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Relevant bibliographies by topics / Hierarchical spatial modeling

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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 (July 22, 2014): 405–17. http://dx.doi.org/10.1002/wics.1315.

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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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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 spatial interactions within the spatial-hierarchical models (HSAM, Hierarchical Spatial Autoregressive Modeling). The aim of the study is to better understand the relationship between consumption and personal income within a hierarchically structured and spatially oriented economic system. The analysis uses the data from 2319 municipalities (i.e. municipal districts or rayons) and urban districts (okrugs) in 84 constituent entities of the Russian Federation for 2018. It showed that 38.4% of the variation among municipalities in terms of sold foods volume is explained by regional factors. The developed hierarchical (two-level) model revealed the positive impact of the volume of social benefits and taxable personal income in the municipality, and the volume of per capita retail trade at the level of the Russian Federation constituent entities, on the volume of foods sold within municipalities, and substantiate the negative impact of the Gini index increase, as well as highlight the positive spatial effect

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Coburn, Timothy C. "Hierarchical Modeling and Analysis for Spatial Data." Mathematical Geology 39, no. 2 (April 14, 2007): 261–62. http://dx.doi.org/10.1007/s11004-006-9076-2.

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Alexander, Neal. "Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology." Journal of the Royal Statistical Society: Series A (Statistics in Society) 174, no. 2 (March 14, 2011): 512–13. http://dx.doi.org/10.1111/j.1467-985x.2010.00681_11.x.

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

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Barber, Jarrett J., and Alan E. Gelfand. "Hierarchical spatial modeling for estimation of population size." Environmental and Ecological Statistics 14, no. 3 (July 10, 2007): 193–205. http://dx.doi.org/10.1007/s10651-007-0021-4.

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Alghamdi, Taghreed, Khalid Elgazzar, and Taysseer Sharaf. "Spatiotemporal Traffic Prediction Using Hierarchical Bayesian Modeling." Future Internet 13, no. 9 (August 30, 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 models. However, the GPP model is not applicable for modeling data with spatial points close to each other. The AR model outperforms the GP models in terms of temporal forecasting. The GP model is used with different covariance matrices: exponential, Gaussian, spherical, and Matérn to capture the spatial correlation. The exponential covariance yields the best precision in spatial analysis with the Gaussian process, while the Gaussian covariance outperforms the others in temporal forecasting.

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Huang, Ling, Xing-Xing Liu, Shu-Qiang Huang, Chang-Dong Wang, Wei Tu, Jia-Meng Xie, Shuai Tang, and Wendi Xie. "Temporal Hierarchical Graph Attention Network for Traffic Prediction." ACM Transactions on Intelligent Systems and Technology 12, no. 6 (December 31, 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 temporal dependencies are only modeled in a regionless network without considering the underlying hierarchical regional structure of the spatial nodes, which is an important structure naturally existing in the real-world road network. Apart from the challenge of modeling the spatial and temporal dependencies like the existing studies, the extra challenge caused by considering the hierarchical regional structure of the road network lies in simultaneously modeling the spatial and temporal dependencies between nodes and regions and the spatial and temporal dependencies between regions. To this end, this article proposes a new Temporal Hierarchical Graph Attention Network (TH-GAT). The main idea lies in augmenting the original road network into a region-augmented network, in which the hierarchical regional structure can be modeled. Based on the region-augmented network, the region-aware spatial dependence model and the region-aware temporal dependence model can be constructed, which are two main components of the proposed TH-GAT model. In addition, in the region-aware spatial dependence model, the graph attention network is adopted, in which the importance of a node to another node, of a node to a region, of a region to a node, and of a region to another region, can be captured automatically by means of the attention coefficients. Extensive experiments are conducted on two real-world traffic datasets, and the results have confirmed the superiority of the proposed TH-GAT model.

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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 (March 1999): 29. http://dx.doi.org/10.2307/1400420.

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Dissertations / Theses on the topic "Hierarchical spatial modeling"

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Ma, Pulong. "Hierarchical Additive Spatial and Spatio-Temporal Process Models for Massive Datasets." University of Cincinnati / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1535635193581096.

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Thomas, Zachary Micah. "Bayesian Hierarchical Space-Time Clustering Methods." The Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1435324379.

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Sengupta, Aritra. "Empirical Hierarchical Modeling and Predictive Inference for Big, Spatial, Discrete, and Continuous Data." The Ohio State University, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=osu1350660056.

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Katzfuss, Matthias. "Hierarchical Spatial and Spatio-Temporal Modeling of Massive Datasets, with Application to Global Mapping of CO₂." The Ohio State University, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=osu1308316063.

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Shi, Hongxiang. "Hierarchical Statistical Models for Large Spatial Data in Uncertainty Quantification and Data Fusion." University of Cincinnati / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1504802515691938.

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Li, Linhua. "A GIS-based Bayesian approach for analyzing spatial-temporal patterns of traffic crashes." [College Station, Tex. : Texas A&M University, 2006. http://hdl.handle.net/1969.1/ETD-TAMU-1766.

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Pfarrhofer, Michael, and Philipp Piribauer. "Flexible shrinkage in high-dimensional Bayesian spatial autoregressive models." Elsevier, 2019. http://epub.wu.ac.at/6839/1/1805.10822.pdf.

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Several recent empirical studies, particularly in the regional economic growth literature, emphasize the importance of explicitly accounting for uncertainty surrounding model specification. Standard approaches to deal with the problem of model uncertainty involve the use of Bayesian model-averaging techniques. However, Bayesian model-averaging for spatial autoregressive models suffers from severe drawbacks both in terms of computational time and possible extensions to more flexible econometric frameworks. To alleviate these problems, this paper presents two global-local shrinkage priors in the context of high-dimensional matrix exponential spatial specifications. A simulation study is conducted to evaluate the performance of the shrinkage priors. Results suggest that they perform particularly well in high-dimensional environments, especially when the number of parameters to estimate exceeds the number of observations. Moreover, we use pan-European regional economic growth data to illustrate the performance of the proposed shrinkage priors.

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Ross, Beth E. "Assessing Changes in the Abundance of the Continental Population of Scaup Using a Hierarchical Spatio-Temporal Model." DigitalCommons@USU, 2012. http://digitalcommons.usu.edu/etd/1147.

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In ecological studies, the goal is often to describe and gain further insight into ecological processes underlying the data collected during observational studies. Because of the nature of observational data, it can often be difficult to separate the variation in the data from the underlying process or `state dynamics.' In order to better address this issue, it is becoming increasingly common for researchers to use hierarchical models. Hierarchical spatial, temporal, and spatio-temporal models allow for the simultaneous modeling of both first and second order processes, thus accounting for underlying autocorrelation in the system while still providing insight into overall spatial and temporal pattern. In this particular study, I use two species of interest, the lesser and greater scaup (Aythya affnis and Aythya marila), as an example of how hierarchical models can be utilized in wildlife management studies. Scaup are the most abundant and widespread diving duck in North America, and are important game species. Since 1978, the continental population of scaup has declined to levels that are 16% below the 1955-2010 average and 34% below the North American Waterfowl Management Plan goal. The greatest decline in abundance of scaup appears to be occurring in the western boreal forest, where populations may have depressed rates of reproductive success, survival, or both. In order to better understand the causes of the decline, and better understand the biology of scaup in general, a level of high importance has been placed on retrospective analyses that determine the spatial and temporal changes in population abundance. In order to implement Bayesian hierarchical models, I used a method called Integrated Nested Laplace Approximation (INLA) to approximate the posterior marginal distribution of the parameters of interest, rather than the more common Markov Chain Monte Carlo (MCMC) approach. Based on preliminary analysis, the data appeared to be overdispersed, containing a disproportionately high number of zeros along with a high variance relative to the mean. Thus, I considered two potential data models, the negative binomial and the zero-inflated negative binomial. Of these models, the zero-inflated negative binomial had the lowest DIC, thus inference was based on this model. Results from this model indicated that a large proportion of the strata were not decreasing (i.e., the estimated slope of the parameter was not significantly different from zero). However, there were important exceptions with strata in the northwest boreal forest and southern prairie parkland habitats. Several strata in the boreal forest habitat had negative slope estimates, indicating a decrease in breeding pairs, while some of the strata in the prairie parkland habitat had positive slope estimates, indicating an increase in this region. Additionally, from looking at plots of individual strata, it seems that the strata experiencing increases in breeding pairs are experiencing dramatic increases. Overall, my results support previous work indicating a decline in population abundance in the northern boreal forest of Canada, and additionally indicate that the population of scaup has increased rapidly in the prairie pothole region since 1957. Yet, by accounting for spatial and temporal autocorrelation in the data, it appears that declines in abundance are not as widespread as previously reported.

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Rice, Ketra Lachell. "A Multi-Method Analysis of the Role of Spatial Factors in Policy Analysis and Health Disparities Research." The Ohio State University, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=osu1365613669.

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Acar, Alper. "Optimal Urban Planning and Housing Prices : a Spatial Analysis." Electronic Thesis or Diss., Bourgogne Franche-Comté, 2024. http://www.theses.fr/2024UBFCG008.

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La thèse étudie l'impact de l'aménagement urbain optimal surla diffusion des prix des logements dans un marché immobilier local. À travers une analyse du champ de la localisation optimale et de l'économétrie spatiale, cette étude vise à considérer comment les propriétés des graphes et les modèles de localisation optimale peuvent contribuer à mieux comprendre et à évaluer les impacts des effets de multiplicateur spatial dans l'économie. Pour ce faire, la recherche s'appuie sur une méthodologie combinant la création d'outils d'aide à la décision et l'étude des prix immobiliers par un modèle économétrique spatiale hiérarchique. Les résultats démontrent que la prise en compte des relations spatiales optimales permet une étude plus précise des impacts de l'aménagement urbain sur la diffusion des prix. A contrario, la considération de relations spatiales “classiques" sur ou sous-estime les impacts
This dissertation studies the effect of optimal urban planning on housing prices diffusion in local real-estate markets. The study uses facility location theory and spatial econometrics to investigate how graph properties and optimal location models can contribute to a better understanding and evaluation of the impact of spatial multiplier effects in the economy. To this end, the research is based on a methodology that combines the creation of decision-support tools and the study of real estate prices using hierarchical spatial econometric models. The results states that using optimal spatial relationships enables a more precise analysis of the impacts of urban planning on the diffusion of prices. Conversely, the consideration of “classical” spatial relationships either underestimates or overestimates the spatial impacts

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Books on the topic "Hierarchical spatial modeling"

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P, Carlin Bradley, and Gelfand Alan E. 1945-, eds. Hierarchical modeling and analysis for spatial data. Boca Raton: Chapman & Hall, 2004.

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Lawson, Andrew. Bayesian disease mapping: Hierarchical modeling in spatial epidemiology. Boca Raton: Taylor & Francis, 2008.

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Dorazio, Robert M. (Robert Matthew) and ScienceDirect (Online service), eds. Hierarchical modeling and inference in ecology: The analysis of data from populations, metapopulations and communities. Amsterdam: Academic, 2008.

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Carlin, Bradley P., Sudipto Banerjee, and Alan E. Gelfand. Hierarchical Modeling and Analysis for Spatial Data. Taylor & Francis Group, 2014.

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Carlin, Bradley P., Sudipto Banerjee, and Alan E. Gelfand. Hierarchical Modeling and Analysis for Spatial Data. Taylor & Francis Group, 2014.

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Carlin, Bradley P., Sudipto Banerjee, Alan E. Gelfand, and Banerjee Sudipto Staff. Hierarchical Modeling and Analysis for Spatial Data. Taylor & Francis Group, 2004.

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Banerjee, Sudipto. Hierarchical Modeling and Analysis for Spatial Data. Taylor & Francis Group, 2003.

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Banerjee, Sudipto, Bradley P. Carlin, and Alan E. Gelfand. Hierarchical Modeling and Analysis for Spatial Data. Chapman and Hall/CRC, 2014. http://dx.doi.org/10.1201/b17115.

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Banerjee, Sudipto, Bradley P. Carlin, Alan E. Gelfand, and Sudipto Banerjee. Hierarchical Modeling and Analysis for Spatial Data. Chapman and Hall/CRC, 2003. http://dx.doi.org/10.1201/9780203487808.

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Lawson, Andrew B. Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology. Taylor & Francis Group, 2008.

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Book chapters on the topic "Hierarchical spatial modeling"

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Porter, Erica M., Christopher T. Franck, and Marco A. R. Ferreira. "Gaussian Spatial Hierarchical Models with ICAR Priors." In Modeling Spatio-Temporal Data, 24–47. Boca Raton: Chapman and Hall/CRC, 2024. http://dx.doi.org/10.1201/9781032623443-2.

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Chountas, Panagiotis, Ermir Rogova, and Krassimir Atanassov. "Expressing Hierarchical Preferences in OLAP Queries." In Uncertainty Approaches for Spatial Data Modeling and Processing, 61–77. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-10663-7_5.

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Murray, Alan T., and Tony H. Grubesic. "Exploring Spatial Patterns of Crime Using Non-hierarchical Cluster Analysis." In Crime Modeling and Mapping Using Geospatial Technologies, 105–24. Dordrecht: Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-4997-9_5.

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Chen, Pingjun, Muhammad Aminu, Siba El Hussein, Joseph D. Khoury, and Jia Wu. "Hierarchical Phenotyping and Graph Modeling of Spatial Architecture in Lymphoid Neoplasms." In Medical Image Computing and Computer Assisted Intervention – MICCAI 2021, 164–74. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87237-3_16.

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Guo, Junpeng, Guohua Jiang, Yuqing Liu, and Yu Tian. "The Hierarchical Model of Spatial Orientation Task in a Multi-module Space Station." In Advances in Ergonomics Modeling, Usability & Special Populations, 129–38. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41685-4_12.

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Bilancia, Massimo, Giusi Graziano, and Giacomo Demarinis. "Geographical Disparities in Mortality Rates: Spatial Data Mining and Bayesian Hierarchical Modeling." In Contributions to Statistics, 1–29. Milano: Springer Milan, 2012. http://dx.doi.org/10.1007/978-88-470-2751-0_1.

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Ilg, Winfried, and Martin Giese. "Modeling of Movement Sequences Based on Hierarchical Spatial-Temporal Correspondence of Movement Primitives." In Biologically Motivated Computer Vision, 528–37. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-36181-2_53.

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Okada, Toshiyuki, Marius George Linguraru, Yasuhide Yoshida, Masatoshi Hori, Ronald M. Summers, Yen-Wei Chen, Noriyuki Tomiyama, and Yoshinobu Sato. "Abdominal Multi-Organ Segmentation of CT Images Based on Hierarchical Spatial Modeling of Organ Interrelations." In Lecture Notes in Computer Science, 173–80. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28557-8_22.

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Samet, Hanan. "Hierarchical Data Structures for Spatial Reasoning." In Mapping and Spatial Modelling for Navigation, 41–58. Berlin, Heidelberg: Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-642-84215-3_3.

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Waller, Lance A., Bradley P. Carlin, and Hong Xia. "Structuring Correlation within Hierarchical Spatio-temporal Models for Disease Rates." In Modelling Longitudinal and Spatially Correlated Data, 309–19. New York, NY: Springer New York, 1997. http://dx.doi.org/10.1007/978-1-4612-0699-6_27.

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Conference papers on the topic "Hierarchical spatial modeling"

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Peng, Mingjun. "Division of urban hierarchical grid based on hierarchical spatial reasoning." In International Symposium on Spatial Analysis, Spatial-temporal Data Modeling, and Data Mining, edited by Yaolin Liu and Xinming Tang. SPIE, 2009. http://dx.doi.org/10.1117/12.838394.

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Xie, Jiong, and Cunjin Xue. "A top-down hierarchical spatio-temporal process description method and its data organization." In International Symposium on Spatial Analysis, Spatial-temporal Data Modeling, and Data Mining, edited by Yaolin Liu and Xinming Tang. SPIE, 2009. http://dx.doi.org/10.1117/12.838353.

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Lin, Zhiyong, and Shuang Liang. "The application of spatial analysis based on rough set theory and hierarchical analysis." In International Symposium on Spatial Analysis, Spatial-temporal Data Modeling, and Data Mining, edited by Yaolin Liu and Xinming Tang. SPIE, 2009. http://dx.doi.org/10.1117/12.838418.

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Papaioannou, Iason, Sebastian Geyer, and Daniel Straub. "Bayesian Hierarchical Spatial Modeling of Soil Properties." In International Symposium for Geotechnical Safety & Risk. Singapore: Research Publishing Services, 2022. http://dx.doi.org/10.3850/978-981-18-5182-7_00-01-004.xml.

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Eitz, Mathias, and Gu Lixu. "Hierarchical Spatial Hashing for Real-time Collision Detection." In IEEE International Conference on Shape Modeling and Applications 2007 (SMI '07). IEEE, 2007. http://dx.doi.org/10.1109/smi.2007.18.

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Qian, Kun, Borivoje Nikolić, and Costas J. Spanos. "Hierarchical modeling of spatial variability with a 45nm example." In SPIE Advanced Lithography, edited by Vivek K. Singh and Michael L. Rieger. SPIE, 2009. http://dx.doi.org/10.1117/12.814226.

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Platonov, Georgiy, Yifei Yang, Haoyu Wu, Jonathan Waxman, Marcus Hill, and Lenhart Schubert. "Modeling Semantics and Pragmatics of Spatial Prepositions via Hierarchical Common-Sense Primitives." In Proceedings of Second International Combined Workshop on Spatial Language Understanding and Grounded Communication for Robotics. Stroudsburg, PA, USA: Association for Computational Linguistics, 2021. http://dx.doi.org/10.18653/v1/2021.splurobonlp-1.4.

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Liu, Bang, Borislav Mavrin, Di Niu, and Linglong Kong. "House Price Modeling over Heterogeneous Regions with Hierarchical Spatial Functional Analysis." In 2016 IEEE 16th International Conference on Data Mining (ICDM). IEEE, 2016. http://dx.doi.org/10.1109/icdm.2016.0134.

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Dai, Zhenxue, Robert W. Ritzi, Jr., David F. Dominic, and Yoram N. Rubin. "Estimating Spatial Correlation Structure for Permeability in Sediments with Hierarchical Organization." In Probabilistic Approaches to Groundwater Modeling Symposium at World Environmental and Water Resources Congress 2003. Reston, VA: American Society of Civil Engineers, 2003. http://dx.doi.org/10.1061/40696(2003)8.

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Montgomery, David E., Mitchel J. Keil, and Arvid Myklebust. "A PHIGS+ Model Rendering System for Simulation of Spatial Mechanisms." In ASME 1991 International Computers in Engineering Conference and Exposition. American Society of Mechanical Engineers, 1991. http://dx.doi.org/10.1115/cie1991-0029.

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Abstract A device independent system is described for the visualization, modeling, and animation of spatial mechanisms and robots. New ideas and methods are presented to simplify the interactive specification of scene rendering and color parameters using the ISO standard for 3-D graphics, the Programmer’s Hierarchical Interactive Graphic System (PHIGS) and its extensions PHIGS+. The parallels between the PHIGS hierarchical structure and spatial mechanism modeling are described from the level of mechanism through links, joints, geometry primitives to PHIGS graphics primitives. Perception and evaluation of spatial mechanism designs are significantly improved by the use of shaded, lighted, depth cued models under animation. The method is coupled to new algorithms for automatic interference detection and reshaping of mechanism links to avoid collisions. The suitability of PHIGS+ for the modeling and simulation of open loop mechanisms is also described. Examples are presented for rendering and animation of spatial mechanisms on a Raster Technologies (Alliant) GX4000 workstation with a hardware based PHIGS+ graphics subsystem, UNIX, NeWS, and C.

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