Using Twitter to understand spatial-temporal changes in urban green space topics based on structural topic modelling

Abstract

Social media data offers urban planners insights into human activities in urban green spaces (UGSs). While recent methods like text-based word frequency analysis provide new perspectives on UGS, they are often lack stationary and non-continuous in nature. This limits their ability to capture the complexity and diversity of UGS use. This study conducts a structural topic model (STM) analysis of geo-referenced Tweets posted in London to investigate the dynamics of UGS-related topics before-, during- and after the COVID-19 outbreaks. Additionally, an approach of inverse distance weighting (IDW) was used to investigate the spatial patterns of topics probabilities. The results found that there were seven main topics categories expressed in UGS over study periods. Specifically, the increasing trends in topics proportions were found for the topics Nature engagement and Dog walking, indicating that these activities became increasingly popular during the pandemic. However, the topic Social events showed a decline in topic proportion, which might be the results of restriction measures such as practicing social distance. This study further discussed the potential factors that affecting the dynamics of these topics in spatial and temporal patterns. The results can potentially support future UGS planning and management especially during a time of crisis.