Mobility Patterns Research Articles

Postgraduate medical education in Public Health: the Italian scenario and a call to action

Abstract Introduction Public health training in Italy takes place within postgraduate medical training in Hygiene and Preventive Medicine. With 39 university schools of public health, spread across 17 of Italy’s 20 regions, the landscape is diverse. The government determines the number of available contracts annually. This study aims to provide an up-to-date analysis of the saturation of places in Public Health programmes, as well as an assessment of mobility trends between northern and southern Italy from 2017 to 2023. Methods Data on admissions, withdrawals and resignations were obtained from the Ministries of Universities and Health and from the archives of the ‘Associazione Liberi Specializzandi’ (ALS). Mobility was assessed as moving from the central-northern regions to the southern-islands of Italy, and vice versa, between registration with the Medical Council and specialisation programme enrollment. Results Between 2017 and 2023, a total of 2301 residents enrolled across Italy, out of 2953 available positions nationwide. During this period the number of available national places, increased from 180 to 576, impacting overall seat saturation. In 2017, 98.3% of positions were covered, compared to only 46.5% in 2023. Regarding the mobility trend, southern students attending northern schools increased from 8.3% in 2017 to 22.4% in 2023, while northern students attending southern schools decreased from 17.9% to 1%. However, the number of dropouts and withdrawals was higher in the North. Mobility students scored lower than non-mobility students nationally. Key messages • From 2017 to 2023, the number of residents enrolled increased, while the allocation rate decreased. There was a shift in mobility patterns, with more southern students moving north. • Addressing the identified inequalities and understanding the underlying factors driving mobility patterns is crucial for effective planning and allocation of resources.

4.Q. Scientific session: Global landscapes of healthcare worker migration: advancing effective and equitable solutions

Abstract Background Healthcare workforce migration not only increased in numbers, the mobility flows and migration patterns are also changing and becoming more diverse. The labour market push-and-pull factors and related risks of ‘brain-drain’ are still relevant and many countries, especially in the western world, heavily rely on foreign-born and foreign-trained healthcare workers to mitigate shortages and respond to the healthcare workforce crisis. However, this migration pattern increasingly co-exists with other patterns, blurring the categories of hosting and sending countries. For instance, some low- and middle-income countries have created the export of skilled healthcare workers as business model, others invest in healthcare worker education without advancing national labour market capacities. There is also an increase in crisis migration caused by wars, climate change, and other conflicts, yet these refugee healthcare workers are often excluded from the formal labour market. These examples illustrate that health policy across countries largely failed to respond to the changing global landscapes of migration and to govern education, recruitment and retention more effectively. The healthcare workforce debate is primarily driven by health labour market needs, while health policy and human rights remain marginal. There is also a lack of comprehensive data and research, that would allow for evidence-based decision-making. Ineffective policy and existing governance gaps exacerbate the global healthcare workforce crisis, increase stress and workload of the healthcare workers, and often ignore worker and human rights. Objectives This workshop looks beyond labour market push-pull factors and highlights the complexity of healthcare worker migration. It makes the diverse needs, stakeholder interests, and institutional frameworks visible that shape mobility patterns of healthcare worker, seeking to identify capacity for more effective governance and good-practice examples. The following major questions will be discussed, among others: What data and research are needed to better understand the global migration landscapes? What can we learn from cross-country knowledge exchange and good practice experiences? How can public health advance more equitable and effective education, recruitment and retention policy, and what role could global contracts and international organisations play? The interactive discussion will be facilitated by a commentary on global capacity building for equitable healthcare worker migration and the action taken by WHO. The presenters and the audience will advance knowledge exchange and critically explore novel solutions to the healthcare workforce crisis that are more sensitive to the needs of both individual migrant healthcare workers and less well-resourced countries, in the global South as well as within Europe. Key messages • Increasingly divers patterns of healthcare worker migration call for novel governance approaches that align labour markets, health policy needs, and human rights. • More effective, equitable and ‘humanised’ governance of healthcare worker migration needs a global public health and human rights approach.

Exploring bike-commuter habits through data physicalisation engagement

ABSTRACT Engagement with real-time GPS data presents an obvious avenue towards behaviour change for sustainable mobility choices, as tracking human activity patterns becomes more ubiquitous. However, tracking data in visual form does not invite the quality of engagement that data physicalisations afford. In this blended method experiment, we study bike-commuter habits by combining data tracking, ethnographic studies and data physicalisation. We invited 12 everyday bicycle commuters to track their mobility patterns through an app, to be video shadowed on their daily commute and finally to engage in workshops with different data physicalisations based on the personal mobility data collected. We employ an analytic framework to indicate how the participant engagements match the theories of Flow State, Play Moods and Small Beginnings. The results of the study show that there is a potential for engaging local communities in reflecting on their day-to-day mobility choices through active engagement with data physicalisations. We also suggest that the framework can be used to develop facilitation practices to further enhance data physicalisation engagement and deepen insights.

Controllable Human Trajectory Generation Using Profile-Guided Latent Diffusion

Trajectory generation is a vital element in AI applications. Firstly, it enables simulation such as traffic simulation and epidemic spreading modeling. Secondly, it can provide synthetic privacy-preserving data for training AI models. Notably, trajectory generation featuring controllable user profiles holds substantial value in generating customized mobility trajectories tailored to diverse requirements. However, relevant work is still lacking. On the one hand, traditional deep generative models fall short in guiding controllable trajectory generation due to the statistical nature of human mobility patterns and the corresponding insufficient control mechanisms. On the other hand, though the diffusion model has demonstrated strong generative capabilities in many fields, to achieve controllable generation on discrete trajectory data, we still need to redesign the structure of the continuous diffusion model. In this paper, we introduce a controllable trajectory generation framework that leverages a continuous diffusion model and classifier guidance for more robust condition control. Our proposed framework comprises two modules: a latent trajectory diffusion model and a trajectory classifier for profile guidance. Experiments on two real-world mobility datasets consistently demonstrate its capability of generating trajectories matching given user profiles and conforming to human mobility patterns. Our source code and trained models are released at https://github.com/tsinghua-fib-lab/User-Profile-Guided-Latent-Diffusion .

Association between land use features and changes in walking patterns from pre-pandemic to post-pandemic: A case study of city of Sydney (2013–2023)

While the impact of the pandemic on active mobility patterns is widely studied in several cities, the underlying characteristics that describe the heterogeneity in changes in active mobility are less understood. This is particularly important for post-pandemic active mobility planning. This study aims to investigate and describe the association between urban population and land-use features, as well as changes in the spatio-temporal patterns of walking from pre-pandemic to post-pandemic through a case study of the city of Sydney, Australia, using 11 years of pedestrian count data from 2013 to 2023. The findings indicate that during the pandemic, the average daily pedestrian traffic in Sydney decreased significantly compared to the pre-pandemic period. However, since experiencing the lowest pedestrian traffic in 2020, activities in the study area have shown signs of partial recovery, with a 51% increase observed in 2023. The observed changes in pedestrian activities are, however, spatially heterogeneous. Modeling results reveal that areas with greater commercial land use, more points of interest (POIs), higher population density, and higher network connectivity experienced a significant negative change in the number of walking trips from the pre-pandemic to the pandemic period. Areas with higher percentages of educational and residential use and with higher personal income experienced smaller changes in pedestrian activities during the pandemic compared to the pre-pandemic period. During the post-pandemic recovery, the influential features remain mostly unchanged; however, the association direction is the opposite.

Analyzing disruptions in post-pandemic population return patterns: A network perspective on Chinese cities

The COVID-19 pandemic has fundamentally reshaped global socio-economic structures, precipitating a profound transformation in people's lifestyles. An in-depth analysis of the disruptions in post-pandemic population returns patterns and the evolving driving factors can facilitate socio-economic recovery, development, and macro-control. This paper employs social network analysis to examine the spatial patterns and evolving urban locational attributes of the Population Mobility Network (PMN) during post-holiday returns in major Chinese cities amid the pandemic, assessing the impact of varying proximities on the PMN. The results indicate that: (1) During the pandemic, the geographic patterns of population mobility in China underwent significant fluctuations, with distinct regional and temporal variations, while intercity connections increasingly shifted toward shorter trips. (2) From 2019 to 2022, major Chinese cities experienced notable changes in status and connectivity, evolving into a multi-centric structure within the national population mobility network. (3) Community clusters predominantly adhere to provincial demarcations, maintaining stable structures within major urban agglomerations despite intense intra-regional competition, with clear distinctions in community stability between northern and southern regions. (4) Beyond traditional socio-economic factors, the level of digital finance has emerged as a new driver of population mobility, with the varied attributes of inter-city relationships also significantly influencing the PMN. Studying spatial patterns and urban location from the perspective of population mobility can inform post-pandemic optimization of national resource allocation and urban recovery strategies.

Dinochromosome Heterotermini with Telosomal Anchorages

Dinoflagellate birefringent chromosomes (BfCs) contain some of the largest known genomes, yet they lack typical nucleosomal micrococcal-nuclease protection patterns despite containing variant core histones. One BfC end interacts with extranuclear mitotic microtubules at the nuclear envelope (NE), which remains intact throughout the cell cycle. Ultrastructural studies, polarized light and fluorescence microscopy, and micrococcal nuclease-resistant profiles (MNRPs) revealed that NE-associated chromosome ends persisted post-mitosis. Histone H3K9me3 inhibition caused S-G2 delay in synchronous cells, without any effects at G1. Differential labeling and nuclear envelope swelling upon decompaction indicate an extension of the inner compartment into telosomal anchorages (TAs). Additionally, limited effects of low-concentration sirtinol on bulk BfCs, coupled with distinct mobility patterns in MNase-digested and psoralen-crosslinked nuclei observed on 2D gels, suggest that telomeric nucleosomes (TNs) are the primary histone structures. The absence of a nucleosomal ladder with cDNA probes, the presence of histone H2A and telomere-enriched H3.3 variants, along with the immuno-localization of H3 variants mainly at the NE further reinforce telomeric regions as the main nucleosomal domains. Cumulative biochemical and molecular analyses suggest that telomeric repeats constitute the major octameric MNRPs that provision chromosomal anchorage at the NE.

Spatio-temporal dynamics and recovery of commuting activities via bike-sharing around COVID-19: A case study of New York

The COVID-19 has led to significant changes in urban travel behaviors, with commuting being one of the most affected travel modes. Commuting cycling by bike-sharing systems (BSS) is regarded as a new transportation mode that is low-carbon and low-cost. However, its dynamic changes and spatiotemporal characteristics in different periods of COVID-19 still lack exploration. Therefore, this study adopts machine learning methods to identify commuter bike-sharing activities and develops a combined analysis method to analyze commuting cycling data via temporal, spatial, and spatiotemporal aggregation. Finally, we select the bike-sharing data in New York City from periods before, during, and after COVID-19 to conduct experiments. It has been found that commuting cycling experienced a “decrease-rebound” trend at the macroscopic level under the pandemic impact. However, at the micro level, urban mobility driven by this travel mode failed to fully recover, as evidenced by significant changes in spatial and temporal mobility patterns. The findings shall not only help traffic operators and managers discover the BSS commuting patterns but also reveal the pandemic impact on the travel behavior of urban residents, promoting the development of intelligent services for urban emergency management and traffic management.

User characteristics and preferences for micromobility use in first- and last-mile journeys in Dublin, Ireland

Micromobility and public transport integration is an area of urban mobility that has been increasingly gaining attention and importance. The necessity to provide improvements to public transport services and their spatial reach in order to reduce the number and volume of car trips in cities is irrefutable. The integration of micromobility and public transport is often presented as a valuable strategy to improve door-to-door connectivity and coverage of existing public transport services, via provision of key first- and last-mile or access-egress connections. The aim of this study was to understand and model the propensity of commuters in County Dublin to use micromobility (private and shared) in the first- and last-miles of their public transport journeys. A stated preference experiment was conducted with a focus on these specific segments of the journey and a mixed logit model was estimated to identify user characteristics and preferences based on socio-economic factors, mobility patterns and attitudes towards and previous experience of shared mobility. The results show that, in this particular case study, respondents with a strong walking preference were less likely to be interested in private micromobility and sharing services for the first- and last-mile. A common assumption that walking is a last resort, and only chosen when there are no other options available was not confirmed by the findings of this study. Moreover, competition between the modes showed that private bicycles are the preferred mode after walking, followed by private e-scooters, while shared modes were less likely to be chosen over walking, and shared e-scooters less so than shared bicycles. Overall, the findings suggest that a small percentage of the sample – mainly young (<35 years old) and male respondents with interest in and experience of e-scooters – were interested in using private and shared e-scooters for the first- and last-mile, but that the majority of the respondents were likely to choose walking, albeit with longer travel times overall.

Discovering resource mobilization pattern of selected necessity entrepreneurs in the Philippines

Discovering resource mobilization pattern of selected necessity entrepreneurs in the Philippines

‘You Know What the Truth Is? There are No Touareg Evacuées Here’: On Crisis and Categories of Migration

This paper analyses the practices of (im)mobilities and the accompanying emotions to question the exceptionalisation of crisis and migratory categorisations. Focusing on Kel Tamasheq (aka Tuareg) mobile livelihoods and their fluid interconnections with kinship networks and emotions across time, it argues that the multidimensionality of causes behind a migratory project, and the importance of historically contextualising the relation between a crisis, here the ‘Malian crisis’, and regional patterns of (im)mobilities, reveal the arbitrariness of making crisis exceptional and migratory categories uniquely discreet. This paper is based on a 10-month ethnography among Kel Tamasheq in Bamako.

The Transition Pathways to Sustainable Urban Mobility: Could They Be Extended to Megacities?

Population concentration in urban areas has placed cities at the forefront of the global struggle to achieve the Sustainable Development Goals. Within cities, current mobility patterns are responsible for a significant proportion of environmental emissions. As a result, cities across the world are seeking to develop transitions towards new and greener mobility systems. This paper analyses the different pathways that explain the readiness, or otherwise negation, of cities in achieving sustainable mobility. Based on a sample of 65 cities from all over the world, and with the application of Set-Theoretic Multi-Method Research and Necessary Conditions Analysis, it is demonstrated that there are necessary conditions for the achievement of sustainable mobility, as well as different terms that explain its achievement and its denial. Moreover, the analysis confirms that both the necessary conditions for sustainable mobility and one of the terms explaining its denial reflect the existence of causal mechanisms. The paper confirms the necessity for an analysis of the transitions towards sustainable mobility to take into account the characteristics of the context. Furthermore, it is not possible to explain the behaviour of megacities on the basis of generalised statements.

From roads to roofs: How urban and rural mobility influence building energy consumption

Understanding the relationship between travel behavior and building energy use at an urban scale is crucial for developing effective energy management strategies. Mobility patterns significantly impact building occupancy, which in turn affects energy consumption. However, existing methods often focus on individual buildings, whereas geographical influences on energy usage are not adequately examined. This study addresses this gap by using transportation origin-destination (OD) data to estimate building occupancy and energy. The proposed method assigns OD trips from census block groups to the building level, incorporating building, travel survey, and census data to derive building occupancy profiles. This method was applied to urban and rural areas with 4062 buildings in 70 census block groups. We found that the OD-informed occupancy profile exhibits smoother energy consumption patterns compared with that of Department of Energy reference occupancy profiles. Our analysis reveals distinct building energy consumption patterns among groups with long and short commutes, emphasizing the effect of commute times and work schedules on residential energy usage. This framework is useful for practitioners in transportation agencies and utility companies, enabling the estimation of building energy based on mobility patterns. Overall, this study shows the potential of integrating transportation and building energy data to inform cross-sector energy management strategies.

Active travel in the university setting: Assessing the effects of social behavior, socioeconomics, and spatial location

University campuses are pooling efforts to promote active mobility to reduce their negative impacts on the urban environment, which is significantly influenced by the overreliance on motorized modes of transport. Providing sufficient and safe accessibility conditions for active travel has been highlighted as a crucial strategy for transforming campuses into more livable and sustainable areas in cities. To further explore the likelihood of active mobility uptake at university campuses, this study explored university students’ mobility patterns over time, examining the role of social behavior, socioeconomics, and spatial location factors. The Faculty of Engineering at the University of Porto, Greater Oporto, Portugal, provided the empirical focus for this research. The data analyzed were acquired through surveys of representative samples and spatial analysis over the academic years of 2012, 2017, and 2023. The statistical analysis explained the tendencies and multifactorial influences on travel behavior among university students. Results indicated that travel distance is associated with housing options and travel costs, whereas access to a metro station was associated with walking or cycling. Hence, this study contributed to a deeper understanding of active travel behavior. It provided insights to guide planning practitioners and decision makers in creating integrated transport policy packages that address the barriers and needs of the university community and the surrounding neighborhoods.

Cross border population movements across three East African states: Implications for disease surveillance and response.

The frequent population movement across the five East African Countries poses risk of disease spread in the region. A clear understanding of population movement patterns is critical for informing cross-border disease control interventions. We assessed population mobility patterns across the borders of the East African states of Kenya, Uganda, and Rwanda. In November 2022, we conducted Focus Group Discussions (FGDs), Key Informant Interviews (KIIs), and participatory mapping. Participants were selected using purposive sampling and a topic guide used during interviews. Key informants included border districts (Uganda and Rwanda) and county health officials (Kenya). FGD participants were identified from border communities and travellers and these included truck drivers, commercial motorcyclists, and businesspersons. During KIIs and FGDs, we conducted participatory mapping using Population Connectivity Across Borders toolkits. Data were analysed using thematic analysis approach using Atlas ti 7 software. Different age groups travelled across borders for various reasons. Younger age groups travelled across the border for education, trade, social reasons, employment opportunities, agriculture and mining. While older age groups mainly travelled for healthcare and social reasons. Other common reasons for crossing the borders included religious and cultural matters. Respondents reported seasonal variations in the volume of travellers. Respondents reported using both official (4 Kenya-Uganda, 5 Rwanda-Uganda borders) and unofficial Points of Entry (PoEs) (14 Kenya-Uganda, 20 Uganda-Rwanda) for exit and entry movements on borders. Unofficial PoEs were preferred because they had fewer restrictions like the absence of health screening, and immigration and customs checks. Key destination points (points of interest) included: markets, health facilities, places of worship, education institutions, recreational facilities and business towns. Twenty-eight health facilities (10- Lwakhakha, Uganda, 10- Lwakhakha, Kenya, and 8- Cyanika, Uganda) along the borders were the most commonly visited by the travellers and border communities. Complex population movement and connectivity patterns were identified along the borders. These were used to guide cross-border disease surveillance and other border health strategies in the three countries. Findings were used to revise district response and preparedness plans by strengthening community-based surveillance in border communities.

GeoGail: A Model-based Imitation Learning Framework for Human Trajectory Synthesizing

Synthesized human trajectories are crucial for a large number of applications. Existing solutions are mainly based on the generative adversarial network (GAN), which is limited due to the lack of modeling the human decision-making process. In this paper, we propose a novel imitation learning based method to synthesize human trajectories. This model utilizes a novel semantics-based interaction mechanism between the decision-making strategy and visitations to diverse geographical locations to model them in the semantic domain in a uniform manner. To augment the modeling ability to the real-world human decision-making policy, we propose a feature extraction model to extract the internal latent factors of variation of different individuals, and then propose a novel self-attention based policy net to capture the long-term correlation of mobility and decision-making patterns. Then, to better reward users’ mobility behavior, we propose a novel multi-scale reward net combined with mutual information to model the instant reward, long-term reward, and individual characteristics in a cohesive manner. Extensive experimental results on two real-world trajectory datasets show that our proposed model can synthesize the most high-quality trajectory data compared with six state-of-the-art baselines in terms of a number of key usability metrics, and can well support practical applications based on trajectory data, demonstrating its effectiveness. Furthermore, our proposed method can learn explainable knowledge automatically from data, including explainable statistical features of trajectories and statistical relation between decision-making policy and features.

The Reign of Racialized Residential Sorting: Gentrification and Residential Mobility in the Twenty-First Century

Despite recent theoretical advances in explaining the persistence of segregation in the United States through structural sorting processes, how the spread and intensification of gentrification intersects with these processes is less clear. Drawing on the 2000–2017 waves of the Panel Study of Income Dynamics (PSID) and fixed effects logistic and linear regression models, we examine gentrification’s relationship with residential mobility patterns and whether this relationship is racially stratified. We do not find evidence that living in gentrifying neighborhoods is associated with higher rates of moving for lower-income respondents compared to those living in nongentrifying neighborhoods nor does this differ by race. However, we find starkly different locational outcomes between Black and White movers across the income spectrum, rather than the gentrification status of one’s neighborhood. We argue that processes of racial stratification in the housing market largely govern residential outcomes, regardless of gentrification.

Student mobility in Southern Italy: An empirical analysis of preferential patterns

This paper presents an empirical analysis aimed at identifying chain migration patterns among university students from Sicily, Sardinia, and Apulia. Utilizing data from the national archive of university students enrolled in an Italian university from 2008 to 2017, we construct origin–destination matrixes and calculate residuals from a log-linear model to detect key student routes. Positive residuals between specific origin–destination pairs that persist over time can suggest significant attraction and potential chain migration. The results support a “large to large” and “small to small” city mobility model, particularly for Sicily and Sardinia. Students from Palermo, Catania, Messina, and Cagliari show strong connections with large cities such as Rome and Milan. Furthermore, results show historical links between Sardinia and Tuscany date back to the 1950s, and the enduring connections between Apulian students and universities in Emilia-Romagna, Veneto, and Lombardy have been evident since the 1970s. In Sicily, mobility patterns towards the Polytechnic of Turin began in the 1970s and persist today. These patterns underscore the influence of prior migrants on the mobility decisions of university students in Southern Italy, highlighting a potential chain migration effect.

Falling sideways? Social status and the true nature of elite downward mobility

Downward mobility is an essential, but commonly overlooked component of social mobility. Existing estimates of downward mobility are routinely based on unidimensional measures of income and social class. This ignores the potential for substantial retention of advantage in other domains of stratification – particularly social status.In this paper, I use highly detailed occupational data from a representative UK sample to examine patterns of multidimensional mobility among those from the most advantaged backgrounds. I find that multidimensional measures reveal dramatically different patterns of downward mobility – particularly for women, who, when downwardly mobile in terms of social class, often retain privileged social status positions.I also find that those whose parents held jobs at the very top of the status distribution were much less likely to be downwardly mobile than previous mobility estimates have suggested – consistent with public perceptions of a ‘glass floor’.

Between land and sea: A multidisciplinary approach to understand the Early Occupation of Sicily (EOS).

The island of Sicily is considered to be among the first occupied by humans in the European Upper Paleolithic. Studies to understand early occupation of the island are mostly concentrated on the northern shores. Our project, Early Occupation of Sicily (EOS), focuses on southeastern Sicily so to address questions regarding dispersal to Mediterranean islands and Late Pleistocene landscapes and environments. Here, we present the initial results of our terrestrial and underwater surveys in combination with archival work and analyses of museum collections. In SE Sicily very few Upper Paleolithic sites have been excavated and analyzed using scientific methods. We have relocated and assessed ~20 caves and rock shelters identified between the 1870s and 1990s, studied museum collections, and collected raw material to reconstruct procurement patterns. To identify new sites, we conducted land and underwater surveys to reconstruct paleo-shorelines and past environments. We have identified three sites, two on land and one partially submerged, that still contain unexplored archaeological sediments, demonstrated in one instance through seismic tomography. This work shows the potential of re-examining minimally studied sites and materials to reconstruct mobility patterns and environmental impact of the first inhabitants of the island.