Local Spatial Correlation Research Articles

Reconstruction based on adaptive group least angle regression for fluorescence molecular tomography.

Fluorescence molecular tomography can combine two-dimensional fluorescence imaging with anatomical information to reconstruct three-dimensional images of tumors. Reconstruction based on traditional regularization with tumor sparsity priors does not take into account that tumor cells form clusters, so it performs poorly when multiple light sources are used. Here we describe reconstruction based on an "adaptive group least angle regression elastic net" (AGLEN) method, in which local spatial structure correlation and group sparsity are integrated with elastic net regularization, followed by least angle regression. The AGLEN method works iteratively using the residual vector and a median smoothing strategy in order to adaptively obtain a robust local optimum. The method was verified using numerical simulations as well as imaging of mice bearing liver or melanoma tumors. AGLEN reconstruction performed better than state-of-the-art methods with different sizes of light sources at different distances from the sample and in the presence of Gaussian noise at 5-25%. In addition, AGLEN-based reconstruction accurately imaged tumor expression of cell death ligand-1, which can guide immunotherapy.

The temporal and spatial pattern evolution of provincial industrial carbon intensity under the carbon neutral target: evidence from China.

Industry is a core area to achieve the carbon neutrality target for most developing countries including China. Hence, it is of great practical significance to study the spatio-temporal characteristics of China's industrial carbon intensity and its evolution. The exploratory spatial data analysis methods were adopted to conduct global and local spatial correlation analysis in this paper. The result shows that (1) the industrial carbon emission intensity decreases year by year, with high industrial carbon emission intensity in the West and low in the East. (2) There is a correlation in the spatial distribution of industrial carbon intensity, with the Moran index experiencing the stage of descending first and then ascending. (3) The local spatial clustering of industrial carbon intensity is obvious. (4) Half of the provinces have experienced a leap, with the majority located in the western part of China. Based on these findings, it is concluded that industrial emission reduction policy synergy between provinces is particularly important, such as low-carbon industrial production policy and green industry development policy.

The digital economy, spatial spillovers and forestry green total factor productivity

Improving forestry green total factor productivity is important for promoting green and high-quality development of the forestry industry. Meanwhile, there are major expectations that the digital economy, which is the new engine driving China's economic development, will help achieve the green transformation and high-quality development of the industry. We systematically examine the impact of the digital economy on forestry green total factor productivity in dynamic and spatial dimensions using a dynamic panel model, mediating effect model and dynamic spatial Durbin model based on the Non-Radial Directional Distance Function-Luenberger Hicks Moorsteen model; furthermore, we measure forestry green total factor productivity in 30 provinces in China from 2013 to 2019. Our results indicate that China's forestry green total factor productivity grew at an average annual rate of 5.03% from 2013 to 2019, maintaining an overall stable growth trend, and the growth rate declined in the order of Central, Eastern, Western, and Northeastern China. At the same time, the positive spatial correlation of green total factor productivity in China's forestry industry increased significantly during this period, and the local spatial correlation showed a trend of gradually shifting to the H–H agglomeration feature, with significant path-dependent characteristics and overall spatial spillover effects. The digital economy can significantly promote an improvement in forestry green total factor productivity, and this conclusion is still valid after robustness tests and endogeneity treatment. The analysis of the mechanism of action shows that industrial structure upgrading and green technology innovation are important mechanisms through which the digital economy can improve forestry green total factor productivity. The heterogeneity analysis shows that the enhancement effect of the digital economy on forestry green total factor productivity is more obvious in the eastern and northeastern regions than in the central and western regions. Finally, according to spatial spillover analysis, the positive effects of the digital economy can have spatial spillover effects on neighboring regions that are geographically close and have similar economies. The research in this paper advances the understanding of the growth drivers of forestry green total factor productivity, the mechanisms, and the regional differences in forestry green total factor productivity as affected by the digital economy.

Spatial-temporal evolution and convergence analysis of agricultural green total factor productivity-evidence from the Yangtze River Delta Region of China.

Measuring regional differences in agricultural green total factor productivity (AGTFP) provides a basis for policy guidance on agricultural green development in the Yangtze River Delta (YRD) region. By constructing a two-period Malmquist-Luenberger index under the carbon emission constraint, we measure the AGTFP of cities in the YRD region from 2001 to 2019. Furthermore, adopting the Moran index method and the hot spot analysis method, this paper analyzes the global spatial correlation and local spatial correlation of AGTFP in this region. Moreover, we investigate its spatial convergence. The results show that the AGTFP of 41 cities in the YRD region is on an increasing trend; the growth of AGTFP in the eastern cities is mainly driven by green technical efficiency, while this growth in the southern cities is mainly stimulated by green technical efficiency and green technological progress. We also find a significant spatial correlation between cities' AGTFP in the YRD region from 2001 to 2019, but with certain fluctuations, showing a U-shaped trend of "strong-weak-strong". In addition, absolute β convergence of the AGTFP exists in the YRD region, and this convergence speed is accelerated with the addition of spatial factors. This evidence provides support for implementing the regional integration development strategy and optimizing the regional agricultural spatial layout. Our findings offer implications for promoting the transfer of green agricultural technology to the southwest of the YRD region, strengthening the construction of agricultural economic belts and agricultural economic circles, and improving the efficiency of agricultural resource use.

Spatial and Temporal Analysis of 2019 Novel Coronavirus (2019-Ncov) Cases in Selangor, Malaysia

The COVID-19 pandemic continues to wreak devastation on public health systems worldwide, particularly in Selangor, Malaysia, COVID-19 was reported from October 2020 to October 2021 at prevalent rate. In order to control and prevent the spread of this pandemic, which is already underway, there is need to comprehend the spatial dimension of this disease. Therefore, the purpose of this study was to describe the patterns of COVID-19 virus transmission in the state of Selangor. Methods: Using a Geographic Information System (GIS), and the Moran’s Index (MI), spatial distribution of COVID-19 across the entire mukim was mapped and spatial statistical analysis was carried out with indications of local spatial correlations. Results: The finding revealed that the clusters were concentrated in the western and southern regions (Global Moran's I = 0.468, p = 0.05, Z = 7.01) of the state of Selangor, thus, this research provides important information on the regional distribution and temporal dynamics of COVID-19. Conclusion: An assessment of COVID-19's geographic spread can help enhance health care programs and resource allocation in Malaysia, specifically Selangor where the COVID-19 is pandemic.

Improving the spatial–temporal aware attention network with dynamic trajectory graph learning for next Point-Of-Interest recommendation

Next Point-Of-Interest (POI) recommendation aim to predict users’ next visits by mining their movement patterns. Existing works attempt to extract spatial–temporal relationships from historical check-ins; however, the following critical factors have not been adequately considered: (1) structured features implied in trajectory that reflect individual visit tendency; (2) collaborative signals from other users and (3) dynamic user preference. To this end, we jointly take into full consideration the graph-structured information as well as sequential effects of user trajectory sequences and propose the Trajectory Graph enhanced Spatial–Temporal aware Attention Network (TGSTAN). Given the general preference among users and the shifts of individual interests over time, we present a novel trajectory-aware dynamic graph convolution network module (TDGCN) to facilitate the capturing of local spatial correlations. Specifically, TDGCN dynamically adjusts the normalized adjacency matrix of the trajectory graph by element-wise multiplication with self-attentive POI representations. The local trajectory graph is generated from the same training batch to reflect real-time and collaborative signals, while also following causality. Moreover, we explicitly integrate spatial–temporal interval information with bilinear interpolation to comprehensively attach relative proximity to attention mechanism when capturing long-term dependence. Extensive experiments on three real-world Location-Based Social Networks datasets (Foursquare_TKY, Weeplaces and Gowalla_CA) demonstrate that the proposed TGSTAN consistently outperforms the existing state-of-the-art baselines with an average of 8.18%, 6.59%, and 9.60% improvement on the three datasets, respectively.

Robust and unbiased estimation of the background distribution for automated quantitative imaging.

Background estimation is the first step in quantitative analysis of images. It has an impact on all subsequent analyses, in particular for segmentation and calculation of ratiometric quantities. Most methods recover only a single value such as the median or yield a biased estimation in non-trivial cases. We introduce, to our knowledge, the first method to recover an unbiased estimation of background distribution. It leverages the lack of local spatial correlation in background pixels to robustly select a subset that accurately represents the background. The resulting background distribution can be used to test for foreground membership of individual pixels or estimate confidence intervals in derived quantities.

Spatiotemporal Dynamic Evolution and Gravity Center Migration of Carbon Emissions in the Main Urban Area of Chongqing over the Past 20 Years

Global warming caused by carbon emissions is an environmental issue that is of great concern to all walks of life. Dynamic monitoring of the spatiotemporal evolution of urban carbon emissions is an important part of achieving the regional double-carbon goals. Taking the main urban area of Chongqing as an example, based on the data of land use and energy consumption, this study estimated the carbon emissions of 153 townships and streets in the main urban area of Chongqing from 2000 to 2020 by using the carbon emission coefficient method. Additionally, using the ESTDA framework to pass the LISA time path, spatiotemporal transition, and the standard deviation ellipse model from the perspective of spatiotemporal interaction, the spatiotemporal dynamic evolution of carbon emissions in the main urban area and the shift in the center of gravity over the past 20 years were analyzed. The results showed that: ① in the past 20 years, the carbon emissions in the main urban and rural areas have had a significant positive spatial correlation, and the spatial convergence showed a trend of first decreasing and then increasing. ② In the past 20 years, there were 126 township streets with low and medium relative lengths (accounting for 82%), indicating that the local spatial structure of township carbon emissions in the main urban area had strong stability; the total number of township streets with low and medium curvatures was 138 (accounting for 90%), indicating that the volatility of the main urban and rural carbon emissions in the direction of spatial dependence was relatively stable; there were 113 township streets (accounting for 74%) of the synergistic growth type, indicating that the main urban and rural carbon emissions were relatively stable. The emission pattern had strong spatial integration. ③ In the past 20 years, the spatiotemporal agglomeration index was greater than 70%, indicating that the local spatial correlation pattern and agglomeration characteristics of carbon emissions in the main urban and rural areas had strong stability. 4 In the past 20 years, the center of carbon emission in the main urban area had been distributed between 106°30'43″-106°32'42″E, 29°33'34″-29°35'56″N, and the center of gravity shifted to the northeast as a whole. The spatial distribution changed from the "northwest-southeast" pattern to the "northeast-southwest" pattern. These results can provide reference for the green and low-carbon sustainable development of Chongqing and the formulation of differentiated emission reduction policies, as well as provide reference for other similar mountain cities in western China.

A point-feature label placement algorithm based on spatial data mining.

The point-feature label placement (PFLP) refers to the process of positioning labels near point features on a map while adhering to specific rules and guidelines, finally obtaining clear, aesthetically pleasing, and conflict-free maps. While various approaches have been suggested for automated point feature placement on maps, few studies have fully considered the spatial distribution characteristics and label correlations of point datasets, resulting in poor label quality in the process of solving the label placement of dense and complex point datasets. In this paper, we propose a point-feature label placement algorithm based on spatial data mining that analyzes the local spatial distribution characteristics and label correlations of point features. The algorithm quantifies the interference among point features by designing a label frequent pattern framework (LFPF) and constructs an ascending label ordering method based on the pattern to reduce interference. Besides, three classical metaheuristic algorithms (simulated annealing algorithm, genetic algorithm, and ant colony algorithm) are applied to the PFLP in combination with the framework to verify the validity of this framework. Additionally, a bit-based grid spatial index is proposed to reduce cache memory and consumption time in conflict detection. The performance of the experiments is tested with 4000, 10000, and 20000 points of POI data obtained randomly under various label densities. The results of these experiments showed that: (1) the proposed method outperformed both the original algorithm and recent literature, with label quality improvements ranging from 3 to 6.7 and from 0.1 to 2.6, respectively. (2) The label efficiency was improved by 58.2% compared with the traditional grid index.

A Data-Driven Optimization Method Considering Data Correlations for Optimal Power Flow Under Uncertainty

This paper introduces a data-driven optimization (DDO) method based on novel strategic sampling (SS) considering data correlations for multiperiod optimal power flow (OPF) considering energy storage devices under uncertainty (OPF-ESDUU) of uncertain renewable energy and power loads (UREPL). This DDO method depends only on the uncertainty samples to yield an optimal solution that satisfies a specific confidence level, which is effective because of two resounding learning algorithms: Bayesian hierarchical modeling (BHM) and determinantal point process (DPP). Considering both the local bus information and spatial correlations over all buses, BHM learns the convex approximation of AC power flow (CAACPF) more accurately than the existing learning methods, converting the originally non-convex OPF-ESDUU to a convex optimization problem. DPP considers the correlations between samples to find a small set of significant samples by measuring the relative weight of each sample using the random matrix theory, significantly decreasing the data samples required by the existing SS. The experimental analysis in IEEE test cases shows that after considering data correlations, 1) BHM learns CAACPF better with 13–90% accuracy improvement on average, compared with the existing learning methods, and 2) the proposed DDO performs more efficiently than the existing DDO as DPP-based SS boosts the sampling efficiency by 50% at least.

Multi-Scale Assessment and Spatio-Temporal Interaction Characteristics of Ecosystem Health in the Middle Reaches of the Yellow River of China

Exploring the assessment methods and multi-scale spatiotemporal interaction characteristics of ecosystem health is of great significance for current ecosystem health theory and application research. Based on the regional differentiation theory and ecosystem service flow theory, the spatial weight coefficient and the modified coefficient of spatial proximity effect were introduced to improve the regional ecosystem health assessment model. Then, the improved VORS model was used to evaluate the ecosystem health level in the Middle Reaches of the Yellow River (MRYR) in China at multiple scales, and the ESTDA method was used to reveal the multi-scale spatiotemporal interaction characteristics of ecosystem health. The results show that: (1) From 1990 to 2018, the ecosystem health level at grid and county scale in the MRYR showed a trend of first decline and then increase, and experienced a slow decline and a steady rise from 1990 to 2005 and 2005 to 2018, respectively. The ecosystem health level at the grid and county scale presented a spatially hierarchical structure with alternating low-value and high-value zones. (2) Compared with the county scale, the grid scale can describe the spatial distribution characteristics of ecosystem health more refined, indicating the existence of spatial scale effects in ecosystem health assessment. (3) The rapid urbanization areas, the ecologically fragile areas in the central and western regions and the transitional zone between mountain and basin have more dynamic spatial structure, and stronger spatio-temporal interaction process. (4) In terms of LISA spatio-temporal transition, the regional system as a whole had strong path-dependent and lock-in characteristics, and the local spatial correlation structure of ecosystem health gradually tended to be stable during the study period. (5) In terms of spatio-temporal interaction network, there were strong spatio-temporal competition in the process of time evolution in six typical regions, such as the surrounding cities of provincial capitals, the fringe areas of cities, the transitional zone between mountain and basin, the transitional zone of ecologically fragile regions, the mountainous areas of western Henan Province, and the areas along rivers.

Spatial–Temporal Evolution Characteristics and Economic Effects of China’s Cultural and Tourism Industries’ Collaborative Agglomeration

In this era of industrial integration, the synergistic energy given collaborative agglomerations of the culture and tourism industries is crucial for fulfilling the potential of the underlying resources. The cultural grasp of artistic depths when fully supported can transform the cultural experiences for tourists and participants alike. In this study, the theory of spatial economics is used to analyze the spatial coupling degree of the Chinese culture and tourism industries from 2010 to 2019, based on the coupling coordination degree model. A spatial correlation test model was used to analyze the spatial–temporal evolution characteristics of industrial collaborative agglomeration, and a spatial vector autoregression model and impulse response function was used to analyze the economic effects of industrial collaborative agglomeration. The results show: (1) A coupling and coordination relationship exists between Chinese culture and the tourism industries. This collaborative bond is in the initial stage. (2) The overall spatial correlation between these industries can potentially provide significant and positive relationships among several components of the community, tourist, and cultural spectrum. The local spatial correlation of culture and tourism industries in Eastern China is ranked the highest; the central region is in the middle. The western region ranks the lowest. (3) The collaborative synergy of the cultural and tourism industries has a nonlinear economic effect on economic development, while the impacts of different industrial collaborative groups have the potential to strengthen the Chinese economy from a more technological perspective. This study provides theoretical support and recommendations for promoting the coordinated development of Chinese culture and tourism industries, which can also serve as an example for other regions seeking a stronger relationship between their culture, economic growth of the region as a whole, and the tourism industries.

Detection of Malware by Deep Learning as CNN-LSTM Machine Learning Techniques in Real Time

Cyber-attacks on the numerous parts of today’s fast developing IoT are only going to increase in frequency and severity. A reliable method for detecting malicious attacks such as botnet in the IoT environment is critical for reducing security risks on IoT devices. Numerous existing methods exist for mining IoT networks for previously discovered patterns that may be exploited to improve security. This study used a hybrid deep learning approach, namely the CNN-LSTM technique, to detect botnet attacks. Any software that infiltrates a computer system or is installed there without the administrators’ knowledge or permission is malicious. There is a wide range of viruses that cyber-criminals use to further their nefarious ends. A revolutionary deep learning system has been developed to counteract the increasing quantity of harmful programs. The system takes advantage of NLP methods as a baseline, mixes CNNs and LSTM neurons to capture local spatial correlations, and learns from successive long-term dependencies. Spatial invariance, often known as symmetry, is the property wherein the dataset size remains constant throughout iterations of an algorithm while undergoing various transformations. Therefore, automated extraction of high-level abstractions and representations aids in the malware categorization process. When compared to its predecessor research study, the current level of categorization accuracy is significantly greater than 0.81. The proposed CNN-LSTM method obtained an R2 = 99.19% in the dataset, with a correlation coefficient for the CNN-LSTM technique of R2 = 100% utilizing the provided dataset. The symmetry correlation of the CNN-LSTM, which illustrates that the CNN-LSTM method has the highest detection accuracy, at 99%, among the other malware detection methods such as the SVM and DT. The rest of classifiers had an accuracy of 98% for DT, and 95% for SVM. The accuracy of the LSTM model is 99%, the precision of the CNN-LSTM is 99%, recall is 99% and F1 score is 1.

The contribution of tourism mobility to tourism economic growth in China.

Mobility is the key factor in promoting tourism economic growth (TEG), and the transportation infrastructure has essential functions for maintaining an orderly flow of tourists. Based on the theory of fluid mechanics, we put forward the indicator of tourism mobility (TM). This study is the first to measure the level of TM in China and analyze the spatiotemporal evolution characteristics of TM. Applying the Exploratory Spatial Data Analysis method, we analyze the global and local spatial correlation characteristics of TM. Moreover, we further estimate the contribution of TM to TEG by econometric models and the LMDI method. The results show that (1) the TM in China has maintained rapid growth for a long time. However, there are differences in the rate of growth in different regions. The TM in each region only showed a significant positive spatial correlation in 2016-2018. The space-time pattern is constantly changing over time. The local spatial autocorrelation results of TM are stable, and various agglomeration states are stably distributed in some provinces. (2) The regression results of the traditional panel data model and spatial panel data model both show that TM has a significant positive effect on TEG. Moreover, TM has a negative spatial spillover effect on neighboring regions. (3) The result from the decomposition of LMDI shows that the overall contribution of TM to TEG is 15.76%. This shows that improving TM is a crucial way to promote the economic growth of tourism.

Identifying Driving Factors of Basin Ecosystem Service Value Based on Local Bivariate Spatial Correlation Patterns

Ecosystem service value (ESV) is a crucial indicator for evaluating ecosystem health, and identifying its spatial driving factors will help to provide scientific decision support for ecological protection and restoration. This study took the Liuxi River Basin in China as the research object and used the value equivalent method to estimate regional ESV. In the process of using the Geodetector model (GDM), the study area was spatially stratified by using the local bivariate spatial correlation pattern to mine the potential driving factors of ESV. The results show that: (1) From 2005 to 2018, the total value of ecosystem services in the Liuxi River Basin showed a fluctuating and increasing trend. ESV had high-value aggregation in the northeastern mountainous areas with high green space coverage and high river distance accessibility and low-value aggregation in the central and southwestern urban areas with frequent human activities. Its spatial heterogeneity and aggregation patterns were of statistical significance. (2) The spatial distribution characteristics of ESV were affected by various driving factors to varying degrees. The order of their degree of influence on ESV was per capita green area > slope > the proportion of urban and rural human settlements > river distance accessibility > population. (3) Compared to the previous study, the stratification method employing the local bivariate spatial correlation pattern more fully considers spatial autocorrelation and spatial heterogeneity. It effectively captured the spatial explanatory power of driving factors. This study can provide new ideas for capturing the driving mechanisms of ESV and insights into the sustainable development of the ecological environment in other regions with similar characteristics worldwide.

Impact of human capital on the green economy: empirical evidence from 30 Chinese provinces.

In China, exploring the relationship between human capital and green economy in the context of economic transformation can promote the construction of an ecological civilization and high-quality economic development. This paper is dedicated to assessing the impact of human capital stock, human capital structure, and the difference between the two on China's green economy. Using the super-efficiency slack-based measure (SBM) model, this paper evaluated the green total factor productivity (GTFP) of 30 Chinese provinces to assess the greenness of its economy and analyzed its spatiotemporal evolution from 2000 to 2017. A spatial error model (SEM) was established to explore the impact of human capital on green economy. The results show that (1) the GTFP of China increased continuously from 0.219 to 0.457, showing a regional spatial differentiation of "eastern region > central region > western region." The spatial gaps of the green economy in the east-west and north-south directions are narrowing. (2) Moran's I index and Moran's I scatter plot indicate a significant spatial correlation between human capital and China's green economy. The local spatial correlation between human capital and green economy is mainly characterized by "high-high" and "low-low" types of agglomeration. (3) The effects of human capital stock and human capital structure on green economy were both positive, with coefficients of 0.0005 and 0.1601, respectively, but the effects of human capital structure were not significant. (4) The results of regional regression show that the difference between the human capital impact coefficients on green economy in the eastern and midwestern regions is small. The impact of human capital stock and human capital structure on green economy is consistent with the national level results. It can be preliminarily concluded that the development of China's green economy relied more on improving the human capital stock than on the improvement of the human capital structure. This study further enriches the literature on the green economy and provides information that can support government policy-making.

Vertical Conductivity and Topography in Electrostrictive Germanium Sulfide Microribbon via Conductive Atomic Force Microscopy.

Layered group IV monochalcogenides are two-dimensional (2D) semiconducting materials with unique crystal structures and novel physical properties. Here, we report the growth of single crystalline GeS microribbons using the chemical vapor transport process. By using conductive atomic force microscopy, we demonstrated that the conductive behavior in the vertical direction was mainly affected by the Schottky barriers between GeS and both electrodes. Furthermore, we found that the topographic and current heterogeneities were significantly different with and without illumination. The topographic deformation and current enhancement were also predicted by our density functional theory (DFT)-based calculations. Their local spatial correlation between the topographic height and current was established. By virtue of 2D fast Fourier transform power spectra, we constructed the holistic spatial correlation between the topographic and current heterogeneity that indicated the diminished correlation with illumination. These findings on layered GeS microribbons provide insights into the conductive and topographic behaviors in 2D materials.

Gray Measure and Spatial Distribution Exploration of Local Emergency Resilience on Compound Disasters.

The complexity and uncertainty of compound disasters highlight the significance of local emergency resilience. This paper puts forward a framework, including the Projection Pursuit Model based on Real-coded Accelerating Genetic Algorithm and the Moran’s Index (Moran’s I), to measure the local emergency resilience and analyze its spatial distribution. An empirical test is conducted with the case of Hubei Province, China. The results show that: (1) the measurement indices related to infrastructure, material reserves, and resource allocation have a larger weight, while those related to personnel and their practice have a smaller weight. (2) The measurement value of local emergency resilience of sub-provincial regions in Hubei Province is vital in the eastern and weak in the western, and there are apparent east-west segmentation and north-south aggregation characteristics. (3) Although the sub-provincial regions do not show significant spatial correlation, the eastern regions centered on Wuhan are negatively correlated, and the western regions are positively correlated. Furthermore, this study provides theories and methods for local emergency resilience evaluation and spatial correlation exploration, and it has specific guidance recommendations for optimizing local emergency management resource allocation and improving local emergency resilience.

Spatio-Temporal Characteristics and Influencing Factors of Basic Public Service Levels in the Yangtze River Delta Region, China

Basic public services are essential to ensure regional social equity and promote regional integrated development. As the Yangtze River Delta region (YRDR) is an example of integrated regional development in China, the integration of basic public services plays a crucial role in promoting regions’ integrated development. However, little studies provide evidence of the characteristics and influencing factors of basic public services in the YRDR. Taking the YRDR as the study area, this paper constructed a comprehensive evaluation index system for the basic public services level (BPSL) in the YRDR from 2010 to 2020. Then, it measured and analyzed its spatio-temporal dynamic evolution characteristics using entropy-weighted TOPSIS and exploratory spatial data analysis methods, as well as analyzed the spatio-temporal heterogeneity of its influencing factors using a geographically and temporally weighted regression model. The results show that: (1) The BPSL in the YRDR generally improved during the study period. There was a huge variation within and between provinces in the BPSL. Over time, the BPSL gradually transitions from unipolar polarization to multipolar differentiation and a flattening trend. (2) Spatial differentiation of the BPSL was evident, with a decreasing gradient from east to west with an inverted U-shape distribution in the north–south direction. The overall spatial circle structure was characterized as being high in the east and low in the west, high in the center, and low in the north and south, forming a spatial distribution pattern of high-level and higher-level grades mainly in Shanghai, southern Jiangsu, and northern Zhejiang. The global spatial correlation characteristics became increasingly significant with time, while the local spatial correlation showed the trend of “spatial club convergence”. (3) Various factors influenced the spatial and temporal evolution of the BPSL, including the urbanization level, the economic development level, the industrial structure level, the degree of external openness, the government action capacity, and the regional population size, each of which had evident spatial and temporal heterogeneity.

Mesoscale numerical investigation on the size effect of concrete uniaxial compressive strength under different contact friction

• The concrete mesoscale concretization model is established to consider the heterogeneity of meso-components. • The effect of contact friction on the compressive failure behavior of concrete is studied. • Increasing the friction coefficient makes the size effect more pronounced. • A modified size effect law DHF-SEL is established. The purpose of this work is to examine the quantitative influence of contact friction on the compressive strength of concrete and the corresponding size effect under uniaxial compression. The concrete mesoscale concretization model (CMCM) is established to consider the heterogeneity of meso-components based on the statistical distribution and local spatial correlation theory. The interaction between concrete and steel loading platens is evaluated by defining the contact friction behavior. The uniaxial compression behavior of concrete with different cross-section diameters, height-diameter ratios, and friction coefficients is simulated to analyze the quantitative effect of contact friction. The numerical results show that the hoop effect caused by contact friction constrains the lateral deformation and damage initiation at the loading boundaries of concrete, thus increasing the compressive strength; when the friction coefficient μ = 0 , the slight size effect is mainly caused by the wall effect, while when the friction coefficient μ > 0 , the severe size effect is primarily driven by the contact friction constraint; increasing the friction coefficient improves the sensitivity of compressive strength to sample size, making the size effect more pronounced. The Size Effect Law considering the diameter, height-diameter ratio and friction coefficient (DHF-SEL) is proposed based on the classical Bažant size effect law and CMCM, and then verified with the numerical results and experimental data.