scholarly journals A Systematic Measurement of Street Quality through Multi-Sourced Urban Data: A Human-Oriented Analysis

2019 ◽  
Vol 16 (10) ◽  
pp. 1782 ◽  
Author(s):  
Lingzhu Zhang ◽  
Yu Ye ◽  
Wenxin Zeng ◽  
Alain Chiaradia
Keyword(s):  
Public Health ◽  
Physical Activity ◽  
Holistic Approach ◽  
Analytical Framework ◽  
Supervised Machine Learning ◽  
Fine Grained ◽  
Quantitative Measurements ◽  
Data Points ◽  
New Research ◽  
Urban Data

Many studies have been made on street quality, physical activity and public health. However, most studies so far have focused on only few features, such as street greenery or accessibility. These features fail to capture people’s holistic perceptions. The potential of fine grained, multi-sourced urban data creates new research avenues for addressing multi-feature, intangible, human-oriented issues related to the built environment. This study proposes a systematic, multi-factor quantitative approach for measuring street quality with the support of multi-sourced urban data taking Yangpu District in Shanghai as case study. This holistic approach combines typical and new urban data in order to measure street quality with a human-oriented perspective. This composite measure of street quality is based on the well-established 5Ds dimensions: Density, Diversity, Design, Destination accessibility and Distance to transit. They are combined as a collection of new urban data and research techniques, including location-based service (LBS) positioning data, points of interest (PoIs), elements and visual quality of street-view images extraction with supervised machine learning, and accessibility metrics using network science. According to these quantitative measurements from the five aspects, streets were classified into eight feature clusters and three types reflecting the value of street quality using a hierarchical clustering method. The classification was tested with experts. The analytical framework developed through this study contributes to human-oriented urban planning practices to further encourage physical activity and public health.

Recent Advances in Treatment of Childhood Obesity

2020 ◽  
Vol 21 (14) ◽  
pp. 1072-1078
Author(s):  
Walter Milano ◽  
Paola Ambrosio ◽  
Francesca Carizzone ◽  
Walter Di Munzio ◽  
Valeria De Biasio ◽  
...  
Keyword(s):  
Public Health ◽  
Physical Activity ◽  
Bariatric Surgery ◽  
Childhood Obesity ◽  
Treatment Strategies ◽  
Lifestyle Changes ◽  
Family Context ◽  
Review Of The Literature ◽  
Biological Factors ◽  
The Family

: Childhood obesity has assumed epidemic proportions and is currently one of the most widespread public health problems. Many are the factors involved in the pathogenesis of excess weight with interactions between genetic, environmental and biological factors and therefore, also the therapeutic approach must be multidisciplinary and multidimensional. In this review of the literature, we report the contiguity of childhood obesity with eating disorders and the importance of involving the family context in order to induce stable lifestyle changes, both in relation to dietary and nutritional habits, but also in increasing physical activity. Finally, among the therapeutic options, although for selected cases, pharmacotherapy and bariatric surgery can be used as treatment strategies.

Application of Inherent Risk of Contagion (IRC) framework & modelling to aid local Covid-19 response & mitigation: Viewpoint (Preprint)

2020 ◽  
Author(s):  
Helmi Zakariah ◽  
Fadzilah bt Kamaluddin ◽  
Choo-Yee Ting ◽  
Hui-Jia Yee ◽  
Shereen Allaham ◽  
...  
Keyword(s):  
Public Health ◽  
Public Health Problem ◽  
Analytical Framework ◽  
Vulnerable Population ◽  
Contact Tracing ◽  
Global Public Health ◽  
Inherent Risk ◽  
Transmission Area ◽  
Current Outbreak ◽  
Novel Coronavirus

UNSTRUCTURED The current outbreak of coronavirus disease 2019 (COVID-19) caused by the novel coronavirus named SARS-CoV-2 has been a major global public health problem threatening many countries and territories. Mathematical modelling is one of the non-pharmaceutical public health measures that plays a crucial role for mitigating the risk and impact of the pandemic. A group of researchers and epidemiologists have developed a machine learning-powered inherent risk of contagion (IRC) analytical framework to georeference the COVID-19 with an operational platform to plan response & execute mitigation activities. This framework dataset provides a coherent picture to track and predict the COVID-19 epidemic post lockdown by piecing together preliminary data on publicly available health statistic metrics alongside the area of reported cases, drivers, vulnerable population, and number of premises that are suspected to become a transmission area between drivers and vulnerable population. The main aim of this new analytical framework is to measure the IRC and provide georeferenced data to protect the health system, aid contact tracing, and prioritise the vulnerable.

scholarly journals The case for tracking misinformation the way we track disease

2021 ◽  
Vol 8 (1) ◽  
pp. 205395172110138
Author(s):  
Erika Bonnevie ◽  
Jennifer Sittig ◽  
Joe Smyser
Keyword(s):  
Public Health ◽  
Real Time ◽  
Supervised Machine Learning ◽  
Disease Spread ◽  
The Public ◽  
Monitoring Tools ◽  
Core Function ◽  
Public Health Organizations ◽  
Front Line Workers ◽  
Media Data

While public health organizations can detect disease spread, few can monitor and respond to real-time misinformation. Misinformation risks the public’s health, the credibility of institutions, and the safety of experts and front-line workers. Big Data, and specifically publicly available media data, can play a significant role in understanding and responding to misinformation. The Public Good Projects uses supervised machine learning to aggregate and code millions of conversations relating to vaccines and the COVID-19 pandemic broadly, in real-time. Public health researchers supervise this process daily, and provide insights to practitioners across a range of disciplines. Through this work, we have gleaned three lessons to address misinformation. (1) Sources of vaccine misinformation are known; there is a need to operationalize learnings and engage the pro-vaccination majority in debunking vaccine-related misinformation. (2) Existing systems can identify and track threats against health experts and institutions, which have been subject to unprecedented harassment. This supports their safety and helps prevent the further erosion of trust in public institutions. (3) Responses to misinformation should draw from cross-sector crisis management best practices and address coordination gaps. Real-time monitoring and addressing misinformation should be a core function of public health, and public health should be a core use case for data scientists developing monitoring tools. The tools to accomplish these tasks are available; it remains up to us to prioritize them.

Physical activity patterns among walkers and compliance with public health recommendations

2002 ◽  
Vol 34 (8) ◽  
pp. 1255-1261 ◽  
Author(s):  
ANN P. RAFFERTY ◽  
MATHEW J. REEVES ◽  
HARRY B. MCGEE ◽  
JAMES M. PIVARNIK
Keyword(s):  
Public Health ◽  
Physical Activity ◽  
Activity Patterns ◽  
Physical Activity Patterns

scholarly journals Urbanization and greenness in HBSC survey: association with overweight and obesity in adolescents

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
V Bellisario ◽  
R Bono ◽  
G Squillacioti ◽  
M Caputo ◽  
I Gintoli ◽  
...  
Keyword(s):  
Public Health ◽  
Physical Activity ◽  
Risk Factors ◽  
Air Pollution ◽  
Population Density ◽  
Weight Status ◽  
Overweight And Obesity ◽  
Green Spaces ◽  
Children Health ◽  
Public Health Strategies

Abstract Background Childhood obesity is an important public health issue worldwide and includes different risk factors, such as environmental pollutants exposure or physical activity. Neighborhood composition and green spaces availability could contrast obesogenic lifestyles and promote healthy habits, whereas, urbanization and traffic volume exposure are inversely associated with physical activity and worsen effects on childhood health. Methods This project analyzed students involved in the HBSC survey from the Piedmont Region. Data were collected in 2018, following the protocol. All the subjects were georeferenced within buffers around schools. Green-spaces availability was measured by Normalised Difference Vegetation Index (NDVI-satellite images) while urbanization was calculated by population density, traffic intensity (satellite measurements) and air pollution concentration (sampling stations). Results Overall, the sample included 3022 subjects, with amount 50% male/female and 30% for each age group (11-13-15 years old). Concerning weight status, above 14% of the all sample is obese or overweight, with, respectively, 20% among boys and 11% among girls. Preliminary analyses showed an association between weight status and population density (rural vs urbanized areas). Currently, we are analyzing the association with greenness and the other measures of urbanization. Conclusions Our preliminary findings suggest that high urbanization levels impact health implementing weight in children. We are testing the hypothesis that greenness positively influences weight status and reduce negative effects of urbanization and air pollution. The managing of these risk factors must be deepened and corroborated by active preventive Public Health strategies for improving children health. Key messages Urbanization and greenness may influence weight status in children. Public Health strategies must be improved for children health.

scholarly journals Health Impacts of Urban Bicycling in Mexico

2021 ◽  
Vol 18 (5) ◽  
pp. 2300
Author(s):  
David Rojas-Rueda
Keyword(s):  
Public Health ◽  
Physical Activity ◽  
Air Pollution ◽  
Health Risks ◽  
Health Benefits ◽  
Health Impacts ◽  
Traffic Fatalities ◽  
Premature Deaths ◽  
Air Pollution Exposure ◽  
Pollution Exposure

Background: Bicycling has been associated with health benefits. Local and national authorities have been promoting bicycling as a tool to improve public health and the environment. Mexico is one of the largest Latin American countries, with high levels of sedentarism and non-communicable diseases. No previous studies have estimated the health impacts of Mexico’s national bicycling scenarios. Aim: Quantify the health impacts of Mexico urban bicycling scenarios. Methodology: Quantitative Health Impact Assessment, estimating health risks and benefits of bicycling scenarios in 51,718,756 adult urban inhabitants in Mexico (between 20 and 64 years old). Five bike scenarios were created based on current bike trends in Mexico. The number of premature deaths (increased or reduced) was estimated in relation to physical activity, road traffic fatalities, and air pollution. Input data were collected from national publicly available data sources from transport, environment, health and population reports, and surveys, in addition to scientific literature. Results: We estimated that nine premature deaths are prevented each year among urban populations in Mexico on the current car-bike substitution and trip levels (1% of bike trips), with an annual health economic benefit of US $1,897,920. If Mexico achieves similar trip levels to those reported in The Netherlands (27% of bike trips), 217 premature deaths could be saved annually, with an economic impact of US $45,760,960. In all bicycling scenarios assessed in Mexico, physical activity’s health benefits outweighed the health risks related to traffic fatalities and air pollution exposure. Conclusion: The study found that bicycling promotion in Mexico would provide important health benefits. The benefits of physical activity outweigh the risk from traffic fatalities and air pollution exposure in bicyclists. At the national level, Mexico could consider using sustainable transport policies as a tool to promote public health. Specifically, the support of active transportation through bicycling and urban design improvements could encourage physical activity and its health co-benefits.

A challenge for historical research: Making data FAIR using a collaborative ontology management environment (OntoME)

Semantic Web ◽  
2020 ◽  
pp. 1-16
Author(s):  
Francesco Beretta
Keyword(s):  
Semantic Analysis ◽  
Historical Research ◽  
Data Modelling ◽  
Fine Grained ◽  
Fair Principles ◽  
Active Research ◽  
New Research ◽  
High Level ◽  
Conceptual Data ◽  
Ontology Management

This paper addresses the issue of interoperability of data generated by historical research and heritage institutions in order to make them re-usable for new research agendas according to the FAIR principles. After introducing the symogih.org project’s ontology, it proposes a description of the essential aspects of the process of historical knowledge production. It then develops an epistemological and semantic analysis of conceptual data modelling applied to factual historical information, based on the foundational ontologies Constructive Descriptions and Situations and DOLCE, and discusses the reasons for adopting the CIDOC CRM as a core ontology for the field of historical research, but extending it with some relevant, missing high-level classes. Finally, it shows how collaborative data modelling carried out in the ontology management environment OntoME makes it possible to elaborate a communal fine-grained and adaptive ontology of the domain, provided an active research community engages in this process. With this in mind, the Data for history consortium was founded in 2017 and promotes the adoption of a shared conceptualization in the field of historical research.

scholarly journals Cox regression survival analysis with compositional covariates: Application to modelling mortality risk from 24-h physical activity patterns

2019 ◽  
Vol 29 (5) ◽  
pp. 1447-1465 ◽  
Author(s):  
DE McGregor ◽  
J Palarea-Albaladejo ◽  
PM Dall ◽  
K Hron ◽  
SFM Chastin
Keyword(s):  
Public Health ◽  
Physical Activity ◽  
Survival Analysis ◽  
Regression Model ◽  
Cox Regression ◽  
Public Health Problem ◽  
Sitting Time ◽  
Cox Regression Model ◽  
Log Ratio ◽  
Activity Behaviour

Survival analysis is commonly conducted in medical and public health research to assess the association of an exposure or intervention with a hard end outcome such as mortality. The Cox (proportional hazards) regression model is probably the most popular statistical tool used in this context. However, when the exposure includes compositional covariables (that is, variables representing a relative makeup such as a nutritional or physical activity behaviour composition), some basic assumptions of the Cox regression model and associated significance tests are violated. Compositional variables involve an intrinsic interplay between one another which precludes results and conclusions based on considering them in isolation as is ordinarily done. In this work, we introduce a formulation of the Cox regression model in terms of log-ratio coordinates which suitably deals with the constraints of compositional covariates, facilitates the use of common statistical inference methods, and allows for scientifically meaningful interpretations. We illustrate its practical application to a public health problem: the estimation of the mortality hazard associated with the composition of daily activity behaviour (physical activity, sitting time and sleep) using data from the U.S. National Health and Nutrition Examination Survey (NHANES).

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