Spatial Weight Research Articles

Estimating urban land subsidence with satellite data using a spatially multiscale geographically weighted regression approach

Land subsidence has attracted widespread attention because of its potential hazards to sustainable urban development. The traditional methods for estimating land subsidence disregard the crucial aspect of altitude by analyzing spatial data solely on the 2D plane (longitude and latitude geographic coordinates). This research incorporates the altitude feature into the construction of the spatial matrix to develop a new method named spatially multiscale geographically weighted regression (SM-GWR) for a better estimation of land subsidence, enabling to determine the optimal bandwidth at 3D spatial scales. By adopting a 3D perspective, the new algorithm accurately fits settlement points based on spatial distances, addressing previous limitations of projecting points on a two-dimensional surface and ignoring height differences, thereby providing a more realistic depiction of spatial relationships. The satellite data is first used to monitor land subsidence through the small baseline subset synthetic aperture radar interferometry (SBAS-InSAR). Then, the SM-GWR model is developed to construct the 3D spatial weight matrix and update estimate parameters through back-fitting calibration. The proposed method's feasibility is confirmed through examples of land subsidence in China and Singapore. The case study results demonstrate that: (1) The SM-GWR model shows significant improvement in fitting land subsidence compared to the traditional method (R2 value of 0.908 and 0.934 in predicting annual subsidence and cumulative subsidence, respectively); (2) The contribution of the influencing factors is highly related to the nature properties and urban development of the city. The novelty of this study lies in the development of an SM-GWR model combined with satellite monitoring to achieve high-precision estimation of land subsidence, considering the 3D structure of spatial geographic data.

Drug-target Affinity Prediction by Molecule Secondary Structure Representation Network.

Identification of drug-target interactions (DTI) is a crucial step in drug development with high specificity and low toxicity. To accelerate the process, computer-aided DTI prediction algorithms have been used to screen compounds or targets rapidly. Furthermore, DTI prediction can be used to identify potential targets for existing drugs, thus uncovering new indications and repositioning them. Therefore, it is of great importance to develop efficient and accurate DTI prediction algorithms. Current algorithms usually represent drugs as extracted features, which are learned by convolutional neural networks (CNNs) from its linear representation, or utilize graph neural networks (GNNs) to learn its graph representation. However, these methods either lose information or fail to capture the structural information of the drug. To address this issue, a novel molecule secondary structure representation network (MSSRN) is proposed to learn drug characterization more accurately. Firstly, the network performs relational graph convolutional networks (R-GCNs) on the drug's molecular graph and integrates drug sequence convolutions to learn the sequential information. Secondly, inspired by the attention mechanism, spatial importance weights of the drug sequence are calculated to guide R-GCNs to learn the topological information of the drug. A drug-target affinity model, called MSSRN-DTA, was then constructed by using MSSRN to learn drug structure and CNN to learn protein sequence. The effectiveness of the proposed method is verified by comparing it with other alternative methods and baseline models on two benchmark datasets.

Insulator defect detection based on improved Yolov5s

The stable operation of a power supply system is inseparable from the work of detecting defects in transmission lines. However, the insulator defect detection model based on deep learning is widely used in wire inspection work. Therefore, this paper proposes an improved YOLOv5s insulator defect detection model in order to solve the problems of insufficient training data and low recognition accuracy of the target detection model in the real-time detection of small target insulator defects. To expand and enhance the training data, experiments were conducted using the addition of noise and random black blocks. The spatial and channel weight coefficients were obtained by adding an attention mechanism (Convolutional Block Attention Module, CBAM), and the dimensions of the input feature maps were transformed to enhance the model’s ability to extract and fuse small target defect features. Experiments show that with Faster RCNN, YOLOv3, SSD and YOLOv4 comparison experiments verified that the algorithm achieves 97.38% detection accuracy for insulators and 93.32% detection accuracy for small target insulator defects with a fast detection speed, which is a better solution to the problem of detecting insulator defects with too small a proportion in the image.

A Methodological Approach for Gap Filling of WFV Gaofen-1 Images from Spatial Autocorrelation and Enhanced Weighting

Clouds and cloud shadow cover cause missing data in some images captured by the Gaofen-1 Wide Field of View (GF-1 WFV) cameras, limiting the extraction and analysis of the image information and further applications. Therefore, this study proposes a methodology to fill GF-1 WFV images using the spatial autocorrelation and improved weighting (SAIW) method. Specifically, the search window size is adaptively determined using Getis-Ord Gi* as a metric. The spatial and spectral weights of the pixels are computed using the Chebyshev distance and spectral angle mapper to better filter the suitable similar pixels. Each missing pixel is predicted using linear regression with similar pixels on the reference image and the corresponding similar pixel located in the non-missing region of the cloudy image. Simulation experiments showed that the average correlation coefficient of the proposed method in this study is 0.966 in heterogeneous areas, 0.983 in homogeneous farmland, and 0.948 in complex urban areas. It suggests that SAIW can reduce the spread of errors in the gap-filling process to significantly improve the accuracy of the filling results and can produce satisfactory qualitative and quantitative fill results in a wide range of typical land cover types and has extensive application potential.

OII: An Orientation Information Integrating Network for Oriented Object Detection in Remote Sensing Images

Oriented object detection for remote sensing images poses formidable challenges due to arbitrary orientation, diverse scales, and densely distributed targets (e.g., across terrain). Current investigations in remote sensing object detection have primarily focused on improving the representation of oriented bounding boxes yet have neglected the significant orientation information of targets in remote sensing contexts. Recent investigations point out that the inclusion and fusion of orientation information yields substantial benefits in training an accurate oriented object system. In this paper, we propose a simple but effective orientation information integrating (OII) network comprising two main parts: the orientation information highlighting (OIH) module and orientation feature fusion (OFF) module. The OIH module extracts orientation features from those produced by the backbone by modeling the frequency information of spatial features. Given that low-frequency components in an image capture its primary content, and high-frequency components contribute to its intricate details and edges, the transformation from the spatial domain to the frequency domain can effectively emphasize the orientation information of images. Subsequently, our OFF module employs a combination of a CNN attention mechanism and self-attention to derive weights for orientation features and original features. These derived weights are adopted to adaptively enhance the original features, resulting in integrated features that contain enriched orientation information. Given the inherent limitation of the original spatial attention weights in explicitly capturing orientation nuances, the incorporation of the introduced orientation weights serves as a pivotal tool to accentuate and delineate orientation information related to targets. Without unnecessary embellishments, our OII network achieves competitive detection accuracy on two prevalent remote sensing-oriented object detection datasets: DOTA (80.82 mAP) and HRSC2016 (98.32 mAP).

Robust Localization-Guided Dual-Branch Network for Camouflaged Object Segmentation

The existence of camouflage targets is widespread in the natural world, as they blend seamlessly or closely resemble their surrounding environment, making it difficult for the human eye to identify them accurately. In camouflage target segmentation, challenges often arise from the high similarity between the foreground and background, resulting in segmentation errors, imprecise edge detection, and overlooking of small targets. To address these issues, this paper presents a robust localization-guided dual-branch network for the recognition of camouflaged targets. Two crucial branches, i.e., a localization branch and an overall refinement branch are designed and incorporated. The localization branch achieves accurate preliminary localization of camouflaged targets by incorporating the robust localization module, which integrates different high-level feature maps in a partially decoded manner. The overall refinement branch optimizes segmentation accuracy based on the output predictions of the localization branch. Within this branch, the edge refinement module is devised to effectively reduce false negative and false positive interference. By conducting context exploration on each feature layer from top to bottom, this module further enhances the precision of target edge segmentation. Additionally, our network employs five jointly trained output prediction maps and introduces attention-guided heads for diverse prediction maps in the overall refinement branch. This design adjusts the spatial positions and channel weights of different prediction maps, generating output prediction maps based on the emphasis of each output, thereby further strengthening the perception and feature representation capabilities of the model. To improve its ability to generate highly confident and accurate prediction candidate regions, tailored loss functions are designed to cater to the objectives of different prediction maps. We conducted experiments on three publicly available datasets for camouflaged object detection to assess our methodology and compared it with state-of-the-art network models. On the largest dataset COD10K, our method achieved a Structure-measure of 0.827 and demonstrated superior performance in other evaluation metrics, outperforming recent network models.

Multi-View Scene Classification Based on Feature Integration and Evidence Decision Fusion

Leveraging multi-view remote sensing images in scene classification tasks significantly enhances the precision of such classifications. This approach, however, poses challenges due to the simultaneous use of multi-view images, which often leads to a misalignment between the visual content and semantic labels, thus complicating the classification process. In addition, as the number of image viewpoints increases, the quality problem for remote sensing images further limits the effectiveness of multi-view image classification. Traditional scene classification methods predominantly employ SoftMax deep learning techniques, which lack the capability to assess the quality of remote sensing images or to provide explicit explanations for the network’s predictive outcomes. To address these issues, this paper introduces a novel end-to-end multi-view decision fusion network specifically designed for remote sensing scene classification. The network integrates information from multi-view remote sensing images under the guidance of image credibility and uncertainty, and when the multi-view image fusion process encounters conflicts, it greatly alleviates the conflicts and provides more reasonable and credible predictions for the multi-view scene classification results. Initially, multi-scale features are extracted from the multi-view images using convolutional neural networks (CNNs). Following this, an asymptotic adaptive feature fusion module (AAFFM) is constructed to gradually integrate these multi-scale features. An adaptive spatial fusion method is then applied to assign different spatial weights to the multi-scale feature maps, thereby significantly enhancing the model’s feature discrimination capability. Finally, an evidence decision fusion module (EDFM), utilizing evidence theory and the Dirichlet distribution, is developed. This module quantitatively assesses the uncertainty in the multi-perspective image classification process. Through the fusing of multi-perspective remote sensing image information in this module, a rational explanation for the prediction results is provided. The efficacy of the proposed method was validated through experiments conducted on the AiRound and CV-BrCT datasets. The results show that our method not only improves single-view scene classification results but also advances multi-view remote sensing scene classification results by accurately characterizing the scene and mitigating the conflicting nature of the fusion process.

Direct and spillover effects of short‐ and long‐term land pricing drivers: Evidence from Italian districts, 1992−2019

AbstractThe global economic and food crisis has increased the demand for land and rekindled the interest in farmland market investments worldwide. This study explores the Italian farmland market, investigating its main influencing factors from 1992 to 2019 using a spatial econometric framework. Traditional land characteristics and location‐specific agricultural factors, as well as non‐agricultural factors, are assessed. The average level of farmland prices and their growth are analyzed by modeling the potential types of spatial interactions, and the results are corroborated by considering different configurations of spatial weight matrices. The results show that farmland markets are influenced by land's current net returns as well as by its potential alternative uses. Therefore, factors considered external to the agricultural dimension, such as population pressure, climate change, and speculative expectations, increasingly shape farmland prices.

Combining Swin Transformer and Attention-Weighted Fusion for Scene Text Detection

The existing text detection algorithms based on Convolutional Neural Networks (CNN) commonly have the problems of insufficient receptive fields and inadequate extraction of spatial positional information, which limit their ability to detect large-scale variation text instances, long-distance and wide-spaced text instances as well as effectively distinguish complex background textures. To address the above problems, in this paper, a scene text detection algorithm combining Swin Transformer and attention-weighted fusion is proposed. Firstly, an attention-weighted fusion (AWF) module is proposed, which embeds a modified coordinate attention module (CAM) in the feature pyramid network (FPN). This module learns spatial positional weights of foreground information in different-scale features while suppressing redundant background information. As a result, the fused features are more focused on the text regions, enhancing the localization ability for text regions and boundaries. Secondly, the window-based self-attention mechanism of the Swin Transformer is utilized to achieve global feature perception on the fused features of the pyramid network. This compensates for the insufficient receptive fields of CNN and enhances the representation capability of global contextual features, thereby further improving the performance of text detection. Experimental results demonstrate that the proposed algorithm achieves competitive performance on three public datasets, namely ICDAR2015, MSRA-TD500, and Total-Text, with F-measure reaching 87.9%, 91.4%, and 86.7%, respectively. Code is available at: https://github.com/xgli411/ST-AWFNet.

Study on the spillover effect of digital economy development on CO2 emissions

To study the spillover effect of the digital economy development on carbon emissions, this study innovatively constructs different spatial weight matrices, based on 2011–2020 panel data covering 30 Chinese provinces, and it explores the direct spillovers, conducted spillovers, and spillovers from different spillover channels, such as human capital, service industry development, and information development of digital economy development on carbon emissions through the spatial Durbin model combined with a mediating effect model. The results show that there is significant spatial heterogeneity in digital economy development; in terms of regions, the eastern region has the highest average development level and the central region has the highest average annual growth rate. Digital economy development can directly suppress carbon emissions, and it can also indirectly suppress carbon emissions by driving technological innovation and optimizing the energy consumption structure, and there exists a spatial spillover effect. Under human capital, service industry development and information development matrices, the spatial spillover effect of digital economy development on carbon emissions is significantly negative. Regions with the same level of information development are more likely to exert a spatial spillover effect of digital economy development on carbon emissions.

Spatial–temporal feature-based End-to-end Fourier network for 3D sign language recognition

Most dynamic sign word misclassifications are caused by redundant spatial–temporal (SPT) feature pruning that lacks language semantic and temporal dependencies. SPT feature recognition is one of the important aspects for the evaluation of the misclassification of dynamic sign words. The redundant pruning of SPT feature space influences the language model of sign confusion, model complexity, and SPT feature similarity. The purpose of this article is to develop a new multi-scale SPT feature-based dynamic sign word recognition approach via a low-cost feature selection method (FS) and End-to-end Fourier convolution neural network (EFCNN). Instead of a sensor fusion technique for obtaining frame position alignment, in the EFCNN, new 3D frame position and coordinates are determined using a pixel weighting and alignment function of the first and succeeding 25 spatial intensities of the 3D video changes across hand motion. The new spatial weight and the original spatial coordinates are fused and truncated in the Fourier domain. We generate the temporal dependence of the fused features. A feature selection known as the FS-EFCNN is introduced to select compact features with a preserved language meaning. Five state-of-the-art feature selection methods, namely Infinite FS (InFS), Relief FS, Fisher, MIM, ILFS, and ensemble FS-EFCNN were deployed to guide and optimize the learning performance of EFCNN. The experimental result analysis highlighted the improved results of the FS-EFCNN method with the best accuracy of 99.86%, 99.89%, and 90.69% on 3D American Sign Language, British Sign Language, and Greek Sign Language data sets, respectively.

Study on the impact of digital finance on agricultural carbon emissions from a spatial perspective: an analysis based on provincial panel data

Abstract China is committed to achieving the goal of ‘carbon peaking and carbon neutrality’, which requires reducing carbon emissions from various sectors, especially agriculture. Agriculture is a major source of greenhouse gas emissions, and its carbon intensity is influenced by many factors, including the development of digital finance. Digital finance, as an important driver of economic growth and technological innovation, can affect the carbon emissions of agricultural production and consumption through various channels. However, the existing literature has not paid enough attention to the spatial effects of digital finance on agricultural carbon emissions, which may lead to biased estimates and policy implications. Therefore, this paper aims to fill this gap by using the Spatial Durbin Model to analyze the impact of digital finance on agricultural carbon emissions from a spatial perspective, based on the provincial panel data of 28 provinces (municipalities and autonomous regions) in China from 2011 to 2020. The main findings are as follows: (1) The development of digital finance has a positive and significant effect on China’s agricultural carbon emissions, and this effect is robust to different spatial weight matrices. (2) The impact mechanism of digital finance on agricultural carbon emissions is mainly through affecting the spatial spillover of carbon emissions and the cross-regional financial coordination. Based on these results, this paper provides some policy suggestions for achieving the goal of ‘carbon peaking and carbon neutrality’ in China’s agricultural sector.

Digitalization, Spillover and Environmental, Social, and Governance Performance: Evidence From China

There is growing interest in the relationship between digitalization and environmental, social, and governance (ESG) performance, but existing research focuses on the one-way relationship and ignores the two-way mechanism. Based on a sample of 3335 listed companies in China in 2020, this study adopts a spatial simultaneous equation model to investigate the bidirectional mechanism between companies’ digitalization and ESG performance. The results show that digitalization and ESG performance have a significant positive two-way mechanism; digitalization enhances ESG performance, while ESG performance promotes digitalization. The results also indicate a significant positive intra-industry spillover effect for both digitization and ESG performance. Further research shows that the relationship between digitization and ESG performance does not differ depending on the definition of spatial weights, however, the spillover effects do differ depending on the definition of spatial weights.

Universal REFoCUS beamforming with spatial weighting

Universal REFoCUS beamforming with spatial weighting

Salient Object Detection With Edge-Guided Learning and Specific Aggregation

Recently, the performance of salient object detection (SOD) has been significantly improved by utilizing edge information for auxiliary training. However, the extraction and utilization of edge cues and multi-level feature fusion are still two issues in existing edge-aware models. In this paper, we devise a novel SOD network with edge-guided learning and specific aggregation, named ELSA-Net, to cooperatively address these two issues. First, we propose the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">edge-guided learning strategy</i> , which utilizes edge cues as low-level guidance to improve saliency prediction. Specifically, we design a two-stream model that uses a saliency branch and an edge branch to detect the interior and the boundary of salient objects, respectively. Then, an edge-guided interaction module (EGI) is further designed to achieve feature enhancement by embedding edge information into the saliency branch as the spatial weights. In addition, two <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">specific aggregation modules</i> are proposed for the progressive fusion of multi-level features in the above two streams, thus making full use of semantic and detailed information. The high-level interactive fusion module (HIF) leverages the correlation between two deeper features to obtain more powerful global contexts. And the low-level weighted fusion module (LWF) focuses on the complement of fine information by selectively integrating input features. Extensive experiments show that the proposed approach outperforms 19 state-of-the-art methods on five datasets, which validates its effectiveness both quantitatively and qualitatively.

Modelling the Count of Diphtheria in West Java in 2022 using Geographically Weighted Zero-Inflated Poisson Regression

The number of death cases caused by infectious diseases, that is to say diphtheria, especially in West Java so that it is categorized as an Extraordinary Event (KLB) generally attacks from the age of toddlers to the elderly. To model diphtheria cases in West Java in 2022 containing over dispersion using Geographically Weighted Zero-Inflated Poisson Regression (GWZIPR). Parameter estimation uses Expectation Maximization (EM) algorithm with spatial weight using Adaptive Gaussian Kernel. The resulting models include a Poisson state model that shows the chance of diphtheria cases occurring with relevant factors of the percentage within the population who apply Hygienic and Healthful Lifestyle (PHBS), who have been vaccinated against DPT-HB3, the number of health workers at Puskesmas, and the percentage of active posyandu and a Zero state model that shows the chance of no diphtheria cases occurring with relevant factors of the percentage who have been vaccinated against DPT-HB3, the number of health workers at Puskesmas, and the percentage of active posyandu.

Does the Collaborative Agglomeration of Logistics and Manufacturing have an Inhibitory Effect on Carbon Emissions? Based on the Spatial Econometric Analysis of Panel Data of 30 Provinces in China

In this paper, panel data from 30 provinces in the Chinese Mainland (excluding Tibet, Hong Kong, Macao, and Taiwan) spanning from 2006 to 2022 is utilized to investigate the relationship between logistics industry agglomeration, manufacturing industry agglomeration, and carbon emissions using spatial econometric analysis methodologies. Specifically, the study focuses on investigating the relationship between the synergistic agglomeration of the logistics and manufacturing industry, and their impact on carbon emissions using the Spatial Durbin Model. The research findings indicate that an increase in manufacturing industry agglomeration significantly reduces carbon emissions, an increase in logistics industry agglomeration result in higher carbon emissions, and the synergistic agglomeration of the two industries plays a significant role in mitigating regional carbon emissions through spatial and geographical mechanisms. Robustness tests, which include the control of additional variables and the use of alternative spatial weight matrices, further support the above conclusions.

Spartus: A 9.4 TOp/s FPGA-Based LSTM Accelerator Exploiting Spatio-Temporal Sparsity.

Long short-term memory (LSTM) recurrent networks are frequently used for tasks involving time-sequential data, such as speech recognition. Unlike previous LSTM accelerators that either exploit spatial weight sparsity or temporal activation sparsity, this article proposes a new accelerator called "Spartus" that exploits spatio-temporal sparsity to achieve ultralow latency inference. Spatial sparsity is induced using a new column-balanced targeted dropout (CBTD) structured pruning method, producing structured sparse weight matrices for a balanced workload. The pruned networks running on Spartus hardware achieve weight sparsity levels of up to 96% and 94% with negligible accuracy loss on the TIMIT and the Librispeech datasets. To induce temporal sparsity in LSTM, we extend the previous DeltaGRU method to the DeltaLSTM method. Combining spatio-temporal sparsity with CBTD and DeltaLSTM saves on weight memory access and associated arithmetic operations. The Spartus architecture is scalable and supports real-time online speech recognition when implemented on small and large FPGAs. Spartus per-sample latency for a single DeltaLSTM layer of 1024 neurons averages 1 μ s. Exploiting spatio-temporal sparsity on our test LSTM network using the TIMIT dataset leads to 46 × speedup of Spartus over its theoretical hardware performance to achieve 9.4-TOp/s effective batch-1 throughput and 1.1-TOp/s/W power efficiency.

The impact of digital finance on the urban-rural income gap: an empirical analysis based on spatial econometric modeling

Abstract China’s economy has stepped into the new normal development stage. Coordinating urban and rural economic development and continuously narrowing the income gap has become a more urgent strategic task, and digital finance is an important engine for economic development and transformation and upgrading, is bound to have a far-reaching impact on the narrowing of the urban-rural income gap. This paper first explores the essential relationship between digital finance and regional economic development and lists the important roles played by digital finance in regional economic development. The entropy power Topsis method is used to select variable indicators and construct the index system. Combining the Moran index, spatial spillover effect, and spatial panel regression model, the regional economic distance is used as the spatial weight matrix, and the corresponding elemental expressions are derived to construct the spatial econometric model of the urban-rural income gap by digital finance, and the model parameters are estimated by the great likelihood method. The model is applied to 31 provinces in China to measure their relevant variables from 2012 to 2022 and analyze the impact of digital finance on the urban-rural income gap from the perspectives of spatial autocorrelation and threshold regression. Through the experiment, it can be seen that the digital finance indexes of the eastern, central, and western regions are all negative, and they are significant at the 5%, 10%, and 10% levels, respectively, which indicates that the digital finance in these three regions stimulates the development of the financial industry while narrowing the urban-rural income gap. When the level of economic development crosses the first threshold value of 10.4365, the regression coefficient of digital finance decreases from −0.0348 to −0.0487, which again confirms that digital finance can reduce the urban-rural income gap.

Comparison of distance-based spatial weight matrix in modeling Internet signal strengths in Tasikmalaya regency using logistic spatial autoregressive model

To ensure that national development objectives in rural areas are achieved evenly and sustainably, the Government of Indonesia applies the principles of Village Sustainable Development Goals (SDGs), which are derivative programs of SDGs. One of the indicators in measuring the progress and independence of villages in Indonesia is the availability of cellular phone signal access. Cellular phone signals have a vital role because most internet users in Indonesia rely on mobile data connections from cellular operators. However, the signal emitted by a provider tower has a limited range. According to the data of the Developing Villages Index in 2022, Tasikmalaya Regency is one of the regencies with the highest number of villages that have weak signal strength in West Java Province, Indonesia. To examine the effect of distance and height difference between the placement of the nearest provider tower and the location of the Village Office on the internet signal strength category in Tasikmalaya Regency, Logistic Spatial Autoregressive modeling is needed. In this study, the Bayesian Markov-Chain Monte Carlo estimation method was used, because it has advantages in flexibility and computational efficiency. In spatial modeling, there is a spatial weight matrix determined by the researcher’s understanding of the observed phenomenon. The variable observed in this study is signal strength, which has an orientation at a distance. However, there are several types of distance-based spatial weight matrices, such as K-nearest neighbor, radial distance, power distance, and exponential distance. To determine the most suitable distance-based spatial weight matrix in internet signal strength modeling, the four (4) weight matrices were compared based on the goodness of fit measure models, calculated from the confusion matrix. The results of the analysis showed that the radial distance weight matrix with a threshold distance of d = 1.7km is the most suitable use of distance-based spatial weight matrix in internet signal modeling in Tasikmalaya Regency. The weight matrix exerted a positive spatial autocorrelation effect of 57.141%. In addition, the height difference factor between the location of the provider tower with the location of the village office has a greater effect than the horizontal distance.