scholarly journals Spatial Autocorrelation Analysis of Multi-Scale Damaged Vegetation in the Wenchuan Earthquake-Affected Area, Southwest China

Forests ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 195
Author(s):  
Jian Li ◽  
Jingwen He ◽  
Ying Liu ◽  
Daojie Wang ◽  
Loretta Rafay ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Linear Regression ◽  
Regression Model ◽  
Spatial Autocorrelation ◽  
Wenchuan Earthquake ◽  
Influencing Factors ◽  
Spatial Patterns ◽  
Linear Regression Model ◽  
Moran's I ◽  
Standard Linear

Major earthquakes can cause serious vegetation destruction in affected areas. However, little is known about the spatial patterns of damaged vegetation and its influencing factors. Elucidating the main influencing factors and finding out the key vegetation type to reflect spatial patterns of damaged vegetation are of great interest in order to improve the assessment of vegetation loss and the prediction of the spatial distribution of damaged vegetation caused by earthquakes. In this study, we used Moran’s I correlograms to study the spatial autocorrelation of damaged vegetation and its potential driving factors in the nine worst-hit Wenchuan earthquake-affected cities and counties. Both dependent and independent variables showed a positive spatial autocorrelation but with great differences at four aggregation levels (625 × 625 m, 1250 × 1250 m, 2500 × 2500 m, and 5000 × 5000 m). Shrubs can represent the characteristics of all damaged vegetation due to the significant linear relationship between their Moran’s I at the four aggregation levels. Clustering of similar high coverage of damaged vegetation occurred in the study area. The residuals of the standard linear regression model also show a significantly positive autocorrelation, indicating that the standard linear regression model cannot explain all the spatial patterns in damaged vegetation. Spatial autoregressive models without spatially autocorrelated residuals had the better goodness-of-fit to deal with damaged vegetation. The aggregation level 8 × 8 is a scale threshold for spatial autocorrelation. There are other environmental factors affecting vegetation destruction. Our study provides useful information for the countermeasures of vegetation protection and conservation, as well as the prediction of the spatial distribution of damaged vegetation, to improve vegetation restoration in earthquake-affected areas.

Locally Optimal Properties of the Durbin-Watson Test

1988 ◽  
Vol 4 (3) ◽  
pp. 509-516 ◽  
Author(s):  
Maxwell L. King ◽  
Merran A. Evans
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Spatial Autocorrelation ◽  
Linear Regression Model ◽  
Null Hypothesis ◽  
Cycle Model ◽  
Disturbance Processes ◽  
Watson Test

Although originally designed to detect AR(1) disturbances in the linear-regression model, the Durbin-Watson test is known to have good power against other forms of disturbance behavior. In this paper, we identify disturbance processes involving any number of parameters against which the Durbin–Watson test is approximately locally best invariant uniformly in a range of directions from the null hypothesis. Examples include the sum of q independent ARMA(1,1) processes, certain spatial autocorrelation processes involving up to four parameters, and a stochastic cycle model.

Spatial Distribution of Chinese Traditional Villages and its Influencing Factors

2020 ◽  
Author(s):  
Yunong Wu ◽  
Bin Zhang ◽  
Burghard C. Meyer ◽  
Duo Xie ◽  
Yong Zeng ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Spatial Autocorrelation ◽  
Influencing Factors ◽  
Distribution Density ◽  
Regional Analysis ◽  
Spatial Differentiation ◽  
Moran’S I ◽  
Statistical Tool ◽  
Aggregation State ◽  
Moran's I

<p>Abstract: Chinese Traditional Villages (TV) were selected from millions of villages based on their important historical and cultural heritage value. The distribution of TV characterized by spatial differentiation is subject to complex and diverse influencing factors. This study takes 6819 TV in China (as of the end of 2019) as research objects to analyse the distribution density of TV in different provinces; the spatial autocorrelation module in ArcGIS' spatial statistical tool was used to analyse the distribution characteristics; a total of 9 factors were selected from the three indicator groups of climate, geography and humanities, and introduced into the clustering and outlier analysis (Anselin Local Moran's I) module to analyse their spatial relationships with TV distribution. The results show that: 1. The spatial distribution of Chinese TV presents an obvious uneven aggregation state. Among them, the highest distribution density was 10.18 per 10,000 km² in Zhejiang province, while less than 0.5 per 10,000 km² in Inner Mongolia, Heilongjiang, Tibet and Xinjiang. The Global Moran's I index of TV distribution is 0.352, and the z-value of normal statistic is 949.76, which has a strong spatial autocorrelation. 2. The distribution of TV is mainly interpreted by humidity index, annual average temperature, elevation, slope, cultural relics, and population. 3. The results of clustering and outlier show that there are significant differences in the effect of the influencing factors on the distribution of TV in different regions. This paper aims to understand the influencing factors that affect the spatial distribution of TV in China and provide more comprehensive research content. This study indicates the importance of further cross-regional analysis of the TV distribution and provides a reference for its environmental management and protective measures and policies.</p>

scholarly journals EXAMINING MULTI-LEVEL POVERTY-CAUSING FACTORS OF FARM HOUSEHOLD

2019 ◽  
Vol XLII-4/W20 ◽  
pp. 49-53
Author(s):  
Y. W. Jiang ◽  
Y. H. Wang ◽  
W. P. Qi
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Research Methods ◽  
Influencing Factors ◽  
Linear Regression Model ◽  
Farm Household ◽  
Research Results ◽  
Multi Level ◽  
At Home

Abstract. In view of the current research on the factors of poverty at home and abroad, most of them have less background effects on scale and the mechanism. This study uses a multi-level linear regression model and a geographic detector to jointly detect the poverty influencing factors in the study area. This study mainly draws the following three conclusions: (1) There are background effects on the scale of the poverty-stricken factors in the study area, and the background effects have a great impact. (2) At different scales there are significant poverty-stricken factors. (3) Different research methods lead to differences in research results.

scholarly journals Stressors of Nursing Interns and Their Influencing Factors: A Cross-Sectional Study

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Xiaoqing Wang ◽  
Yu Han ◽  
Runyu Chai ◽  
Rong Chai
Keyword(s):  
Clinical Practice ◽  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Influencing Factors ◽  
Linear Regression Model ◽  
Protective Factor ◽  
Multiple Linear Regression Model ◽  
Medical Industry ◽  
Partial Regression Coefficient

Objective. It is still unknown whether the stress level and stressors in Chinese nursing interns are influenced by teacher-related factors. This research was carried out for better understanding of the stress in nursing interns and distribution of stressors during their clinical practice and targeted measures to unwind the stress of nursing interns. Methods. A questionnaire survey, titled Questionnaire on Stressors of Nursing Interns during Clinical Practice, was conducted on nursing interns at a 3A Grade Hospital in Shandong Province. Characteristics of the nursing interns and stressors of nursing interns were collected. A multiple-linear regression model was used to explore the influencing factors of nursing interns’ scores. Results. A total of 132 nursing interns were investigated in this study, and the overall stress scores were calculated. The stressors during the internship include the nature and content of the job, role orientation, workload, conflict between study and work, practice preparation, and interpersonal relationships. Gender, education level, instructor encouragement, and parents engaged in the medical industry were adjusted in the multiple-linear regression model as covariates. All of these factors had significant impacts on the scores of stressors ( P  < 0.05), with the partial regression coefficient values of 13.38, −10.52, −5.02, 3.4, −9.89, −14.77, and −15.83 for factors like female, undergraduates, graduate students, never obtained encouragement from teachers, obtained encouragement from teachers occasionally, obtained encouragement from teachers frequently, and parents engaged in the medical industry, respectively. Conclusion. The stressors of nursing interns are mostly work-wise, and teachers’ encouragement is an important protective factor for nursing interns to reduce stress. Therefore, clinical instructors should take targeted measures in teaching methods and work arrangements according to the needs of interns.

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