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

325
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...
325
Levels of Use of a GIS01:29

Levels of Use of a GIS

457
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...
457
Manipulation and Analysis01:21

Manipulation and Analysis

326
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...
326
GIS Software, Hardware, and Sources of GIS Data01:23

GIS Software, Hardware, and Sources of GIS Data

1.0K
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...
1.0K
Introduction to GIS01:28

Introduction to GIS

744
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...
744
Thematic Layering in GIS01:30

Thematic Layering in GIS

434
In the past, planning projects such as schools or public facilities required extensive manual effort to gather and compile data. Information such as property boundaries, soil characteristics, road networks, zoning regulations, and flood zones had to be sourced individually from courthouses, utility providers, and registry offices. Assembling these datasets into a coherent format often took several months, delaying project timelines.The introduction of Geographic Information Systems (GIS)...
434

You might also read

Related Articles

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

Sort by
Same author

Charge density wave in a band insulator.

Nature communications·2026
Same author

Gallic Acid Protects Against DSS-Induced Colitis by Modulating Gut Microbiota and Suppressing the Activation of NF-κB/MAPK Signaling Pathway.

Molecular nutrition & food research·2026
Same author

Interlayer Charge-Density-Wave Vector Phase Induced Structural Chirality.

Physical review letters·2026
Same author

Gallic acid alleviates colitis by restoring intestinal barrier function and enriching butyrate-producing bacteria.

International immunopharmacology·2026
Same author

Construction and validation of risk prediction model for uterine fibroids: a retrospective cohort study based on MIMIC database.

Reproductive biology and endocrinology : RB&E·2026
Same author

Development and validation of a nomogram model for predicting in-hospital cardiac arrest risk: A prospective multi-center observational study.

Medicine·2025

Related Experiment Video

Updated: Apr 7, 2026

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

14.3K

Distributed Storage Algorithm for Geospatial Image Data Based on Data Access Patterns.

Shaoming Pan1, Yongkai Li2, Zhengquan Xu1

  • 1State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, Hubei, China; Collaborative Innovation Center for Geospatial Technology, Wuhan, Hubei, China.

Plos One
|July 17, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces a novel system for storing small geospatial image data files in distributed environments. By analyzing access patterns, the system enhances parallel I/O performance and query response times.

More Related Videos

Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging
09:19

Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging

Published on: April 18, 2025

1.6K
Reefshape: A System for the Efficient Collection and Automated Processing of Time-Series Underwater Photogrammetry Data for Benthic Habitat Monitoring
13:35

Reefshape: A System for the Efficient Collection and Automated Processing of Time-Series Underwater Photogrammetry Data for Benthic Habitat Monitoring

Published on: June 13, 2025

1.9K

Related Experiment Videos

Last Updated: Apr 7, 2026

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

14.3K
Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging
09:19

Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging

Published on: April 18, 2025

1.6K
Reefshape: A System for the Efficient Collection and Automated Processing of Time-Series Underwater Photogrammetry Data for Benthic Habitat Monitoring
13:35

Reefshape: A System for the Efficient Collection and Automated Processing of Time-Series Underwater Photogrammetry Data for Benthic Habitat Monitoring

Published on: June 13, 2025

1.9K

Area of Science:

  • Geospatial data management
  • Distributed systems
  • Data storage optimization

Background:

  • Declustering techniques improve query response time in distributed systems by parallel I/O.
  • Small geospatial image data files pose challenges for traditional splitting methods in distributed storage.

Purpose of the Study:

  • To propose a theoretical system for distributed storage of small geospatial image data files.
  • To leverage historical access log information for optimizing data distribution.

Main Methods:

  • Developed an algorithm to construct an access correlation matrix from log data.
  • Implemented a heuristic algorithm to determine optimal data distribution based on the matrix.
  • Conducted comparative experiments to evaluate performance.

Main Results:

  • The proposed algorithm achieved a 10-15% higher total parallel access probability compared to other methods.
  • Performance improved by over 20% when combined with a copy storage strategy.
  • Demonstrated effective parallel I/O realization in distributed environments.

Conclusions:

  • The developed system offers an effective solution for distributed storage of small geospatial image data.
  • Access pattern mining is crucial for optimizing distributed data storage performance.
  • The approach significantly enhances system performance and query response times.