Jove
Visualize
Contact Us

Related Concept Videos

Design Example: Identifying the Locations of Monuments in the Field Using Global Positioning System Device01:30

Design Example: Identifying the Locations of Monuments in the Field Using Global Positioning System Device

210
Surveyors use Global Positioning System (GPS) technology to measure the precise location and elevation of points on Earth. In a recent survey, GPS receivers were used to determine the coordinates and elevations of two park monuments. The process involved careful mission planning, data collection, and correction to ensure accuracy. The survey began with mission planning to identify optimal satellite visibility and minimize Position Dilution of Precision (PDOP). A geodetic control point...
210
Types of Global Positioning System Surveys01:30

Types of Global Positioning System Surveys

159
GPS surveying methods vary in application, accuracy, and data collection techniques, catering to diverse surveying and mapping needs. Static GPS, kinematic GPS, and real-time kinematic (RTK) surveying are widely used. Each technique offers distinct advantages.Static GPS involves placing one receiver at a known reference point and another at the target point. It collects exact positional data by observing multiple satellite ranges over an extended period, achieving centimeter-level accuracy for...
159
Field Application of Global Positioning System01:28

Field Application of Global Positioning System

139
The Global Positioning System (GPS) has become an indispensable tool in fieldwork, offering unparalleled precision and efficiency for surveying, navigation, and infrastructure development. By harnessing signals from a constellation of satellites, GPS receivers determine the location of objects with remarkable speed and accuracy, often completing calculations within a second.Advantages of Modern GPS TechnologyContemporary GPS receivers are designed to meet the practical demands of field...
139
Cluster Sampling Method01:20

Cluster Sampling Method

13.3K
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...
13.3K
Selected Data About Geographic Locations01:25

Selected Data About Geographic Locations

106
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...
106
Levels of Use of a GIS01:29

Levels of Use of a GIS

143
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...
143

You might also read

Related Articles

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

Sort by
Same author

Understanding the impact of spray drying temperature on the dissolution behavior of a HPMC-AS spray dried formulation.

Journal of pharmaceutical sciences·2026
Same author

A Vision Language-Based Framework for Detecting Industrial Mechanical, Electrical, and Plumbing Assets Using Unlabelled Data.

Sensors (Basel, Switzerland)·2026
Same author

GICEDCam: A Geospatial Internet of Things Framework for Complex Event Detection in Camera Streams.

Sensors (Basel, Switzerland)·2025
Same author

A Systematic Review of Event-Matching Methods for Complex Event Detection in Video Streams.

Sensors (Basel, Switzerland)·2024
Same author

An Interoperable Architecture for the Internet of COVID-19 Things (IoCT) Using Open Geospatial Standards-Case Study: Workplace Reopening.

Sensors (Basel, Switzerland)·2020
Same author

Community-Based Groundwater Monitoring Network Using a Citizen-Science Approach.

Ground water·2015
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
See all related articles
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 Experiment Video

Updated: Oct 25, 2025

The HoneyComb Paradigm for Research on Collective Human Behavior
06:48

The HoneyComb Paradigm for Research on Collective Human Behavior

Published on: January 19, 2019

9.6K

A Blockchain-Based Spatial Crowdsourcing System for Spatial Information Collection Using a Reward Distribution.

Masoud Kamali1, Mohammad Reza Malek1, Sara Saeedi2

  • 1Department of GIS, Faculty of Geodesy and Geomatics Engineering, K. N. Toosi University of Technology, Tehran 19967-15433, Iran.

Sensors (Basel, Switzerland)
|August 10, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a novel blockchain-based spatial crowdsourcing system that enhances data accuracy and participant rewards. The decentralized approach improves security and transparency in collecting location-based information.

Keywords:
blockchaincrowdsourcinglocation-based servicesreward algorithm

More Related Videos

An Open-Source Virtual Reality System for the Measurement of Spatial Learning in Head-Restrained Mice
08:59

An Open-Source Virtual Reality System for the Measurement of Spatial Learning in Head-Restrained Mice

Published on: March 3, 2023

2.3K
Photorealistic Learned Landscapes for Augmented Reality
06:54

Photorealistic Learned Landscapes for Augmented Reality

Published on: June 27, 2025

335

Related Experiment Videos

Last Updated: Oct 25, 2025

The HoneyComb Paradigm for Research on Collective Human Behavior
06:48

The HoneyComb Paradigm for Research on Collective Human Behavior

Published on: January 19, 2019

9.6K
An Open-Source Virtual Reality System for the Measurement of Spatial Learning in Head-Restrained Mice
08:59

An Open-Source Virtual Reality System for the Measurement of Spatial Learning in Head-Restrained Mice

Published on: March 3, 2023

2.3K
Photorealistic Learned Landscapes for Augmented Reality
06:54

Photorealistic Learned Landscapes for Augmented Reality

Published on: June 27, 2025

335

Area of Science:

  • Geoinformatics
  • Computer Science
  • Data Science

Background:

  • Location-based services increasingly rely on accurate spatial data.
  • Traditional crowdsourcing methods often lack security and transparency.
  • Blockchain technology offers potential for secure and transparent data collection.

Purpose of the Study:

  • To propose a novel blockchain-based spatial crowdsourcing system.
  • To enhance the accuracy and security of spatial information collection.
  • To incentivize user participation through a comprehensive reward mechanism.

Main Methods:

  • Developed a decentralized system using blockchain technology.
  • Implemented a consensus-based approach for task verification (confirming/rejecting reports).
  • Incorporated spatial and non-spatial factors for user rewards.

Main Results:

  • Achieved a 40% increase in information accuracy.
  • Reduced report review time by 30%.
  • Ensured privacy preservation and security of spatial data.

Conclusions:

  • The proposed blockchain system significantly improves spatial data accuracy and collection efficiency.
  • The novel reward mechanism encourages broader and more accurate participation.
  • This system offers a secure, transparent, and privacy-preserving alternative to traditional crowdsourcing.