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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Transient Stability Assessment Model With Sample Selection Method Based on Spatial Distribution by Yongbin Li, Yiting Wang, Jian Li, Huanbei Zhao, Huaiyuan Wang, Litao Hu

“…Sample selection aims to optimize the training set to speed up the training process while improving the preference of the TSA model. The typical samples which can accurately express the spatial distribution of the raw dataset are selected by the proposed method. …”
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Global air quality index prediction using integrated spatial observation data and geographics machine learning by Tania Septi Anggraini, Hitoshi Irie, Anjar Dimara Sakti, Ketut Wikantika

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A Bayesian Hierarchical Model for Estimation of Abundance and Spatial Density of Aedes aegypti. by Daniel A M Villela, Claudia T Codeço, Felipe Figueiredo, Gabriela A Garcia, Rafael Maciel-de-Freitas, Claudio J Struchiner

“…Our approach is useful in strategies such as population suppression or the displacement of wild vector populations by refractory Wolbachia-infected mosquitoes, since the invasion dynamics have been shown to follow threshold conditions dictated by mosquito abundance. The presence of spatially distributed abundance hotspots is also formally addressed under this modeling framework and its knowledge deemed crucial to predict the fate of transmission control strategies based on the replacement of vector populations.…”
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Norway’s electric vehicle revolution: unveiling greenhouse gas emissions reductions and material use of passenger cars across space and time by Lola S A Rousseau, Jan Sandstad Næss, Marine Lhuillier, Romain G Billy, Peter Schön, Edgar G Hertwich

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Vehicle Trajectory Prediction Algorithm Based on Hybrid Prediction Model with Multiple Influencing Factors by Tao Wang, Yiming Fu, Xing Cheng, Lin Li, Zhenxue He, Yuchi Xiao

“…In light of this limitation, we propose a vehicle trajectory prediction algorithm predicated on a hybrid prediction model. …”
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Research on the Evaluation and Spatial Distribution Optimization of the Field Intensity Effect of Rural Basic Education Infrastructure in Wuhan’s New Urban District: A Case Study o... by Liang Jiang, Jie Chen, Jing Luo, Guanghui Tian

Subjects: “…rural basic education infrastructure…”
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Advanced Heart Disease Prediction Through Spatial and Temporal Feature Learning with SCN-Deep BiLSTM by Vivek Pandey, Umesh Kumar Lilhore, Ranjan Walia

“…Abstract Heart disease prediction using machine learning methods faces various challenges, such as low data quality, missing irrelevant values, and underfit and overfit problems, which increase the time complexity and degrade the model's prediction performance. …”
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Multi-channel spatial-temporal traffic flow prediction based on hybrid static-dynamic graph convolution by Xiongtao ZHANG, Jingyu ZHENG, Qing SHEN, Danfeng SUN, Yunliang JIANG

“…Aiming at the problem that the traffic flow prediction model did not consider the correlation of road context and the dynamics of spatial dependency, a multi-channel spatial-temporal traffic flow prediction based on hybrid static-dynamic graph convolution (MHGCN) was proposed.A sandwich structure (i.e.multi-channel spatial module in the middle and temporal module on both sides) was used in the model to extract spatial-temporal features, and the multi-channel spatial module was divided into static graph convolution module and dynamic graph convolution module.The static graph convolution module simultaneously extracted specific and common features from topological spatial structures, semantic spatial structures, and their combinations.The dynamic graph convolution module assigned different weights to different features and extracts dynamic spatial features from unknown graph structures.In the temporal module, the multi-head attention mechanism was used to extract the global temporal features, and the temporal gating mechanism extracted the local temporal features.The model extracted spatial information from different spatial structures and temporal information from different time intervals to establish a global and comprehensive spatial-temporal relationship.The experimental results show that the MHGCN performs better than the existing traffic flow prediction models on four real world traffic flow datasets.…”
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Multi-featured spatial-temporal and dynamic multi-graph convolutional network for metro passenger flow prediction by Chuan Zhao, Xin Li, Zezhi Shao, HongJi Yang, Fei Wang

“…To address these challenges, we developed a novel model called the multi-featured spatial-temporal (MFST) and dynamic multi-graph convolutional network (DMGCN) model. …”
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Predicting the Spatial Distribution of Geological Hazards in Southern Sichuan, China, Using Machine Learning and ArcGIS by Ruizhi Zhang, Dayong Zhang, Bo Shu, Yang Chen

“…Geological hazards in Southern Sichuan have become increasingly frequent, posing severe risks to local communities and infrastructure. This study aims to predict the spatial distribution of potential geological hazards using machine learning models and ArcGIS-based spatial analysis. …”
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Method and experimental verification of spatial attitude prediction for an advanced hydraulic support system under mining influence by Zhuang Yin, Kun Zhang, ZengBao Zhang, Hongyue Chen, Lingyu Meng, Zhen Wang, Mingchao Du, Xiangpeng Hu, Defu Zhao, Dan Tian

“…A spatial attitude prediction method for the advanced hydraulic support group based on WOA-LSTM was proposed. …”
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Deep learning-based spatial analysis on tumor and immune cells of pathology images predicts MIBC prognosis. by Chao Hu, Fan Wang, Hui Xu, XiQi Dong, XiuJuan Xiong, Yun Zhang, TianCheng Zhao, YuanQiao He, LiBin Deng, XiongBing Lu

“…A convolutional neural network (CNN) classification model was constructed. Based on the definition of the border region of tumor cell nests, we assessed 12 spatial indicators for different patch types within, around and outside the tumor cluster. …”
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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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