FEN-MRMGCN: A Frontend-Enhanced Network Based on Multi-Relational Modeling GCN for Bus Arrival Time Prediction by Ting Qiu, Chan-Tong Lam, Bowie Liu, Benjamin K. Ng, Xiaochen Yuan, Sio Kei Im

“…The proposed module captures spatial relationships in dense, multi-route areas by using graph convolution layers based on multi-relational modeling to aggregate spatial information. …”
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Integration of deep learning and railway big data for environmental risk prediction models and analysis of their limitations by Liuhui Quan, Minjie Wang, Lyu Baihang, Zhang Ziwen, Zhang Ziwen, Zhang Ziwen

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The spatial resolution of epidemic peaks. by Harriet L Mills, Steven Riley

“…Here, we used a spatially-explicit stochastic meta-population model of arbitrary spatial resolution to determine the effect of resolution on model-derived epidemic trajectories. …”
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Spatial Association Network of Land-Use Carbon Emissions in Hubei Province: Network Characteristics, Carbon Balance Zoning, and Influencing Factors by Yong Huang, Zhong Wang, Heng Zhao, Di You, Wei Wang, Yanran Peng

“…This study constructs a LUCE spatial association network for Hubei Province using a modified gravity model to uncover the spatial linkages in carbon emissions. …”
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GeNetFormer: Transformer-Based Framework for Gene Expression Prediction in Breast Cancer by Oumeima Thaalbi, Moulay A. Akhloufi

“…<i>Background:</i> Histopathological images are often used to diagnose breast cancer and have shown high accuracy in classifying cancer subtypes. Prediction of gene expression from whole-slide images and spatial transcriptomics data is important for cancer treatment in general and breast cancer in particular. …”
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Exploring the Structure of Spatial Representations. by Tamas Madl, Stan Franklin, Ke Chen, Robert Trappl, Daniela Montaldi

“…We show that these learned metrics, together with a probabilistic model of clustering based on the Bayesian cognition paradigm, allow prediction of participants' cognitive map structures in advance. …”
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Modeling and predicting land use and land cover changes using remote sensing in tropical coastal ecosystems of southern Peru by Ellen Delgado, Elgar Barboza, Nilton B. Rojas-Briceño, Alexander Cotrina-Sanchez, Armando Valdés-Velásquez, Rocío López de la Lama, Camila Llerena-Cayo, Santiago de la Puente

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Parameter-Efficient Vehicle Trajectory Prediction Based on Attention-Enhanced Liquid Structural Neural Model by Ruochen Wang, Yue Chen, Renkai Ding, Qing Ye

“…In this paper, we propose a parameter-efficient trajectory prediction model that integrates Liquid Time-Constant (LTC) networks with attention mechanisms, termed the Attn-LTC model. …”
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Evaluating the Accuracy of Land-Use Change Models for Predicting Vegetation Loss Across Brazilian Biomes by Macleidi Varnier, Eliseu José Weber

“…Land-use change models are used to predict future land-use scenarios. …”
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Spatiotemporal Deformation Prediction Model for Retaining Structures Integrating ConvGRU and Cross-Attention Mechanism by Yanyong Gao, Zhaoyun Xiao, Zhiqun Gong, Shanjing Huang, Haojie Zhu

“…However, existing models often overlook the spatial deflection correlations among monitoring points. …”
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Spatial assessment employing fusion logistic regression and frequency ratio models to monitor landslide susceptibility in the upper Blue Nile basin of Ethiopia: Muger watershed by Samuel Hailu, Kiros Tsegay Deribew, Ermias Teferi, Mitiku Badasa Moisa, Zenebe Reta Roba, Shimelis Sishah Dagne, Muluneh Woldetsadik

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Active travel modelling: a methodological approach to networks for walking and cycling commuting analysis by Ivann Schlosser, Valentina Marin, Richard Milton, Elsa Arcaute, Michael Batty

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Influences of Sampling Design and Model Selection on Predictions of Chemical Compounds in Petroferric Formations in the Brazilian Amazon by Niriele Bruno Rodrigues, Theresa Rocco Barbosa, Helena Saraiva Koenow Pinheiro, Marcelo Mancini, Quentin D. Read, Joshua Blackstock, Edwin H. Winzeler, David Miller, Phillip R. Owens, Zamir Libohova

“…Relatively, RF, GLMET, and KNN performed better, compared to other models. The terrain attributes were significantly more successful as to the spatial predictions of the elements contained in laterites than were the remote sensing spectral indices, likely due to the fact that the underlying spatial structures of the two formations (laterite and talus) occur at different elevations.…”
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Real-Time Adaptive Traffic Flow Prediction Based on a GE-GRU-KNN Model by Xiangyu YI, Hongmei ZHOU, Shaopeng ZHONG

“…The results show that compared with traditional methods, the prediction error of this method is reduced by 1.08%–14.71%, indicating that the hybrid GE-GRU-KNN model exhibits good performance.…”
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A Deep Learning Model with Conv-LSTM Networks for Subway Passenger Congestion Delay Prediction by Wei Chen, Zongping Li, Can Liu, Yi Ai

“…Experimental results show that Conv-LSTM is better than the benchmark models in capturing spatial and temporal correlation.…”
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Improved digital mapping of soil texture using the kernel temperature–vegetation dryness index and adaptive boosting by Xu Zhai, Yuzhong Liu, Yuanyuan Hong, Yunjie Yang, Pengju Wang, Zhicheng Ye, Xiaoyan Liu, Tianlong She, Lihui Wang, Chen Xu, Lili Zhang, Qiang Wang

“…In this study, we collected 399 soil samples collected from Mingguang City in southeast China and made spatial predictions of soil texture based on remote sensing indices such as the kernel normalized difference vegetation index computed from Landsat8 data and topographic attributes computed via digital elevation model as environmental covariates. …”
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Multi-model learning for vessel ETA prediction in inland waterways using multi-attribute data by Abdullah Al Noman, Anton Zitnikov, Aaron Heuermann, Klaus-Dieter Thoben

“…Existing ETA prediction models largely rely on Automatic Identification System (AIS) data but often overlook additional factors. …”
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Spatiotemporal prediction of soil organic carbon density in Europe (2000–2022) using earth observation and machine learning by Xuemeng Tian, Sytze de Bruin, Rolf Simoes, Mustafa Serkan Isik, Robert Minarik, Yu-Feng Ho, Murat Şahin, Martin Herold, Davide Consoli, Tomislav Hengl

“…Prediction accuracy varies by land cover, depth interval and year of prediction with the worst accuracy for shrubland and deeper soils 100–200 cm. …”
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From Prediction to Explanation: Using Explainable AI to Understand Satellite-Based Riot Forecasting Models by Scott Warnke, Daniel Runfola

“…This study investigates the application of explainable AI (XAI) techniques to understand the deep learning models used for predicting urban conflict from satellite imagery. …”
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Predicting ecotopes from hydrodynamic model data: Towards an ecological assessment of nature-based solutions by Soesja Brunink, Gijs G. Hendrickx

“…Quantifying the current ecological state and future ecological shifts faces challenges, including variable dependencies, spatial-temporal disparities, and the limitations in available information. …”
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