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

Selected Data About Geographic Locations01:25

Selected Data About Geographic Locations

211
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...
211
Relative Frequency Histogram01:14

Relative Frequency Histogram

6.2K
The relative frequency depicts the proportion of data points that have each value. The frequency tells the number of data points that have each value. Like the histogram, a relative frequency histogram also has the same shape with a horizontal scale (the x-axis), but the vertical scale (the y-axis) is marked with relative frequencies (percentages of the whole) instead of actual frequencies. A relative frequency histogram is a graphical representation of a frequency distribution where the...
6.2K
Cluster Sampling Method01:20

Cluster Sampling Method

13.8K
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...
13.8K
Levels of Use of a GIS01:29

Levels of Use of a GIS

264
Geographic Information Systems (GIS) operate across three levels of application, each representing an increasing degree of complexity: data management, analysis, and prediction. These levels reflect the expanding functionality and versatility of GIS technology in handling spatial data for diverse purposes.Data ManagementAt its foundational level, GIS serves as a tool for data management, enabling the input, storage, retrieval, and organization of spatial data. This level is often employed in...
264

You might also read

Related Articles

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

Sort by
Same author

Retraction notice to "Analyzing trends in the spatial-temporal visitation patterns of mainland Chinese tourists in Sabah, Malaysia based on Weibo social big data" [Heliyon 9 (2023) e15526].

Heliyon·2025
Same author

Esophageal Involvement in Eosinophilic Granulomatosis With Polyangiitis.

Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association·2025
Same author

Analyzing trends in the spatial-temporal visitation patterns of mainland Chinese tourists in Sabah, Malaysia based on Weibo social big data.

Heliyon·2023
Same author

Investigating COVID-19 Vaccine Uptake Intention Using an Integrated Model of Protection Motivation Theory and an Extended Version of the Theory of Planned Behavior.

Health communication·2023
Same author

Predicting Health Insurance Policy Subscription Intention: An Empirical Study.

Social work in public health·2022
Same author

Unearthing mask waste separation behavior in COVID-19 pandemic period: An empirical evidence from Ghana using an integrated theory of planned behavior and norm activation model.

Current psychology (New Brunswick, N.J.)·2021

Related Experiment Video

Updated: Dec 22, 2025

Trajectory Data Analyses for Pedestrian Space-time Activity Study
16:14

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

14.0K

Correction: Measuring multi-spatiotemporal scale tourist destination popularity based on text granular computing.

Chi Yunxian, Li Renjie, Zhao Shuliang

    Plos One
    |May 6, 2020
    PubMed
    Summary

    This study corrects a previously published article DOI. The correction ensures accurate referencing for scientific research and data integrity. Further details are available via the corrected DOI.

    More Related Videos

    Quantifying Spatiotemporal Parameters of Cellular Exocytosis in Micropatterned Cells
    10:21

    Quantifying Spatiotemporal Parameters of Cellular Exocytosis in Micropatterned Cells

    Published on: September 16, 2020

    6.4K
    Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
    11:52

    Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

    Published on: February 9, 2017

    6.3K

    Related Experiment Videos

    Last Updated: Dec 22, 2025

    Trajectory Data Analyses for Pedestrian Space-time Activity Study
    16:14

    Trajectory Data Analyses for Pedestrian Space-time Activity Study

    Published on: February 25, 2013

    14.0K
    Quantifying Spatiotemporal Parameters of Cellular Exocytosis in Micropatterned Cells
    10:21

    Quantifying Spatiotemporal Parameters of Cellular Exocytosis in Micropatterned Cells

    Published on: September 16, 2020

    6.4K
    Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
    11:52

    Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

    Published on: February 9, 2017

    6.3K

    Area of Science:

    • Scientific publishing
    • Scholarly communication
    • Research integrity

    Background:

    • Accurate citation is crucial for scientific reproducibility.
    • Digital Object Identifiers (DOIs) ensure persistent access to research.
    • Errors in DOIs can impede the retrieval and verification of scientific literature.

    Purpose of the Study:

    • To correct an erroneous Digital Object Identifier (DOI) for a published article.
    • To ensure accurate citation and retrieval of the scientific work.
    • To uphold standards of research integrity and scholarly communication.

    Main Methods:

    • Identification of the incorrect DOI in the original publication.
    • Submission of a correction request to the relevant authorities.
    • Publication of a corrigendum with the correct DOI.

    Main Results:

    • The incorrect DOI has been identified and rectified.
    • The corrected DOI is now associated with the article.
    • Improved accessibility and accurate referencing of the research are established.

    Conclusions:

    • The correction ensures that researchers can reliably access and cite the article.
    • This action reinforces the importance of meticulous record-keeping in scientific publishing.
    • Maintaining accurate metadata is essential for the integrity of the scientific record.