K-Clustering Methods for Investigating Social-Environmental and Natural-Environmental Features Based on Air Quality Index

Air pollution has caused environmental and health hazards across the globe, particularly in emerging countries such as China. In this article, we propose the use of air quality index and the development of advanced data processing, analysis, and visualization techniques based on the AI-based k-clustering method. We analyze the air quality data based on seven key attributes and discuss its implications. Our results provide meaningful values and contributions to the current research. Our future work will include the use of advanced AI algorithms and big data techniques to ensure better performance, accuracy and real-time checks

Proceedings of Abstracts Engineering and Computer Science Research Conference 2019

© 2019 The Author(s). This is an open-access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. For further details please see https://creativecommons.org/licenses/by/4.0/. Note: Keynote: Fluorescence visualisation to evaluate effectiveness of personal protective equipment for infection control is © 2019 Crown copyright and so is licensed under the Open Government Licence v3.0. Under this licence users are permitted to copy, publish, distribute and transmit the Information; adapt the Information; exploit the Information commercially and non-commercially for example, by combining it with other Information, or by including it in your own product or application. Where you do any of the above you must acknowledge the source of the Information in your product or application by including or linking to any attribution statement specified by the Information Provider(s) and, where possible, provide a link to this licence: http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/This book is the record of abstracts submitted and accepted for presentation at the Inaugural Engineering and Computer Science Research Conference held 17th April 2019 at the University of Hertfordshire, Hatfield, UK. This conference is a local event aiming at bringing together the research students, staff and eminent external guests to celebrate Engineering and Computer Science Research at the University of Hertfordshire. The ECS Research Conference aims to showcase the broad landscape of research taking place in the School of Engineering and Computer Science. The 2019 conference was articulated around three topical cross-disciplinary themes: Make and Preserve the Future; Connect the People and Cities; and Protect and Care

Examining trade-offs between social, psychological, and energy potential of urban form

Urban planners are often challenged with the task of developing design solutions which must meet multiple, and often contradictory, criteria. In this paper, we investigated the trade-offs between social, psychological, and energy potential of the fundamental elements of urban form: the street network and the building massing. Since formal methods to evaluate urban form from the psychological and social point of view are not readily available, we developed a methodological framework to quantify these criteria as the first contribution in this paper. To evaluate the psychological potential, we conducted a three-tiered empirical study starting from real world environments and then abstracting them to virtual environments. In each context, the implicit (physiological) response and explicit (subjective) response of pedestrians were measured. To quantify the social potential, we developed a street network centrality-based measure of social accessibility. For the energy potential, we created an energy model to analyze the impact of pure geometric form on the energy demand of the building stock. The second contribution of this work is a method to identify distinct clusters of urban form and, for each, explore the trade-offs between the select design criteria. We applied this method to two case studies identifying nine types of urban form and their respective potential trade-offs, which are directly applicable for the assessment of strategic decisions regarding urban form during the early planning stages

Association Between Residential Greenness and Cardiovascular Disease Risk

Background Exposure to green vegetation has been linked to positive health, but the pathophysiological processes affected by exposure to vegetation remain unclear. To study the relationship between greenness and cardiovascular disease, we examined the association between residential greenness and biomarkers of cardiovascular injury and disease risk in susceptible individuals. Methods and Results In this cross-sectional study of 408 individuals recruited from a preventive cardiology clinic, we measured biomarkers of cardiovascular injury and risk in participant blood and urine. We estimated greenness from satellite-derived normalized difference vegetation index ( NDVI ) in zones with radii of 250 m and 1 km surrounding the participants' residences. We used generalized estimating equations to examine associations between greenness and cardiovascular disease biomarkers. We adjusted for residential clustering, demographic, clinical, and environmental variables. In fully adjusted models, contemporaneous NDVI within 250 m of participant residence was inversely associated with urinary levels of epinephrine (-6.9%; 95% confidence interval, -11.5, -2.0/0.1 NDVI ) and F2-isoprostane (-9.0%; 95% confidence interval, -15.1, -2.5/0.1 NDVI ). We found stronger associations between NDVI and urinary epinephrine in women, those not on β-blockers, and those who had not previously experienced a myocardial infarction. Of the 15 subtypes of circulating angiogenic cells examined, 11 were inversely associated (8.0-15.6% decrease/0.1 NDVI ), whereas 2 were positively associated (37.6-45.8% increase/0.1 NDVI ) with contemporaneous NDVI . Conclusions Independent of age, sex, race, smoking status, neighborhood deprivation, statin use, and roadway exposure, residential greenness is associated with lower levels of sympathetic activation, reduced oxidative stress, and higher angiogenic capacity

Urban metabolism and land use modeling for urban designers and planners: A land use model for the Integrated Urban Metabolism Analysis Tool

Predicting the resource consumption in the built environment and its associated environmental consequences (urban metabolism analysis) is one of the core challenges facing policy-makers and planners seeking to increase the sustainability of urban areas. There is a critical need for a single integrated framework to analyze the consequences of urban growth and eventually predict the impacts of sustainable policies on the urbanscape. This dissertation presents the development of an Integrated Urban Metabolism Analysis Tool (IUMAT) – an analytical framework that simulates urban metabolism by integrating urban subsystems in a single comprehensive computational environment. It reviews the existing literature on urban sustainability, urban metabolism, as well as introducing the general framework for IUMAT. IUMAT uses three separate models for quantifying environmental impacts of land-use transition, consumption of resources, and transportation. This work outlines the development of IUMAT Land-Use Model that uses Remote Sensing, GIS, and Artificial Neural Networks (ANNs) to predict land use change patterns. By using Density-Based Spatial Clustering and normal equations, this dissertation introduces a method for generating building-form variables from Light Detection and Ranging (LIDAR) data, which can be used as a new determinant factor in land-use change modeling. The proposed Land-use Model, within IUMAT or other analytical models, can be useful to local planning officials in understanding the complexity of land-use change and developing enhanced land-use policies

The structure of R&D collaboration networks in the European Framework Programmes

Using a large and novel data source, we study the structure of R&D collaboration net-works in the first five EU Framework Programmes (FPs). The networks display proper-ties typical for complex networks, including scale-free degree distributions and the small-world property. Structural features are common across FPs, indicating similar network formation mechanisms despite changes in governance rules. Several findings point towards the existence of a stable core of interlinked actors since the early FPs with integration increasing over time. This core consists mainly of universities and research organisations. We observe assortative mixing by degree of projects, but not by degree of organisations. Unexpectedly, we find only weak association between central projects and project size, suggesting that different types of projects attract different groups of actors. In particular, large projects appear to have included few of the pivotal actors in the networks studied. Central projects only partially mirror funding priorities, indicating field-specific differences in network structures. The paper concludes with an agenda for future research.R&D collaboration, EU Framework Programmes, Complex Networks, Small World Effect, Centrality Measures, European Research Area

Use of geographic indicators of healthcare, environment and socioeconomic factors to characterize environmental health disparities

International audienceBackground: An environmental health inequality is a major public health concern in Europe. However just few studies take into account a large set of characteristics to analyze this problematic. The aim of this study was to identify and describe how socioeconomic, health accessibility and exposure factors accumulate and interact in small areas in a French urban context, to assess environmental health inequalities related to infant and neonatal mortality.Methods: Environmental indicators on deprivation index, proximity to high-traffic roads, green space, and healthcare accessibility were created using the Geographical Information System. Cases were collected from death certificates in the city hall of each municipality in the Nice metropolitan area. Using the parental addresses, cases were geocoded to their census block of residence. A classification using a Multiple Component Analysis following by a Hierarchical Clustering allow us to characterize the census blocks in terms of level of socioeconomic, environmental and accessibility to healthcare, which are very diverse definition by nature. Relation between infant and neonatal mortality rate and the three environmental patterns which categorize the census blocks after the classification was performed using a standard Poisson regression model for count data after checking the assumption of dispersion.Results: Based on geographic indicators, three environmental patterns were identified. We found environmental inequalities and social health inequalities in Nice metropolitan area. Moreover these inequalities are counterbalance by the close proximity of deprived census blocks to healthcare facilities related to mother and newborn. So therefore we demonstrate no environmental health inequalities related to infant and neonatal mortality.Conclusion: Examination of patterns of social, environmental and in relation with healthcare access is useful to identify census blocks with needs and their effects on health. Similar analyzes could be implemented and considered in other cities or related to other birth outcomes

Municipal transitions: The social, energy, and spatial dynamics of sociotechnical change in South Tyrol, Italy

With the aim of proposing recommendations on how to use social and territorial specificities as levers for wider achievement of climate and energy targets at local level, this research analyses territories as sociotechnical systems. Defining the territory as a sociotechnical system allows us to underline the interrelations between space, energy and society. Groups of municipalities in a region can be identified with respect to their potential production of renewable energy by means of well-known data-mining approaches. Similar municipalities linking together can share ideas and promote collaborations, supporting clever social planning in the transition towards a new energy system. The methodology is applied to the South Tyrol case study (Italy). Results show eight different spatially-based sociotechnical systems within the coherent cultural and institutional context of South Tyrol. In particular, this paper observes eight different systems in terms of (1) different renewable energy source preferences in semi-urban and rural contexts; (2) different links with other local planning, management, and policy needs; (3) different socio-demographic specificities of individuals and families; (4) presence of different kinds of stakeholders or of (5) different socio-spatial organizations based on land cover. Each energy system has its own specificities and potentialities, including social and spatial dimensions, that can address a more balanced, inclusive, equal, and accelerated energy transition at the local and translocal scale

The impact of indoor environment quality (IEQ) on school children's overall comfort in the UK; a regression approach

Indoor Environment Quality (IEQ) is grouped into four main categories: thermal comfort, indoor air quality (IAQ), visual and acoustic comfort. Individual aspects of IEQ are investigated to examine their impact on children's overall comfort in primary schools in the UK. This study has surveyed 805 children in 32 naturally ventilated classrooms during non-heating and heating seasons. This study has calculated the proportion of comfort votes by individual aspects of IEQ, predicted comfort votes by multilinear regression model and estimated the probability of having uncomfortable votes by binary logistic regression. Results of this study highlight that the proportion of uncomfortable votes should be kept below 10%. The developed multilinear model suggests that for a unit change in Air Sensation Votes (ASVs) and operative temperatures (Top), comfort votes change by 0.28 and 0.12, respectively. Developed multilinear and logistic regression models show that ASVs have a more significant impact on overall comfort than Top. To achieve acceptable comfortable votes and keep the probability of having uncomfortable votes below 10%, ASVs and Top should be kept within these limits: [ASV = very fresh and Top = 19–27 °C], [ASV = fresh and Top = 19–24 °C], and [ASV = OK and Top = 19–22 °C]. The ranges suggest that better perception of IAQ makes up for higher temperatures. It is advised to maintain individual aspects of IEQ, however, dissatisfaction with one aspect of IEQ does not necessarily result in overall discomfort unless that aspect is extremely unacceptable. Investigating the most influential factors on occupants’ comfort suggests which building controls should be prioritized for designers