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Related Concept Videos

Sampling Methods: Overview01:06

Sampling Methods: Overview

A sample refers to a smaller subset representative of a larger population. In analytical chemistry, studying or analyzing an entire population is often impractical or impossible. Therefore, samples are used to draw inferences and generalize the whole population. The sampling method selects individuals or items from a population to create a sample. Standard sampling methods include random, judgemental, systematic, stratified, and cluster sampling. 
In analytical chemistry, the choice of sampling...
Sampling Plans01:23

Sampling Plans

Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
Manipulation and Analysis01:21

Manipulation and Analysis

GIS manipulation and analysis functions are vital for decision-making and planning. These activities range from data retrieval tasks, such as selecting information based on specific criteria, to advanced analytical techniques that address complex spatial problems.One critical GIS analysis method is overlaying, which combines multiple data layers to examine impacts. For example, overlaying a river-dammed lake boundary with road networks can identify affected infrastructure. Another common...
Selected Data About Geographic Locations01:25

Selected Data About Geographic Locations

Geographic Information Systems (GIS) rely on two core types of data: spatial data and attribute data.Spatial DataSpatial data defines the physical location of features within a coordinate system, typically expressed in terms of latitude and longitude. It provides precise positioning for elements like roads, rivers, or buildings.Attribute DataAttribute data complements spatial data by adding descriptive information about these features. For example, a road's spatial data includes its start and...
Convenience Sampling Method00:55

Convenience Sampling Method

Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population.
Convenience sampling is a non-random method of sample selection; this method selects individuals that are easily accessible and may result in biased data. For example, a marketing...
Stratified Sampling Method01:16

Stratified Sampling Method

Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a stratified sample, divide the population into groups called strata and then take a...

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Related Experiment Video

Updated: May 24, 2026

Sampling Soils in a Heterogeneous Research Plot
07:11

Sampling Soils in a Heterogeneous Research Plot

Published on: January 7, 2019

Sampling and kriging spatial means: efficiency and conditions.

Jin-Feng Wang1, Lian-Fa Li, George Christakos

  • 1Institute of Geographic Sciences & Nature Resources Research, Chinese Academy of Sciences, Beijing, China;

Sensors (Basel, Switzerland)
|February 21, 2012
PubMed
Summary
This summary is machine-generated.

Mean Kriging estimation for spatial attributes outperforms traditional methods by accounting for spatial correlation. This technique improves accuracy for geographical data like temperature and pollution levels.

Keywords:
GISmean Krigingrandom fieldspatial dependence

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Measuring and Mapping Patterns of Soil Erosion and Deposition Related to Soil Carbonate Concentrations Under Agricultural Management
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Measuring and Mapping Patterns of Soil Erosion and Deposition Related to Soil Carbonate Concentrations Under Agricultural Management

Published on: September 12, 2017

Related Experiment Videos

Last Updated: May 24, 2026

Sampling Soils in a Heterogeneous Research Plot
07:11

Sampling Soils in a Heterogeneous Research Plot

Published on: January 7, 2019

Measuring and Mapping Patterns of Soil Erosion and Deposition Related to Soil Carbonate Concentrations Under Agricultural Management
08:09

Measuring and Mapping Patterns of Soil Erosion and Deposition Related to Soil Carbonate Concentrations Under Agricultural Management

Published on: September 12, 2017

Area of Science:

  • Geospatial analysis
  • Statistical modeling
  • Environmental science

Background:

  • Accurate estimation of spatially varying geographical attributes is crucial for various applications.
  • Spatial sampling design and statistical inference modeling significantly impact estimation accuracy.

Purpose of the Study:

  • To compare spatial sampling and Kriging means for areal attribute estimation.
  • To evaluate their performance in terms of mean values, variances, and efficiencies.

Main Methods:

  • Comparative analysis of spatial sampling techniques.
  • Application and evaluation of Kriging means for areal attribute estimation.
  • Theoretical analysis and empirical study of estimation methods.

Main Results:

  • Mean Kriging significantly outperforms commonly-used spatial estimation techniques.
  • Methods accounting for spatial correlation are more efficient than those that do not.
  • Comparative efficiencies vary depending on surface features.

Conclusions:

  • Mean Kriging is a superior technique for areal attribute estimation.
  • Accounting for spatial dependence enhances estimation efficiency.
  • The mean Kriging technique is adaptable for diverse spatially distributed attributes.