Modeling the spatial distribution of African buffalo (Syncerus caffer) in the Kruger National Park, South Africa. by Kristen Hughes, Geoffrey T Fosgate, Christine M Budke, Michael P Ward, Ruth Kerry, Ben Ingram

“…Spatial distribution models were created using buffalo census information and archived data from previous research. …”
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Modeling consequences of spatial closures for offshore energy: Loss of fishing grounds and fishery‐independent data by M. Campbell, J. F. Samhouri, J. W. White

“…This produced two effects in the model: initial loss of fishery yield due to the closure and reductions in fishing effort when the loss of data triggered precautionary measures in the harvest control rule. …”
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Spatially varying parameters improve carbon cycle modeling in the Amazon rainforest with ORCHIDEE r8849 by L. Zhu, L. Zhu, L. Zhu, L. Zhu, P. Ciais, Y. Yao, D. Goll, S. Luyssaert, I. Martínez Cano, A. Fendrich, A. Fendrich, L. Li, H. Yang, S. Saatchi, W. Li, W. Li

“…<p>Uncertainty in the dynamics of the Amazon rainforest poses a critical challenge for accurately modeling the global carbon cycle. Current dynamic global vegetation models (DGVMs), which use one or two plant functional types for tropical rainforests, fail to capture observed biomass and mortality gradients in this region, raising concerns about their ability to predict forest responses to global change drivers. …”
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STVMamba: precipitation nowcasting with spatiotemporal prediction model by Maoyang Zou, Longrui Wen, Yuanyuan Huang, Yuan He, Jingzhong Xiao

“…The Spatial-Temporal Vision Mamba (STVMamba) is proposed, a novel spatiotemporal prediction model specifically designed for precipitation nowcasting. …”
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A multimodal model for protein function prediction by Yu Mao, WenHui Xu, Yue Shun, LongXin Chai, Lei Xue, Yong Yang, Mei Li

“…Protein structure provides richer spatial and functional insights, which can significantly improve prediction accuracy. …”
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Six-Dimensional Spatial Dimension Chain Modeling via Transfer Matrix Method with Coupled Form Error Distributions by Lu Liu, Xin Jin, Huan Guo, Chaojiang Li

“…The experimental validation on an aero-engine casing assembly shows that the SDC model captures multidimensional closed-loop spatial errors, with absolute errors of max–min closed-loop distances below 9.3 μm and coaxiality prediction errors under 8.3%. …”
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Synergistic Evolution and Spatial-Temporal Differences in Green Technological Innovation and Carbon Emission Reduction in the Construction Industry from the Perspective of New Prod... by Zihao Niu, Qingjie Xie

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Gradual Variation-Based Dual-Stream Deep Learning for Spatial Feature Enhancement With Dimensionality Reduction in Early Alzheimer&#x2019;s Disease Detection by Najmul Hassan, Abu Saleh Musa Miah, Taro Suzuki, Jungpil Shin

“…These features are processed through a dual-stream DL model, where each stream captures complementary spatial features. …”
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Building the optimal hybrid spatial Data-Driven Model: Balancing accuracy and complexity by Emanuele Barca, Maria Clementina Caputo, Rita Masciale

“…Based on these findings, we have developed a methodology that employs a series of statistical tests and data analytics to identify essential features hidden in spatial data in order to assess the predictive model (of white/grey kind) that best approximates underlying spatial processes. …”
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Drivers of Future Indian Ocean Warming and Its Spatial Pattern in CMIP Models by S. Gopika, K. Sadhvi, J. Vialard, V. Danielli, S. Neetu, M. Lengaigne

“…Abstract Coupled Model Intercomparison Project phases 5 and 6 (CMIP5/6) projections display substantial inter‐model diversity in the future tropical Indian Ocean warming magnitude and spatial pattern. …”
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Spatial Modeling of Trace Element Concentrations in PM₁₀ Using Generalized Additive Models (GAMs) by Mariacarmela Cusano, Alessandra Gaeta, Raffaele Morelli, Giorgio Cattani, Silvia Canepari, Lorenzo Massimi, Gianluca Leone

“…A stepwise procedure was followed to determine the model with the optimal set of covariates. A leave-one-out cross-validation method was used to estimate the prediction error. …”
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Temporal and spatial pattern analysis and forecasting of methane: Satellite image processing by Fatima Elshukri, Noor Hussam Abusirriya, Nathan Joseph Braganza, Abdulkarem Amhamed, Odi Fawwaz Alrebei

“…Atmospheric dispersion modeling is a critical tool in environmental research, offering insights into spatial and temporal patterns of pollutants. …”
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Reduced temporal and spatial stability of neural activity patterns predict cognitive control deficits in children with ADHD by Zhiyao Gao, Katherine Duberg, Stacie L. Warren, Li Zheng, Stephen P. Hinshaw, Vinod Menon, Weidong Cai

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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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Leveraging Spatial and Temporal Data to Predict Heavy Freight Vehicle Traffic Flow on Rural Road Network by Alireza Gholami, Seyedehsan Seyedabrishami

“…The extreme gradient boosting (XGBoost) model surpasses the time-series model in predictive accuracy, yielding average R-squared values of 84.7% and 85.8% on the test data for trucks and tractor-trailers, respectively. …”
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Digitally mapping soil health at regional scale: disentangling drivers and predicting spatial land use effects by Valentina Rubio, Joseph Amsili, David G. Rossiter, Andrew McDonald, Harold van Es

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Spatial Prediction of Soil Total Phosphorus in a Karst Area: Comparing GWR and Residual-Centered Kriging by Laimou Lu, Penghui Li, Liang Zhong, Mingbao Luo, Liyuan Xing, Chunlai Zhang

“…GWRK also achieved the highest R² (0.67), demonstrating robust predictive capability. MM_OK and MC_OK models performed well and showed smoother spatial transitions, while the OK model displayed the lowest predictive accuracy (62%). …”
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Spatial Weight Matrix Comparison of SAR-X Model using Casetti Approach by Almeira Tsanawafa, Dianne Amor Kusuma, Budi Nurani Ruchjana

“…The Spatial Autoregressive Exogenous (SAR-X) model with the Casetti approach is used to describe the influence of location and exogenous variables in the description and prediction of spatial observations, namely, people's habits and behavior towards culture in Java Island. …”
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Modelling the soil microclimate: does the spatial or temporal resolution of input parameters matter? by Anna Carter, Michael Kearney, Nicola Mitchell, Stephen Hartley, Warren Porter, Nicola Nelson

“…<div class="WordSection1"><p>The urgency of predicting future impacts of environmental change on vulnerable populations is advancing the development of spatially explicit habitat models. …”
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Unsupervised feature correlation-based spatial stratification for local context-aware modelling by Jinyu Meng, Zengchuan Dong, Yongze Song

“…Context-aware modelling improves the accuracy of spatial inferences through using local environmental conditions, spatial dependency, and heterogeneity. …”
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