical description of it. In the last few years, much theoretical research has addressed this issue by combining the physics of complex networks with a description of interactions in terms of evolutionary game theory. We here take this research a step further by introducing a most salient societal factor such as the individuals' preferences, a characteristic that is key to understand much of the social phenomenology these days. We consider a heterogeneous, agent-based model in which agents interact strategically with their neighbors but their preferences and payoffs for the possible actions differ. We study how such a heterogeneous network behaves under evolutionary dynamics and different strategic interactions, namely coordination games and best shot games. With this model we study the emergence of the equilibria predicted analytically in random graphs under best response dynamics, and we extend this test to unexplored contexts like proportional imitation and scale free networks. We show that some theoretically predicted equilibria do not arise in simulations with incomplete Information, and we demonstrate the importance of the graph topology and the payoff function parameters for some games. Finally, we discuss our results with available experimental evidence on coordination games, showing that our model agrees better with the experiment that standard economic theories, and draw hints as to how to maximize social efficiency in situations of conflicting preferences.
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Submitted 22 June, 2017;
originally announced June 2017.