An optimal parameters-based geographical detector model enhances geographic characteristics of explanatory variables for spatial heterogeneity analysis: cases with different types of spatial data

Song, Yongze; Wang, Jinfeng; Ge, Yong; Xu, Chengdong

doi:10.1080/15481603.2020.1760434

Cited by 480 publications

(243 citation statements)

References 47 publications

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“…General geographical detectors include factor, interaction, risk, and ecological detectors. The factor detector reveals the relative importance of the explanatory variables [37]. The power of the determinants is computed using a Q-statistic:…”

Section: Geographical Detector Modelmentioning

confidence: 99%

NDVI Indicates Long-Term Dynamics of Vegetation and Its Driving Forces from Climatic and Anthropogenic Factors in Mongolian Plateau

Guo

Wang

et al. 2021

Remote Sensing

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In recent years, global warming and intense human activity have been responsible for significantly altering vegetation dynamics on the Mongolian Plateau. Understanding the long-term vegetation dynamics in this region is important to assess the impact of these changes on the local ecosystem. Long-term (1982–2015), satellite-derived normalized difference vegetation index (NDVI) datasets were used to analyse the spatio-temporal patterns of vegetation activities using linear regression and the breaks for additive season and trend methods. The links between these patterns and changes in temperature, precipitation (PRE), soil moisture (SM), and anthropogenic activity were determined using partial correlation analysis, the residual trends method, and a stepwise multiple regression model. The most significant results indicated that air temperature and potential evapotranspiration increased significantly, while the SM and PRE had markedly decreased over the past 34 years. The NDVI dataset included 71.16% of pixels showing an increase in temperature and evaporation during the growing season, particularly in eastern Mongolia and the southern border of the Inner Mongolia Autonomous region, China. The proportion indicating the breakpoint of vegetation dynamics was 71.34% of pixels, and the trend breakpoints mainly occurred in 1993, 2003, and 2010. The cumulative effects of PRE and SM in the middle period, coupled with the short-term effects of temperature and potential evapotranspiration, have had positive effects on vegetation greening. Anthropogenic factors appear to have positively impacted vegetation dynamics, as shown in 81.21% of pixels. We consider rapid economic growth, PRE, and SM to be the main driving factors in Inner Mongolia. PRE was the main climatic factor, and combined human and livestock populations were the primary anthropogenic factors influencing vegetation dynamics in Mongolia. This study is important in promoting the continued use of green projects to address environmental change in the Mongolian Plateau.

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Section: Geographical Detector Modelmentioning

confidence: 99%

NDVI Indicates Long-Term Dynamics of Vegetation and Its Driving Forces from Climatic and Anthropogenic Factors in Mongolian Plateau

Guo

Wang

et al. 2021

Remote Sensing

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“…The Hurst exponent and R/S analysis have developed a fractal theory for studying time series and have been widely used in the research of urban environmental change, climate change, population, and economic development [67,68]. R/S analysis can be used to predict future trends in vegetation cover, wherein the main principle is to construct a time series that defines an average series { ξ(t), t = 1, 2 • • • •} , for any positive integer τ ≥ 1.…”

Section: Hurst Exponent and R/s Analysismentioning

confidence: 99%

Monitoring Vegetation Change and Its Potential Drivers in Inner Mongolia from 2000 to 2019

et al. 2021

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Inner Mongolia in China is a typically arid and semi-arid region with vegetation prominently affected by global warming and human activities. Therefore, investigating the past and future vegetation change and its impact mechanism is important for assessing the stability of the ecosystem and the ecological policy formulation. Vegetation changes, sustainability characteristics, and the mechanism of natural and anthropogenic effects in Inner Mongolia during 2000–2019 were examined using moderate resolution imaging spectroradiometer normalized difference vegetation index (NDVI) data. Theil–Sen trend analysis, Mann–Kendall method, and the coefficient of variation method were used to analyze the spatiotemporal variability characteristics and sustained stability of the NDVI. Furthermore, a trend estimation method based on a Seasonal Trend Model (STM), and the Hurst index was used to analyze breakpoints and change trends, and predict the likely future direction of vegetation, respectively. Additionally, the mechanisms of the compound influence of natural and anthropogenic activities on the vegetation dynamics in Inner Mongolia were explored using a Geodetector Model. The results show that the NDVI of Inner Mongolia shows an upward trend with a rate of 0.0028/year (p < 0.05) from 2000 to 2019. Spatially, the NDVI values showed a decreasing trend from the northeast to the southwest, and the interannual variation fluctuated widely, with coefficients of variation greater than 0.15, for which the high-value areas were in the territory of the Alxa League. The areas with increased, decreased, and stable vegetation patterns were approximately equal in size, in which the improved areas were mainly distributed in the northeastern part of Inner Mongolia, the stable and unchanged areas were mostly in the desert, and the degraded areas were mainly in the central-eastern part of Inner Mongolia, it shows a trend of progressive degradation from east to west. Breakpoints in the vegetation dynamics occurred mainly in the northwestern part of Inner Mongolia and the northeastern part of Hulunbuir, most of which occurred during 2011–2014. The future NDVI trend in Inner Mongolia shows an increasing trend in most areas, with only approximately 10% of the areas showing a decreasing trend. Considering the drivers of the NDVI, we observed annual precipitation, soil type, mean annual temperature, and land use type to be the main driving factors in Inner Mongolia. Annual precipitation was the first dominant factor, and when these four dominant factors interacted to influence vegetation change, they all showed interactive enhancement relationships. The results of this study will assist in understanding the influence of natural elements and human activities on vegetation changes and their driving mechanisms, while providing a scientific basis for the rational and effective protection of the ecological environment in Inner Mongolia.

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“…Each has its advantages and drawbacks, but it is worth noting that current methods have proven to be measurable and with specified analysis and software. For example, a great deal of academic work has involved grey relational analysis [41], geographically weighted regression (GWR) [42], visualization and spatial measurement methods using ArcGIS, MATLAB, STARS, and others [26,28,43], geographical detector models [44], Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) models [45][46][47], and the Logarithmic Mean Divisia Index (LMDI) decomposition method [48]. Hybrid Single-Particle Lagrangian Integrated Trajectory Models (HYSPLIT-4), Potential Source Contribution Function (PSCF), and Concentration Weighted Trajectory (CWT) are commonly associated with the trans-regional transportation of atmospheric particulates and the identification of potential source regions.…”

Section: Literature Reviewmentioning

confidence: 99%

How Does Local Real Estate Investment Influence Neighborhood PM2.5 Concentrations? A Spatial Econometric Analysis

Bao

Shan

Wang

et al. 2021

Land

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Real estate investment has been an important driving force in China’s economic growth in recent years, and the relationship between real estate investment and PM2.5 concentrations has been attracting widespread attention. Based on spatial econometric modelling, this paper explores the relationships between real estate investment and PM2.5 concentrations using multi-source panel data from 30 provinces in China between 1987 and 2017. The results demonstrate that compared with static spatial panel modelling, using a dynamic spatial Durbin lag model (DSDLM) more accurately reflects the influences of real estate investment on PM2.5 concentrations in China, and that PM2.5 concentrations show significant superposition effects and spillover effects. Moreover, there is an inverted U-shaped relationship between real estate investment and PM2.5 concentrations in the Eastern and Central Regions of China. At the national level, the impacts of real estate investment on land urbanization and PM2.5 concentrations first increased and then decreased over time. The key implications of this analysis are as follows. (1) it highlights the need for a unified PM2.5 monitoring platform among Chinese regions; (2) the quality of population urbanization rather than land urbanization should be given more attention; and (3) the speed of construction of green cities and building of green transportation systems and green town systems should be increased.

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An optimal parameters-based geographical detector model enhances geographic characteristics of explanatory variables for spatial heterogeneity analysis: cases with different types of spatial data

Cited by 480 publications

References 47 publications

NDVI Indicates Long-Term Dynamics of Vegetation and Its Driving Forces from Climatic and Anthropogenic Factors in Mongolian Plateau

NDVI Indicates Long-Term Dynamics of Vegetation and Its Driving Forces from Climatic and Anthropogenic Factors in Mongolian Plateau

Monitoring Vegetation Change and Its Potential Drivers in Inner Mongolia from 2000 to 2019

How Does Local Real Estate Investment Influence Neighborhood PM2.5 Concentrations? A Spatial Econometric Analysis

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