Knowledge diffusion of Geodetector: A perspective of the literature review and Geotree

Liang, Yuting; Xu, Chengdong

doi:10.1016/j.heliyon.2023.e19651

Cited by 18 publications

(6 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…GD model is a statistical method that reveals the driving factors behind research phenomena and the interaction relationships between various factors ( 32 ). GD includes risk detection, factor detection, ecological detection, and interaction detection.…”

Section: Data Source and Research Methodsmentioning

confidence: 99%

“…First, we explore the spatial distribution pattern of the longevity a in Yunnan Province through spatial analysis methods. Then, we use the Geodetector (GD) and Geographically Weighted Regression (GWR) to explore the driving factors (wellness elements) that form this pattern ( 31 , 32 ). The use of GD and GWR is due to their ability to effectively handle the spatial heterogeneity of geographical phenomena, thereby helping us better understand and explain the relationship between longevity phenomena and geographical environment.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A longevity level-oriented wellness target area identification method: a case study of Yunnan Province, China

Wang,

Wang

2024

Front. Public Health

View full text Add to dashboard Cite

BackgroundAging, as a global demographic issue, is characterized by its rapid growth, which drives an increase in people's healthcare awareness. The emergence of wellness bases caters to this market demand. Therefore, the identification of potential areas suitable for wellness activities and the construction of wellness bases, referred to as Wellness Target Areas (WTAs), becomes a crucial first step. Currently, commonly used identification methods are mostly based on traditional statistical approaches, which are often complex, cumbersome, and subject to potential risks of subjective assumptions, affecting the reliability of WTAs identification results. Longevity level serves as a comprehensive indicator reflecting the natural and socio-economic environment of a region, making it the most indicative of the regional wellness environment status.MethodsThis study proposes using longevity level as the benchmark for WTAs identification to simplify the identification process and reduce the impact of subjective bias on the results. The study focuses on 129 county-level units in Yunnan Province. Firstly, the Geodetector (GD) is utilized to explore the complex interaction between the longevity level and the geographical environment to determine regional wellness factors. Secondly, using ArcGIS and geographical weighted regression (GWR), the study investigates the role of different wellness factors, ultimately classifying and grading the WTAs.ResultsThe longevity level in Yunnan Province exhibits a pattern of multi-point clustering, forming three major longevity regions. Factors that significantly influence longevity level include annual average precipitation, sunshine duration, PM2.5 content, per capita disposable income, density of tourist attractions, and distance from residential areas to hospitals. Based on the degree of longevity and the contribution rate of influencing factors, Yunnan Province's WTAs are classified into three levels and two types (natural and comprehensive).ConclusionOur study aims to establish a connection between longevity level and the selection of wellness bases, exploring regional wellness factors through the relationship between longevity phenomena and geographical environment, identifying potential construction areas for wellness bases (i.e., WTAs), providing new insights for the precise selection of wellness bases, effectively enhancing the scientificity of site selection, promoting population health, and contributing to the global aging process with better health.

show abstract

Section: Data Source and Research Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A longevity level-oriented wellness target area identification method: a case study of Yunnan Province, China

Wang,

Wang

2024

Front. Public Health

View full text Add to dashboard Cite

show abstract

“…The Geodetector comprises a suite of statistical tools tailored for identifying spatial heterogeneity and discerning the driving mechanisms beneath [40]. It encompasses several modules: the factor detector, the risk detector, the interaction detector, and the ecological detector.…”

Section: Geodetectormentioning

confidence: 99%

“…In terms of land-use carbon emission driving factors, traditional econometric methods like the logarithmic mean divisia index (LMDI) decomposition method [37], stochastic impacts by regression on population, affluence, the technology (STIRPAT) model [38], and the grey correlation model [39] are commonly used, but often fail to consider spatial differences among influencing factors and the interactive effects of these factors on the spatial differentiation of land-use carbon emissions, thereby not fully elucidating the drivers behind spatial disparities. Geodetector [40], by contrast, offers a more robust mechanism for detecting and quantifying the distinct contributions and interplays of diverse factors affecting the spatial variance in land-use carbon emissions, thus facilitating a deeper exploration of the intricate causal networks embedded within spatial datasets. Regarding land-use carbon emission optimization, regional discrepancies and ecological complexity limit the generalizability of existing findings [41].…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Effects and Optimization Strategies of Land-Use Carbon Emissions at the County Scale: A Case Study of Shaanxi Province, China

Zhang,

Li,

Liu

et al. 2024

Sustainability

View full text Add to dashboard Cite

Land use, as one of the major sources of carbon emissions, has profound implications for global climate change. County-level land-use systems play a critical role in national carbon emission management and control. Consequently, it is essential to explore the spatiotemporal effects and optimization strategies of land-use carbon emissions at the county scale to promote the achievement of regional dual carbon targets. This study, focusing on Shaanxi Province, analyzed the spatiotemporal characteristics of land use from 2000 to 2020. By establishing a carbon emission evaluation model, the spatiotemporal effects of county-level carbon emissions were clarified. Utilizing Geodetector and K-means clustering methods, the driving mechanisms and clustering characteristics of county-level carbon emissions were elucidated, and optimization strategies for land use carbon emission were explored. The results showed that during 2000–2020, land use in Shaanxi Province underwent significant spatiotemporal changes, with constructed land increasing by 97.62%, while cultivated land and grassland were substantially reduced. The overall county-level carbon emissions exhibited a pattern of North > Central > South. The total carbon emissions within the province increased nearly fourfold over 20 years, reaching 1.00 × 108 tons. Constructed land was the primary source of emissions, while forest land contributed significantly to the carbon sink of the study area. Interactions among factors had significant impacts on the spatial differentiation of total county-level carbon emissions. For counties with different types of carbon emissions, differentiated optimization strategies were recommended. Low-carbon emission counties should intensify ecological protection and rational utilization, medium-carbon emission counties need to strike a balance between economic development and environmental protection, while high-carbon emission counties should prioritize profound emission reduction and structural transformation.

show abstract

“…It has been widely used to analyze the drivers of ESs, urban expansion, population distribution, vegetation change, extreme events, etc. [54,56,57].…”

Section: Introductionmentioning

confidence: 99%

Mapping and Analyzing the Spatiotemporal Patterns and Drivers of Multiple Ecosystem Services: A Case Study in the Yangtze and Yellow River Basins

Yu,

Xiao,

Bruzzone

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

The Yangtze River Basin (YZRB) and the Yellow River Basin (YRB), which are crucial for ecology and economy in China, face growing challenges to ecosystem service (ES) functions due to global population growth, urbanization, and climate change. This study assessed the spatiotemporal dynamics of ESs in the YZRB and the YRB between 2001 and 2021, comprehensively encompassing essential aspects such as water yield (WY), carbon sequestration (CS), soil conservation (SC), and habitat quality (HQ) while also analyzing the trade-offs and synergies among these ESs at the grid cells. The GeoDetector was employed to ascertain individual or interactive effects of natural and anthropogenic factors on these ESs and their trade-offs/synergies. The results showed that (1) from 2001 to 2021, the four ESs exhibited significant spatial disparities in the distribution within two basins, with the overall trend of ESs mainly increasing. YZRB consistently exhibited substantially higher ES values than the YRB. (2) Complex trade-offs and synergies among these ESs were apparent in both basins, characterized by distinct spatial heterogeneity. The spatial relationships of WY–CS, WY–SC, CS–SC, and CS–HQ were mainly synergistic. (3) Precipitation, potential evapotranspiration, elevation, land use and land cover (LULC), and slope influenced ESs in both basins. Notably, interactive factors, particularly the interactions involving LULC and other factors, demonstrated more robust explanatory power for ESs and their trade-offs/synergies than individual drivers. These findings significantly affect the refined ecosystem management and sustainable development decision-making in large rivers or regions.

show abstract

Knowledge diffusion of Geodetector: A perspective of the literature review and Geotree

Cited by 18 publications

References 50 publications

A longevity level-oriented wellness target area identification method: a case study of Yunnan Province, China

A longevity level-oriented wellness target area identification method: a case study of Yunnan Province, China

Spatiotemporal Effects and Optimization Strategies of Land-Use Carbon Emissions at the County Scale: A Case Study of Shaanxi Province, China

Mapping and Analyzing the Spatiotemporal Patterns and Drivers of Multiple Ecosystem Services: A Case Study in the Yangtze and Yellow River Basins

Contact Info

Product

Resources

About