Downscaling Land Surface Temperature Using Multiscale Geographically Weighted Regression Over Heterogeneous Landscapes in Wuhan, China

Yang, Chen; Zhan, Qiwen; Lv, Yunzhe; Liu, Huimin

doi:10.1109/jstars.2019.2955551

Cited by 42 publications

(20 citation statements)

References 46 publications

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“…A set of local parameter estimates obtained from a small optimized bandwidth indicates that the associated spatial process represented by the parameter estimates varies over relatively short distances, while a set of local parameter estimates obtained from a large bandwidth is indicative of a relationship that exhibits variation only over large distances or is constant over space. Recent research in different fields ranging from public health to environmental analysis supports the view that different processes may exhibit different degrees of spatial heterogeneity, which can be modeled with MGWR (Cupido, Fotheringham, & Jevtic, 2021;Fotheringham, Yue, & Li, 2019;Oshan, Smith, & Fotheringham, 2020;Yang, Zhan, Lv, & Liu, 2019).…”

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confidence: 99%

The spatial and temporal dynamics of voter preference determinants in four U.S. presidential elections (2008–2020)

Fotheringham

2021

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confidence: 99%

The spatial and temporal dynamics of voter preference determinants in four U.S. presidential elections (2008–2020)

Fotheringham

2021

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“…In this study, to explore the driving forces of PM2.5 population exposure in Wuhan, the RF regression has been adopted to quantify the impacts of external drives. As a robust and commonused machine learning method, RF has been widely adopted in environment-related studies (Yang et al, 2019;Yang et al, 2018a;Zhang et al, 2018). As an extension of the decision tree regression, the samples of each decision tree in the RF are obtained from the training set through the relocation sampling.…”

Section: Random Forest (Rf) Regressionmentioning

confidence: 99%

Identifying the Driving Factors of Population Exposure to Fine Particulate Matter (PM2.5) in Wuhan, China

Yang

Zhan

Xiao

et al. 2020

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Characterizing the spatiotemporal dynamics of population exposure to fine particulate matter (PM2.5) and the underlying external forcing can provide proactive implication for public health precautions. In this study, satellite-derived surface-level PM2.5 concentration as well as landscape factors and socioeconomic data are collected to identify the inter-annual variations and potential driving forces of population exposure to fine particulate matter (PM2.5) in Wuhan, China from 2000 to 2015. The fine-scale PM2.5 exposures in 2000, 2005, 2010 and 2015 were first estimated. Then the contributions of landscape factors and socioeconomic forcing are quantified by a machine learning method (i.e. Random Forest). The results revealed that the population in Wuhan faced increasing and more clustering PM2.5 threats from 2000 to 2010. Then a weakened and dispersed health threat of PM2.5 was witnessed in 2015. In general, the Gross Domestic Product (GDP) contributed the most to high-level PM2.5 exposure in the period of 2000–2015, i.e. variable importance (VIM) equalled to xxx. Among all the biophysical and landscape characteristics, the percentage of urban landscape (PLAND_UA) and urban area fraction were attributed the most to the PM2.5 population exposure. In parallel, precipitation played a crucial part in the mitigation of PM2.5 exposure. The identification of inter-annual dynamics of population PM2.5 exposure and the underlying forcing can facilitate the decision making and epidemiological precautions in the evaluation and alleviation of population exposure risks.

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“…MGWR provides a richer but more parsimonious quantitative representation of the determinants of obesity rates. Additionally, Yang et al [38] introduced MGWR into land surface temperature downscaling to establish a multiscale and nonstationary relationship between LST and biophysical indices and develop a hybrid method (MGWRK) coupled with area-to-point kriging. The results show that in LST downscaling, MGWR is capable of meticulously delineating nonstationary relationships between LST and multiple biophysical indices at different scales, which is expected to improve the temperature fidelity and spatial information enrichment in urban regions with diversiform landscapes and intricate land-atmospheric interactions.…”

Section: Introductionmentioning

confidence: 99%

Multi-Scale Geographically Weighted Regression Modeling of Urban and Rural Construction Land Fragmentation–A Case Study of the Yangtze River Delta Region

Geng

Ming

et al. 2022

IEEE Access

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Landscape fragmentation, which can be natural or man-made, leads to decreasing species diversity and environmental degradation. The analysis of landscape fragmentation helps people understand the relationship between land and ecosystems in order to achieve sustainable development. Using the data from 306 counties in the Yangtze River Delta region, we calculated the landscape pattern index and constructed an evaluation index for urban and rural construction land fragmentation. We used a multi-scale geographically weighted regression model to analyze the determinants of land fragmentation and their spatial heterogeneity. We found that land fragmentation has significant spatial autocorrelation. The spatial distribution of the fragmentation is "ladder-like" and higher in the southwest and lower in the northeast. The determinants' spatial scales from largest to smallest are average yearly precipitation, population (PS), temperature (TEM), gross domestic product (GDP) per capita (GPC), GDP per unit area (GPUA), proportion of secondary production (PSP), slope (SLO), and elevation (ELE). The mean contributions of the regression coefficient from highest to lowest were PER, ELE, TEM, location, GPC, SLO, GPUA, PSP, and PS. The evaluation index could help policymakers make urbanization plans and implement effective urban and rural spatial structures.INDEX TERMS multi-scale geographically weighted regression, urban and rural construction land, fragmentation evaluation

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Downscaling Land Surface Temperature Using Multiscale Geographically Weighted Regression Over Heterogeneous Landscapes in Wuhan, China

Cited by 42 publications

References 46 publications

The spatial and temporal dynamics of voter preference determinants in four U.S. presidential elections (2008–2020)

The spatial and temporal dynamics of voter preference determinants in four U.S. presidential elections (2008–2020)

Identifying the Driving Factors of Population Exposure to Fine Particulate Matter (PM2.5) in Wuhan, China

Multi-Scale Geographically Weighted Regression Modeling of Urban and Rural Construction Land Fragmentation–A Case Study of the Yangtze River Delta Region

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