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

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...
Finding Critical Values for Chi-Square01:18

Finding Critical Values for Chi-Square

Consider a curve representing sample data drawn randomly from a normally distributed population. One must construct confidence intervals to estimate or to test a claim regarding the population standard deviation. For example, a 95% confidence interval covers 95% of the area under the curve, and the remaining 5% is equally distributed on either side of the curve. To achieve such confidence intervals, one must determine the critical values. The critical values are simply the values separating the...
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...
Critical Values01:31

Critical Values

A critical value is a definite value obtained from a particular probability distribution at a predecided confidence level (or a predecided significance level) for a given population parameter. The critical value provides demarcation that separates the sample statistics that are likely to occur from the ones that are unlikely to occur based on the given probability distribution and the population parameter to be estimated. The critical value for normal distribution is obtained from the z...
Confidence Coefficient01:24

Confidence Coefficient

The confidence coefficient is also known as the confidence level or degree of confidence. It is the percent expression for the probability, 1-α, that the confidence interval contains the true population parameter assuming that the confidence interval is obtained after sufficient unbiased sampling; for example, if the CL = 90%, then in 90 out of 100 samples the interval estimate will enclose the true population parameter. Here α is the area under the curve, distributed equally under both the...
Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor 't,' or...

You might also read

Related Articles

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

Sort by
Same author

Spatiotemporal Changes in the Slavery-Inequality Relationship: The Diffusion of the Legacy of Slavery.

Demography·2024
Same author

Clustered spatio-temporal varying coefficient regression model.

Statistics in medicine·2020
See all related articles

Related Experiment Video

Updated: May 26, 2026

Assessment and Communication for People with Disorders of Consciousness
07:37

Assessment and Communication for People with Disorders of Consciousness

Published on: August 1, 2017

Confidence Set for the Cluster of a Spatial Scan Statistic.

Junho Lee1

  • 1Department of Experimental Statistics, Louisiana State University, Baton Rouge, Louisiana, USA.

Statistics in Medicine
|May 25, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a new framework for spatial cluster detection, providing confidence sets to quantify and visualize uncertainty. The method enhances traditional approaches by offering more robust and interpretable cluster identification in spatial data analysis.

Keywords:
cluster delineationhotspot detectionspatial cluster detectionuncertaintyuncertainty quantificationuncertainty visualization

More Related Videos

Infinium Assay for Large-scale SNP Genotyping Applications
13:33

Infinium Assay for Large-scale SNP Genotyping Applications

Published on: November 19, 2013

Related Experiment Videos

Last Updated: May 26, 2026

Assessment and Communication for People with Disorders of Consciousness
07:37

Assessment and Communication for People with Disorders of Consciousness

Published on: August 1, 2017

Infinium Assay for Large-scale SNP Genotyping Applications
13:33

Infinium Assay for Large-scale SNP Genotyping Applications

Published on: November 19, 2013

Area of Science:

  • Spatial statistics
  • Geographic Information Science
  • Epidemiology

Background:

  • Spatial cluster detection identifies unusual patterns in geographic data, crucial for epidemiology and environmental science.
  • Traditional methods like spatial scan statistics lack robust uncertainty quantification and visualization tools.
  • Existing techniques struggle with non-circular or multiple spatial clusters, limiting their practical application.

Purpose of the Study:

  • To develop a novel framework for constructing confidence sets for spatial clusters, addressing uncertainty in detection.
  • To provide tools for quantifying and visualizing the uncertainty associated with identified spatial clusters.
  • To offer a principled alternative to traditional significance-based methods in spatial cluster analysis.

Main Methods:

  • Developed a likelihood-based framework for constructing confidence sets applicable to Poisson and Normal models.
  • Utilized the p-value of the likelihood ratio test statistic to ensure empirical coverage near nominal levels.
  • Implemented a binary search algorithm for efficient determination of significance thresholds and introduced confidence heatmaps and envelopes for visualization.

Main Results:

  • The proposed framework successfully constructs confidence sets for spatial clusters with empirical coverage close to nominal levels.
  • Simulation studies and real-world cancer mortality data analysis demonstrated the method's interpretability and robustness.
  • The confidence heatmap and envelopes effectively visualized cluster uncertainty, enhancing practical utility.

Conclusions:

  • The novel framework provides a principled and effective approach to spatial cluster detection with uncertainty quantification.
  • The method is robust to various cluster shapes and numbers, offering practical advantages over traditional techniques.
  • This approach opens new possibilities for extensions in spatio-temporal analysis and spatially varying regression models.