Search Results (56)

A geographically and temporally weighted regression (GTWR) model is an effective tool for dealing with spatial heterogeneity and temporal non-stationarity simultaneously. As an important characteristic of spatiotemporal data, spatiotemporal autocorrelation should be considered when constructing spatiotemporally varying coefficient models. The proposed local autoregressive geographically and temporally weighted regression (GTWRLAR) model can simultaneously handle spatiotemporal autocorrelations among response variables and the spatiotemporal heterogeneity of regression relationships. The two-stage weighted least squares (2SLS) estimation can effectively reduce computational complexity. However, the weighted least squares estimation is essentially a Nadaraya–Watson kernel-smoothing approach for nonparametric regression models, and it suffers from a boundary effect. For spatiotemporally varying coefficient models, the three-dimensional spatiotemporal coefficients (longitude, latitude, and time) inherently exhibit larger boundaries than one-dimensional intervals. Therefore, the boundary effect of the 2SLS estimation of GTWRLAR will be more serious. A local–linear geographically and temporally weighted 2SLS (GTWRLAR-L) estimation is proposed to correct the boundary effect in both the spatial and temporal dimensions of GTWRLAR and simultaneously improve parameter estimation accuracy. The simulation experiment shows that the GTWRLAR-L method reduces the root mean square error (RMSE) of parameter estimates compared to the standard GTWRLAR approach. Empirical analyses of carbon emissions in China’s Yellow River Basin (2017–2021) show that GTWRLAR-L enhances the adjusted $R^2$ from 0.888 to 0.893. Full article

Under the combined influence of climate change and human activities, the non-stationarity of reservoir runoff has significantly intensified, posing challenges for traditional statistical models to accurately capture its multi-scale abrupt changes. This study focuses on Qianping (QP) Reservoir and systematically integrates climate-driven mechanisms with machine learning approaches to uncover the patterns of runoff evolution and develop high-precision prediction models. The findings offer a novel paradigm for adaptive reservoir operation under non-stationary conditions. In this paper, we employ methods including extreme-point symmetric mode decomposition (ESMD), Bayesian ensemble time series decomposition (BETS), and cross-wavelet transform (XWT) to investigate the variation trends and mutation features of the annual runoff in QP Reservoir. Additionally, four models—ARIMA, LSTM, LSTM-RF, and LSTM-CNN—are utilized for runoff prediction and analysis. The results indicate that: (1) the annual runoff of QP Reservoir exhibits a quasi-8.25-year mid-short-term cycle and a quasi-13.20-year long-term cycle on an annual scale; (2) by using Bayesian estimators based on abrupt change year detection and trend variation algorithms, an abrupt change point with a probability of 79.1% was identified in 1985, with a confidence interval spanning 1984 to 1986; (3) cross-wavelet analysis indicates that the periodic associations between the annual runoff of QP Reservoir and climate-driving factors exhibit spatiotemporal heterogeneity: the AMO, AO, and PNA show multi-scale synergistic interactions; the DMI and ENSO display only phase-specific weak coupling; while solar sunspot activity modulates runoff over long-term cycles; and (4) The NSE of the ARIMA, LSTM, LSTM-RF, and LSTM-CNN models all exceed 0.945, the RMSE is below 0.477 × 10⁹ m³, and the MAE is below 0.297 × 10⁹ m³, Among them, the LSTM-RF model demonstrated the highest accuracy and the most stable predicted fluctuations, indicating that future annual runoff will continue to fluctuate but with a decreasing amplitude. Full article

Land use and landscape changes undermine the balance between humans and the environment, threatening sustainable regional development, yet their driving mechanisms and future trends remain insufficiently understood, particularly in arid areas. This study establishes a long-term analytical framework for the temporal evolution and driving mechanisms of land use and landscape patterns in arid areas, based on Landsat remote sensing imagery and socio-economic data. We investigate spatiotemporal evolution trends, driving mechanisms, and spatial non-stationarity of regional landscapes, and apply the Patch-generating Land Use Simulation (PLUS) model to predict future landscape changes under business-as-usual (BAU), economic development (ED), and ecological protection (EP) scenarios. The results show that: (1) Grassland and unused land together account for over 80% of the total area. From 1990 to 2020, built-up land expanded by 1471.58 km², an increase of 190.09%. The comprehensive land use dynamic degree in the Urumqi–Changji–Shihezi (UCS) region was 0.22%, with the highest value observed between 2000 and 2010. (2) At the class level, spatial heterogeneity and fragmentation of different landscape types increased, enhancing regional landscape diversity. (3) Spatiotemporal changes in land use and landscape patterns were driven by the combined effects of natural factors, socio-economic conditions, and policy influences. (4) By 2030, under all three scenarios, unused land is expected to decrease, with the most significant reduction under the EP scenario. Grassland will increase most notably under the EP scenario, built-up land will expand, especially under the ED scenario, and cropland will also grow, mainly under the EP scenario. Forest and water areas will show slight decreases with minimal fluctuations. Overall, the proposed framework effectively captures the spatiotemporal dynamics and driving forces of land use and landscape changes, providing support for the formulation of long-term sustainable development policies. Full article

The Multiscale Geographically and Temporally Weighted Regression model overcomes the limitation of estimating spatiotemporal variation characteristics of regression coefficients for different variables under a single scale, making it a powerful tool for exploring the spatiotemporal scale characteristics of regression relationships. Currently, the most widely used estimation method for multiscale spatiotemporal geographically weighted models is the backfitting-based iterative approach. However, the iterative process of this method leads to a substantial computational burden and the accumulation of errors during iteration. This paper proposes a non-iterative estimation method for the MGTWR model, combining local linear fitting and two-step weighted least squares estimation techniques. Initially, a reduced bandwidth is used to fit a local linear GTWR model to obtain the initial estimates. Then, for each covariate, the optimal bandwidth and regression coefficients are estimated by substituting the initial estimates into a localized least squares problem. Simulation experiments are conducted to evaluate the performance of the proposed non-iterative method compared to traditional methods and the backfitting-based approach in terms of coefficient estimation accuracy and computational efficiency. The results demonstrate that the non-iterative estimation method for MGTWR significantly enhances computational efficiency while effectively capturing the scale effects of spatiotemporal variation in the regression coefficient functions for each predictor. Full article

Community regeneration plays a pivotal role in creating human-centered spaces by transforming spatial configurations, enhancing multifunctional uses, and optimizing designs that promote sustainability and vibrancy. However, the influence of such regeneration on spatial vitality—particularly its spatial heterogeneity and nonlinear effects—remains insufficiently explored. This study presents a comprehensive framework that combines the Difference-in-Differences (DID) method with multiple socio-spatial correlated factors, including place agglomeration, individual agglomeration, and social perception, offering a systematic assessment of urban vitality and evaluating the impact of regeneration interventions. By leveraging street-level imagery to capture environmental changes pre- and post-regeneration, this research applies Gradient Boosting Decision Tree Regression (GBDT) to uncover nonlinear built environment dynamics affecting urban vitality. Empirical analysis from six districts in Suzhou reveals the following: (1) A pronounced increase in urban vitality is seen in core areas, while peripheral districts exhibit more moderate improvements, highlighting spatially uneven regeneration outcomes. (2) In historically significant areas such as Wuzhong, limited vitality gains underscore the complex interplay among historical preservation, spatial configurations, and urban development trajectories. (3) Furthermore, environmental transformations, including variations in sky visibility, nonprivate vehicles, architectural elements, and the introduction of glass-wall structures, exhibit nonlinear impacts with distinct threshold effects. This study advances the discourse on sustainable urban regeneration by proposing context-sensitive, data-driven assessment tools that reconcile heritage conservation with contemporary urban regeneration goals. It underscores the need for integrated, adaptive regeneration strategies that align with local conditions, historical contexts, and urban development trajectories, informing policies that promote green, inclusive, and digitally transformed cities. Full article

The sustainable management of state-owned forest regions is significant for improving the nationally determined contribution and achieving carbon neutrality. The administrative area of key state-owned forest regions in northeast China and Inner Mongolia, hereafter referred to as forest regions, spans a forested area of 27.16 million hectares and a forest coverage rate of 82.97%. This represents China’s largest state-owned forest resource base, with extensive and concentrated forest areas. However, despite this vast forest coverage, the region’s forest stand density remains below the national and global average, underscoring the need for improved carbon sequestration performance. This study used the Stochastic Frontier Analysis (SFA) method to measure the carbon sequestration efficiency of key state-owned forest regions in northeast China and Inner Mongolia. A spatiotemporal Geographically and Temporally Weighted Regression model (GTWR) was employed to reveal the spatiotemporal non-stationarity of the driving mechanism of carbon sequestration efficiency. Finally, the benchmarking management method was applied to predict the carbon sequestration potential. The results indicated that the carbon sequestration efficiency of forest regions exhibited an overall increasing trend over time, with significant spatial and temporal heterogeneity among forest industry enterprises (forest farms). Specifically, the carbon sequestration efficiency ranked from highest to lowest is as follows: Greater Khingan Forestry Group, Inner Mongolia Forestry Industry Group, Longjiang Forestry Industry Group, Changbai Mountain Forestry Industry Group, Jilin Forestry Industry Group, and Yichun Forestry Industry Group. Furthermore, carbon sequestration efficiency was driven by both natural and socioeconomic factors, but the effects of these factors were spatiotemporally non-stationary. Generally, enterprise output value, labor compensation, tending, and accumulated temperature had positive effects on carbon sequestration efficiency, while capital structure, altitude, and precipitation had negative effects. Finally, our findings revealed that the carbon sequestration potential of forest regions is substantial. If technical efficiency is improved, the carbon sequestration potential of forest regions could expand by 0.86 times the current basis, reaching 31.29 mtCO₂ by 2030. These results underscore the importance of respecting the differences and conditionality of forest development paths and promoting the sustainable management of key state-owned forest regions through scientific approaches, which is crucial for achieving carbon neutrality goals. Full article

Classification of neurocognitive states from Electroencephalography (EEG) data is complex due to inherent challenges such as noise, non-stationarity, non-linearity, and the high-dimensional and sparse nature of connectivity patterns. Graph-theoretical approaches provide a powerful framework for analysing the latent state dynamics using connectivity measures across spatio-temporal-spectral dimensions. This study applies the graph Koopman embedding kernels (GKKE) method to extract latent neuro-markers of seizures from epileptiform EEG activity. EEG-derived graphs were constructed using correlation and mean phase locking value (mPLV), with adjacency matrices generated via threshold-binarised connectivity. Graph kernels, including Random Walk, Weisfeiler–Lehman (WL), and spectral-decomposition (SD) kernels, were evaluated for latent space feature extraction by approximating Koopman spectral decomposition. The potential of graph Koopman embeddings in identifying latent metastable connectivity structures has been demonstrated with empirical analyses. The robustness of these features was evaluated using classifiers such as Decision Trees, Support Vector Machine (SVM), and Random Forest, on Epilepsy-EEG from the Children’s Hospital Boston’s (CHB)-MIT dataset and cognitive-load-EEG datasets from online repositories. The classification workflow combining mPLV connectivity measure, WL graph Koopman kernel, and Decision Tree (DT) outperformed the alternative combinations, particularly considering the accuracy (91.7%) and F1-score (88.9%), The comparative investigation presented in results section convinces that employing cost-sensitive learning improved the F1-score for the mPLV-WL-DT workflow to 91% compared to 88.9% without cost-sensitive learning. This work advances EEG-based neuro-marker estimation, facilitating reliable assistive tools for prognosis and cognitive training protocols. Full article

The development of inner harbors has been accompanied by the destruction of natural landscapes, which in turn has led to numerous ecological problems. However, the temporal and spatial relationships between changes in the inner harbor landscape and ecological effects are not yet clear, and there are relatively few studies at smaller scales such as villages. In this study, we investigated Xieqian Harbor in Xiangshan County, along the eastern coast of China, and then analyzed the landscape change and evolutionary characteristics of the effects of carbon storage, soil conservation, and water yield at the village scale for the years 2000, 2010, and 2020. We then used the geographically and temporally weighted regression (GTWR) model to explore the spatiotemporal relationships between landscape variables and ecological effects. The results showed that the fragmentation and diversity of landscape patches increased from 2000 to 2020 due to reclamation and aquaculture, tourism development, and harbor construction, as reflected by the edge density (ED) and the Shannon diversity index (SHDI), which increased by 11.31% and 2.82%, respectively. This change resulted in a notable reduction of 572.6 thousand tons in carbon sequestration, 853 million tons in soil conservation, and 19 million cubic meters in water yield over the past 20 years. When temporal non-stationarity and spatial heterogeneity were combined, the relationship between landscape change and ecological effects became highly intricate, with varying responses across different time periods and locations. The area-weighted mean patch shape index (AWMSI) was a key factor affecting the three ecological effects. Our research confirmed that there was significant spatiotemporal heterogeneity in the effects of different landscape variables on ecological effects in inner harbors at the village scale. Compared with larger-scale studies, the results of village-scale studies revealed more precisely the impacts of localized landscape changes on ecological effects, providing support for the sustainable management of inner harbors and providing a new approach to integrating GTWR into landscape ecological time–space analysis research. Full article

Rapid economic growth in China has brought about a significant challenge: the widening gap in regional development. Addressing this disparity is crucial for ensuring sustainable development. However, existing studies have largely overlooked the intrinsic spatial and temporal dynamics of regional disparities on various levels. This study thus employed five advanced multiscale geographically and temporally weighted regression models—GWR, MGWR, GTWR, MGTWR, and STWR—to analyze the spatio-temporal relationships between ten key conventional socio-economic indicators and per capita GDP across different administrative levels in China from 2000 to 2019. The findings highlight a consistent increase in regional disparities, with secondary industry emerging as a dominant driver of long-term economic inequality among the indicators analyzed. While a clear inland-to-coastal gradient underscores the persistence of regional disparity determinants, areas with greater economic disparities exhibit pronounced spatio-temporal heterogeneity. Among the models, STWR outperforms others in capturing and interpreting local variations in spatio-temporal disparities, demonstrating its utility in understanding complex regional dynamics. This study provides novel insights into the spatio-temporal determinants of regional economic disparities, offering a robust analytical framework for policymakers to address region-specific variables driving inequality over time and space. These insights contribute to the development of targeted and dynamic policies for promoting balanced and sustainable regional growth. Full article

In active distribution networks (ADNs), the extensive deployment of distributed generations (DGs) heightens system nonlinearity and non-stationarity, which can weaken fault characteristics and reduce fault detection accuracy. To improve fault detection accuracy in distribution networks, a method combining improved multivariate variational mode decomposition (IMVMD) and YOLOv10 network for active distribution network fault detection is proposed. Firstly, an MVMD method optimized by the northern goshawk optimization (NGO) algorithm named IMVMD is introduced to adaptively decompose zero-sequence currents at both ends of line sources and loads into intrinsic mode functions (IMFs). Secondly, considering the spatio-temporal correlation between line sources and loads, a dynamic time warping (DTW) algorithm is utilized to determine the optimal alignment path time series for corresponding IMFs at both ends. Then, the Markov transition field (MTF) transforms the 1D time series into 2D spatio-temporal images, and the MTF images of all lines are concatenated to obtain a comprehensive spatio-temporal feature map of the distribution network. Finally, using the spatio-temporal feature map as input, the lightweight YOLOv10 network autonomously extracts fault features to achieve precise fault-line selection. Experimental results demonstrate the robustness of the proposed method, achieving a fault detection accuracy of 99.88%, which can ensure accurate fault-line selection under complex scenarios involving simultaneous phase-to-ground faults at two points. Full article

Investigating the spatiotemporal dynamics of vegetation net primary productivity (NPP) and its influencing factors are crucial for green and low-carbon development and facilitate human well-being in the Yellow River Basin (YRB). Although the research on NPP has advanced rapidly, in view of the regional particularity of the YRB, the persistence of its NPP change trend needs to be further discussed and more comprehensive impact factors need to be included in the analysis. Meanwhile, the spatial non-stationarity and scale effects of the impact on NPP when multiple factors are involved remain uncertain. Here, we selected a total of twelve natural and anthropogenic factors and used multi-scale geographically weighted regression (MGWR) to disentangle the spatial non-stationary relationship between vegetation NPP and related factors and identify the impact scale difference in the YRB. Additionally, we analyze the spatiotemporal variation trend and persistence of NPP during 2000–2020. The results revealed the following: (1) The annual NPP showed a fluctuating increasing trend, and the vegetation NPP in most regions will exhibit a future trend of increasing to decreasing. (2) The effects of different factors show significant spatial non-stationarity. Among them, the intensity of the impact of most natural factors shows a clear strip-shaped distribution in the east-west direction. It is closely related to the spatial distribution characteristics of natural factors in the YRB. In contrast, the regularity of anthropogenic influences is less obvious. (3) The impact scales of different factors on vegetation NPP were significantly different, and this scale changed with time. The factors with small impact scales could better explain the change in vegetation NPP. Interestingly, the impact size and scale of relative humidity on NPP in the YRB are both larger. This may be due to the arid and semi-arid characteristics of the YRB. Our findings could provide policy makers with specific and quantitative insights for protecting the ecological environment in the YRB. Full article

To objectively evaluate the road traffic safety levels across different provinces in China, this study investigated the spatiotemporal heterogeneity characteristics of macro factors influencing road traffic accidents. Panel data from 31 provinces in China from 2009 to 2021 were collected, and after data preprocessing, traffic accident data were selected as the dependent variables. Population size, economic level, motorization level, highway mileage, unemployment rate, and passenger volume were selected as explanatory variables. Based on the spatiotemporal non-stationarity testing of traffic accident data, three models, namely, ordinary least squares (OLS), geographically weighted regression (GWR), and geographically and temporally weighted regression (GTWR), were constructed for empirical research. The results showed that the spatiotemporal heterogeneity characterizing the macro factors of traffic accidents could not be ignored. In terms of impact effects, highway mileage, population size, motorization level and passenger volume had positive promoting effects on road traffic accidents, while economic level and unemployment rate mainly exhibited negative inhibitory effects. In terms of impact magnitude, highway mileage had the greatest impact on traffic accidents, followed by population size, motorization level, and passenger volume. Comparatively, the impact magnitude of economic level and unemployment rate was relatively small. The conclusions were aimed at contributing to the objective evaluation of road traffic safety levels in different provinces and providing a basis for the formulation of reasonable macro traffic safety planning and management decisions. The findings offer valuable insights that can be used to optimize regional traffic safety policies and strategies, thereby enhancing road safety. Full article

To optimize growth and management, precision agriculture relies on a deep understanding of agricultural dynamics, particularly crop water status analysis. Leveraging unmanned aerial vehicles, we can efficiently acquire high-resolution spatiotemporal samples by utilizing remote sensors. However, non-linear relationships among data features, localized within specific subgroups, frequently emerge in agricultural data. Interpreting these complex patterns requires sophisticated analysis due to the presence of noise, high variability, and non-stationarity behavior in the collected samples. Here, we introduce Local Biplot, a methodological framework tailored for discerning meaningful data patterns in non-stationary contexts for precision agriculture. Local Biplot relies on the well-known uniform manifold approximation and projection method, such as UMAP, and local affine transformations to codify non-stationary and non-linear data patterns while maintaining interpretability. This lets us find important clusters for transformation and projection within a single global axis pair. Hence, our framework encompasses variable and observational contributions within individual clusters. At the same time, we provide a relevance analysis strategy to help explain why those clusters exist, facilitating the understanding of data dynamics while favoring interpretability. We demonstrated our method’s capabilities through experiments on both synthetic and real-world datasets, covering scenarios involving grass and rice crops. Moreover, we use random forest and linear regression models to predict water status variables from our Local Biplot-based feature ranking and clusters. Our findings revealed enhanced clustering and prediction capability while emphasizing the importance of input features in precision agriculture. As a result, Local Biplot is a useful tool to visualize, analyze, and compare the intricate underlying patterns and internal structures of complex agricultural datasets. Full article

The ride-hailing market often experiences significant fluctuations in traffic demand, resulting in supply-demand imbalances. In this regard, the dynamic subsidy strategy is frequently employed by ride-hailing platforms to incentivize drivers to relocate to zones with high demand. However, determining the appropriate amount of subsidy at the appropriate time remains challenging. First, traffic demand exhibits high non-stationarity, characterized by multi-context patterns with time-varying statistical features. Second, high-dimensional state/action spaces contain multiple spatiotemporal dimensions and context patterns. Third, decision-making should satisfy real-time requirements. To address the above challenges, we first construct a Non-Stationary Markov Decision Process (NSMDP) based on the assumption of ride-hailing service systems dynamics. Then, we develop a solution framework for the NSMDP. A change point detection method based on feature-enhanced LSTM within the framework can identify the changepoints and time-varying context patterns of stochastic demand. Moreover, the framework also includes a deterministic policy deep reinforcement learning algorithm to optimize. Finally, through simulated experiments with real-world historical data, we demonstrate the effectiveness of the proposed approach. It performs well in improving the platform’s profits and alleviating supply-demand imbalances under the dynamic subsidy strategy. The results also prove that a scientific dynamic subsidy strategy is particularly effective in the high-demand context pattern with more drastic fluctuations. Additionally, the profitability of dynamic subsidy strategy will increase with the increase of the non-stationary level. Full article

In solar forecasting, it is common practice for solar data (be it irradiance or photovoltaic power) to be converted into a stationary index (e.g., clear-sky or clearness index) before being used as inputs for solar-forecasting models. However, its actual impact is rarely quantified. Thus, this paper aims to study the impact of including this processing step in the modeling workflow within the scope of very-short-term spatio-temporal forecasting. Several forecasting models are considered, and the observed impact is shown to be model-dependent. Persistence does not benefit from this for such short timescales; however, the statistical models achieve an additional 0.5 to 2.5 percentual points (PPs) in terms of the forecasting skill. Machine-learning (ML) models achieve 0.9 to 1.9 more PPs compared to a linear regression, indicating that stationarization reveals non-linear patterns in the data. The exception is Random Forest, which underperforms in comparison with the other models. Lastly, the inclusion of solar elevation and azimuth angles as inputs is tested since these are easy to compute and can inform the model on time-dependent patterns. Only the cases where the input is not made stationary, or the underperforming Random Forest model, seem to benefit from this. This indicates that the apparent Sun position data can compensate for the lack of stationarization in the solar inputs and can help the models to differentiate the daily and seasonal variability from the shorter-term, weather-driven variability. Full article