Spatial Correlation Research Articles

LGIT: local–global interaction transformer for low-light image denoising

Transformer-based methods effectively capture global dependencies in images, demonstrating outstanding performance in multiple visual tasks. However, existing Transformers cannot effectively denoise large noisy images captured under low-light conditions owing to (1) the global self-attention mechanism causing high computational complexity in the spatial dimension owing to a quadratic increase in computation with the number of tokens; (2) the channel-wise self-attention computation unable to optimise the spatial correlations in images. We propose a local–global interaction Transformer (LGIT) that employs an adaptive strategy to select relevant patches for global interaction, achieving low computational complexity in global self-attention computation. A top-N patch cross-attention model (TPCA) is designed based on superpixel segmentation guidance. TPCA selects top-N patches most similar to the target image patch and applies cross attention to aggregate information from them into the target patch, effectively enhancing the utilisation of the image’s nonlocal self-similarity. A mixed-scale dual-gated feedforward network (MDGFF) is introduced for the effective extraction of multiscale local correlations. TPCA and MDGFF were combined to construct a hierarchical encoder-decoder network, LGIT, to compute self-attention within and across patches at different scales. Extensive experiments using real-world image-denoising datasets demonstrated that LGIT outperformed state-of-the-art (SOTA) convolutional neural network (CNN) and Transformer-based methods in qualitative and quantitative results.

Dynamic category-sensitive hypergraph inferring and homo-heterogeneous neighbor feature learning for drug-related microbe prediction.

The microbes in human body play a crucial role in influencing the functions of drugs, as they can regulate the activities and toxicities of drugs. Most recent methods for predicting drug-microbe associations are based on graph learning. However, the relationships among multiple drugs and microbes are complex, diverse and heterogeneous. Existing methods often fail to fully model the relationships. In addition, the attributes of drug-microbe pairs exhibit long-distance spatial correlations, which previous methods have not integrated effectively. We propose a new prediction method named DHDMP which is designed to encode the relationships among multiple drugs and microbes and integrate the attributes of various neighbor nodes along with the pairwise long-distance correlations. First, we construct a hypergraph with dynamic topology, where each hyperedge represents a specific relationship among multiple drug nodes and microbe nodes. Considering the heterogeneity of node attributes across different categories, we developed a node category-sensitive hypergraph convolution network to encode these diverse relationships. Second, we construct homogeneous graphs for drugs and microbes respectively, as well as drug-microbe heterogeneous graph, facilitating the integration of features from both homogeneous and heterogeneous neighbors of each target node. Third, we introduce a graph convolutional network with cross-graph feature propagation ability to transfer node features from homogeneous to heterogeneous graphs for enhanced neighbor feature representation learning. The propagation strategy aids in the deep fusion of features from both types of neighbors. Finally, we design spatial cross-attention to encode the attributes of drug-microbe pairs, revealing long-distance correlations among multiple pairwise attribute patches. The comprehensive comparison experiments showed our method outperformed state-of-the-art methods for drug-microbe association prediction. The ablation studies demonstrated the effectiveness of node category-sensitive hypergraph convolution network, graph convolutional network with cross-graph feature propagation, and spatial cross-attention. Case studies on 3 drugs further showed DHDMP's potential application in discovering the reliable candidate microbes for the interested drugs. Source codes and supplementary materials are available at https://github.com/pingxuan-hlju/DHDM. Supplementary data are available at Bioinformatics online.

Whole genome regulatory effect of MoISW2 and consequences for the evolution of the rice plant pathogenic fungus Magnaporthe oryzae.

Isw2 proteins, ubiquitous across eukaryotes, exhibit a propensity for DNA binding and exert dynamic influences on local chromosome condensation in an ATP-dependent fashion, thereby modulating the accessibility of neighboring genes to transcriptional machinery. Here, we report the deletion of a putative MoISW2 gene, yielding substantial ramifications on plant pathogenicity. Subsequent gene complementation and chromatin immunoprecipitation sequencing (ChIP-seq) analyses were conducted to delineate binding sites. RNA sequencing (RNA-seq) assays revealed discernible impacts on global gene regulation along chromosomes in both mutant and wild-type strains, with comparative analyses against 55 external RNA-seq data sets corroborating these findings. Notably, MoIsw2-mediated binding and activities delineate genomic loci characterized by pronounced gene expression variability proximal to MoIsw2 binding sites, juxtaposed with comparatively stable expression in surrounding regions. The contingent genes influenced by MoIsw2 activity predominantly encompass niche-determinant genes, including those encoding secreted proteins, secondary metabolites, and stress-responsive elements, alongside avirulence genes. Furthermore, our investigations unveil a spatial correlation between MoIsw2 binding motifs and known transposable elements (TEs), suggesting a potential interplay wherein TE transposition at these loci could modulate the transcriptional landscape of Magnaporthe oryzae in a strain-specific manner. Collectively, these findings position MoIsw2 as a plausible master regulator orchestrating the delicate equilibrium between genes vital for biomass proliferation, akin to housekeeping genes, and niche-specific determinants crucial for ecological adaptability. Stress-induced TE transposition, in conjunction with MoIsw2 activity, emerges as a putative mechanism fostering enhanced mutagenesis and accelerated evolution of niche-determinant genes relative to housekeeping counterparts.IMPORTANCEIsw2 proteins are conserved in plants, fungi, animals, and other eukaryotes. We show that a fungal Isw2 protein in the rice pathogen Magnaporthe oryzae binds to retrotransposon (RT) DNA motifs and affects the epigenetic gene expression landscape of the fungal genome. Mainly ecological niche determinant genes close to the binding motifs are affected. RT elements occur frequently in DNA between genes in most organisms. They move place and multiply in the genome, especially under physiological stress. We further discuss the Isw2 and RT combined activities as a possible sought-after mechanism that can cause biased mutation rates and faster evolution of genes necessary for reacting to abiotic and biotic challenges. The most important biotic challenges for plant pathogens are the ones from the host plants' innate immunity. The overall result of these combined activities will be an adaptation-directed evolution of niche-determinant genes.

Hydraulic fracturing in the overpressured, isotropically stressed Cane Creek Unit, Paradox Basin

The Cane Creek Unit within the Paradox Basin is an unconventional oil play that poses substantial geomechanical and hydrological challenges. With hundreds of millions of oil barrels in place, this resource is a viable target, but a greater understanding of the subsurface geomechanics is required for more reliable and consistent production. We analyzed a previous hydraulic stimulation of the State 16-2 well, located in the northern part of the basin, to produce recommendations for how future stimulations might be improved. Specifically, we look at three components of the completed stimulation: 1) Spatial correlations of various well logs to identify regions favorable for retaining hydrocarbons within natural fractures; 2) Analysis of pressure-time data from the hydraulic fracturing stages during well stimulation to assess the complexity of the fracture networks; and 3) Evaluation of poroelastic effects from reservoir depletion to assess the potential for future restimulation. Results of our analysis provide quantitative insight about the State 16-2 stimulation and yield specific recommendations. The most important recommendation is to stimulate regions with an intermediate fracture density due to their ability to retain hydrocarbons. Restimulation, if needed, may be more effective if completed after significant depletion due to poroelastic stress changes. While the direction of maximum horizontal stress is a critical factor, it might be less crucial if restimulation is conducted after considerable reservoir depletion.#xD;

Global dynamic features and information of adjacent hidden layer enhancement based on autoencoder for industrial process soft sensor application

AbstractThere is a lack of consideration of temporal and spatial correlation in the process variables and adjacent hidden layers correlation in the soft sensor model of stacked autoencoders. To address the issue, a novel global dynamic adjacent layer information enhancement auto encoder (GD‐ALIEAE) method is proposed to improve the poor prediction performance. The gated recurrent unit (GRU) and uniform manifold approximation and projection (UMAP) are applied to the GD‐ALIEAE model for obtaining global dynamic features of the temporal and spatial information of process variables by parallel computation. An adjacent layer information correlation algorithm is proposed to avoid the loss of hidden layers information during the stacking process. The algorithm enhances the features of the low layer through nonlinear mapping, combining the low layer and its adjacent layer as input. The input then is fed to the multi‐head attention mechanism to obtain features that contain adjacent layer correlation. Finally, a prediction model is established through a fully connected layer. Through simulation experiments on two industrial cases of sulphur recovery unit and thermal power plant, and compared with models of stacked autoencoder (SAE), stacked isomorphic autoencoder (SIAE), and target‐related stacked autoencoder (TSAE), the effectiveness of the proposed method was verified.

Ecological dynamics and coexistence patterns of wild and domestic mammals in an abandoned landscape

The issue of agricultural land abandonment in southern Europe has raised concerns about its impact on biodiversity. While abandoned areas can lead to positive developments like creating new habitats and restoring native vegetation, they can also result in human–wildlife conflicts, particularly in areas with extensive farming and free‐ranging livestock. To understand habitat selection and use of livestock and wild ungulates, it is essential to study their spatial and temporal distribution patterns. In this context, we conducted a long‐term large mammal monitoring project using camera traps in the Peneda‐Gerês National Park in northern Portugal. Our primary focus was on exploring habitat preferences, occupancy dynamics, and potential spatial use correlations between domestic and wild species, utilizing dynamic occupancy models. Most wild species exhibited stable area use patterns, while domestic species experienced marginal declines, and the Iberian ibex displayed signs of repopulation. We observed distinct effects of habitat variables on occupancy, colonization, and extinction, revealing species‐specific patterns of habitat utilization. Human disturbance had a notable impact on domestic species but did not affect wild ones. Camera sensitivity emerged as a critical factor, enhancing detection probability for all species. Additionally, habitat and weather variables exerted varying effects on detection probabilities, underscoring the necessity of accounting for these factors in modeling the detection process. We found shared habitat preferences between cattle and horses, both positively correlated with wolves, suggesting potential human–wildlife conflicts. Despite extensive spatial overlap, domestic and wild species seem to exhibit ecological independence possibly due to distinct strategies and low predation pressure. Overall, the study emphasizes the multifaceted factors influencing habitat use. The observed species associations contribute to understanding ecological relationships and potential resource competition, emphasizing the importance of considering environmental variables for effective wildlife conservation and management.

RESEARCH ON DIGITAL ECONOMY AND GREEN TOTAL FACTOR PRODUCTIVITY GROWTH OF CHINA’S INDUSTRY AND SPATIAL EFFECTS

This paper evaluates the impact and role of digital economy on industrial green total factor productivity based on the perspective of China’s industrial green transformation and provides a realistic basis and decision-making reference for industrial green transformation. This paper constructs the industrial green total factor productivity and digital economy development index of 30 provinces from 2013 to 2019, and empirically tests the spatial-temporal evolution relationship between them and the impact of digital economy on industrial green development by using time-fixed effect and spatial Durbin model. The results show that the digital economy and industrial green total factor productivity digital economy are unevenly developed, basically showing a distribution trend of high in the east and low in the west, and the two have a significant positive spatial correlation in the years observed. The results of local spatial autocorrelation analysis show that there is a high clustering of digital economy and total factor productivity in the eastern coastal region and low clustering in the western region. The regional heterogeneity analysis shows that the development of digital economy is beneficial to optimize the distortion of capital and labor allocation and thus improve industrial green total factor productivity, but the indirect effect of digital economy on industrial green total factor productivity is not significant and the direct effect plays a dominant role.

Relative Residence Time Can Account for Half of the Anatomical Variation in Fatty Streak Prevalence Within the Right Coronary Artery.

The patchy anatomical distribution of atherosclerosis has been attributed to variation in haemodynamic wall shear stress (WSS). The consensus is that low WSS and a high Oscillatory Shear Index (OSI) trigger the disease. We found that atherosclerosis at aortic branch sites correlates threefold better with transverse WSS (transWSS), a metric which quantifies multidirectional near-wall flow. Coronary artery disease has greater clinical significance than aortic disease but computation of WSS metrics is complicated by the substantial vessel motion occurring during each cardiac cycle. Here we present the first comparison of the distribution of atherosclerosis with WSS metrics computed for moving coronary arteries. Maps of WSS metrics were computed using dynamic geometries reconstructed from angiograms of ten non-stenosed human right coronary arteries (RCAs). They were compared with maps of fatty streak prevalence derived from a previous study of 1852 RCAs. Time average WSS (TAWSS), OSI, transWSS and the cross-flow index (CFI), a non-dimensional form of the transWSS, gave non-significant or significant but low spatial correlations with lesion prevalence. The highest correlation coefficient (0.71) was for the relative residence time (RRT), a metric that decreases with TAWSS and increases with OSI. The coefficient was not changed if RRT was calculated using CFI, which captures multidirectional WSS only, rather than OSI, which encompasses both multidirectional and oscillatory WSS. Contrary to our earlier findings in the aorta, low WSS in combination with highly multidirectional flow correlates best with lesion location in the RCA, explaining approximately half of its anatomical variation.

Vehicle trajectory prediction method integrating spatiotemporal relationships with hybrid time-step scene interaction

In vehicle trajectory prediction, constructing the interactive relationships among vehicles within the traffic environment poses a significant challenge. Existing models predominantly focus on temporal dependencies within vehicle histories and spatial correlations among neighboring vehicles, overlooking the continuous influence of historical vehicle states on the current time step and the interplay of multiple sequences over time. To address these limitations, we propose a method for multimodal vehicle trajectory prediction that integrates Hybrid Time-step Scene Interaction (HTSI) into the spatiotemporal relationships. Firstly, we introduce the HTSI module, comprising Multi-step Temporal Information Aggregation (MTIA) and Single-step Temporal Information Aggregation (STIA) methods. MTIA utilizes multi-head attention mechanisms to capture temporal dependencies between consecutive frames, thereby generating new time series amalgamating the ongoing influence of historical time states on the current timestamp. Simultaneously, STIA employs multi-head attention mechanisms to capture the spatial dimension weights of multiple time series and, by aggregating spatial interaction features at each timestamp, generates new time series fused with spatial interaction influences. Subsequently, feature extraction is performed through LSTM layers. Moreover, we propose an improved DIPM pooling module, improving the model’s long-term prediction capability by selectively reusing historical hidden states. Ultimately, based on training results from the HighD and NGSIM datasets, our model demonstrates significant advantages in long-term prediction compared to other state-of-the-art trajectory prediction models. Specifically, within the 5 s prediction window, the model achieved a root mean square error (RMSE) of 2.79 m on the NGSIM dataset, representing a 33.62% improvement over the baseline model’s average accuracy. Additionally, on the HighD dataset, the model attained an RMSE of 2.16 m, reflecting a 33.43% enhancement. The crucial code can be obtained from the provided link: https://github.com/gyhhq/Prediction-trajectory .

Combining molecular transmission network analysis and spatial epidemiology to reveal HIV-1 transmission pattern among the older people in Nanjing, China

BackgroundIn China, the problem of HIV infection among the older people has become increasingly prominent. This study aimed to analyze the pattern and influencing factors of HIV transmission based on a genomic and spatial epidemiological analysis among this population.MethodsA total of 432 older people who were aged ≥ 50 years, newly diagnosed with HIV-1 between January 2018 and December 2021 and without a history of ART were enrolled. HIV-1 pol gene sequence was obtained by viral RNA extraction and nested PCR. The molecular transmission network was constructed using HIV-TRACE and the spatial distribution analyses were performed in ArcGIS. The multivariate logistic regression analysis was performed to analyze the factors associated with clustering.ResultsA total of 382 sequences were successfully sequenced, of which CRF07_BC (52.3%), CRF01_AE (32.5%), and CRF08_BC (6.8%) were the main HIV-1 strains. A total of 176 sequences entered the molecular network, with a clustering rate of 46.1%. Impressively, the clustering rate among older people infected through commercial heterosexual contact was as high as 61.7% and three female sex workers (FSWs) were observed in the network. The individuals who were aged ≥ 60 years and transmitted the virus by commercial heterosexual contact had a higher clustering rate, while those who were retirees or engaged other occupations and with higher education degree were less likely to cluster. There was a positive spatial correlation of clustering rate (Global Moran I = 0.206, P < 0.001) at the town level and the highly aggregated regions were mainly distributed in rural area. We determined three large clusters which mainly spread in the intra-region of certain towns in rural areas. Notably, 54.5% of cases in large clusters were transmitted through commercial heterosexual contact.ConclusionsOur joint analysis of molecular and spatial epidemiology effectively revealed the spatial aggregation of HIV transmission and highlighted that towns of high aggregation were mainly located in rural area. Also, we found vital role of commercial heterosexual contact in HIV transmission among older people. Therefore, health resources should be directed towards highly aggregated rural areas and prevention strategy should take critical persons as entry points.

Examining intermediate soil properties variability through spatial interpolation methods in GIS

This study compares Kriging and Inverse Distance Weighting (IDW) spatial interpolation methods for estimating intermediate soil properties at a construction site in Astana, Kazakhstan. Using data from eight boreholes, seven engineering geological elements (EGE) were identified and analyzed at 6.5 m and 11.5 m depths. Kriging produced deformation modulus values ranging from -0.29 to 18.99 MPa at 6.5 m and -0.51 to 23.94 MPa at 11.5 m, capturing more spatial variability compared to IDW, which provided ranges of 3.3 to 18.99 MPa and 2.6 to 23.99 MPa, respectively. Kriging’s ability to account for spatial correlations resulted in more accurate predictions, particularly in areas with complex subsurface variability. Meanwhile, IDW offered reliable localized results, effective in more uniform geological conditions. The findings demonstrate that both methods are valuable for geotechnical applications, with the choice depending on data density and site variability.

Seismic response prediction method of train-bridge coupled system based on convolutional neural network-bidirectional long short-term memory-attention modeling

Seismic response prediction is crucial for the safety analysis of train-bridge coupled systems. However, due to the complexity, suddenness, and high-risk nature of earthquakes, there are strong nonlinear relationships among different parts of bridges, making it challenging to express their spatial correlations using analytical models and traditional neural networks. To address this, this paper establishes a ballast track shaker scaling model and employs the grating monitoring measurement method to construct a spatial quasi-distributed monitoring system for the ballast track, thereby collecting seismic strain responses of the train-bridge coupled system under various seismic conditions. A hybrid neural network method is proposed for predicting the seismic responses of the train-bridge coupled system. This hybrid neural network integrates the features of a Convolutional Neural Network (CNN), Bidirectional Long Short-Term Memory Neural Network (BiLSTM), and the attention mechanism, thereby termed the CNN-BiLSTM-attention hybrid neural network. The model was validated using strain responses from 54 seismic scenarios. The results indicate that the model has a Mean Absolute Error (MAE) of 0.2349 and a coefficient of determination (R2) of 0.9446. Comparing the prediction results with those from RNN and LSTM models, it was found that the CNN effectively extracts features under various seismic parameters, while the BiLSTM better captures the temporal information of the strain responses, ensuring effective prediction regardless of the magnitude of strain responses. Therefore, the CNN-BiLSTM-attention hybrid neural network model is recommended for predicting seismic response.

DGD-CNet: Denoising Gated Recurrent Unit with a Dropout-Based CSI Network for IRS-Aided Massive MIMO Systems

For the deployment of Sixth Generation (6G) networks, integrating Massive Multiple-Input Multiple-Output (Massive MIMO) systems with Intelligent Reflecting Surfaces (IRS) is highly recommended due to its significant benefits in reducing communication losses for Non-Line-of-Sight (NLoS) conditions. However, the use of passive IRS presents challenges in channel estimation, mainly due to the significant feedback overhead required in Frequency Division Duplex (FDD)-based Massive MIMO systems. To address these challenges, this paper introduces a novel Denoising Gated Recurrent Unit with a Dropout-based Channel state information Network (DGD-CNet). The proposed DGD-CNet model is specifically designed for FDD-based IRS-aided Massive MIMO systems, aiming to reduce the feedback overhead while improving the channel estimation accuracy. By leveraging the Dropout (DO) technique with the Gated Recurrent Unit (GRU), the DGD-CNet model enhances the channel estimation accuracy and effectively captures both spatial structures and time correlation in time-varying channels. The results show that the proposed DGD-CNet model outperformed existing models in the literature, achieving at least a 26% improvement in Normalized Mean Square Error (NMSE), a 2% increase in correlation coefficient, and a 4% in system accuracy under Low-Compression Ratio (Low-CR) in indoor situations. Additionally, the proposed model demonstrates effectiveness across different CRs and in outdoor scenarios.

CRNN-Refined Spatiotemporal Transformer for Dynamic MRI reconstruction

Magnetic Resonance Imaging (MRI) plays a pivotal role in modern clinical practice, providing detailed anatomical visualization with exceptional spatial resolution and soft tissue contrast. Dynamic MRI, aiming to capture both spatial and temporal characteristics, faces challenges related to prolonged acquisition times and susceptibility to motion artifacts. Balancing spatial and temporal resolutions becomes crucial in real-world clinical scenarios. In the realm of dynamic MRI reconstruction, while Convolutional Recurrent Neural Networks (CRNNs) struggle with long-range dependencies, CRNNs require extensive iterations, impacting efficiency. Transformers, known for their effectiveness in high-dimensional imaging, are underexplored in dynamic MRI reconstruction. Additionally, prevailing algorithms fall short of achieving superior results in demanding generative reconstructions at high acceleration rates. This research proposes a novel approach for dynamic MRI reconstruction, named CRNN-Refined Spatiotemporal Transformer Network (CST-Net). The spatiotemporal Transformer initiates reconstruction, modeling temporal and spatial correlations, followed by refinement using the CRNN. This integration mitigates inaccuracies caused by damaged frames and reduces CRNN iterations, enhancing computational efficiency without compromising reconstruction quality. Our study compares the performance of the proposed CST-Net at 6 × and 12 × undersampling rates, showcasing its superiority over existing algorithms. Particularly, in challenging 25× generative reconstructions, the CST-Net outperforms current methods. The comparison includes experiments under both radial and Cartesian undersampling patterns. In conclusion, CST-Net successfully addresses the limitations inherent in existing generative reconstruction algorithms, thereby paving the way for further exploration and optimization of Transformer-based approaches in dynamic MRI reconstruction. Code and Datasets can be available: https://github.com/XWangBin/CST-Net.

Optimal Deployment of Monitoring Points Considering Spatial Deformation Correlation of Slopes

Optimal Deployment of Monitoring Points Considering Spatial Deformation Correlation of Slopes

Correlation structures of statistically isotropic stiffness and compliance TRFs through upscaling

This paper reports a procedure to develop random fields of material properties on a mesoscale level, coarser than the microscale level of heterogeneous material microstructure. Since the anisotropy of properties at the mesoscale level is unavoidable, tensor-valued random fields (TRFs) need to be constructed. The construction satisfies three criteria: (i) the passage from the micro to mesoscale must be conducted according to micromechanics, (ii) any anisotropic properties must be grasped, and (iii) full (spatial) correlation structure must be accounted for. The construction is illustrated in the example of a linear elastic microstructure modeling a planar interpenetrating phase composite (IPC), where each phase is interconnected throughout the microstructure. The most general representation of a statistically homogeneous and isotropic correlation structure of the TRFs of stiffness and compliance is employed. Four material functions of the representation are determined through scale-dependent homogenization, which grasps any auto- and cross-correlations within/among different components of TRFs. The Gaussianity of the TRFs is also assessed with a finding that, overall, the smaller is the mesoscale and the more pronounced is the microstructural randomness, the more non-Gaussian are the mesoscale TRFs.

Resilience evaluation and spatio-temporal evolution analysis of China's science and technology financial ecosystem

ABSTRACT This paper discusses the resilience of science and technology (sci-tech) financial ecosystem. It constructs an evaluation system from four dimensions: resistance, adaptability, recovery and innovation. The data are from 29 provinces in China from 2010 to 2020. It is evaluated using entropy weights–TOPSIS and dynamic evaluation model. The spatial evolution law is revealed by spatial autocorrelation analysis. The results show that: (1) The overall resilience level of sci-tech financial ecosystem is not high. Its evolution and development are generally in a relatively stable state. It has significant regional heterogeneity, showing a distribution pattern of ‘high east and low west’. (2) It has significant spatial positive correlation. The structure of the sci-tech financial ecosystem in the eastern region is highly complex, most provinces are in high–high agglomeration area. Provinces in other regions are mainly in low–low agglomeration area. Only a few provinces have experienced changes in the spatial distribution. (3) The dynamic value in the eastern region shows polarisation characteristics. Although the static value of resilience in the central and western regions is low, the dynamic evaluation value in some provinces in the central and western regions is high. The subsequent growth momentum of resilience in the north-eastern region is insufficient.

Spatial correlation assessment of multiple earthquake intensity measures using physics-based simulated ground motions

Predictive models for spatial correlation play an effective role in the assessment of seismic risk associated with distributed infrastructure and building portfolios. However, existing models often rely on simplified approaches, assuming isotropy and stationarity. This paper verifies these assumptions by presenting a comprehensive study using a database of 3D physics-based simulated broadband ground motions for Istanbul, generated by the SPEED software. The results reveal significant event-to-event variability and nonstationary and anisotropic characteristics of spatial correlation influenced by source, path, and site effects. The development of nonstationary correlation models requires exploring influential metrics beyond spatial proximity and gaining a deep understanding of their impact, which is the focus of this study. Analysis of the spatial correlations of peak ground displacement, peak ground velocity, peak ground acceleration, and response spectral accelerations at different periods, employing both stationary and nonstationary correlation modelling methods and considering the finite fault model, indicates that the slip distribution pattern, direction and distance of station pairs relative to earthquake rupture, soil softness, and homogeneity of soil properties significantly influence the spatial correlations of near-field earthquake ground motions. Implementation of the introduced parameters in predictive spatial correlation models enhances the precision of regional seismic hazard assessments.

Accounting for continuous correlations among alternatives in the context of spatial choice modelling using high resolution mobility data

Accounting for similarity among alternatives is important for having unbiased estimates and behaviourally reasonable substitutions. Capturing similarity in a spatial context is a challenging task and the common approach of discretising space into a number of disjoint nests generally leads to uncaptured spatial correlations. On the other hand, relying on more complex error structures quickly leads to computational issues. In the present paper, we propose an alternative approach, where a Cross-Nested Logit (CNL) modelling framework with a flexible correlation structure is used, where space is treated as continuous, while the allocation can be parameterised based on a range of similarity factors. The proposed structure is applied in the context of mode and destination choices of shopping trips using a smart-phone GPS panel survey from Leeds, UK. Results indicate that in addition to the improvements in model fit, the proposed CNL specification is able to uncover interesting findings about individual mobility behaviour.

Spatial spillover effects and correlation network analysis of green construction development efficiency in China

Green construction has been implemented globally to address resource depletion and environmental degradation. The effectiveness of green construction development depends on the efficiency of resource utilization and the common progress of all regions. However, existing studies have paid limited attention to green construction development efficiency (GCDE) and its spatial correlation. This study uses the super-efficiency slacks-based measure (Super-SBM) model, spatial econometric models, and social network analysis (SNA) method to reveal the spatial distribution, spatial spillover effects, and spatial correlation of GCDE across 30 provinces in China. The results indicate significant disparities in GCDE among regions, with the eastern region exhibiting considerably higher GCDE than the western and northeastern regions. There is evidence of spatial spillover effects, and the spatial correlation of GCDE is strengthening. Provinces with high GCDE occupy central positions in the GCDE network and exert spillover effects on other provinces, while underdeveloped provinces have opportunities to benefit from regional cooperation. The findings provide policy insights for the implementation and enhancement of green construction development actions. Moreover, they provide Chinese experience for green construction practices in other areas of the world.