K
Kangjae Lee
Researcher at University of Illinois at Urbana–Champaign
Publications - 29
Citations - 763
Kangjae Lee is an academic researcher from University of Illinois at Urbana–Champaign. The author has contributed to research in topics: Computer science & Recreation. The author has an hindex of 9, co-authored 16 publications receiving 443 citations. Previous affiliations of Kangjae Lee include Yonsei University & Seoul National University.
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Journal ArticleDOI
Within What Distance Does "Greenness" Best Predict Physical Health? A Systematic Review of Articles with GIS Buffer Analyses across the Lifespan.
TL;DR: It is found that larger buffer sizes, up to 2000 m, better predicted physical health than smaller ones, and it is recommended that future analyses use nested rather than overlapping buffers to evaluate to what extent greenness not immediately around a person’s home predicts physical health.
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Access to Urban Green Space in Cities of the Global South: A Systematic Literature Review
TL;DR: In this paper, a systematic review examines disparities in access to urban green space (UGS) based on socioeconomic status (SES) and race-ethnicity in Global South cities.
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Might School Performance Grow on Trees? Examining the Link Between "Greenness" and Academic Achievement in Urban, High-Poverty Schools
TL;DR: Interactions between greenness and Disadvantage suggest that the greenness-academic achievement link is different for student bodies with different levels of disadvantage, and future research should assess whether greening schoolyards boost school performance.
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Physical activity classification in free-living conditions using smartphone accelerometer data and exploration of predicted results
Kangjae Lee,Mei Po Kwan +1 more
TL;DR: The findings of this study indicate that random forest and gradient boosting enable accurate physical activity classification in free-living conditions.
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Location-based service using ontology-based semantic queries: A study with a focus on indoor activities in a university context
TL;DR: This study proposes a location-based service (LBS) using ontology-based semantic queries with a focus on the indoor activities in a university context to retrieve and provide information about places relevant to a destination with keywords given by users.