How do taxi drivers expose to fine particulate matter (PM2.5) in a Chinese megacity: a rapid assessment incorporating with satellite-derived information and urban mobility data.

BACKGROUND: Taxi drivers in a Chinese megacity are frequently exposed to traffic-related particulate matter (PM2.5) due to their job nature, busy road traffic, and urban density. A robust method to quantify dynamic population exposure to PM2.5 among taxi drivers is important for occupational risk prevention, however, it is limited by data availability. METHODS: This study proposed a rapid assessment of dynamic exposure to PM2.5 among drivers based on satellite-derived information, air quality data from monitoring stations, and GPS-based taxi trajectory data. An empirical study was conducted in Wuhan, China, to examine spatial and temporal variability of dynamic exposure and compare whether drivers' exposure exceeded the World Health Organization (WHO) and China air quality guideline thresholds. Kernel density estimation was conducted to further explore the relationship between dynamic exposure and taxi drivers' activities. RESULTS: The taxi drivers' weekday and weekend 24-h PM2.5 exposure was 83.60 µg/m3 and 55.62 µg/m3 respectively, 3.4 and 2.2 times than the WHO's recommended level of 25 µg/m3. Specifically, drivers with high PM2.5 exposure had a higher average trip distance and smaller activity areas. Although major transportation interchanges/terminals were the common activity hotspots for both taxi drivers with high and low exposure, activity hotspots of drivers with high exposure were mainly located in busy riverside commercial areas within historic and central districts bounded by the "Inner Ring Road", while hotspots of drivers with low exposure were new commercial areas in the extended urbanized area bounded by the "Third Ring Road". CONCLUSION: These findings emphasized the need for air quality management and community planning to mitigate the potential health risks of taxi drivers.

Exploring data sources and mathematical approaches for estimating human mobility rates and implications for understanding COVID-19 dynamics: a systematic literature review.

Human mobility, which refers to the movement of people from one location to another, is believed to be one of the key factors shaping the dynamics of the COVID-19 pandemic. There are multiple reasons that can change human mobility patterns, such as fear of an infection, control measures restricting movement, economic opportunities, political instability, etc. Human mobility rates are complex to estimate as they can occur on various time scales, depending on the context and factors driving the movement. For example, short-term movements are influenced by the daily work schedule, whereas long-term trends can be due to seasonal employment opportunities. The goal of the study is to perform literature review to: (i) identify relevant data sources that can be used to estimate human mobility rates at different time scales, (ii) understand the utilization of variety of data to measure human movement trends under different contexts of mobility changes, and (iii) unraveling the associations between human mobility rates and social determinants of health affecting COVID-19 disease dynamics. The systematic review of literature was carried out to collect relevant articles on human mobility. Our study highlights the use of three major sources of mobility data: public transit, mobile phones, and social surveys. The results also provides analysis of the data to estimate mobility metrics from the diverse data sources. All major factors which directly and indirectly influenced human mobility during the COVID-19 spread are explored. Our study recommends that (a) a significant balance between primitive and new estimated mobility parameters need to be maintained, (b) the accuracy and applicability of mobility data sources should be improved, (c) encouraging broader interdisciplinary collaboration in movement-based research is crucial for advancing the study of COVID-19 dynamics among scholars from various disciplines.

A data-driven method for identifying the locations of hurricane evacuations from mobile phone location data.

How evacuations are managed can substantially impact the risks faced by affected communities. Having a better understanding of the mobility patterns of evacuees can improve the planning and management of these evacuations. Although mobility patterns during evacuations have traditionally been studied through surveys, mobile phone location data can be used to capture these movements for a greater number of evacuees over a larger geographic area. Several approaches have been used to identify hurricane evacuation patterns from location data; however, each approach relies on researcher judgment to first determine the areas from which evacuations occurred and then identify evacuations by determining when an individual spends a specified number of nights away from home. This approach runs the risk of detecting non-evacuation behaviors (e.g., work trips, vacations, etc.) and incorrectly labeling them as evacuations where none occurred. In this article, we developed a data-driven method to determine which areas experienced evacuations. With this approach, we inferred home locations of mobile phone users, calculated their departure times, and determined if an evacuation may have occurred by comparing the number of departures around the time of the hurricane against historical trends. As a case study, we applied this method to location data from Hurricanes Matthew and Irma to identify areas that experienced evacuations and illustrate how this method can be used to detect changes in departure behavior leading up to and following a hurricane. We validated and examined the inferred homes for representativeness and validated observed evacuation trends against past studies.

Leveraging big data for outdoor recreation management: A case study from the York river in Virginia.

Outdoor recreation is important for improving quality of life, well-being, and local economies, but quantifying its value without direct monetary transactions can be challenging. This study explores combining non-market valuation techniques with emerging big data sources to estimate the value of recreation for the York River and surrounding parks in Virginia. By applying the travel cost method to anonymous human mobility data, we gain deeper insights into the significance of recreational experiences for visitors and the local economy. Results of a zero-inflated Negative Binomial model show a mean consumer surplus value of $26.91 per trip, totaling $15.5 million across nearly 600,000 trips observed in 2022. Further, weekends, holidays, and the summer and fall months are found to be peak visitation times, whereas those with young children and who are Hispanic or over 64 years old are less likely to visit. These findings shed light on various factors influencing visitation patterns and recreation values, including temporal effects and socio-demographics, revealing disparities that warrant targeted efforts for inclusivity and accessibility. Policymakers can use these insights to make informed and sustainable choices in outdoor recreation management, fostering the preservation of natural resources for the benefit of both visitors and the environment.

Greenspace use during the COVID-19 pandemic: A longitudinal population mobility study in the United Kingdom.

BACKGROUND: The coronavirus pandemic greatly disrupted the lives of people. Restrictions introduced worldwide to limit the spread of infection included stay-at-home orders, closure of venues, restrictions to travel and limits to social contacts. During this time, parks and outdoor greenspaces gained prominent attention as alternative location for respite. Population mobility data offers a unique opportunity to understand the impact of the pandemic on outdoor behaviour. We examine the role of the restrictions on park use throughout the full span of the pandemic while controlling for weather and region. METHODS: This study provides a longitudinal population analysis of park visitation using Google COVID-19 Community Mobility Reports data in the UK. Daily park visitation was plotted and ANOVA analyses tested season and year effects in visitation. Then, regressions examined park visitation beyond weather (temperature and rain), according to COVID-19 restrictions, while controlling for region specificities through unit fixed effect models. RESULTS: Time series and ANOVA analyses documented the significant decrease in park visitation in the spring of 2020, the seasonal pattern in visitation, and an overall sustained and elevated use over nearly three years. Regressions confirmed park visitation increased significantly when temperature was greater and when it rained less. More visitation was also seen when there were fewer COVID-19 cases and when the stringency level of restrictions was lower. Of special interest, a significant interaction effect was found between temperature and stringency, with stringency significantly supressing the effect of higher temperature on visitation. CONCLUSIONS: COVID-19 restrictions negatively impacted park visitation on warm days. Given the general health, social, and wellbeing benefits of greenspace use, one should consider the collateral negative impact of restrictions on park visitation. When social distancing of contacts is required, the few remaining locations where it can safely occur should instead be promoted.

COVID-19, stigma, and habituation: evidence from mobility data.

BACKGROUND: The Japanese government has restricted people's going-out behavior by declaring a non-punitive state of emergency several times under COVID-19. This study aims to analyze how multiple policy interventions that impose non-legally binding restrictions on behavior associate with people's going-out. THEORY: This study models the stigma model of self-restraint behavior under the pandemic with habituation effects. The theoretical result indicates that the state of emergency's self-restraint effects weaken with the number of times. METHODS: The empirical analysis examines the impact of emergency declarations on going-out behavior using a prefecture-level daily panel dataset. The dataset includes Google's going-out behavior data, the Japanese government's policy interventions based on emergency declarations, and covariates that affect going-out behavior, such as weather and holidays. RESULTS: First, for multiple emergency declarations from the beginning of the pandemic to 2021, the negative association between emergency declarations and mobility was confirmed in a model that did not distinguish the number of emergency declarations. Second, in the model that considers the number of declarations, the negative association was found to decrease with the number of declarations. CONCLUSION: These empirical analyses are consistent with the results of theoretical analyses, which show that the negative association between people's going-out behavior and emergency declarations decreases in magnitude as the number of declarations increases.

Characterizing superspreading events and age-specific infectiousness of SARS-CoV-2 transmission in Georgia, USA.

It is imperative to advance our understanding of heterogeneities in the transmission of SARS-CoV-2 such as age-specific infectiousness and superspreading. To this end, it is important to exploit multiple data streams that are becoming abundantly available during the pandemic. In this paper, we formulate an individual-level spatiotemporal mechanistic framework to integrate individual surveillance data with geolocation data and aggregate mobility data, enabling a more granular understanding of the transmission dynamics of SARS-CoV-2. We analyze reported cases, between March and early May 2020, in five (urban and rural) counties in the state of Georgia. First, our results show that the reproductive number reduced to below one in about 2 wk after the shelter-in-place order. Superspreading appears to be widespread across space and time, and it may have a particularly important role in driving the outbreak in rural areas and an increasing importance toward later stages of outbreaks in both urban and rural settings. Overall, about 2% of cases were directly responsible for 20% of all infections. We estimate that the infected nonelderly cases (<60 y) may be 2.78 [2.10, 4.22] times more infectious than the elderly, and the former tend to be the main driver of superspreading. Our results improve our understanding of the natural history and transmission dynamics of SARS-CoV-2. More importantly, we reveal the roles of age-specific infectiousness and characterize systematic variations and associated risk factors of superspreading. These have important implications for the planning of relaxing social distancing and, more generally, designing optimal control measures.

Disruptions to in-person medical visits across the United States during the COVID-19 pandemic: evolving disparities by medical specialty and socio-economic status.

OBJECTIVES: This study aimed to investigate how people's health-seeking behaviors evolve in the COVID-19 pandemic by community and medical service category. STUDY DESIGN: This is a longitudinal study using mobility data from 19 million mobile devices of visits to all types of health facility locations for all US states. METHODS: We examine the variations in weekly in-person medical visits across county, neighborhood, and specialty levels. Different regression models are used for each level to investigate factors that influence the disparities in medical visits. County-level analysis explores associations between county medical visit patterns, political orientation, and COVID-19 infection rate. Neighborhood-level analysis focuses on neighborhood socio-economic compositions as potential determinants of medical visit levels. Specialty-level analysis compares the evolution of visit disruptions in different specialties. RESULTS: A more left-leaning political orientation and a higher local infection rate were associated with larger decreases in in-person medical visits, and these associations became stronger, moving from the initial period of stay-at-home orders into the post-lockdown period. Initial reactions were strongest for seniors and those of high socio-economic status, but this reversed in post-lockdown period where socio-economically disadvantaged communities stabilized at a lower level of medical visits. Neighborhoods with more female and young people exhibited larger decreases in in-person medical visits throughout the initial and post-lockdown periods. The evolution of disruptions diverges across medical specialties, from only short-term disruption in specialties such as dentistry to increasing disruption, as in cardiology. CONCLUSIONS: Given distinct patterns in visit between communities, medical service categories, and between different periods in the pandemic, policy makers, and providers should concentrate on monitoring patients in disrupted specialties who overlap with the at-risk contexts and socio-economic factors in future health emergencies.

Polygon Simplification for the Efficient Approximate Analytics of Georeferenced Big Data.

The unprecedented availability of sensor networks and GPS-enabled devices has caused the accumulation of voluminous georeferenced data streams. These data streams offer an opportunity to derive valuable insights and facilitate decision making for urban planning. However, processing and managing such data is challenging, given the size and multidimensionality of these data. Therefore, there is a growing interest in spatial approximate query processing depending on stratified-like sampling methods. However, in these solutions, as the number of strata increases, response time grows, thus counteracting the benefits of sampling. In this paper, we originally show the design and realization of a novel online geospatial approximate processing solution called GeoRAP. GeoRAP employs a front-stage filter based on the Ramer-Douglas-Peucker line simplification algorithm to reduce the size of study area coverage; thereafter, it employs a spatial stratified-like sampling method that minimizes the number of strata, thus increasing throughput and minimizing response time, while keeping the accuracy loss in check. Our method is applicable for various online and batch geospatial processing workloads, including complex geo-statistics, aggregation queries, and the generation of region-based aggregate geo-maps such as choropleth maps and heatmaps. We have extensively tested the performance of our prototyped solution with real-world big spatial data, and this paper shows that GeoRAP can outperform state-of-the-art baselines by an order of magnitude in terms of throughput while statistically obtaining results with good accuracy.

Modelling the geographical spread of HIV among MSM in Guangdong, China: a metapopulation model considering the impact of pre-exposure prophylaxis.

Men who have sex with men (MSM) make up the majority of new human immunodeficiency virus (HIV) diagnoses among young people in China. Understanding HIV transmission dynamics among the MSM population is, therefore, crucial for the control and prevention of HIV infections, especially for some newly reported genotypes of HIV. This study presents a metapopulation model considering the impact of pre-exposure prophylaxis (PrEP) to investigate the geographical spread of a hypothetically new genotype of HIV among MSM in Guangdong, China. We use multiple data sources to construct this model to characterize the behavioural dynamics underlying the spread of HIV within and between 21 prefecture-level cities (i.e. Guangzhou, Shenzhen, Foshan, etc.) in Guangdong province: the online social network via a gay social networking app, the offline human mobility network via the Baidu mobility website, and self-reported sexual behaviours among MSM. Results show that PrEP initiation exponentially delays the occurrence of the virus for the rest of the cities transmitted from the initial outbreak city; hubs on the movement network, such as Guangzhou, Shenzhen, and Foshan are at a higher risk of 'earliest' exposure to the new HIV genotype; most cities acquire the virus directly from the initial outbreak city while others acquire the virus from cities that are not initial outbreak locations and have relatively high betweenness centralities, such as Guangzhou, Shenzhen and Shantou. This study provides insights in predicting the geographical spread of a new genotype of HIV among an MSM population from different regions and assessing the importance of prefecture-level cities in the control and prevention of HIV in Guangdong province. This article is part of the theme issue 'Data science approach to infectious disease surveillance'.

COVID-19 effects on urban driving, walking, and transit usage trends: Evidence from Indian metropolitan cities.

The outbreak of the COVID-19 pandemic disrupted all walks of life, including the transportation sector. Fear of the contagion coupled with government regulations to restrict mobility altered the travel behavior of the public. This study proposes integrating freely accessible aggregate mobility datasets published by tech giants Apple and Google, which opens a broader avenue for mobility research in the light of difficult data collection circumstances. A comparative analysis of the changes in usage of different mobility modes during the national lockdown and unlock policy periods across 6 Indian cities (Bangalore, Chennai, Delhi, Hyderabad, Mumbai, and Pune) explain the spatio-temporal differences in mode usages. The study shows a preference for individual travel modes (walking and driving) over public transit. Comparisons with pre-pandemic mode shares present evidence of inertia in the choice of travel modes. Association investigations through generalized linear mixed-effects models identify income, vehicle registrations, and employment rates at the city level to significantly impact the community mobility trends. The methods and interpretations from this study benefit government, planners, and researchers to boost informed policymaking and implementation during a future emergency demanding mobility regulations in the high-density urban conglomerations.

Variation in human mobility and its impact on the risk of future COVID-19 outbreaks in Taiwan.

BACKGROUND: As COVID-19 continues to spread around the world, understanding how patterns of human mobility and connectivity affect outbreak dynamics, especially before outbreaks establish locally, is critical for informing response efforts. In Taiwan, most cases to date were imported or linked to imported cases. METHODS: In collaboration with Facebook Data for Good, we characterized changes in movement patterns in Taiwan since February 2020, and built metapopulation models that incorporate human movement data to identify the high risk areas of disease spread and assess the potential effects of local travel restrictions in Taiwan. RESULTS: We found that mobility changed with the number of local cases in Taiwan in the past few months. For each city, we identified the most highly connected areas that may serve as sources of importation during an outbreak. We showed that the risk of an outbreak in Taiwan is enhanced if initial infections occur around holidays. Intracity travel reductions have a higher impact on the risk of an outbreak than intercity travel reductions, while intercity travel reductions can narrow the scope of the outbreak and help target resources. The timing, duration, and level of travel reduction together determine the impact of travel reductions on the number of infections, and multiple combinations of these can result in similar impact. CONCLUSIONS: To prepare for the potential spread within Taiwan, we utilized Facebook's aggregated and anonymized movement and colocation data to identify cities with higher risk of infection and regional importation. We developed an interactive application that allows users to vary inputs and assumptions and shows the spatial spread of the disease and the impact of intercity and intracity travel reduction under different initial conditions. Our results can be used readily if local transmission occurs in Taiwan after relaxation of border control, providing important insights into future disease surveillance and policies for travel restrictions.

Socioeconomic Disparities in Social Distancing During the COVID-19 Pandemic in the United States: Observational Study.

BACKGROUND: Eliminating disparities in the burden of COVID-19 requires equitable access to control measures across socio-economic groups. Limited research on socio-economic differences in mobility hampers our ability to understand whether inequalities in social distancing are occurring during the SARS-CoV-2 pandemic. OBJECTIVE: We aimed to assess how mobility patterns have varied across the United States during the COVID-19 pandemic and to identify associations with socioeconomic factors of populations. METHODS: We used anonymized mobility data from tens of millions of devices to measure the speed and depth of social distancing at the county level in the United States between February and May 2020, the period during which social distancing was widespread in this country. Using linear mixed models, we assessed the associations between social distancing and socioeconomic variables, including the proportion of people in the population below the poverty level, the proportion of Black people, the proportion of essential workers, and the population density. RESULTS: We found that the speed, depth, and duration of social distancing in the United States are heterogeneous. We particularly show that social distancing is slower and less intense in counties with higher proportions of people below the poverty level and essential workers; in contrast, we show that social distancing is intensely adopted in counties with higher population densities and larger Black populations. CONCLUSIONS: Socioeconomic inequalities appear to be associated with the levels of adoption of social distancing, potentially resulting in wide-ranging differences in the impact of the COVID-19 pandemic in communities across the United States. These inequalities are likely to amplify existing health disparities and must be addressed to ensure the success of ongoing pandemic mitigation efforts.

QoS-Aware Approximate Query Processing for Smart Cities Spatial Data Streams.

Large amounts of georeferenced data streams arrive daily to stream processing systems. This is attributable to the overabundance of affordable IoT devices. In addition, interested practitioners desire to exploit Internet of Things (IoT) data streams for strategic decision-making purposes. However, mobility data are highly skewed and their arrival rates fluctuate. This nature poses an extra challenge on data stream processing systems, which are required in order to achieve pre-specified latency and accuracy goals. In this paper, we propose ApproxSSPS, which is a system for approximate processing of geo-referenced mobility data, at scale with quality of service guarantees. We focus on stateful aggregations (e.g., means, counts) and top-N queries. ApproxSSPS features a controller that interactively learns the latency statistics and calculates proper sampling rates to meet latency or/and accuracy targets. An overarching trait of ApproxSSPS is its ability to strike a plausible balance between latency and accuracy targets. We evaluate ApproxSSPS on Apache Spark Structured Streaming with real mobility data. We also compared ApproxSSPS against a state-of-the-art online adaptive processing system. Our extensive experiments prove that ApproxSSPS can fulfill latency and accuracy targets with varying sets of parameter configurations and load intensities (i.e., transient peaks in data loads versus slow arriving streams). Moreover, our results show that ApproxSSPS outperforms the baseline counterpart by significant magnitudes. In short, ApproxSSPS is a novel spatial data stream processing system that can deliver real accurate results in a timely manner, by dynamically specifying the limits on data samples.

Meteorological and human mobility data on predicting COVID-19 cases by a novel hybrid decomposition method with anomaly detection analysis: A case study in the capitals of Brazil.

In 2020, Brazil was the leading country in COVID-19 cases in Latin America, and capital cities were the most severely affected by the outbreak. Climates vary in Brazil due to the territorial extension of the country, its relief, geography, and other factors. Since the most common COVID-19 symptoms are related to the respiratory system, many researchers have studied the correlation between the number of COVID-19 cases with meteorological variables like temperature, humidity, rainfall, etc. Also, due to its high transmission rate, some researchers have analyzed the impact of human mobility on the dynamics of COVID-19 transmission. There is a dearth of literature that considers these two variables when predicting the spread of COVID-19 cases. In this paper, we analyzed the correlation between the number of COVID-19 cases and human mobility, and meteorological data in Brazilian capitals. We found that the correlation between such variables depends on the regions where the cities are located. We employed the variables with a significant correlation with COVID-19 cases to predict the number of COVID-19 infections in all Brazilian capitals and proposed a prediction method combining the Ensemble Empirical Mode Decomposition (EEMD) method with the Autoregressive Integrated Moving Average Exogenous inputs (ARIMAX) method, which we called EEMD-ARIMAX. After analyzing the results poor predictions were further investigated using a signal processing-based anomaly detection method. Computational tests showed that EEMD-ARIMAX achieved a forecast 26.73% better than ARIMAX. Moreover, an improvement of 30.69% in the average root mean squared error (RMSE) was noticed when applying the EEMD-ARIMAX method to the data normalized after the anomaly detection.

Give more data, awareness and control to individual citizens, and they will help COVID-19 containment.

The rapid dynamics of COVID-19 calls for quick and effective tracking of virus transmission chains and early detection of outbreaks, especially in the "phase 2" of the pandemic, when lockdown and other restriction measures are progressively withdrawn, in order to avoid or minimize contagion resurgence. For this purpose, contact-tracing apps are being proposed for large scale adoption by many countries. A centralized approach, where data sensed by the app are all sent to a nation-wide server, raises concerns about citizens' privacy and needlessly strong digital surveillance, thus alerting us to the need to minimize personal data collection and avoiding location tracking. We advocate the conceptual advantage of a decentralized approach, where both contact and location data are collected exclusively in individual citizens' "personal data stores", to be shared separately and selectively (e.g., with a backend system, but possibly also with other citizens), voluntarily, only when the citizen has tested positive for COVID-19, and with a privacy preserving level of granularity. This approach better protects the personal sphere of citizens and affords multiple benefits: it allows for detailed information gathering for infected people in a privacy-preserving fashion; and, in turn this enables both contact tracing, and, the early detection of outbreak hotspots on more finely-granulated geographic scale. The decentralized approach is also scalable to large populations, in that only the data of positive patients need be handled at a central level. Our recommendation is two-fold. First to extend existing decentralized architectures with a light touch, in order to manage the collection of location data locally on the device, and allow the user to share spatio-temporal aggregates-if and when they want and for specific aims-with health authorities, for instance. Second, we favour a longer-term pursuit of realizing a Personal Data Store vision, giving users the opportunity to contribute to collective good in the measure they want, enhancing self-awareness, and cultivating collective efforts for rebuilding society.

Human mobility and COVID-19 initial dynamics.

Countries in Europe took different mobility containment measures to curb the spread of COVID-19. The European Commission asked mobile network operators to share on a voluntarily basis anonymised and aggregate mobile data to improve the quality of modelling and forecasting for the pandemic at EU level. In fact, mobility data at EU scale can help understand the dynamics of the pandemic and possibly limit the impact of future waves. Still, since a reliable and consistent method to measure the evolution of contagion at international level is missing, a systematic analysis of the relationship between human mobility and virus spread has never been conducted. A notable exceptions are France and Italy, for which data on excess deaths, an indirect indicator which is generally considered to be less affected by national and regional assumptions, are available at department and municipality level, respectively. Using this information together with anonymised and aggregated mobile data, this study shows that mobility alone can explain up to 92% of the initial spread in these two EU countries, while it has a slow decay effect after lockdown measures, meaning that mobility restrictions seem to have effectively contribute to save lives. It also emerges that internal mobility is more important than mobility across provinces and that the typical lagged positive effect of reduced human mobility on reducing excess deaths is around 14-20 days. An analogous analysis relative to Spain, for which an IgG SARS-Cov-2 antibody screening study at province level is used instead of excess deaths statistics, confirms the findings. The same approach adopted in this study can be easily extended to other European countries, as soon as reliable data on the spreading of the virus at a suitable level of granularity will be available. Looking at past data, relative to the initial phase of the outbreak in EU Member States, this study shows in which extent the spreading of the virus and human mobility are connected. The findings will support policymakers in formulating the best data-driven approaches for coming out of confinement and mostly in building future scenarios in case of new outbreaks.

The Value of Greenspace Under Pandemic Lockdown.

The COVID-19 outbreak resulted in unprecedented restrictions on citizen's freedom of movement as governments moved to institute lockdowns designed to reduce the spread of the virus. While most out-of-home leisure activities were prohibited, in England the lockdown rules allowed for restricted use of outdoor greenspace for the purposes of exercise and recreation. In this paper, we use data recorded by Google from location-enabled mobile devices coupled with a detailed recreation demand model to explore the welfare impacts of those constraints on leisure activities. Our analyses reveals evidence of large-scale substitution of leisure time towards recreation in available greenspaces. Indeed, despite the restrictions the economic value of greenspace to the citizens of England fell by only £150 million over lockdown. Examining the outcomes of counterfactual policies we find that the imposition of stricter lockdown rules would have reduced welfare from greenspace by £1.14 billion. In contrast, more relaxed lockdown rules would have delivered an aggregate increase in the economic value of greenspace equal to £1.47 billion.

Lateral Motion Prediction of On-Road Preceding Vehicles: A Data-Driven Approach.

Drivers' behaviors and decision making on the road directly affect the safety of themselves, other drivers, and pedestrians. However, as distinct entities, people cannot predict the motions of surrounding vehicles and they have difficulty in performing safe reactionary driving maneuvers in a short time period. To overcome the limitations of making an immediate prediction, in this work, we propose a two-stage data-driven approach: classifying driving patterns of on-road surrounding vehicles using the Gaussian mixture models (GMM); and predicting vehicles' short-term lateral motions (i.e., left/right turn and left/right lane change) based on real-world vehicle mobility data, provided by the U.S. Department of Transportation, with different ensemble decision trees. We considered several important kinetic features and higher order kinematic variables. The research results of our proposed approach demonstrate the effectiveness of pattern classification and on-road lateral motion prediction. This methodology framework has the potential to be incorporated into current data-driven collision warning systems, to enable more practical on-road preprocessing in intelligent vehicles, and to be applied in autopilot-driving scenarios.

Detecting and Reducing Biases in Cellular-Based Mobility Data Sets.

Correctly estimating the features characterizing human mobility from mobile phone traces is a key factor to improve the performance of mobile networks, as well as for mobility model design and urban planning. Most related works found their conclusions on location data based on the cells where each user sends or receives calls or messages, data known as Call Detail Records (CDRs). In this work, we test if such data sets provide enough detail on users' movements so as to accurately estimate some of the most studied mobility features. We perform the analysis using two different data sets, comparing CDRs with respect to an alternative data collection approach. Furthermore, we propose three filtering techniques to reduce the biases detected in the fraction of visits per cell, entropy and entropy rate distributions, and predictability. The analysis highlights the need for contextualizing mobility results with respect to the data used, since the conclusions are biased by the mobile phone traces collection approach.