Relevant bibliographies by topics / Spatial analysis (Statistics)

Academic literature on the topic 'Spatial analysis (Statistics)'

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Published: 4 June 2021
Last updated: 30 July 2024

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Journal articles on the topic "Spatial analysis (Statistics)"

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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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Riffo-Campos, Angela L., Guillermo Ayala, and Francisco Montes. "Gene Set Analysis Using Spatial Statistics." Mathematics 9, no. 5 (March 3, 2021): 521. http://dx.doi.org/10.3390/math9050521.

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Gene differential expression consists of the study of the possible association between the gene expression, evaluated using different types of data as DNA microarray or RNA-Seq technologies, and the phenotype. This can be performed marginally for each gene (differential gene expression) or using a gene set collection (gene set analysis). A previous (marginal) per-gene analysis of differential expression is usually performed in order to obtain a set of significant genes or marginal p-values used later in the study of association between phenotype and gene expression. This paper proposes the use of methods of spatial statistics for testing gene set differential expression analysis using paired samples of RNA-Seq counts. This approach is not based on a previous per-gene differential expression analysis. Instead, we compare the paired counts within each sample/control using a binomial test. Each pair per gene will produce a p-value so gene expression profile is transformed into a vector of p-values which will be considered as an event belonging to a point pattern. This would be the first component of a bivariate point pattern. The second component is generated by applying two different randomization distributions to the correspondence between samples and treatment. The self-contained null hypothesis considered in gene set analysis can be formulated in terms of the associated point pattern as a random labeling of the considered bivariate point pattern. The gene sets were defined by the Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. The proposed methodology was tested in four RNA-Seq datasets of colorectal cancer (CRC) patients and the results were contrasted with those obtained using the edgeR-GOseq pipeline. The proposed methodology has proved to be consistent at the biological and statistical level, in particular using Cuzick and Edwards test with one realization of the second component and between-pair distribution.
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NL, Julian Besag, and James Simpson. "Spatial Statistics and Digital Image Analysis." Journal of the American Statistical Association 89, no. 427 (September 1994): 1149. http://dx.doi.org/10.2307/2290961.

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Unwin, A., and D. Unwin. "Spatial Data Analysis with Local Statistics." Journal of the Royal Statistical Society: Series D (The Statistician) 47, no. 3 (September 1998): 415–21. http://dx.doi.org/10.1111/1467-9884.00143.

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Lee, E. Stanley. "Neuro-Fuzzy Estimation in Spatial Statistics." Journal of Mathematical Analysis and Applications 249, no. 1 (September 2000): 221–31. http://dx.doi.org/10.1006/jmaa.2000.6938.

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Yang, Hong, Xiao Ya Dong, Min Wang, and Yu Guo. "GIS-Aided Evolvement Analysis of Spatial-Temporal Pattern of Regional Tourism Industry Environment." Advanced Materials Research 726-731 (August 2013): 4690–93. http://dx.doi.org/10.4028/www.scientific.net/amr.726-731.4690.

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Regional tourism is a part of China's economic income. It is of great significance for tourism economy development to study spatial-temporal evolvement. This study analyzed time-based characteristics and spatial cluster characteristics through methods including Spatial Weight Matrix, global spatial autocorrelation (Morans I) statistic, spatial Statistics (Getis-Ord Gi*) and local spatial autocorrelation calculations. Results show that the overall spatial autocorrelation model changed slowly from negative (-0.05) to positive (0.08) while eastern part of study area clustered as hotspot and western part clustered as coldspot. It can be concluded that the spatial distribution pattern of the tourism economy in study area from 2002 to 2007 was increasingly clustered and the tourism development of each units in study area will be much more spatially inter-dependent.
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Wagner, Helene H., and Marie-Josée Fortin. "SPATIAL ANALYSIS OF LANDSCAPES: CONCEPTS AND STATISTICS." Ecology 86, no. 8 (August 2005): 1975–87. http://dx.doi.org/10.1890/04-0914.

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Javizadeh, Saeed, and Zahra Hejazizadeh. "Analysis of Drought Spatial Statistics in Iran." Journal of Applied researches in Geographical Sciences 19, no. 53 (July 1, 2019): 251–77. http://dx.doi.org/10.29252/jgs.19.53.251.

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Smith, Chad M., Daniel C. Brown, Anthony P. Lyons, and Thomas Gabrielson. "Analysis of split-beam spatial coherence statistics for discerning compact from non-compact littoral sonar clutter." Journal of the Acoustical Society of America 151, no. 4 (April 2022): A280. http://dx.doi.org/10.1121/10.0011341.

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Active sonar systems operating in the littoral environment are often reverberation-limited, which impacts detection performance by reducing the effective signal-to-noise ratio. Additionally, irregularities in the environment lead to excessive false alarms commonly referred to as sonar clutter. Clutter is found in all environments, but shallow-water littoral regions have been observed to be especially challenging. The irregularities that cause sonar clutter can have spatial scales ranging from much smaller to much greater than the dimensions of the sonar resolution cell. This talk will discuss physical interpretation and experimental assessment of signal statistics related to the transverse horizontal spatial coherence of reverberation estimated via split-beam processed data obtained from a line array. Multiple boundary interaction in shallow water depths and spatial variability randomize reverberation and may allow the use of Gaussian noise models. While match-filtered signal intensity is an optimal detection statistic in a Gaussian noise environment, spatial reverberation coherence statistics may provide complimentary information of the spatial scale of clutter features relative to the width of the beampattern. It is found these statistics may be used to help discern clutter types that are spatially compact from those that are non-compact when compared to the width of the sonar resolution cell.
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Wang, Fen-Jiao, Chang-Lin Mei, Zhi Zhang, and Qiu-Xia Xu. "Testing for Local Spatial Association Based on Geographically Weighted Interpolation of Geostatistical Data with Application to PM2.5 Concentration Analysis." Sustainability 14, no. 21 (November 7, 2022): 14646. http://dx.doi.org/10.3390/su142114646.

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Using local spatial statistics to explore local spatial association of geo-referenced data has attracted much attention. As is known, a local statistic is formulated at a particular sampling unit based on a prespecific proximity relationship and the observations in the neighborhood of this sampling unit. However, geostatistical data such as meteorological data and air pollution data are generally collected from meteorological or monitoring stations which are usually sparsely located or highly clustered over space. For such data, a local spatial statistic formulated at an isolate sampling point may be ineffective because of its distant neighbors, or the statistic is undefinable in the sub-regions where no observations are available, which limits the comprehensive exploration of local spatial association over the whole studied region. In order to overcome the predicament, a local-linear geographically weighted interpolation method is proposed in this paper to obtain the predictors of the underlying spatial process on a lattice spatial tessellation, on which a local spatial statistic can be well formulated at each interpolation point. Furthermore, the bootstrap test is suggested to identify the locations where local spatial association is significant using the interpolated-value-based local spatial statistics. Simulation with comparison to some existing interpolation and test methods is conducted to assess the performance of the proposed interpolation and the suggested test methods and a case study based on PM2.5 concentration data in Guangdong province, China, is used to demonstrate their applicability. The results show that the proposed interpolation method performs accurately in retrieving an underlying spatial process and the bootstrap test with the interpolated-value-based local statistics is powerful in identifying local patterns of spatial association.
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Dissertations / Theses on the topic "Spatial analysis (Statistics)"

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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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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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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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Butler, Thomas W. "Spatial statistics and analysis of earth's ionosphere." Thesis, Boston University, 2013. https://hdl.handle.net/2144/10950.

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Thesis (Ph.D.)--Boston University
The ionosphere, a layer of Earths upper atmosphere characterized by energetic charged particles, serves as a natural plasma laboratory and supplies proxy diagnostics of space weather drivers in the magnetosphere and the solar wind. The ionosphere is a highly dynamic medium, and the spatial structure of observed features (such as auroral light emissions, charge density, temperature, etc.) is rich with information when analyzed in the context of fluid, electromagnetic, and chemical models. Obtaining measurements with higher spatial and temporal resolution is clearly advantageous. For instance, measurements obtained with a new electronically-steerable incoherent scatter radar (ISR) present a unique space-time perspective compared to those of a dish-based ISR. However, there are unique ambiguities for this modality which must be carefully considered. The ISR target is stochastic, and the fidelity of fitted parameters (ionospheric densities and temperatures) requires integrated sampling, creating a tradeoff between measurement uncertainty and spatio-temporal resolution. Spatial statistics formalizes the relationship between spatially dispersed observations and the underlying process(es) they represent. A spatial process is regarded as a random field with its distribution structured (e.g., through a correlation function) such that data, sampled over a spatial domain, support inference or prediction of the process. Quantification of uncertainty, an important component of scientific data analysis, is a core value of spatial statistics. This research applies the formalism of spatial statistics to the analysis of Earth's ionosphere using remote sensing diagnostics. In the first part, we consider the problem of volumetric imaging using phased-array ISR based on optimal spatial prediction ("kriging"). In the second part, we develop a technique for reconstructing two-dimensional ion flow fields from line-of-sight projections using Tikhonov regularization. In the third part, we adapt our spatial statistical approach to global ionospheric imaging using total electron content (TEC) measurements derived from navigation satellite signals.
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Ho, Lai Ping. "Complete spatial randomness tests, intensity-dependent marking and neighbourhood competition of spatial point processes with applications to ecology." HKBU Institutional Repository, 2006. http://repository.hkbu.edu.hk/etd_ra/770.

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Maimon, Geva. "A Bayesian spatial analysis of glass data /." Thesis, McGill University, 2004. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=82284.

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In criminal investigations involving glass evidence, refractive index (RI) is the property of glass most commonly used by forensic examiners to determine the association between control samples of glass obtained at the crime scene, and samples of glass found on a suspect. Previous studies have shown that an intrinsic variability of RI exists within a pane of float glass. In this thesis, we attempt to determine whether this variability is spatially determined or random in nature, the conclusion of which plays an important role in the statistical interpretation of glass evidence. We take a Bayesian approach in fitting a spatial model to our data, and utilize the WinBUGS software to perform Gibbs sampling. To test for spatial variability, we propose two test quantities, and employ Bayesian Monte Carlo significance tests to test our data, as well as nine other specifically formulated data-sets.
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Oleson, Jacob J. "Bayesian spatial models for small area estimation /." free to MU campus, to others for purchase, 2002. http://wwwlib.umi.com/cr/mo/fullcit?p3052203.

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Assefa, Yared. "Time series and spatial analysis of crop yield." Thesis, Kansas State University, 2012. http://hdl.handle.net/2097/15142.

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Master of Science
Department of Statistics
Juan Du
Space and time are often vital components of research data sets. Accounting for and utilizing the space and time information in statistical models become beneficial when the response variable in question is proved to have a space and time dependence. This work focuses on the modeling and analysis of crop yield over space and time. Specifically, two different yield data sets were used. The first yield and environmental data set was collected across selected counties in Kansas from yield performance tests conducted for multiple years. The second yield data set was a survey data set collected by USDA across the US from 1900-2009. The objectives of our study were to investigate crop yield trends in space and time, quantify the variability in yield explained by genetics and space-time (environment) factors, and study how spatio-temporal information could be incorporated and also utilized in modeling and forecasting yield. Based on the format of these data sets, trend of irrigated and dryland crops was analyzed by employing time series statistical techniques. Some traditional linear regressions and smoothing techniques are first used to obtain the yield function. These models were then improved by incorporating time and space information either as explanatory variables or as auto- or cross- correlations adjusted in the residual covariance structures. In addition, a multivariate time series modeling approach was conducted to demonstrate how the space and time correlation information can be utilized to model and forecast yield and related variables. The conclusion from this research clearly emphasizes the importance of space and time components of data sets in research analysis. That is partly because they can often adjust (make up) for those underlying variables and factor effects that are not measured or not well understood.
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Wilson, Helen Elizabeth. "Statistical analysis of replicated spatial point patterns." Thesis, Lancaster University, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.268009.

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The field of pathology provides us with many opportunities for collecting replicated spatial data. Using an ordinary microscope, for example, we can digitise cell positions within windows imposed on pieces of tissue. Suppose now that we have some such replicated spatial data from several groups of individuals, where each point in each window represents a cell position. We seek to determine whether the spatial arrangement of cells differs between the groups. We propose and develop a new method which allows us to answer such questions, and apply it to some spatial neuro-anatomical data. We introduce point process theory, and extend the existing second order methods to deal with replicated spatial data. We conclude the first part of the thesis by defining Sudden Infant Death Syndrome (S.LD.S.) and Intra-Uterine Growth Retardation (LU.G.R.), and stating why these conditions are neuro-anato,mically interesting. We develop and validate a method for comparing groups of spatial data, which is motivated by analysis of variance, and uses a Monte Carlo procedure to attach significance to between-group differences. Having carried out our initial investigative work looking exclusively at the one-way set up, we extend the new methods to cope with two and higher way set ups, and again carry out some validation. We turn our attention to practical issues which arise in the collection of spatial neuroanatomical data. How, for example, should we collect the data to ensure the unbiasedness of any inference we may draw from it? We introduce the field of stereology which facilitates the unbiased sampling of tissue. We note a recent proposal to assess spatial distribution of cells using a stereological approach, and compare it with an existing second order method. We also note the level of structural heterogeneity within the brain, and consider the best way to design a sampling protocol. We conclude with a spatial analysis of cell position data, collected using our specified design, from normal birth-weight non S.LD.S., normal birth-weight S.I.D.S and low birth-weight S.LD.S cases.
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AvRuskin, Gillian. "Towards A Spatial Model of Rurality." Fogler Library, University of Maine, 2000. http://www.library.umaine.edu/theses/pdf/AvRuskinG2000.pdf.

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Books on the topic "Spatial analysis (Statistics)"

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

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1945-, Gelfand Alan E., ed. Handbook of spatial statistics. Boca Raton: Taylor & Francis, 2010.

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

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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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Jesper, Møller, ed. Spatial statistics and computational methods. New York: Springer, 2003.

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

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Marc, Moore, ed. Spatial statistics: Methodological aspects and applications. New York: Springer, 2001.

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Dale, Mark R. T. Spatial analysis: A guide for ecologists. Cambridge: Cambridge University Press, 2014.

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Stewart, Fotheringham A., and Rogerson Peter, eds. The SAGE handbook of spatial analysis. Los Angeles: SAGE Publications, 2009.

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Book chapters on the topic "Spatial analysis (Statistics)"

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Oliveira, Victor, and A. Alexandre Trindade. "Spatial Statistics." In Encyclopedia of Social Network Analysis and Mining, 1–15. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4614-7163-9_167-1.

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Oliveira, Victor, and A. Alexandre Trindade. "Spatial Statistics." In Encyclopedia of Social Network Analysis and Mining, 1976–90. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-6170-8_167.

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Oliveira, Victor, and A. Alexandre Trindade. "Spatial Statistics." In Encyclopedia of Social Network Analysis and Mining, 2882–95. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-7131-2_167.

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Ramsay, James O., Tim Ramsay, and Laura M. Sangalli. "Spatial Functional Data Analysis." In Contributions to Statistics, 269–75. Heidelberg: Physica-Verlag HD, 2011. http://dx.doi.org/10.1007/978-3-7908-2736-1_42.

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Scott, Lauren M., and Mark V. Janikas. "Spatial Statistics in ArcGIS." In Handbook of Applied Spatial Analysis, 27–41. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03647-7_2.

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Rura, Melissa J., and Daniel A. Griffith. "Spatial Statistics in SAS." In Handbook of Applied Spatial Analysis, 43–52. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03647-7_3.

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Nogueira, Pedro M. "Basic Statistics." In Spatial Analysis in Geology Using R, 71–90. Boca Raton: Chapman and Hall/CRC, 2024. http://dx.doi.org/10.1201/9781032651880-7.

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Fortin, Marie-Josée. "Spatial Statistics in Landscape Ecology." In Landscape Ecological Analysis, 253–79. New York, NY: Springer New York, 1999. http://dx.doi.org/10.1007/978-1-4612-0529-6_12.

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Nagel, Matthias. "Interactive Analysis of Spatial Data." In Contributions to Statistics, 295–314. Heidelberg: Physica-Verlag HD, 1994. http://dx.doi.org/10.1007/978-3-642-57991-2_17.

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Chainey, Spencer, and Jerry Ratcliffe. "Spatial Statistics for Crime Analysis." In GIS and Crime Mapping, 115–43. Chichester, West Sussex, England: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118685181.ch5.

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Conference papers on the topic "Spatial analysis (Statistics)"

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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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Liu, Guifang, and Heli Lu. "Statistics analysis on SPOT 5 classification accuracy of different data fusion methods." 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/10.1117/12.838450.

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Chen, Rongguo, and Siqing Chen. "Statistics analysis embedded in spatial DBMS." In Geoinformatics 2006: Geospatial Information Technology, edited by Huayi Wu and Qing Zhu. SPIE, 2006. http://dx.doi.org/10.1117/12.712952.

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CAKMAK, SABIT, RICK BURNETT, MICHAEL JERRETT, MARK S. GOLDBERG, ARDEN POPE, RENJUN MA, and DANIEL KREWSKI. "SPATIAL ASSOCIATION BETWEEN COMMUNITY AIR POLLUTION AND HEART DISEASE: ANALYSIS OF CORRELATED DATA." In Proceedings of Statistics 2001 Canada: The 4th Conference in Applied Statistics. PUBLISHED BY IMPERIAL COLLEGE PRESS AND DISTRIBUTED BY WORLD SCIENTIFIC PUBLISHING CO., 2002. http://dx.doi.org/10.1142/9781860949531_0007.

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Bhattacharya, Prosun, Julian Ijumulana, and Felix Mtalo. "SPATIAL STATISTICS: A TOOL FOR SPATIAL ANALYSIS OF GEOCONTAMINANTS IN GROUNDWATER." In GSA Annual Meeting in Seattle, Washington, USA - 2017. Geological Society of America, 2017. http://dx.doi.org/10.1130/abs/2017am-307971.

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Burgess, Arthur E. "Mammographic structure: data preparation and spatial statistics analysis." In Medical Imaging '99, edited by Kenneth M. Hanson. SPIE, 1999. http://dx.doi.org/10.1117/12.348620.

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Lan, Keyi, and Yanbing Liu. "A Spatial-Statistics based Framework of Spatial Correlation Analysis of Retail Sales*." In 2022 International Conference on Computers, Information Processing and Advanced Education (CIPAE). IEEE, 2022. http://dx.doi.org/10.1109/cipae55637.2022.00095.

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BOULEMNADJEL, Amel, Ferhat KAABACHE, Soumeya KHARFOUCHI, and Fella HACHOUF. "Higher-Order Spatial Statistics: A Strong Alternative In Image Processing." In 2018 3rd International Conference on Pattern Analysis and Intelligent Systems (PAIS). IEEE, 2018. http://dx.doi.org/10.1109/pais.2018.8598513.

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Meshkova, Elena, Janne Riihijärvi, Junaid Ansari, and Petri Mähönen. "Indoor coverage estimation from unreliable measurements using spatial statistics." In MSWiM '13: 16th ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems. New York, NY, USA: ACM, 2013. http://dx.doi.org/10.1145/2507924.2507957.

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Miranda, E. "Failure analysis of MIM and MIS structures using spatial statistics." In 2013 Spanish Conference on Electron Devices (CDE). IEEE, 2013. http://dx.doi.org/10.1109/cde.2013.6481384.

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Reports on the topic "Spatial analysis (Statistics)"

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Volpe Martincus, Christian, Pablo Sanguinetti, and Iulia Traistaru. Economic Integration and Location of Production Activities: The Case of Mercosur. Inter-American Development Bank, February 2004. http://dx.doi.org/10.18235/0008723.

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This research aims at answering the following questions: How do specialization patterns look like in member countries of Mercosur and how have they evolved over time? How do concentration patterns look like and how have they evolved over time? What are the main determinants of locational patterns? Did Mercosur have an impact on location of economic activities? What are the consequences for the smaller countries? Do we see production clusters? We analyse specialization, concentration, and locational patterns in Mercosur using production value data for the period 1971-1998. We identify the determinants of those patterns during the period 1985-1998 using econometric techniques. In addition, we calculate intraindustry trade statistics within Mercosur and with the Rest of the World for the period 1986-2001 in order to investigate the formation of clusters and uncover their determinants. We complement this analysis with a brief description of production developments in border regions with the purpose of determining whether this phenomenon has a specific spatial dimension. Finally, we draw some conclusions about the implications for smaller countries within the bloc and some lessons for upcoming trade initiatives such as a deepening of integration within the region, the establishment of FTAA, and a free trade agreement with the European Union.
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Fuentes, Anthony, Michelle Michaels, and Sally Shoop. Methodology for the analysis of geospatial and vehicle datasets in the R language. Cold Regions Research and Engineering Laboratory (U.S.), November 2021. http://dx.doi.org/10.21079/11681/42422.

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The challenge of autonomous off-road operations necessitates a robust understanding of the relationships between remotely sensed terrain data and vehicle performance. The implementation of statistical analyses on large geospatial datasets often requires the transition between multiple software packages that may not be open-source. The lack of a single, modular, and open-source analysis environment can reduce the speed and reliability of an analysis due to an increased number of processing steps. Here we present the capabilities of a workflow, developed in R, to perform a series of spatial and statistical analyses on vehicle and terrain datasets to quantify the relationship between sensor data and vehicle performance in winter conditions. We implemented the R-based workflow on datasets from a large, coordinated field campaign aimed at quantifying the response of military vehicles on snow-covered terrains. This script greatly reduces processing times of these datasets by combining the GIS, data-assimilation and statistical analyses steps into one efficient and modular interface.
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Stjernberg, Mats, Anna Vasilevskaya, and Oskar Penje. Towards a grid-based Nordic territorial typology - A new tool for analysis across the urban-rural continuum. Nordregio, March 2024. http://dx.doi.org/10.6027/r2024:91403-2503.

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This report presents the grid-based Nordic urban–rural typology, which was developed as a new analytical tool for studying different types of spatial phenomena across Nordic territories. In this study this meant developing a typology that classifies all Nordic territories into seven different typology classes based on different degrees of urbanity and rurality. A key starting point for this work was the need for a territorial typology that would help enrich and provide new understanding of different types of urban and rural areas across the Nordic countries and shed light on how they are developing. This report first presents how the typology was created, including the rationale behind the typology, key considerations at different stages of the work, and the main operational steps taken. The main purpose was to create a new territorial typology, to which different types of data could be combined, thus helping to provide a more nuanced and fine-grained understanding of territorial differences across the Nordic countries. Several key principles were specified early in the work. These include that the typology should be created at grid-level (1 x 1 km) as this allows identifying the characteristics of different types of areas at a very detailed territorial level. Another key decision was to create the typology mainly using open-source data and following a replicable method, to make any possible future updates to the typology easier and less costly. For the development of the Nordic typology, the Finnish grid-based urban–rural classification (Kaupunki-maaseutuluokitus) was the main source of inspiration. This Nordic typology and population data at grid level (linked to the typology) is then used as an analytical lens for studying territorial differences, settlement pattens and demographic change dynamics in the five Nordic countries. According to the typology, the Nordic countries are predominantly rural when considering how their land areas are classified. However, an examination of settlement patterns according to the Nordic typology shows that the settlements are rather unevenly distributed in all the Nordic countries, and the majority of the population live relatively concentrated in areas that are classified as urban. In general, the population is largely concentrated in coastal areas and along waterways, where the major urban regions are found, reflecting historical patterns and features of physical geography. The Nordic typology is also used to examine what types of population change dynamics occurred in the Nordic countries during the period 2008–2022. The analysis shows that urbanisation has been a general trend during the past couple of decades, with the largest population growth occurring in the typology classes inner urban and outer urban. A relatively noticeable increase in population is also evident in peri-urban areas, suggesting suburbanisation and that intermediate areas located on the urban fringes have increasingly attracted new residents. In rural areas, the general trend shows that depopulation has occurred in many rural localities, but different types of rural areas have developed quite differently. Based on the analysis, rural areas that are in the vicinity of cities and towns appear to have become more attractive places for people to settle, while sparsely populated rural areas seem to be less favourably placed and have generally witnessed population decrease. This report shows how this typology and more fine-grained data can help reveal territorial differences that cannot be observed with more general statistics and data. The grid-based Nordic typology shows that many municipalities are at the same time both urban, intermediate, and rural, and in many cases these different categories seem to be undergoing quite different types of development. While the Nordic urban–rural typology is used in this study to examine settlement patterns and population change dynamics, it should be stressed that the typology is also well-suited to be used in combination with other types of data and as an analytical framework for studying also other types of spatial phenomena across the urban–rural continuum.
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Anderson, Andrew, and Mark Yacucci. Inventory and Statistical Characterization of Inorganic Soil Constituents in Illinois: Appendices. Illinois Center for Transportation, June 2021. http://dx.doi.org/10.36501/0197-9191/21-007.

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This report presents detailed histograms of data from the Regulated Substances Library (RSL) developed by the Illinois Department of Transportation (IDOT). RSL data are provided for state and IDOT region, IDOT district, and county spatial subsets to examine the spatial variability and its relationship to thresholds defining natural background concentrations. The RSL is comprised of surficial soil chemistry data obtained from rights-of-way (ROW) subsurface soil sampling conducted for routine preliminary site investigations. A selection of 22 inorganic soil analytes are examined in this report: Al, Sb, As, Ba, Be, Cd, Ca, Cr, Co, Cu, Fe, Pb, Mg, Mn, Hg, Ni, K, Se, Na, Tl, V, and Zn. RSL database summary statistics, mean, median, minimum, maximum, 5th percentile, and 95th percentile, are determined for Illinois counties and for recognized environmental concern, non-recognized environmental concern, and de minimis site contamination classifications.
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Zandiatashbar, Ahoura, Jochen Albrecht, and Hilary Nixon. A Bike System for All in Silicon Valley: Equity Assessment of Bike Infrastructure in San José, CA. Mineta Transportation Institute, October 2023. http://dx.doi.org/10.31979/mti.2023.2162.

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Investing in sustainable, multimodal infrastructure is of increasing importance throughout the United States and worldwide. Cities are increasingly making strategic capital investment decisions about bicycle infrastructure—decisions that need planning efforts that accurately assess the equity aspects of developments, achieve equitable distribution of infrastructures, and draw upon accurate assessment methods. Toward these efforts, this project uses a granular bike network dataset with statistical and geospatial analyses to quantify a bike infrastructure availability score (i.e., bike score) that accounts for the safety and comfort differences in bike path classes in San José, California. San José is the 10th largest U.S. city and a growing tech hub with a booming economy, factors that correlate with increased traffic congestion if adequate multimodal and active transportation infrastructure are not in place. Therefore, San José has been keen on becoming “one of the most bike-friendly communities in North America.” The City’s new plan, which builds on its first bike plan adopted in 2009, envisions a 557-mile network of allages-and-abilities bikeways to support a 20% bicycle mode split (i.e., 20% of all trips to be made by bike) by 2050. Hence, San José makes a perfect study area for piloting this project’s methodology for accurately assessing the equity of urban bike plans and infrastructures. The project uses the above-mentioned bike score (representing the bike infrastructure supply status) and San José residents’ bike travel patterns (to show bike trip demand status) utilizing StreetLight data to answer the following questions: (1) Where are San José's best (bike paradise) and worst (bike desert) regions for cycling? (2) How different are the socioeconomic attributes of San José’s bike desert and paradise residents? (3) Has San José succeeded in achieving an equitable infrastructure distribution and, if so, to what extent? And, (4) has the availability of infrastructure attracted riders from underserved communities and, if so, to what extent? Using the bike infrastructure availability score, this research measures and maps the City of San José's best and worst regions for cycling through geospatial analyses to answer Question 1 above. Further spatial and statistical analyses including t-tests, Pairwise Pearson correlation analysis, descriptive analysis, spatial visualization, principal component analysis (PCA), and multiple regression models to answer Questions 2, 3, and 4. In addition to this report, the findings are used to develop an open access web-tool, the San José Bike Equity Web Map (SJ-BE iMap). This research contributes to the critical assessment and planning efforts of sustainable, multimodal infrastructure in California and beyond.
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Liu, Yining, Jesus Osorio, and Yanfeng Ouyang. Quantifying Impacts of the COVID-19 Pandemic on Ridership of CTA Rail and Bus Systems in Chicago. Illinois Center for Transportation, April 2023. http://dx.doi.org/10.36501/0197-9191/23-002.

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This study’s research team conducted a comprehensive statistical analysis to help transit agencies better understand factors that may have contributed to transit ridership loss and the extent of its impacts. Building off ICT-IDOT project R27-SP45, they developed a series of statistical models for the Chicago Transit Authority’s rail and bus systems. Data-driven analysis of the COVID-19 pandemic’s impacts on CTA bus and rail ridership can help the Illinois Department of Transportation and Regional Transportation Authority, as well as other transit agencies, make policy decisions on planning resources and services during and after the pandemic. This study’s research team observed that most of the identified pandemic and socioeconomic factors, especially work occupancy rates, vaccination rates, discount programs, and crime rates, have affected over 80 to 90 percent of all CTA rail stations and bus lines. It is also observed that different population groups may react differently to policy decisions. The fare discount program, for example, may be most successful in attracting transit trips from the employed population in the health and retail industries. In particular, the temporal and spatial analyses show that work occupancy rates are crucial to answering most of the ridership loss at all of CTA’s bus lines and rail stations because workplace commute trips have driven a large proportion of CTA ridership. Therefore, transit ridership recovery may depend on individual industries’ remote work policies rather than city-wide quarantine executive orders. This could further suggest that transit agencies may need to collaborate closely with specific industry sectors to expedite the recovery of public transit ridership.
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Saltus, Christina, Richard Johansen, Molly Reif, Weston Nowlin, Benjamin Schwartz, and Joshuah Perkins. Next Generation Ecological Models - Central Texas Watersheds: Geospatial Layers and Related Tables. Engineer Research and Development Center (U.S.), September 2023. http://dx.doi.org/10.21079/11681/47608.

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Relevant geospatial data layers are required for developing next generation ecological response models for specific reaches of 5 rivers in Central Texas: Colorado, Concho, San Saba, Llano, and Pedernales Rivers. Therefore, a collaborative effort between Engineer Research and Development Center (ERDC), Texas State University, and Texas A&M University was undertaken to acquire and curate a collection of biological and physical datasets to be utilized as inputs for next generation ecological response models at various spatial scales (watershed, river buffer, and point). The objective was to aggregate and process GIS and remote sensing data layers using advanced geospatial analytics to generate relevant metrics (e.g., landcover, elevation, soil erodibility). Next, subsets of the priority datasets were extracted based on two spatial units of interest (local buffer and watershed). The priorities include (1) Percent land use and land cover information from National Land Cover Dataset (NLCD) land use/land cover class types (forest, cultivated land, open water, etc.) and soil composition (%); LU change, (2) Geomorphology for each sampling point, (3) Physiographic information on each sampling point, and (4) Linear stream distance between sampling points (5 Secondary) Distance/network analyses. This work was funded through ERDC’s Next Generation Ecological Modeling Program under the work unit titled, “Remote Sensing and statistical support for multi-scale field-based data collection”.
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Johansen, Richard, Alan Katzenmeyer, Kaytee Pokrzywinski, and Molly Reif. A review of sensor-based approaches for monitoring rapid response treatments of cyanoHABs. Engineer Research and Development Center (U.S.), July 2023. http://dx.doi.org/10.21079/11681/47261.

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Water quality sensors are dynamic and vary greatly both in terms of utility and data acquisition. Data collection can range from single-parameter and one-dimensional to highly complex multiparameter spatiotemporal. Likewise, the analytical and statistical approaches range from relatively simple (e.g., linear regression) to more complex (e.g., artificial neural networks). Therefore, the decision to implement a particular water quality monitoring strategy is dependent upon many factors and varies widely. The purpose of this review was to document the current scientific literature to identify and compile approaches for water quality monitoring as well as statistical methodologies required to analyze and visualize highly diverse spatiotemporal water quality data. The literature review identified two broad categories: (1) sensor-based approaches for monitoring rapid response treatments of cyanobacterial harmful algal blooms (cyanoHABs), and (2) analytical tools and techniques to analyze complex high resolution spatial and temporal water quality data. The ultimate goal of this review is to provide the current state of the science as an array of scalable approaches, spanning from simple and practical to complex and comprehensive, and thus, equipping the US Army Corps of Engineers (USACE) water quality managers with options for technology-analysis combinations that best fit their needs.
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Jury, William A., and David Russo. Characterization of Field-Scale Solute Transport in Spatially Variable Unsaturated Field Soils. United States Department of Agriculture, January 1994. http://dx.doi.org/10.32747/1994.7568772.bard.

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This report describes activity conducted in several lines of research associated with field-scale water and solute processes. A major effort was put forth developing a stochastic continuum analysis for an important class of problems involving flow of reactive and non reactive chemicals under steady unsaturated flow. The field-scale velocity covariance tensor has been derived from local soil properties and their variability, producing a large-scale description of the medium that embodies all of the local variability in a statistical sense. Special cases of anisotropic medium properties not aligned along the flow direction of spatially variable solute sorption were analysed in detail, revealing a dependence of solute spreading on subtle features of the variability of the medium, such as cross-correlations between sorption and conductivity. A novel method was developed and tested for measuring hydraulic conductivity at the scale of observation through the interpretation of a solute transport outflow curve as a stochastic-convective process. This undertaking provided a host of new K(q) relationships for existing solute experiments and also laid the foundation for future work developing a self-consistent description of flow and transport under these conditions. Numerical codes were developed for calculating K(q) functions for a variety of solute pulse outflow shapes, including lognormal, Fickian, Mobile-Immobile water, and bimodal. Testing of this new approach against conventional methodology was mixed, and agreed most closely when the assumptions of the new method were met. We conclude that this procedure offers a valuable alternative to conventional methods of measuring K(q), particularly when the application of the method is at a scale (e.g. and agricultural field) that is large compared to the common scale at which conventional K(q) devices operate. The same problem was approached from a numerical perspective, by studying the feasibility of inverting a solute outflow signal to yield the hydraulic parameters of the medium that housed the experiment. We found that the inverse problem was solvable under certain conditions, depending on the amount of noise in the signal and the degree of heterogeneity in the medium. A realistic three dimensional model of transient water and solute movement in a heterogeneous medium that contains plant roots was developed and tested. The approach taken was to generate a single realization of this complex flow event, and examine the results to see whether features were present that might be overlooked in less sophisticated model efforts. One such feature revealed is transverse dispersion, which is a critically important component in the development of macrodispersion in the longitudinal direction. The lateral mixing that was observed greatly exceeded that predicted from simpler approaches, suggesting that at least part of the important physics of the mixing process is embedded in the complexity of three dimensional flow. Another important finding was the observation that variability can produce a pseudo-kinetic behavior for solute adsorption, even when the local models used are equilibrium.
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Psuty, Norbert, Chritopher Menke, Andrea Habeck, and Jordan Raphael. Shoreline position monitoring at Fire Island National Seashore: 2017–2022 and 2007–2022 trend report. National Park Service, June 2023. http://dx.doi.org/10.36967/2299483.

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The Shoreline Position Trend Report summarizes the results of data collected in 2017 through 2022 along the ocean-facing shoreline of Fire Island National Seashore in addition to data collected in 2007 through 2017. Data sets include a spatial depiction of annual shoreline position as well as statistical analysis describing annual and 5-year, and 15-year changes, all following the model presented in Psuty et al. (2022b). Generally, the largest vectors of shoreline position change were produced by natural events such as storms and by human-induced cultural events such as beach nourishment or dredging. The most mobile portion of shoreline was at Democrat Point. At this site, the shoreline downdrift of the jetty was extending westward and was cut back due to dredging. The other portion of the shoreline with the largest vectors of change was in the Otis Pike Fire Island High Dune Wilderness. The NPS lands and community lands had different rates of change, with higher rates of erosion along the western portion of Fire Island. Beach nourishment episodes at Smith Point County Park and within community lands tempered the rates of inland displacement. This is the third trend report for Fire Island National Seashore produced for the Northeast Coastal and Barrier Network in accordance with the protocol for shoreline position monitoring. The first trend report covered from 2007 through 2012 (Psuty et al. 2017), the second trend report covered 2012 through 2017 (Psuty et al. 2018). Generally, the most recent trend is similar to the earlier reporting periods, with storminess and anthropogenic influences affecting shoreline position.
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