Work

Enhancing Systematic Review Efficiency with AIGC: Applications of Perception Data in Built Environment Audits

Year: 2025

Type: review

Abstract: With the growing use of human perception data streams in audits of the built environment, their value for enhancing objectivity and human-centeredness has become increasingly evident. This review synthesizes 63 publications through July 2024, providing a comprehensive analysis of perception data types, collection modalities and spatial strategies. This review introduces an Artificial Intelligence (AI)-enabled framework and utilizes Artificial Intelligence-Generated Content (AIGC) to assist literature retrieval and analysis, improving efficiency and transparency. The results indicate that heart rate and mood are currently the most frequently used perception data types in built-environment audits. Existing audit practices primarily focus on roads, green spaces, and residential areas at community and block-scale settings, with data choices varying by spatial typology. This review advances a systematic understanding of the application of perception data streams in built-environment audits and offers evidence-based recommendations for data collection, thereby providing stronger data support for future research.

Source: Buildings

DOI: 10.3390/buildings15203684

Authors: Anjun Tao, Zhijie Yang, Wenbo Ou

Institutions: Southeast University, Chinese Academy of Sciences, Institute of Geographic Sciences and Natural Resources Research, State Key Laboratory of Resources and Environmental Information System

Language: English

Topic: Urban Design and Spatial Analysis

Subfield: Building and Construction

Field: Engineering

Domain: Physical Sciences

SDG: Sustainable cities and communities

Open Access status: gold

Estimated APC paid: $3,137