Text mining public feedback on urban densification plan change in Hamilton, New Zealand

Abstract

Cities worldwide are commonly aspiring to transition from inefficient urban sprawl patterns to more compact and sustainable urban forms. However, urban densification efforts often face significant public resistance or skepticism, hindering at-scale implementation. There is a scarcity of empirical studies identifying the rationale and mechanisms underpinning public opposition to urban density. This study aims to bridge this gap by leveraging novel natural language processing techniques (NLP), combined with mixed-methods analysis of a unique, highly detailed public dataset on urban intensification in Hamilton. This research stands out by proposing a transferable model for rapidly generating insights from large public feedback datasets, and also unveils the polarized and complex, self-interest-driven mechanisms, including NIMBYism (Not In My Back Yard), behind public support or opposition to urban densification. NLP techniques, such as sentiment analysis, topic modeling, and ChatGPT, can be used to offer rapid insights into a large, unstructured public feedback dataset. When combined with submitters’ individual interest representation and identifies, these AI-generated summaries can offer important insights into the hidden rationales behind public opinions, and, more importantly, be used to design tailored public engagement activities to obtain community buy-in.