Urban Housing Conflicts in Large Canadian Cities: A Spatio-Temporal and Semantic Analysis Using Large Language Models

This paper introduces a comparative analysis of urban housing conflicts across eight major Canadian cities, Toronto, Vancouver, Québec, Ottawa, Calgary, Edmonton, St. John’s, and Halifax, over a 20-year period. Using Large Language Models (LLMs), we implement a structured workflow to extract, classify, and organize more than one thousand conflict instances from diverse textual sources, including municipal reports, media archives, and non-governmental organization publications. The methodological contribution lies in demonstrating how an LLM-assisted pipeline, combining schema-based extraction, prompt perturbation, and a two-phase calibration procedure, can generate structured, multi-city conflict datasets while addressing challenges such as output homogenization and sensitivity to prompt design. The findings highlight both shared national tendencies and city-specific configurations with post-2020 conflicts intensifying. Overall, the study proposes a transparent workflow for applying LLMs to conflict-related text analysis and offers an exploratory overview of the spatial, temporal, and semantic regularities of housing conflicts in Canadian cities.