Human mobility networks reveal increased segregation in large cities

InclusiViz : Visual Analytics of Human Mobility Data for Understanding and Mitigating Urban Segregation

Urban segregation refers to the physical and social division of people, often driving inequalities within cities and exacerbating socioeconomic and racial tensions. While most studies focus on residential spaces, they often neglect segregation across "activity spaces" where people work, socialize, and engage in leisure. Human mobility data offers new opportunities to analyze broader segregation patterns, encompassing both residential and activity spaces, but challenges existing methods in capturing the complexity and local nuances of urban segregation. This work introduces InclusiViz, a novel visual analytics system for multi-level analysis of urban segregation, facilitating the development of targeted, data-driven interventions. Specifically, we developed a deep learning model to predict mobility patterns across social groups using environmental features, augmented with explainable AI to reveal how these features influence segregation. The system integrates innovative visualizations that allow users to explore segregation patterns from broad overviews to fine-grained detail and evaluate urban planning interventions with real-time feedback. We conducted a quantitative evaluation to validate the model's accuracy and efficiency. Two case studies and expert interviews with social scientists and urban analysts demonstrated the system's effectiveness, highlighting its potential to guide urban planning toward more inclusive cities.

Learning the complexity of urban mobility with deep generative network

City-scale individual movements, population flows, and urban morphology are intricately intertwined, collectively contributing to the complexity of urban mobility and impacting critical aspects of a city, from socioeconomic exchanges to epidemic transmission. Existing models, derived from fundamental laws of human mobility, often capture only partial facets of this complexity. This article introduces DeepMobility, a powerful deep generative collaboration network designed to encapsulate the multifaceted nature of complex urban mobility within one unified model, bridging the gap between the heterogeneous behaviors of individuals and the collective behaviors emerging from the entire population. As the first generative deep learning model to integrate micro- and macrolevel dynamics through bidirectional collaboration, DeepMobility generates high-fidelity synthetic mobility data, overcoming key limitations of prior approaches. Our experiments, conducted on mobility trajectories and flows in cities of China and Senegal, reveal that unlike state-of-the-art deep learning models that tend to "memorize" observed data, DeepMobility excels in learning the intricate data distribution and successfully reproduces the existing universal scaling laws that characterize human mobility behaviors at both individual and population levels. DeepMobility also exhibits robust generalization capabilities, enabling it to generate realistic trajectories and flows for cities lacking corresponding training data. Our approach underscores the feasibility of employing generative deep learning to model the underlying mechanism of human mobility and establishes a versatile framework for mobility data generation that supports sustainable and livable cities.

Associations Between Measures of Structural Racism and Receipt of Acute Ischemic Stroke Interventions in the United States

BACKGROUND

Structural racism and rural/urban differences in stroke care affect care delivery and outcomes. We explored the interplay among structural racism, urbanity, and intravenous thrombolysis (tissue plasminogen activator) and endovascular thrombectomy (ET).

METHODS AND RESULTS

In this retrospective study using complete, deidentified inpatient Medicare data (2016-2019), we identified incident acute ischemic stroke admissions, demographics, and hospital-level variables. Medicare beneficiaries aged ≥65 years with incident acute ischemic stroke admission in large metropolitan and nonurban settings were included. Validated structural racism metrics at the county level and a composite structural racism score that incorporated measures of segregation, housing, employment, education, and income were studied. Among 951 914 patients, rural hospitals demonstrated lower intensive care unit capacity (27.5% versus 88.6%), stroke certification (5.3% versus 38.4%), and rates of tissue plasminogen activator (1.6% versus 12.3%) and ET (<1% versus 3.8%). Large metropolitan areas demonstrated higher levels of income inequality (Gini index -0.15 versus 0.11 SD), and racial segregation (dissimilarity index 0.29 SD higher than the US mean). The composite structural racism score was associated with increased odds of tissue plasminogen activator receipt (odds ratio, 1.47 [95% CI, 1.33-1.63]) and ET (odds ratio, 4.15 [95% CI, 2.98-5.79]). Despite greater access to stroke care in urban areas, a persistent racial disparity remained, with Black patients less likely to receive tissue plasminogen activator (odds ratio, 0.70 [95% CI, 0.68-0.72]) and ET (odds ratio, 0.63 [95% CI, 0.60-0.66]) compared with White patients.

CONCLUSIONS

We found persistent disparities in stroke care access and outcomes, influenced by structural racism and rural-urban differences. Further research should explore interactions between structural racism, urbanity, and health care delivery to inform effective interventions.

The Universal Neighborhood Effect Averaging in Mobility-Dependent Environmental Exposures

The neighborhood effect averaging problem (NEAP) is a fundamental statistical phenomenon in mobility-dependent environmental exposures. It suggests that individual environmental exposures tend toward the average exposure in the study area when considering human mobility. However, the universality of the NEAP across various environmental exposures and the mechanisms underlying its occurrence remain unclear. Here, using a large human mobility data set of more than 27 000 individuals in the Chicago Metropolitan Area, we provide robust evidence of the existence of the NEAP in a range of individual environmental exposures, including green spaces, air pollution, healthy food environments, transit accessibility, and crime rates. We also unveil the social and spatial disparities in the NEAP's influence on individual environmental exposure estimates. To further reveal the mechanisms behind the NEAP, we perform multiscenario analyses based on environmental variation and human mobility simulations. The results reveal that the NEAP is a statistical phenomenon of regression to the mean (RTM) under the constraints of spatial autocorrelation in environmental data. Increasing travel distances and out-of-home durations can intensify and promote the NEAP's impact, particularly for highly dynamic environmental factors like air pollution. These findings illuminate the complex interplay between human mobility and environmental factors, guiding more effective public health interventions.

Using smartphone user mobility to unveil actual travel time to healthcare: An example of mental health facilities

Travel time to health facilities is one of the most important factors in evaluating health disparity. Previous extensive research has primarily leveraged the driving time to the nearest health facility to gauge travel time. However, such ideal travel time (ITT) may not accurately represent real individual travel time to health services and is often underestimated. This study aims to systematically understand such gaps by comparing ITT to actual travel time (ATT) derived from smartphone-based human mobility data and further identifying how various population groups across regions are most likely to be affected. This study takes mental health as an example and compares ATT with ITT to mental health facilities. Results indicate that ITT and ATT demonstrate significant disparities between urban and rural areas. ITT is consistently underestimated across the contiguous US. We compare travel times among diverse sociodemographic groups across eight geographical regions. The findings suggest that different age groups have similar travel times to mental health facilities. However, racial groups exhibit varied travel times. Hispanics have a larger percentage of the population experiencing longer ATT than ITT. We also employed spatial and non-spatial regression models, such as Ordinary Least Squares, Spatial Lag Model, and Spatial Error Model, to quantify the correlation between travel times and socioeconomic status. The results revealed that the proportion of older adults and high school dropouts positively correlates with travel times in most regions. Areas with more non-Hispanics show positive correlations with both travel times. Overall, this study reveals pronounced discrepancies between ITT and ATT, underscoring the importance of using smartphone-derived ATT to measure health accessibility.

Riascos A, Mateos JL. Pattern detection in the vehicular activity of bus rapid transit systems

In this paper, we explore different methods to detect patterns in the activity of bus rapid transit (BRT) systems focusing on two aspects of transit: infrastructure and the movement of vehicles. To this end, we analyze records of velocity and position of each active vehicle in nine BRT systems located in the Americas. We detect collective patterns that characterize each BRT system obtained from the statistical analysis of velocities in the entire system (global scale) and at specific zones (local scale). We analyze the velocity records at the local scale applying the Kullback-Leibler divergence to compare the vehicular activity between zones. This information is organized in a similarity matrix that can be represented as a network of zones. The resulting structure for each system is analyzed using network science methods. In particular, by implementing community detection algorithms on networks, we obtain different groups of zones characterized by similarities in the movement of vehicles. Our findings show that the representation of the dataset with information of vehicles as a network is a useful tool to characterize at different scales the activity of BRT systems when geolocalized records of vehicular movement are available. This general approach can be implemented in the analysis of other public transportation systems.

Disparities in Liver Transplant Outcomes: Race/Ethnicity and Individual- and Neighborhood-Level Socioeconomic Status

Race/ethnicity and individual-level socioeconomic status (SES) may contribute to health disparities in liver transplant (LT) outcomes. The socioeconomic conditions of a neighborhood may either mitigate or exacerbate these health disparities. This retrospective study investigated the relationship between race/ethnicity, individual- and neighborhood-level SES, and LT outcomes, and whether neighborhood-level SES modified the relationship between individual factors and LT outcomes. Adult individuals who underwent LT between 2010 and 2019 (n = 55,688) were identified from the United Network for Organ Sharing database. Primary exposures were race/ethnicity, education, primary insurance type, and the Social Deprivation Index (SDI) scores. Education and primary insurance type were used as proxies for individual-level SES, while SDI scores were used as a proxy for neighborhood-level SES. The primary outcome was time to occurrence of graft failure or mortality. Cox proportional hazard models were used to examine the associations between the exposures and outcomes. LT recipients who were Black (hazard ratio [HR]: 1.27, p < .0001), completed high school or less (HR: 1.06, p = .002), and had public insurance (HR: 1.14, p < .0001) had a higher rate of graft failure or mortality than those who were White, completed more than high school, and had private insurance, respectively. The SDI scores were not significantly associated with LT outcomes when adjusting for individual factors (HR: 1.02, p = .45) and did not modify the associations between individual factors and LT outcomes. Findings of this study suggest that disparities based on individual factors were not modified by neighborhood-level SES. Tailored interventions targeting the unique needs associated with race/ethnicity and individual-level SES are needed to optimize LT outcomes.

Predictability of human mobility during the COVID-19 pandemic in the United States

Human mobility is fundamental to a range of applications including epidemic control, urban planning, and traffic engineering. While laws governing individual movement trajectories and population flows across locations have been extensively studied, the predictability of population-level mobility during the COVID-19 pandemic driven by specific activities such as work, shopping, and recreation remains elusive. Here we analyze mobility data for six place categories at the US county level from 2020 February 15 to 2021 November 23 and measure how the predictability of these mobility metrics changed during the COVID-19 pandemic. We quantify the time-varying predictability in each place category using an information-theoretic metric, permutation entropy. We find disparate predictability patterns across place categories over the course of the pandemic, suggesting differential behavioral changes in human activities perturbed by disease outbreaks. Notably, predictability change in foot traffic to residential locations is mostly in the opposite direction to other mobility categories. Specifically, visits to residences had the highest predictability during stay-at-home orders in March 2020, while visits to other location types had low predictability during this period. This pattern flipped after the lifting of restrictions during summer 2020. We identify four key factors, including weather conditions, population size, COVID-19 case growth, and government policies, and estimate their nonlinear effects on mobility predictability. Our findings provide insights on how people change their behaviors during public health emergencies and may inform improved interventions in future epidemics.

Investigation on the coupling coordination of pseudo human settlements in the urban agglomerations in eastern China

In the information age, digital information technology has gradually become a new quality of productivity to improve international competitiveness, and the status and importance of the pseudo human settlements(PHS) constructed by digital and information has been increasing in the human settlements(HS) system. In this context, exploring the coupling coordination of the internal systems of the PHS provides a theoretical basis for promoting the comprehensive improvement of the quality of the HS in the urban agglomerations, provides a theoretical reference for rationally interpreting the new type of human-land relationship in the information age, and provide a new perspective for the study of the human settlements geography. Currently, research on PHS is in its nascent stages, therefore, we construct a theoretical framework for the coupling coordination of the "three states" of the HS, takes the internal system of PHS as the entry point, empirically analyze the spatiotemporal characteristics of the PHS coupling coordination degree within urban agglomerations in eastern China and the driving factors by using the coupling coordination degree, spatial autocorrelation, the center of gravity and the ellipse of the standard deviation, and geo-detector models. Research shows: (1) Time course: exhibits an upward "inverted L" trend. (2) Spatial pattern: exhibits a general spatial pattern of "high in the north and low in the south". (3) Spatial correlation: shows a spatially positively correlated clustering trend. (4) Spatial evolution: demonstrates a decentralized pattern of migration from the northeast to the southwest, indicating that the coupling coordination grows faster in the southwest than in the northeast. (5) Driving factor: the development of coupling coordination degree of PHS results from multiple factors and systems. This research provides theoretical support for promoting the comprehensive improvement of the quality of PHS in the urban agglomerations in eastern China, and offers scientific reference for the construction of PHS in other regions of China.

How people are exposed to neighborhoods racially different from their own

In US cities, neighborhoods have long been racially segregated. However, people do not spend all their time in their neighborhoods, and the consequences of residential segregation may be tempered by the contact people have with other racial groups as they traverse the city daily. We examine the extent to which people's regular travel throughout the city is to places "beyond their comfort zone" (BCZ), i.e., to neighborhoods of racial composition different from their own-and why. Based on travel patterns observed in more than 7.2 million devices in the 100 largest US cities, we find that the average trip is to a neighborhood less than half as racially different from the home neighborhood as it could have been given the city. Travel to grocery stores is least likely to be BCZ; travel to gyms and parks, most likely; however, differences are greatest across cities. For the first ~10 km people travel from home, neighborhoods become increasingly more BCZ for every km traveled; beyond that point, whether neighborhoods do so depends strongly on the city. Patterns are substantively similar before and after COVID-19. Our findings suggest that policies encouraging more 15-min travel-that is, to amenities closer to the home-may inadvertently discourage BCZ movement. In addition, promoting use of certain "third places" such as restaurants, bars, and gyms, may help temper the effects of residential segregation, though how much it might do so depends on city-specific conditions.

Temporal dynamics of public transportation ridership in Seoul before, during, and after COVID-19 from urban resilience perspective

We delve into the temporal dynamics of public transportation (PT) ridership in Seoul, South Korea, navigating the periods before, during, and after the COVID-19 pandemic through a spatial difference-in-difference model (SDID). Rooted in urban resilience theory, the study employs micro-level public transportation card data spanning January 2019 to December 2023. Major findings indicate a substantial ridership decline during the severe COVID impact phase, followed by a period in the stable and post-COVID phases. Specifically, compared to the pre-COVID phase, PT ridership experienced a 32.1% decrease in Severe, followed by a reduced magnitude of 21.8% in Stable and 13.5% in post-COVID phase. Interestingly, the observed decrease implies a certain level of adaptability, preventing a complete collapse. Also, contrasting with findings in previous literature, our study reveals a less severe impact, with reductions ranging from 27.0 to 34.9%. Moreover, while the ridership in the post-COVID phase exhibits recovery, the ratio (Post/Pre) staying below 1.0 suggests that the system has not fully returned to its pre-pandemic state. This study contributes to the urban resilience discourse, illustrating how PT system adjusts to COVID, offering insights for transportation planning.

Urban segregation on multilayered transport networks: a random walk approach

We present a novel method for analysing socio-spatial segregation in cities by considering constraints imposed by transportation networks. Using a multilayered network approach, we model the interaction probabilities of socio-economic groups with random walks and Lévy flights. This method allows for evaluation of new transport infrastructure's impact on segregation while quantifying each network's contribution to interaction opportunities. The proposed random walk segregation index measures the probability of individuals encountering diverse social groups based on their available means of transit via random walks. The index incorporates temporal constraints in urban mobility with a parameter, α ∈ [ 0 , 1 ) , of the probability of the random walk continuing at each time step. By applying this to a toy model and conducting a sensitivity analysis, we explore how the index changes dependent on this temporal constraint. When the parameter equals zero, the measure simplifies to an isolation index. When the parameter approaches one it represents the city's overall socio-economic distribution by mirroring the steady-state of the random walk process. Using Cuenca, Ecuador as a case study, we illustrate the method's applicability in transportation planning as a valuable tool for policymakers, addressing spatial distribution of socio-economic groups and the connectivity of existing transport networks, thus promoting equitable interactions throughout the city.