Hyojoong Kim

dblp:343/3021 · DBLP profile ↗
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2ranked-venue papers in the field
0as first author
2since 2021 · last 2025
—ORCID · unresolved

Domains — venue-derived; a paper can count in several

Big Data, Cloud & Distributed Data Systems · 2
YearPublicationVenuePosition
2025 A Study on Compact Data Structure-Based Compression Techniques for Grid Data
abstract
To utilize grid data containing location identifiers such as street names or parcel numbers on mobile devices for location verification, the data must store various location identifiers within each grid cell, resulting in large data volumes. Therefore, there is a need to reduce the storage capacity of the data. Since grid data has a structure similar to raster data, a recent approach proposes applying Compact Data Structures (CDS)-a method that simultaneously stores attribute values and indexing in a compressed format-for data compression. When the CDS-based compression method was applied to 10m grid data containing location identifiers such as road names and land-lot numbers, the storage capacity was reduced compared to the Network Common Data Form (NetCDF) format, which stores array data. Additionally, the memory usage was also reduced. Consequently, even when using grid data on mobile devices, the reduced data size allows for faster search and visualization.
Hyojoong Kim
IEEE Big Data2
2024 3-D land use index: Vertical land use analysis based on multi-dimensional geospatial data cube
abstract
In response to the concentration of population in cities and the increasing land value, cities are being developed in a more vertical and complex manner. To quantify vertical land use in three dimensions, a three-dimensional land use index (3D LUI) was proposed. For this purpose, heterogeneous time-series spatial data were used to create a multidimensional geospatial data cube (GDC) consisting of the four dimensions. The cell size of the GDC was determined by using the 3D Moran's I index, which minimizes the effect of modifiable areal unit problem (MAUP) when mapping point data to cells, and thus the smallest cell value was chosen as the initial cell size. The 3D LUI was calculated by linearly summing the spatial accessibility (kernal density factor) and diversity of uses (enrichment factor) between cells, weighted by the values derived from the entropy weighting method (EWM). After applying this method to the Seocho-gu, Seoul, Republic of Korea in June 2024, visual evaluation using KakaoMap’s Street View revealed that in mixed-use buildings or buildings with commercial functions on the ground floor, the commercial and residential functions were separated vertically.
Hyojoong Kim
IEEE Big Data2