用大数据分析东京租房空间开放度，揭示其与租金和设计的关系。
Multifaceted Exploration of Spatial Openness in Rental Housing: A Big Data Analysis in Tokyo's 23 Wards
- 从2D平面可视性与3D图像语义分割双角度量化空间开放度。
- 发现1990年代开放度达峰值，且开放度高的房源租金更高。
- 开放度对感知空间影响有限，家具布局起更大作用。
理解空间开放度对提升居住品质至关重要，但现有研究常孤立分析其影响因素。本研究构建了二维（2D）与三维（3D）双维度量化框架，基于东京23区4,004套出租公寓数据，分析开放度的时空变化及其与租金和房屋属性的关系。2D开放度通过楼面图的可视性图分析（VGA）计算，3D开放度则利用Mask2Former语义分割模型从室内图像中识别墙、顶、地、窗等要素获取。结果显示，客厅可视性上升，整体开放度在1990年代达到峰值。空间分析表明开放度、租金与建筑特征存在部分相关性，反映城市更新趋势。尽管2D与3D开放度指标无直接相关，但较高开放度通常对应更高租金。现有模型预测的感知评分与开放度关联较弱，说明室内设计与家具布置对空间感知影响更显著。本研究提出一种多维度数据驱动框架，实现住宅空间开放度的量化，并链接城市与市场动态。
原文摘要 · Abstract (English)
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, rent, and building characteristics, reflecting urban redevelopment trends. Although the 2D and 3D openness indicators were not directly correlated, higher openness tended to correspond to higher rent. The impression scores predicted by the existing models were only weakly related to openness, suggesting that the interior design and furniture more strongly shape perceived space. This study offers a new multidimensional data-driven framework for quantifying residential spatial openness and linking it with urban and market dynamics.
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