Search Results (377)

As an important coal-producing region in China, open-pit coal mining in Shaoyang, Hunan Province, has a significant impact on the ecological environment. This study focuses on the three major open-pit mining areas in the city, utilizing remote sensing data from 1998 to 2024. By calculating the normalized difference vegetation index (NDVI) and fractional vegetation cover (FVC), and combining climate factors such as temperature and precipitation with Net Primary Productivity (NPP), this study analyzes the spatiotemporal evolution characteristics of vegetation cover and carbon sinks, and explores the impact of climate and environmental policies on vegetation recovery. The study employed trend analysis and autoregressive integrated moving average (ARIMA) model predictions, which showed that vegetation cover in the mining areas decreased overall from 1998 to 2011, gradually recovered after 2011, and reached a relatively high level by 2024. Changes in carbon sinks were consistent with the trends in vegetation cover. Spatially, the north mining area experienced the most severe vegetation degradation in the early stages, the middle area recovered earliest, and the south area had the fastest vegetation cover recovery rate. Climate factors had a certain influence on vegetation recovery, but precipitation, temperature, and FVC showed no significant correlation. The study indicates that vegetation recovery in mining areas is jointly influenced by mining intensity, climate conditions, and policy interventions, with geological environment management policies in Hunan mining areas playing a key role in promoting vegetation recovery. Full article

This study examines how hotel characteristics and urban spatial context influence tourist satisfaction in Seoul, South Korea, by integrating sentiment analysis of online reviews with regression modeling. Drawing on 4500 TripAdvisor reviews from 75 hotels, sentiment scores were extracted using aspect-based sentiment analysis, and two regression approaches, ordinary least squares (OLS) and spatial autoregressive combined models, were applied to evaluate how hotel specific features, such as the age and scale of the hotels and room rates, and their geographic characteristics, such as the proximity to airports and cultural landmarks, affect both emotional sentiment and formal hotel ratings. The OLS model for sentiment scores identified the scale and rating of the hotels as well as the proximity to the airports as key predictors. Additionally, the spatial autoregressive combined model was also statistically significant, suggesting spatial spillover effects. A separate model for the traditional rating revealed weaker associations, with only the hotel’s opening year reaching significance. These findings highlight a divergence between emotional responses and structured ratings, with sentiment scores more sensitive to spatial context. This study offers practical implications for hotel managers and urban planners, emphasizing the value of incorporating spatial factors into hospitality research to better understand the tourist experience. Full article

With the deep integration of edge computing and Internet of Things (IoT) technologies, the computational capabilities of intelligent edge cameras continue to advance, providing new opportunities for the local deployment of video understanding algorithms. However, existing video captioning models suffer from high computational complexity and large parameter counts, making them challenging to meet the real-time processing requirements of resource-constrained IoT edge devices. In this work, we propose EdgeVidCap, a lightweight video captioning model specifically designed for IoT edge cameras. Specifically, we design a hybrid module termed Synergetic Attention State Mamba (SASM) that incorporates channel attention mechanisms to enhance feature selection capabilities and leverages State Space Models (SSMs) to efficiently capture long-range spatial dependencies, achieving efficient spatiotemporal modeling of multimodal video features. In the caption generation stage, we propose an adaptive attention-guided LSTM decoder that can dynamically adjust feature weights according to video content and auto-regressively generate semantically rich and accurate textual descriptions. Comprehensive evaluations of EdgeVidCap on mainstream datasets, including MSR-VTT and MSVD are analyzed. Experimental results demonstrate that our system demonstrated enhanced precision relative to existing investigations, and our streamlined frame filtering mechanism yielded greater processing efficiency while creating more dependable descriptions following frame selection. Full article

Multivariate space–time datasets are often collected at discrete, regularly monitored time intervals and are typically treated as components of time series in environmental science and other applied fields. To effectively characterize such data in geostatistical frameworks, valid and practical covariance models are essential. In this work, we propose several classes of multivariate spatio-temporal covariance matrix functions to model underlying stochastic processes whose discrete temporal margins correspond to well-known autoregressive and moving average (ARMA) models. We derive sufficient and/or necessary conditions under which these functions yield valid covariance matrices. By leveraging established methodologies from time series analysis and spatial statistics, the proposed models are straightforward to identify and fit in practice. Finally, we demonstrate the utility of these multivariate covariance functions through an application to Kansas weather data, using co-kriging for prediction and comparing the results to those obtained from traditional spatio-temporal models. Full article

At a critical juncture in the global low-carbon transition, the role of artificial intelligence (AI) in facilitating low-carbon growth has become increasingly significant. To accelerate the integration of AI with socio-economic development, China has established National New Generation Artificial Intelligence Innovation and Development Pilot Zones (AIPZ). However, the specific impact of these zones on low-carbon development remains unclear. This study utilized panel data from 30 provinces in China from 2013 to 2022 and employed the multi-period difference-in-differences (DID) model and the spatial autoregressive difference-in-differences (SARDID) model to examine the carbon emissions reduction effects of the AIPZ policy and its spatial spillover effects. The findings revealed that the policy significantly reduced carbon emissions intensity (CEI) across provinces, with an average reduction effect of 6.9%. The analysis of the impact mechanism confirmed the key role of human, technological, and financial resources. Heterogeneity analysis indicated varying effects across regions, with more significant reductions in eastern and energy-rich areas. Further analysis using the SARDID model confirmed spatial spillover effects on CEI. This paper aims to enhance understanding of the relationship between AIPZ and CEI and provide empirical evidence for policymakers during the low-carbon transition. By exploring the potential of the AIPZ policy in emissions reduction, it proposes targeted strategies and implementation pathways for policymakers and industry participants to promote the sustainable development of China’s low-carbon economy. Full article

Communication delay, as a key factor restricting the rapid and accurate transmission of data in the smart grid, will affect the collaborative operation of power electronic devices represented by the Distributed Power Flow Controller (DPFC), and further affect the construction and safe and stable operation of the new power system. Aiming at the problem of DPFC communication delay prediction, this paper proposes a new SAR-ARIMA-LSTM hybrid prediction model. This model introduces the spatial autoregressive model (SAR) on the basis of the traditional ARIMA-LSTM model to extract the spatial correlation between devices caused by geographical location and communication load, and then combines ARIMA-LSTM prediction. The experimental structure shows that compared with the traditional ARIMA-LSTM model, the model proposed in this paper predicts that RMSE decreases from 1.59 to 1.2791 and MAE decreases from 1.27 to 1.0811, with a reduction of more than 14%. The method proposed in this paper can effectively reduce the communication delay prediction data of DPFC at different spatial positions, has a stronger ability to handle high-delay fluctuations, and provides a new technical approach for improving the reliability of the power grid communication network. Full article

Accurate dengue forecasting is essential for sustainable public health planning, especially in tropical regions where the disease remains a persistent threat. This study evaluates the predictive performance of seven modeling approaches—Seasonal Autoregressive Integrated Moving Average (SARIMA), Extreme Gradient Boosting (XGBoost), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), Convolutional LSTM (CNN–LSTM), and a Bayesian spatiotemporal model—using monthly dengue incidence data from 2009 to 2023 in Bandung City, Indonesia. Model performance was assessed using MAE, sMAPE, RMSE, and Pearson’s correlation (R). Among all models, the Bayesian spatiotemporal model achieved the best performance, with the lowest MAE (5.543), sMAPE (62.137), and RMSE (7.482), and the highest R (0.723). While SARIMA and XGBoost showed signs of overfitting, the Bayesian model not only delivered more accurate forecasts but also produced spatial risk estimates and identified high-risk hotspots via exceedance probabilities. These features make it particularly valuable for developing early warning systems and guiding targeted public health interventions, supporting the broader goals of sustainable disease management. Full article

Most existing precipitation data fusion methods rely on reliable precipitation values, such as those observed from ground-based rain gauges, to correct the satellite precipitation estimates (SPEs) that often involve systematic biases. However, such reliable data are rarely available in many regions of the world, especially in rugged terrain and hostile regions, rendering the correction suboptimal. To address this limitation, we propose a novel data fusion method—Triple Collocation Spatial Autoregression under Dirichlet distribution (TCSpAR-Dirichlet)—which eliminates the need for reliable data while still having the capability to effectively capture true precipitation patterns. The key idea in our method is using the variance of the precipitation estimates at each grid location obtained from each satellite to optimally leverage the associated satellite’s weight in data fusion, then characterizing the weights on all locations by a spatial autoregression model, and finally using the fitted weights to fuse the multi-sourced SPEs at all grid locations. We apply this method to SPEs in Nepal, which does not have ground gauges in many of its mountainous areas, to collect reliable precipitation data, to produce a fused precipitation dataset with uniform spatial coverage and high measurement accuracy. Full article

As shared micromobility grows quickly in metropolitan settings, e-scooter safety issues have become more urgent. This paper uses a Bayesian hierarchical model applied to census block groups in several Texas metropolitan areas to construct a spatial risk assessment methodology for e-scooter crashes. Based on crash statistics from 2018 to 2024, we develop a severity-weighted crash risk index and combine it with variables related to land use, transportation, demographics, economics, and other factors. The model comprises a geographically structured random effect based on a Conditional Autoregressive (CAR) model, which accounts for residual spatial clustering after capture. It also includes fixed effects for covariates such as car ownership and nightlife density, as well as regional random intercepts to account for city-level heterogeneity. Markov Chain Monte Carlo is used for model fitting; evaluation reveals robust spatial calibration and predictive ability. The following key predictors are statistically significant: a higher share of working-age residents shows a positive association with crash frequency (incidence rate ratio (IRR): ≈1.55 per +10% population aged 18–64), as does a greater proportion of car-free households (IRR ≈ 1.20). In the built environment, entertainment-related employment density is strongly linked to elevated risk (IRR ≈ 1.37), and high intersection density similarly increases crash risk (IRR ≈ 1.32). In contrast, higher residential housing density has a protective effect (IRR ≈ 0.78), correlating with fewer crashes. Additionally, a sensitivity study reveals that the risk index is responsive to policy scenarios, including reducing car ownership or increasing employment density, and is sensitive to varying crash intensity weights. Results show notable collision hotspots near entertainment venues and central areas, as well as increased baseline risk in car-oriented urban environments. The results provide practical information for targeted initiatives to lower e-scooter collision risk and safety planning. Full article

How to balance the relationship between economic development and environmental protection is a common challenge faced by developing countries. Based on panel data from 30 Chinese provinces between 2008 to 2021, we analyze the impact of economic growth targets (EGTs) on environmental pollution (EP) using a spatial autoregressive threshold panel (SARTP) model. The empirical findings are as follows. (1) A 1% increase in the EP index in adjacent provinces leads to a 0.5870% increase in the observing province. (2) For provinces with EGTs above 7.5%, a 1% increase in the EGT results in a 0.3799% increase in the EP index. Conversely, its impact on EP is not significant. (3) As EGTs increase, the EP effect intensifies in central provinces, weakens in western provinces, and remains insignificant in eastern provinces; the EP effect of EGTs is significantly greater in provinces with a large population size and a low proportion of tertiary industry. (4) When the provincial EGT exceeds the central target by 0.5%, a 1% increase in the EGT results in a 0.4469% increase in the EP index. Our paper offers theoretical and empirical insights for alleviating EP and promoting sustainable economic development. Full article

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

In the context of the new developmental philosophy, this study aimed to address the bottleneck of regional sustainable development; it constructs a three-system evaluation indicator system for Industrial Structure Optimization (ISO), Ecological Environment Management (EEM), and Socio-economic Development (SED), based on panel data from 20 cities in the Western Taiwan Straits Economic Zone between 2011 and 2023. To reveal how the synergistic development of the three subsystems in different domains can achieve sustainable development through their interactions and to analyze the dynamic patterns of the three subsystems, this study employed the panel vector autoregression (PVAR) model to examine the interactions between subsystems. Additionally, drawing on the framework of evolutionary economics, the study quantified the temporal evolution and spatial characteristics of the coupling coordination level among the three subsystems based on the results of the degree of coupling coordination model. The results indicate the following: (1) ISO shows a significant upward trend, EEM slightly declines, and SED experiences minor fluctuations before accelerating. (2) ISO, EEM, and SED exhibited self-reinforcing effects. (3) The degree of coupling, coordination, and coupling coordination all exhibit a trend of “fluctuating and increasing initially, followed by steady growth”. The spatial patterns of the degree of coupling, coordination, and coupling coordination have shifted from “decentralized” to “centralized”, with clear signs of synergistic development. (4) The difference in the degree of coupling coordination along the north–south direction remained the primary factor contributing to inter-regional disparities. Regions with the higher degrees of coupling coordination were concentrated in the southeastern coastal areas, while those with the lower degrees of coupling coordination appeared in the northeastern mountainous areas and southwestern coastal areas. (5) The spatial connection in the strength of the degree of coupling coordination has gradually increased, with notable intra-provincial connections and weakened inter-city connections across the province. The study’s results provided decision-making references for the construction of a sustainable development community. Full article

The eastern imperial eagle (Aquila heliaca) has shown a marked population increase in the past decades in Hungary. The breeding range is expanding towards homogeneous agricultural habitats of the Hungarian Plain, where the already existing and recently growing infrastructural network is thought to be one of the main factors limiting distribution. We used data from 508 breeding attempts between 1989 and 2008 to assess the effects of infrastructural networks on breeding distribution. We constructed a single cumulative infrastructure effect (CIE) variable based on the avoidance of different infrastructure types by eagles in the past 20 years. Conditional autoregressive models were built in a Bayesian framework to quantify the effects of infrastructures on the spatial breeding pattern in a pre-defined core study area. Both multivariate and CIE models were able to classify the presence of breeding attempts with high accuracy. The CIE variable was used to build a predictive distribution model for the Hungarian Plain. The results suggest that infrastructure has a significant local effect but does not necessarily hinder the future range expansion of imperial eagles, as two-thirds of the prediction area seems to be suitable for the species. The methods and results described enable conservation managers and policy makers to assess the trade-off between infrastructural development and nature conservation priorities. Full article

Landscape narrative theory, which focuses on the interaction between space, culture, and human experience, provides a practical and interdisciplinary framework for guiding the integration of culture and tourism. By incorporating storytelling elements into tourism planning, it helps transform static cultural assets into engaging visitor experiences. This approach is particularly relevant in provincial contexts where cultural resources are unevenly distributed. Taking Anhui Province, China, as a case study, this research builds a five-dimensional evaluation system covering culture–tourism economy, cultural resources, tourism resources, transportation accessibility, and policy support. Using spatial analytical methods such as Moran’s I and the Spatial Autoregressive (SAR) model, the study identifies clear spatial clustering patterns and influential factors. The SAR model results show that transportation accessibility (coefficient = 0.685, p < 0.01) and policy support (coefficient = 0.736, p < 0.01) significantly promote integration. In contrast, cultural resources (coefficient = −0.352, p < 0.01) and tourism resources (p ≈ 0.11) have limited or no significant direct economic impact. Based on these findings, this paper proposes targeted strategies such as building regional narrative networks, enhancing infrastructure and policy coordination, and fostering collaborative development. The key contribution of this study lies in applying landscape narrative theory at the provincial level to construct a “Theory–Indicators–Method–Strategy” framework, offering new perspectives for promoting high-quality regional culture–tourism integration. Full article

Spatial data not only enables smart cities to visualize, analyze, and interpret data related to location and space, but also helps departments make more informed decisions. We apply a Bayesian quantile regression (BQR) of the partially linear varying coefficient spatial autoregressive (PLVCSAR) model for spatial data to improve the prediction of performance. It can be used to capture the response of covariates to linear and nonlinear effects at different quantile points. Through an approximation of the nonparametric functions with free-knot splines, we develop a Bayesian sampling approach that can be applied by the Markov chain Monte Carlo (MCMC) approach and design an efficient Metropolis–Hastings within the Gibbs sampling algorithm to explore the joint posterior distributions. Computational efficiency is achieved through a modified reversible-jump MCMC algorithm incorporating adaptive movement steps to accelerate chain convergence. The simulation results demonstrate that our estimator exhibits robustness to alternative spatial weight matrices and outperforms both quantile regression (QR) and instrumental variable quantile regression (IVQR) in a finite sample at different quantiles. The effectiveness of the proposed model and estimation method is demonstrated by the use of real data from the Boston median house price. Full article