Spatial features of tumor-infiltrating lymphocytes in primary lesions of lung adenocarcinoma predict lymph node metastasis by Huibo Zhang, Ming Luo, Junwei Feng, Juan Tan, Yan Jiang, Dmitrij Frishman, Yang Liu

“…Random forest models incorporating clinical/pathological data with (M1) and without (M2) TIL features (TIL scores and sTILCs) were developed on a training cohort (N = 312) to predict LNM, and performance was compared across validation (N = 78) and independent test cohorts (N = 148). …”
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A High Accuracy Spatial Reconstruction Method Based on Surface Theory for Regional Ionospheric TEC Prediction by Jian Wang, Yi‐ran Liu, Ya‐fei Shi

“…Abstract In order to achieve more accurate spatial reconstruction of ionospheric total electron content (TEC) and promote improved satellite positioning and ranging applications, a high accuracy spatial reconstruction (HASR) method for TEC is proposed based on the surface theory. …”
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A deep learning short-term traffic flow prediction method considering spatial-temporal association by Yang ZHANG, Yue HU, Dongrong XIN

“…The short-term traffic flow prediction is too dependent on the time correlation characteristics, which due to the problems that the correlation factors of the spatial correlation characteristics are too complicated and difficult to quantify.In response to this defect, a deep learning short-term traffic flow prediction method considering spatial-temporal association was proposed.Firstly, by constructing a spatial association measurement function that simultaneously considers distance, flow similarity, and speed similarity, the spatial correlation between the target road segment and the surrounding associated road segments was quantified and predicted.Then, a convolutional neural network model with long short-term memory neurons embedded was constructed.The long short-term memory neurons were used to extract the temporal correlation characteristics between the data, and the spatial correlation metric and the convolution transmission of traffic data were used to extract the spatial correlation characteristics between the data, so as to realize the traffic flow prediction considering the spatial-temporal association.The experimental results show that the proposed method can adapt to short-term forecasting under different traffic flow characteristics such as weekdays and weekends, and the prediction accuracy is better than that of the classical methods.In weekdays and weekends, the forecast bias are 10.45% and 12.35% respectively.…”
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Improved and Interpretable Solar Flare Predictions With Spatial and Topological Features of the Polarity Inversion Line Masked Magnetograms by Hu Sun, Ward Manchester IV, Yang Chen

“…We found that using the new features can improve the skill scores of the flare classification model and new features tend to have higher feature importance, especially the spatial statistics features. …”
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Spatially predicting ecosystem service patterns in boreal drained peatlands forests using multisource satellite data by Kaapro Keränen, Anwarul Islam Chowdhury, Parvez Rana

“…Incorporating auxiliary variables from seven-year-old LiDAR data improved model R2 value by 1–24 %. We successfully scaled ESs predictions to map spatial distributions across the study area, with high ESs value in closed-canopy areas. …”
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Predicted Spatial Patterns of Suitable Habitats for <i>Troides aeacus</i> Under Different Climate Scenarios by Biyu Liu, Xinqi Deng, Zhiqian Liu, Xinju Wei, Honghua Zhang, Danping Xu, Zhihang Zhuo

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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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Linear attention based spatiotemporal multi graph GCN for traffic flow prediction by Yanping Zhang, Wenjin Xu, Benjiang Ma, Dan Zhang, Fanli Zeng, Jiayu Yao, Hongning Yang, Zhenzhen Du

“…This study introduces the Linear Attention Based Spatial-Temporal Multi-Graph Convolutional Neural Network (LASTGCN), a novel deep learning model tailored for traffic flow prediction. …”
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Assessment of Machine Learning Models for Predicting Aboveground Biomass in the Indian Subcontinent by S. Mamgain, B. Ghale, H. C. Karnatak, A. Roy

“…The predictions reveal significant spatial variation in biomass density, reflecting region's diverse ecological zones &amp; land-use patterns. …”
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Predicting communities with high tuberculosis case-finding efficiency to optimise resource allocation in Pakistan: comparing the performance of a negative binomial spatial lag model with a Bayesian machine-learning model by Hasan Tahir, Frank Cobelens, Christina Mergenthaler, Mirjam I Bakker, Tanveer Ahmed, Jake D Mathewson, Daniella Brals, Abdullah Latif, Stephanie Lako, Andreas Werle van der Merwe, Matthys Potgieter, Vincent Meurrens, Zia Samad, Ente Rood

“…A predictive negative binomial regression (NBR) model was created, and the presence of spatial autocorrelation was examined to account for spatial dependencies in the outcome variable. …”
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Interpretable machine learning unveils threshold responses and spatial patterns of global soil respiration by Junjie Jiang, Lingxia Feng, Junguo Hu, Chao Zhu

“…Additionally, we found significant biases in the annual Rs calculated by area weighting based on climate and ecosystem classifications because these factors characterise spatial heterogeneity differently. Such dynamics should be considered when modelling global Rs and analysing the results because they can help improve the estimation accuracy of global Rs prediction models.…”
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Integrating Machine Learning Techniques for Enhanced Safety and Crime Analysis in Maryland by Zeinab Bandpey, Soroush Piri, Mehdi Shokouhian

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Machine Learning and Deep Learning for Wildfire Spread Prediction: A Review by Henintsoa S. Andrianarivony, Moulay A. Akhloufi

“…The emergence of machine learning (ML) and, more specifically, deep learning (DL) has introduced new techniques that significantly enhance prediction accuracy. ML models, such as support vector machines and ensemble models, use tabular data points to identify patterns and predict fire behavior. …”
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Actual and Predictive Transport Modeling of Fluoride Contamination of the Sfax-Agareb Coastal Aquifer in the Mediterranean Basin by Samira Melki, Amina Mabrouk El Asmi, Mohamadou Ould Baba Sy, Moncef Gueddari

“…This study, using numerical modeling, gives new insights for short- and medium-term prediction of eventual fluoride concentrations in the saturated zone of the Sfax-Agareb aquifer.…”
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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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Predictive modeling of soil profiles for precision agriculture: a case study in safflower cultivation environments by Megha Sharma, Shailendra Goel, Ani A. Elias

“…The robustness, versatility, and predictive ability of two statistical learning models in correctly classifying the soil profile to clusters were tested. …”
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A monthly sub-national Harmonized Food Insecurity Dataset for comprehensive analysis and predictive modeling by Melissande Machefer, Michele Ronco, Anne-Claire Thomas, Michael Assouline, Melanie Rabier, Christina Corbane, Felix Rembold

“…The scientific community can also leverage the HFID to develop data-driven predictive models, enhancing the capacity to forecast and prevent future food crises.…”
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Artificial intelligence-based predictive modeling for imaging neutral particle analyzers on the DIII-D tokamak by Alvin Garcia, Sydney Liu, Xiaodi Du, Max Curie, Azarakhsh Jalalvand, Peter Steiner, Egemen Kolemen

“…This improvement highlights the model’s enhanced predictive accuracy for measured images from the validation set. …”
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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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SMOPCA: spatially aware dimension reduction integrating multi-omics improves the efficiency of spatial domain detection by Mo Chen, Ruihua Cheng, Jianuo He, Jun Chen, Jie Zhang

“…Despite these advancements, there is a notable lack of effective methods for modeling spatial multi-omics data. We introduce SMOPCA, a Spatial Multi-Omics Principal Component Analysis method designed to perform joint dimension reduction on multimodal data while preserving spatial dependencies. …”
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