On robust estimation of the location parameter

Statistics and Data Analysis in Geology

Foundations Of Statistics

Statistics For Spatial Data

Examples are discussed to show the differences among discriminant analysis, logistic regression, and multiple regression. Chapter 6, “Multivariate Analysis of Variance,” presents advantages of multivariate analysis of variance (MANOVA) over univariate analysis of variance (ANOVA), discusses assumptions of MANOVA, and assesses validations of MANOVA assumptions and model estimation. The authors also discuss post hoc tests of MANOVA and multivariate analysis of covariance. Chapter 7, “Conjoint Analysis,” explains what conjoint analysis does and how it is different from other multivariate techniques. Guidelines of selecting attributes, models, and methods of data collection are presented. Chapter 8, “Cluster Analysis,” studies objectives, roles, and limitations of cluster analysis. Two basic concepts: similarity and distance are discussed. The authors also discuss details of five most popular hierarchical algorithms (singlelinkage, complete-linkage, average-linkage, centroid method, Ward’s method) and three nonhierarchical algorithms (the sequential threshold method, the parallel threshold method, and the optimizing procedure). Profiles of clusters and guidelines for cluster validation are studied as well. Chapter 9, “Multidimensional Scaling and Correspondence Analysis,” introduces two interdependence techniques to display the relationships in the data. The book describes clearly and intuitively the differences between the two techniques and how these two techniques are performed. Chapters 10–12 cover topics in SEM. Chapter 10, “Structural Equation Modeling: An Introduction,” introduces SEM and related concepts such as exogenous, endogenous constructs, and so on, points out the differences between SEM and other multivariate techniques, overviews the decision process of SEM. Chapter 11, “Confirmatory Factor Analysis,” explains the differences between exploratory and confirmatory factor analysis, discusses how to construct, validate, and assess the goodness of fit of a measurement model in SEM by confirmatory factor analysis. Chapter 12, “Testing a Structural Model,” presents some methods of SEM in examining the relationships between latent constructs. The book is an excellent book for people in management and marketing. For the Technometrics audience, this book does not have much flavor of physical, chemical, and engineering sciences. For example, partial least squares, a very popular method in Chemometrics, is discussed but not as detailed as other techniques in the book. Furthermore, due to the amount of materials covered in the book, it might be inappropriate for someone who is new to multivariate analysis.

The Grammar of Graphics

The Sitno Natura 2000 Site covers an area of 935,56 hectares. The Sitno region is significant due to the number of rare and endangered species of plants, and as a result is considered a location of great importance to the maintenance of floral gene pools. The study area suffers human impacts in the form of tourism. The main purpose of this study is to the measure landscape elements, determine the ecological significance of habitats within the Sitno area, and from this data, organize the study area into conservation zones. The results of this landscape quantification are numerical values that can be used to interpret the quality of ongoing ecological processes within individual landscape types. Interpretation of this quantified data can be used to determine the ecological significance of landscapes in other study areas. This research examines the habitats of Natura 2000 Sites by a set of landscape metrics for habitat area, size, density, and shape, such as Number of patches (NP), Patch density (PD), Mean patch size (MPS), Patch size standard deviation (PSSD) and Mean shape index (MSI). The classification of land cover patches is based on the Annex Code system.

Determination of ecological significance based on geostatistical assessment: a case study from the Slovak Natura 2000 protected area

This paper introduces an application of R programming language for geostatistical data processing with a case study of the Mariana Trench, Pacific Ocean. The formation of the Mariana Trench, the deepest among all hadal oceanic depth trenches, is caused by complex and diverse geomorphic factors affecting its development. Mariana Trench crosses four tectonic plates: Mariana, Caroline, Pacific and Philippine. The impact of the geographic location and geological fac- tors on its geomorphology has been studied by methods of statistical analysis and data visualization using R libraries. The methodology includes following steps. Firstly, vector thematic data were processed in QGIS: tectonics, bathymetry, geo- morphology and geology. Secondly, 25 cross-section profiles were drawn across the trench. The length of each profile is 1000-km. The attribute information has been derived from each profile and stored in a table containing coordinates, depths and thematic information. Finally, this table was processed by methods of the statistical analysis on R. The programming codes and graphical results are presented. The results include geospatial comparative analysis and estimated effects of the data distribution by tectonic plates: slope angle, igneous volcanic areas and depths. The innovativeness of this paper consists in a cross-disciplinary approach combining GIS, statistical analysis and R programming.

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Statistical Analysis of the Mariana Trench Geomorphology Using R Programming Language

Current research details methodological framework for the land planning of the recreational activities based on ecologic approach. Human impacts on landscapes caused by touristic activities should be in accordance with sustainability level, i.e. without changing natural landscape elements, their function and processes, as well as environmental quality. Region of Stiavnicke Bane in Slovakia represents a case study area that is completely under nature and landscape conservation. The methodological framework for the planning of recreational activities is based on the methodology of ecologic carrying capacity which is implemented by the Landscape ecological planning. The main result from this work is suitable tourism activities determined by the ecological approach. Methodological steps include spatial analysis, interpretation, evaluations and propositions which were suggested for recreational activities. The most suitable activities for winter periods are downhill skiing, cross-country skiing and winter tourism. For the summer period the best activities are the following ones: hiking, water sports and recreational activities linked to watering and sport fishing.
The most suitable activities for the year-round period are service facilities and therapeutic recreational facilities. This sum of the activities represents the level of the land using that has not any negative environmental impact.

https://hal.archives-ouvertes.fr/hal-02022151/document

Land planning as a support for sustainable development based on tourism: a case study of slovak rural region

The study area is focused on the Mariana Trench, west Pacific Ocean. The research aim is to investigate correlation between various factors, such as bathymetric depths, geomorphic shape, geographic location on four tectonic plates of the sampling points along the trench, and their influence on the geologic sediment thickness. Technically, the advantages of applying Python programming language for oceanographic data sets were tested. The methodological approaches include GIS data collecting, data analysis, statistical modelling, plotting and visualizing. Statistical methods include several algorithms that were tested: 1) weighted least square linear regression between geological variables, 2) autocorrelation; 3) design matrix, 4) ordinary least square regression, 5) quantile regression. The spatial and statistical analysis of the correlation of these factors aimed at the understanding, which geological and geodetic factors affect the distribution of the steepness and shape of the trench. Following factors were analysed: geology (sediment thickness), geographic location of the trench on four tectonics plates: Philippines, Pacific, Mariana and Caroline and bathymetry along the profiles: maximal and mean, minimal values, as well as the statistical calculations of the 1st and 3rd quantiles. The study revealed correlations between the sediment thickness and distinct variations of the trench geomorphology and sampling locations across various segments along the crescent of the trench.

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Testing Linear Regressions by StatsModel Library of Python for Oceanological Data Interpretation

The study area is located in western Pacific Ocean, Mariana Trench. The aim of the data analysis is to analyze the potential influence of how various geological and tectonic factors may affect the geomorphological shape of the Mariana Trench.  Statistical analysis of the data set in marine geology and oceanography requires an adequate strategy on big data processing. In this context, current research proposes a combination of the Python-based methodology that couples GIS geospatial data analysis. The Quantum GIS part of the methodology produces an optimized representative sampling dataset consisting of 25 cross-section profiles having in total 12,590 bathymetric observation points. The sampling of the geospatial dataset are located across the Mariana Trench. The second part of the methodology consists of statistical data processing by means of high-level programming language Python. Current research uses libraries Pandas, NumPy and SciPy. The data processing also involves the subsampling of two auxiliary masked data frames from the initial large data set that only consists of the target variables: sediment thickness, slope angle degrees and bathymetric observation points across four tectonic plates: Pacific, Philippine, Mariana, and Caroline. Finally, the data were analyzed by several approaches: 1) Kernel Density Estimation (KDE) for analysis of the probability of data distribution; 2) stacked area chart for visualization of the data range across various segments of the trench; 3) spacial series of radar charts; 4) stacked bar plots showing the data distribution by tectonic plates; 5) stacked bar charts for correlation of sediment thickness by profiles, versus distance from the igneous volcanic areas; 6) circular pie plots visualizing data distribution by 25 profiles; 7) scatterplot matrices for correlation analysis between marine geologic variables. The results presented a distinct correlation between the geologic, tectonic and oceanographic variables. Six Python codes are provided in full for repeatability of this research.

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Polina Lemenkova

Papers

Determination of ecological significance based on geostatistical assessment: a case study from the Slovak Natura 2000 protected area

Statistical Analysis of the Mariana Trench Geomorphology Using R Programming Language

Land planning as a support for sustainable development based on tourism: a case study of slovak rural region

Testing Linear Regressions by StatsModel Library of Python for Oceanological Data Interpretation

Processing oceanographic data by python libraries numpy, scipy and pandas