Sensing pedestrian flows for real-time assessment of non-pharmaceutical policy interventions during the COVID-19 pandemic

IntroductionTo combat and mitigate the transmission of the SARS-CoV-2 virus, reducing the number of social contacts within a population is highly effective. Non-pharmaceutical policy interventions, e.g. stay-at-home orders, closing schools, universities, and (non-essential) businesses, are expected to decrease pedestrian flows in public areas, leading to reduced social contacts. The extent to which such interventions show the targeted effect is often measured retrospectively by surveying behavioural changes. Approaches that use data generated through mobile phones are hindered by data confidentiality and privacy regulations and complicated by selection effects. Furthermore, access to such sensitive data is limited. However, a complex pandemic situation requires a fast evaluation of the effectiveness of the introduced interventions aiming to reduce social contacts. Location-based sensor systems installed in cities, providing objective measurements of spatial mobility in the form of pedestrian flows, are suited for such a purpose. These devices record changes in a population’s behaviour in real-time, do not have privacy problems as they do not identify persons, and have no selection problems due to ownership of a device. ObjectiveThis work aimed to analyse location-based sensor measurements of pedestrian flows in 49 metropolitan areas at 100 locations in Germany to study whether such technology is suitable for the real-time assessment of behavioural changes during a phase of several different pandemic-related policy interventions. MethodsSpatial mobility data of pedestrian flows was linked with policy interventions using the date as a unique linkage key. Data was visualised to observe potential changes in pedestrian flows before or after interventions. Furthermore, differences in time series of pedestrian counts between the pandemic and the pre-pandemic year were analysed. ResultsThe sensors detected changes in mobility patterns even before policy interventions were enacted. Compared to the pre-pandemic year, pedestrian counts were 85% lower. ConclusionsThe study illustrated the practical value of sensor-based real-time measurements when linked with non-pharmaceutical policy intervention data. This study’s core contribution is that the sensors detected behavioural changes before enacting or loosening non-pharmaceutical policy interventions. Therefore, such technologies should be considered in the future by policymakers for crisis management and policy evaluation.

Mobility Patterns and Mode Choice Preferences during the COVID-19 Situation

The empirical research on the COVID-19 epidemic’s consequences suggests a major drop in human mobility and a significant shift in travel patterns across all forms of transportation. We can observe a shift from public transport and an increase in car use, and in some cases also increase of cycling and (less often) walking. Furthermore, it seems that micromobility and, more generally, environmentally friendly and comanaged mobility (including shared services), are gaining ground. In previous research, much attention was paid to the mode choice preferences during lockdown, or early stages of the SARS-CoV-2 situation. The blind spot, and aim of this work, is how long observed changes in mode choice last and when or if we can expect the mode choice to shift back to the situation before the SARS-CoV-2 episodes. The research sample consisted of 636 cases; in total, 10 countries contributed to the sample examined in this study. The data were collected in two phases: the first in the spring of 2020 and the second in the fall of the same year. Results showed that respondents reduced mobility by car, local public transport and walking, but not bicycling during the lockdown, compared to the time before the pandemic started. When the easing came, respondents assessed their own use of the car and walking as almost back to normal. They also reported an increase in the use of public transport, but not reaching the level prior the pandemic by far. It seems that cycling was affected least by the pandemic; use of a bicycle hardly changed at all. As for the implication of our study, it is evident that special attention and actions will be needed to bring citizens back to public transport, as it seems that the impact of the pandemic on public transport use will last much longer than the pandemic itself.

Mobility in Unsupervised Word Embeddings for Knowledge Extraction—The Scholars’ Trajectories across Research Topics

In the knowledge discovery field of the Big Data domain the analysis of geographic positioning and mobility information plays a key role. At the same time, in the Natural Language Processing (NLP) domain pre-trained models such as BERT and word embedding algorithms such as Word2Vec enabled a rich encoding of words that allows mapping textual data into points of an arbitrary multi-dimensional space, in which the notion of proximity reflects an association among terms or topics. The main contribution of this paper is to show how analytical tools, traditionally adopted to deal with geographic data to measure the mobility of an agent in a time interval, can also be effectively applied to extract knowledge in a semantic realm, such as a semantic space of words and topics, looking for latent trajectories that can benefit the properties of neural network latent representations. As a case study, the Scopus database was queried about works of highly cited researchers in recent years. On this basis, we performed a dynamic analysis, for measuring the Radius of Gyration as an index of the mobility of researchers across scientific topics. The semantic space is built from the automatic analysis of the paper abstracts of each author. In particular, we evaluated two different methodologies to build the semantic space and we found that Word2Vec embeddings perform better than the BERT ones for this task. Finally, The scholars’ trajectories show some latent properties of this model, which also represent new scientific contributions of this work. These properties include (i) the correlation between the scientific mobility and the achievement of scientific results, measured through the H-index; (ii) differences in the behavior of researchers working in different countries and subjects; and (iii) some interesting similarities between mobility patterns in this semantic realm and those typically observed in the case of human mobility.

Modelling imperfect knowledge via location semantics for realistic privacy risks estimation in trajectory data

Mobility patterns of vehicles and people provide powerful data sources for location-based services such as fleet optimization and traffic flow analysis. Location-based service providers must balance the value they extract from trajectory data with protecting the privacy of the individuals behind those trajectories. Reaching this goal requires measuring accurately the values of utility and privacy. Current measurement approaches assume adversaries with perfect knowledge, thus overestimate the privacy risk. To address this issue, we introduce a model of an adversary with imperfect knowledge about the target. The model is based on equivalence areas, spatio-temporal regions with a semantic meaning, e.g. the target’s home, whose size and accuracy determine the skill of the adversary. We then derive the standard privacy metrics of k-anonymity, l-diversity and t-closeness from the definition of equivalence areas. These metrics can be computed on any dataset, irrespective of whether and what kind of anonymization has been applied to it. This work is of high relevance to all service providers acting as processors of trajectory data who want to manage privacy risks and optimize the privacy vs. utility trade-off of their services.

Spatial Analysis of Environmental Impacts Linked to Changes in Urban Mobility Patterns during COVID-19: Lessons Learned from the Cartagena Case Study

The impact of the pandemic caused by COVID-19 on urban pollution in our cities is a proven fact, although its mechanisms are not known in great detail. The change in urban mobility patterns due to the restrictions imposed on the population during lockdown is a phenomenon that can be parameterized and studied from the perspective of spatial analysis. This study proposes an analysis of the guiding parameters of these changes from the perspective of spatial analysis. To do so, the case study of the city of Cartagena, a medium-sized city in Spain, has been analyzed throughout the period of mobility restrictions due to COVID-19. By means of a geostatistical analysis, changes in urban mobility patterns and the modal distribution of transport have been correlated with the evolution of environmental air quality indicators in the city. The results show that despite the positive effect of the pandemic in its beginnings on the environmental impact of urban mobility, the changes generated in the behavior patterns of current mobility users favor the most polluting modes of travel in cities.

Impact of Human Mobility Trends on the Outbreak of COVID-19 Across Different States of India

The explosion of coronavirus has posed challenges to public health infrastructure in India. This pandemic can be contained with social distancing and isolation. The analysis of human mobility trends plays a decisive role in the spread of the pandemic. These movement patterns are extracted from Google COVID-19 Community Mobile Reports. These reports help to analyze the human mobility trends to various frequently visited places across different states of India. This work focuses on analyzing mobility trends in India and their effect on the spread of pandemic in terms of number of active cases and death rate. The mobility patterns, number of tests conducted, population density across different states in India are explored to understand their effect on the severity of epidemic. These features are correlated using statistical methods. This study lays the foundation in building a framework to contain the contributors for the spread of pandemics and provide insights to the regulatory bodies to strategize enforcing or revoking lockdown restrictions across regions in the country.

Comparison of human and faunal enamel isotopes reveals diverse paleodiet and exchange patterns at the highland Maya Site of Kaminaljuyu, Guatemala

This study provides an isotopic examination of both human and animal paleodiets and mobility patterns at a highland Maya community. Kaminaljuyu, Guatemala, was a large Prehispanic center located in a distinctly cooler, drier setting compared with the majority of Maya sites in the surrounding lowlands. Previous archaeological research at Kaminaljuyu revealed it played an important political and economic role in the Maya region, assisting in the obsidian trade network and maintaining ties with communities as far away as Teotihuacan in central Mexico. By examining the strontium (87Sr/86Sr), carbon (δ13C), and oxygen (δ18O) isotope values from dental enamel of humans and terrestrial mammals at the site, this study provides direct evidence of long-distance animal trade, explores the nature and timing of such activities, and compares highland dietary patterns with faunal studies in the lowlands. Our results indicate that isotopically non-local humans and animals are most frequently found in special and ceremonial contexts, indicating that long-distance movements of people and products were motivated for politically or ritually significant events. Although dietary patterns showed cross-species variation, diets within species were similar between highland and lowland settings.