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Yufeng Wang, Xue Chen and Feng Xue
Spatial epidemiology investigates the patterns and determinants of health outcomes over both space and time. Within this field, Bayesian spatiotemporal models have gained popularity due to their capacity to incorporate spatial and temporal dependencies, ...
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Yunfei Zhang, Fangqi Zhu, Qiuping Li, Zehang Qiu and Yajun Xie
Exploring spatiotemporal patterns of traffic accidents from historic crash databases is one essential prerequisite for road safety management and traffic risk prevention. Presently, with the emergence of GIS and data mining technologies, numerous geospat...
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Yusi Liu, Xiang Gao, Disheng Yi, Heping Jiang, Yuxin Zhao, Jun Xu and Jing Zhang
Massive taxi trajectory data can be easily obtained in the era of big data, which is helpful to reveal the spatiotemporal information of human travel behavior but neglects activity semantics. The activity semantics reflect people?s daily activities and t...
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Taghreed Alghamdi, Khalid Elgazzar and Taysseer Sharaf
Hierarchical Bayesian models (HBM) are powerful tools that can be used for spatiotemporal analysis. The hierarchy feature associated with Bayesian modeling enhances the accuracy and precision of spatiotemporal predictions. This paper leverages the hierar...
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Xu Zhang, Chao Song, Chengwu Wang, Yili Yang, Zhoupeng Ren, Mingyu Xie, Zhangying Tang and Honghu Tang
Understanding geospatial impacts of multi-sourced drivers on the tourism industry is of great significance for formulating tourism development policies tailored to regional-specific needs. To date, no research in China has explored the combined impacts o...
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Alejandro Ivan Aguirre-Salado, Humberto Vaquera-Huerta, Carlos Arturo Aguirre-Salado, José del Carmen Jiménez-Hernández, Franco Barragán and María Guzmán-Martínez
We introduced a novel spatial model based on the distribution of generalized extreme values (GEV) to analyze the maximum intensity levels of earthquakes with incomplete data (randomly censored) on the Pacific coast of southern Mexico using a random censo...
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Chawarat Rotejanaprasert and Andrew B. Lawson
Quantile modeling has been seen as an alternative and useful complement to ordinary regression mainly focusing on the mean. To directly apply quantile modeling to areal data the discrete conditional quantile function of the data can be an issue. Although...
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Jérémy Roos, Gérald Gavin, Stéphane Bonnevay
Pág. 53 - 61
We propose an approach to forecast the short-term passenger flows of the urban rail network of Paris. Based on dynamic Bayesian networks, this approach is designed to perform even in case of incomplete data. The structure of the model is built so that th...
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Yaseen A. Hamaamin, Amir Pouyan Nejadhashemi, Zhen Zhang, Subhasis Giri, Sean A. Woznicki
Pág. 1 - 15
Accurate and efficient estimation of streamflow in a watershed?s tributaries is prerequisite parameter for viable water resources management. This study couples process-driven and data-driven methods of streamflow forecasting as a more efficient and cost...
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Hwa-Lung Yu, Chih-Hsih Wang, Ming-Che Liu and Yi-Ming Kuo
Fine airborne particulate matter (PM2.5) has adverse effects on human health. Assessing the long-term effects of PM2.5 exposure on human health and ecology is often limited by a lack of reliable PM2.5 measurements. In Taipei, PM2.5 levels were not system...
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