The Surface Soil Moisture (SSM) products derived from microwave remote sensing have a coarse spatial resolution, therefore downscaling is required to obtain accurate SSM at high spatial resolution. An effective way to handle the stratified heterogeneity is to model for various stratifications, however the number of samples is often limited under each stratification, influencing the downscaling accuracy. In this study, a machine learning-based geostatistical model, which combines various ancillary information at fine spatial scale, is developed for spatial downscaling. The proposed support vector area-to-area regression kriging (SVATARK) model incorporates support vector regression and area-to-area kriging by considering the nonlinear relationships among variables for various stratifications. SVATARK also considers the change of support problem in the downscaling interpolation process as well as for solving the small sample size in trend prediction. The SVATARK method is evaluated in the Naqu region on the Tibetan Plateau, China to downscale the European Space Agency's (ESA) 25-km-resolution SSM product. The 1-km-resolution SSM predictions have been produced every 8 days over a six-year period (2010-2015). Compared with other five downscaling methods, the downscaled predictions from the SVATARK method performs the best with in-situ observations, resulting in a 24.4 percent reduction in root mean square error with 0.08 m3.m-3 and a 8.2 percent increase in correlation coefficient with 0.72, on average. Additionally, anomalously low SSM values, an indicator of drought, had a record low anomaly in mid-July for 2015, as noted by previous studies, indicating that SVATARK could be utilized for drought monitoring.

Spatial-temporal k-anonymity has become a mainstream approach among techniques for protection of users’ privacy in location-based services (LBS) applications, and has been applied to several variants such as LBS snapshot queries and continuous queries. Analyzing large-scale spatial-temporal anonymity sets may benefit several LBS applications. In this paper, we propose two location prediction methods based on transition probability matrices constructing from sequential rules for spatial-temporal k-anonymity dataset. First, we define single-step sequential rules mined from sequential spatial-temporal k-anonymity datasets generated from continuous LBS queries for multiple users. We then construct transition probability matrices from mined single-step sequential rules, and normalize the transition probabilities in the transition matrices. Next, we regard a mobility model for an LBS requester as a stationary stochastic process and compute the n-step transition probability matrices by raising the normalized transition probability matrices to the power n. Furthermore, we propose two location prediction methods: rough prediction and accurate prediction. The former achieves the probabilities of arriving at target locations along simple paths those include only current locations, target locations and transition steps. By iteratively combining the probabilities for simple paths with n steps and the probabilities for detailed paths with n-1 steps, the latter method calculates transition probabilities for detailed paths with n steps from current locations to target locations. Finally, we conduct extensive experiments, and correctness and flexibility of our proposed algorithm have been verified.

Analyzing large-scale spatial-temporal k-anonymity datasets recorded in location-based service (LBS) application servers can benefit some LBS applications. However, such analyses can allow adversaries to make inference attacks that cannot be handled by spatial-temporal k-anonymity methods or other methods for protecting sensitive knowledge. In response to this challenge, first we defined a destination location prediction attack model based on privacy-sensitive sequence rules mined from large scale anonymity datasets. Then we proposed a novel on-line spatial-temporal k-anonymity method that can resist such inference attacks. Our anti-attack technique generates new anonymity datasets with awareness of privacy-sensitive sequence rules. The new datasets extend the original sequence database of anonymity datasets to hide the privacy-sensitive rules progressively. The process includes two phases: off-line analysis and on-line application. In the off-line phase, sequence rules are mined from an original sequence database of anonymity datasets, and privacy-sensitive sequence rules are developed by correlating privacy-sensitive spatial regions with spatial grid cells among the sequence rules. In the on-line phase, new anonymity datasets are generated upon LBS requests by adopting specific generalization and avoidance principles to hide the privacy-sensitive sequence rules progressively from the extended sequence anonymity datasets database. We conducted extensive experiments to test the performance of the proposed method, and to explore the influence of the parameter K value. The results demonstrated that our proposed approach is faster and more effective for hiding privacy-sensitive sequence rules in terms of hiding sensitive rules ratios to eliminate inference attacks. Our method also had fewer side effects in terms of generating new sensitive rules ratios than the traditional spatial-temporal k-anonymity method, and had basically the same side effects in terms of non-sensitive rules variation ratios with the traditional spatial-temporal k-anonymity method. Furthermore, we also found the performance variation tendency from the parameter K value, which can help achieve the goal of hiding the maximum number of original sensitive rules while generating a minimum of new sensitive rules and affecting a minimum number of non-sensitive rules.

Applying spatial clustering algorithms on large-scale spatial interactive dataset to find urban hot/cold spots is a new idea to assist urban management. However, the research usually focuses on the dataset with spatio-temporal proximity, rather than remote dataset. This article proposes a spatial hot/cold spot detection method for human communication by auto-correlating the PageRank values of the spatial interaction networks constructed by records. Milan was selected as the study area, and the spatial interaction records reflected by telephone calls, the land-use dataset, and the POI dataset were used as experimental data. The results showed that the proposed method can be applied to long-distance spatial interactive recording data, and the hot/cold spot were clearly distinguished by the statistical distribution of the containing land-use dataset and the POI dataset. These differences were consistent with the actual situation in the study area, indicating the accuracy of the proposed method for detecting hot/cold areas.

Reliable radio coverage is essential for the deployment of 5G for railway (5G-R) communication systems in complex railway-line environments. Previous simulation- and measurement-based studies have mainly focused on main-track railway scenarios, while the propagation characteristics in railway-side obstructed environments remain insufficiently characterized. To address this gap, this paper investigates large-scale propagation characteristics using passive synchronization signal reference signal received power (SS-RSRP) measurements collected from a 5G-R test network. Typical railway-line scenarios, including open line-of-sight (LOS) propagation, building-obstructed railway-side sections, viaduct-blocked regions, and depot-like environments, are analyzed to reveal the influence of railway-side structures on large-scale signal behavior. A floating-intercept (FI) model is adopted to characterize scenario-dependent path loss, and a height-corrected FI refinement is further introduced for building-obstructed sections. The results show that local railway-side structures introduce distinct and quantifiable excess propagation loss beyond conventional distance-dependent path loss. The obtained model parameters can support large-scale propagation modeling, link-budget margin design, coverage-hole identification, and wireless coverage evaluation for 2100 MHz 5G-R systems in obstructed railway-side environments.

With the rise of location-based services applications, research on point-of-interest (POI) recommendation has gained significant attention. Conventional federated POI recommendation studies suffer from insufficient prediction accuracy. This study introduces a novel POI federated recommendation method: Spatio-Temporal self-Attention Recommendation for POI (STAR-POI). STAR-POI employs a spatiotemporal self-attention network to learn the spatial and temporal relationships between discontinuous check-ins, a projection enhancement gradient similarity index to cluster clients with their model updates. Model accuracy comparisons showed that STAR-POI outperforms three baseline models across all metrics, as it can capture more nuanced user behaviors. Further, variation analysis of model accuracy revealed the method's saturation points concerning the number of clients, evaluation metrics, data distribution in the study area, and the projection enhancement factor λ. These insights can guide the selection of optimal parameters for model training and POI recommendation.