Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Types of Global Positioning System Surveys

Types of Global Positioning System Surveys

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...

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

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

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 served as...

Levels of Use of a GIS

Levels of Use of a GIS

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...

Selected Data About Geographic Locations

Selected Data About Geographic Locations

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...

Manipulation and Analysis

Manipulation and Analysis

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...

Design Example: Alignment of a Road Line Using GIS

Design Example: Alignment of a Road Line Using GIS

The alignment of a road line using Geographic Information Systems (GIS) is a critical process in civil engineering, combining advanced technology with practical decision-making. This methodology begins with the collection of geospatial data, including information on land cover, geomorphology, drainage patterns, slope, and contour details. Such data is typically acquired through satellite imagery and GIS tools, offering a comprehensive understanding of the terrain.Once the data is gathered, it...

You might also read

Related Articles

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

Sort by

Same author

Determinants and relationships of climate change, climate change hazards, mental health, and well-being: a systematic review.

Frontiers in psychiatry·2025

Same author

Design and implementation of a data sharing API for supporting urban governance schemes in environmental and traffic monitoring.

MethodsX·2025

Same author

Utilizing hydrophobic sand to construct an air permeable aquiclude to enhance rice yields.

Scientific reports·2025

Same author

Optimizing spatial survey administration adopting RT-GSCS: A statistical perspective on performance metrics.

MethodsX·2024

Same author

Statistical processing of building and neighborhood data considering energy ratings in Dublin, Ireland.

Data in brief·2024

Same author

Evaluating background and local contributions and identifying traffic-related pollutant hotspots: insights from Google Air View mobile monitoring in Dublin, Ireland.

Environmental science and pollution research international·2024

Search research articles

Related Experiment Video

Updated: Jun 28, 2026

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

Exploring hybrid models for identifying locations for active mobility pathways using real-time spatial Delphi and

Yuri Calleo¹, Nadia Giuffrida², Francesco Pilla¹

¹University College Dublin, Dublin, Ireland.

European Transport Research Review

|November 11, 2024

Summary

This study introduces a hybrid model using Real-Time Spatial Delphi and Generative Adversarial Networks (GANs) for optimal urban planning of bike lanes and pedestrian zones. The approach aids stakeholders in visualizing and assessing proposed transportation infrastructure changes.

Keywords:

Artificial intelligence Generative adversarial networks Real-time spatial Delphi Spatial planning

More Related Videos

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

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

Published on: August 26, 2018

Modeling the Functional Network for Spatial Navigation in the Human Brain

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

Related Experiment Videos

Last Updated: Jun 28, 2026

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

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

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

Published on: August 26, 2018

Modeling the Functional Network for Spatial Navigation in the Human Brain

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

Area of Science:

Urban Planning and Spatial Analysis
Artificial Intelligence in Urban Development
Sustainable Transportation Infrastructure

Background:

Spatial planning is complex, requiring multidisciplinary approaches for effective urban development.
Prioritizing bike lanes, stations, and pedestrian zones enhances urban sustainability, public health, and safety.
Current planning methods may lack tools for visualizing the real-world impact of proposed interventions.

Purpose of the Study:

To propose and validate a hybrid model for identifying optimal locations for bike lanes, bike stations, and pedestrian zones.
To integrate expert consensus with AI-driven visualization for enhanced spatial planning.
To support decision-making in urban transportation infrastructure development.

Main Methods:

A hybrid model combining Real-Time Spatial Delphi (RTSD) and Generative Adversarial Networks (GANs).
RTSD facilitates expert consensus on optimal locations through real-time feedback and visualization.
GANs generate visual representations of proposed urban interventions based on expert judgments.

Main Results:

The hybrid model effectively identifies optimal locations for bike lanes, stations, and pedestrian zones.
The application in Dublin demonstrated the model's utility in visualizing the impact of proposed changes.
Stakeholders, policymakers, and citizens can better understand and evaluate proposed urban interventions.

Conclusions:

The proposed hybrid model offers a novel and effective approach to complex spatial planning challenges.
Integrating expert knowledge with AI visualization tools improves the planning and implementation of sustainable urban infrastructure.
This method enhances collaborative decision-making and public engagement in urban development projects.