Lecture

Sampling: procedures and constraints

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Description

This lecture discusses the use of sampling procedures in analyzing continuous phenomena in geographical space, such as precipitation levels and chemical properties of water bodies, due to the impracticality of measuring all points. The instructor explains how sampling precedes interpolation methods, which help predict continuous variables by inferring values. Different sampling procedures are applied based on the territory and analyzed phenomena, aiming to minimize uncertainty by meeting specific constraints like field delimitation, spatial representativeness, and uniform density of measurement points.

In MOOC

Geographical Information Systems 2

The second part of the course deals with spatial analysis methods and georeferenced information representation techniques. In particular, you will learn how to:

Analyze the spatial properties of discrete variables, for example by quantifying spatial autocorrelation;
Work with continuous variables (sampling, interpolation and construction of isolines)
Use digital elevation models (DEMs) and their derivatives (slope, orientation, etc.);
Use geodata superposition techniques;
Produce cartographic docu

Official source

Related concepts (15)

Interpolation

In the mathematical field of numerical analysis, interpolation is a type of estimation, a method of constructing (finding) new data points based on the range of a discrete set of known data points. In engineering and science, one often has a number of data points, obtained by sampling or experimentation, which represent the values of a function for a limited number of values of the independent variable. It is often required to interpolate; that is, estimate the value of that function for an intermediate value of the independent variable.

Multivariate interpolation

In numerical analysis, multivariate interpolation is interpolation on functions of more than one variable (multivariate functions); when the variates are spatial coordinates, it is also known as spatial interpolation. The function to be interpolated is known at given points and the interpolation problem consists of yielding values at arbitrary points . Multivariate interpolation is particularly important in geostatistics, where it is used to create a digital elevation model from a set of points on the Earth's surface (for example, spot heights in a topographic survey or depths in a hydrographic survey).

Polynomial interpolation

In numerical analysis, polynomial interpolation is the interpolation of a given bivariate data set by the polynomial of lowest possible degree that passes through the points of the dataset. Given a set of n + 1 data points , with no two the same, a polynomial function is said to interpolate the data if for each . There is always a unique such polynomial, commonly given by two explicit formulas, the Lagrange polynomials and Newton polynomials.

Nearest-neighbor interpolation

Nearest-neighbor interpolation (also known as proximal interpolation or, in some contexts, point sampling) is a simple method of multivariate interpolation in one or more dimensions. Interpolation is the problem of approximating the value of a function for a non-given point in some space when given the value of that function in points around (neighboring) that point. The nearest neighbor algorithm selects the value of the nearest point and does not consider the values of neighboring points at all, yielding a piecewise-constant interpolant.

Hermite interpolation

In numerical analysis, Hermite interpolation, named after Charles Hermite, is a method of polynomial interpolation, which generalizes Lagrange interpolation. Lagrange interpolation allows computing a polynomial of degree less than n that takes the same value at n given points as a given function. Instead, Hermite interpolation computes a polynomial of degree less than mn such that the polynomial and its m − 1 first derivatives have the same values at n given points as a given function and its m − 1 first derivatives.

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