No previous study has simultaneously examined the influence of diverse feature types, scales, and thresholds on simplification performance. We compared ten algorithms using six measures, seven datasets, and eleven thresholds. Results revealed that 'Ramer-Douglas-Peucker,' 'Sleeve-fitting,' and 'Before Opening Window' produced the largest changes in angularity and vector displacement, while minimizing percentage changes in coordinates and curvilinear segments. The opposite holds for Triangular Routine, Euclidean Distance, and Perpendicular Distance. Performance varies with feature type, scale, and threshold, revealing anomalous trends requiring further investigation. Statistical validation supports these findings, and guidelines help avoid misleading comparisons of newly developed algorithms.
To improve PPP-RTK reliability under ionospheric scintillation, this study proposes a ROTI-aided ionosphere-weighted PPP-RTK model. The Rate Of TEC Index detection method adaptively adjusts ionospheric constraints. On the server side, ionospheric activity for each receiver-satellite pair is monitored in real time, and spatial or temporal constraints are dynamically switched. On the user side, two strategies are offered: applying server ionospheric products directly or performing local ROTI-based monitoring. Experiments using Hong Kong Satellite Positioning Reference Network data on February 24, 2023, demonstrate that the ROTI-aided model effectively reduces positioning errors during scintillations, with the second strategy showing more significant improvements.
Land subsidence along the northern coast of Java, Indonesia, represents a critical geohazard due to its substantial socioeconomic impacts, particularly the exacerbation of coastal flooding and structural damage. Quantification of spatial and temporal variability in subsidence rates is imperative for effective mitigation strategies. This study aimed to analyze these patterns using precise geodetic observations. Daily GNSS data collected from five stations of the Ina-CORS network (Tegal, Pekalongan, Semarang, Jepara, and Rembang) were rigorously processed using GAMIT/GLOBK software. The results demonstrate heterogeneous land subsidence rates, with Semarang (-89.08 mm/yr), Pekalongan (-78.84 mm/yr), and Tegal (-20.33 mm/yr) exhibiting the highest subsidence rates. In contrast, Jepara and Rembang CORS stations remained relatively stable, indicating the influence of localized factors on the deformation mechanism.
This paper proposes an IFC-BIM enrichment approach that links process-related entities in the IFC standard with temporal information for 3D cadastral objects in multi-storey properties, enabling spatio-temporal (4D) cadastre. Unlike existing BIM-based cadastre models focused on spatial representation, the method associates time-based processes with legal and spatial objects in the IFC schema. The model was implemented on a real building using Revit, Blender, and IFC. The enriched IFC supports registration and visualization of 3D legal spaces, ownership zones, easements, and boundaries with time attributes. Evaluation by 21 experts yielded a score of 4.48/5, demonstrating applicability and consistency.
To reduce the calculation burden of GPS and BDS-2 triple-frequency precise point positioning (PPP), a short-term inter-frequency clock bias (IFCB) prediction method was proposed for the GPS Block IIF satellites and BDS-2 satellites. The important innovation of this method is to fit the period lag time relative to 24 hours instead of estimating the IFCB period, and only a short IFCB basic sequence is needed to perform high-precision forecasting with multiple days of lag. After applying these predicted IFCBs, the static/kinematic positioning accuracies can be improved by 16-25%/25-33% for GPS and by 10-19%/22-27% for GPS/BDS-2.
Predicting human mobility requires choices about how to represent people, places, and time. This paper provides a graph-construction and task-formulation framework for mobility analysis using passively collected trajectories. Using the Humob 2023 dataset, we instantiate three commonly used graph representations then use Graph Attention Networks (GAT) and Graph Convolutional Network (GCN) baselines to separate representation effects from architectural effects. The results indicate that representation and task definition dominate performance differences, while the gap between GAT and GCN is comparatively smaller in this setting. We distil practical considerations for selecting mobility graph representations and discuss limitations, especially the need for explicit dynamic-graph or sequence modelling when fine-grained temporal evolution is central.
Existing 3D spatial relation models depend on holistic bounding volume strategies, which cannot capture local vertical variations of morphologically complex objects. High uncertainty in topological reasoning also restricts their practical application. This paper presents 3D-SCRM, a 3D Spatial Comprehensive Relation Model based on Spatial Slicing, which discretizes 3D space into 2D vertical slices. It integrates three synergistic sub-models for directional, topological and distance relations, and adopts a compositional reasoning method to reduce topological ambiguity via directional and distance constraints. Unlike traditional models, 3D-SCRM precisely depicts dynamic spatial relation changes with altitude, supporting urban planning and 3D geospatial modeling.
This study presents the first comprehensive evaluation of SouthPAN-SBAS-aided real-time precise point positioning (RT-PPP) for high-rate applications. A single-axis shake table was used to simulate harmonic oscillations and earthquake-induced ground motions, and GNSS data was collected at 20 Hz. Solution accuracy was evaluated by comparison with relative GNSS positioning, post-processed PPP, conventional RT-PPP, and Linear Variable Differential Transformer (LVDT) reference. Results indicate that SBAS-aided RT-PPP accurately captures oscillation frequencies and dynamic displacements. These findings demonstrate the strong potential of SouthPAN SBAS-assisted RT-PPP for GNSS seismology and structural health monitoring in the Australia-New Zealand and broader Asia-Pacific region.
As spatial applications become increasingly complex, accurate 3D directional reasoning becomes essential. Existing models suffer from inaccuracies in handling complex 3D spatial relations. To address this issue, we propose the 3DR52 direction relation model, which improves directional representation by subdividing the exterior of the minimum bounding box. Experiments show it enhances accuracy over traditional methods. We further examine the dynamic properties of 3D direction relations, analyzing size variations, dynamic adjacent relations, and migration patterns,and propose an algorithm for continuity testing. Theoretical and experimental results confirm that the proposed approach effectively supports complex 3D dynamic direction reasoning.
Remote sensing has revolutionized forest inventory by enabling accurate, efficient and scalable assessments. This review focuses on two essential aspects: mapping individual trees through detection and crown delineation, and characterizing species composition. Recent advances in optical imagery, laser scanning-based sensing and multi-sensor data integration have significantly improved tree-level mapping and species discrimination. In addition, machine learning (ML) and deep learning (DL) models have enhanced analytical precision and classification performance. By synthesizing data sources, processing techniques and modelling approaches, this review provides a comprehensive overview of the state-of-the-art in remote sensing for forest inventory.
This study models the spatial and spatio-temporal relative risk of Dengue transmission in Bandung using Bayesian inference. Applying the Besag-York-Molli & eacute; (BYM) framework, we evaluated separable and inseparable space-time models. Results show that Integrated Nested Laplace Approximation (INLA) produces estimates comparable to Markov chain Monte Carlo (MCMC) but is significantly faster. Evaluated via Deviance Information Criterion (DIC), the INLA-estimated inseparable spatio-temporal model performed best (DIC: 1795.190), effectively identifying high-risk areas like Arcamanik, Lengkong, and southeastern Bandung. This highlights INLA's computational efficiency and the necessity of space-time interactions in robust disease mapping.
The growing complexity of spatial data management in Architecture, Engineering, and Construction requires integration of Building Information Modeling and Geospatial Information Systems. This study proposes a topological ontology that formally represents adjacency, intersection, and containment relationships to support spatial reasoning in built environments. Utilizing Semantic Web technologies, Industry Foundation Classes data are converted into Resource Description Framework to enhance interoperability and enable efficient spatial queries. A case study on a multi-story building demonstrates improved data management; RDF files are 12.8 times smaller than IFC files with sub-second performance for complex topological queries.
Research on urban agglomerations often models population density attenuation from centers to peripheries, yet some traditional methods overlook spatial interactions. This study examines China's 14 national urban agglomerations, tracing their evolution from monocentric to polycentric structures and strengthened inter-core collaboration, reflecting expanded resource agglomeration. It quantitatively identifies three key mechanisms: new core emergence, core diffusion, and spatial scale expansion, while also noting cases where collaboration remains limited.
This study was conducted to determine the impact of the Covid-19 on road traffic accidents across Turkey's primary roads from 2015 to 2022. The accidents were analyzed within a space-time cube, characterized by spatial bins of 5 and 10 kilometers and temporal steps of 1 and 3 months using Emerging Hot Spot Analysis. The temporal behavior of the data was evaluated using the Mann-Kendall and Sen's slope methods, while stationarity using the Augmented Dickey-Fuller. On national scale, there was a significant decrease in accidents by 27.19% from 2019 to 2020, in injuries by 29.68%, and in deaths by 14.39%.
In September 2023, Hong Kong experienced a severe rainfall event. This study, based on PPP-AR technology, used BDS/GPS/Galileo data from 9 reference stations in Hong Kong to retrieve ZTD, PWV, and tropospheric horizontal gradients. By combining IGS products, ERA5 data, and measured rainfall data, we analyze the spatiotemporal characteristics of water vapor parameters and their coupling mechanism with rainfall. The results show that the RMSE of the PPPAR-ZTD relative to IGS-ZTD ranges from 6.95 to 7.23 mm, and the RMSE of PPPAR-PWV relative to ERA5-PWV ranges from 2.23 to 4.53 mm, indicating acceptable accuracy. Prior to the heavy rainfall, water vapor accumulated steadily, peaking during the rainfall and gradually decreasing afterward. High PWV areas coincide with the center of intense rainfall. The horizontal gradient increases 0.5 to 3 hours in advance, and the gradient direction aligns with the shifting of the rainfall center. This study reveals the spatiotemporal evolution of water vapor and its response mechanism to extreme rainfall, providing insights for improving heavy rainfall prediction models..
Changes in land cover were analysed for seasonal (winter and summer) and spatiotemporal patterns of land surface temperature (LST) over the 1990-2023 period for the metropolitan city of Bulawayo in Zimbabwe using the Local Indices of Spatial Associations (LISA). Local Moran's I of the Normalized Difference Vegetation Index (NDVI) revealed that clustered vegetation cover was beneficial in reducing the LST in both the winter and summer seasons. However, as the city got more urbanized, the spatial clustering of heavily built-up areas raised the LST, as seen by the positive relationship between Getis-Ord Gi* of built-up areas (NDBI) and LST.
This study investigates the relationship between nighttime light (NTL) intensity and rural multidimensional poverty across 75 Moroccan provinces, using VIIRS and MODIS satellite data with spatial econometric models (SAR, SEM, SDM). A strong negative correlation (R-2 = 0.83, p < 0.01) confirms NTL as a reliable poverty proxy. Spatial spillover effects (gamma = 0.031, p < 0.05) highlight the influence of neighboring provinces, suggesting regionally interconnected poverty mechanisms. NTL decomposition underscores the role of agricultural areas development. Hotspot analysis reveals persistent deprivation in the northeast and south. The study supports data-driven, spatially informed strategies for rural poverty alleviation.
A geographically and temporally weighted regression model with a spatiotemporally autoregressive term in the response variable (termed LARGTWR model) can simultaneously handle spatiotemporal heterogeneity in the regression relationship and spatiotemporal autocorrelation in the response variable. Statistical inference issues related to spatiotemporal heterogeneity in both the regression relationship and the spatiotemporal lag term are crucial for accurately interpreting the spatiotemporal characteristics of the regression relationship and the response variable's autocorrelation. To this end, we propose two residual-based bootstrap tests using the local linear two-stage least squares (2SLS-LL) estimation method: one targeting spatiotemporal lag term heterogeneity and the other examining regression coefficient non-stationarity. Conducting simulation experiments aims to evaluate the performances of the proposed bootstrap tests. The results demonstrate that the proposed tests have satisfactory power and robustness to the model error distribution. Moreover, a real-life dataset is analyzed to demonstrate the application of the proposed tests.
