Heat and park attendance: Evidence from “small data” and “big data” in Hong Kong - Hao, Tongping; Chang, Haoliang; Liang, Sisi; Jones, Phil; Chan, P.W.; Li, Lishuai; Huang, Jianxiang (Building and environment, 2023)
Urban heat disrupts the use of parks, although the extent of such disruptions remains disputed. Literature relies on “small data” methods, such as questionnaires, field studies, or human-subject experiments, to capture the behavioural response to heat. Their findings are often in contradiction with each other, possibly due to the small sample sizes, the short study period, or the few sites available in a single study. The rise of “big data” such as social media offers new opportunities, yet its reliability and usefulness remain unknown. This paper describes a study using Twitter data (tweets) to study park attendance under the influence of hot weather. Some 20,000 tweets geo-coded within major parks were obtained in Hong Kong over a period of three years. Field studies have been conducted in parallel in a large park covering the hot and cool seasons and some 40,000 attendance were recorded over three months. Both the “small” and “big data” were analyzed and compared to each other. Findings suggest that a 1 °C increase in temperature was associated with some 4% drop in park attendance and some 1% drop in park tweets. The differences between the two data sources be explained by the ‘leakage’ of indoor tweets to parks caused by GPS drift near buildings. The Universal Thermal Climate Index can better predict self-reported thermal sensations, compared with other biometeorological indicators. This study has contributed to methodologies and new evidence to the study of behaviors and thermal adaptations in an outdoor space, and geo-coded tweets can serve as a powerful data source. •Twitter data were used to study park attendance influenced by hot weather in Hong Kong.•“big data” and “small data” confirmed negative correlations between attendance and temperature.•‘Hotspots’ identified from the two data sources were in agreement with each other.•The new data analysis protocol is of value for follow-up studies in monitoring outdoor activities.•UTCI can better explain thermal sensations compared with WBGT, OUT_SET* or PET.