Characterizing US Spatial Connectivity and Implications for Geographical Disease Dynamics and Metapopulation Modeling: Longitudinal Observational Study by Giulia Pullano, Lucila Gisele Alvarez-Zuzek, Vittoria Colizza, Shweta Bansal

“…ObjectiveThis study aimed to address the questions that are critical for developing accurate transmission models, predicting the spatial propagation of disease across scales, and understanding the optimal geographical and temporal scale for the implementation of control policies. …”
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Spatial-Temporal Fusion Graph Neural Networks With Mixed Adjacency for Weather Forecasting by Ang Guo, Yanghe Liu, Shiyu Shao, Xiaowei Shi, Zhenni Feng

“…Rapidly accumulating, large-scale and long-term meteorological data provide unprecedented opportunities for data-driven meteorological models and fine-grained numerical weather prediction. …”
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Preliminary Mapping of the Spatial Variability in the Microclimate in Tropical Greenhouses: A Pepper Crop Perspective by Angel Triana, Alfonso Llanderal, Pedro García-Caparrós, Manuel Donoso, Rafael Jiménez-Lao, John Eloy Franco Rodríguez, María Teresa Lao

“…The objectives of this experiment were to (1) discern the spatial variability in climatic parameters within a greenhouse throughout different phenological stages of pepper cultivation and (2) develop an empirical model aimed at establishing predictive equations for temperature, relative humidity, vapor pressure deficit, and crop evapotranspiration (ETc) within the greenhouse considering the climatic parameters recorded on the outside. …”
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OPTIMIZING ROAD SAFETY: THE ROLE OF GEOGRAPHIC INFORMATION SYSTEMS (GIS) IN TRAFFIC ACCIDENT ANALYSIS AND PREDICTION by Mohammed Shukur Alfaras, Oğuz Karan, Sefer Kurnaz

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Random Cross-Validation Produces Biased Assessment of Machine Learning Performance in Regional Landslide Susceptibility Prediction by Chandan Kumar, Gabriel Walton, Paul Santi, Carlos Luza

“…Machine learning (ML) models are extensively used in spatial predictive modeling, including landslide susceptibility prediction. …”
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Application of Radionuclides Migration Software(MNS) in Radionuclides Diffusion Modeling by Yao LI, Rui-hao LI, Duo ZHOU, Xi CHEN, Yu-wei XU, Bo WANG

“…Pre-processing involves the construction of spatial models and parameter assignment, such as porosity, mineral density, radionuclide diffusion and migration parameters, and water flow velocity. …”
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Temporal–Spatial Partial Differential Equation Modeling of Land Cover Dynamics via Satellite Image Time Series and Sparse Regression by Ming Kang, Zheng Zhang, Zhitao Zhao, Keli Shi, Junfang Zhao, Ping Tang

“…Land cover dynamics play a critical role in understanding environmental changes, but accurately modeling these dynamics remains a challenge due to the complex interactions between temporal and spatial factors. …”
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Spatial Analysis of Rural Architecture Structure in Passive Defense by VIKOR Modeling; Case study: Yaseh Chai Village by Kourosh Momeni, Mostafa Mohebian, Elias Mavedat

“…Based on the results obtained from the VIKOR modeling, the spatial analysis of the architectural and urban structure of Yaseh Chai village is based on non-operational defense criteria, such as "hiding the village's appearance with local materials" and "predicting the stair-shaped form of houses to reduce damage caused by the destruction of houses" as well as "suitable village location based on suitable and fertile soil for agriculture, horticulture and farming to provide for the economic needs of the inhabitants." …”
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SCS-Net: Stratified Compressive Sensing Network for Large-Scale Crowd Flow Prediction by Xiaoyong Tan, Kaiqi Chen, Min Deng, Baoju Liu, Zhiyuan Zhao, Youjun Tu, Sheng Wu

“…However, achieving accurate and efficient prediction remains challenging. Most existing models overlook spatial heterogeneity, employing unified parameters to fit diverse crowd flow patterns across different spatial units, which limits their accuracy. …”
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A method for spatial interpretation of weakly supervised deep learning models in computational pathology by Abhinav Sharma, Bojing Liu, Mattias Rantalainen

“…Such information is also needed for any further spatial interpretation of predictions from such models. …”
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Spatial characterization of tertiary lymphoid structures as predictive biomarkers for immune checkpoint blockade in head and neck squamous cell carcinoma by Daniel A. Ruiz-Torres, Michael E. Bryan, Shun Hirayama, Ross D. Merkin, Evelyn Luciani, Thomas J. Roberts, Manisha Patel, Jong C. Park, Lori J. Wirth, Peter M. Sadow, Moshe Sade-Feldman, Shannon L. Stott, Daniel L. Faden

“…Tertiary Lymphoid Structures (TLS) have shown promising potential for predicting response to ICB. However, their exact composition, size, and spatial biology in HNSCC remain understudied. …”
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Surrogate model for calculating spatial magnetic track forces in permanent magnet levitation transportation by LIU Haodong, FENG Yang, ZHAO Chunfa, LUO Shihui, SONG Xiaolin

“…Developing a method for rapidly and accurately calculating magnetic forces, which considers multi-degree-of-freedom spatial attitudes, is essential for the dynamic prediction and assessment of PML vehicles. …”
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Spatial-Temporal Distribution Prediction of Electric Vehicle Charging Load Considering Charging Behavior and Real-Time SOC by ZHANG Linjuan, LI Wenfeng, XU Changqing, GUO Jianyu, ZHANG Xiawei, YUAN Jia, WANG Yaoqiang

“…[Methods] The influence of traffic conditions and ambient temperature on EV energy consumption and charging behavior is analyzed，and road traffic network and comprehensive energy consumption models are established. Based on the user's travel chain，the user's travel characteristics are analyzed，the shortest time method is used to plan the driving path，and a spatial-temporal distribution prediction model of the EV charging load is built considering the charging queue time and real-time SOC. …”
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Genetic Programming-Based Prediction Model for Microseismic Data by Man Wang, Hongwei Zhou, Dongming Zhang, Yingwei Wang, Weihang Du, Beichen Yu

“…Therefore, we collected a large amount of microseismic data obtained from the working faces of Shoushan Mine of Pingdingshan Coal Group in China and filtered the data, then we constructed a prediction model of microseismic data based on underground spatial three-dimensional coordinates using genetic programming (GP), which can realize real-time monitoring and disaster warning of microseismic signals. …”
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Spatial age-stratified epidemiological model with applications to South African COVID-19 pandemic by Raeesa Manjoo-Docrat, Nada Abdelatif, Jenny Holloway, Nontembeko Dudeni-Tlhone, Claudia Dresselhaus, Elona Mbayise, Charl Janse van Rensburg, Inger Fabris-Rotelli, Pravesh Debba, Sibusisiwe Makhanya

“…Results: The spatial age-stratified model produced more biologically plausible and accurate predictions compared to non-stratified models investigated. …”
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Temporal and Spatial Variation in Habitat Quality in Guangxi Based on PLUS-InVEST Model by Chuntian Pan, Jun Wen, Jianing Ma

“…This study systematically analyzes the spatial and temporal dynamics of land use and habitat quality in Guangxi from 2000 to 2020 using the PLUS-InVEST model and simulates future scenarios for 2030. …”
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Spatially Explicit Model for Assessing the Impacts of Groundwater Protection Measures in the Vicinity of the Hranice Abyss by Jozef Sedláček, Hana Vavrouchová, Kryštof Chytrý, Ondřej Ulrich, Petra Oppeltová, Milan Geršl, Kristýna Kohoutková, Radim Klepárník, Petr Kučera, Vítězslav Vlček, Jana Šimečková, Eva Žallmannová

“…The model employs a multi-criteria decision analysis, integrated with hydrological modeling and a high-resolution random forest-based prediction algorithm, to downscale land surface temperature (LST) in order to obtain high-resolution 1 × 1 m spatial results. …”
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Research on rock burst prediction based on an integrated model by Junming Zhang, Qiyuan Xia, Hai Wu, Sailei Wei, Zhen Hu, Bing Du, Yuejing Yang, Huaixing Xiong

“…Additionally, the sparrow search algorithm (SSA) is employed to optimize hyperparameters, further improving the model’s performance. Unlike traditional approaches that rely on time-axis-based analysis, this study uses the working face advancement distance as the basis for prediction, which better reveals the potential spatial correlations of rockburst occurrences, aligning with engineering practice needs.Validation using microseismic monitoring data from a coal mine demonstrates that the proposed model achieves a prediction accuracy of 93.62% and an F1-score of 93.54%. …”
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Increasing neural network robustness improves match to macaque V1 eigenspectrum, spatial frequency preference and predictivity. by Nathan C L Kong, Eshed Margalit, Justin L Gardner, Anthony M Norcia

“…They also suggest that it may be useful to penalize slow-decaying eigenspectra or to bias models to extract features of lower spatial frequencies during task-optimization in order to improve robustness and V1 neural response predictivity.…”
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Coupling coordination between agricultural carbon emission efficiency and food security in China: The spatial-temporal evolution and prediction. by Xixian Zheng, Wenmei Liao

“…Additionally, a Combination Forecasting Model predicts CCD trends through 2030. The findings indicate positive trends in both ACEE and FS, albeit with significant regional disparities and a notable lag of FS behind ACEE improvement. …”
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