A Segmented Processing Approach of Eigenvector Spatial Filtering Regression for Normalized Difference Vegetation Index in Central China

Yang, Jiaxin; Chen, Yumin; Chen, Meijie; Yang, Fan; Yao, Ming

doi:10.3390/ijgi7080330

Cited by 8 publications

(2 citation statements)

References 37 publications

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“…We will continue to look for better data sources in further research. Considering the limitations of the ESFLR method for large-scale landslide data due to the computational demands, we consulted with other scholars and determined that the segmented processing approach [47] or fast-ESF method [48] might be helpful, which will be evaluated in our subsequent research.…”

Section: Discussionmentioning

confidence: 99%

Eigenvector Spatial Filtering-Based Logistic Regression for Landslide Susceptibility Assessment

Chen

Deng

et al. 2019

IJGI

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Logistic regression methods have been widely used for landslide research. However, previous studies have seldom paid attention to the frequent occurrence of spatial autocorrelated residuals in regression models, which indicate a model misspecification problem and unreliable results. This study accounts for spatial autocorrelation by implementing eigenvector spatial filtering (ESF) into logistic regression for landslide susceptibility assessment. Based on a landslide inventory map and 11 landslide predisposing factors, we developed the eigenvector spatial filtering-based logistic regression (ESFLR) model, as well as a conventional logistic regression (LR) model and an autologistic regression (ALR) model for comparison. The three models were evaluated and compared in terms of their prediction capability and model fit. The ESFLR model performed better than the other two models. The overall predictive accuracy of the ESFLR model was 90.53%, followed by the ALR model (76.21%) and the LR model (74.76%), and the areas under the ROC curves for the ESFLR, ALR and LR models were 0.957, 0.828 and 0.818, respectively. The ESFLR model adequately addressed the spatial autocorrelation of residuals by reducing the Moran's I value of the residuals to 0.0270. In conclusion, the ESFLR model is an effective and flexible method for landslide analysis.implies that these models failed to explain all of the spatial patterns inherent in the landslide data, and may have led to misspecification errors in the model.This study attempted to eliminate the negative influence of spatial autocorrelation on landslide susceptibility assessments by introducing eigenvector spatial filtering (ESF) into logistic regression. Spatial filtering, as discussed by Getis [21,22] and Griffith [23], was considered to be an effective approach for addressing spatial autocorrelation. The ESF method proposed by Griffith, utilizes eigenvectors generated from a given spatial connectivity matrix to account for redundant locational information resulting from spatial autocorrelation [24]. Several significant eigenvectors selected with a stepwise regression procedure are added to the linear regression model as independent variables to filter the spatial autocorrelation out from the regression residuals.This study was conducted in Wulong county and involved four main steps. First, the predisposing factors responsible for landslide occurrence were determined. Subsequently, landslide susceptibility models were developed. Then, evaluations were conducted and comparisons were made between the ESF-based logistic regression model and other models, including ordinary logistic regression and autologistic regression, to assess model performance. Finally, the models were employed to map landslide susceptibility throughout the study area. Study Area and Data Study AreaThe study area of Wulong county is located in the southeast section of the Chongqing Municipality (Figure 1) between longitudes (107 • 14 ) E and (108 • 5 ) E and latitudes (29 • 2 ) N and (29 • 40 ) N, and covers an a...

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Section: Discussionmentioning

confidence: 99%

Eigenvector Spatial Filtering-Based Logistic Regression for Landslide Susceptibility Assessment

Chen

Deng

et al. 2019

IJGI

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“…ESF regression was developed by Griffith et al and is becoming recognized because of its accuracy and usability [38]. ESF regression has been used to model PM2.5, the normalized difference vegetation index (NDVI), and landslide risk [39][40][41][42][43].…”

Section: Introductionmentioning

confidence: 99%

Modeling China’s Prefecture-Level Economy Using VIIRS Imagery and Spatial Methods

et al. 2020

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Nighttime light (NTL) data derived from the Visible Infrared Imaging Radiometer Suite (VIIRS), carried by the Suomi National Polar Orbiting Partnership (NPP) satellite, has been widely used to evaluate gross domestic product (GDP). Nevertheless, due to the monthly VIIRS data fluctuation and missing data (excluded by producers) over high-latitude regions, the suitability of VIIRS data for longitudinal city-level economic estimation needs to be examined. While GDP distribution in China is always accompanied by regional disparity, previous studies have hardly considered the spatial autocorrelation of the GDP distribution when using NTL imagery. Thus, this paper aims to enhance the precision of the longitudinal GDP estimation using spatial methods. The NTL images are used with road networks and permanent resident population data to estimate the 2013, 2015, and 2017 3-year prefecture-level (342 regions) GDP in mainland China, based on eigenvector spatial filtering (ESF) regression (mean R2 = 0.98). The ordinary least squares (OLS) (mean R2 = 0.86) and spatial error model (SEM) (mean pseudo R2 = 0.89) were chosen as reference models. The ESF regression exhibits better performance for root-mean-square error (RMSE), mean absolute relative error (MARE), and Akaike information criterion (AIC) than the reference models and effectively eliminated the spatial autocorrelation in the residuals in all 3 years. The results indicate that the spatial economic disparity, as well as population distribution across China’s prefectures, is decreasing. The ESF regression also demonstrates that the population is crucial to the local economy and that the contribution of urbanization is growing.

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Characterizing spatiotemporal properties of precipitation in the middle Mahanadi subdivision, India during 1901–2017

Sahu

Verma

et al. 2023

Acta Geophys.

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A Segmented Processing Approach of Eigenvector Spatial Filtering Regression for Normalized Difference Vegetation Index in Central China

Cited by 8 publications

References 37 publications

Eigenvector Spatial Filtering-Based Logistic Regression for Landslide Susceptibility Assessment

Eigenvector Spatial Filtering-Based Logistic Regression for Landslide Susceptibility Assessment

Modeling China’s Prefecture-Level Economy Using VIIRS Imagery and Spatial Methods

Characterizing spatiotemporal properties of precipitation in the middle Mahanadi subdivision, India during 1901–2017

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