Testing the Importance of the Explanatory Variables in a Mixed Geographically Weighted Regression Model

10.1068/a3768 ◽  
2006 ◽  
Vol 38 (3) ◽  
pp. 587-598 ◽  
Author(s):  
Chang-Lin Mei ◽  
Ning Wang ◽  
Wen-Xiu Zhang
Keyword(s):  
Regression Model ◽  
Spatial Data ◽  
Geographically Weighted Regression ◽  
Statistical Modelling ◽  
Spatial Data Analysis ◽  
Weighted Regression ◽  
P Value ◽  
Bootstrap Procedure ◽  
Explanatory Variables ◽  
Geographically Weighted Regression Model

A mixed geographically weighted regression (MGWR) model is a kind of regression model in which some coefficients of the explanatory variables are constant, but others vary spatially. It is a useful statistical modelling tool in a number of areas of spatial data analysis. After an MGWR model is identified and calibrated, which has been well studied recently, one of the important inference problems is to evaluate the influence of the explanatory variables in the constant-coefficient part on the response of the model. This is useful in the selection of the variables and for the purpose of explanation. In this paper, a statistical inference framework for this issue is suggested and, besides the F-approximation, which has been frequently used in the literature of the geographically weighted regression technique, a bootstrap procedure for deriving the p-value of the test is also suggested. The performance of the test is investigated by extensive simulations. It is demonstrated that both the F-approximation and the bootstrap procedure work satisfactorily.

scholarly journals Spatial interaction of groundwater and surface topographic using geographically weighted regression in built-up area

2020 ◽  
Author(s):  
Asep Mulyadi ◽  
Moh. Dede ◽  
Millary Agung Widiawaty
Keyword(s):  
Regression Model ◽  
Geographically Weighted Regression ◽  
Flat Surface ◽  
Spatial Interaction ◽  
Weighted Regression ◽  
P Value ◽  
Groundwater Levels ◽  
Geographically Weighted Regression Model ◽  
Primary Water ◽  
Global Regression

Groundwater is a primary water resource for human living. In Indonesia, excessive exploitation of groundwater generally occurs in the built-up area due to over-discharge processes characterized by a cone of depression. This research revealed the spatial interaction between groundwater levels and surface topographic using geographically weighted regression in built-up area. Groundwater levels data are obtained from 72 wells in Cikembang, Bandung Regency, whereas surface topographic based on BIG's DEMNas data which has 8 meters spatial resolution. This study showed significant spatial interaction between groundwater levels and surface topographic in the built-up area. The interaction has a clustered pattern with p-value less than 0.01. It indicated in the area with flat surface topographic has lower groundwater levels than others. There are several points who indicated the cone of depression in the built-up area with flat topographic. The geographically weighted regression model has high spatial variability and better results than the global regression model to assess groundwater level interaction with surface topographic.

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.

scholarly journals Spatial Analysis of Housing Prices and Market Activity with the Geographically Weighted Regression

2020 ◽  
Vol 9 (6) ◽  
pp. 380
Author(s):  
Radosław Cellmer ◽  
Aneta Cichulska ◽  
Mirosław Bełej
Keyword(s):  
Spatial Heterogeneity ◽  
Housing Market ◽  
Spatial Data ◽  
Geographically Weighted Regression ◽  
Housing Prices ◽  
Spatial Diversity ◽  
Spatial Data Analysis ◽  
Weighted Regression ◽  
Market Activity ◽  
Administrative Units

The main part of the study will be to demonstrate that models taking into account spatial heterogeneity (Geographically Weighted Regression and Mixed Geographically Weighted Regression) which reproduce housing market determinants better reflect market relationships than conventional regression models. The spatial heterogeneity of the housing market determinants results in the spatial diversity of the market activity, as well as of real estate prices and values. The main aim of the study was to analyse an effect of these socio-demographic and environmental factors on average housing property prices and on the number of transactions in a spatial approach. In previous research conducted on a national scale, usually all variables were treated in a similar way, i.e., as global or local variables. During the research, an attempt was also made to answer the question of which of the variables adopted for analysis have a local impact on prices and market activity, and which are global. The study was conducted in Poland and used data from the year 2018 on 380 counties (Local Administrative Units). The study showed that determinants both for average prices and for the housing market activity show spatial autocorrelation with high–high and low–low cluster groups. Owing to these models, it was possible to draw specific conclusions on local determinants of flat prices and the market activity in Poland. The study findings have confirmed that they are an extremely effective tool for spatial data analysis.

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