Multifaceted Exploration of Spatial Openness in Rental Housing: A Big Data Analysis in Tokyo's 23 Wards
Takuya OKi, Yuan Liu

TL;DR
This study introduces a comprehensive data-driven framework to quantify and analyze spatial openness in Tokyo's rental housing, integrating 2D and 3D perspectives and examining their relationships with rent and urban trends.
Contribution
It develops a novel multidimensional approach combining visual analysis and semantic segmentation to evaluate spatial openness in residential units.
Findings
Higher openness correlates with higher rent.
Spatial openness increased in the 1990s.
Interior design influences perceived space more than openness measures.
Abstract
Understanding spatial openness is vital for improving residential quality and design; however, studies often treat its influencing factors separately. This study developed a quantitative framework to evaluate the spatial openness in housing from two- (2D) and three- (3D) dimensional perspectives. Using data from 4,004 rental units in Tokyo's 23 wards, we examined the temporal and spatial variations in openness and its relationship with rent and housing attributes. 2D openness was computed via planar visibility using visibility graph analysis (VGA) from floor plans, whereas 3D openness was derived from interior images analysed using Mask2Former, a semantic segmentation model that identifies walls, ceilings, floors, and windows. The results showed an increase in living room visibility and a 1990s peak in overall openness. Spatial analyses revealed partial correlations among openness,…
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Taxonomy
TopicsUrban Design and Spatial Analysis · Urban and spatial planning · 3D Modeling in Geospatial Applications
