Abstract
The rapid development of big data analytics provides tremendous possibilities to investigate large-scale patterns in both the spatial and temporal dimensions. In this research, we utilize a unique open dataset, the Global Database on Events, Location, and Tone (GDELT), and a geotagged social media dataset (Weibo) to analyze connections between Chinese provinces. Specifically, this study constructs a gravity model to compare the distance decay effect between the GDELT data (i.e., mass media data) and the Weibo data (i.e., location-based social media [LBSM] data). The results demonstrate that mass media data possess a weaker distance decay effect than LBSM data for Chinese provinces. This study generates valuable input to interpret regional relations in a fast-growing, developing country—China. It also provides methodological references to explore urban relations in other countries and regions in the big data era.
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Notes
- 1.
- 2.
Conflict and Mediation Event Observations (CAMEO) is a framework for coding event data.
- 3.
From the GDELT codebook (http://data.gdeltproject.org/documentation/GDELT-Global_Knowledge_Graph_Codebook.pdf).
- 4.
Here user IDs are long integers generated by Weibo.com and are not directly connected to any personally identifiable information (PII), unless the users volunteer to make such information publicly accessible.
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Yuan, Y. (2017). Exploring the Spatial Decay Effect in Mass Media and Location-Based Social Media: A Case Study of China. In: Griffith, D., Chun, Y., Dean, D. (eds) Advances in Geocomputation. Advances in Geographic Information Science. Springer, Cham. https://doi.org/10.1007/978-3-319-22786-3_13
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