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

Manipulation and Analysis01:21

Manipulation and Analysis

304
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...
304
Levels of Use of a GIS01:29

Levels of Use of a GIS

406
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...
406
Selected Data About Geographic Locations01:25

Selected Data About Geographic Locations

281
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...
281
Applications of GIS: Disaster Management and Emergency Response01:29

Applications of GIS: Disaster Management and Emergency Response

564
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...
564
Introduction to GIS01:28

Introduction to GIS

612
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...
612
Unsoundness of Aggregate due to Volume Change01:26

Unsoundness of Aggregate due to Volume Change

406
Unsoundness in aggregates due to volume changes is primarily caused by the physical alterations aggregates undergo, such as freezing and thawing, thermal changes, and wetting and drying. Unsound aggregates, when subjected to these changes, result in volume change upon disintegration. This, in turn, contributes to the deterioration of concrete, including scaling, pop-outs, and cracking. Particular types of aggregates, such as porous flints, cherts, and those containing clay minerals, are...
406

You might also read

Related Articles

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

Sort by
Same author

Research and innovation paving the way for climate neutrality in urban transport: Analysis of 362 cities on their journey to zero emissions.

Transport policy·2024
Same author

Integrated Spatial Simulation of Population and Urban Land Use: a Pan-European Model Validation.

Applied spatial analysis and policy·2023
Same author

Modelling agricultural land abandonment in a fine spatial resolution multi-level land-use model: An application for the EU.

Environmental modelling & software : with environment data news·2021
Same author

Population growth, accessibility spillovers and persistent borders: Historical growth in West-European municipalities.

Journal of transport geography·2017
Same author

Assessing uncertainties in land cover projections.

Global change biology·2016
Same author

More green infrastructure is required to maintain ecosystem services under current trends in land-use change in Europe.

Landscape ecology·2015

Related Experiment Video

Updated: Feb 20, 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

Reducing the uncertainty induced by spatial aggregation in accessibility and spatial interaction applications.

Marcin Stępniak1, Chris Jacobs-Crisioni2

  • 1Institute of Geography and Spatial Organization, Polish Academy of Sciences, ul. Twarda 51/55, 00-818 Warsaw, Poland.

Journal of Transport Geography
|October 24, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces a novel method for calculating interaction-weighted travel times, improving accuracy by reducing sensitivity to spatial aggregation. Population-weighted centroids are recommended over geographically-weighted ones for better spatial interaction analysis.

Keywords:
AccessibilityDistance decayMAUPScaleSpatial interaction

More Related Videos

A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants
06:28

A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants

Published on: August 26, 2018

6.3K
Evaluating Usability Aspects of a Mixed Reality Solution for Immersive Analytics in Industry 4.0 Scenarios
06:02

Evaluating Usability Aspects of a Mixed Reality Solution for Immersive Analytics in Industry 4.0 Scenarios

Published on: October 6, 2020

2.7K

Related Experiment Videos

Last Updated: Feb 20, 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
A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants
06:28

A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants

Published on: August 26, 2018

6.3K
Evaluating Usability Aspects of a Mixed Reality Solution for Immersive Analytics in Industry 4.0 Scenarios
06:02

Evaluating Usability Aspects of a Mixed Reality Solution for Immersive Analytics in Industry 4.0 Scenarios

Published on: October 6, 2020

2.7K

Area of Science:

  • Spatial analysis
  • Transportation geography
  • Geographic information systems (GIS)

Background:

  • Spatial interaction analyses often face uncertainty due to spatially dispersed interaction masses within zones affecting travel times.
  • Existing methods for estimating interaction-weighted travel times can be sensitive to spatial aggregation issues.

Purpose of the Study:

  • To propose a new method for computing intra-zonal, interaction-weighted travel times.
  • To reduce the sensitivity of travel time estimates to spatial aggregation.
  • To compare the effectiveness of population-weighted versus geographically-weighted centroids.

Main Methods:

  • Computed interaction-weighted travel times from a fine-resolution point matrix.
  • Integrated secondary data to refine travel time estimations.
  • Developed a novel approach for intra-zonal travel time calculations.

Main Results:

  • The proposed method for intra-zonal, interaction-weighted travel times is less sensitive to spatial aggregation.
  • Demonstrated that population-weighted centroids yield improved estimates compared to geographically-weighted centroids.
  • Enhanced the accuracy of spatial interaction models by addressing intra-zonal travel time uncertainties.

Conclusions:

  • The developed method offers a more robust approach to calculating interaction-weighted travel times, particularly within zones.
  • Utilizing population-weighted centroids is crucial for more accurate spatial interaction modeling.
  • This research contributes to improving the precision of spatial analysis and transportation planning.