Research on Post-Use Evaluation of Community Green Space Rectification Based on a Multi-Dimensional Perception System: A Case Study of Jiayuan Sanli Community in Beijing

Abstract

Community green spaces (CGSs) constitute a crucial element of urban land use, playing a pivotal role in maintaining the stability of urban ecosystems and enhancing the overall quality of the urban environment. Through the post-occupancy evaluation (POE) of green spaces, we can gain insights into residents’ actual needs and usage habits, providing scientific evidence for the planning, design, and management of green spaces. This ensures that CGSs better meet residents’ needs and improve their quality of life. The POE of CGSs relies heavily on high-precision data support. However, the current POE system for CGSs faces challenges, such as limited data collection methods, incomplete indicator systems, and excessive manual involvement. To address these limitations in data collection, this study proposes a comprehensive, dynamically monitored, objective, and sustainable POE system for CGSs. This system incorporates a multi-dimensional perception system that integrates the Internet of Things (IoT) and sensors to collect data from various sources. It establishes an evaluation framework from the perspectives of policy guidance and usage needs for CGSs, utilizing neural network systems and artificial intelligence techniques to compute the evaluation results. Using the Jiayuan Sanli Community in Beijing as a case study, this paper demonstrates the feasibility of the proposed system. A comparison between the POE results obtained using the multi-dimensional perception technique and those obtained manually reveals an 87% improvement in the accuracy of the evaluation results based on the multi-dimensional perception system. This system bridges the gap between planning perspectives and user experiences, contributing significantly to future urban land planning and land policy formulation.