Unlocking the potential of NLP in text data analysis for sustainable urban development

This paper reports on results of the SURE facilitation and synthesis research (FSR) project for the funding priority SURE (Sustainable Development of Urban Regions) of the German Federal Ministry of Education and Research (BMBF). SURE engages ten collaborative projects which develop concepts and test locally implementable solutions and strategies for sustainable transformation of fast-growing urban regions in Southeast Asia and China. SURE aims to create conceptual, theoretical, methodological, and translational innovations that integrate and move beyond discipline-specific approaches to address issues of sustainable urban development. The paper discusses the application of Natural Language Processing (NLP) as one form of Artificial Intelligence (AI) to support data and knowledge synthesis in sustainable urban development research. The abundant urban data and recent advancements in the field of AI have the potential to transform how urban researchers perceive and tackle sustainable development-related problems of cities. The research team employs various NLP algorithms to assess text data with the goal to analyse patterns in order to explore intra-project synergies and research intelligence on future trends. NLP has exhibited an ability to digest copious textual data and improve the usability of urban corpora, improving study scope and reducing resources required for research. However, the implementation of NLP to study issues related to sustainable urban development is a relatively novel. Predominantly used NLP modules are unable to identify contextual relations amongst multiple words which is essential in urban region study. To overcome this issue, algorithms employed were trained to identify various word classifications related to urban study discipline for precise output. We discuss the preliminary results of the ongoing exploration and show how it could contribute to an understanding of large text-based data sets in urban knowledge management. We examine the possibilities and limitations of such an approach and discuss the implications of AI as part of a multi-methodological approach to carry out a synthesis of sustainable urban development research efforts across an entire region covered under SURE framework. The paper also gives an outlook on utilising new AI based algorithms to generate text-based data analysis channel as well as indicate the limits, successes, challenges and constraints of such approaches.