Relevant bibliographies by topics / Spatial analysis (Statistics) Regression analysis

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

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

Author: Grafiati
Published: 4 June 2021
Last updated: 7 February 2022

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

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Dusek, Tamás. "Bidimensional Regression in Spatial Analysis." Regional Statistics 2, no. 1 (2012): 61–73. http://dx.doi.org/10.15196/rs02105.

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Gamerman, Dani, and Ajax R. B. Moreira. "Multivariate spatial regression models." Journal of Multivariate Analysis 91, no. 2 (November 2004): 262–81. http://dx.doi.org/10.1016/j.jmva.2004.02.016.

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Rahman, S., L. C. Munn, R. Zhang, and G. F. Vance. "Rocky Mountain forest soils: Evaluating spatial variability using conventional statistics and geostatistics." Canadian Journal of Soil Science 76, no. 4 (November 1, 1996): 501–7. http://dx.doi.org/10.4141/cjss96-062.

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Spatial variability of soils is a landscape attribute which soil scientists must identify and understand if they are to construct useful soils maps. This paper describes the spatial variability of soils in a forested watershed in the Medicine Bow Mountains, Wyoming, using both conventional statistics and geostatistics. Principle Components Analysis indicated that flow accumulation and aspect were the two terrain attributes that most economically described terrain variability. Thickness of A and B horizons, organic carbon and solum coarse fragments were variable in the study area (CVs of 40 to 58%). Simple correlation and regression analyses suggested there were no statistically significant relationships between soil properties (texture, pH, coarse fragments, organic carbon content) and terrain attributes (elevation, slope gradient, slope shape, flow accumulation, aspect). Geostatistical analysis indicated thickness and coarse fragment contents of the A and B horizons, and solum thickness were spatially independent variables; however, pH, organic carbon content, and solum coarse fragment content were spatially correlated. Spatial variability was described by both linear (pH and organic carbon content) and spherical (solum coarse fragment) models. Use of geostatistics provided insight into the nature of variability in soil properties across the landscape of the Libby Creek watershed when conventional statistics (analysis of variance and regression analysis) did not. Key words: Rocky Mountains, Medicine Bow Mountains, forest soils, spatial variability, principle component analysis, geostatistics
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Bernardi, Mara S., Michelle Carey, James O. Ramsay, and Laura M. Sangalli. "Modeling spatial anisotropy via regression with partial differential regularization." Journal of Multivariate Analysis 167 (September 2018): 15–30. http://dx.doi.org/10.1016/j.jmva.2018.03.014.

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SALTYTE-BENTH, JURATE, and KESTUTIS DUCINSKAS. "Linear Discriminant Analysis of Multivariate Spatial-Temporal Regressions." Scandinavian Journal of Statistics 32, no. 2 (June 2005): 281–94. http://dx.doi.org/10.1111/j.1467-9469.2005.00421.x.

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Fried, Roland. "An investigation of humus disintegration by spatial-temporal regression analysis." Journal of Agricultural, Biological, and Environmental Statistics 9, no. 2 (June 2004): 138–57. http://dx.doi.org/10.1198/1085711043127_a.

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Hepple, Leslie W. "Multiple Regression and Spatial Policy Analysis: George Udny Yule and the Origins of Statistical Social Science." Environment and Planning D: Society and Space 19, no. 4 (August 2001): 385–407. http://dx.doi.org/10.1068/d291.

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Studies on the history of statistics by MacKenzie and on quantitative geography by Barnes have suggested that the lineaments and assumptions of statistical methods such as correlation and regression are closely related to their origin in biometrics and eugenics. This paper challenges that view by examining in detail the work of George Udny Yule. Yule was a colleague of Karl Pearson in the 1890s, but was interested in social science and social policy applications, not eugenics. In the late 1890s he constructed both the theory and application of multiple regression analysis, using geographical data. The paper examines Yule's work and its context, relating it to debates on the history of statistics, and traces the subsequent early diffusion of regression and correlation into the social sciences. The paper concludes by arguing for greater recognition of Yule's pivotal role, and also for further studies on the history of quantitative social science.
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Rojas-Gualdrón, Diego Fernando. "Comparing definitions of spatial relations for the analysis of geographic disparities in mortality within a Bayesian mixed-effects framework." Revista Brasileira de Epidemiologia 20, no. 3 (July 2017): 487–500. http://dx.doi.org/10.1590/1980-5497201700030011.

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ABSTRACT: Objective: To analyze the conceptual and technical differences between three definitions of spatial relations within a Bayesian mixed-effects framework: classical multilevel definition, spatial multiple membership definition and conditional autoregressive definition with an illustration of the estimate of geographic disparities in early neonatal mortality in Colombia, 2011-2014. Methods: A registry based cross-sectional study was conducted. Births and early neonatal deaths were obtained from the Colombian vital statistics registry for 2011-2014. Crude and adjusted Bayesian mixed effects regressions were performed for each definition of spatial relation. Model fit statistics, spatial autocorrelation of residuals and estimated mortality rates, geographic disparity measures, relative ratios and relative differences were compared. Results: The definition of spatial relations between municipalities based on the conditional autoregressive prior showed the best performance according to both fit statistics and residual spatial pattern analyses. Spatial multiple membership definition had a poor performance. Conclusion: Bayesian mixed effects regression with conditional autoregressive prior as an analytical framework may be an important contribution to epidemiological design as an improved alternative to ecological methods in the analyses of geographic disparities of mortality, considering potential ecological bias and spatial model misspecification.
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Sifriyani, Sifriyani, Ruslan Ruslan, and Susanty, F. H. "Mapping and Analysis Factors of Affecting Productivity Tropical Rain Forests in East Kalimantan." Modern Applied Science 13, no. 10 (September 24, 2019): 112. http://dx.doi.org/10.5539/mas.v13n10p112.

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Up to 2019, tropical rainforests in East Kalimantan has been experiencing very rapid degradation and continues to shrink. Therefore, it is necessary to evaluate mapping and analysis of factors affecting the productivity of tropical rain forests in East Kalimantan. The purpose of this study was to determine the factors that cause shrinkage of tropical rainforests in East Kalimantan based on spatial statistical perspectives. The data used were secondary data from the Indonesian Ministry of Forestry and the Central Bureau of Statistics. The data consisted of 10 districts/cities from East Kalimantan Province. Those data were influenced by spatial dependence and spatial heterogeneity. Nonparametric Geospatial Regression (NGR) is one of the spatial statistical methods used to overcome spatial dependence and spatial heterogeneity. The results of the study obtained was a Nonparametric Geospatial Regression modeling using the Gaussian Kernel geographical weighting function with a minimum CV value of 1.48. The model had R2 values for each district/city ranging from 74.39% - 88.65%.  The goodness of fit of the NGR model was shown by the value of R2 = 0.8865, which stated that the variables that significantly affect the preservation of tropical rainforest by 88.65%  were the area of protected forests, nature reserves and nature preservation, production forests, area of each district/city, economic growth rate and regional development index.
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Su, Liyun, Changhai Liang, Xiaohe Yang, and Yang Liu. "Influence Factors Analysis of Provincial Divorce Rate Spatial Distribution in China." Discrete Dynamics in Nature and Society 2018 (July 16, 2018): 1–11. http://dx.doi.org/10.1155/2018/6903845.

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Divorce is the primary factor affecting the harmony and stability of the family and society. This paper uses spatial statistics to analyze the potential social causes of influencing the spatial distribution of divorce rates in various provinces of China. Firstly, the factors of social influence, family cohesion, and ethnic customs are constructed by factor analysis, then the spatial interaction effect of divorce rate in each province is brought into the model, and the spatial regression analysis of these three factors is carried out. The results show that social influence, especially the tertiary industry share of GDP, has a significant influence on the divorce rate, family cohesion has a distinct negative effect on the divorce rate, and ethnic customs have a noteworthy impact on the divorce rate. It is reflected in the high divorce rate of the majority of ethnic minority population, while, in the spatial data processing, the factor spatial lag model (FSLM) is better than the ordinary least square (OLS) regression model.
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Dissertations / Theses on the topic "Spatial analysis (Statistics) Regression analysis"

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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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Sikdar, Khokan Chandra. "Application of geographically weighted regression for assessing spatial non-stationarity /." Internet access available to MUN users only, 2003. http://collections.mun.ca/u?/theses,172881.

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Wheeler, David C. "Diagnostic tools and remedial methods for collinearity in linear regression models with spatially varying coefficients." Columbus, Ohio : Ohio State University, 2006. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1155413322.

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Burke, Tommy. "Evaluation of visualisations of geographically weighted regression, with perceptual stability." Thesis, University of St Andrews, 2016. http://hdl.handle.net/10023/15680.

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Given the large volume of data that is regularly accumulated, the need to properly manage, efficiently display and correctly interpret, becomes more important. Complex analysis of data is best performed using statistical models and in particular those with a geographical element are best analysed using Spatial Statistical Methods, including local regression. Spatial Statistical Methods are employed in a wide range of disciplines to analyse and interpret data where it is necessary to detect significant spatial patterns or relationships. The topic of the research presented in this thesis is an exploration of the most effective methods of visualising results. A human being is capable of processing a vast amount of data as long as it is effectively displayed. However, the perceptual load will at some point exceed the cognitive processing ability and therefore the ability to comprehend data. Although increases in data scale did increase the cognitive load and reduce processing, prior knowledge of geographical information systems did not result in an overall processing advantage. The empirical work in the thesis is divided into two parts. The first part aims to gain insight into visualisations which would be effective for interpretation and analysis of Geographically Weighted Regression (GWR), a popular Spatial Statistical Method. Three different visualisation techniques; two dimensional, three dimensional and interactive, are evaluated through an experiment comprising two data set sizes. Interactive visualisations perform best overall, despite the apparent lack of researcher familiarity. The increase in data volume can present additional complexity for researchers. Although the evaluation of the first experiment augments understanding of effective visualisation display, the scale at which data can be adequately presented within these visualisations is unclear. Therefore, the second empirical investigation seeks to provide insight into data scalability, and human cognitive limitations associated with data comprehension. The general discussion concludes that there is a need to better inform researchers of the potential of interactive visualisations. People do need to be properly trained to use these systems, but the limits of human perceptual processing also need to be considered in order to permit more efficient and insightful analysis.
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Kordi, Maryam. "Geographically weighted spatial interaction (GWSI)." Thesis, University of St Andrews, 2013. http://hdl.handle.net/10023/4112.

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One of the key concerns in spatial analysis and modelling is to study and analyse similarities or dissimilarities between places over geographical space. However, ”global“ spatial models may fail to identify spatial variations of relationships (spatial heterogeneity) by assuming spatial stationarity of relationships. In many real-life situations spatial variation in relationships possibly exists and the assumption of global stationarity might be highly unrealistic leading to ignorance of a large amount of spatial information. In contrast, local spatial models emphasise differences or dissimilarity over space and focus on identifying spatial variations in relationships. These models allow the parameters of models to vary locally and can provide more useful information on the processes generating the data in different parts of the study area. In this study, a framework for localising spatial interaction models, based on geographically weighted (GW) techniques, has been developed. This framework can help in detecting, visualising and analysing spatial heterogeneity in spatial interaction systems. In order to apply the GW concept to spatial interaction models, we investigate several approaches differing mainly in the way calibration points (flows) are defined and spatial separation (distance) between flows is calculated. As a result, a series of localised geographically weighted spatial interaction (GWSI) models are developed. Using custom-built algorithms and computer code, we apply the GWSI models to a journey-to-work dataset in Switzerland for validation and comparison with the related global models. The results of the model calibrations are visualised using a series of conventional and flow maps along with some matrix visualisations. The comparison of the results indicates that in most cases local GWSI models exhibit an improvement over the global models both in providing more useful local information and also in model performance and goodness-of-fit.
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Wang, Zilong. "Analysis of Binary Data via Spatial-Temporal Autologistic Regression Models." UKnowledge, 2012. http://uknowledge.uky.edu/statistics_etds/3.

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Spatial-temporal autologistic models are useful models for binary data that are measured repeatedly over time on a spatial lattice. They can account for effects of potential covariates and spatial-temporal statistical dependence among the data. However, the traditional parametrization of spatial-temporal autologistic model presents difficulties in interpreting model parameters across varying levels of statistical dependence, where its non-negative autocovariates could bias the realizations toward 1. In order to achieve interpretable parameters, a centered spatial-temporal autologistic regression model has been developed. Two efficient statistical inference approaches, expectation-maximization pseudo-likelihood approach (EMPL) and Monte Carlo expectation-maximization likelihood approach (MCEML), have been proposed. Also, Bayesian inference is considered and studied. Moreover, the performance and efficiency of these three inference approaches across various sizes of sampling lattices and numbers of sampling time points through both simulation study and a real data example have been studied. In addition, We consider the imputation of missing values is for spatial-temporal autologistic regression models. Most existing imputation methods are not admissible to impute spatial-temporal missing values, because they can disrupt the inherent structure of the data and lead to a serious bias during the inference or computing efficient issue. Two imputation methods, iteration-KNN imputation and maximum entropy imputation, are proposed, both of them are relatively simple and can yield reasonable results. In summary, the main contributions of this dissertation are the development of a spatial-temporal autologistic regression model with centered parameterization, and proposal of EMPL, MCEML, and Bayesian inference to obtain the estimations of model parameters. Also, iteration-KNN and maximum entropy imputation methods have been presented for spatial-temporal missing data, which generate reliable imputed values with the reasonable efficient imputation time.
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Sha, Zhe. "Estimation of conditional auto-regressive models." Thesis, University of Oxford, 2016. https://ora.ox.ac.uk/objects/uuid:6cc56943-2b4d-4931-895a-f3ab67e48e3a.

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Conditional auto-regressive (CAR) models are frequently used with spatial data. However, the likelihood of such a model is expensive to compute even for a moderately sized data set of around 1000 sites. For models involving latent variables, the likelihood is not usually available in closed form. In this thesis we use a Monte Carlo approximation to the likelihood (extending the approach of Geyer and Thompson (1992)), and develop two strategies for maximising this. One strategy is to limit the step size by defining an experimental region using a Monte Carlo approximation to the variance of the estimates. The other is to use response surface methodology. The iterative procedures are fully automatic, with user-specified options to control the simulation and convergence criteria. Both strategies are implemented in our R package mclcar. We demonstrate aspects of the algorithms on simulated data on a torus, and achieve similar results to others in a short computational time on two datasets from the literature. We then use the methods on a challenging problem concerning forest restoration with data from around 7000 trees arranged in transects within study plots. We modelled the growth rate of the trees by a linear mixed effects model with CAR spatial error and CAR random e ects for study plots in an acceptable computational time. Our proposed methods can be used for similar models to provide a clearly defined framework for maximising Monte Carlo approximations to likelihoods and reconstructing likelihood surfaces near the maximum.
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Martinho, Maria. "Spatial analysis of exposure coefficients with applications to stomach cancer." Thesis, University of Oxford, 2007. http://ora.ox.ac.uk/objects/uuid:427fe13e-39b1-4bfd-a3a8-be957120cf44.

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Earlier ecological studies on the relation between H. pylori infection and stomach cancer have considered that the relation between these two variables, as estimated by the exposure coefficient, is constant. However, there is evidence to suggest that this relation changes geographically due to differences in strains of H. pylori. Since the prevalence of H. pylori varies with socio-economic status, the association between the latter and stomach cancer mortality may also vary geographically. This thesis studies stomach cancer by taking into account the geographical variability of the exposure coefficients. The study proposes the use of regression mixtures, clustering models and spatially varying regressions for the study of varying exposure coefficients. The effect of transformations of variables in these models appears to have been little considered. We provide new necessary conditions for invariance under transformations of variables for mixed effect models in general, and for the proposed models in particular. In addition, we show that varying exposure coefficients may induce a varying baseline risk. The regression mixtures and the clustering model are applied to a data set on stomach cancer incidence and H. pylori prevalence in 57 countries worldwide. We extend the clustering model to reflect any distance measure between the geographical units, including the Euclidean distance, in the formation of clusters. We also show that the clustering model performs better than the regression mixture model when the aim is to identify connected clusters and the observations present large variance. The results obtained with the clustering model supported the existence of three clusters where the interaction between the human and H. pylori populations have similar characteristics. Spatially varying regressions are applied to a data set of areal death counts of stomach cancer and spending power in 275 counties in continental Portugal. We provide an original strategy for implementing multivectorial intrinsic autoregressions as the distribution for the random effects. The results obtained with the application of this methodology were consistent with a varying exposure coefficient of spending power.
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Huang, Fang. "Modeling patterns of small scale spatial variation in soil." Link to electronic thesis, 2006. http://www.wpi.edu/Pubs/ETD/Available/etd-011106-155345/.

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Thesis (M.S.)--Worcester Polytechnic Institute.
Keywords: spatial variations; nested random effects models; semivariogram models; kriging methods; multiple logistic regression models; missing; multiple imputation. Includes bibliographical references (p. 35-36).
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Chun, Yongwan. "Behavioral specifications of network autocorrelation in migration modeling an analysis of migration flows by spatial filtering /." Columbus, Ohio : Ohio State University, 2007. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1187188476.

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Books on the topic "Spatial analysis (Statistics) Regression analysis"

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Ward, Michael Don. Spatial regression models. Thousand Oaks: Sage Publications, 2008.

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Griffith, Daniel A. Spatial regression analysis on the PC: Spatial statistics using Minitab. [Ann Arbor, Mich., U.S.A.] (2790 Briarcliff, Ann Arbor 48105): Institute of Mathematical Geography, 1989.

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Griffith, Daniel A. Spatial regression analysis on the PC: Spatial statistics using SAS. Washington, D.C: Association of American Geographers, 1993.

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

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Chris, Brunsdon, and Charlton Martin, eds. Geographically weighted regression: The analysis of spatially varying relationships. Chichester, England: Wiley, 2002.

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Thomas, Kneib, ed. Bayesian smoothing and regression for longitudinal, spatial and event history data. Oxford: Oxford University Press, 2011.

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Johnson, Laura D. Smoothing spatial data by estimating mean local variance. Monterey, Calif: Naval Postgraduate School, 1988.

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Petrucci, Alessandra. The application of a spatial regression model to the analysis and mapping of poverty. Rome: Food and Agriculture Organization of the United Nations, 2003.

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Understanding regression analysis. New York: Plenum Press, 1997.

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S, Hadi Ali, ed. Regression analysis by example. Hoboken, New Jersey: Wiley, 2012.

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

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Zakaria, Syerrina, and Nuzlinda Abdul Rahman. "Explorative Spatial Analysis of Crime Rates Among the District of Peninsular Malaysia: Geographically Weighted Regression." In Proceedings of the International Conference on Computing, Mathematics and Statistics (iCMS 2015), 145–56. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-2772-7_15.

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Rees, D. G. "Regression analysis." In Essential Statistics, 153–66. Boston, MA: Springer US, 1989. http://dx.doi.org/10.1007/978-1-4899-7260-6_14.

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Christensen, Ronald. "Regression Analysis." In Springer Texts in Statistics, 110–42. New York, NY: Springer New York, 1996. http://dx.doi.org/10.1007/978-1-4757-2477-6_6.

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Christensen, Ronald. "Regression Analysis." In Springer Texts in Statistics, 122–55. New York, NY: Springer New York, 2002. http://dx.doi.org/10.1007/978-0-387-21544-0_6.

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Christensen, Ronald. "Regression Analysis." In Springer Texts in Statistics, 145–95. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-32097-3_6.

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Christensen, Ronald. "Regression Analysis." In Springer Texts in Statistics, 121–61. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-9816-3_6.

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Etzioni, Ruth, Micha Mandel, and Roman Gulati. "Regression Analysis." In Springer Texts in Statistics, 37–63. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59889-1_3.

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Christensen, Ronald. "Regression Analysis." In Springer Texts in Statistics, 85–112. New York, NY: Springer New York, 1987. http://dx.doi.org/10.1007/978-1-4757-1951-2_6.

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Filzmoser, Peter, Karel Hron, and Matthias Templ. "Regression Analysis." In Springer Series in Statistics, 181–205. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96422-5_10.

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Sahu, Pradip Kumar. "Regression Analysis." In Applied Statistics for Agriculture, Veterinary, Fishery, Dairy and Allied Fields, 223–75. New Delhi: Springer India, 2016. http://dx.doi.org/10.1007/978-81-322-2831-8_8.

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

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Wang, Hui, Saumuy Suriano, Liang Zhou, and S. Jack Hu. "High-Definition Metrology Based Spatial Variation Pattern Analysis for Machining Process Monitoring and Diagnosis." In ASME 2009 International Manufacturing Science and Engineering Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/msec2009-84154.

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Non-contact high-definition measurement technology, such as laser holographic interferometry, makes it feasible to quickly inspect dimensional variation at micron level, providing up to 2 million data points over a surface area of up to 300×300 mm2. Such high-definition metrology (HDM) data contain rich spatial variation information but it is challenging to utilize this information for process monitoring and control. The spatial distribution of the data is in high-dimensional form and may show nonlinear patterns. Conventional statistical process monitoring and diagnostic schemes based on simple test statistics and linear statistical process models are incapable of capturing the complex surface characteristics as reflected by large amounts of spatial data. This paper develops a framework for efficient monitoring of spatial variation in HDM data using principal curves and quality control charts. Since large scale surface variation patterns (caused by fixturing and part bending) may camouflage those in the smaller scale (generally associated with tooling conditions), it is essential to separate the patterns in these scales and monitor them individually. At each scale, process monitoring is implemented in a sequential manner by monitoring the overall spatial features followed by localized variation identification if an out-of-control condition is detected. To examine the overall spatial characteristics, a principal-component-analysis (PCA) filtered principal curve regression is proposed in conjunction with multivariate control charts whereby nonlinear patterns of spatial data are extracted and monitored. When the overall monitoring indicates a problem, the identification of a surface variation change can be achieved through localized monitoring over each surface region based on variogram pattern analysis and control charts. The location of surface region change provides clues for variation source diagnosis. The proposed method is illustrated using simulated HDM data.
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Qi, Huimin. "Analysis on Integration Path of Urban and Rural Industries Based on Economic Data Model. A Case Study of Strategy Planning of Taiyuan Rural Revitalization." In 55th ISOCARP World Planning Congress, Beyond Metropolis, Jakarta-Bogor, Indonesia. ISOCARP, 2019. http://dx.doi.org/10.47472/jubr5968.

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In the background of ongoing urbanization in China and prominent “dualistic” contradiction between urban and rural areas, rural revitalization is extremely urgent. Currently, common problems concerning industry, ecology and humanities exist in rural areas. This paper attempts to figure out the causes for differences in industrial development in rural areas on the basis of macro data analysis and industrial spatial distribution. Given the lack of quantitative analysis of the relationship between urban and rural development and industrial structure, this paper adopts SPSS statistical software to conduct regression analysis on the statistical data of Taiyuan City in the past ten years. Based on the relationship between industrial proportion and urban-rural income ratio, this paper proposes how the adjustment of urban industrial structure promotes the industrial development in surrounding rural areas and the narrowing of urban-rural income gap. From the perspective of rural industry undertaking or complementation with urban industry, this paper then puts forward the idea of undertaking the transfer industry within the scope of ensuring the aggregation effect of the city center and the carrying capacity of the ecological environment, proposing an industrial development path from agriculture to processing industry and then to culture, tourism and recreation industry for the villages in Taiyuan.
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Li, Xiumin. "Multivariate Regression Analysis Using Statistics with R." In 2nd International Conference On Systems Engineering and Modeling. Paris, France: Atlantis Press, 2013. http://dx.doi.org/10.2991/icsem.2013.130.

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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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Pires, Rubiane Maria, and Carlos Alberto Ribeiro Diniz. "Bayesian residual analysis for beta-binomial regression models." In XI BRAZILIAN MEETING ON BAYESIAN STATISTICS: EBEB 2012. AIP, 2012. http://dx.doi.org/10.1063/1.4759610.

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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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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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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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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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Rachmawati, Ro’fah Nur, Anik Djuraidah, Aji Hamim Wigena, and I. Wayan Mangku. "Spatio-temporal Bayes Regression with INLA in Statistical Downscaling Modeling for Estimating West Java Rainfall." In Proceedings of the 1st International Conference on Statistics and Analytics, ICSA 2019, 2-3 August 2019, Bogor, Indonesia. EAI, 2020. http://dx.doi.org/10.4108/eai.2-8-2019.2290346.

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Reports on the topic "Spatial analysis (Statistics) Regression analysis"

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Over, Thomas, Riki Saito, Andrea Veilleux, Padraic O’Shea, Jennifer Sharpe, David Soong, and Audrey Ishii. Estimation of Peak Discharge Quantiles for Selected Annual Exceedance Probabilities in Northeastern Illinois. Illinois Center for Transportation, June 2016. http://dx.doi.org/10.36501/0197-9191/16-014.

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Abstract:
This report provides two sets of equations for estimating peak discharge quantiles at annual exceedance probabilities (AEPs) of 0.50, 0.20, 0.10, 0.04, 0.02, 0.01, 0.005, and 0.002 (recurrence intervals of 2, 5, 10, 25, 50, 100, 200, and 500 years, respectively) for watersheds in Illinois based on annual maximum peak discharge data from 117 watersheds in and near northeastern Illinois. One set of equations was developed through a temporal analysis with a two-step least squares-quantile regression technique that measures the average effect of changes in the urbanization of the watersheds used in the study. The resulting equations can be used to adjust rural peak discharge quantiles for the effect of urbanization, and in this study the equations also were used to adjust the annual maximum peak discharges from the study watersheds to 2010 urbanization conditions. The other set of equations was developed by a spatial analysis. This analysis used generalized least-squares regression to fit the peak discharge quantiles computed from the urbanization-adjusted annual maximum peak discharges from the study watersheds to drainage-basin characteristics. The peak discharge quantiles were computed by using the Expected Moments Algorithm following the removal of potentially influential low floods defined by a multiple Grubbs-Beck test. To improve the quantile estimates, regional skew coefficients were obtained from a newly developed regional skew model in which the skew increases with the urbanized land use fraction. The skew coefficient values for each streamgage were then computed as the variance-weighted average of at-site and regional skew coefficients. The drainage-basin characteristics used as explanatory variables in the spatial analysis include drainage area, the fraction of developed land, the fraction of land with poorly drained soils or likely water, and the basin slope estimated as the ratio of the basin relief to basin perimeter. This report also provides: (1) examples to illustrate the use of the spatial and urbanization-adjustment equations for estimating peak discharge quantiles at ungaged sites and to improve flood-quantile estimates at and near a gaged site; (2) the urbanization-adjusted annual maximum peak discharges and peak discharge quantile estimates at streamgages from 181 watersheds including the 117 study watersheds and 64 additional watersheds in the study region that were originally considered for use in the study but later deemed to be redundant. The urbanization-adjustment equations, spatial regression equations, and peak discharge quantile estimates developed in this study will be made available in the web-based application StreamStats, which provides automated regression-equation solutions for user-selected stream locations. Figures and tables comparing the observed and urbanization-adjusted peak discharge records by streamgage are provided at http://dx.doi.org/10.3133/sir20165050 for download.
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