Impact of Urban-Rural Integrated Development on Carbon Emission Intensity of Land Use: A Case Study of the Changsha–Zhuzhou–Xiangtan Urban Agglomeration by Li Xiaoqing, Zhou Guohua, Cui Shuqiang, Wang Hua, Liu Hui, Yu Xuexia

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A dynamic prediction method for surface mining subsidence based on dynamic probability integral model and Logistic model by Lei HAN, Xinxin QI, Tianjun WANG, Chunmin PEI, Xudong WANG

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Survival Prediction of Esophageal Cancer Using 3D CT Imaging: A Context-Aware Approach With Non-Local Feature Aggregation and Graph-Based Spatial Interaction by Fuce Guo, Chen Huang, Shengmei Lin, Yongmei Dai, Qianshun Chen, Shu Zhang, Xunyu XU

“…In the current study, we aimed to develop an effective EC survival risk prediction using only 3D computed tomography (CT) images.The proposed model consists of two essential components: 1) non-local feature aggregation module(NFAM) that integrates visual features from tumor and lymph nodes at both local and global scales, 2) graph-based spatial interaction module(GSIM) that explores the latent contextual interactions between tumors and lymph nodes.The experimental results demonstrate that our model achieves superior performance compared to state-of-the-art survival prediction methods, emphasizing its robust predictive capability. …”
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A Systematic Literature Review on the Application of Machine Learning for Predicting Stunting Prevalence in Indonesia (2020–2024) by Emilda Indrisari, Hidayat Febiansyah, Bambang Adiwinoto

“…The findings indicate that Random Forest, Support Vector Machine (SVM), and Artificial Neural Network (ANN) are the most frequently used algorithms, with prediction accuracy ranging from 72% to 99.92%. Dominant predictor variables include maternal education, economic status, sanitation, and spatial-temporal data. …”
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Enhanced streamflow prediction with SWAT using support vector regression for spatial calibration: A case study in the Illinois River watershed, U.S. by Lifeng Yuan, Kenneth J Forshay

“…However, the highly non-linear relationship between rainfall and runoff makes prediction difficult with desirable accuracy. To improve the accuracy of monthly streamflow prediction, a seasonal Support Vector Regression (SVR) model coupled to the Soil and Water Assessment Tool (SWAT) model was developed for 13 subwatersheds in the Illinois River watershed (IRW), U.S. …”
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Golden eagle optimized CONV-LSTM and non-negativity-constrained autoencoder to support spatial and temporal features in cancer drug response prediction by Wesam Ibrahim Hajim, Suhaila Zainudin, Kauthar Mohd Daud, Khattab Alheeti

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Precise Spatial Prediction of Rice Seedlings From Large-Scale Airborne Remote Sensing Data Using Optimized Li-YOLOv9 by Jayakrishnan Anandakrishnan, Arun Kumar Sangaiah, Hendri Darmawan, Nguyen Khanh Son, Yi-Bing Lin, Mohammed J. F. Alenazi

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Analyzing the determinant factors of spatial groundwater availability in the Akaki catchment, Central Ethiopia by Getamesay Nigussie, Mekuria Argaw, Dessie Nedaw, Tsegaye Tadesse, Andreas Hartmann

“…Hence, this study investigated the spatial availability of groundwater within the catchment by considering eight different factors. …”
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Predicting the spatial demand for public charging stations for EVs using multi-source big data: an example from jinan city, china by Qimeng Ren, Ming Sun

“…By using multi-source big data, this paper analyzes the population distribution, traffic organization, infrastructure, land use and regional economy of Jinan urban area, China, and constructs a comprehensive evaluation index system to predict the spatial demand of PCS for EVs. We analyse: (1) Distribution of population activities on weekday and rest days, the closeness and betweenness of road network, high-density area, commerce, public service facilities, parks, transportation facilities, residential area, building coverage, floor area ratio, economic development area and housing price level. (2) Correlation and influence weights of 14 evaluation indexes and PCS layout. (3) Prediction of spatial demand distribution of PCS. (4) Comparison of current PCS distribution and spatial demand prediction results. …”
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Spatial patterns and MRI-based radiomic prediction of high peritumoral tertiary lymphoid structure density in hepatocellular carcinoma: a multicenter study by Juan Chen, Xiong Chen, Kai Fu, Lan Zhou, Shichao Long, Mengsi Li, Linhui Zhong, Aerzuguli Abudulimu, Wenguang Liu, Deng Pan, Ganmian Dai, Yigang Pei, Wenzheng Li

“…This study aimed to elucidate biological differences related to pTLS density and develop a radiomic classifier for predicting pTLS density in HCC, offering new insights for clinical diagnosis and treatment.Methods Spatial transcriptomics (n=4) and RNA sequencing data (n=952) were used to identify critical regulators of pTLS density and evaluate their prognostic significance in HCC. …”
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Spatial Analysis of Dust Storms in Iran based on Climatic and Vegetation Characteristics by Farzaneh Borzou, Hasan Zolfaghari, Jafar Masoompour Samakosh, Jalil Sahraei

“…The present study aims to identify and spatially analyze the sources of Iran dust storms by using the National Centers for Environmental Prediction (NCEP/DOE); European Centre for Medium-Range Weather Forecasts operational (ECMWF) ERA-Interim reanalysis datasets and the records of 52 synoptic stations from 1984 to 2016. …”
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Graph neural network driven traffic prediction technology:review and challenge by Yi ZHOU, Shuting HU, Wei LI, Nan CHENG, Ning LU, Xuemin(Sherman) SHEN

“…With the rapid development of Internet of things and artificial intelligence technology, accurate analysis and prediction of traffic data have become the primary target of intelligent transportations.In recent years, the method of traffic forecasting has gradually changed from the classical model-driven type to the data-driven type.However, how to effectively analyze the spatial-temporal characteristics of road networks through big data is one of the key issues in the traffic prediction process.Spatiotemporal big data analysis is a powerful tool for the traffic prediction.The traffic network can be modeled as a graph network, while the deep learning method can be extended on the graph network.Utilizing graph neural networks, we can build the spatiotemporal prediction model, and obtain the spatial-temporal correlation between the sensor nodes in road networks effectively by using graph convolution, which can significantly improve the accuracy of traffic prediction models.The traffic forecasting technology driven by graph neural networks was explored, and two kinds of traffic prediction models based on the analysis of deep spatial-temporal characteristics were extracted.The actual cases were analyzed and evaluated to discuss the technical advantages and key challenges of graph neural networks in the traffic prediction.The potential issues of graph neural network driven prediction mechanisms were also excavated.…”
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Graph neural network driven traffic prediction technology:review and challenge by Yi ZHOU, Shuting HU, Wei LI, Nan CHENG, Ning LU, Xuemin(Sherman) SHEN

“…With the rapid development of Internet of things and artificial intelligence technology, accurate analysis and prediction of traffic data have become the primary target of intelligent transportations.In recent years, the method of traffic forecasting has gradually changed from the classical model-driven type to the data-driven type.However, how to effectively analyze the spatial-temporal characteristics of road networks through big data is one of the key issues in the traffic prediction process.Spatiotemporal big data analysis is a powerful tool for the traffic prediction.The traffic network can be modeled as a graph network, while the deep learning method can be extended on the graph network.Utilizing graph neural networks, we can build the spatiotemporal prediction model, and obtain the spatial-temporal correlation between the sensor nodes in road networks effectively by using graph convolution, which can significantly improve the accuracy of traffic prediction models.The traffic forecasting technology driven by graph neural networks was explored, and two kinds of traffic prediction models based on the analysis of deep spatial-temporal characteristics were extracted.The actual cases were analyzed and evaluated to discuss the technical advantages and key challenges of graph neural networks in the traffic prediction.The potential issues of graph neural network driven prediction mechanisms were also excavated.…”
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MODIFIED WEIGHT MATRIX USING PRIM’S ALGORITHM IN MINIMUM SPANNING TREE (MST) APROACH FOR GSTAR(1;1) MODEL by Nur'ainul Miftahul Huda, Fransiskus Fran, Yundari Yundari, Lisa Fikadila, Fauziah Safitri

“…The existence of a weight matrix is one of the aspects that established this model. The matrix illustrates the spatial impact that occurs between locations. …”
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Impact of low carbon orientation on green finance in highly polluted areas based on STIRPAT spatial panel model by Yunyan Yang

“…This study aims to analyze the impact of low-carbon orientation on green finance, clarify the intrinsic relationship between the two, and provide strategic recommendations for the development of low-carbon emission reduction finance in high pollution areas. To achieve the research objectives, this study used a random effects regression model and a spatial panel data model to conduct in-depth analysis of the carbon emission index in high pollution areas. …”
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Modelling and simulation of the block pouring construction system considering spatial–temporal conflict of construction machinery in arch dams by Zhipeng Liang, Jiayao Peng, Chunju Zhao, Huawei Zhou, Dongfeng Li, Yihong Zhou, Quan Liu, Xiaodong Li, Cheng Zhang, Fang Wang

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Analysis of the spatial pattern and causes of ecological environmental quality in Myanmar based on the RSEI model and the Geodetector-GCCM method by Shuangfu Shi, Shuangfu Shi, Shuangyun Peng, Shuangyun Peng, Zhiqiang Lin, Zhiqiang Lin, Bangmei Huang, Dongling Ma, Dongling Ma, Ziyi Zhu, Ziyi Zhu, Yilin Zhu, Yilin Zhu, Rui Zhang, Ting Li

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Modeling the effects of land use change on agricultural carrying capacity and food security by R. Harini, R. Rijanta, E.H. Pangaribowo, R.F. Putri, I. Sukri

“…Predictions of spatial land changes will reveal changes in land function, carrying capacity and food security between regions.METHODS: Land changes were studied using remote sensing imagery-based mapping methods and spatial simulations using the cellular automata approach. …”
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Topographic Position Index Predicts Within-Field Yield Variation in a Dryland Cereal Production System by Jacob A. Macdonald, David M. Barnard, Kyle R. Mankin, Grace L. Miner, Robert H. Erskine, David J. Poss, Sushant Mehan, Adam L. Mahood, Maysoon M. Mikha

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A spatially explicit containment modelling approach for escaped wildfires in a Mediterranean climate using machine learning by Gbenga Lawrence Alawode, Pere Joan Gelabert, Marcos Rodrigues

“…Effectively suppressing large wildfires requires a thorough understanding of containment opportunities across landscapes, to which empirical spatial modelling can contribute largely. The previous containment model in Catalonia failed to account for the crucial roles of weather conditions, lacked temporal prediction and could not forecast windows for containment opportunities, prompting this research. …”
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