Digital participation in urban design and planning: Addressing Data Translation Challenges in Urban Policy- and Decision-Making through Visualization Techniques

Digital technologies and online platforms, such as eparticipation and crowdsourcing tools, are revolutionizing citizen engagement in urban design and planning by enabling large-scale, asynchronous, and individual participation processes. This evolution towards more inclusive and representative decision- and policymaking, however, presents a significant challenge: the effective utilization of the vast amounts of textual data generated. This difficulty arises from distilling the most relevant information from the extensive datasets and the lack of suitable methodologies for the visual representation of qualitative data in urban practices. Addressing this, the paper deploys AI-based analysis methods, including Natural Language Processing (NLP), Topic Modeling (TM), and sentiment analysis, to efficiently analyze these datasets and extract relevant information. It then advances into the realm of data representation, proposing innovative approaches for the visual translation of this textual data into multi-layered narratives. These approaches, designed to comply with a comprehensive set of both quantitative and qualitative interpretation criteria, aim to offer deeper insights, thus fostering equitable and inclusive governance. The goal of this research is to harness the power of qualitative textual data derived from online participation platforms to inform and enhance decision- and policymaking processes in urban design and planning, thereby contributing to more informed, inclusive, and effective urban governance.