Related Socio-economic Data Research Articles

Prevalence of smoking during pregnancy and associated social inequalities in developed countries over the 1995-2020 period: A systematic review.

Smoking during pregnancy (SDP) is an important source of preventable morbidity and mortality for both mother and child. The aim of this study was to describe changes in the prevalence of SDP over the last 25 years in developed countries (Human Development Index >0.8 in 2020) and associated social inequalities. A systematic review was conducted based on a search in PubMed, Embase and PsycInfo databases and government sources. Published studies between January 1995 and March 2020, for which the primary outcome was to assess the national prevalence of SDP and the secondary outcome was to describe related socio-economic data were included in the analysis. The selected articles had to be written in English, Spanish, French or Italian. The articles were selected after successive reading of the titles, abstracts and full-length text. An independent double reading with intervention of a third reader in case of disagreement allowed including 35 articles from 14 countries in the analysis. The prevalence of SDP differed across the countries studied despite comparable levels of development. After 2015, the prevalence of SDP ranged between 4.2% in Sweden and 16.6% in France. It was associated with socio-economic factors. The prevalence of SDP slowly decreased over time, but this overall trend masked inequalities within populations. In Canada, France and the United States, the prevalence decreased more rapidly in women of higher socio-economic status, and inequalities in maternal smoking were more marked in these countries. In the other countries, inequalities tended to decrease but remained significant. During pregnancy, that is a period described as a window of opportunity, smoking and social vulnerability factors need to be detected to implement targeted prevention strategies aiming at reducing related social inequalities.

The Impact of Urbanization on the Relationship between Carbon Storage Supply and Demand in Mega-Urban Agglomerations and Response Measures: A Case of Yangtze River Delta Region, China.

Rapid urbanization in mega-urban agglomerations disturbs the balance of carbon storage supply and demand (CSD) and constrains the achievement of sustainable development goals. Here, we developed a socio-ecological system (SES) framework coupled with ecosystem services (ES) cascade and DPSIR model to systematically analyze the impacts and responses of urbanization affecting CSD. We quantified urbanization and CSD using multi-source remote sensing data, such as land use and night lighting, together with related socio-economic data, such as total energy consumption, population and GDP. We found that from 2000 to 2020, the urbanization of Yangtze River Delta region (YRD) led to a decrease of 2.75% in carbon storage supply and an increase of 226.45% in carbon storage demand. However, carbon storage supply was still larger than carbon storage demand, and the spatial mismatch of CSD is the most important problem at present. Therefore, it is necessary to explore the response measures from the comprehensive perspective of SES. We identified key ecological conservation areas using a Marxan model to protect the carbon storage capacity in ecological subsystems, and promoted a carbon compensation scheme based on both the grandfather principle and the carbon efficiency principle, reconciling the contradiction between ecological conservation and socio-economic development in the social subsystem. Finally, this study quantified the threshold of urbanization based on the carbon neutrality target at which CSD reaches an equilibrium state. This study proposed a SES framework, and a set of methodologies to quantify the relationship between urbanization and CSD, which will help mega-urban agglomerations to promote harmonious development of urbanization and ecological conservation and to achieve the carbon peak and carbon neutrality targets proposed by the Chinese government.

Cloud4NFICA-Nearness Factor-Based Incremental Clustering Algorithm Using Microsoft Azure for the Analysis of Intelligent Meter Data

Intelligent electricity meters (IEMs) form a key infrastructure necessary for the growth of smart grids. IEMs generate a considerable amount of electricity data incrementally. However, on an influx of new data, traditional clustering task re-cluster all of the data from scratch. The incremental clustering method is an essential way to solve the problem of clustering with dynamic data. Given the volume of IEM data and the number of data types involved, an incremental clustering method is highly complex. Microsoft Azure provide the processing power necessary to handle incremental clustering analytics. The proposed Cloud4NFICA is a scalable platform of a nearness factor-based incremental clustering algorithm. This research uses the real dataset of Irish households collected by IEMs and related socioeconomic data. Cloud4NFICA is incremental in nature, hence accommodates the influx of new data. Cloud4NFICA was designed as an infrastructure as a service. It is visible from the study that the developed system performs well on the scalability aspect.

Algorithmic analysis of intelligent electricity meter data for reduction of energy consumption and carbon emission

Intelligent Electricity Meters (IEMs) generate a considerable amount of household electricity usage data incrementally. Obviously, for the clustering task, it is better to incrementally update the new clustering results based on the old data rather than to recluster all the data from scratch. The gradational clustering is an essential way to accommodate the influx of new data seamlessly for accurate analysis. However, given the volume of IEM data and the number of data types involved makes the gradational clustering highly complex. Microsoft Azure provides the processing power necessary to handle gradational clustering analytics. The paper aim is to develop a Distributed Log-likelihood Based Gradational Clustering Algorithm on Microsoft Azure for analysis of IEM data. This research uses the real dataset of Irish households collected by IEMs and related socioeconomic data, including the geographic information, demographic data. It is visible from the study that algorithmic analysis helps the household customers to monitor and improvise electricity consumption patterns, Utility providers to reduce power outage and avoid capital expenses of building new plants. This research will be extremely useful for maintaining the environment by reducing pollution via carbon production by power plants.

Eligibility for social services: social policy development in an international context

The article provides a frame of reference for reviewing the basic processes underlying the development of formal social policy. The bases for the article were provided by: (1) review of the literature in related social services policy development areas; (2) analyses and review of the literature specific to service eligibility; (3) analyses of related socio-economic data from international organizations and registries. Eligibility is proposed as an essential element of social services within the context of citizens’ rights, cultural mores and customs, political processes and economic conditions of the six contributing countries. These data sources were augmented by review of summary narratives of the history and status of social services development.

Land use transitions and their effects on water environment in Huang-Huai-Hai Plain, China

This paper analyzes the spatio-temporal dynamic patterns of land use in Huang-Huai-Hai Plain, one of the China's most important grain production bases experiencing rapid urban–rural transformation development, using high-resolution Landsat TM (Thematic Mapper) data and series data of water environmental quality monitoring in 2000 and 2010, and related socio-economic data from government departments. After assessing the change of water environmental quality of Huang-Huai-Hai Plain during 2000–2010, three spatial econometric regression models including Spatial Lag Model (SLM), Spatial Error Model (SEM) and ordinary least squares (OLS) are used to explore the correlationships between land use transitions and water environmental changes. The outcomes indicated that, during the research period, land use pattern changes in the study area were characterized by the loss of large quantities of farmland and the increase of construction land and water body. On the whole, the water environment in the study area was obviously improved, but there also existed partial deterioration as evidenced by the increase of monitoring sections with water environmental quality below Class V. In general, the increases of grassland, forested land and water body have positive effects on water environmental quality while farmland and construction land have negative effects on that. The authors argue that construction land use and farmland use have almost equal negative effects on water environmental quality, and managing agricultural non-point source pollution is pivotal to improve local water environmental quality. Finally, some of the major implications for managing the land and water resources in the plain areas of China as well as other developing countries undergoing rapid urban–rural transformation development are discussed in the aspects of ecological farming practices, rural construction land management, land use planning and urban planning.