arXiv subjects

Gabriele Manoli

Publications and source records attributed to Gabriele Manoli.

8 recordsLinked to original sources

arXiv2026

Emergent regularity of walking patterns: evidence from Swiss cities

Human travel behaviour is characterised by high temporal and spatial regularity, as demonstrated by the Exploration and Preferential Return (EPR) mechanism, whereby individuals alternate between exploring new locations and preferentially returning to previously visited places. Yet, existing evidence is based on aggregated mobility data that cannot distinguish short trips or separate active mobility (i.e., walking and biking) from other mobility modes. This limits our ability to assess the benefits of walking activities and proximity-based policies such as the 15-minute city. Here, we analyse the spatial distribution and duration of walking habits of 1,318 participants, tracked during a 4-week time use survey in major Swiss cities - which are very close to the 15-minute city model. We find that walking activity is highly concentrated around individuals' home and work locations. These two attractors create walking patterns that are fundamentally different from other transport modes, which are instead used for longer trips and to connect home-workplace. We then introduce a dual-center EPR model, with empirically derived parameters, to simulate the decaying probability of walking away from the centers. Combining empirical data and the EPR framework, we find that groups with higher walking times show a stronger tendency to take long-distance walks and explore more destinations. In general, walking behavior reflects both individual circumstances and residential context: walking durations are shorter among people without a public transport pass, car owners, and residents of low-density rural areas. These findings provide a basis for more targeted assessments of walkability and active-mobility-oriented urban design.

physics.soc-ph↗

arXiv2026

Urban climate regressions require representative monitoring networks

Land use regression models relate intra-urban air temperature to spatial predictors such as building volume, tree canopy cover and topography, and are increasingly used to produce fine-scale temperature maps. However, the accuracy of these estimates can be compromised by unrepresentative monitoring networks. Here we show that reference weather station networks, designed for regional rather than intra-urban monitoring, sample only a narrow portion of a city's spatial heterogeneity, missing both the densely built core and the cool, vegetated periphery that set the two ends of the urban temperature range. As a result, city-wide predictions underestimate temperature differences, in ways that cross-validation against the station measurements does not detect. Using Zurich, Switzerland, as a case study, we compare multiple linear regression, generalized additive models and random forests as up to 117 low-cost temperature sensors are added to the four reference weather stations. When trained on reference stations alone, all three models achieve comparable cross-validated performance. As low-cost sensors broaden the sampled spatial heterogeneity, multiple linear regression scores lower on both metrics, whereas the non-linear models remain at their reference-only level. Although a third of the low-cost sensors already recovers nearly the full predicted temperature range, which predictors are identified, and with which sign, is determined by the station sample rather than the model class. At full coverage, only multiple linear regression leaves significant residual spatial autocorrelation, suggesting a linear form unable to capture non-linear responses of air temperature to urban form. Drawing on landscape ecology research on focal site study design, we provide a transferable framework for diagnosing station network representativeness and improving the reliability of city-wide environmental maps.

physics.ao-ph↗

arXiv2026

Mobility shapes heat exposure inequalities in cities

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.

physics.soc-ph↗

arXiv2026

Scaling intra-urban climate fluctuations

Urban-induced microclimate variations, such as urban heat islands and air pollution, scale with city size, producing distinctive relations between average climate variables and city-scale quantities (e.g., total population). However, these relations are sensitive to city boundary definitions and overlook intra-urban variability. Here, we overcome these limitations by using high-resolution data of urban temperatures, air quality, population, and street networks from 142 cities worldwide, showing that their marginal and joint probability distributions collapse onto a set of general functions inspired by finite-size scaling in statistical physics. Through a logarithmic relation linking urban spatial features to climate variables, we find that average street network properties are sufficient to characterize the full variability of temperature and air pollution fields within and across cities. These findings show that intra-urban climate variability follows general scaling functions, enabling the integration of climate information into reduced-complexity models of urban systems to better inform future urban planning.

physics.soc-ph↗

arXiv2026

Disorder Crossover in Urban-Front Growth

Urban expansion fronts display a robust local roughness exponent together with strongly dispersed growth and nonuniversal dynamic exponents. We show that this coexistence can arise from a disorder-controlled crossover in projected-front growth. Introducing a minimal Eden model, in which geographic constraints act as quenched dilution and coalescence as quenched local acceleration, we demonstrate that the resulting front enters a long disorder-dominated preasymptotic regime, whose scaling near threshold is set by ordinary two-dimensional percolation. In this regime, the local roughness remains close to $1/2$, while the large-scale exponents vary broadly with disorder and acceleration. These results provide a minimal explanation of urban-front roughening and suggest a more general mechanism for stochastic growth in heterogeneous media.

physics.soc-ph↗

arXiv2026

Modelling human activities in a system of cities

Cities host most of the world population with diverse services and activities. One key challenge in urban modelling is the quantification of intra- and inter-city mobility patterns and the associated space-time dynamics of population density and anthropogenic activities. To address this, we apply the novel agent-based urban model DAVE (Dynamic Anthropogenic actiVities and feedback to Emissions) to simulate population behaviour and mobility in the Vaud and Geneva Cantons, a system of small- to medium-size cities in Switzerland. Simulation results provide detailed temporal (10 min) and spatial (500 m) population dynamics for different age groups and day types. DAVE further models the time-varying population distribution in 11 different microenvironments (e.g., home, work, leisure, outdoor) and the travel flows by different modes. Simulation results align with observations, confirming the possibility of driving urban system modelling with statistical information on residents' behaviour. Sustainability and health indicators like daily driving distance and walking time for each neighbourhood are also reflected by the model with urban-rural gradients displayed. This work serves as a foundation for future applications of DAVE to study bottom-up human-built environment interactions, from anthropogenic emissions and building energy to urban climate, exposure, and health in cities around the world.

physics.soc-ph↗

arXiv2025

Dynamic roughening of cities driven by multiplicative noise

The evolution of urban landscapes is rapidly altering the surface of our planet. Yet, our understanding of the urbanisation phenomenon remains far from complete. A fundamental challenge is to describe spatiotemporal changes in the built environment. A dynamic theory of urban evolution should account for both vertical and horizontal city expansion, analogous to the dynamical behaviour of surface growth in physical and biological systems. Here we show that building-height dynamics in cities around the world are well described by a zero-dimensional geometric Brownian motion (GBM), where multiplicative noise drives stochastic fluctuations around a deterministic drift associated with economic growth. To account for intra-city correlations, we extend the GBM with spatial coupling, revealing how local interactions effectively mitigate noise-driven fluctuations and shape urban morphology. The continuum limit of this spatial model can be recasted into the Kardar-Parisi-Zhang (KPZ) equation and we find that empirical estimates of the roughness exponent are in the range of the KPZ prediction for most cities. Together, these results show that multiplicative noise, moderated by local interactions, governs the evolution of urban roughness, anchoring spatiotemporal city dynamics in a well-established statistical physics framework.

physics.soc-ph↗

arXiv2025

Meteorological assessment of vertical axis wind turbine energy generation potentials across two Swiss cities in complex terrain

Wind energy is the most mature renewable energy technology, however, its exploitation in cities is often met with skepticism. Thanks to their ability to operate effectively at low wind from any direction, vertical axis wind turbines (VAWTs) offer an attractive opportunity for wind energy harvesting in cities, but limited evidence exists on their potential in complex urban environments, and the role of different geographical settings, local meteorological conditions, and urban characteristics remains unclear. Here we use realistic Weather Research and Forecast model high-resolution wind speed simulations alongside representative VAWT power curves to quantify the range of micro-generation potentials at the annual, seasonal, and diurnal scale across two Swiss cities (Lausanne and Geneva) residing in complex terrain. Our results show that Lausanne generally experiences higher (+24%) wind speed than Geneva. Both cities present the greatest micro-generation potential during the summer months, although Lausanne shows non-negligible potential also during the wintertime. Wind speed is higher during the nighttime in Lausanne and during the daytime in Geneva, due to the different interaction between the local lake-breeze circulation and the synoptic flow. Simulated performance of case-study VAWTs is dominated by cut-in wind speed and power curve inflection point. On average, in 2022, an individual VAWT would have produced 2665 kWh of total annual electricity, equivalent to 16.5 square meters of photovoltaic panels. These results highlight the need for research on urban wind energy featuring detailed city-scale assessments that account for urban heterogeneities and regional circulation patters, to inform future planning investment and engineering development.

physics.soc-ph↗