Drivers of afforestation in Northern Vietnam: Assessing local variations using geographically weighted regression

This article identifies drivers of forest transition in a province of Northern Vietnam between 1993 and 2000 by applying geographically weighted regression (GWR) analysis to remotely sensed and statistical data. The regression model highlighted the spatial variation of the relationship between the percentage of land afforested and its proximate causes. Factors identified as having a major impact on afforestation are: the presence or proximity of a wood-processing industry, the distance to highways, and land allocation to households. Whereas the two former variables are in most areas of the province positively correlated with afforestation, an unexpected negative correlation was observed for the latter. The analysis of these results, supported by an in-depth knowledge of the area and of the political context, leads to the conclusion that, during the time period considered, afforestation was largely driven by state organisations on protected state-owned land, and forestry was not a significant component of household economic activities.     

A bootstrap test for constant coefficients in geographically weighted regression models

Statistical tests for whether some coefficients really vary over space play an important role in using the geographically weighted regression (GWR) to explore spatial non-stationarity of the regression relationship. In view of some shortcomings of the existing inferential methods, we propose a residual-based bootstrap test to detect the constant coefficients in a GWR model. The proposed test is free of the assumption that the model error term is normally distributed and admits some useful extensions for identifying more complicated spatial patterns of the coefficients. Some simulation with comparison to the existing test methods is conducted to assess the test performance, including the accuracy of the bootstrap approximation to the null distribution of the test statistic, the power in identifying spatially varying coefficients and the robustness to collinearity among the explanatory variables. The simulation results demonstrate that the bootstrap test works quite well. Furthermore, a real-world data set is analyzed to illustrate the application of the proposed test.     

The Minkowski approach for choosing the distance metric in geographically weighted regression

In this study, the geographically weighted regression (GWR) model is adapted to benefit from a broad range of distance metrics, where it is demonstrated that a well-chosen distance metric can improve model performance. How to choose or define such a distance metric is key, and in this respect, a ‘Minkowski approach’ is proposed that enables the selection of an optimum distance metric for a given GWR model. This approach is evaluated within a simulation experiment consisting of three scenarios. The results are twofold: (1) a well-chosen distance metric can significantly improve the predictive accuracy of a GWR model; and (2) the approach allows a good approximation of the underlying ‘optimal distance metric’, which is considered useful when the ‘true’ distance metric is unknown.     

Predictive mapping of soil total nitrogen at a regional scale: A comparison between geographically weighted regression and cokriging

Accurately mapping the spatial distribution of soil total nitrogen is important to precision agriculture and environmental management. Geostatistical methods have been frequently used for predictive mapping of soil properties. Recently, a local regression method, geographically weighted regression (GWR), got the attention of environmentalists as an alternative in spatial modeling of environmental attributes, due to its capability of incorporating various auxiliary variables with spatially varied correlation coefficients. The objective of this study is to compare GWR and ordinary cokriging (OCK) in predictive mapping of soil total nitrogen (TN) using multiple environmental variables. 353 soil Samples within the surface horizon of 0–20 cm in a study area were collected, and their TN contents were measured for calibrating and validating the GWR and OCK interpolations. The environmental variables finally chosen as auxiliary data include elevation, land use types, and soil types. Results indicate that, although OCK is slightly better than GWR in global accuracy of soil TN prediction (the adjusted R² for GWR and OCK are 0.5746 and 0.6858, respectively), the soil TN map interpolated by GWR shows many details reflecting the spatial variations of major auxiliary variables while OCK smoothes out almost all local details. Geographically weighted regression could account for both the spatial trend and local variations, whilst OCK had difficulties to capture local variations. It is concluded that GWR is a more promising spatial interpolation method compared to OCK in predicting soil TN and potentially other soil properties, if a suitable set of auxiliary variables are available and selected.     

Using multilevel modeling and geographically weighted regression to identify spatial variations in the relationship between place-level disadvantages and obesity in Taiwan

Objectives

We examined whether and to what extent the relationship between township disadvantages and obesity varied across geographical areas.

Methods

A cross-sectional analysis of a population-based sample of Taiwanese adults (N = 25,985) from the 2005 Social Development Trend Survey on Health and Safety was performed. Multilevel models integrated with geographically weighted regressions were employed to analyze the spatially varying association between area disadvantages and obesity. The dependent variable was body mass index calculated from respondents’ self-reported weight and height. The key explanatory variable was a township disadvantage index made of poverty level, minority composition, and social disorder. Other individual socio-demographic characteristics were included to account for the compositional effect.

Results

The association between township disadvantages and elevated obesity risk in Taiwan was found to be area-specific. In contrast to results from the commonly used global regression, geographically weighted regression model showed that township disadvantages elevated obesity level only in certain areas.

Conclusions

We found heterogeneity of place-level determinants of obesity across geographical areas. Adoption of population approach to curb obesity would require area-specific strategies for most needed areas.     

Spatial non-stationary characteristics between grass yield and its influencing factors in the Ningxia temperate grasslands based on a mixed geographically weighted regression model

Spatial models are effective in obtaining local details on grassland biomass, and their accuracy has important practical significance for the stable management of grasses and livestock. To this end, the present study utilized measured quadrat data of grass yield across different regions in the main growing season of temperate grasslands in Ningxia of China (August 2020), combined with hydrometeorology, elevation, net primary productivity (NPP), and other auxiliary data over the same period. Accordingly, non-stationary characteristics of the spatial scale, and the effects of influencing factors on grass yield were analyzed using a mixed geographically weighted regression (MGWR) model. The results showed that the model was suitable for correlation analysis. The spatial scale of ratio resident-area index (PRI) was the largest, followed by the digital elevation model, NPP, distance from gully, distance from river, average July rainfall, and daily temperature range; whereas the spatial scales of night light, distance from roads, and relative humidity (RH) were the most limited. All influencing factors maintained positive and negative effects on grass yield, save for the strictly negative effect of RH. The regression results revealed a multiscale differential spatial response regularity of different influencing factors on grass yield. Regression parameters revealed that the results of Ordinary least squares (OLS) (Adjusted R² = 0.642) and geographically weighted regression (GWR) (Adjusted R² = 0.797) models were worse than those of MGWR (Adjusted R² = 0.889) models. Based on the results of the RMSE and radius index, the simulation effect also was MGWR > GWR > OLS models. Ultimately, the MGWR model held the strongest prediction performance (R² = 0.8306). Spatially, the grass yield was high in the south and west, and low in the north and east of the study area. The results of this study provide a new technical support for rapid and accurate estimation of grassland yield to dynamically adjust grazing decision in the semi-arid loess hilly region.     

Geographically and temporally weighted regression for modeling spatio-temporal variation in house prices

By incorporating temporal effects into the geographically weighted regression (GWR) model, an extended GWR model, geographically and temporally weighted regression (GTWR), has been developed to deal with both spatial and temporal nonstationarity simultaneously in real estate market data. Unlike the standard GWR model, GTWR integrates both temporal and spatial information in the weighting matrices to capture spatial and temporal heterogeneity. The GTWR design embodies a local weighting scheme wherein GWR and temporally weighted regression (TWR) become special cases of GTWR. In order to test its improved performance, GTWR was compared with global ordinary least squares, TWR, and GWR in terms of goodness-of-fit and other statistical measures using a case study of residential housing sales in the city of Calgary, Canada, from 2002 to 2004. The results showed that there were substantial benefits in modeling both spatial and temporal nonstationarity simultaneously. In the test sample, the TWR, GWR, and GTWR models, respectively, reduced absolute errors by 3.5%, 31.5%, and 46.4% relative to a global ordinary least squares model. More impressively, the GTWR model demonstrated a better goodness-of-fit (0.9282) than the TWR model (0.7794) and the GWR model (0.8897). McNamara's test supported the hypothesis that the improvements made by GTWR over the TWR and GWR models are statistically significant for the sample data.     

The supporting capability of Water and land resources for sustainable increase of yield in North China Plain

ＴｈｅｓｕｐｐｏｒｔｉｎｇｃａｐａｂｉｌｉｔｙｏｆｗａｔｅｒａｎｄｌａｎｄｒｅｓｏｕｒｃｅｓｆｏｒｓｕｓｔａｉｎａｂｌｅｉｎｃｒｅａｓｅｏｆｙｉｅｌｄｉｎＮｏｒｔｈＣｈｉｎａＰｌａｉｎＺＨＡＮＧＨｏｎｇｙｅＩｎｓｔｉｔｕｔｅｏｆＧｅｏｇｒａｐｈｙ,...     

四川紫色土地区鹤鸣观小流域分布式侵蚀产沙模型

从四川省南部县鹤鸣观小流域Ⅱ号支沟为研究区,构建了适合紫色土地区小流域分布式侵蚀产沙模型。该模型以20m×20m栅格为空间步长,以10min为时间步长,定量分析鹤鸣观小流域Ⅱ号支沟水土流失程度,模拟了各时段每个栅格次降雨侵蚀产沙过程,计算了每个栅格次降雨径流量、侵蚀量与沉积量,并且运用递归算法计算出整个流域次降雨侵蚀产沙量,模型能够评价流域下垫面各因子空间分布不均匀性和人类活动的影响。在鹤鸣观小流域Ⅱ号支沟进行了模型的检验,模拟过程与实测结果符合较好。