Relevant bibliographies by topics / Spatial analysis

Academic literature on the topic 'Spatial analysis'

Author: Grafiati
Published: 4 June 2021
Last updated: 29 July 2024

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Journal articles on the topic "Spatial analysis"

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Sapaty, P. S. "Holistic spatial analysis of distributed worlds." Mathematical machines and systems 2 (2022): 3–18. http://dx.doi.org/10.34121/1028-9763-2022-2-3-18.

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The paper investigates extended applications of the developed spatial grasp model and technology for analyzing large distributed systems and environments, as well as some examples of solving typical problems in them in the Spatial Grasp Language (SGL). The Spatial Grasp (SG) paradigm allows solving complex problems in a holistic and fully distributed way. It develops in distributed spaces as active ubiquitous waves or even viruses and grasps solutions to spatial problems in parallel pattern-matching mode, fundamentally differing from traditional representations of systems and their solutions as parts that exchange messages. The resultant Spatial Grasp Technology details are briefed where its SGL interpreters can be networked as powerful spatial computers covering any terrestrial and celestial environments and solving problems without any centralized resources. The extended areas for new SG applications include basic environmental issues, global systems, discovering new worlds, Earth science, and planetary exploration activities at NASA. The paper contains descriptions of solutions in SGL to practical problems related to different worlds, including group behavior of marine animals, details of geographical terrain, management of transport networks, and investigation of information networks. The developed paradigm allows direct expression of top semantics and holistic methods for solving complex problems and dynamically composes the needed implementation environments, thus providing the strictest way from problem definition to a practical solution. The formula-like high-level solutions in SGL are extremely compact, often a hundred times shorter than in other languages, and its implementation can be accomplished on any existing platforms, as for the previous language versions in different countries.
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Azhari, Septian Cahya, Iman Hilman, Siti Fadjarajani, and Guruh Sukmo. "A Bibliometric Analysis: Remote Sensing Literature in Dimensions.ai Database." Jurnal Spatial Wahana Komunikasi dan Informasi Geografi 22, no. 2 (December 8, 2022): 79–94. http://dx.doi.org/10.21009/spatial.222.01.

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This research using bibliometric analysis aims to identify and visualize research developments in the field of remote sensing studies. The data is obtained from 2021-2022 through dimensions.ai with criteria for articles that have a digital object identifier (DOI). The data generated were 2645 articles which were then analyzed using the VosViewer device. The data analyzed includes the development of popular keywords, affiliations, countries and authors who do a lot of research in the field of remote sensing. The results of the study found 4 clusters with 216 items for keywords, 6 clusters and 36 items for authors, 15 clusters and 323 items for affiliation, 7 clusters and 61 items for country analysis which mostly conducted research on remote sensing.
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Azhari, Septian Cahya, Iman Hilman, Siti Fadjarajani, and Guruh Sukmo. "A Bibliometric Analysis: Remote Sensing Literature in Dimensions.ai Database." Jurnal Spatial Wahana Komunikasi dan Informasi Geografi 22, no. 2 (December 8, 2022): 91–106. http://dx.doi.org/10.21009/spatial.222.001.

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This research using bibliometric analysis aims to identify and visualize research developments in the field of remote sensing studies. The data is obtained from 2021-2022 through dimensions.ai with criteria for articles that have a digital object identifier (DOI). The data generated were 2645 articles which were then analyzed using the VosViewer device. The data analyzed includes the development of popular keywords, affiliations, countries and authors who do a lot of research in the field of remote sensing. The results of the study found 4 clusters with 216 items for keywords, 6 clusters and 36 items for authors, 15 clusters and 323 items for affiliation, 7 clusters and 61 items for country analysis which mostly conducted research on remote sensing.
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Unwin, David J. "GIS, spatial analysis and spatial statistics." Progress in Human Geography 20, no. 4 (December 1996): 540–51. http://dx.doi.org/10.1177/030913259602000408.

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Ertan, Hayri. "Exploratory spatial analysis of hit distribution in archery." International Journal of Academic Research 5, no. 6 (December 10, 2013): 112–18. http://dx.doi.org/10.7813/2075-4124.2013/5-6/a.15.

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Liu, Yupeng, Wei-Qiang Chen, Tao Lin, and Lijie Gao. "How Spatial Analysis Can Help Enhance Material Stocks and Flows Analysis?" Resources 8, no. 1 (March 4, 2019): 46. http://dx.doi.org/10.3390/resources8010046.

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Spatial information can be integrated into almost all fields of industrial ecology. Many researchers have shown that spatial proximity affects a variety of behaviors and interactions, and thus matters for materials stocks and flows analysis. However, normal tools or models in industrial ecology based on temporal dependence cannot be simply applied to the case of spatial dependence. This paper proposes a framework integrating material stocks and flows analysis with spatial analysis. We argue that spatial analysis can help data management and visualization, determine spatio-temporal patterns-processes-drivers, and finally develop dynamic and spatially explicit models, to improve the performance of simulating and assessing stocks and flows of materials. Scaling in spatial, temporal, and organizational dimensions and other current limitations are also discussed. Combined with spatial analysis, industrial ecology can really be more powerful in achieving its origin and destination—sustainability.
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Banerjee, Sudipto. "Spatial Data Analysis." Annual Review of Public Health 37, no. 1 (March 18, 2016): 47–60. http://dx.doi.org/10.1146/annurev-publhealth-032315-021711.

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Tobler, Waldo. "Global spatial analysis." Computers, Environment and Urban Systems 26, no. 6 (November 2002): 493–500. http://dx.doi.org/10.1016/s0198-9715(02)00017-0.

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Merriam, D. F. "Introductory spatial analysis." Computers & Geosciences 12, no. 6 (January 1986): 819. http://dx.doi.org/10.1016/0098-3004(86)90032-4.

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Zhou, Qiming, and Jianfeng Li. "Geo-Spatial Analysis in Hydrology." ISPRS International Journal of Geo-Information 9, no. 7 (July 11, 2020): 435. http://dx.doi.org/10.3390/ijgi9070435.

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With the increasing demand for accurate and reliable hydrological information, geo-spatial analysis plays a more and more important role in hydrological studies. The development of the geo-spatial technique advances our understanding of the complex and spatially heterogeneous hydrological systems. Meanwhile, how to efficiently and effectively process and analyze multi-source geo-spatial data has become more challenging in the fields of hydrology. In this editorial, we first review the development and application of geo-spatial analysis in three major topics in hydrological studies, namely the scaling issue, extraction of basin characteristics, and hydrological modelling. We hence introduce the articles of the Special Issue. These studies present the latest results of geo-spatial analysis in different topics in hydrology, and improve geo-spatial analytic methods for better accuracy and reliability.
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Dissertations / Theses on the topic "Spatial analysis"

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Fischer, Manfred M. "Spatial Analysis." WU Vienna University of Economics and Business, 1999. http://epub.wu.ac.at/4145/1/WSG_DP_6699.pdf.

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This article views spatial analysis as a research paradigm that provides a unique set of specialised techniques and models for a wide range of research questions in which the prime variables of interest vary significantly over space. The heartland of spatial analysis is concerned with the analysis and modeling of spatial data. Spatial point patterns and area referenced data represent the most appropriate perspectives for applications in the social sciences. The researcher analysing and modeling spatial data tends to be confronted with a series of problems such as the data quality problem, the ecological fallacy problem, the modifiable areal unit problem, boundary and frame effects, and the spatial dependence problem. The problem of spatial dependence is at the core of modern spatial analysis and requires the use of specialised techniques and models in the data analysis. The discussion focuses on exploratory techniques and model-driven [confirmatory] modes of analysing spatial point patterns and area data. In closing, prospects are given towards a new style of data-driven spatial analysis characterized by computational intelligence techniques such as evolutionary computation and neural network modeling to meet the challenges of huge quantities of spatial data characteristic in remote sensing, geodemographics and marketing. (author's abstract)
Series: Discussion Papers of the Institute for Economic Geography and GIScience
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Yan, Hongjia. "Statistical analysis of spatial dynamic pattern in spatial data analysis." Thesis, University of York, 2013. http://etheses.whiterose.ac.uk/4495/.

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In this thesis, inspired by the Boston House Price data, we propose a semiparametric spatial dynamic model, that extends the ordinary spatial autoregressive models to accommodate the effects of some covariates associated with the House price. A profile likelihood-based estimation procedure is proposed and the asymptotic normality of the proposed estimators are derived. We also investigate the connection between cross-validation method and AIC/BIC methods in the semiparametric family. In the proposed model, it is easier to apply the AIC/BIC method than the 'cross-validation' method. We illustrate how to identify the parametric/nonparametric components in the proposed semiparametric model. We also show how many unknown parameters an unknown bivariate function amounts to, and propose an AIC/BIC nonparametric model selection. Simulation studies are conducted to examine the performance of the proposed methods, and their results show that the methods work very well. Finally, we apply the proposed methods to analyze the Boston House Price data, which lead to some interesting findings.Although, the proposed model and methodology are stimulated by the Boston House Price data, they could be widely used in many other scientific problems.
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Yue, Yu. "Spatially adaptive priors for regression and spatial modeling." Diss., Columbia, Mo. : University of Missouri-Columbia, 2008. http://hdl.handle.net/10355/6059.

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Thesis (Ph. D.)--University of Missouri-Columbia, 2008.
The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file (viewed on August 3, 2009) Vita. Includes bibliographical references.
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Zhang, Jun. "Nearest neighbor queries in spatial and spatio-temporal databases /." View abstract or full-text, 2003. http://library.ust.hk/cgi/db/thesis.pl?COMP%202003%20ZHANG.

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Zhang, Xiang. "Analysis of Spatial Data." UKnowledge, 2013. http://uknowledge.uky.edu/statistics_etds/4.

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In many areas of the agriculture, biological, physical and social sciences, spatial lattice data are becoming increasingly common. In addition, a large amount of lattice data shows not only visible spatial pattern but also temporal pattern (see, Zhu et al. 2005). An interesting problem is to develop a model to systematically model the relationship between the response variable and possible explanatory variable, while accounting for space and time effect simultaneously. Spatial-temporal linear model and the corresponding likelihood-based statistical inference are important tools for the analysis of spatial-temporal lattice data. We propose a general asymptotic framework for spatial-temporal linear models and investigate the property of maximum likelihood estimates under such framework. Mild regularity conditions on the spatial-temporal weight matrices will be put in order to derive the asymptotic properties (consistency and asymptotic normality) of maximum likelihood estimates. A simulation study is conducted to examine the finite-sample properties of the maximum likelihood estimates. For spatial data, aside from traditional likelihood-based method, a variety of literature has discussed Bayesian approach to estimate the correlation (auto-covariance function) among spatial data, especially Zheng et al. (2010) proposed a nonparametric Bayesian approach to estimate a spectral density. We will also discuss nonparametric Bayesian approach in analyzing spatial data. We will propose a general procedure for constructing a multivariate Feller prior and establish its theoretical property as a nonparametric prior. A blocked Gibbs sampling algorithm is also proposed for computation since the posterior distribution is analytically manageable.
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White, Gentry. "Bayesian semiparametric spatial and joint spatio-temporal modeling." Diss., Columbia, Mo. : University of Missouri-Columbia, 2006. http://hdl.handle.net/10355/4450.

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Thesis (Ph.D.)--University of Missouri-Columbia, 2006.
The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file viewed on (May 2, 2007) Vita. Includes bibliographical references.
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Hashimoto, Tomoyuki. "Spatial analysis of pedestrian accidents." [Tampa, Fla.] : University of South Florida, 2005. http://purl.fcla.edu/fcla/etd/SFE0001072.

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Katz, Ronit. "Spatial analysis of pore images." Thesis, University of Oxford, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.260735.

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Mugglestone, Moira A. "Spectral analysis of spatial processes." Thesis, University of Edinburgh, 1990. http://hdl.handle.net/1842/15450.

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This thesis is concerned with the development of two-dimensional spectral analysis as a technique for investigating spatial pattern, and the stochastic processes which generate pattern. The technique is discussed for two types of data: first, quantitative measurements associated with a rectangular grid, or lattice; secondly, analysis of spatial point patterns using the coordinates which describe the locations of events. Spectral analysis of lattice data is applied to two examples of remotely sensed digital imagery. The first example consists of digitised aerial photographs of glaciated terrain in Canada. Spectral analysis is used to detect geological lineations which are visible in the photographs, and to study the structure of the land surface beneath the lineations. The second example is meteorological satellite imagery. Spectral analysis is used to develop a system for discrimination between different cloud types. Point spectral analysis is used as the basis of formal tests for randomness, against alternatives such as clustering or inhibition. Spectral theory for univariate spatial point patterns is extended to cross-spectral analysis of bivariate point patterns. In particular, we show how cross-spectral functions indicate the type of interaction between the events of two patterns. A test for independent components is composed, and the application of the test is demonstrated using a variety of real and artificial patterns. A further extension, to bispectral analysis of third-order properties of spatial point patterns, is also discussed. This type of analysis is used to distinguish between processes which have the same first- and second-order properties, but different third-order properties. Finally, we show how Greig-Smith analysis of quadrant count data can be interpreted as a type of two-dimensional spectral analysis based on a set of orthogonal square waves known as Walsh functions. This representation indicates why Greig Smith's method is entirely dependent on the starting point of the grid.
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Basnet, Shiva. "Spatial Analysis of Rock Textures." Bowling Green State University / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1349988757.

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Books on the topic "Spatial analysis"

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Murtagh, Fionn. Handbook of cluster analysis. Edited by Meilă Marina 1962 editor. Boca Raton: Taylor & Francis, 2016.

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Fischer, Manfred M., and Jinfeng Wang. Spatial Data Analysis. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21720-3.

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Sibley, David. Spatial applications of exploratory data analysis. Norwich: Geo Books, 1988.

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Andrew, Lawson, and Denison David G. T, eds. Spatial cluster modelling. Boca Raton, FL: Chapman & Hall/CRC, 2002.

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Bailey, Trevor C. Interactive spatial data analysis. Harlow Essex, England: Longman Scientific & Technical, 1995.

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Ripley, Brian D. Spatial statistics. Hoboken, N.J: Wiley-Interscience, 2004.

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Kovalerchuk, Boris, and James Schwing, eds. Visual and Spatial Analysis. Dordrecht: Springer Netherlands, 2004. http://dx.doi.org/10.1007/978-1-4020-2958-5.

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Páez, Antonio, Julie Gallo, Ron N. Buliung, and Sandy Dall'erba, eds. Progress in Spatial Analysis. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-03326-1.

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Sanders, Lena, ed. Models in Spatial Analysis. London, UK: ISTE, 2007. http://dx.doi.org/10.1002/9780470612255.

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Okabe, Atsuyuki, and Kokichi Sugihara. Spatial Analysis along Networks. Chichester, UK: John Wiley & Sons, Ltd, 2012. http://dx.doi.org/10.1002/9781119967101.

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Book chapters on the topic "Spatial analysis"

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Gaydarska, Bisserka. "Spatial Analysis." In SpringerBriefs in Archaeology, 71–75. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-09819-7_9.

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Isik, Fikret, James Holland, and Christian Maltecca. "Spatial Analysis." In Genetic Data Analysis for Plant and Animal Breeding, 203–26. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-55177-7_7.

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Schulze Bäing, Andreas. "Spatial Analysis." In Encyclopedia of Quality of Life and Well-Being Research, 6259–61. Dordrecht: Springer Netherlands, 2014. http://dx.doi.org/10.1007/978-94-007-0753-5_2818.

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Gleeson, A. C. "Spatial analysis." In Statistical Methods for Plant Variety Evaluation, 68–85. Dordrecht: Springer Netherlands, 1997. http://dx.doi.org/10.1007/978-94-009-1503-9_5.

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Linke, Andrew M., and John O'Loughlin. "Spatial Analysis." In The Wiley Blackwell Companion to Political Geography, 187–205. Chichester, UK: John Wiley & Sons, Ltd, 2015. http://dx.doi.org/10.1002/9781118725771.ch15.

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Fox, Charles. "Spatial Analysis." In Springer Textbooks in Earth Sciences, Geography and Environment, 107–23. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-72953-4_8.

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Shekhar, Shashi, and Hui Xiong. "Spatial Analysis." In Encyclopedia of GIS, 1073. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-35973-1_1240.

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Awange, Joseph, and John Kiema. "Spatial Analysis." In Environmental Geoinformatics, 237–48. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03017-9_18.

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Awange, Joseph L., and John B. Kyalo Kiema. "Spatial Analysis." In Environmental Geoinformatics, 225–36. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-34085-7_17.

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Šegina, Ela. "Spatial Analysis." In Springer Theses, 89–146. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-61449-2_3.

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Conference papers on the topic "Spatial analysis"

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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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Yi, Wenbin, and Hong Tang. "Experimental analysis on classification of unmanned aerial vehicle images using the probabilistic latent semantic 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/10.1117/12.838283.

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Wang, Zhongyuan, and Zongyi He. "Spatial clustering analysis based on graphics." 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/10.1117/12.838631.

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Ke, Xi-lin, Qing-sheng Guo, Yue-peng Zhang, and Ping Gao. "Multi-scale terrain representation and terrain 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/10.1117/12.837528.

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Wang, Hong, Chengqi Cheng, and Tinghua Ai. "Pattern analysis of geo-referenced motion processes." 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/10.1117/12.838124.

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Serrano, E., M. P. Y. Desmulliez, S. M. Prince, H. Inbar, and B. S. Wherrett. "Multiple-Quantum-Well Binary-Phase Modulators: Design and Tolerance Analysis." In Spatial Light Modulators. Washington, D.C.: Optica Publishing Group, 1997. http://dx.doi.org/10.1364/slmo.1997.stue.9.

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Over the last fifteen years, various types of semiconductor electro-absorption modulators have been fabricated and analysed. The potential use of such devices in photonic switching fabrics and inter-chip interconnections has provoked numerous studies of their performance capabilities and design trade-offs [1,2]. Of particular concern has been the impossibility, until recently, to manufacture fast 2-D multiple quantum well (MQW) phase modulator arrays. Trezza and coworkers reported a π-phase change in a reflection-mode vertical cavity asymmetric multiple quantum well [3], in which the absorption variation induced by an external applied field causes a change in the dominant role played by one of the cavity minors. Moreover, the device, named a phase-flip modulator, is designed so that no change in throughput is induced by the bias voltage. The π phase change at constant reflectivity requires a careful cavity design and the precise determination of the operating wavelength and voltage swing. A systematic study demands the implementation of a tolerance methodology. Results, based on such a methodology, will be presented that analyse which parameters are in achieving the best overall device performance, the optimum operating conditions and the higher degree of robustness (the tolerance) to the variations in device characteristics.
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Wang, Changwei, Deren Li, Yueming Hu, Xiaofang Wu, and Yu Qi. "Research of spatio-temporal analysis of agricultural pest." 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/10.1117/12.838413.

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Shu, Hong, Chao Zhao, and Aiping Xu. "Spatio-temporal statistics for exploratory NDVI image 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/10.1117/12.838576.

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Li, Sha, Hong Shu, and Zhengquan Xu. "A spatial-temporal covariance model for rainfall 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/10.1117/12.838632.

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Zhang, Xia, and Qing Zhu. "Scheme evaluation of urban design using 3D visual 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/10.1117/12.838542.

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Reports on the topic "Spatial analysis"

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Thiessen, R. L., and J. R. Eliason. Geologic spatial analysis. Office of Scientific and Technical Information (OSTI), January 1989. http://dx.doi.org/10.2172/6160871.

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Affleck, Rosa T., and Sally A. Shoop. Spatial Analysis of Thaw Depth. Fort Belvoir, VA: Defense Technical Information Center, January 2001. http://dx.doi.org/10.21236/ada392172.

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Crovella, Mark, and Eric Kolaczyk. Graph Wavelets for Spatial Traffic Analysis. Fort Belvoir, VA: Defense Technical Information Center, July 2002. http://dx.doi.org/10.21236/ada442573.

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Stone, Gregory. Discussion & analysis: Spatial reasoning assessment. The Learning Partnership, January 2021. http://dx.doi.org/10.51420/report.2021.2.

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A complete Rasch multi-faceted analysis was performed on the draft Spatial Reasoning Assessment. While the psychometric report presents the complete analysis for the examination, this discussion proceeds step-by-step to understand the way in which the analysis proceeded, and the findings therein. The findings were that holistically, the instrument performed admirably. As a pretest, it is likely that students were not expected to demonstrate certain reasoning skills (e.g., GIS) as indeed they did not. The rating scale functions well to capture the examiner judgement. Overall, the instrument works together as a functional assessment, capturing the general construct of Spatial Reasoning.
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Grunsky, E. Spatial factor analysis: a technique to assess the spatial relationships of multivariate data. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1990. http://dx.doi.org/10.4095/128074.

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Wheelock, David C., Gary A. Wagner, and Thomas A. Garrett. A Spatial Analysis of State Banking Regulation. Federal Reserve Bank of St. Louis, 2003. http://dx.doi.org/10.20955/wp.2003.044.

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YEON, Yeong Kwang, Jong Gyu HAN, and Hye Ja HYUN. Spatial Prediction Analysis Using a Neural Network. Cogeo@oeaw-giscience, September 2011. http://dx.doi.org/10.5242/iamg.2011.0058.

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Liebhold, Andrew M., and Hope R. Barrett, eds. Proceedings: Spatial analysis and forest pest management. Radnor, PA: U.S. Department of Agriculture, Forest Service, Northeastern Forest Experiment Station, 1993. http://dx.doi.org/10.2737/ne-gtr-175.

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Read, Andrew, Patrick Halpin, Benjamin Best, Ei Fujioka, Caroline Good, Lucie Hazen, Erin LaBrecque, Song Qian, and Robert Schick. Predictive Spatial Analysis of Marine Mammal Habitats. Fort Belvoir, VA: Defense Technical Information Center, January 2010. http://dx.doi.org/10.21236/ada520623.

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Harrell, Krystle. Homelessness in Portland, Oregon: An Analysis of Homeless Campsite Spatial Patterns and Spatial Relationships. Portland State University Library, January 2000. http://dx.doi.org/10.15760/geogmaster.24.

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You might also be interested in the extended bibliographies on the topic 'Spatial analysis' for particular source types:
Journal articles Dissertations / Theses Books
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