{"uid": "COVID-19_b", "time_update": 1585306988, "title": "Spatial Analysis of COVID-19-related Weibo", "cnt_html": "<div align=center>\n<img src=\"http://drr.ikcest.org/static/upload/4f/4fb71928-701a-11ea-94ca-00163e0618d6_m.jpg\" style=\"width:50%\"/>\n</div>\n\n<div align=\"center\">(a)</div>\n\n<div align=center>\n<img src=\"http://drr.ikcest.org/static/upload/5c/5cbe8cd2-701a-11ea-868c-00163e0618d6_m.jpg\" style=\"width:50%\"/>\n</div>\n\n<div align=\"center\">(b)</div>\n\n<div align=\"center\">Figure 2 The spatial distribution of Weibo related to COVID-19.</div>\n\n<p>The spatial distribution of Weibo related to COVID-19 is shown in Figure 2. The Weibo \nnumbers were mainly concentrated in the east-central parts of China, as shown in Figure \n4a. There were more than 5,000 Weibo texts in Shandong Province (the capital of Jinan),\nHubei Province (capital of Wuhan), Henan (capital of Zhengzhou), Guangdong (capital of\nGuangzhou), Sichuan (the capital of Chengdu), and Jiangsu (capital of Nanjing), Anhui \nProvince (capital of Hefei), Hebei Province (capital of Shijiazhuang), Beijing, Shaanxi \n(capital of Xi\u2019an), Liaoning (capital of Shenyang), Hunan (capital of Changsha), and \nShanxi (capital of Taiyuan). Figure 4b shows the spatial distribution of the kernel \ndensity with a search radius of 100 km, indicating that the high-density areas of Weibo \nrelated to COVID-19 were in Wuhan, Beijing, Shanghai, Guangzhou, Chengdu, Xi\u2019an, and \nZhengzhou, and presents a continuous trend among the hot points of Wuhan, Beijing, and \nShanghai.</p>"}