Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

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...
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...
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...
Cluster Sampling Method01:20

Cluster Sampling Method

Appropriate sampling methods ensure 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 cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
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...
Random Sampling Method01:09

Random 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. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Directional Rates of Change Under Spatial Process Models.

Journal of the American Statistical Association·2025
Same author

On prior smoothing with discrete spatial data in the context of disease mapping.

Statistical methods in medical research·2025
Same author

Century-long West Antarctic snow accumulation changes induced by tropical teleconnections.

Science advances·2025
Same author

Spatial Modeling With Spatially Varying Coefficient Processes.

Journal of the American Statistical Association·2024
Same author

Swirls and scoops: Ice base melt revealed by multibeam imagery of an Antarctic ice shelf.

Science advances·2024
Same author

Resonance between projected Tibetan Plateau surface darkening and Arctic climate change.

Science bulletin·2023

Related Experiment Video

Updated: May 7, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

On the Effect of Preferential Sampling in Spatial Prediction.

Alan E Gelfand1, Sujit K Sahu, David M Holland

  • 1Institute of Statistics and Decisions, Duke University, Durham, NC, USA.

Environmetrics
|October 1, 2013
PubMed
Summary
This summary is machine-generated.

Preferential sampling in spatial networks, common in environmental monitoring, biases exposure surface predictions. This study introduces a simulation approach to quantify this bias by comparing surfaces from selective versus random sampling.

Keywords:
fitting modelhierarchical modelinformative covariateintensitysampling modelspatial point pattern

More Related Videos

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments
13:00

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Published on: January 23, 2017

Related Experiment Videos

Last Updated: May 7, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments
13:00

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Published on: January 23, 2017

Area of Science:

  • Environmental Science
  • Spatial Statistics
  • Geostatistics

Background:

  • Spatial network sampling is often non-random, prioritizing areas with high response values (e.g., air pollution).
  • This preferential sampling can introduce bias in model estimation and prediction of exposure surfaces.
  • Prediction accuracy is often the primary goal in spatial modeling, making bias in predictive surfaces a critical concern.

Purpose of the Study:

  • To develop and present a simulation-based methodology for assessing the impact of preferential sampling on spatial predictive surfaces.
  • To compare predictive surfaces generated under preferential sampling versus complete spatial randomness.
  • To provide a framework for understanding and quantifying the effects of non-random data collection in spatial analysis.

Main Methods:

  • A simulation-based approach is proposed to evaluate preferential sampling effects.
  • Two predictive surfaces are compared: one based on an 'operating' intensity driving site selection, and another assuming complete spatial randomness.
  • Various response models can be incorporated, reflecting operating intensity, covariates, or flexible spatial structures.
  • Data are generated under specified models, followed by model fitting and kriging interpolation to create predictive surfaces.
  • Expected comparisons of random predictive surfaces are emphasized, requiring suitable comparison metrics.

Main Results:

  • The study outlines a method to generate and compare spatial predictive surfaces under different sampling schemes.
  • It highlights the importance of assessing bias in the predictive surface, rather than solely in parameter estimates.
  • The simulation approach allows for quantifying the discrepancy introduced by preferential sampling.

Conclusions:

  • Preferential sampling significantly impacts the accuracy of spatial exposure surface predictions.
  • The proposed simulation-based method offers a direct way to assess and quantify these impacts.
  • This approach is crucial for reliable environmental monitoring and exposure assessment where data collection is often selective.