Exploring aspects of transport-induced gentrification across the project lifecycle: An NLP approach

Abstract

Public transport projects reshape neighbourhoods, creating opportunities for urban revitalisation, place-based and transit-oriented development. However, project planning processes may create social disbenefits, enacting gentrification and community displacement. This paper explores the relationship between Light Rail Transit (LRT) and economic and socio-demographic aspects that provide early indications of gentrification processes, by analysing public engagement sources. We adopt a mixed-method case study approach using Natural Language Processing (NLP) and document review to analyse public engagement data during the design, construction and operation phases of the project. A case study of the CBD and South-East Light Rail Project in Sydney, Australia, is employed. Our results indicate that while early indications show gentrification processes may be occurring, they are not reflected in the project-led public engagement or project documentation. Identifying aspects of gentrification processes earlier through public engagement during the design and construction phases could facilitate timely intervention to mitigate adverse impacts on local communities. We conclude that metropolitan governments and project leaders must improve engagement practices about the potential effects of gentrification processes and work with local authorities and communities to be transparent about the significance of effects over time.