Application of geographically weighted regression model to analysis of spatiotemporal varying relationships between groundwater quantity and land use changes (case study: Khanmirza Plain, Iran)

2014 ◽  
Vol 186 (5) ◽  
pp. 3123-3138 ◽  
Author(s):  
Shahabeddin Taghipour Javi ◽  
Bahram Malekmohammadi ◽  
Hadi Mokhtari
Keyword(s):  
Land Use ◽  
Regression Model ◽  
Geographically Weighted Regression ◽  
Land Use Changes ◽  
Weighted Regression ◽  
Geographically Weighted Regression Model ◽  
Groundwater Quantity

scholarly journals Analysis of Beijing’s Working Population Based on Geographically Weighted Regression Model

2020 ◽  
Vol 12 (12) ◽  
pp. 5018
Author(s):  
Yanyan Chen ◽  
Hanqiang Qian ◽  
Yang Wang
Keyword(s):  
Land Use ◽  
Population Density ◽  
Regression Model ◽  
Mobile Phone ◽  
Geographically Weighted Regression ◽  
Mobile Phone Data ◽  
Weighted Regression ◽  
Working Population ◽  
Development Status ◽  
Geographically Weighted Regression Model

Evaluation of urban planning and development is becoming more and more important due to the large-scale urbanization of the world. With the application of mobile phone data, people can analyze the development status of cities from more perspectives. By using the mobile phone data of Beijing, the working population density in different regions was identified. Taking the working population density in Beijing as the research object and combining the land use of the city, the development status of Beijing was evaluated. A geographically weighted regression model (GWR) was used to analyze the difference in the impact of land use on the working population between different regions. By establishing a correlation model between the working population and land use, not only can the city’s development status be evaluated, but it can also help city managers and planners to make decisions to promote better development of Beijing.

scholarly journals Spatiotemporal Changes in Vegetation Cover and Its Influencing Factors in the Loess Plateau of China Based on the Geographically Weighted Regression Model

Forests ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 673
Author(s):  
Chen Yang ◽  
Meichen Fu ◽  
Dingrao Feng ◽  
Yiyu Sun ◽  
Guohui Zhai
Keyword(s):  
Regression Model ◽  
Influencing Factors ◽  
Loess Plateau ◽  
Geographically Weighted Regression ◽  
Vegetation Cover ◽  
Weighted Regression ◽  
Vegetation Coverage ◽  
The Loess Plateau ◽  
Geographically Weighted Regression Model ◽  
Spatiotemporal Changes

Vegetation plays a key role in ecosystem regulation and influences our capacity for sustainable development. Global vegetation cover has changed dramatically over the past decades in response to both natural and anthropogenic factors; therefore, it is necessary to analyze the spatiotemporal changes in vegetation cover and its influencing factors. Moreover, ecological engineering projects, such as the “Grain for Green” project implemented in 1999, have been introduced to improve the ecological environment by enhancing forest coverage. In our study, we analyzed the changes in vegetation cover across the Loess Plateau of China and the impacts of influencing factors. First, we analyzed the latitudinal and longitudinal changes in vegetation coverage. Second, we displayed the spatiotemporal changes in vegetation cover based on Theil-Sen slope analysis and the Mann-Kendall test. Third, the Hurst exponent was used to predict future changes in vegetation coverage. Fourth, we assessed the relationship between vegetation cover and the influence of individual factors. Finally, ordinary least squares regression and the geographically weighted regression model were used to investigate the influence of various factors on vegetation cover. We found that the Loess Plateau showed large-scale greening from 2000 to 2015, though some regions showed decreasing vegetation cover. Latitudinal and longitudinal changes in vegetation coverage presented a net increase. Moreover, some areas of the Loess Plateau are at risk of degradation in the future, but most areas showed a sustainable increase in vegetation cover. Temperature, precipitation, gross domestic product (GDP), slope, cropland percentage, forest percentage, and built-up land percentage displayed different relationships with vegetation cover. Geographically weighted regression model revealed that GDP, temperature, precipitation, forest percentage, cropland percentage, built-up land percentage, and slope significantly influenced (p < 0.05) vegetation cover in 2000. In comparison, precipitation, forest percentage, cropland percentage, and built-up land percentage significantly affected (p < 0.05) vegetation cover in 2015. Our results enhance our understanding of the ecological and environmental changes in the Loess Plateau.

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