Visual analytics for spatio-temporal air quality data

Air pollution is the second biggest environmental concern for Europeans after climate change and the major risk to public health. It is imperative to monitor the spatio-temporal patterns of urban air pollution. The TRAFAIR air quality dashboard is an effective web application to empower decision-makers to be aware of the urban air quality conditions, define new policies, and keep monitoring their effects. The architecture copes with the multidimensionality of data and the real-time visualization challenge of big data streams coming from a network of low-cost sensors. Moreover, it handles the visualization and management of predictive air quality maps series that is produced by an air pollution dispersion model. Air quality data are not only visualized at a limited set of locations at different times but in the continuous space-time domain, thanks to interpolated maps that estimate the pollution at un-sampled locations

GIS-Based Geospatial Data Analysis: the Security of Cycle Paths in Modena

The use of fossil fuels is contributing to the global climate crisis and is threatening the sustainability of the planet. Bicycles are a vital component of the solution, as they can help mitigate the effects of climate change and improve the quality of life for all. However, cities need to be equipped with the necessary infrastructure to support their use guaranteeing safety for cyclists. Moreover, cyclists should plan their route considering the level of security associated with the different available options to reach their destination. The paper tests and presents a method that aims to integrate geographical data from various sources with different geometries and formats into a single view of the cycle paths in the province of Modena, Italy. The Geographic Information System (GIS) software functionalities have been exploited to classify paths in 5 categories: from protected bike lanes to streets with no bike infrastructure. The type of traffic that co-exists in each cycle path was analysed too. The main outcome of this research is a visualization of the cycle paths in the province of Modena highlighting the security of paths, the discontinuity of the routes, and the less covered areas. Moreover, a cycle paths graph data model was generated to perform routing based on the security level

From Sensors Data to Urban Traffic Flow Analysis

By 2050, almost 70% of the population will live in cities. As the population grows, travel demand increases and this might affect air quality in urban areas. Traffic is among the main sources of pollution within cities. Therefore, monitoring urban traffic means not only identifying congestion and managing accidents but also preventing the impact on air pollution. Urban traffic modeling and analysis is part of the advanced traffic intelligent management technologies that has become a crucial sector for smart cities. Its main purpose is to predict congestion states of a specific urban transport network and propose improvements in the traffic network that might result into a decrease of the travel times, air pollution and fuel consumption. This paper describes the implementation of an urban traffic flow model in the city of Modena based on real traffic sensor data. This is part of a wide European project that aims at studying the correlation among traffic and air pollution, therefore at combining traffic and air pollution simulations for testing various urban scenarios and raising citizen awareness about air quality where necessary

Using real sensors data to calibrate a traffic model for the city of Modena

In Italy, road vehicles are the preferred mean of transport. Over the last years, in almost all the EU Member States, the passenger car fleet increased. The high number of vehicles complicates urban planning and often results in traffic congestion and areas of increased air pollution. Overall, efficient traffic control is profitable in individual, societal, financial, and environmental terms. Traffic management solutions typically require the use of simulators able to capture in detail all the characteristics and dependencies associated with real-life traffic. Therefore, the realization of a traffic model can help to discover and control traffic bottlenecks in the urban context. In this paper, we analyze how to better simulate vehicle flows measured by traffic sensors in the streets. A dynamic traffic model was set up starting from traffic sensors data collected every minute in about 300 locations in the city of Modena. The reliability of the model is discussed and proved with a comparison between simulated values and real values from traffic sensors. This analysis pointed out some critical issues. Therefore, to better understand the origin of fake jams and incoherence with real data, we approached different configurations of the model as possible solutions

Procedural pain management in Italy: learning from a nationwide survery involving centers of the Italian Association of Pediatric Hematology-Oncology

Procedural pain is an important aspect of care in pediatrics, and particularly in pediatric oncology where children often consider this to be the most painful experience during their illness. Best recommended practice to control procedural pain includes both sedative-analgesic administration and non-pharmacological treatments, practiced in an adequate and pleasant setting by skilled staff. A nationwide survey has been conducted among the Italian Centers of Pediatric Hematology-Oncology to register operators' awareness on procedural pain, state of the art procedural pain management, operators' opinions about pain control in their center, and possible barriers impeding sedation-analgesia administration. Based on indications in the literature, we discuss the results of the survey to highlight critical issues and suggest future directions for improvement. Future objectives will be to overcome differences depending on size, improve operators' beliefs about the complexity of pain experience, and promote a global approach to procedural pain

The opinion of clinical staff regarding painfulness of procedures in pediatric hematology-oncology: an Italian survey

<p>Abstract</p> <p>Background</p> <p>Beliefs of caregivers about patient's pain have been shown to influence assessment and treatment of children's pain, now considered an essential part of cancer treatment. Painful procedures in hematology-oncology are frequently referred by children as the most painful experiences during illness. Aim of this study was to evaluate professionals' beliefs about painfulness of invasive procedures repeatedly performed in Pediatric Hemato-Oncology Units.</p> <p>Methods</p> <p>Physicians, nurses, psychologists and directors working in Hemato-Oncology Units of the Italian Association of Pediatric Hematology-Oncology (AIEOP) were involved in a wide-nation survey. The survey was based on an anonymous questionnaire investigating beliefs of operators about painfulness of invasive procedures (lumbar puncture, bone marrow aspirate and bone marrow biopsy) and level of pain management.</p> <p>Results</p> <p>Twenty-four directors, 120 physicians, 248 nurses and 22 psychologists responded to the questionnaire. The score assigned to the procedural pain on a 0-10 scale was higher than 5 in 77% of the operators for lumbar puncture, 97.5% for bone marrow aspiration, and 99.5% for bone marrow biopsy. The scores assigned by nurses differed statistically from those of the physicians and directors for the pain caused by lumbar puncture and bone marrow aspiration. Measures adopted for procedural pain control were generally considered good.</p> <p>Conclusions</p> <p>Invasive diagnostic-therapeutic procedures performed in Italian Pediatric Hemato-Oncology Units are considered painful by all the caregivers involved. Pain management is generally considered good. Aprioristically opinions about pain depend on invasiveness of the procedure and on the professional role.</p

The impact of law on moral and social norms: evidence from facemask fines in the UK

We measure the extent that a prevailing law can affect moral and social norms. We use variations in facemask fines in the UK during the COVID-19 pandemic as a case study, as it presents us with many features of a natural experiment. Our analysis provides two important findings. First, we find that the legal status of a behaviour, that is whether the behaviour is illegal or not, is important in influencing stated compliance and moral and social norms. In contrast we find no evidence that a variation in penalties has any effect on stated compliance or moral or social norms. We consider these results are important for law makers and society, as both moral and social norms are known to be important drivers of social change, and our results show that legality influences both—thus highlighting an endogenous relationship between the law—and moral and social norms