Spatial Dependence Research Articles

Interaction of a Dense Layer of Solid Particles with a Shock Wave Propagating in a Tube

A numerical simulation of an unsteady gas flow containing inert solid particles in a shock tube is carried out using the interpenetrating continuum model. The gas and dispersed phases are characterized by governing equations that express the concepts of mass, momentum, and energy conservation as well as an equation that shows the change of the volume fraction of the dispersed phase. Using a Godunov-type approach, the hyperbolic governing equations are solved numerically with an increased order of accuracy. The working section of the shock tube containing air and solid particles of various sizes is considered. The shock wave structure is discussed and computational results provide the spatial and temporal dependencies of the particle concentration and other flow quantities. The numerical simulation results are compared with available experimental and computational data.

On the Stationarity of the Global Spatial Dependency of Heat Risk on Drought

AbstractLand surface processes such as the soil moisture—air temperature coupling influence compound climate anomalies like heatwaves and droughts, yet the spatial and temporal variability of the coupling strength is still understudied. We assess global land exposure to concurrent heat waves and drought since the 1980s. We found that drought significantly shapes the spatial distribution of the risk of heat waves. We show that the portion of global land experiencing drought‐conditioned heat anomalies more than tripled in less than 3 decades. However, using such traditional heat waves indicators, the level of spatial coupling between heat waves and drought seems to decline. Conversely, time‐dependent approaches accounting for the baseline climate change offer a more stable perspective. We conclude that tailoring hazard definitions to specific processes and impacts is crucial. Early warning systems can play a prominent role in mitigating the impacts of global warming to society.

A spatial Durbin model for interval-valued data with t-distribution

Interval-valued data have received much attention due to their wide applications in finance, econometrics, meteorology and medicine. However, most regression models developed for interval-valued data assume that the observations are mutually independent, which can not be adapted to scenarios where individuals are spatially correlated. We propose a new linear model to account for areal-type spatial dependencies present in interval-valued data. Specifically, two spatial Durbin models are separately built for the centers and log-ranges of the interval-valued response. To make the new model more robust to heavy-tailed noise, we assume the error term follows a t-distribution. The parameters are obtained using an expectation-maximization (EM) algorithm. Numerical experiments are designed to examine the performance of the proposed method. We also use a weather dataset to demonstrate the usefulness of our model.

An approach for constructing spatially paired pseudo repeat-sales housing price indices in China

AbstractConstructing a reliable housing price index is crucial for accurately reflecting housing price dynamics and enhancing transparency in the housing market. However, existing methods for constructing housing price indices often confront many challenges, such as omitted variable issues in the hedonic model and constrained samples used in the repeat-sales model. To address these challenges, this study identifies a spatially paired relationship between complexes and develops a Spatially Paired Pseudo Repeat-Sales model to construct the housing price index. This approach offers two significant advantages: first, it enlarges the sample size used in the repeat-sales model at least 1.8 times; second, it effectively estimates the effects of spatial dependency and physical housing factors on prices while mitigating the impacts of unobservable factors through differentiation. The findings of this paper suggest that using the spatially paired pseudo repeat-sales model can significantly improve the estimation of housing price volatility, by approximately 13%. Moreover, the constructed housing price index model demonstrates significant robustness, even when the spatial weight settings are altered. This research provides convincing evidence of improved housing market transparency for stakeholders, including governments, institutional developers, and individual investors. Additionally, the constructed model can inform other similar research in housing price index construction by providing a spatially pairing perspective on housing complexes.

THE SPATIAL LINKAGES BETWEEN INTERNATIONAL MIGRATION AND SECURITY: THE EMPIRICAL FINDINGS FROM TURKEY HOSTING MOST REFUGEE IN THE WORLD

The increasing international migration in the world, especially due to the conflict, have potential negative impacts in immigrant countries. An increase in crime rates caused by unemployed immigrants who cannot meet their food and housing needs is one of them. Accordingly, the study investigates the effects of international immigrants, whose numbers reached about 3,9 million in Turkey, on violent and financial crime rates. The Spatial Error Panel Model with Fixed Effects covering Turkey’s 26 NUTS-II regions and 2016-2019 period indicate that there are positive and statistically significant relationships between immigrants and these crime rates. However, the negative impacts of international migration on security are mainly observed in financial crimes rather than violent crimes. Furthermore, the most important determinants of these crimes are high food and housing costs. And also, there is a negative relationship between uneducated employment and financial crimes rather than violent crimes. On the other hand, the findings indicate that regions have spatial dependency positively in terms of violent crimes. It means that as average violent crimes increase in the neighbors of a region, the violent crimes of that region also increase.

Waiting times spillovers in a National Health Service hospital network: a little organizational diversity can go a long way

BackgroundThe objective of this study is to assess if waiting times for elective surgeries within the Portuguese National Health Service (NHS) are influenced by the waiting times at neighboring hospitals. Recognizing these interdependencies, and their extent, is crucial for understanding how hospital network dynamics affect healthcare delivery efficiency and patient access.MethodsWe utilized patient-level data from all elective surgeries conducted in Portuguese NHS hospitals to estimate a hospital-specific index for waiting times. This index served as the dependent variable in our analysis. We applied a spatial lag model to examine the potential strategic interactions between hospitals concerning their waiting times.ResultsOur analysis revealed a significant positive endogenous spatial dependence, indicating that waiting times in NHS hospitals are strategic complements. Furthermore, we found that NHS contracts with private not-for-profit hospitals not only reduce waiting times within these hospitals but also exert positive spillover effects on other NHS hospitals.ConclusionsThe findings suggest that diversifying the organization of the NHS hospital network, particularly through contracts with private entities for marginal patients, can significantly enhance competitive dynamics and reduce waiting times. This effect persists even when patient choice is confined to a small fraction of the patient population, highlighting a strategic avenue for policy optimization in healthcare service delivery.

EHNet: Efficient Hybrid Network with Dual Attention for Image Deblurring

The motion of an object or camera platform makes the acquired image blurred. This degradation is a major reason to obtain a poor-quality image from an imaging sensor. Therefore, developing an efficient deep-learning-based image processing method to remove the blur artifact is desirable. Deep learning has recently demonstrated significant efficacy in image deblurring, primarily through convolutional neural networks (CNNs) and Transformers. However, the limited receptive fields of CNNs restrict their ability to capture long-range structural dependencies. In contrast, Transformers excel at modeling these dependencies, but they are computationally expensive for high-resolution inputs and lack the appropriate inductive bias. To overcome these challenges, we propose an Efficient Hybrid Network (EHNet) that employs CNN encoders for local feature extraction and Transformer decoders with a dual-attention module to capture spatial and channel-wise dependencies. This synergy facilitates the acquisition of rich contextual information for high-quality image deblurring. Additionally, we introduce the Simple Feature-Embedding Module (SFEM) to replace the pointwise and depthwise convolutions to generate simplified embedding features in the self-attention mechanism. This innovation substantially reduces computational complexity and memory usage while maintaining overall performance. Finally, through comprehensive experiments, our compact model yields promising quantitative and qualitative results for image deblurring on various benchmark datasets.

Beyond Parametric Bounds: Exploring Regional Unemployment Patterns Using Semiparametric Spatial Autoregression

Abstract Background It is a well-known phenomenon that nonlinearities that are inherent in the relationship among economic variables negatively affect the commonly used estimators in the econometric models. The nonlinearities cause an instability of the estimated parameters that, in particular, are unable to capture a local relationship between the response and the covariate. Objectives The main aim of the paper is the simultaneous consideration of spatial effects as well as nonlinearities through an advanced semiparametric spatial autoregressive econometric model. The paper seeks to contribute to empirical studies of regional science focused on the application of semiparametric spatial autoregressive econometric models. Methods/Approach We outline an approach that can be used to correct nonlinearities by incorporating a semiparametric idea within the framework of econometric models. We use an expansion by penalised basis splines that are highly flexible and are able to capture local nonlinearities between variables. Results In the empirical study, we fit different econometric models that attempt to explain the dynamics of the European Union's regional unemployment. Conclusions The results show that regional unemployment exhibits significant spatial dependence, indicating interconnectedness among neighbouring regions and suggesting the adoption of a semiparametric spatial autoregressive model for improved modelling flexibility, surpassing traditional parametric approaches.

Efficient self-attention based joint optimization for lithology and petrophysical parameter estimation in the Athabasca Oil Sands

Accurately identifying lithology and petrophysical parameters, such as porosity and water saturation, are essential in reservoir characterization. Manual interpretation of well-log data, the conventional approach, is not only labor-intensive but also susceptible to human errors. To address these challenges of lithology identification and petrophysical parameter estimation in the Athabasca Oil Sands area, this study introduces an AutoRegressive Vision Transformer (ARViT) model for lithology and petrophysical parameter prediction. The effectiveness of ARViT lies in its self-attention mechanism and its ability to handle data sequentially, allowing the model to capture important spatial dependencies within the well-log data. This mechanism enables the model to identify subtle spatial and temporal relationships among various geophysical measurements. The model is also interpretable and can serve as an assistive tool for geoscientists, enabling faster interpretation while reducing human bias. The interpretable nature of the model should assist geoscientists in conducting faster quality checks of the predictions, ensuring that errors are not propagated to subsequent stages. This study adopts a multitask learning approach, jointly optimizing the model's performance across multiple tasks simultaneously. To evaluate the effectiveness of the ARViT model, we conducted series of experiments and comparisions, testing it against traditional artificial neural networks (ANN), Long Short-Term Memory (LSTM), and Vision Transformer (ViT) models. To showcase the versatility of ARViT, we apply Low-Rank Adaptation (LoRA) to a different smaller dataset, showing its potential to adapt to different geological contexts. LoRA not only helps in model adaptability but also helps to reduce the number of trainable parameters. Our findings demonstrate that ARViT outperforms ANN, LSTM, and ViT in estimating lithological and petrophysical parameters. While lithology prediction has been a well-explored field, ARViT's unique blend of features, including its self-attention mechanism, autoregression, and multitask approach along with efficient fine tuning using LoRA, sets it apart as a valuable tool for the complex task of lithology prediction and petrophysical parameter estimation.

Fungal community composition varies spatially in a commercial potato field in response to soil properties and topographic features

The factors influencing the spatial distribution of fungal communities are commonly examined over large spatial scales but not at smaller scales. Given this, the extent to which soil properties and topographic features contribute to the diversity and distribution of fungal communities in an agricultural field needs to be further explored. We investigated the spatial distribution of soil fungal community composition from a ~1100 m long transect with 83 sampling points in a commercial potato field with a rolling landform. The relative abundance of Ascomycota, Basidiomycota and Mortierellomycota showed medium to strong spatial dependence with an autocorrelation range varying from ~43 to 92 m, similar to the autocorrelation range of soil properties and topographic features. Most of the variability in fungal and saprotrophic community composition was explained by soil properties (15% and 11%, respectively) and spatial distance (16% and 15% respectively). While topographic features contributed 8% and 5% of variability to total fungi and saprotrophic community composition, respectively. The fungal and saprotrophic community compositions were correlated with SOC, pH and slope curvature, however, richness and Pielou’s evenness of the fungal communities and fungal biomass were not correlated with soil properties or topographic features. The results suggest that the spatial variation in fungal and saprotrophic community composition in response to soil properties and topographic features in this agricultural landscape was due to differences in assemblages of fungal Amplicon Sequence Variants (ASVs) but not in differences in the number of fungal ASVs or fungal biomass measured using phospholipids fatty acids.

Impact of local sports policies in the prevalence of cardiovascular disease: An assessment with spatial data analysis

According to the World Health Organization, 17 million people die every year of cardiovascular diseases (CVD), particularly heart attacks and strokes. More than half of all deaths across the Europe are caused by CVDs. 80 % of premature deaths from these causes could be avoided by controlling the main risk factors: tobacco, unhealthy diet and physical inactivity.Local authorities provide sport and recreation facilities to their communities. They also have a central role in forging partnerships with public and private sector to enhance the local sport delivery system.The present paper consists of an empirical exercise about the statistical relationship between the financial effort of Portuguese municipalities in the implementation of sports policies and the prevalence of CVDs. The estimation of a Spatial Autocorrelation Model confirms the strong spatial dependence between neighbouring municipalities and validates the existence of a positive impact of local sports policies on mortality rate from cardiovascular diseases. The results raise the need to reinforce local policies to promote active living based on holistic approaches to combating heart disease and promoting healthy lifestyles in partnerships with health professionals, educators, and community leaders.

Environmental Quality, Extreme Heat, and Healthcare Expenditures

Although the effects of the environment on human health are well-established, the literature on the relationship between the quality of the environment and expenditures on healthcare is relatively sparse and disjointed. In this study, the Environmental Quality Index developed by the Environmental Protection Agency and heatwave days were compared against per capita Medicare spending at the county level. A general additive model with a Markov Random Field smoothing term was used for the analysis to ensure that spatial dependence did not undermine model results. The Environmental Quality Index was found to hold a statistically significant (p < 0.05), multifaceted nonlinear association with spending, as was the average seasonal maximum heat index. The same was not true of heatwave days, however. In a secondary analysis on the individual domains of the index, the social and built environment components were significantly related to spending, but the air, water, and land domains were not. These results provide initial support for the simultaneous benefits of healthcare financing systems to mitigate some dimensions of poor environmental quality and consistently high air temperatures.

A multimodal multistream multilevel fusion network for finger joint angle estimation with hybrid sEMG and FMG sensing

Finger joint angle (FJA) estimation, as a dynamic and fine-grained decoding mode, can support intuitive and natural human–machine interactions. This study is pioneering work dedicated to achieving accurate and clinically friendly FJA estimation. In this context, a novel surface electromyography and force myography-based multimodal multistream multilevel fusion network (MMMFNet) is proposed. MMMFNet utilises gate-recurrent units (GRU) networks to capture spatial dependencies among features and adopts a multilevel fusion strategy to extract more comprehensive representations from information sources than a single-level fusion. A cross-stream interaction (CSI) block is added between the GRU networks to focus on important information in the prediction and optimise the learning process and representation capability by facilitating information interaction among branch networks. The experimental results suggest that the MMMFNet has higher estimation accuracy (RMSE: 6.225±0.276 vs. 6.953±0.282, R2: 0.879±0.019 vs 0.846±0.021), shorter training time (4.146±0.074 min vs. 16.536±0.430 min), and lower computational complexity (5.4×106 FLOPs vs 8.8×106 FLOPs) than state-of-the-art fusion methods. Moreover, MMMFNet, which quantifies feature importance using attention weights, is the first explainable multimodal model for hand movement intention decoding. In summary, compared with state-of-the-art methods, MMMFNet can not only estimate FJAs more accurately but is also more clinically friendly.

Composite dyadic models for spatio-temporal data.

Mechanistic statistical models are commonly used to study the flow of biological processes. For example, in landscape genetics, the aim is to infer spatial mechanisms that govern gene flow in populations. Existing statistical approaches in landscape genetics do not account for temporal dependence in the data and may be computationally prohibitive. We infer mechanisms with a Bayesian hierarchical dyadic model that scales well with large data sets and that accounts for spatial and temporal dependence. We construct a fully connected network comprising spatio-temporal data for the dyadic model and use normalized composite likelihoods to account for the dependence structure in space and time. We develop a dyadic model to account for physical mechanisms commonly found in physical-statistical models and apply our methods to ancient human DNA data to infer the mechanisms that affected human movement in Bronze Age Europe.

Efficient Modeling of Spatial Extremes over Large Geographical Domains

Various natural phenomena exhibit spatial extremal dependence at short spatial distances. However, existing models proposed in the spatial extremes literature often assume that extremal dependence persists across the entire domain. This is a strong limitation when modeling extremes over large geographical domains, and yet it has been mostly overlooked in the literature. We here develop a more realistic Bayesian framework based on a novel Gaussian scale mixture model, with the Gaussian process component defined by a stochastic partial differential equation yielding a sparse precision matrix, and the random scale component modeled as a low-rank Pareto-tailed or Weibull-tailed spatial process determined by compactly-supported basis functions. We show that our proposed model is approximately tail-stationary and that it can capture a wide range of extremal dependence structures. Its inherently sparse structure allows fast Bayesian computations in high spatial dimensions based on a customized Markov chain Monte Carlo algorithm prioritizing calibration in the tail. We fit our model to analyze heavy monsoon rainfall data in Bangladesh. Our study shows that our model outperforms natural competitors and that it fits precipitation extremes well. We finally use the fitted model to draw inference on long-term return levels for marginal precipitation and spatial aggregates.

Research on coastline extraction and dynamic change from remote sensing images based on deep learning

Accurate coastline extraction is crucial for the scientific management and protection of coastal zones. Due to the diversity of ground object details and the complexity of terrain in remote sensing images, the segmentation of sea and land faces challenges such as unclear segmentation boundaries and discontinuous coastline contours. To address these issues, this study improve the accuracy and efficiency of coastline extraction by improving the DeepLabv3+ model. Specifically, this study constructs a sea-land segmentation network, DeepSA-Net, based on strip pooling and coordinate attention mechanisms. By introducing dynamic feature connections and strip pooling, the connection between different branches is enhanced, capturing a broader context. The introduction of coordinate attention allows the model to integrate coordinate information during feature extraction, thereby allowing the model to capture longer-distance spatial dependencies. Experimental results has shown that the model can achieves a land-sea segmentation mean intersection over union (mIoU) ration and Recall of over 99% on all datasets. Visual assessment results show more complete edge details of sea-land segmentation, confirming the model’s effectiveness in complex coastal environments. Finally, using remote sensing data from a coastal area in China as an application instance, coastline extraction and dynamic change analysis were implemented, providing new methods for the scientific management and protection of coastal zones.

Phase unwrapping via fully exploiting global and local spatial dependencies

Phase unwrapping (PU) is the process of extracting the authentic phase image from its noisy wrapped measurements, playing a crucial role in scientific imaging techniques. PU requires solving a challenging non-linear ill-posed problem, particularly in the presence of noticeable noise. In recent years, deep learning has emerged as a promising approach for PU. Inspired by the success of convolutional neural networks (CNNs) in image restoration, many existing works trained CNNs for PU. However, due to the locality of convolutional kernels, CNNs are not efficient in capturing global spatial dependencies, a critical cue for PU. As an alternate, recent studies employed recurrent neural networks (RNNs) defined on handcrafted pixel paths. Nonetheless, a limited number of pre-defined pixel paths cannot fully exploit global spatial dependencies existing in complex phase structures. In this paper, we introduce a vision transformer (ViT) model that effectively captures both global and local spatial dependencies using a hierarchical structure with a multi-scale process. The proposed ViT model employs a series of global transformer blocks to capture global spatial dependencies at the roughest scale. The resulting global features are used to guide a set of local transformer blocks to analyze local spatial dependencies in a coarse-to-fine progressive manner for unwrapping. Extensive experiments show that, our proposed ViT model produces higher-quality unwrapped phases over existing CNN/RNN-based methods, while maintaining a lightweight nature.

MFH‐Net: A Hybrid CNN‐Transformer Network Based Multi‐Scale Fusion for Medical Image Segmentation

ABSTRACTIn recent years, U‐Net and its variants have gained widespread use in medical image segmentation. One key aspect of U‐Net's design is the skip connection, facilitating the retention of detailed information and leading to finer segmentation results. However, existing research often concentrates on enhancing either the encoder or decoder, neglecting the semantic gap between them, and resulting in suboptimal model performance. In response, we introduce Multi‐Scale Fusion module aimed at enhancing the original skip connections and addressing the semantic gap. Our approach fully incorporates the correlation between outputs from adjacent encoder layers and facilitates bidirectional information exchange across multiple layers. Additionally, we introduce Channel Relation Perception module to guide the fused multi‐scale information for efficient connection with decoder features. These two modules collectively bridge the semantic gap by capturing spatial and channel dependencies in the features, contributing to accurate medical image segmentation. Building upon these innovations, we propose a novel network called MFH‐Net. On three publicly available datasets, ISIC2016, ISIC2017, and Kvasir‐SEG, we perform a comprehensive evaluation of the network. The experimental results show that MFH‐Net exhibits higher segmentation accuracy in comparison with other competing methods. Importantly, the modules we have devised can be seamlessly incorporated into various networks, such as U‐Net and its variants, offering a potential avenue for further improving model performance.

Vehicle miles traveled induced demand, rebound effect, and price and income elasticities: A US spatial econometric analysis

The economic literature on transportation is extensive, with travel demand being a well-studied topic. However, research on light-duty vehicle travel has not fully explored spatial dependencies arising from neighboring regions' driving activities. This study uses state-level panel data from 2000 to 2019 to examine U.S. vehicle miles traveled (VMT), focusing on spatial spillover effects through a Spatial Durbin Model. Our results reveal significant spatial interactions between states. Increased VMT in one state leads to spillover effects, increasing VMT in neighboring states. We present our findings as marginal effects, distinguishing between direct and indirect impacts. The direct price elasticity is estimated at −0.257, with a positive spillover impact of 0.036, yielding a total elasticity of −0.221. Induced travel exhibits a larger indirect effect than a direct effect, resulting in an overall impact of 0.276. Additionally, direct and total income elasticities are 0.194 and 0.146, respectively, while direct and total fuel economy rebound effects are 0.546 and 0.496, respectively. Moreover, higher numbers of licensed drivers, registered vehicles, and urbanization significantly influence a state's VMT. This research highlights the importance of regional planning among states, especially considering transportation infrastructure and environmental concerns related to driving and vehicle emissions.

Exploring the Coupling Relationship and Driving Factors of Land Use Conflicts and Ecosystem Services Supply–Demand Balances in Different Main Functional Areas, Southwest China

ABSTRACTConflicts between different types of land use, driven by rapid urbanization, are altering ecosystem services supply–demand balances (ESDB), and the reduction of ESDB will threaten regional sustainable development and human welfare. However, there are few studies on the interrelationships and their drivers between land use conflicts (LUCs) and ESDB from a coupling perspective, especially in different main functional areas. Therefore, this study focused on Southwest China. Firstly, the coupling coordination degree model was employed to measure the ESDB‐LUCs relationship and analyze its dynamic changes from 1990 to 2020. Then, the RDA method was used to explore the driving factors of the ESDB‐LUCs relationship in different functional areas (i.e., urban development area (UDA), agricultural development area (APA), and ecological protection area (EPA)). The results show that the LUCs index displayed a downward trend in the SW during 1990–2020, with a decreasing distribution pattern from the northeast to the southwest. The ESDB index exhibited a downward and then upward trend, with an increasing distribution mode from the northeast to the southwest. There was a spatial dependence between LUCs and ESDB. The type of coupling coordination between LUCs and ESDB was dominated by moderate coordination, with the index showing a spatial pattern of UDA > APA > EPA. Among these, the proportion of cropland and the proportion of urban land were the main factors influencing the degree of coordination of the UDA (explanation rate > 80%), showing positive and negative effects, respectively. The proportion of forestland and the proportion of cropland were the main factors influencing the APA and EPA (explanation rate > 70%), with negative and positive effects, respectively. Therefore, mitigating the conflict between cropland and urban land, cropland and forest land is essential to achieve ecosystem balance in the SW.