Temporal and Spatial Evolution of Public–Private Partnership (PPP) Project Risks in China: 2003–2019 by Yanwei Wang, Lili Gong, Shanfeng Zheng, Xiaoqing Han, Jiajin Zhang, Yi Huang

“…Then the spatial variation, standard deviation ellipse, and gray dynamic model were used to analyze the spatial–temporal dynamic evolution characteristics of the risk level of PPP projects from 2003 to 2019 and to make reasonable predictions of the future spatial distribution pattern. …”
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Characteristics of Spatial and Temporal Evolution of Extreme Precipitation in Haihe River Basin and Analysis of Future Trends by LI Jia, HUANG Lingmei, SHEN Ao, SHI Rongqing, SHEN Manhua, YU Xiaobo

“…The results indicate that: ①The data simulation effect after Delta deviation correction is relatively good and is suitable for the prediction of extreme precipitation. ② In the historical period (1980–2014), in terms of temporal changes, the total annual precipitation, the number of days with precipitation exceeding 10 mm, and the maximum five-day precipitation show a significant upward trend, and other indicators increase but do not reach a significant level; in terms of spatial changes, the extreme precipitation index generally shows the pattern of "low in the northwest and high in the southeast". ③ In the future period (2021–2100), the extreme precipitation index will generally show a strengthening trend in terms of temporal changes. …”
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Vegetation resistance to compound drought and heatwave events buffers the spatial shift velocities of vegetation vulnerability by Wenbo Yan, Jian Zhou, Xiaopeng Wang, Jinyi Luo, Feiling Yang, Ruidong Wu

“…This elevates vegetation loss probability. Despite spatial shifts in vegetation loss probability being crucial for predicting spatial redistribution patterns of vegetation vulnerability across terrestrial ecosystems, they remain poorly understood under compound drought and heatwave events. …”
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Spatial Influence Analysis of Traffic Safety in Diverging Areas between Freeway Segments and Off Ramps by Cuiping Zhang, Xuedong Yan, Meiwu An, Hui Zhao

“…Data from a geocoded GIS crash database for Colorado Springs metropolitan area was used; the statistically significant factors associated with crash frequency were examined for the spatial influence of freeway diverging segments. Also, the generalized linear models with negative binomial link function were applied to predict the crash frequency for freeway diverging segments and off ramps based on the influential area. …”
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Hyperspectral Image Joint Super-Resolution via Local Implicit Spatial-Spectral Function Learning by Yanan Zhang, Jizhou Zhang, Sijia Han

“…The model consists of a deep feature encoder and a spatial-spectral intensity decoder. …”
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Ecosystem Services in Agricultural Landscapes: A Spatially Explicit Approach to Support Sustainable Soil Management by Mohsen Forouzangohar, Neville D. Crossman, Richard J. MacEwan, D. Dugal Wallace, Lauren T. Bennett

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Spatial and temporal variation of Antarctic microbial interactions: a study around the west Antarctic Peninsula by Swan L. S. Sow, Willem H. van de Poll, Rachel Eveleth, Jeremy J. Rich, Hugh W. Ducklow, Patrick D. Rozema, Catherine M. Luria, Henk Bolhuis, Michael P. Meredith, Linda A. Amaral-Zettler, Julia C. Engelmann

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Evaluating Land use Mixed-ness on Street Level through Spatial Analyses and Gini Method by Hamid Motieyan, Mohammad Azmoodeh

“…Therefore, in the first step, the layers of streets and land uses of the Valiasr neighborhood, located in District 6 of Tehran, which have the ability to perform spatial analysis, have been collected. In the second step, in order to present the calculation model, the streets that have a length of more than 200 meters, which play higher importance in the distribution of land uses in the neighborhood, are selected and form the layer of analysis. …”
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Spatiotemporal evolution and prediction of land use change and carbon storage in ionic rare earth mining areas based on the YOLOv11–SegFormer–InVEST–PLUS integrated model by Zhenghao Zhu, Shen Zhao, Qiangfeng Li, Zongbin Shi, Yuxuan Wu, Lu Wang

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Comparative Analysis of Prediction Models for Trawling Grounds of the Argentine Shortfin Squid <i>Illex argentinus</i> in the Southwest Atlantic High Seas Based on Vessel Position... by Delong Xiang, Yuyan Sun, Hanji Zhu, Jianhua Wang, Sisi Huang, Shengmao Zhang, Famou Zhang, Heng Zhang

“…A CNN-Attention deep learning model was applied to each dataset to develop <i>Illex argentinus</i> trawling ground prediction models. …”
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Characteristics of Flux Footprint over Typical Underlying Surface of Qinghai-Xizang Plateau by Zixin WANG, Lei ZHONG, Yaoming MA, Yunfei FU

“…The heterogeneity of the underlying surface affects the accuracy and representativeness of the land-atmosphere flux observation.The study on the flux footprint distribution of complex underlying surface over Qinghai-Xizang Plateau （QXP） is of great significance to the observation and simulation of land-atmosphere interaction and its influence on weather and climate.Flux footprint analysis plays a pivotal role in investigating the spatial representativeness of flux observation information.The Flux Footprint Prediction （FFP） model represents a proficient methodology for computing the flux footprint.Based on the observation data from multiple research stations， including the Qomolangma Atmospheric and Environmental Observation and Research Station， the Ngari Desert Observation and Research Station， the Nam Co Monitoring and Research Station for Multisphere Interactions， the Muztagh Ata Westerly Observation and Research Station， the Southeast Tibet Observation and Research Station for the Alpine Environment in 2013， the FFP model was utilized to investigate the sensitivity of model parameters concerning flux footprint distribution.Additionally， the spatiotemporal characteristics and specific influencing factors of flux footprint distribution at different stations were discussed， thereby providing valuable insights for the erection of future observing stations.The results reveal that the primary determinants of flux footprint are measurement height， wind speed and wind direction.Characterized by an underlying surface of evergreen coniferous forest， flux footprint at Linzhi station exhibits greater sensitivity to measurement height and planetary boundary layer depth compared to the other stations.In the QXP， the spatial extent of the flux footprint derived from the ultrasonic anemometer measurements ranges from approximately 250 m to 500 m.Among the five stations， Qomo station exhibited the lowest frequency of stable stratification times during daytime， representing 15.69% of the daytime data points， whereas Ali station had the lowest occurrence of unstable stratification times during nighttime， comprising for 13.32% of the nighttime data points.At these five stations on the TP， the nocturnal flux footprints demonstrate greater width and extent compared to their daytime counterparts.In summer， due to the influence of monsoon， the axis of flux footprint tends to be more consistent.Lake-land breeze at Nam Co station is the main factor affecting flux footprint， whereas glacier wind at Qomo station is the dominant factor.Linzhi station possesses the smallest footprint due to the smallest mean wind speed， thus demonstrating the highest level of representativeness among these five stations.Lowering the height of observation instruments at Qomo and Nam Co stations could potentially enhance the representativeness of in situ measurements.…”
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Impact of Underground Space Height and BMI on Children’s Fatigue During Ascending Evacuation: An Experimental Study and Intelligent Assistive Implications by Ming Liu, Hu Zhang, Xin Guo, Yongbo Feng, Xiaochen Zhao, Changzheng Xuan, Xiaohu Jia

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Comprehensive prediction of potential spatiotemporal distribution patterns, priority planting regions, and introduction adaptability of Elymus sibiricus in the Chinese region by Huan-Huan Lu, Yu-Ying Zheng, Yong-Sen Qiu, Liu-Ban Tang, Yan-Cui Zhao, Wen-Gang Xie

“…In this study, the geographical distribution and environmental data of E. sibiricus in China were collected, and the potential spatiotemporal distribution pattern, planting pattern, and introduction adaptability of E. sibiricus were comprehensively predicted by using ensembled ecological niche model and Marxan model. …”
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Current and Projected Future Spatial Distribution Patterns of <i>Prunus microcarpa</i> in the Kurdistan Region of Iraq by Renas Y. Qadir, Nabaz R. Khwarahm

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A Machine Learning Approach to Adapt Local Land Use Planning to Climate Change by Julia Forster, Stefan Bindreiter, Birthe Uhlhorn, Verena Radinger-Peer, Alexandra Jiricka-Pürrer

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IoT-Based Traffic Prediction for Smart Cities by Zhinong Miao, Qilong Liao

“…The primary objective was to develop a predictive model that improves traffic forecasting accuracy, reduces congestion, and optimizes real-time traffic management. …”
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Unifying spatiotemporal and frequential attention for traffic prediction by Qi Guo, Qi Tan, Jun Tang, Benyun Shi

“…By leveraging deep learning to capture spatial correlations in traffic flow and applying spectral analysis to fuse time series data with underlying periodic correlations in both the time and frequency domains, we develop an innovative traffic prediction model called the Space-Time-Frequency Attention Network (STFAN). …”
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Intelligent Prediction and Numerical Simulation of Landslide Prediction in Open-Pit Mines Based on Multi-Source Data Fusion and Machine Learning by Li Qing, Linfeng Xu, Juehao Huang, Xiaodong Fu, Jian Chen

“…This model allows for a more precise analysis of the lithology and fault spatial distances. …”
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Regularity and predictability of human mobility in personal space. by Daniel Austin, Robin M Cross, Tamara Hayes, Jeffrey Kaye

“…Studying a data set of almost 15 million observations from 19 adults spanning up to 5 years of unobtrusive longitudinal home activity monitoring, we find that in-home mobility is not well represented by a universal scaling law, but that significant structure (predictability and regularity) is uncovered when explicitly accounting for contextual data in a model of in-home mobility. …”
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Spatial Distribution and Management of Trace Elements in Arid Agricultural Systems: A Geostatistical Assessment of the Jordan Valley by Mamoun A. Gharaibeh, Bernd Marschner, Nicolai Moos, Nikolaos Monokrousos

“…Trace element concentrations were consistently higher in TWW-irrigated soils, although all values remained below WHO/FAO recommended thresholds for agricultural use. Spatial modeling was conducted using both ordinary kriging (OK) and empirical Bayesian kriging (EBK), with EBK showing greater prediction accuracy based on cross-validation statistics. …”
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