Relevant bibliographies by topics / Hierarchical spatial modeling

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  1. Journal articles

Academic literature on the topic 'Hierarchical spatial modeling'

Author: Grafiati
Published: 11 January 2025

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Journal articles on the topic "Hierarchical spatial modeling"

1

Lawson, Andrew B. "Hierarchical modeling in spatial epidemiology." Wiley Interdisciplinary Reviews: Computational Statistics 6, no. 6 (2014): 405–17. http://dx.doi.org/10.1002/wics.1315.

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2

Gelfand, Alan E. "Hierarchical modeling for spatial data problems." Spatial Statistics 1 (May 2012): 30–39. http://dx.doi.org/10.1016/j.spasta.2012.02.005.

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3

Timiryanova, Venera, Kasim Yusupov, and Ruzel Salimyanov. "Relationship Between Consumption and Personal Income Within a Hierarchically Structured Spatial System." Spatial Economics 16, no. 4 (2020): 91–112. http://dx.doi.org/10.14530/se.2020.4.091-112.

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Differentiation in the level of socio-economic development of territories is largely manifested in both inter-regional and intra-regional differences in personal income and consumption of goods. In this regard the methods of hierarchical analysis (HLM, Hierarchical Linear Modeling) that make it possible to study variation at several levels taking into account both municipal and regional factors are acquiring special relevance. Along with hierarchical effects, neighborhood effects can be distinguished. This is possible due to the imposition of a spatial adjacency matrix on the data observing sp
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4

Coburn, Timothy C. "Hierarchical Modeling and Analysis for Spatial Data." Mathematical Geology 39, no. 2 (2007): 261–62. http://dx.doi.org/10.1007/s11004-006-9076-2.

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5

Alexander, Neal. "Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology." Journal of the Royal Statistical Society: Series A (Statistics in Society) 174, no. 2 (2011): 512–13. http://dx.doi.org/10.1111/j.1467-985x.2010.00681_11.x.

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6

Yasenovskiy, Vladimir, and John Hodgson. "Hierarchical Location-Allocation with Spatial Choice Interaction Modeling." Annals of the Association of American Geographers 97, no. 3 (2007): 496–511. http://dx.doi.org/10.1111/j.1467-8306.2007.00560.x.

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7

Barber, Jarrett J., and Alan E. Gelfand. "Hierarchical spatial modeling for estimation of population size." Environmental and Ecological Statistics 14, no. 3 (2007): 193–205. http://dx.doi.org/10.1007/s10651-007-0021-4.

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8

Alghamdi, Taghreed, Khalid Elgazzar, and Taysseer Sharaf. "Spatiotemporal Traffic Prediction Using Hierarchical Bayesian Modeling." Future Internet 13, no. 9 (2021): 225. http://dx.doi.org/10.3390/fi13090225.

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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 hierarchy of the Bayesian approach using the three models; the Gaussian process (GP), autoregressive (AR), and Gaussian predictive processes (GPP) to predict long-term traffic status in urban settings. These models are applied on two different datasets with missing observation. In terms of modeling sparse datasets, the GPP model outperforms the other
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9

Huang, Ling, Xing-Xing Liu, Shu-Qiang Huang, et al. "Temporal Hierarchical Graph Attention Network for Traffic Prediction." ACM Transactions on Intelligent Systems and Technology 12, no. 6 (2021): 1–21. http://dx.doi.org/10.1145/3446430.

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As a critical task in intelligent traffic systems, traffic prediction has received a large amount of attention in the past few decades. The early efforts mainly model traffic prediction as the time-series mining problem, in which the spatial dependence has been largely ignored. As the rapid development of deep learning, some attempts have been made in modeling traffic prediction as the spatio-temporal data mining problem in a road network, in which deep learning techniques can be adopted for modeling the spatial and temporal dependencies simultaneously. Despite the success, the spatial and tem
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10

Royle, J. Andrew, and L. Mark Berliner. "A Hierarchical Approach to Multivariate Spatial Modeling and Prediction." Journal of Agricultural, Biological, and Environmental Statistics 4, no. 1 (1999): 29. http://dx.doi.org/10.2307/1400420.

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