scholarly journals Determination of Stunting Risk Factors Using Spatial Interpolation Geographically Weighted Regression Kriging in Malang

2020 ◽  
Vol 20 (2) ◽  
Author(s):  
Henny Pramoedyo ◽  
Mudjiono Mudjiono ◽  
Adji Achmad Fernandes ◽  
Deby Ardianti ◽  
Kurniawati Septiani
Keyword(s):  
Risk Factors ◽  
Geographically Weighted Regression ◽  
Spatial Interpolation ◽  
Weighted Regression ◽  
Regression Kriging

scholarly journals Modeling and Predictive Mapping of Soil Organic Carbon Density in a Small-Scale Area Using Geographically Weighted Regression Kriging Approach

2020 ◽  
Vol 12 (22) ◽  
pp. 9330
Author(s):  
Tao Liu ◽  
Huan Zhang ◽  
Tiezhu Shi
Keyword(s):  
Environmental Factors ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Geographically Weighted Regression ◽  
Environmental Variables ◽  
Spatial Interpolation ◽  
Weighted Regression ◽  
Regression Kriging ◽  
Carbon Density ◽  
Soil Organic Carbon Density

Different natural environmental variables affect the spatial distribution of soil organic carbon (SOC), which has strong spatial heterogeneity and non-stationarity. Additionally, the soil organic carbon density (SOCD) has strong spatial varying relationships with the environmental factors, and the residuals should keep independent. This is one hard and challenging study in digital soil mapping. This study was designed to explore the different impacts of natural environmental factors and construct spatial prediction models of SOC in the junction region (with an area of 2130.37 km2) between Enshi City and Yidu City, Hubei Province, China. Multiple spatial interpolation models, such as stepwise linear regression (STR), geographically weighted regression (GWR), regression kriging (RK), and geographically weighted regression kriging (GWRK), were built using different natural environmental variables (e.g., terrain, environmental, and human factors) as auxiliary variables. The goodness of fit (R2), root mean square error, and improving accuracy were used to evaluate the predicted results of the spatial interpolation models. Results from Pearson correlation coefficient analysis and STR showed that SOCD was strongly correlated with elevation, topographic position index (TPI), topographic wetness index (TWI), slope, and normalized difference vegetation index (NDVI). GWRK had the highest simulation accuracy, followed by RK, whereas STR was the weakest. A theoretical scientific basis is, therefore, provided for exploring the relationship between SOCD and the corresponding environmental variables as well as for modeling and estimating the regional soil carbon pool.

Effects of different sampling densities on geographically weighted regression kriging for predicting soil organic carbon

2017 ◽  
Vol 20 ◽  
pp. 76-91 ◽  
Author(s):  
Huichun Ye ◽  
Wenjiang Huang ◽  
Shanyu Huang ◽  
Yuanfang Huang ◽  
Shiwen Zhang ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Geographically Weighted Regression ◽  
Weighted Regression ◽  
Regression Kriging

scholarly journals Geospatial modeling approaches for mapping topsoil organic carbon stock in northern part of Mongolia

2019 ◽  
pp. 4-17
Author(s):  
Samdandorj M ◽  
Purevdorj Ts
Keyword(s):  
Organic Carbon ◽  
Root Mean Square Error ◽  
Geographically Weighted Regression ◽  
Carbon Stock ◽  
Vegetation Index ◽  
Weighted Regression ◽  
Mean Square ◽  
Regression Kriging ◽  
Soc Stock ◽  
Organic Carbon Stock

Soil organic carbon (SOC) is one of the most important indicators of soil quality and agricultural productivity. This paper presents the application of Regression Kriging (RK), geographically weighted regression (GWR) and Geographically Weighted Regression Kriging (GWRK) for prediction of topsoil organic carbon stock in Tarialan. A total of 25 topsoil (0-30 cm) samples were collected from Tarialan soum of Khuvsgul aimag in Mongolia. In this study, seven independent variables were used including normalised difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), normalised difference moisture index (NDMI), land surface temperature (LST) and terrain factors (DEM, Slope, Aspect). We used root-mean-square error (RMSE), mean error (ME) and determination coefficient (R2) to evaluate the performance of these methods. Validation results showed that performance of the GWRK, GWR, and RK approaches were good with not only low values of root-mean-square error (1.38 kg/m2, 1.48 kg/m2, 0.69 kg/m2), mean error (0.28 kg/m2, -0.22 kg/m2, 0.17 kg/m2) but also high values of R2 (0.76, 0.72, 0.94). The estimated SOC stock values ranged from 0.28-16.26 kg/m2, 0.72–15.24 kg/m2, 0.16–15.83 kg/m2 using GWRK, GWR, RK approaches in the study area. The highest average SOC stock value was in the wetland (6.47 kg/m2, 6.08 kg/m2, 6.44 kg/m2) and the lowest was in cropland (1.63 kg/m2, 1.48 kg/m2, 1.80 kg/m2) using these approaches. According to the validation, GWRK, GWR, and RK approaches produced satisfactory results for estimating and mapping SOC stock. However, Regression Kriging was the best model, followed by GWRK and GWR to predict topsoil organic carbon stock in Tarialan.

scholarly journals Determination of the Peak Hour Ridership of Metro Stations in Xi’an, China Using Geographically-Weighted Regression

2020 ◽  
Vol 12 (6) ◽  
pp. 2255 ◽  
Author(s):  
Lijie Yu ◽  
Yarong Cong ◽  
Kuanmin Chen
Keyword(s):  
Land Use ◽  
Land Use Planning ◽  
Geographically Weighted Regression ◽  
Urban Land Use ◽  
Weighted Regression ◽  
Metro Station ◽  
Geographically Weighted Regression Model ◽  
Extra Peak ◽  
Deviation Coefficient

The ridership of a metro station during a city’s peak hour is not always the same as that during the station’s own peak hour. To investigate this inconsistency, this study introduces the peak deviation coefficient to describe this phenomenon. Data from 88 metro stations in Xi’an, China, are used to analyze the peak deviation coefficient based on the geographically weighted regression model. The results demonstrate that when the land around a metro station is mainly land for work, primary and middle schools, and residences, its station’s peak hour is consistent with the city’s peak hour. Additionally, the station’s peak hour is more likely to deviate from the city’s peak hour for suburban stations. There are two ridership options when designing stations, namely the extra peak hour ridership during a city’s peak hour and that during a station’s peak hour, and the larger of the two is used to design metro stations. The mixed land use ratio must be considered in urban land use planning, because although non-commuting land can mitigate the traffic pressure of a city’s peak hour, it may cause the deviation of the station’s peak hours from that of the city.

scholarly journals Augmenting Geostatistics with Matrix Factorization: A Case Study for House Price Estimation

2020 ◽  
Vol 9 (5) ◽  
pp. 288
Author(s):  
Aisha Sikder ◽  
Andreas Züfle
Keyword(s):  
Matrix Factorization ◽  
Geographically Weighted Regression ◽  
House Prices ◽  
Housing Prices ◽  
House Price ◽  
Weighted Regression ◽  
Regression Kriging ◽  
Truncated Svd ◽  
Latent Features ◽  
Kriging Approach

Singular value decomposition (SVD) is ubiquitously used in recommendation systems to estimate and predict values based on latent features obtained through matrix factorization. But, oblivious of location information, SVD has limitations in predicting variables that have strong spatial autocorrelation, such as housing prices which strongly depend on spatial properties such as the neighborhood and school districts. In this work, we build an algorithm that integrates the latent feature learning capabilities of truncated SVD with kriging, which is called SVD-Regression Kriging (SVD-RK). In doing so, we address the problem of modeling and predicting spatially autocorrelated data for recommender engines using real estate housing prices by integrating spatial statistics. We also show that SVD-RK outperforms purely latent features based solutions as well as purely spatial approaches like Geographically Weighted Regression (GWR). Our proposed algorithm, SVD-RK, integrates the results of truncated SVD as an independent variable into a regression kriging approach. We show experimentally, that latent house price patterns learned using SVD are able to improve house price predictions of ordinary kriging in areas where house prices fluctuate locally. For areas where house prices are strongly spatially autocorrelated, evident by a house pricing variogram showing that the data can be mostly explained by spatial information only, we propose to feed the results of SVD into a geographically weighted regression model to outperform the orginary kriging approach.

Sign in / Sign up

Export Citation Format

Share Document