Dynamic changes in the spatial-temporal patterns of urban green space topics on Twitter during the COVID-19 pandemic Nan Cui*1, Vikki Houlden1, Nick Malleson1 and Alexis Comber1 1School of Geography, University of Leeds, Leeds LS2 9JT, UK GISRUK 2023 Summary This study aims to detect spatial-temporal changes in urban green space (UGS) topics pre-, during, and after the peak of the COVID-19 pandemic. Twitter data was selected as data source. Structural topic modelling (STM) was used to identify UGS topics and detect the trends of all topics over time. The inverse distance weighted (IDW) interpolation method was used to show the spatial distributions of all topics over all periods. The research found that the topic Nature observation was the most popular among all topics and showed an increasing trend in topic proportions and dynamic changes in spatial- temporal patterns. KEYWORDS: Urban green space, Topic detection, Spatial-temporal analysis, COVID-19 1. Introduction Urban green space (UGS) plays an essential role in supporting people’s daily (Houlden, de Albuquerque, Weich, & Jarvis, 2019). For example, UGS provides people with outdoor activity areas such as running tracks, tennis courts, and rowing places; and entertainment activity areas such as flower gardens, grasslands, and picnic areas. During the COVID-19 pandemic period, in order to mitigate the virus spread, a series of public health measures were implemented such as travel restrictions, quarantine, closing non-essential business services, and requiring citizens to stay at home (Cameron-Blake et al., 2020). UGS therefore became a major outdoor destination during the COVID-19 pandemic period. In this context, questions about what types of topics people discussed and how these topics changed over space and time became increasingly popular research issues (Geng, Innes, Wu, & Wang, 2021). Popular social media platforms such as Twitter provide new data sources on important events, providing rich knowledge about urban systems and human dynamics (Ilieva & McPhearson, 2018). In this study, Twitter was selected as a data resource due to its popularity (Cui, Malleson, Houlden, & Comber, 2021), and all Tweets used included geo-reference positions within UGS in Greater London. This study aims to develop a method to detect the impact of the COVID-19 pandemic on topics in urban green space (UGS). This method can effectively track important information during accident events like the pandemic and help with the planning and management of UGS. The method includes three steps: data collection and data pre-processing, dynamic topics generation, and investigation of spatial- temporal patterns of topics. In detail, the first step was geo-referenced Twitter data collection, Tweets cleaning and data pre-processing. The second step refers to using STM to identify UGS topics and detect the trends in topic proportion and topic label words. The third step focuses on monitoring the changes in UGS topics in semantic, spatial, and temporal dimensions pre-, during- and after the COVID- 19 pandemic. 2. Data collection and analysis 2.1 Data collection The Twitter datasets used for this study were downloaded via the Twitter academic research API. The API call selected geo-referenced Tweets located in London. They covered a three-month period (23rd March to 23rd Jun) for three consecutive years: 2019, 2020, and 2021. The downloaded Twitter data needed to be thoroughly cleaned. First, only English Tweets were selected, Tweets with fewer than three words were removed, and stop-words in each of the Tweets were also removed. Second, the