Designing an optimal indoor space is challenging in interior architecture. The optimal space design requires a comprehensive analysis of the living situation of residents in a space. However, it is extremely difficult to collect data from the space where daily life occurs. Many spatial analysis sensors are required because various daily life data must be collected precisely. Hence, it is difficult for indoor space designers to use the daily-life information of users when managing indoor layouts or floor plans. In this paper, we introduce a technique to solve this problem: simple mobile application (app) logs are used to identify the daily-life patterns of users in an indoor space, and the results are used to create the optimal space layout. We collect and process key information from the mobile app logs and Google app servers to generate a high-dimensional dataset required for user behavior analysis. Subsequently, we suggest a floor plan that minimizes the living cost using a two-dimensional genetic algorithm. Our method will facilitate the spatial analysis of currently inhabited indoor space and reduce the space utilization feedback costs of users.

设计最佳的室内空间在室内建筑中具有挑战性。优化的空间设计需要对空间内居民的居住状况进行综合分析。然而，从日常生活发生的空间收集数据是极其困难的。由于必须精确收集各种日常生活数据，因此需要许多空间分析传感器。因此，室内空间设计师在管理室内布局或平面图时很难使用用户的日常生活信息。在本文中，我们介绍了一种解决此问题的技术：使用简单的移动应用程序 (app) 日志来识别用户在室内空间中的日常生活模式，并将结果用于创建最佳空间布局。我们从移动应用日志和谷歌应用服务器收集和处理关键信息，以生成用户行为分析所需的高维数据集。随后，我们建议使用二维遗传算法最小化生活成本的平面图。我们的方法将有助于对当前有人居住的室内空间进行空间分析，降低用户的空间利用反馈成本。