SPATIALLY INFORMED INSIGHTS: MODELING PERCENTAGE POVERTY IN EAST JAVA PROVINCE USING SEM WITH SPATIAL WEIGHT VARIATIONS by Ashabul Akbar Maulana, Achmad Fauzan

“…Diverse weighting schemes are applied based on both distance (1) and contiguity (2). The optimal predictive model utilized is the Spatial Error Model (SEM) incorporating a Distance Band Weighing (DBW) mechanism with a designated maximum distance ( ) of 75000 meters. …”
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Integrating proximal geophysical sensing and machine learning for digital soil mapping: Spatial prediction and model evaluation using a small dataset by Danilo César de Mello, Gustavo Vieira Veloso, Murilo Ferre de Mello, Marcos Guedes de Lana, Isabelle de Angeli Oliveira, Fellipe Alcantara de Oliveira Mello, Rafael Gomes Siqueira, Lucas Carvalho Gomes, Elpídio Inácio Fernandes-Filho, Carlos Ernesto Gonçalves Reynaud Schaefer, Márcio Rocha Francelino, Emilson Pereira Leite, Tiago Osório Ferreira, José Alexandre Melo Demattê

“…In this research, we aimed to model and predict the spatial distribution of soil geophysical properties using parent material and terrain attributes with machine learning algorithms. …”
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SpatConv Enables the Accurate Prediction of Protein Binding Sites by a Pretrained Protein Language Model and an Interpretable Bio-spatial Convolution by Mingming Guan, Jiyun Han, Shizhuo Zhang, Hongyu Zheng, Juntao Liu

“…Traditional protein binding site prediction models usually extract residue features manually and then employ a graph or point-cloud-based architecture borrowed from other fields. …”
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Analysis and Prediction of Spatial and Temporal Land Use Changes in the Urban Agglomeration on the Northern Slopes of the Tianshan Mountains by Xiaoxu He, Zhaojin Yan, Yicong Shi, Zhe Wei, Zhijie Liu, Rong He

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Spatial and Temporal Characteristics of Land Use Changes in the Yellow River Basin from 1990 to 2021 and Future Predictions by Yali Cheng, Yangbo Chen

“…Studying spatial and temporal characteristics of land use changes and the driving factors in the Yellow River Basin as well as simulating and predicting future land use is crucial for resource management, ecological protection, and regional sustainable development in the Yellow River Basin. …”
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Predictive quality of census-based socio-economic indicators on Covid-19 infection risk at a fine spatial scale in France by Nicolas Romain-Scelle, Benjamin Riche, Thomas Benet, Muriel Rabilloud

“…Ten census-based ecological covariates were evaluated as predictors of case incidence using a Poisson regression with conditional autoregressive (CAR) spatial effects. Benefits of CAR effects and covariates on model predictive ability was assessed comparing posterior predictive distribution of case incidence with the observed value for each statistical unit. …”
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Revisiting the "satisfaction of spatial restraints" approach of MODELLER for protein homology modeling. by Giacomo Janson, Alessandro Grottesi, Marco Pietrosanto, Gabriele Ausiello, Giulia Guarguaglini, Alessandro Paiardini

“…The most frequently used approach for protein structure prediction is currently homology modeling. The 3D model building phase of this methodology is critical for obtaining an accurate and biologically useful prediction. …”
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Spatial Autoregressive Modeling on Linear Mixed Models for Dependency Between Regions by Timbang Sirait

“…In this study, we are concerned with the spatial lag or SAR models because dependency between variables of interest is easier to predict. …”
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Combining habitat selection, behavioural states, and individual variation to predict fish spatial usage near a barrier by Rachel Mawer, Jelger Elings, Stijn P. Bruneel, Ine S. Pauwels, Eliezer Pickholtz, Renanel Pickholtz, Johan Coeck, Peter L.M. Goethals

“…Model results were explored to assess the benefits of including behavioural state and understand state-specific habitat preferences, then cross-validated and used to develop an individual based model to predict fish spatial usage. …”
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Spatial-Temporal Coordination of Agricultural Quality and Water Carrying Capacity in Chengdu-Chongqing by Bingchang Li, Xinlan Liang, Cuihua Bian, Fengxin Sun, Zichen Xia, Binghao Sun, Ying Cao

“…Employing the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) model, obstacle degree analysis, standard deviational ellipse, and grey prediction modeling, the study finds that AQI exhibits a sustained upward trend—doubling in over half of the region’s cities—while WCI shows fluctuating growth, constrained by climatic extremes and uneven water distribution. …”
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Analysis of Temporal and Spatial Evolution Characteristics and Peak Prediction of Carbon Emissions in China Under the Dual-Carbon Target: A Case Study of Heilongjiang Province by Zhongxia Yu, Mingcong Zhang, Yingce Zhan, Yongxia Guo, Yuxian Zhang, Xiaoyan Liang, Chen Wang, Yuxin Fan, Mingfen Shan, Haiqing Guo, Wei Zhou

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Spatial analysis and prediction of psittacosis in Zhejiang Province, China, 2019–2024 by Zheyuan Ding, Haocheng Wu, Chen Wu, Kui Liu, Qinbao Lu, Xinyi Wang, Tianying Fu, Junjie Li, Ke Yang, Queping Song, Junfen Lin

“…This study aimed to characterize the epidemiological patterns and spatiotemporal distribution of psittacosis in Zhejiang Province, China, and to identify high-risk clusters through predictive modeling.MethodsWe conducted a comprehensive analysis of reported psittacosis cases in Zhejiang Province from January 2019 to June 2024. …”
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STGAT: Spatial–Temporal Graph Attention Neural Network for Stock Prediction by Ruizhe Feng, Shanshan Jiang, Xingyu Liang, Min Xia

“…Additionally, deep learning methods, especially temporal convolution networks and graph attention networks, have been introduced in this area and have achieved significant improvements in both stock price prediction and portfolio optimization. Therefore, this study proposes a Spatial–Temporal Graph Attention Network (STGAT) that integrates STL decomposition components and graph structures to model both temporal patterns and asset correlations. …”
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New multifactor spatial prediction method based on Bayesian maximum entropy by YANG Yong, ZHANG Chutian, HE Liyuan

“…Currently, the spatial distribution of soil properties is usually predicted with classical geostatistics or environmental correlation. …”
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A Hybrid Long Short-Term Memory-Graph Convolutional Network Model for Enhanced Stock Return Prediction: Integrating Temporal and Spatial Dependencies by Songze Shi, Fan Li, Wei Li

“…This study proposes a hybrid model integrating long short-term memory (LSTM) networks and graph convolutional networks (GCNs) to enhance accuracy by capturing both temporal dynamics and spatial inter-stock relationships. …”
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A reliability model to predict failure behaviour of overlying strata in groundwater-rich coal mine by Ruirui Li, Xiaowei Hou, Luwang Chen, Yingxin Wang, Fuyou Huang, Lanting Wang

“…In this study, a reliability model with consideration of spatial variability and uncertainty of strength parameters was proposed to predict the failure behaviour of overlying strata during coal mining in groundwater-rich coalfields. …”
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Spatiotemporal patterns and prediction of multi-region house prices via functional mixed effects model by Yilin Chen, Haitao Zheng

Subjects: “…house price prediction…”
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Assessing the spatial-temporal performance of machine learning in predicting grapevine water status from Landsat 8 imagery via block-out and date-out cross-validation by Eve Laroche-Pinel, Vincenzo Cianciola, Khushwinder Singh, Gaetano A. Vivaldi, Luca Brillante

“…The results of the study demonstrate that machine learning is accurate in predicting vine water status spatially within the training measurement dates with low errors (NRMSEΨstem = 2.7 %, NRMSEgs = 16.2 %, NRMSEAN = 11.2 %) and a high degree of accuracy (R2 greater than 0.8 in the prediction of all three measurements) as assessed by block-out cross-validation. …”
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Enhancing land use planning through integrating landscape analysis and flood inundation prediction Bekasi City’s in 2030 by Novia Fitriyati, Hadi Susilo Arifin, R. L. Kaswanto, Marimin

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Extended Generalized Skew Laplace Random Field: Spatial Autoregressive and Moving Average Model for Prediction of Missing Data in Skew and Heavy Tailed Data by Mohammad Mehdi Saber, Alireza Nematollahi, Mohsen Mohammadzadeh

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