Research on Urban Precision Governance Based on Behavioural Science Theory and Data-Driven Methods

National Social Science Fund General Project (2022-2025), Completed

Introduction:  This study examines methods and application frameworks for data-driven urban governance refinement through public participation. Adopting a behavioural science perspective, it utilises common public engagement data in urban governance—including resident complaints, social media check-ins, and crowdsourced videos—to: 1. Analyse the operational mechanisms of three behavioural effects within such data: perceptual bias, action preference, and feedback influence. This enables the decomposition of these effects' pure contributions, establishing a methodological framework for discerning diverse behavioural effects within public participation data; Subsequently, it explores pathways for each effect: precise characterisation of urban governance issues through perception bias correction; accurate attribution of urban governance problems via action preference tracing; and targeted intervention in governance challenges by leveraging feedback effects. This establishes an application framework for data-driven, refined urban governance through public participation.