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

Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

394
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
394
Selected Data About Geographic Locations01:25

Selected Data About Geographic Locations

176
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...
176
Applications of GIS: Disaster Management and Emergency Response01:29

Applications of GIS: Disaster Management and Emergency Response

323
Geographic Information System (GIS) technology is essential for risk identification, action prioritization, and resource optimization in critical situations like flooding and earthquakes. By integrating spatial and demographic data, GIS provides a comprehensive framework for emergency response.GIS integrates data layers, like rainfall intensity, topography, elevation profiles, and river levels, to model high-risk flood zones. These layers assess areas susceptible to flooding based on their...
323

You might also read

Related Articles

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

Sort by
Same author

Smart Solutions to Keep Your Mental Balance.

Procedia computer science·2022
See all related articles

Related Experiment Video

Updated: Dec 6, 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

13.9K

A Spatial-Temporal Model for Event Detection in Social Media.

Șerban Boghiu1, Daniela Gîfu1,2

  • 1Faculty of Computer Science, "Alexandru Ioan Cuza" University, General Berthelot, 16, 700483, Iasi, Romania.

Procedia Computer Science
|October 12, 2020
PubMed
Summary
This summary is machine-generated.

This study reviews models for analyzing complex spatiotemporal patterns in social media data. It aims to improve the understanding of event localization, semantic interpretation, and relationships between time and space.

Keywords:
event analysisformalismspatialitytemporality

More Related Videos

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

6.2K
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

16.0K

Related Experiment Videos

Last Updated: Dec 6, 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

13.9K
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

6.2K
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

16.0K

Area of Science:

  • Data Science
  • Computer Science
  • Social Network Analysis

Background:

  • Increasing interest in spatial-temporal data modeling.
  • Social network data presents complex spatiotemporal patterns.
  • Conventional methods struggle with analyzing event data complexity.

Purpose of the Study:

  • Review models for social media data processing.
  • Formalize a novel theory of action and time.
  • Enhance understanding of spatiotemporal event analysis.

Main Methods:

  • Literature review of existing models and techniques.
  • Focus on social media data processing.
  • Analysis of spatial-temporal localization and semantic interpretation.

Main Results:

  • Identified key models for spatiotemporal data analysis.
  • Highlighted challenges in classifying complex patterns.
  • Established a foundation for deciphering events in text.

Conclusions:

  • Need for advanced models to analyze complex spatiotemporal patterns.
  • Importance of spatial-temporal localization and semantic interpretation.
  • Proposed framework for understanding events in time and space.