Spatio-temporal characteristics of soil Cd pollution and its influencing factors: A Geographically and temporally weighted regression (GTWR) method

Zhao, Menglu; Wang, Huijuan; Sun, Jiaxun; Tang, Rui; Cai, Boya; Song, Xiaoyong; Huang, Xinmiao; Huang, Jian; Fan, Zhengqiu

doi:10.1016/j.jhazmat.2022.130613

Cited by 35 publications

(9 citation statements)

References 49 publications

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“…The regression coefficient of each independent variable can determine the impact on the dependent variable. The contribution rate of explanatory variables to the expansion or contraction of the oasis is presented in Equations 10 and 11 (Zhao et al., 2023).

\mathrm{C}{\mathrm{T}}_{k,i}=\frac{\vert {\beta }_{0}\left({u}_{i},{v}_{i},{t}_{i}\right)\vert }{{\sum }_{k}\vert \left({u}_{i},{v}_{i},{t}_{i}\right)\vert }\times 100\%

\mathrm{C}{\mathrm{T}}_{k}=\frac{1}{i}\times \mathrm{C}{\mathrm{R}}_{k,i}

…”

Section: Methodsmentioning

confidence: 99%

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Distribution and Growth Drivers of Oases at a Global Scale

Cui,

Gui,

Liu

et al. 2024

Earth's Future

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The human‐environmental system in drylands is centered on oases. Despite its extent and socio‐ecological importance, understanding the dynamic changes of global oases and their human and environmental driving forces is imperative for sustainable development in drylands under global warming. Nevertheless, the dynamic changes of global oases and how they respond to the evolving environment are not well established. In this study, three criteria were summarized (i.e., existing in dryland climates, surrounded or partially surrounded by desert terrain, having a reliable source of freshwater and forming landscape units with higher vegetation coverage/productivity). A global oasis distribution map from 1995 to 2020 was generated using European Space Agency Climate Change Initiative Land Cover and GIMMS‐3G+ data (overall accuracy within a 95% confidence interval is 0.85 ± 0.01) based on overlay analysis and visual interpretation. In addition, we used geographic and temporal weighted regression methods to evaluate the potential macro‐level elements affecting both global and local oasis growth. The result showed that the global oases area in 2020 occupied an area of 191.91 Mha, and most oases existed in Asia (77.3%). The global oases area has significantly increased from 1995 to 2020 (+8.65 Mha). However, about 13.43 Mha of the global oases are desertified, indicating a high risk of desertification. Water resources, contributing 51.36% to the total driver's contribution, are key to the global oasis expansion. In the context of climate (climate variability and climate change), this research highlights the need for improved holistic water resource management for long‐term global oasis growth, particularly in developing countries where the oases' development is threatened by water scarcity and desertification.

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\mathrm{C}{\mathrm{T}}_{k,i}=\frac{\vert {\beta }_{0}\left({u}_{i},{v}_{i},{t}_{i}\right)\vert }{{\sum }_{k}\vert \left({u}_{i},{v}_{i},{t}_{i}\right)\vert }\times 100\%

\mathrm{C}{\mathrm{T}}_{k}=\frac{1}{i}\times \mathrm{C}{\mathrm{R}}_{k,i}

…”

Section: Methodsmentioning

confidence: 99%

Section: Geographic and Temporal Weighted Regression (Gtwr)mentioning

confidence: 99%

Distribution and Growth Drivers of Oases at a Global Scale

Cui,

Gui,

Liu

et al. 2024

Earth's Future

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“…The proposed future research endeavor involves the collection of multiple sets of historical maps at 5-year intervals to construct a multi-dimensional data structure with spatiotemporal attributes. Then, we suggest incorporating time dimension attributes to enhance the GWR method, known as GTWR (geographical and temporal weighted regression) [53], for a dynamic urban development situation. (3) The research area serves as an exemplary illustration of a metropolitan city characterized by a polycentric geographical structure and concentric road networks.…”

Section: Limitationsmentioning

confidence: 99%

Measurement of 2D and 3D Fractal Features of Urban Morphology from an Architectural View and Its Influencing Factors

Zhang,

Ping,

Fan

et al. 2024

Fractal Fract

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Urban morphology has been empirically demonstrated to be self-organized and can be quantified by fractal dimension. However, the spatial variation rule of fractal features at the sub-zone scale has yet to be uncovered, as well as the relationship between fractal dimension values and road network or land-use patterns. In this study, the urban area is partitioned into 158 grid units, with subsequent calculations conducted to determine the fractal dimensions (using 2D box-counting and 3D voxel-counting methods), road network characteristics, and land-use patterns within each individual unit. The pattern of how architectures fill into the 2D or 3D embedding space at the grid level is revealed. Moreover, the spatial relationship between the road network, land-use, and their impacts on the local architectural layout is elucidated by employing MGWR, a model that incorporates the principles of fitting localized spatial regression. The results are as follows: (1) urban morphology follows fractal laws at a sub-zone scale, both in a 2D plane and 3D volume; (2) the filling degree of architecture is high in the urban center but low in the periphery areas; (3) the selected variables fit well with the regression models; (4) there is spatial heterogeneity regarding the influence of each factor. The research findings provide valuable insights into the theoretical relationship between urban morphology and the composite structure of road networks and land use. This facilitates identifying crucial areas and priority directions for urban renewal construction, as well as optimizing architectural design to improve efficiency and functionality.

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“…This research presents innovations in the field of statistics and statistical modeling, especially geographically weighted models. The spatio-temporal model is a model that represents observed natural phenomena in spatial and temporal dimensions [1] , [2] , [3] . Data analysis on the spatio-temporal model considers the spatial dependency between observation areas and the correlation between one or several time lags [ 4 , 5 ].…”

Section: Introductionmentioning

confidence: 99%

Determination of the best geographic weighted function and estimation of spatio temporal model – Geographically weighted panel regression using weighted least square

Sifriyani,

Budiantara,

Mardianto

et al. 2024

MethodsX

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Spatio-temporal characteristics of soil Cd pollution and its influencing factors: A Geographically and temporally weighted regression (GTWR) method

Cited by 35 publications

References 49 publications

Distribution and Growth Drivers of Oases at a Global Scale

Distribution and Growth Drivers of Oases at a Global Scale

Measurement of 2D and 3D Fractal Features of Urban Morphology from an Architectural View and Its Influencing Factors

Determination of the best geographic weighted function and estimation of spatio temporal model – Geographically weighted panel regression using weighted least square

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