Dynamic Spatial Patterns Research Articles

Analysis of Spatial Distribution Pattern and Driving Factors of Nature Reserves in Xinjiang

Establishing nature reserves is fundamental to promoting ecological civilization and ensuring the ecological security of sustainable development, as well as a key strategy for maintaining the integrity of the national ecosystem. In recent years, with the enhancement in environmental awareness, the role of nature reserves in maintaining ecosystem stability, protecting biodiversity, and promoting sustainable development has become increasingly prominent. As a crucial ecological defense line in China, studying the spatial distribution and influencing factors of nature reserves in Xinjiang is vital for regional ecological protection and resource management. In this study, we focused on the nature reserves in Xinjiang and used kernel density and standard deviation ellipse analysis methods to examine data from 1980, 1990, 2000, 2010, and 2020 in order to reveal the dynamics of spatial distribution, clustering characteristics, and evolutionary patterns, providing a scientific basis for optimizing the spatial layout of nature reserves in Xinjiang. We also employed a geographical detector model to assess the main influencing factors. This study shows that the number, area, and types of nature reserves in Xinjiang have rapidly increased, forming an integrated system predominantly composed of nature reserves. Their spatial distribution exhibits a northeast–southwest orientation and a ring-centered clustering pattern. This spatial differentiation is the result of the interaction between natural and human factors, manifesting as an enhanced effect among these factors.

Experimental Study on Concentration and Size Distribution Characteristics of Particulate Matter in Cold and Hot Rolling

Workers’ health is jeopardized in steel rolling workshops due to the production of particulate matter. These particles’ physicochemical properties have a direct impact on their effects on human health, emphasizing the importance of understanding these properties for exposure control. This study investigated particulate matter emissions in hot and cold steel rolling workshops, focusing on mass concentrations, particle size distributions, and elemental compositions. The findings revealed that particles predominantly range from 0.3 to 1.0 μm in size, with irregular block-like and fibrous morphologies. Elemental analysis showed distinct compositions: the main components in the hot rolling workshop were oxygen, calcium, silicon, carbon, and iron while those in the cold rolling workshop were oxygen, silicon, aluminum, carbon, and iron. The particulate matter concentrations were higher in the hot rolling workshop than in the cold rolling workshop. The Rosin–Rammler particle size distribution function was applied to characterize particle size distribution at emission sources. This paper highlights the dynamic variations and spatial distribution patterns of particulate matter during rolling processes, providing key data for understanding particulate matter behavior in industrial environments and informing targeted pollution control strategies.

Ecosystem Services’ Response to Land Use Intensity: A Case Study of the Hilly and Gully Region in China’s Loess Plateau

The hilly and gully region of the Loess Plateau represents one of China’s most ecologically vulnerable landscapes, characterized by severe soil erosion, intensive land use, and pronounced disturbances to the structure and functionality of ecosystem services. Taking Zichang City as a case study, this research integrates grid-scale analysis with the InVEST-PLUS model and bivariate spatial autocorrelation techniques to examine the spatiotemporal dynamics and inter-relations of four critical ecosystem services—carbon storage, water yield, biodiversity, and soil retention—under varying land use intensity scenarios from 1990 to 2035. The findings indicate that (1) between 1990 and 2020, land use intensity in Zichang City steadily declined, exhibiting a spatial distribution pattern typified by central-area clustering and gradual peripheral transitions. (2) Across three development scenarios, the spatial distribution of the four ecosystem services aligned with the patterns observed in 2020, with central areas showing pronounced fluctuations, whereas peripheral regions experienced relatively minor changes. Specifically, from 1990 to 2020, the proportion of low-carbon storage areas increased by 2.89%, and high water yield areas expanded by 9.45%, while the shares of low habitat quality and low soil retention areas decreased by 5.59% and 6.25%, respectively. (3) A significant spatial autocorrelation was observed between land use intensity and the four ecosystem services, with widespread cold and hot spots reflecting dynamic spatial clustering patterns. These results offer valuable insights for optimizing land use strategies, improving ecosystem service performance, and advancing ecological conservation and sustainable development initiatives.

MTLPM: a long-term fine-grained PM2.5 prediction method based on spatio-temporal graph neural network.

The concentration of PM2.5 is one of the air quality indicators that the public pays the most attention to. Existing methods for PM2.5 prediction primarily analyze and forecast data from individual monitoring stations, without considering the mutual influence among multiple stations caused by natural environmental factors, e.g., air circulation. Moreover, the existing methods are mostly short-term predictions and perform poorly in long-term forecasting. In this paper, we propose MTLPM, i.e., a spatio-temporal graph neural network model based on an encoder-decoder architecture, which fully exploits the spatial dynamic patterns and long-term dependencies. Firstly, we adopt a message passing mechanism combined with spatial features and complex environmental factors (e.g., temperature, humidity, and wind direction) to update station data, capturing real-time spatial dynamic information. Secondly, we adopt the Multi-head ProbSparse Self-attention to extract temporal features, learning the long-term dependency relationships among the data. Finally, we adopt a generative one-step decoder structure to simultaneously forecast the data for multiple stations over a long period. We conducted experiments on both the project dataset and the publicly available dataset. Compared to existing state-of-the-art methods, MTLPM achieved an average reduction of approximately 1.6 in mean absolute error (MAE) and approximately 0.02 in symmetric mean absolute percentage error (SMAPE) in predicting results. The relevant source code is publicly available on GitHub1.

Global Assessment of Mesoscale Eddies with TOEddies: Comparison Between Multiple Datasets and Colocation with In Situ Measurements

The present study introduces a comprehensive, open-access atlas of mesoscale eddies in the global ocean, as identified and tracked by the TOEddies algorithm implemented on a global scale. Unlike existing atlases, TOEddies detects eddies directly from absolute dynamic topography (ADT) without spatial filtering, preserving the natural spatial variability and enabling precise, high-resolution tracking of eddy dynamics. This dataset provides daily information on eddy characteristics, such as size, intensity, and polarity, over a 30-year period (1993–2023), capturing complex eddy interactions, including splitting and merging events that often produce networks of interconnected eddies. This unique approach challenges the traditional single-trajectory perspective, offering a nuanced view of eddy life cycles as dynamically linked trajectories. In addition to traditional metrics, TOEddies identifies both the eddy core (characterized by maximum azimuthal velocity) and the outer boundary, offering a detailed representation of eddy structure and enabling precise comparisons with in situ data. To demonstrate its value, we present a statistical overview of eddy characteristics and spatial distributions, including generation, disappearance, and merging/splitting events, alongside a comparative analysis with existing global eddy datasets. Among the multi-year observations, TOEddies captures coherent, long-lived eddies with lifetimes exceeding 1.5 years, while highlighting significant differences in the dynamic properties and spatial patterns across datasets. Furthermore, this study integrates TOEddies with 23 years of colocalized Argo profile data (2000–2023), allowing for a novel examination of eddy-induced subsurface variability and the role of mesoscale eddies in the transport of global ocean heat and biogeochemical properties. This atlas aims to be a valuable resource for the oceanographic community, providing an open dataset that can support diverse applications in ocean dynamics, climate research, and marine resource management.

An improved machine learning-based model for prediction of diurnal and spatially continuous near surface air temperature

Near-surface air temperature (Tair) is crucial for assessing urban thermal conditions and their impact on human health. Traditional Tair estimation methods, reliant on sparse weather stations, often miss spatial variability. This study proposes a novel framework using a federated learning artificial neural network (FLANN) for fine-scale Tair prediction. Leveraging spatially complete thermal data from Landsat 8/9, Sentinel 3, and Himawari 8/9 (105 acquisition days, 2013–2023), and data from automatic weather stations, 23 predictor variables were extracted. After rigorous selection processes, nine variables significantly correlated with Tair were identified. Comparative analysis against established machine learning and linear models, using cross-validation data, showed FLANN’s superior performance with a Pearson correlation coefficient (r) of 0.98 and a root mean square error (RMSE) of 0.97 K, compared to r and RMSE of 0.85 and 1.09, respectively, for the linear model. FLANN showed greater improvements for urban stations with r and RMSE differences of 0.19 and − 2.03 K. Application of FLANN to predict Tair in Hong Kong in July 2023 enabled detailed urban heat island (UHI) analysis, revealing dynamic spatial and temporal UHI patterns. This study highlights FLANN’s potential for accurate Tair prediction and UHI analysis, enhancing urban thermal environment management.

Patterns of spatial dynamics and distribution of african elephants (Loxodonta africana) in the Central Kalahari Game Reserve, Botswana

Patterns of spatial dynamics and distribution of african elephants (Loxodonta africana) in the Central Kalahari Game Reserve, Botswana

A dataset of NDVI for the vegetation growing season in Central Asia with a resolution of 250 m (2001–2020) and 30 m (2020)

Central Asia is the largest arid and semi-arid region in the Northern Hemisphere, and its ecological environment is extremely fragile and susceptible to the effects of global climate change. Maintaining the stability of the region's ecosystem is crucial to global economic and social development due to its unique geographic location. Vegetation serves as a significant indicator of the ecological environment, and the spatial and temporal distribution pattern, as well as the change trend of vegetation, are important indicators in evaluating the ecological status of the region. The Normalized Difference Vegetation Index (NDVI) is a commonly used remote sensing index to study vegetation, which characterizes the spatio-temporal changes of vegetation. This dataset utilized MODIS13Q1 to generate long-term time series growing season mean NDVI data with a spatial resolution of 250m in Central Asia from 2001 to 2020. To obtain the 2020 growing season mean NDVI data with a higher spatial resolution of 30m, the Cubist algorithm based on rule segmentation regression was utilized to fuse the Landsat data and MODIS data. Meanwhile, this dataset carried out quality control of data products from three aspects: data source quality control, model training optimization, and model independent verification to ensure the accuracy and reliability of data. The generation of this dataset provides powerful data support for the analysis of vegetation dynamic change and spatial pattern in Central Asia.

Transcriptional repression and enhancer decommissioning silence cell cycle genes in postmitotic tissues.

The mechanisms that maintain a non-cycling status in postmitotic tissues are not well understood. Many cell cycle genes have promoters and enhancers that remain accessible even when cells are terminally differentiated and in a non-cycling state, suggesting their repression must be maintained long term. In contrast, enhancer decommissioning has been observed for rate-limiting cell cycle genes in the Drosophila wing, a tissue where the cells die soon after eclosion, but it has been unclear if this also occurs in other contexts of terminal differentiation. In this study, we show that enhancer decommissioning also occurs at specific, rate-limiting cell cycle genes in the long-lived tissues of the Drosophila eye and brain, and we propose this loss of chromatin accessibility may help maintain a robust postmitotic state. We examined the decommissioned enhancers at specific rate-limiting cell cycle genes and showed that they encode for dynamic temporal and spatial expression patterns that include shared, as well as tissue-specific elements, resulting in broad gene expression with developmentally controlled temporal regulation. We extend our analysis to cell cycle gene expression and chromatin accessibility in the mammalian retina using a published dataset and find that the principles of cell cycle gene regulation identified in terminally differentiating Drosophila tissues are conserved in the differentiating mammalian retina. We propose a robust, non-cycling status is maintained in long-lived postmitotic tissues through a combination of stable repression at most cell cycle genes, alongside enhancer decommissioning at specific rate-limiting cell cycle genes.

Transformer-Based Spatiotemporal Graph Diffusion Convolution Network for Traffic Flow Forecasting

Accurate traffic flow forecasting is a crucial component of intelligent transportation systems, playing a pivotal role in enhancing transportation intelligence. The integration of Graph Neural Networks (GNNs) and Transformers in traffic flow forecasting has gained significant adoption for enhancing prediction accuracy. Yet, the complex spatial and temporal dependencies present in traffic data continue to pose substantial challenges: (1) Most GNN-based methods assume that the graph structure reflects the actual dependencies between nodes, overlooking the complex dependencies present in the real-world context. (2) Standard time-series models are unable to effectively model complex temporal dependencies, hindering prediction accuracy. To tackle these challenges, the authors propose a novel Transformer-based Spatiotemporal Graph Diffusion Convolution Network (TSGDC) for Traffic Flow Forecasting, which leverages graph diffusion and transformer to capture the complexity and dynamics of spatial and temporal patterns, thereby enhancing prediction performance. The authors designed an Efficient Channel Attention (ECA) that learns separately from the feature dimensions collected by traffic sensors and the temporal dimensions of traffic data, aiding in spatiotemporal modeling. Chebyshev Graph Diffusion Convolution (GDC) is used to capture the complex dependencies within the spatial distribution. Sequence decomposition blocks, as internal operations of transformers, are employed to gradually extract long-term stable trends from hidden complex variables. Additionally, by integrating multi-scale dependencies, including recent, daily, and weekly patterns, accurate traffic flow predictions are achieved. Experimental results on various public datasets show that TSGDC outperforms conventional traffic forecasting models, particularly in accuracy and robustness.

Alterations of resting-state network dynamics in Alzheimer's disease based on leading eigenvector dynamics analysis.

Alzheimer's disease (AD) is a neurodegenerative disease, and mild cognitive impairment (MCI) is considered a transitional stage between healthy aging and dementia. Early detection of MCI can help slow down the progression of AD. At present, there are few studies exploring the characteristics of abnormal dynamic brain activity in AD. This article uses a method called leading eigenvector dynamics analysis (LEiDA) to study resting-state functional magnetic resonance imaging (rs-fMRI) data of AD, MCI, and cognitively normal (CN) participants. By identifying repetitive states of phase coherence, intergroup differences in brain dynamic activity indicators are examined, and the neurobehavioral scales were used to assess the relationship between abnormal dynamic activities and cognitive function. The results showed that in the indicators of occurrence probability and lifetime, the globally synchronized state of the patient group decreased. The activity state of the limbic regions significantly detected the difference between AD and the other two groups. Compared to CN, AD and MCI have varying degrees of increase in default and visual region activity states. In addition, in the analysis related to the cognitive scales, it was found that individuals with poorer cognitive abilities were less active in the globally synchronized state and more active in limbic region activity state and visual region activity state. Taken together, these findings reveal abnormal dynamic activity of resting-state networks in patients with AD and MCI, provide new insights into the dynamic analysis of brain networks, and contribute to a deeper understanding of abnormal spatial dynamic patterns in AD patients.NEW & NOTEWORTHY Alzheimer's disease (AD) is a neurodegenerative disease, but few studies have explored the characteristics of abnormal dynamic brain activity in AD patients. Here, our report reveals the abnormal dynamic activity of the patients' resting-state network, providing new insights into the dynamic analysis of brain networks and helping to gain a deeper understanding of the abnormal spatial dynamic patterns in AD patients.

Spatial–temporal evaluation of total-factor energy efficiency in Chinese construction industry based on three-stage super-efficiency SBM-DEA model

PurposeThe study aims to enhance energy efficiency within the high-energy consuming construction industry. It explores the spatial-temporal dynamics and distribution patterns of total factor energy efficiency (TFEE) across China’s construction industry, aiming to inform targeted emission reduction policies at provincial and city levels.Design/methodology/approachUtilizing a three-stage super-efficiency SBM-DEA model that integrates carbon emissions, the TFEE in 30 Chinese provinces and cities from 2004 to 2019 is assessed. Through kernel density estimation and exploratory spatial data analysis, the dynamic evolution and spatial patterns of TFEE are examined.FindingsAnalysis reveals that environmental investments positively impact TFEE, whereas Gross Regional Product (GRP) exerts a negative influence. R&D expenditure intensity and marketization show mixed effects. Excluding environmental and random factors, TFEE averages declined, aligning more closely with actual development trends, showing a gradual decrease from east to west. TFEE exhibited fluctuating growth with a trend moving from inefficient clusters to a more even distribution. Spatially, TFEE demonstrated aggregation effects and characteristics of space-time transition.Originality/valueThis research employs the three-stage super-efficiency SBM-DEA model to measure the total factor energy efficiency of the construction industry, taking into account external environment, random disturbances, and multiple effective decision-making units. It also evaluates energy efficiency changes before and after removing disturbances and comprehensively examines regional and temporal differences from static and dynamic, overall and phased perspectives. Additionally, Moran scatter plots and LISA cluster maps are used to objectively analyze the spatial agglomeration and factors influencing energy efficiency.

Enhancing multivariate, multi-step residential load forecasting with spatiotemporal graph attention-enabled transformer

Enhancing multivariate, multi-step residential load forecasting with spatiotemporal graph attention-enabled transformer

The spatiotemporal evolution of monogenetic scoria cones in the Paricutin-Tancítaro region, Mexico: Results from a Morpho-chronological analysis and its consequences on the distributed volcanic hazard

The spatiotemporal evolution of monogenetic scoria cones in the Paricutin-Tancítaro region, Mexico: Results from a Morpho-chronological analysis and its consequences on the distributed volcanic hazard

STFEformer: Spatial–Temporal Fusion Embedding Transformer for Traffic Flow Prediction

In the realm of Intelligent Transportation Systems (ITSs), traffic flow prediction is crucial for multiple applications. The primary challenge in traffic flow prediction lies in the handling and modeling of the intricate spatial–temporal correlations inherent in transport data. In recent years, many studies have focused on developing various Spatial–Temporal Graph Neural Networks (STGNNs), and researchers have also begun to explore the application of transformers to capture spatial–temporal correlations in traffic data. However, GNN-based methods mainly focus on modeling spatial correlations statically, which significantly limits their capacity to discover dynamic and long-range spatial patterns. Transformer-based methods have not sufficiently extracted the comprehensive representation of traffic data features. To explore dynamic spatial dependencies and comprehensively characterize traffic data, the Spatial–Temporal Fusion Embedding Transformer (STFEformer) is proposed for traffic flow prediction. Specifically, we propose a fusion embedding layer to capture and fuse both native information and spatial–temporal features, aiming to achieve a comprehensive representation of traffic data characteristics. Then, we introduce a spatial self-attention module designed to enhance detection of dynamic and long-range spatial correlations by focusing on interactions between similar nodes. Extensive experiments conducted on three real-world datasets demonstrate that STFEformer significantly outperforms various baseline models, notably achieving up to a 5.6% reduction in Mean Absolute Error (MAE) on the PeMS08 dataset compared to the next-best model. Furthermore, the results of ablation experiments and visualizations are employed to clarify and highlight our model’s performance. STFEformer represents a meaningful advancement in traffic flow prediction, potentially influencing future research and applications in ITSs by providing a more robust framework for managing and analyzing traffic data.

A novel full-resolution convolutional neural network for urban-fringe-rural identification: A case study of urban agglomeration region

A novel full-resolution convolutional neural network for urban-fringe-rural identification: A case study of urban agglomeration region

Spatial Analysis of Internally Displaced Persons’ Camps in Borno State, Nigeria

The situation in Borno State, Nigeria has been exacerbated by the ongoing insurgency led by Boko Haram in northeast Nigeria, leading to widespread displacement and the establishment of Internally Displaced Persons (IDP) camps. This study aims to identify the locations of IDP camps in Borno State and analyze their spatial distribution, density, and clustering patterns. Furthermore, it seeks to investigate the geospatial implications of these patterns. To achieve this, the study employs geospatial analysis techniques such as Kernel Density Estimation, Cluster and Outlier Analysis, Nearest Neighbour Analysis, and Spatial Autocorrelation Analysis. The data collected for this study includes location coordinates, satellite imagery, demographic information, and administrative maps. The findings reveal that the distribution of IDP camps in Borno State is non-random and demonstrates varying camp densities across senatorial districts, with predominant high-high clusters and a deliberate, non-random arrangement. These results align with spatial analysis theories, emphasizing the importance of recognizing hidden inequalities in IDP camps and informing targeted interventions and resource planning for a more effective and equitable humanitarian response in Borno State. The implications extend to policy planning and enhanced vulnerability assessment for effective humanitarian responses, stressing the need for tailored interventions based on identified hotspots that address root causes and implement both immediate and long-term solutions. Future research directions could involve more in-depth investigations into the influencing factors, socio-economic impacts, and temporal dynamics of spatial patterns in displacement.

Thalamic contributions to psychosis susceptibility: Evidence from co-activation patterns accounting for intra-seed spatial variability (μCAPs).

The temporal variability of the thalamus in functional networks may provide valuable insights into the pathophysiology of schizophrenia. To address the complexity of the role of the thalamic nuclei in psychosis, we introduced micro-co-activation patterns (μCAPs) and employed this method on the human genetic model of schizophrenia 22q11.2 deletion syndrome (22q11.2DS). Participants underwent resting-state functional MRI and a data-driven iterative process resulting in the identification of six whole-brain μCAPs with specific activity patterns within the thalamus. Unlike conventional methods, μCAPs extract dynamic spatial patterns that reveal partially overlapping and non-mutually exclusive functional subparts. Thus, the μCAPs method detects finer foci of activity within the initial seed region, retaining valuable and clinically relevant temporal and spatial information. We found that a μCAP showing co-activation of the mediodorsal thalamus with brain-wide cortical regions was expressed significantly less frequently in patients with 22q11.2DS, and its occurrence negatively correlated with the severity of positive psychotic symptoms. Additionally, activity within the auditory-visual cortex and their respective geniculate nuclei was expressed in two different μCAPs. One of these auditory-visual μCAPs co-activated with salience areas, while the other co-activated with the default mode network (DMN). A significant shift of occurrence from the salience+visuo-auditory-thalamus to the DMN + visuo-auditory-thalamus μCAP was observed in patients with 22q11.2DS. Thus, our findings support existing research on the gatekeeping role of the thalamus for sensory information in the pathophysiology of psychosis and revisit the evidence of geniculate nuclei hyperconnectivity with the audio-visual cortex in 22q11.2DS in the context of dynamic functional connectivity, seen here as the specific hyper-occurrence of these circuits with the task-negative brain networks.

Mapping dynamic spatial patterns of brain function with spatial-wise attention

Objective: Using functional magnetic resonance imaging (fMRI) and deep learning to discover the spatial pattern of brain function, or functional brain networks (FBNs) has been attracted many reseachers. Most existing works focus on static FBNs or dynamic functional connectivity among fixed spatial network nodes, but ignore the potential dynamic/time-varying characteristics of the spatial networks themselves. And most of works based on the assumption of linearity and independence, that oversimplify the relationship between blood-oxygen level dependence signal changes and the heterogeneity of neuronal activity within voxels. Approach: To overcome these problems, we proposed a novel spatial-wise attention (SA) based method called Spatial and Channel-wise Attention Autoencoder (SCAAE) to discover the dynamic FBNs without the assumptions of linearity or independence. The core idea of SCAAE is to apply the SA to generate FBNs directly, relying solely on the spatial information present in fMRI volumes. Specifically, we trained the SCAAE in a self-supervised manner, using the autoencoder to guide the SA to focus on the activation regions. Experimental results show that the SA can generate multiple meaningful FBNs at each fMRI time point, which spatial similarity are close to the FBNs derived by known classical methods, such as independent component analysis. Main results: To validate the generalization of the method, we evaluate the approach on HCP-rest, HCP-task and ADHD-200 dataset. The results demonstrate that SA mechanism can be used to discover time-varying FBNs, and the identified dynamic FBNs over time clearly show the process of time-varying spatial patterns fading in and out. Significance: Thus we provide a novel method to understand human brain better. Code is available at https://github.com/WhatAboutMyStar/SCAAE.

Archetypal analysis of COVID-19 in Montana, USA, March 13, 2020 to April 26, 2022.

Infectious disease data can often involve complex spatial patterns intermixed with temporal trends. Archetypal Analysis is a method to mine complex spatio-temporal data, and can be used to discover the dynamics of spatial patterns. The application of Archetypal Analysis to epidemiological data is relatively new, and here we present one of the first applications on COVID-19 data from March 13, 2020 to April 26, 2022, for the counties of Montana, USA. We present three views of the data set decomposed with Archetypal Analysis. First, we evaluate the entire 56 county data set. Second, we use a mutual information calculation to remove counties whose dynamics are mainly independent from the other counties, reducing the set to 17 counties. Finally, we analyze the top ten counties in terms of population size to focus on the dynamics in the large cities in the state. For each data set, we analyze four significant disease outbreaks across Montana. Archetypal Analysis uncovers distinct spatial patterns for each outbreak and demonstrates that each has a unique trajectory across the state.