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

Applications of GIS: Disaster Management and Emergency Response01:29

Applications of GIS: Disaster Management and Emergency Response

353
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...
353
GIS Software, Hardware, and Sources of GIS Data01:23

GIS Software, Hardware, and Sources of GIS Data

592
A Geographic Information System (GIS) combines specialized software and hardware to effectively manage, analyze, and present spatial and related data. GIS software includes critical functionalities such as a user interface for easy navigation, database management tools for handling spatial and attribute data, and data retrieval features for efficient access. Analytical tools transform raw data into insights, while display functions produce maps and reports in various formats for effective...
592
Levels of Use of a GIS01:29

Levels of Use of a GIS

251
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...
251
Manipulation and Analysis01:21

Manipulation and Analysis

224
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...
224
Selected Data About Geographic Locations01:25

Selected Data About Geographic Locations

199
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...
199
Introduction to GIS01:28

Introduction to GIS

404
Geographic Information Systems (GIS) are tools for storing, analyzing, and displaying spatial data alongside related attributes. Unlike traditional information systems that address general queries, GIS incorporates spatial components, enabling users to answer "where" and "how far." For example, GIS can process housing data linked to geographic locations like zip codes, allowing insights into population density or housing distribution through thematic maps.GIS integrates technologies such as...
404

You might also read

Related Articles

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

Sort by
Same author

The longer, the better? Investigating the effect of prolonged acoustic stimulation on brief acoustic tinnitus suppression.

BMC neurology·2026
Same author

Trajectory of COVID-related tinnitus over the pandemic timeline.

Brazilian journal of otorhinolaryngology·2026
Same author

Antagonizing NRG1-ERBB4 signaling pathway with spironolactone for the treatment of schizophrenia: results of a randomized controlled drug repositioning clinical trial.

Communications medicine·2026
Same author

Tinnitus and tinnitus disorder: Genetic, neurobiological, and clinical differentiation.

iScience·2026
Same author

Sound hypersensitivity phenotypes and sound hypersensitivity disorder.

Neuroscience and biobehavioral reviews·2026
Same author

E-field guided repetitive transcranial magnetic stimulation modulates oscillatory brain activity dynamics in tinnitus.

Brain research bulletin·2026

Related Experiment Video

Updated: Dec 17, 2025

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data
11:21

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

Published on: July 27, 2018

8.5K

Efficient Processing of Geospatial mHealth Data Using a Scalable Crowdsensing Platform.

Robin Kraft1,2, Ferdinand Birk1, Manfred Reichert1

  • 1Institute of Databases and Information Systems, Ulm University, 89081 Ulm, Germany.

Sensors (Basel, Switzerland)
|June 24, 2020
PubMed
Summary

This study introduces a new architecture for mobile crowdsensing data management. It enables real-time noise mapping for health-conscious individuals, like tinnitus patients, to avoid high sound levels.

Keywords:
architectural designcloud-nativecrowdsensinggeospatial datamHealthscalabilitystream processingtinnitus

More Related Videos

Author Spotlight: Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring
06:32

Author Spotlight: Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring

Published on: July 14, 2023

1.7K
An Application for Pairing with Wearable Devices to Monitor Personal Health Status
06:58

An Application for Pairing with Wearable Devices to Monitor Personal Health Status

Published on: February 3, 2022

3.2K

Related Experiment Videos

Last Updated: Dec 17, 2025

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data
11:21

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

Published on: July 27, 2018

8.5K
Author Spotlight: Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring
06:32

Author Spotlight: Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring

Published on: July 14, 2023

1.7K
An Application for Pairing with Wearable Devices to Monitor Personal Health Status
06:58

An Application for Pairing with Wearable Devices to Monitor Personal Health Status

Published on: February 3, 2022

3.2K

Area of Science:

  • Environmental Health
  • Computer Science
  • Data Science

Background:

  • Smart sensors and smartphones are increasingly used for environmental data collection.
  • Existing monolithic backends struggle with flexible, scalable processing of crowdsensing data.
  • Mobile crowdsensing offers opportunities for capturing phenomena of common interest with spatio-temporal data.

Purpose of the Study:

  • To design an architectural approach for managing geospatial data in healthcare crowdsensing.
  • To enable users, particularly tinnitus patients, to access interactive environmental noise maps.
  • To provide a foundation for scalable crowdsensing platforms in various healthcare scenarios.

Main Methods:

  • Development of a cloud-native architectural design.
  • Integration of Big Data and stream processing concepts.
  • Focus on managing spatio-temporal sensor data from mobile crowdsensing.

Main Results:

  • A proposed architecture capable of handling challenging crowdsensing healthcare data.
  • Demonstration of an interactive map for environmental noise levels.
  • The approach facilitates data processing for health-conscious individuals.

Conclusions:

  • The developed architecture offers a flexible, efficient, and scalable solution for crowdsensing data management.
  • The approach can empower individuals to make informed decisions about environmental noise exposure.
  • The architectural design is adaptable for diverse healthcare crowdsensing applications.