An user-friendly Python Application for exploratory and structural spatial dependence analysis for sample points of spatial attributes (original) (raw)

Geography, Spatial Data Analysis, and Geostatistics: An Overview

Geographical Analysis, 2010

Geostatistics is a distinctive methodology within the field of spatial statistics. In the past, it has been linked to particular problems (e.g., spatial interpolation by kriging) and types of spatial data (attributes defined on continuous space). It has been used more by physical than human geographers because of the nature of their types of data. The approach taken by geostatisticians has several features that distinguish it from the methods typically used by human geographers for analyzing spatial variation associated with regional data, and we discuss these. Geostatisticians attach much importance to estimating and modeling the variogram to explore and analyze spatial variation because of the insight it provides. This article identifies the benefits of geostatistics, reviews its uses, and examines some of the recent developments that make it valuable for the analysis of data on areal supports across a wide range of problems.

Software and software design issues in the exploration of local dependence

Journal of the Royal Statistical Society: Series D (The Statistician), 1998

Alternative software implementations for use in the exploration of local dependence are discussed in relation to six design criteria. Since exploratory spatial data analysis is largely based on the study of local dependence, and because software tools now make it feasible to examine multiple views of spatial data sets, it becomes important for researchers to compare the outcomes of analyses using varying implementations. Having established and discussed chosen design criteria, some implementations are compared. Special weight is given to the extensibility of the implementation, to permit the user more control over the analysis. It is concluded that such analyses should proceed with discretion, and that often prior or contextual knowledge is necessary for an adequate interpretation of results.

GSTools v1.3: A toolbox for geostatistical modelling in Python

2021

Geostatistics as a subfield of statistics accounts for the spatial correlations encountered in many applications of e.g. Earth Sciences. Valuable information can be extracted from these correlations, also helping to address the often encountered burden of data scarcity. Despite the value of additional data, the use of geostatistics still falls short of its potential. This problem is often connected to the lack of user-friendly software hampering the use and application of geostatistics. We therefore present GSTools, a Python-based software suite for solving a wide range of geostatistical problems. We chose Python due to its unique balance between usability, flexibility, and efficiency and due to its adoption in the scientific community. GSTools provides methods for generating random fields, it can perform kriging and variogram estimation and much more. We demonstrate its abilities by virtue of a series of example application detailing their use.

Geostatistical Tools for Modeling and Interpreting Ecological Spatial Dependence

Ecological Monographs, 1992

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G-Sivar: A Global Spatial Indicator Based on Variogram

Boletim de Ciências Geodésicas

Among the exploratory spatial data analysis tools, there are indicators of spatial association, which measure the degree of spatial dependence of analysed data and can be applied to quantitative data. Another procedure available is geostatistics, which is based on the variogram, describing quantitatively and qualitatively the spatial structure of a variable. The aim of this paper is to use the concept of the variogram to develop a global indicator of spatial association (Global Spatial Indicator Based on Variogram-G-SIVAR). The G-SIVAR indicator has a satisfactory performance for spatial association, with sensibility for anisotropy cases. Because the indicator is based on geostatistics, it is appropriate for quantitative and qualitative data. The developed indicator is derived from theoretical global variogram, providing more details of the spatial structure of the data. The G-SIVAR indicator is based on spatial dissimilarity, while traditional indexes, such as Moran's I, are based on spatial similarity.

Computational system for geostatistical analysis

Scientia Agricola, 2004

Geostatistics identifies the spatial structure of variables representing several phenomena and its use is becoming more intense in agricultural activities. This paper describes a computer program, based on Windows Interfaces (Borland Delphi), which performs spatial analyses of datasets through geostatistic tools: Classical statistical calculations, average, cross- and directional semivariograms, simple kriging estimates and jackknifing calculations. A published dataset of soil Carbon and Nitrogen was used to validate the system. The system was useful for the geostatistical analysis process, for the manipulation of the computational routines in a MS-DOS environment. The Windows development approach allowed the user to model the semivariogram graphically with a major degree of interaction, functionality rarely available in similar programs. Given its characteristic of quick prototypation and simplicity when incorporating correlated routines, the Delphi environment presents the main ad...

Spatial decorrelation methods : beyond MAF and PCA

2012

In the geostatistical treatment of multivariate data sets the joint modelling of their spatial continuity is usually required. While it is possible to automate the inference of a suitable variogram model a transformation of the set of attributes into spatially uncorrelated factors that can be simulated independently, might be desirable. Standard methods used in geostatistics for this purpose are principal component analysis (PCA) and the method of minimum/maximum autocorrelation factors (MAF). Both methods have restrictions in their applicability and a more flexible approach may be more suitable such as that offered by approximate joint diagonalisation (AJD) methods common in Blind Source Separation. The application of two AJD methods to a family of experimental semivariogram matrices is explored here and the performance is assessed on a number of simulated data sets with different spatial characteristics. A comparison with MAF and PCA shows that the use of AJD algorithm results in ...