基于多源数据的非正式学习空间使用调查研究——以西建大东楼建筑广场为例

Research on the Informal Learning Space Based on Multi-Source Data: A Case Study of the Architectural Square in XAUAT

随着大数据时代的到来,环境行为研究试图采取更加量化的手段对建成环境及使用者行为规律进行调查。非正式学习空间中行为的多样性,易导致不同学习行为相互干扰,即行为与采光、噪声等环境差异化要求不匹配等问题,也增加了调研的难度。本文选取尺度适宜且承载行为复杂的某高校非正式学习空间作为实验对象,通过对室内定位、物理环境以及用户属性等相关数据的需求分析、采集、预处理、可视化、关联分析完成整个调研工作。并基于时间和空间维度建立不同类型数据之间的关联,以此呈现多源数据综合分析方法在非正式学习环境行为研究中的潜力。研究表明,多源数据关联分析的行为研究对揭示不同情境下的行为特征提供有力的方法,为既有环境的评估提供理论依据,并有潜力为设计优化提供支持。

With the advent of the big data era, environmental behaviour research has attempted to take more quantitative means to investigate the built environment and user behaviour patterns.The diversity of behaviours in informal learning space can easily lead to the interference between different learning behaviours and mismatches between behaviours and environmental differentiation requirements such as lighting and noise, which adds to the difficulty of investigation. This paper selects the informal learning space of a university with appropriate scale and complex behaviours as the research object. The entire study is completed through demand analysis, collection, pre-processing, visualisation and correlation analysis of indoor positioning, physical environment and user attributes. Based on the time and space dimensions, the association between different types of data is established, so as to show the potential of an integrated multi-source data analysis method for behavioural research in informal learning environments. The research shows that the behavioural research with multi-source data correlation analysis provides a powerful method to reveal the behavioural characteristics in different contexts, and supports the evaluation and design optimisation of the existing environment.