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Global cognitive changes in older age impact driving behaviour and road safety, but how spatial orientation changes impact driving behaviours is unknown on a population level, despite clear implications for driving policy and evaluation during ageing. The present study aimed to establish how spatial navigation changes impact driving behaviour and road safety within a large cohort of older adults. 804 participants (mean age: 71.05) were recruited for a prospective cohort study. Participants self-reported driving behaviour followed by spatial orientation (allocentric & egocentric) testing and a broader online cognitive battery (visuomotor speed, processing speed, executive functioning, spatial working memory, episodic memory, visuospatial functioning). It was found that 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 behaviour in under 70 and over 70 age groups, a common age threshold for driving evaluation in older age. In conclusion, 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 ageing driving assessments, as it is has clear relevance for road safety within the ageing population.

Due to the limitation of data collection techniques and privacy issues, the problem of missing spatial origin-destination flows frequently occurs. Data imputation provides great support for the acquisition of complete flow data, which enables us to better understand regional connections and mobility patterns. However, existing models or approaches neglect the network structure of spatial flows, thus resulting in inappropriate estimates and a low performance. The development of graph neural networks offers a powerful tool to deal with graph-structured data. In this article, we proposed a spatial interaction graph convolutional network model, which combines graph convolution and a mapping function to predict flow data from the perspective of network learning. This model utilizes geographical unit embedding in local spatial networks to improve prediction accuracy. A negative sampling technique is adopted to reduce misestimation. Experiments on Beijing taxi trip data verified the usefulness of our model in spatial flow prediction. We also demonstrated that a biased training sample had a negative impact on the model's performance. More attributes of geographical units, a more proper negative sampling rate and a larger training set can increase the prediction accuracy of flow data.

Spatial boundaries play an important role in defining spaces, structuring memory and supporting planning during navigation. Recent models of hierarchical route planning use boundaries to plan efficiently first across regions and then within regions. However, it remains unclear which structures (e.g. parks, rivers, major streets, etc.) will form salient boundaries in real-world cities. This study tested licensed London taxi drivers, who are unique in their ability to navigate London flexibly without physical navigation aids. They were asked to indicate streets they considered as boundaries for London districts or dividing areas. It was found that agreement on boundary streets varied considerably, from some boundaries providing almost no consensus to some boundaries consistently noted as boundaries. Examining the properties of the streets revealed that a key factor in the consistent boundaries was the near rectilinear nature of the designated region (e.g. Mayfair and Soho) and the distinctiveness of parks (e.g. Regent's Park). Surprisingly, the River Thames was not consistently considered as a boundary. These findings provide insight into types of environmental features that lead to the perception of explicit boundaries in large-scale urban space. Because route planning models assume that boundaries are used to segregate the space for efficient planning, these results help make predictions of the likely planning demands of different routes in such complex large-scale street networks. Such predictions could be used to highlight information used for navigation guidance applications to enable more efficient hierarchical planning and learning of large-scale environments.

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.

Agent-based modelling (ABM) is a facet of wider Multi-Agent Systems (MAS) research that explores the collective behaviour of individual ‘agents’, and the implications that their behaviour and interactions have for wider systemic behaviour. The method has been shown to hold considerable value in exploring and understanding human societies, but is still largely confined to use in academia. This is particularly evident in the field of Urban Analytics; one that is characterised by the use of new forms of data in combination with computational approaches to gain insight into urban processes. In Urban Analytics, ABM is gaining popularity as a valuable method for understanding the low-level interactions that ultimately drive cities, but as yet is rarely used by stakeholders (planners, governments, etc.) to address real policy problems. This paper presents the state-of-the-art in the application of ABM at the interface of MAS and Urban Analytics by a group of ABM researchers who are affiliated with the Urban Analytics programme of the Alan Turing Institute in London (UK). It addresses issues around modelling behaviour, the use of new forms of data, the calibration of models under high uncertainty, real-time modelling, the use of AI techniques, large-scale models, and the implications for modelling policy. The discussion also contextualises current research in wider debates around Data Science, Artificial Intelligence, and MAS more broadly.

Movement and contacts are central to the transmission of infectious diseases and, within the hospital setting, healthcare worker (HCW) mobility and their contact with patients play an important role in the spread of nosocomial disease. Yet data relating to HCW behaviours associated with mobility and contacts in the healthcare environment are often limited. This paper proposes a framework for integrating several electronic data sources routinely-collected by modern hospitals, to enable the measurement of HCW behaviours relevant to the transmission of infections. Using data from a London teaching hospital during the COVID-19 pandemic, we demonstrate how, at an aggregate level, electronic medical records (EMRs) and door access logs can be used to establish changes in HCW mobility and patient contacts. In addition, to show the utility of these data sources in supporting infection prevention and control (IPC), we 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). Average daily rates of patient contacts are computed and found to be higher throughout the pandemic compared to that pre-pandemic, while the average daily rates of HCW mobility remained stable until the second wave, where they surpassed pre-pandemic levels. The response of HCW behaviour to the pandemic was not equal between floors, whereby the highest increases in patient contacts and mobility were on floors handling the majority of COVID-19 patients. The first wave of COVID-19 patients resulted in changes to the flow of HCWs between floors, but the interconnectivity between COVID-19 and non COVID-19 wards was evident throughout the pandemic. Daily rates of indirect contact between patients provided evidence for reactive staff cohorting, whereby indirect contact rates between COVID-19 positive and negative patients were lowest during peaks in COVID-19 hospital admissions. We propose that IPC practitioners use these routinely collected data on HCW behaviour to support infection control activities and to help better protect hospital staff and patients from nosocomial outbreaks of communicable diseases.

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.

By 2020, over 100 countries expanded electric and plug-in hybrid electric vehicle (EV/PHEV) technologies, with global sales surpassing 7 million units. Governments are adopting cleaner vehicle technologies due to proven environmental and health implications of internal combustion engine vehicles (ICEVs), evidenced by the recent COP26 meeting. This article proposes an agent-based model of vehicle activity as a tool for quantifying energy consumption by simulating a fleet of EV/PHEVs within an urban street network at various spatio-temporal resolutions. Driver behaviour plays a significant role in fuel consumption, thus, simulating various levels of individual behaviour enhancing heterogeneity should provide more accurate results of potential energy demand in cities. The study found that 1) energy consumption is lowest when speed limit adherence increases (low variance in behaviour) and is highest when acceleration/deceleration patterns vary (high variance in behaviour) and 2) on average, for tested vehicles, EV/PHEVs were £116.33 cheaper to run than ICEVs across all experiment conditions. The difference in the average fuel costs (electricity and petrol) shrinks at the vehicle level as driver behaviour is less varied (more homogeneous). This research should allow policymakers to quantify the demand for energy and subsequent fuel costs in cities.

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, data 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 a heavy requirement on dashboards as information systems, resulting in poor usability and ineffective design. Understanding these shortfalls is a challenge given the absence of a consistent and comprehensive approach to dashboard evaluation. In this paper we systematically review literature on dashboard implementation in the healthcare domain, a field where dashboards have been employed widely, and in which 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 a dashboard task-based classification, which underpin our subsequent analysis. From a total of 81 papers we derive 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 subtle and less well posed questions. We conclude by outlining a number of active discussion points and a set of dashboard evaluation best practices for the academic, clinical and software development communities alike.

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.

Pedestrian navigation decisions take place simultaneously at multiple spatial scales. Yet most models of pedestrian behaviour focus either on local physical interactions or optimisation of routes across a road network. We present a novel hierarchical pedestrian route choice framework that integrates dynamic, perceptual decisions at the street level with abstract, network-based decisions at the neighbourhood level. The framework is based on construal level theory which states that decision makers construe decisions based on their psychological distance from the object of the decision. We implement this route choice framework in a spatial agent-based model in which pedestrian and vehicle agents complete trips in an urban environment. Using global sensitivity analysis techniques, we demonstrate the interaction between route choice components representing decision making at different spatial and temporal scales. Additionally, through comparison to a least cost network model, we demonstrate the increased route heterogeneity produced by this approach. This work could form the basis of an alternative method for producing pedestrian route alternatives. The granularity and scale of the modelled pedestrian trajectories could also help improve appraisals of street infrastructure.

Background The SARS-CoV-2 pandemic has caused an unprecedented strain on healthcare systems worldwide, including the United Kingdom National Health Service (NHS). We conducted an observational cohort study of SARS-CoV-2 infection in frontline healthcare workers (HCW) working in an acute NHS Trust during the first wave of the pandemic, to answer emerging questions surrounding SARS-CoV-2 infection, diagnosis, transmission and control. Methods Using self-collected weekly saliva and twice weekly combined oropharyngeal/nasopharyngeal (OP/NP) samples, in addition to self-assessed symptom profiles and isolation behaviours, we retrospectively compared SARS-CoV-2 detection by RT-qPCR of saliva and OP/NP samples. We report the association with contemporaneous symptoms and isolation behaviour. Results Over a 12-week period from 30th March 2020, 40·0% (n = 34/85, 95% confidence interval 31·3–51·8%) HCW had evidence of SARS-CoV-2 infection by surveillance OP/NP swab and/or saliva sample. Symptoms were reported by 47·1% (n = 40) and self-isolation by 25·9% (n = 22) participants. Only 44.1% (n = 15/34) participants with SARS-CoV-2 infection reported any symptoms within 14 days of a positive result and only 29·4% (n = 10/34) reported self-isolation periods. Overall agreement between paired saliva and OP/NP swabs was 93·4% (n = 211/226 pairs) but rates of positive concordance were low. In paired samples with at least one positive result, 35·0% (n = 7/20) were positive exclusively by OP/NP swab, 40·0% (n = 8/20) exclusively by saliva and in only 25·0% (n = 5/20) were the OP/NP and saliva result both positive. Conclusions HCW are a potential source of SARS-CoV-2 transmission in hospitals and symptom screening will identify the minority of infections. Without routine asymptomatic SARS-CoV-2 screening, it is likely that HCW with SARS-CoV-2 infection would continue to attend work. Saliva, in addition to OP/NP swab testing, facilitated ascertainment of symptomatic and asymptomatic SARS-CoV-2 infections. Combined saliva and OP/NP swab sampling would improve detection of SARS-CoV-2 for surveillance and is recommended for a high sensitivity strategy.

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.

Licenced London taxi drivers have been found to show changes in the grey 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 examined the process of how this knowledge is acquired and we detail key steps that include: systematic study of maps, travel on selected overlapping routes, the mental visualisation of places and the optimal use of subgoals. We provide the first map of the street network covered by the routes used to learn, allowing insight into where gaps in the network exist. The methods could be widely applied to aid spatial learning in the general population and may provide insights for artificial intelligence (AI) systems to efficiently learn new environments.

Roadside collisions are a significant problem faced by all countries. Urbanisation has led to an increase in traffic congestion and roadside vehicle collisions. According to the UK Government’s Department for Transport, most vehicle collisions occur on urban roads, with empirical evidence showing drivers are more likely to break local and fixed speed limits in urban environments. Analysis conducted by the Department for Transport found that the UK’s accident prevention measure’s cost is estimated to be £33bn per year. Therefore, there is a strong motivation to investigate the causes of roadside collisions in urban environments to better prepare traffic management, support local council policies, and ultimately reduce collision rates. This study utilises agent-based modelling as a tool to plan, experiment and investigate the relationship between speeding and vehicle density with collisions. The study found that higher traffic density results in more vehicles travelling at a slower speed, regardless of the degree to which drivers comply with speed restrictions. Secondly, collisions increase linearly as speed compliance is reduced for all densities. Collisions are lowest when all vehicles comply with speed limits for all densities. Lastly, higher global traffic densities result in higher local traffic densities near-collision sites across all adherence levels, increasing the likelihood of congestion around these sites. This work, when extended to real-world applications using empirical data, can support effective road safety policies.

Cities are continuing to develop and are grappling with uncertainties and difficulties as they do so. It has therefore become essential to understand how urban spatial structure changes, particularly with the increasingly available sources of ‘big data’. However, most studies mainly focus on delineating the spatial structure and its variations. Only a few have investigated the incentives behind the movement dynamics. To identify the urban structure of Greater London and uncover how it co-evolves with socio-economic and spatial policy factors, this study applies network community detection, using smart card data derived from the years 2013, 2015 and 2017, respectively. Our findings show that, firstly, between 2013 and 2017, London's urban structure moved towards a more polycentric and compact pattern. Secondly, it is found that Greater London can be clustered into five communities based on the characteristics of passengers' travel patterns. Thirdly, the dynamics of structural change in different urban clusters differ both in terms of changing intensity and potential motivation. In addition to spatial impact and spatial strategic policies, our results show that employment density and residential densities are also the main indicators that affected the interaction between Londoners in different areas on various levels.

The COVID-19 pandemic has had a profound impact on the life of our cities. While we have observed major disruption to collective activity, aspects of urban life have continued. In this study, we explore the changed spatiotemporal nature of mobility demand. Through a comparative analysis of New York City taxi records data, we observe how relative demand for taxis has displaced across land use zones and concentrated during daylight hours. We also find changes in payment methods that counter public health advice. These findings point to the fundamental mobility needs during the pandemic.

BackgroundRoutine asymptomatic testing using RT-PCR of people who interact with vulnerable populations, such as medical staff in hospitals or care workers in care homes, has been employed to help prevent outbreaks among vulnerable populations. Although the peak sensitivity of RT-PCR can be high, the probability of detecting an infection will vary throughout the course of an infection. The effectiveness of routine asymptomatic testing will therefore depend on testing frequency and how PCR detection varies over time.MethodsWe fitted a Bayesian statistical model to a dataset of twice weekly PCR tests of UK healthcare workers performed by self-administered nasopharyngeal swab, regardless of symptoms. We jointly estimated times of infection and the probability of a positive PCR test over time following infection, we then compared asymptomatic testing strategies by calculating the probability that a symptomatic infection is detected before symptom onset and the probability that an asymptomatic infection is detected within 7 days of infection.FindingsWe estimated that the probability that the PCR test detected infection peaked at 77% (54 - 88%) 4 days after infection, decreasing to 50% (38 - 65%) by 10 days after infection. Our results suggest a substantially higher probability of detecting infections 1–3 days after infection than previously published estimates. We estimated that testing every other day would detect 57% (33-76%) of symptomatic cases prior to onset and 94% (75-99%) of asymptomatic cases within 7 days if test results were returned within a day.InterpretationOur results suggest that routine asymptomatic testing can enable detection of a high proportion of infected individuals early in their infection, provided that the testing is frequent and the time from testing to notification of results is sufficiently fast.FundingWellcome Trust, National Institute for Health Research (NIHR) Health Protection Research Unit, Medical Research Council (UKRI)

Spatial cognition is fundamental to the behaviour and activity of humans in urban space. Humans perceive their environments with systematic biases and errors, and act upon these perceptions, which in turn form urban patterns of activity. These perceptions are influenced by a multitude of factors, many of them relating to the static urban form. Yet much of geographic analysis ignores the influence of urban form, instead referring most commonly to the Euclidean arrangement of space. In this paper, we propose a novel spatial modelling framework for estimating cognitive distance in urban space. This framework is constructed from a wealth of research describing the effect of environmental factors on distance estimation, and produces a quantitative estimate of the effect based on standard GIS data. Unlike other cost measures, the cognitive distance estimate integrates systematically observed distortions and biases in spatial cognition. As a proof-of-concept, the framework is implemented for 26 cities worldwide using open data, producing a novel comparative measure of ‘cognitive accessibility’. The paper concludes with a discussion of the potential of this approach in analysing and modelling urban systems, and outlines areas for further research.

Agent-based modeling is a powerful simulation technique that allows one to build artificial worlds and populate these worlds with individual agents. Each agent or actor has unique behaviors and rules which govern their interactions with each other and their environment. It is through these interactions that more macro-phenomena emerge: for example, how individual pedestrians lead to the emergence of crowds. Over the past two decades, with the growth of computational power and data, agent-based models have evolved into one of the main paradigms for urban modeling and for understanding the various processes which shape our cities. Agent-based models have been developed to explore a vast range of urban phenomena from that of micro-movement of pedestrians over seconds to that of urban growth over decades and many other issues in between. In this chapter, we introduce readers to agent-based modeling from simple abstract applications to those representing space utilizing geographical data not only for the creation of the artificial worlds but also for the validation and calibration of such models through a series of example applications. We will then discuss how big data, data mining, and machine learning techniques are advancing the field of agent-based modeling and demonstrate how such data and techniques can be leveraged into these models, giving us a new way to explore cities.

Background SARS-CoV-2 infection in Healthcare Workers (HCWs) is a public health concern during the pandemic. Little description has been made of their antibody response over time in the presence or absence detectable SARS-CoV-2 RNA and of symptoms. We followed a cohort of patient-facing HCWs at an acute hospital in London to measure seroconversion and RNA detection at the peak of the pandemic in London. Methods We enrolled 200 front-line HCWs between 26 March and 8 April 2020 and collected twice-weekly self-administered nose and throat swabs and monthly blood samples. Baseline and regular symptom data were also collected. Swabs were tested for SARS-CoV-2 RNA by polymerase chain reaction, and serum for IgM, IgA and IgG antibodies to the virus spike protein by enzyme-linked immunosorbent assay and flow cytometry. Findings We enrolled HCWs with a variety of roles who worked in areas where COVID-19 patients were admitted and cared for. During the first month of observation, 42/200 (21%) HCWs were PCR positive in at least one nose and throat swab. Only 8/42 HCW (19%) who were PCR positive during the study period had symptoms that met the current case definition. Of 181 HCWs who provided enrollment and follow-up blood samples, 82/181 (45.3%) were seropositive; 36/181 (19.9%) seroconverted during the study and 46/181 (25.4%) were seropositive at both time points. In 33 HCWs who had positive serology at baseline but were PCR negative, 32 remained PCR negative throughout follow-up. One HCW had a PCR positive swab six days after enrollment, likely representing a waning infection. Interpretation The extremely high seropositivity and RNA detection in this cohort of front-line HCWs who worked during the peak of the pandemic brings policies to protect staff and patients in the hospital environment into acute focus. Our findings have implications for planning for the expected second wave and for future vaccination roll out campaigns in similar settings. The further evidence of asymptomatic SARS-CoV-2 infection indicates that asymptomatic surveillance of HCWs is essential while our study sets the foundations to answer pertinent questions around the duration of protective immune response and the risk of re-infection.

Uncertainty and interconnectedness in complex engineering and engineered systems such as power-grids and telecommunication networks are sources of vulnerability compromising the resilience of these systems. Conditions of uncertainty and interconnectedness change over time and depend on emerging socio-technical contexts, thus conventional methods which can conduct normative, descriptive and prescriptive assessment of complex engineering and engineered systems resilience are limited. This paper brings together contributions of experts in complex engineering and engineered systems who have identified six methods, three each for uncertainty and interconnectedness, which form the foundational methods for knowing complex engineering and engineered systems resilience. The paper has reviewed how these methods contribute to overcoming uncertainty or interconnectedness and how they are implemented using case studies in order to illustrate essential approaches to enhancing resilience. It is hoped that this approach will allow the subject to be quantified and best practice standards to develop.

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.