China’s population spatialization based on three machine learning models

Song, Zhao; Liu, Yanxu; Fu, Bojie

doi:10.1016/j.jclepro.2020.120644

Cited by 36 publications

(35 citation statements)

References 65 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…High spatial resolution population data is necessary to obtain reliable exposure evaluation results at the intra-urban scale. The commonly used methods for population spatialization include areal weighting, dasymetric mapping, and statistics regression models [40,41]. In this study, residential buildings were screened and classified on the basis of high-resolution remote sensing images.…”

Section: Comprehensive Discussionmentioning

confidence: 99%

High-Resolution Population Exposure to PM<sub>2.5</sub> in Nanchang Urban Region Using Multi-Source Data

Yang¹,

Guo²,

Leng³

2021

Pol. J. Environ. Stud.

View full text Add to dashboard Cite

Long-term exposure to PM 2.5 can lead to great adverse health effect on human health. To better guide public policies that aim to reduce PM 2.5 population exposure, this work combined multi-source data to realize high-resolution PM 2.5 exposure risk assessment in Nanchang urban region. The land use regression (LUR) model was used to simulate the seasonal-spatial variations of PM 2.5 concentrations at 100-m resolution, and building information extracted from IKONOS image was applied to spatialize population at 100-m resolution. An improved piece-wise population exposure approach was introduced to evaluate the exposure risk, and results were compared with two classical approaches. In all seasons, results by the absolute concentration approach are very different from the other two, showing obvious spatial smoothing effect. Results by population-weighted and piece-wise exposure approaches are similar in spring and autumn, and different in summer and winter. In winter, the area and population percentages divided to severity level 7 by population-weighted exposure approach are 5.21% and 2.35% lower than that by piece-wise exposure approach. When in summer, the area and population percentages divided to severity level 7 by population-weighted exposure approach are 6.77% and 24.79% higher than that by piece-wise exposure approach. The absolute concentration approach is disadvantageous for the identification of high-risk areas, the population-weighted exposure approach would underestimate or overestimate the population exposure when air is seriously polluted or remarkably clean, and the proposed piece-wise exposure approach would be more reasonable. The integrated methodology is effective in exposure risk assessment and can be applied to other regions and pollutants.

show abstract

Section: Comprehensive Discussionmentioning

confidence: 99%

High-Resolution Population Exposure to PM<sub>2.5</sub> in Nanchang Urban Region Using Multi-Source Data

Yang¹,

Guo²,

Leng³

2021

Pol. J. Environ. Stud.

View full text Add to dashboard Cite

show abstract

“…La inteligencia artificial, podría ayudarnos a cuantificar las redes comerciales productor-consumidor a nivel global y así conocer el impacto de la ciudad en el campo. En relación al incremento de áreas urbanas, numerosos estudios combinan datos, que incluyen imágenes a partir de drones, aviones o satélites, con inteligencia artificial para elaborar mapas urbanos precisos, diseñar redes de abastecimiento, predecir la expansión del territorio urbano (Khryashchev et al 2018;Grekousis 2019;Li et al 2019;Oh et al 2020;Phan et al 2020;Wang et al 2020b; Ghaffarian y Emtehani 2021) o ayudar en la planificación urbana (Arribas-Bel et al 2019;He et al 2020;Xue et al 2020;Zhao et al 2020).…”

Section: Variables Socio-económicas En El Estudio De La Desertificaciónunclassified

Potential of artificial intelligence to advance the study of desertification

Guirado

Martínez‐Valderrama

2021

ECOS

View full text Add to dashboard Cite

La desertificación es un problema global que afecta a más de 1.500 millones de personas que viven en los lugares más pobres y vulnerables del planeta. En los últimos años numerosos estudios han contribuido a aportar información para evaluar el problema. Algunos de ellos se basan en analizar variables biofísicas y socio-económicas mediante técnicas de inteligencia artificial. Por ejemplo, se han usado para completar datos de anomalías en la estimación de almacenamiento de agua, la identificación precisa de cobertura del suelo, estimación de la radiación solar diaria a nivel global y mejora en predicciones climáticas, entre otras. Si bien su uso todavía no está muy extendido, el futuro en los estudios sobre desertificación parece prometedor. En este trabajo revisamos el potencial de las técnicas de inteligencia artificial (aprendizaje automático y aprendizaje profundo) en el estudio de la desertificación y su reciente crecimiento en los últimos años. Durante el periodo 2015-2020 el número de publicaciones que implementan el aprendizaje profundo se incrementó un 63%, mientras que para el aprendizaje automático su crecimiento fue más modesto, del 3%. En particular, cuando buscamos estudios relacionados con la desertificación, las cifras de crecimiento son más llamativas: un incremento medio del 103% en estudios con aprendizaje profundo, y del 43% en aprendizaje automático. Sin embargo, se requieren más estudios y esfuerzos que agrupen todas las disciplinas implicadas en el estudio de la desertificación para obtener una visión global y transversal de este fenómeno y así diseñar acciones efectivas para mitigar sus efectos adversos o anticiparse a ellos.

show abstract

“…As the largest populated and most active economic development country, China's vast population provides a large consumer market with more business opportunities for enterprises 5 – 8 . However, overpopulation negatively influences natural resources, the ecological environment, and global climate change 9 , 10 .…”

Section: Introductionmentioning

confidence: 99%

Evaluation and analysis of the projected population of China

Dai

Shen

Cheng

2022

Sci Rep

View full text Add to dashboard Cite

The population has a significant influence on economic growth, energy consumption, and climate change. Many scholars and organizations have published projections for China's future population due to its substantial population amounts. However, these projections have not been evaluated or analyzed, which may lead confusion to extensional studies based on these datasets. This manuscript compares several China's projection datasets at multiscale and analyzes the impacting factors affecting projection accuracy. The results indicate that the slow of actual population growth rates from 2017 is earlier than most datasets projected. Therefore, the turning point of population decline probably comes rapidly before these datasets expected during 2024 and 2034. Furthermore, the projections do not reveal the population decline from 2010 in the Northeast provinces such as Jilin and Heilongjiang, and underrate the population increase in the southern provinces such as Guangdong and Chongqing. According to the results of regression models, the rate of population changes and the number of migrations people play a significant role in projection accuracy. These findings provide meaningful guidance for scholars to understand the uncertainty of those projection datasets. Moreover, for researchers performing population projections, our discoveries provide insights to increase the projection accuracy.

show abstract

China’s population spatialization based on three machine learning models

Cited by 36 publications

References 65 publications

High-Resolution Population Exposure to PM<sub>2.5</sub> in Nanchang Urban Region Using Multi-Source Data

High-Resolution Population Exposure to PM<sub>2.5</sub> in Nanchang Urban Region Using Multi-Source Data

Potential of artificial intelligence to advance the study of desertification

Evaluation and analysis of the projected population of China

Contact Info

Product

Resources

About