The Impact of Spatial Orientation Changes on Driving Behavior in Healthy Aging.

OBJECTIVES: Global cognitive changes in older age affect driving behavior and road safety, but how spatial orientation differences affect driving behaviors is unknown on a population level, despite clear implications for driving policy and evaluation during aging. The present study aimed to establish how spatial navigation changes affect driving behavior and road safety within a large cohort of older adults. METHODS: Eight hundred and four participants (mean age: 71.05) were recruited for a prospective cohort study. Participants self-reported driving behavior followed by spatial orientation (allocentric and egocentric) testing and a broader online cognitive battery (visuomotor speed, processing speed, executive functioning, spatial working memory, episodic memory, visuospatial functioning). RESULTS: Spatial orientation performance significantly predicted driving difficulty and frequency. Experiencing more driving difficulty was associated with worse allocentric spatial orientation, processing speed, and source memory performance. Similarly, avoiding challenging driving situations was associated with worse spatial orientation and episodic memory. Allocentric spatial orientation was the only cognitive domain consistently affecting driving behavior in under 70 and over 70 age groups, a common age threshold for driving evaluation in older age. DISCUSSION: We established for the first time that worse spatial orientation performance predicted increased driving difficulty and avoidance of challenging situations within an older adult cohort. Deficits in spatial orientation emerge as a robust indicator of driving performance in older age, which should be considered in future aging driving assessments, as it has clear relevance for road safety within the aging population.

Assessing spatiotemporal variability in SARS-CoV-2 infection risk for hospital workers using routinely-collected data.

The COVID-19 pandemic has emphasised the need to rapidly assess infection risks for healthcare workers within the hospital environment. Using data from the first year of the pandemic, we investigated whether an individual's COVID-19 test result was associated with behavioural markers derived from routinely collected hospital data two weeks prior to a test. The temporal and spatial context of behaviours were important, with the highest risks of infection during the first wave, for staff in contact with a greater number of patients and those with greater levels of activity on floors handling the majority of COVID-19 patients. Infection risks were higher for BAME staff and individuals working more shifts. Night shifts presented higher risks of infection between waves of COVID-19 patients. Our results demonstrate the epidemiological relevance of deriving markers of staff behaviour from electronic records, which extend beyond COVID-19 with applications for other communicable diseases and in supporting pandemic preparedness.

A synthetic population for agent-based modelling in Canada.

In order to anticipate the impact of local public policies, a synthetic population reflecting the characteristics of the local population provides a valuable test bed. While synthetic population datasets are now available for several countries, there is no open-source synthetic population for Canada. We propose an open-source synthetic population of individuals and households at a fine geographical level for Canada for the years 2021, 2023 and 2030. Based on 2016 census data and population projections, the synthetic individuals have detailed socio-demographic attributes, including age, sex, income, education level, employment status and geographic locations, and are related into households. A comparison of the 2021 synthetic population with 2021 census data over various geographical areas validates the reliability of the synthetic dataset. Users can extract populations from the dataset for specific zones, to explore 'what if' scenarios on present and future populations. They can extend the dataset using local survey data to add new characteristics to individuals. Users can also run the code to generate populations for years up to 2042.

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 behavior and provide useful tools for modelers. Finally, we discuss how progress on these research directions may help us address some of the challenges our society faces today.

Investigating healthcare worker mobility and patient contacts within a UK hospital during the COVID-19 pandemic.

BACKGROUND: Insights into behaviours relevant to the transmission of infections are extremely valuable for epidemiological investigations. Healthcare worker (HCW) mobility and patient contacts within the hospital can contribute to nosocomial outbreaks, yet data on these behaviours are often limited. METHODS: Using electronic medical records and door access logs from a London teaching hospital during the COVID-19 pandemic, we derive indicators for HCW mobility and patient contacts at an aggregate level. We assess the spatial-temporal variations in HCW behaviour and, to demonstrate the utility of these behavioural markers, investigate changes in the indirect connectivity of patients (resulting from shared contacts with HCWs) and spatial connectivity of floors (owing to the movements of HCWs). RESULTS: Fluctuations in HCW mobility and patient contacts were identified during the pandemic, with the most prominent changes in behaviour on floors handling the majority of COVID-19 patients. The connectivity between floors was disrupted by the pandemic and, while this stabilised after the first wave, the interconnectivity of COVID-19 and non-COVID-19 wards always featured. Daily rates of indirect contact between patients provided evidence for reactive staff cohorting in response to the number of COVID-19 patients in the hospital. CONCLUSIONS: Routinely collected electronic records in the healthcare environment provide a means to rapidly assess and investigate behaviour change in the HCW population, and can support evidence based infection prevention and control activities. Integrating frameworks like ours into routine practice will empower decision makers and improve pandemic preparedness by providing tools to help curtail nosocomial outbreaks of communicable diseases.

Movement of healthcare workers and their patient contacts can contribute to outbreaks of infection in the healthcare environment. We use electronic medical records and door access logs from a London hospital to derive indicators for staff behaviour during the COVID-19 pandemic. Changes in staff behaviour were most prominent on floors handling the majority of COVID-19 patients. We also show how the flow of staff between COVID-19 and non-COVID-19 wards continued throughout the pandemic, but find evidence that indirect contact between COVID-19 positive and negative patients reduced as COVID-19 prevalence increased. We suggest these routinely collected data on HCW behaviour should be used to support decision makers in activities to help curtail disease outbreaks in healthcare settings.

A Framework for Evaluating Dashboards in Healthcare.

In the era of 'information overload', effective information provision is essential for enabling rapid response and critical decision making. In making sense of diverse information sources, dashboards have become an indispensable tool, providing fast, effective, adaptable, and personalized access to information for professionals and the general public alike. However, these objectives place heavy requirements on dashboards as information systems in usability and effective design. Understanding these issues is challenging given the absence of consistent and comprehensive approaches to dashboard evaluation. In this article we systematically review literature on dashboard implementation in healthcare, where dashboards have been employed widely, and where there is widespread interest for improving the current state of the art, and subsequently analyse approaches taken towards evaluation. We draw upon consolidated dashboard literature and our own observations to introduce a general definition of dashboards which is more relevant to current trends, together with seven evaluation scenarios - task performance, behaviour change, interaction workflow, perceived engagement, potential utility, algorithm performance and system implementation. These scenarios distinguish different evaluation purposes which we illustrate through measurements, example studies, and common challenges in evaluation study design. We provide a breakdown of each evaluation scenario, and highlight some of the more subtle questions. We demonstrate the use of the proposed framework by a design study guided by this framework. We conclude by comparing this framework with existing literature, outlining a number of active discussion points and a set of dashboard evaluation best practices for the academic, clinical and software development communities alike.

London taxi drivers: A review of neurocognitive studies and an exploration of how they build their cognitive map of London.

Licensed London taxi drivers have been found to show changes in the gray matter density of their hippocampus over the course of training and decades of navigation in London (UK). This has been linked to their learning and using of the "Knowledge of London," the names and layout of over 26,000 streets and thousands of points of interest in London. Here we review past behavioral and neuroimaging studies of London taxi drivers, covering the structural differences in hippocampal gray matter density and brain dynamics associated with navigating London. We examine the process by which they learn the layout of London, detailing the key learning steps: systematic study of maps, travel on selected overlapping routes, the mental visualization of places and the optimal use of subgoals. Our analysis provides the first map of the street network covered by the routes used to learn the network, allowing insight into where there are gaps in this network. The methods described could be widely applied to aid spatial learning in the general population and may provide insights for artificial intelligence systems to efficiently learn new environments.

Household visitation during the COVID-19 pandemic.

The COVID-19 pandemic has posed novel risks related to the indoor mixing of individuals from different households and challenged policymakers to adequately regulate this behaviour. While in many cases household visits are necessary for the purpose of social care, they have been linked to broadening community transmission of the virus. In this study we propose a novel, privacy-preserving framework for the measurement of household visitation at national and regional scales, making use of passively collected mobility data. We implement this approach in England from January 2020 to May 2021. The measures expose significant spatial and temporal variation in household visitation patterns, impacted by both national and regional lockdown policies, and the rollout of the vaccination programme. The findings point to complex social processes unfolding differently over space and time, likely informed by variations in policy adherence, vaccine relaxation, and regional interventions.

Future Developments in Geographical Agent-Based Models: Challenges and Opportunities.

Despite reaching a point of acceptance as a research tool across the geographical and social sciences, there remain significant methodological challenges for agent-based models. These include recognizing and simulating emergent phenomena, agent representation, construction of behavioral rules, and calibration and validation. While advances in individual-level data and computing power have opened up new research avenues, they have also brought with them a new set of challenges. This article reviews some of the challenges that the field has faced, the opportunities available to advance the state-of-the-art, and the outlook for the field over the next decade. We argue that although agent-based models continue to have enormous promise as a means of developing dynamic spatial simulations, the field needs to fully embrace the potential offered by approaches from machine learning to allow us to fully broaden and deepen our understanding of geographical systems.

Perception of urban subdivisions in pedestrian movement simulation.

The perception of urban subdivisions, deriving from regionalisation processes and the identification of separating elements (barriers), has proven to dynamically shape peoples' cognitive representations of space and route choice behaviour in cities. However, existing Agent-Based Models (ABMs) for pedestrian simulation have not accounted for these particular cognitive mapping processes. The aim of this paper is to explore the behaviour of pedestrian agents endowed with knowledge about urban subdivisions. Drawing from literature in spatial cognition, we adapted a region-based route choice model, which contemplates a high- and a local planning level, and advanced a barrier-based route choice model, wherein the influence of separating elements is manipulated. Finally, we combined these two approaches in a region-barrier based model. The patterns emerging from the movement of agents employing such approaches were examined in the city centres of London and Paris. The introduction of regions in the routing mechanisms reduced the unbalanced concentration of agents across the street network brought up by the widely employed least cumulative angular change model (-.08 Gini coefficient). The inclusion of barriers further raised the dispersal of the agents through secondary roads, while leading agents to walk along waterfronts and across parks; it also yielded a more regular usage of pedestrian roads. Moreover, the region- and the region-barrier based routes showed deviation ratio values from the road distance shortest path (region-based: 1.18 London, 1.16 Paris, region-barrier based: 1.43 London, 1.33 Paris) consistent with empirical observations from pedestrian behaviour research. A further evaluation of the model with macro-level observational data may enhance the understanding of pedestrian dynamics and help tuning the interplay amongst urban salient elements at the agent level. Yet, we consider the movement flows arising from our current implementation insightful for assessing the distribution of pedestrians and testing possible interventions for the design of legible and walkable spaces.

Impact of road network structure on dementia-related missing incidents: a spatial buffer approach.

Dementia-related missing incidents are a highly prevalent issue worldwide. Despite being associated with potentially life-threatening consequences, very little is still known about what environmental risk factors may potentially contribute to these missing incidents. The aim of this study was to conduct a retrospective, observational analysis using a large sample of police case records of missing individuals with dementia (n = 210). Due to the influence that road network structure has on our real world navigation, we aimed to explore the relationship between road intersection density, intersection complexity, and orientation entropy to the dementia-related missing incidents. For each missing incident location, the above three variables were computed at a 1 km radius buffer zone around these locations; these values were then compared to that of a set of random locations. The results showed that higher road intersection density, intersection complexity, and orientation entropy were all significantly associated with dementia-related missing incidents. Our results suggest that these properties of road network structure emerge as significant environmental risk factors for dementia-related missing incidents, informing future prospective studies as well as safeguarding guidelines.

Digital technologies in the public-health response to COVID-19.

Digital technologies are being harnessed to support the public-health response to COVID-19 worldwide, including population surveillance, case identification, contact tracing and evaluation of interventions on the basis of mobility data and communication with the public. These rapid responses leverage billions of mobile phones, large online datasets, connected devices, relatively low-cost computing resources and advances in machine learning and natural language processing. This Review aims to capture the breadth of digital innovations for the public-health response to COVID-19 worldwide and their limitations, and barriers to their implementation, including legal, ethical and privacy barriers, as well as organizational and workforce barriers. The future of public health is likely to become increasingly digital, and we review the need for the alignment of international strategies for the regulation, evaluation and use of digital technologies to strengthen pandemic management, and future preparedness for COVID-19 and other infectious diseases.

Physical activity trails in an urban setting and cardiovascular disease morbidity and mortality in Winnipeg, Manitoba, Canada: a study protocol for a natural experiment.

INTRODUCTION: Aspects of the built environment that support physical activity are associated with better population health outcomes. Few experimental data exist to support these observations. This protocol describes the study of the creation of urban trials on cardiovascular disease (CVD)-related morbidity and mortality in a large urban centre. METHODS AND ANALYSIS: Between 2008 and 2010, the city of Winnipeg, Canada, built four, paved, multiuse (eg, cycling, walking and running), two-lane trails that are 5-8 km long and span ~60 neighbourhoods. Linking a population-based health data with census and environmental data, we will perform an interrupted time series analysis to assess the impact of this natural experiment on CVD-related morbidity and mortality among individuals 30-65 years of age residing within 400-1200 m of the trail. The primary outcome of interest is a composite measure of incident major adverse CVD events (ie, CVD-related mortality, ischaemic heart disease, stroke and congestive heart failure). The secondary outcome of interest is a composite measure of incident CVD-related risk factors (ie, diabetes, hypertension and dyslipidaemia). Outcomes will be assessed quarterly in the 10 years before the intervention and 5 years following the intervention, with a 4-year interruption. We will adjust analyses for differences in age, sex, ethnicity, immigration status, income, gentrification and other aspects of the built environment (ie, greenspace, fitness/recreation centres and walkability). We will also assess trail use and trail user profiles using field data collection methods. ETHICS AND DISSEMINATION: Ethical approvals for the study have been granted by the Health Research Ethics Board at the University of Manitoba and the Health Information Privacy Committee within the Winnipeg Regional Health Authority. We have adopted an integrated knowledge translation approach. Information will be disseminated with public and government partners. TRIAL REGISTRATION NUMBER: NCT04057417.

The role of worldviews in the governance of sustainable mobility.

Sustainable development aims for a viable interaction between human and physical nature. However, how do we perceive the social and natural world, rationalize our behavior, and modify our ways of life? Here, we apply the idea of worldviews to cognition and rationality in transport since a transition to sustainable mobility is crucial in dealing with global climate change. We utilize Cultural Theory and the British Social Attitudes survey (N = 1,120) to study how three worldviews-egalitarianism, hierarchy, and individualism-relate to people's attitudes to sustainable mobility. First, we use factor analysis to extract the three worldviews or ways of life in Great Britain. Second, we construct hypotheses concerning the correlations between the worldviews and social attitudes to sustainable mobility. Our statistical analysis of 11 mobility issues in the survey confirms our hypotheses, elucidating the cultural cognition or rationality that underlies people's transport decision-making. Egalitarianism favors demand control, environmental friendliness, and action driven by inner conviction; hierarchy privileges conformity, order, and security; and individualism embraces freedom, speed, and external incentives. The findings show that the worldviews have a systematic and comprehensive impact on how people assess sustainable mobility debates. Moreover, we perform regression analysis to investigate how these cultural styles are associated with British people's sociodemographics and political party identification, which can help identify the characteristics of stakeholders in sustainability planning and engagement. We conclude that the worldviews form the bedrock of individual decisions on sustainable mobility and have a wider significance for holistic sustainability governance.

Walking on Water-A Natural Experiment of a Population Health Intervention to Promote Physical Activity after the Winter Holidays.

Background: Very few experimental studies exist describing the effect of changes to the built environment and opportunities for physical activity (PA). We examined the impact of an urban trail created on a frozen waterway on visitor counts and PA levels. Methods: We studied a natural experiment in Winnipeg, Manitoba, Canada that included 374,204 and 237,362 trail users during the 2017/2018 and 2018/2019 winter seasons. The intervention was a 10 km frozen waterway trail lasting 8-10 weeks. The comparator conditions were the time periods immediately before and after the intervention when ~10 kms of land-based trails were accessible to the public. A convenience sample of 466 participants provided directly measured PA while on the frozen waterway. Results: Most trail users were 35 years or older (73%), Caucasian (77%), and had an annual household income >$50,000 (61%). Mean daily trail network visits increased ~four-fold when the frozen waterway was open (median and interquartile range (IQR) = 710 (239-1839) vs. 2897 (1360-5583) visits/day, p < 0.001), compared with when it was closed. Users achieved medians of 3852 steps (IQR: 2574-5496 steps) and 23 min (IQR: 13-37 min) of moderate to vigorous intensity PA (MVPA) per visit, while 37% of users achieved ≥30 min of MVPA. Conclusion: A winter-specific urban trail network on a frozen waterway substantially increased visits to an existing urban trail network and was associated with a meaningful dose of MVPA. Walking on water could nudge populations living in cold climates towards more activity during winter months.

Developing a Data Dashboard Framework for Population Health Surveillance: Widening Access to Clinical Trial Findings.

BACKGROUND: Population surveillance sites generate many datasets relevant to disease surveillance. However, there is a risk that these data are underutilized because of the volumes of data gathered and the lack of means to quickly disseminate analysis. Data visualization offers a means to quickly disseminate, understand, and interpret datasets, facilitating evidence-driven decision making through increased access to information. OBJECTIVES: This paper describes the development and evaluation of a framework for data dashboard design, to visualize datasets produced at a demographic health surveillance site. The aim of this research was to produce a comprehensive, reusable, and scalable dashboard design framework to fit the unique requirements of the context. METHODS: The framework was developed and implemented at a demographic surveillance platform at the Africa Health Research Institute, in KwaZulu-Natal, South Africa. This context represents an exemplar implementation for the use of data dashboards within a population health-monitoring setting. Before the full launch, an evaluation study was undertaken to assess the effectiveness of the dashboard framework as a data communication and decision-making tool. The evaluation included a quantitative task evaluation to assess usability and a qualitative questionnaire exploring the attitudes to the use of dashboards. RESULTS: The evaluation participants were drawn from a diverse group of users working at the site (n=20), comprising of community members, nurses, scientific and operational staff. Evaluation demonstrated high usability for the dashboard across user groups, with scientific and operational staff having minimal issues in completing tasks. There were notable differences in the efficiency of task completion among user groups, indicating varying familiarity with data visualization. The majority of users felt that the dashboards provided a clear understanding of the datasets presented and had a positive attitude to their increased use. CONCLUSIONS: Overall, this exploratory study indicates the viability of the data dashboard framework in communicating data trends within population surveillance setting. The usability differences among the user groups discovered during the evaluation demonstrate the need for the user-led design of dashboards in this context, addressing heterogeneous computer and visualization literacy present among the diverse potential users present in such settings. The questionnaire highlighted the enthusiasm for increased access to datasets from all stakeholders highlighting the potential of dashboards in this context.

Global Determinants of Navigation Ability.

Human spatial ability is modulated by a number of factors, including age [1-3] and gender [4, 5]. Although a few studies showed that culture influences cognitive strategies [6-13], the interaction between these factors has never been globally assessed as this requires testing millions of people of all ages across many different countries in the world. Since countries vary in their geographical and cultural properties, we predicted that these variations give rise to an organized spatial distribution of cognition at a planetary-wide scale. To test this hypothesis, we developed a mobile-app-based cognitive task, measuring non-verbal spatial navigation ability in more than 2.5 million people and sampling populations in every nation state. We focused on spatial navigation due to its universal requirement across cultures. Using a clustering approach, we find that navigation ability is clustered into five distinct, yet geographically related, groups of countries. Specifically, the economic wealth of a nation was predictive of the average navigation ability of its inhabitants, and gender inequality was predictive of the size of performance difference between males and females. Thus, cognitive abilities, at least for spatial navigation, are clustered according to economic wealth and gender inequalities globally, which has significant implications for cross-cultural studies and multi-center clinical trials using cognitive testing.

Spatiotemporal variation in travel regularity through transit user profiling.

New smart card datasets are providing new opportunities to explore travel behaviour in much greater depth than anything accomplished hitherto. Part of this quest involves measuring the great array of regular patterns within such data and explaining these relative to less regular patterns which have often been treated in the past as noise. Here we use a simple method called DBSCAN to identify clusters of travel events associated with particular individuals whose behaviour over space and time is captured by smart card data. Our dataset is a sequence of three months of data recording when and where individual travellers start and end rail and bus travel in Greater London. This dataset contains some 640 million transactions during the period of analysis we have chosen and it enables us to begin a search for regularities at the most basic level. We first define measures of regularity in terms of the proportions of events associated with temporal, modal (rail and bus), and service regularity clusters, revealing that the frequency distributions of these clusters follow skewed distributions with different means and variances. The analysis then continues to examine how regularity relative to irregular travel across space, demonstrating high regularities in the origins of trips in the suburbs contrasted with high regularities in the destinations in central London. This analysis sets the agenda for future research into how we capture and measure the differences between regular and irregular travel which we discuss by way of conclusion.

Variability in Regularity: Mining Temporal Mobility Patterns in London, Singapore and Beijing Using Smart-Card Data.

To discover regularities in human mobility is of fundamental importance to our understanding of urban dynamics, and essential to city and transport planning, urban management and policymaking. Previous research has revealed universal regularities at mainly aggregated spatio-temporal scales but when we zoom into finer scales, considerable heterogeneity and diversity is observed instead. The fundamental question we address in this paper is at what scales are the regularities we detect stable, explicable, and sustainable. This paper thus proposes a basic measure of variability to assess the stability of such regularities focusing mainly on changes over a range of temporal scales. We demonstrate this by comparing regularities in the urban mobility patterns in three world cities, namely London, Singapore and Beijing using one-week of smart-card data. The results show that variations in regularity scale as non-linear functions of the temporal resolution, which we measure over a scale from 1 minute to 24 hours thus reflecting the diurnal cycle of human mobility. A particularly dramatic increase in variability occurs up to the temporal scale of about 15 minutes in all three cities and this implies that limits exist when we look forward or backward with respect to making short-term predictions. The degree of regularity varies in fact from city to city with Beijing and Singapore showing higher regularity in comparison to London across all temporal scales. A detailed discussion is provided, which relates the analysis to various characteristics of the three cities. In summary, this work contributes to a deeper understanding of regularities in patterns of transit use from variations in volumes of travellers entering subway stations, it establishes a generic analytical framework for comparative studies using urban mobility data, and it provides key points for the management of variability by policy-makers intent on for making the travel experience more amenable.

Estimating Urban Traffic Patterns through Probabilistic Interconnectivity of Road Network Junctions.

The emergence of large, fine-grained mobility datasets offers significant opportunities for the development and application of new methodologies for transportation analysis. In this paper, the link between routing behaviour and traffic patterns in urban areas is examined, introducing a method to derive estimates of traffic patterns from a large collection of fine-grained routing data. Using this dataset, the interconnectivity between road network junctions is extracted in the form of a Markov chain. This representation encodes the probability of the successive usage of adjacent road junctions, encoding routes as flows between decision points rather than flows along road segments. This network of functional interactions is then integrated within a modified Markov chain Monte Carlo (MCMC) framework, adapted for the estimation of urban traffic patterns. As part of this approach, the data-derived links between major junctions influence the movement of directed random walks executed across the network to model origin-destination journeys. The simulation process yields estimates of traffic distribution across the road network. The paper presents an implementation of the modified MCMC approach for London, United Kingdom, building an MCMC model based on a dataset of nearly 700000 minicab routes. Validation of the approach clarifies how each element of the MCMC framework contributes to junction prediction performance, and finds promising results in relation to the estimation of junction choice and minicab traffic distribution. The paper concludes by summarising the potential for the development and extension of this approach to the wider urban modelling domain.