The rapid growth of tourist-generated content demands scalable and reliable quality assessment methods. This study introduces an LLM-driven framework that combines parameter-efficient fine-tuning and prompt engineering to evaluate content quality accurately and interpretably. Applied to 484,930 reviews from MaFengWo, TripAdvisor, and Ctrip, the approach achieves superior performance (RMSE=0.3040, NDCG@100=0.500, BERTScore=77.95%) with 9 × higher efficiency. Spatial-temporal-semantic analyses reveal platform-specific quality patterns: MaFengWo exhibits prominent spatial centrality and stable temporal cointegration; TripAdvisor demonstrates simplified core-periphery structures with high volatility; Ctrip presents dynamic multicentricity particularly in Shanghai. Two domestic platforms, MaFengWo and Ctrip, expose systematic deficiency on the theme ‘ Decision-making Plan ’ (92.9∼96.4% lacking operational suggestions), while international TripAdvisor emphasizes ‘ Practical Information ’ and ‘ Consumption Activity ’ but 40.76% neglects original viewpoints. Heterogeneous network analysis identifies the behavioral signatures of high-reliability user—preference attachment, quality stability, and profile homogeneity. This work bridges theoretical rigor with operational scalability, demonstrating the potential of LLMs in content governance for digital tourism.

GNSS coordinate time series reflects the combined influence of geophysical factors on stations around the land surface. Although some traditional parameterized methods are helpful to determine the magnitude of the seasonal signal at GNSS stations, the annual variation characteristics of the stations are not static, thus it is quite necessary to extract finer periodic signals with time-varying coefficients (PSTC) from stations’ position time series. This paper focuses on the height time series of 243 stations from the Crustal Movement Observation Network of China (CMONOC) and employs singular spectrum analysis (SSA) to extract PSTC. The results show that SSA method can effectively extract the time-varying trend and periodic terms from the original time series, which cannot be perfectly achieved by parameterized methods. SSA method reduces the RMSE value of the residual time series at 90.5% CMONOC stations, compared with the results of maximum likelihood estimation (MLE). Its function in extracting the PSTC from CMONOC stations is significant for further explaining the generation mechanism of the land surface nonlinear deformation in China. Different from MLE method which only considers the given epochs of offsets, SSA method can effectively fit the original time series through singular value decomposition (SVD) and signal reconstruction, despite there are unrecognized offsets contained in GNSS time series. It still works well when there is an offset up to 20 mm, which would reduce the traditional workload of offset detection by sight. SSA method manages to distinguish large unknown offsets, showing as negative improvement rates.

Short-term traffic forecasting is important for the development of an intelligent traffic management system. Critical to the performance of the traffic prediction model utilized in such a system is accurate representation of the spatiotemporal traffic characteristics. This can be achieved by integrating spatiotemporal traffic information or the dynamic traffic characteristics in the modeling process. The currently employed spatiotemporal k-nearest neighbor (STKNN) model is based on the spatial heterogeneity and adaptive spatiotemporal parameters of the traffic to improve the prediction accuracy. However, the non-stationary characteristics of the traffic cannot be fully represented by simply modeling the entire time range or all the time partitions based on experience. We therefore developed a dynamic STKNN model (D-STKNN) for short-term traffic forecasting based on the non-stationary spatiotemporal pattern of the road traffic. The different traffic patterns along the road are first automatically determined using an affinity propagation clustering algorithm. The Warped K-Means algorithm is then used to automatically partition the time periods for each traffic pattern. Finally, the D-STKNN model is developed based on the three-dimensional spatiotemporal tensor data models for the different road segments with different traffic patterns during different time periods. The D-STKNN model was verified through extensive experiments performed using actual vehicular speed datasets collected from city roads in Beijing, China, and expressways in California, U.S.A. The proposed model outperforms existing seven baselines in different time periods under different traffic patterns. The results confirmed the imperative of considering the non-stationary spatiotemporal traffic pattern in developing a model for short-term traffic prediction.

Missing data is a common problem in the analysis of geospatial information. Existing methods introduce spatiotemporal dependencies to reduce imputing errors yet ignore ease of use in practice. Classical interpolation models are easy to build and apply; however, their imputation accuracy is limited due to their inability to capture spatiotemporal characteristics of geospatial data. Consequently, a lightweight ensemble model was constructed by modelling the spatiotemporal dependencies in a classical interpolation model. Temporally, the average correlation coefficients were introduced into a simple exponential smoothing model to automatically select the time window which ensured that the sample data had the strongest correlation to missing data. Spatially, the Gaussian equivalent and correlation distances were introduced in an inverse distance-weighting model, to assign weights to each spatial neighbor and sufficiently reflect changes in the spatiotemporal pattern. Finally, estimations of the missing values from temporal and spatial were aggregated into the final results with an extreme learning machine. Compared to existing models, the proposed model achieves higher imputation accuracy by lowering the mean absolute error by 10.93 to 52.48% in the road network dataset and by 23.35 to 72.18% in the air quality station dataset and exhibits robust performance in spatiotemporal mutations.