Spillover Effect of the Internet on Trade Performance Based on a Vision of the Public's Sleep Health: A Spatial Study of the Global Network

This is the first study to analyze the spatial spillover effect of the internet on trade performance based on a vision of the public's sleep health. The internet's effect on trade performance has been enhanced in a new economy consisting of larger global markets. An overall improvement in health gradually impacts economic development. In this study, hierarchical modeling is applied to reveal the effect of the internet on trade performance at a fundamental level, and the effect of sleep health on trade performance at general level. The global network is structured by a spatial weight matrix based on the Mahalanobis distance of the internet and sleep health. Furthermore, spatial autoregressive modeling is applied to study the effect of the spatial weight matrix based on the Mahalanobis distance matrix of the internet and sleep health on trade performance. The spatial Durbin modeling is applied to further analyze the interaction effect of the spatial weight matrix and countries' factors on trade performance. It was found that the internet has a positive effect on trade performance, and good sleep health can be helpful to the spillover effect of the internet on trade performance. The interaction of the spatial weight matrix and gross domestic product (GDP) can further enhance the effect. This research can assist global managers to further understand the spatial spillover effect of the internet on trade performance based on a vision of the public's sleep health.

Superpixel Weighted Low-rank and Sparse Approximation for Hyperspectral Unmixing

We propose a superpixel weighted low-rank and sparse unmixing (SWLRSU) method for sparse unmixing. The proposed method consists of two steps. In the first step, we segment hyperspectral image into superpixels which are defined as the homogeneous regions with different shape and sizes according to the spatial structure. Then, an efficient method is proposed to obtain a spatial weight term using superpixels to capture the spatial structure of hyperspectral data. In the second step, we solve a superpixel guided low-rank and spatially weighted sparse approximation problem in which spatial weight term obtained in the first step is used as a weight term in sparsity promoting norm. This formulation exploits the spatial correlation of the pixels in the hyperspectral image efficiently, which yields satisfactory unmixing results. The experiments are conducted on simulated and real data sets to show the effectiveness of the proposed method.

Superpixel Weighted Low-rank and Sparse Approximation for Hyperspectral Unmixing

We propose a superpixel weighted low-rank and sparse unmixing (SWLRSU) method for sparse unmixing. The proposed method consists of two steps. In the first step, we segment hyperspectral image into superpixels which are defined as the homogeneous regions with different shape and sizes according to the spatial structure. Then, an efficient method is proposed to obtain a spatial weight term using superpixels to capture the spatial structure of hyperspectral data. In the second step, we solve a superpixel guided low-rank and spatially weighted sparse approximation problem in which spatial weight term obtained in the first step is used as a weight term in sparsity promoting norm. This formulation exploits the spatial correlation of the pixels in the hyperspectral image efficiently, which yields satisfactory unmixing results. The experiments are conducted on simulated and real data sets to show the effectiveness of the proposed method.

How Does Human Capital Spillover Inflow of Foreign Workers Affect Economic Growth?

The ASEAN Economic Community provides opportunities for foreign workers to enter Indonesia, including Central Java Province. The existence of these foreign workers tends to negatively and positively impact the regional economic growth of the country. Therefore, this study aims to analyze the effects of foreign workers’ human capital spillover inflow on the economic growth of Central Java. The Euclidean distance spatial weight matrix was used to calculate the spatial autoregressive model from 2015 to 2020. These results indicate that the presence of skilled foreign workers positively impacts increasing economic growth in Central Java Province. The influx of foreign workers, along with the influx of investment, encourages local workers to follow the performance of foreign workers. This study suggests a policy to encourage technology transfer from foreign workers to local workers. The government is also expected to strengthen local workers’ productivity to compete with foreign workers.

Industrial Efficiency Algorithm Based on Spatio-Temporal-Data-Driven

Stochastic frontier model is an important and effective method to calculate industry efficiency. However, when dealing with temporal and spatial data from the industry, it is difficult to accurately calculate the industrial production efficiency due to the influence of spatial correlation and time lag effect. If the traditional spatial statistical method is used, the setting method of spatial weight matrix is often questioned. To solve this series of problems, one possible idea is to design a spatial data mining process based on stochastic frontier analysis. Firstly, the stochastic frontier model should be improved to analyze spatio-temporal data. In order to accurately measure the technical efficiency in the case of dual correlation between time and space, a more effective spatio-temporal stochastic frontier model method is proposed. Meanwhile, based on the idea of generalized moment estimation, an estimation method of spatiotemporal stochastic frontier model is designed, and the consistency of estimators is proved. In order to ensure that the most appropriate spatial weight matrix can be selected in the process of model construction, the K -fold crossvalidation method is adopted to evaluate the prediction effect under the data-driven idea. This set of spatio-temporal data mining methods will be used to measure the technical efficiency of high-tech industries in various provinces of China.

Research on the Spatial Correlation and Spatial Lag of COVID-19 Infection Based on Spatial Analysis

COVID-19 has spread throughout the world since the virus was discovered in 2019. Thus, this study aimed to identify the global transmission trend of the COVID-19 from the perspective of the spatial correlation and spatial lag. The research used primary data collected of daily increases in the amount of COVID-19 in 14 countries, confirmed diagnosis, recovered numbers, and deaths. Findings of the Moran index showed that the propagation of infection was aggregated between 9 May and 21 May based on the composite spatial weight matrix. The results from the Lagrange multiplier test indicated the COVID-19 patients can infect others with a lag.

Modelling Representative Population Mobility for COVID-19 Spatial Transmission in South Africa

The COVID-19 pandemic starting in the first half of 2020 has changed the lives of everyone across the world. Reduced mobility was essential due to it being the largest impact possible against the spread of the little understood SARS-CoV-2 virus. To understand the spread, a comprehension of human mobility patterns is needed. The use of mobility data in modelling is thus essential to capture the intrinsic spread through the population. It is necessary to determine to what extent mobility data sources convey the same message of mobility within a region. This paper compares different mobility data sources by constructing spatial weight matrices at a variety of spatial resolutions and further compares the results through hierarchical clustering. We consider four methods for constructing spatial weight matrices representing mobility between spatial units, taking into account distance between spatial units as well as spatial covariates. This provides insight for the user into which data provides what type of information and in what situations a particular data source is most useful.

Geospatial Least Squares Support Vector Regression Fused with Spatial Weight Matrix

Due to the increasingly complex objects and massive information involved in spatial statistics analysis, least squares support vector regression (LS-SVR) with a good stability and high calculation speed is widely applied in regression problems of geospatial objects. According to Tobler’s First Law of Geography, near things are more related than distant things. However, very few studies have focused on the spatial dependence between geospatial objects via SVR. To comprehensively consider the spatial and attribute characteristics of geospatial objects, a geospatial LS-SVR model for geospatial data regression prediction is proposed in this paper. The 0–1 type and numeric-type spatial weight matrices are introduced as dependence measures between geospatial objects and fused into a single regression function of the LS-SVR model. Comparisons of the results obtained with the proposed and conventional models and other traditional models indicate that fusion of the spatial weight matrix can improve the prediction accuracy. The proposed model is more suitable for geospatial data regression prediction and enhances the ability of geospatial phenomena to explain geospatial data.

Transformation of Regional Budgetary Independence and Security: Spatial Analysis

The issue of increasing budgetary independence and security is relevant for the majority of territorial systems, both at the regional and municipal levels. It was hypothesised that changes in the structure of regional public debt have a negative impact on their budgetary security. According to this hypothesis, an increase in the proportion of bank borrowing and corresponding decrease in the issue of debt securities by the constituent entities of the Russian Federation leads to a greater overall debt burden on the regional budget. In order to study transformation processes affecting budgetary independence and regional security. We developed a methodology to permit a separate assessment of these concepts. According to this approach, we propose to evaluate the budgetary independence of regional systems in terms of: (1) the balance of the budget (ratio of internal tax and non-tax revenues to budget expenditures); (2) financial dependence on transfers and subsidies from budgets at other levels; (3) budget security, taking into account gratuitous and non-gratuitous transfers. Thus, budget ary security can be assessed in accordance with the public debt dynamics, as well as the level of budgetary debt covered by the region’s own tax and non-tax revenues. The novelty of the presented methodological approach consists in its systematic use of Moran’s I for various spatial weight matrices combined with regression analysis methods based on panel data. Testing this methodology demonstrated the spatial heterogeneity of regional fiscal capacity, highlighting the financial dependence of most regions on federal and other gratuitous transfers. Autocorrelation analysis carried out according to Moran’s I using various spatial weight matrices confirmed the increasing tendency of the budgetary debt of Russian regions towards spatial heterogeneity. Future studies will focus on simulating the influence of various factors on regional budgetary security in order to predict the dynamics of its change.

New Robust PCA for Outliers and Heavy Sparse Noises’ Detection via Affine Transformation, the L ∗ , w and L 2,1 Norms, and Spatial Weight Matrix in High-Dimensional Images: From the Perspective of Signal Processing

In this paper, we propose a novel robust algorithm for image recovery via affine transformations, the weighted nuclear, L ∗ , w , and the L 2,1 norms. The new method considers the spatial weight matrix to account the correlated samples in the data, the L 2,1 norm to tackle the dilemma of extreme values in the high-dimensional images, and the L ∗ , w norm newly added to alleviate the potential effects of outliers and heavy sparse noises, enabling the new approach to be more resilient to outliers and large variations in the high-dimensional images in signal processing. The determination of the parameters is involved, and the affine transformations are cast as a convex optimization problem. To mitigate the computational complexity, alternating iteratively reweighted direction method of multipliers (ADMM) method is utilized to derive a new set of recursive equations to update the optimization variables and the affine transformations iteratively in a round-robin manner. The new algorithm is superior to the state-of-the-art works in terms of accuracy on various public databases.