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

Evolutionary Relationships through Genome Comparisons

Evolutionary Relationships through Genome Comparisons

Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...

You might also read

Related Articles

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

Sort by

Same author

A Bayesian Framework for the Network Analysis of Transmission Dynamics in Infectious Disease.

Journal of molecular evolution·2026

Same author

Pathways, outputs and impact of NIH-supported bioinformatics and genomics graduate trainees in Africa.

Briefings in bioinformatics·2026

Same author

The Role of Community Networks in the Transmission and Persistence of <i>M. tuberculosis</i> in Urban Africa with Endemic Tuberculosis.

Open forum infectious diseases·2025

Same author

Detect early TB infection, evaluate contacts for TB (DETEC-TB) - a protocol for a prospective cohort study on active case finding for tuberculosis disease and tuberculosis infection source tracing.

BMC infectious diseases·2025

Same author

Bayesian estimation of transmission networks for infectious diseases.

Journal of mathematical biology·2025

Same author

Phylogenetic inference of inter-population transmission rates for infectious diseases.

Briefings in bioinformatics·2024

Same journal

Beyond absolute space: modeling disease dispersion and reactive actions from a multi-spatialization perspective.

International journal of geographical information science : IJGIS·2026

Same journal

Pharos: a mobile GIS application for network assessment and ground-truth mapping to support mHealth study design in low-resource settings.

International journal of geographical information science : IJGIS·2026

Same journal

Deconstructing rurality to better "place" health data.

International journal of geographical information science : IJGIS·2025

Same journal

SpaCE: a spatial counterfactual explainable deep learning model for predicting out-of-hospital cardiac arrest survival outcome.

International journal of geographical information science : IJGIS·2025

Same journal

A research agenda for GIScience in a time of disruptions.

International journal of geographical information science : IJGIS·2025

Same journal

Visit probability and accessibility within space-time prism of activity program.

International journal of geographical information science : IJGIS·2025

See all related articles

Search research articles

Related Experiment Video

Updated: May 22, 2025

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

Exploring Human Mobility: A Time-Informed Approach to Pattern Mining and Sequence Similarity.

Hao Yang¹, X Angela Yao¹, Christopher C Whalen²

¹Department of Geography, University of Georgia, Athens, U.S.

International Journal of Geographical Information Science : IJGIS

|March 17, 2025

Summary

This summary is machine-generated.

This study introduces novel methods for analyzing human mobility patterns from spatial big data. The developed framework effectively identifies distinct daily mobility behaviors, such as "stay-at-home" and "work-oriented" patterns.

Keywords:

Sequential Pattern mining data mining human mobility pattern mobile phone data sequence similarity measurement

More Related Videos

Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior

Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior

Published on: April 13, 2016

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

Published on: December 11, 2015

Related Experiment Videos

Last Updated: May 22, 2025

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior

Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior

Published on: April 13, 2016

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

Published on: December 11, 2015

Area of Science:

Spatial data science
Human mobility research
Computational social science

Background:

The increasing volume of spatial big data fuels interest in human mobility patterns.
Discovering and comparing these patterns from big data presents significant analytical challenges.

Purpose of the Study:

To introduce novel methods for discovering and assessing human mobility patterns.
To present an analytical framework for analyzing mobility at individual and aggregated levels.
To demonstrate the framework's application and effectiveness using a real-world case study.

Main Methods:

Developed Time-Informed pattern mining (TiPam) for frequent pattern discovery.
Introduced a Time-Aware Longest Common Subsequence (T-LCS) algorithm for sequence similarity assessment.
Integrated these into a comprehensive analytical framework for human mobility analysis.

Main Results:

Applied the framework to daily mobile phone data from 135 users in Kampala, Uganda.
Identified four distinct mobility groups: "stay-at-home," "unoccupied," "education-oriented," and "work-oriented."
Demonstrated the framework's efficiency and the utility of the novel algorithms.

Conclusions:

The proposed framework and algorithms effectively analyze human mobility patterns.
The approach is versatile and applicable to diverse datasets and research fields.
Provides a robust method for understanding individual and group mobility behaviors.