Hyeongmo Koo

Assistant Professor

Seoul National University

South Korea

Research Interests

Explore related searches

Professors in South Korea

Contact this professor

Get a free first email built on one of their papers, ready for you to edit and send yourself.

About

Hyeongmo Koo is an Assistant Professor at the University of Seoul, South Korea. His research focuses on spatio-temporal variation, machine learning applications in spatial analysis, and the optimization of public facility locations. Recent publications include exploring the impact of spatial autocorrelation in model evaluations and investigating the effects of COVID-19 on local business sales through spatio-temporal analyses.

Articles (16)

Year:

2025

Year:

2025

Year:

2024

Year:

2024

Year:

2024

Year:

2024

Year:

2024

Year:

2024

Year:

2023

Year:

2022

Year:

2021

Year:

2020

Year:

2020

Year:

2020

Year:

2020

Year:

2020

Collaborators (3)

Yongwan Chun

University of Texas at Dallas

UNITED STATES

View details

Jiyeong Lee

Seoul National University

SOUTH KOREA

View details

David W. S. Wong

George Mason University

UNITED STATES

View details

ApplyKite TwinLearns as you apply

Make your next move.

Prepare your application

Review drafts before you send them.

Hyeongmo Koo

Research Interests

Explore related searches

Contact this professor

About

Articles (16)

Evaluating Spatio-Temporal Kriging with Machine Learning Considering the Sources of Spatio-Temporal Variation

Impacts of Spatio-Temporal Variation on Estimation of Spatial and Spatio-Temporal Kriging

Optimal location selection for smart public multi-use facilities in Gwangju using machine learning and maximum covering location models

Exploring the impact of spatial autocorrelation on optimistic bias in cross-validation and assessing the effectiveness of spatial cross-validation

Varying Prediction Accuracies of Spatially Constrained Hierarchical Clustering-Based Machine Learning with Different Numbers of Clusters

Exploring Discontinuous Spatial Heterogeneity in the Determinants of Apartment Sales Price in Seoul

Spatial Clustering with Contiguity Constraint for Improving Prediction Accuracy in Spatial Machine Learning

Exploring Effectiveness of Spatial Features for Incorporating Spatial Data into Machine Learning

Exploring the spatio-temporal clusters of closed restaurants after the COVID-19 outbreak in Seoul using relative risk surfaces

Spatio-Temporal Variability of the Impact of Population Mobility on Local Business Sales in Response to COVID-19 in Seoul, Korea

Measuring Local Spatial Autocorrelation with Data Reliability Information

Position paper: Sensitivity analysis of spatially distributed environmental models- a pragmatic framework for the exploration of uncertainty sources

The inequality in education and its spatio-temporal changes

The Spatio-Temporal Distribution of Urban Commercial Structure in Seoul based on Kernel Weighted Location Quotients

A global sensitivity analysis approach for identifying critical sources of uncertainty in non-identifiable, spatially distributed environmental models: A holistic analysis applied to SWAT for input datasets and model parameters

Exploring the Spatio-Temporal Distribution of Commercial Gentrification Processes in Seoul: Focus on the Change in Coffee Shops Densities

Collaborators (3)

Yongwan Chun

Jiyeong Lee

David W. S. Wong

Make your next move.