Spatiotemporal analysis and forecasting of public attention to China's five major religions

Abstract

In the digital era, leveraging search engine data to gain insights into public interests and attitudes towards religion has become increasingly important. To examine the spatiotemporal evolution and develop a forecasting model of public attention to China's five major religions (Buddhism, Taoism, Catholicism, Christianity and Islam), this study introduces geographic information system technology, forecasting technology and spatiotemporal analysis methods into religious research, using Baidu Index data from 2020 to 2024. The results show that most religions exhibit stable annual public attention, although significant changes occur during specific seasons, festivals or events. Spatial variation in public attention is moderate, with a relatively balanced regional distribution. However, a distinct east-west clustering pattern is evident, reflecting spatial aggregation. The SARIMA-based forecasting model effectively captures temporal dynamics and demonstrates strong forecasting performance. Findings reveal the spatiotemporal distribution patterns of religious public attention and highlight the practical value of the forecasting model, thereby emphasising the importance of considering spatial factors and forecasting models when addressing the dissemination of religion in the digital age. This study provides new methods and perspectives for religious research, promoting an interdisciplinary synthesis of religious studies, sociology and geography. It offers new insights into global religious studies, religious communication strategies and cultural exchange. Furthermore, it contributes to advancing cross-cultural and cross-regional religious research and offers valuable references for religious organisations and policymakers in managing religious affairs, adjusting policies and optimising communication strategies in a globalised context.

Keywords: Forecasting; GIS technology; Public attention; Religion; Spatiotemporal analysis.

Fig. 1

The methodology of the SARIMA model.

Fig. 2

The changing trend of public attention to China’s five major religions (2020–2024).

Fig. 3

Local spatial correlation types of public attention to China’s five major religions in 2024.

Fig. 4

Normalised local spatial correlation types of public attention to China’s five major religions in 2024.

Fig. 5

ACF and PACF of the first-order differenced series.

Fig. 6

Forecasting result of SARIMA model of Religions in 2024.