中国COVID-19疫情的空间格局和时空演化

金丽娟; 许泉立

doi:10.16462/j.cnki.zhjbkz.2021.11.015

中国COVID-19疫情的空间格局和时空演化

doi: 10.16462/j.cnki.zhjbkz.2021.11.015

金丽娟,
许泉立^,

650500 昆明，云南师范大学地理学部，云南省地理空间信息技术工程技术研究中心，云南省高校资源与环境遥感重点实验室

基金项目:

国家自然科学基金 42161065

国家自然科学基金 41461038

云南省科技基础专项重点项目 202001AS070032

云南省中青年学术技术带头人后备人才项目 202105AC160059

详细信息

通讯作者:
许泉立，E-mail: go2happiness@163.com

中图分类号: K901;R181
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- 被引次数: 0
出版历程
- 收稿日期: 2021-01-06
- 修回日期: 2021-04-28
- 网络出版日期: 2021-12-04
- 刊出日期: 2021-11-10

The spatial pattern and spatio-temporal evolution of COVID-19 in China

JIN Li-juan,
XU Quan-li^,

Faculty of Geography, Yunnan Normal University, Geospatial Information Technology Engineering Research Center of Yunnan Province, Key Laboratory of Resources and Environmental Remote Sensing for Universities in Yunnan, Kunming 650500, China

Funds:

National Natural Science Foundation of China 42161065

National Natural Science Foundation of China 41461038

Yunnan Science and Technology Foundation Special Key Projects 202001AS070032

Young and Middle-Aged Academic and Technical Leaders Reserve Talents Project in Yunnan Province 202105AC160059

More Information

Corresponding author: XU Quan-li, E-mail: go2happiness@163.com

摘要

摘要: 目的掌握COVID-19疫情的空间格局和时空变化特征，探索其分布和扩散规律，有助于更好地防控COVID-19疫情扩散。方法本文以2020年1月22日-5月26日中国COVID-19疫情省级逐日发病率数据为数据源，利用空间自相关技术分析COVID-19疫情的空间格局，并利用重心轨迹迁移算法对其进行时空演化分析。结果在研究时间段范围内，中国COVID-19疫情在省级尺度上具有较强的空间依赖性。2020年1月22日-5月26日，中国COVID-19疫情的全局空间关联程度呈由强变弱再增强的发展趋势，Moran's I在(-0.04，-0.02)区间内，均为负值且波动范围小；疫情总体呈现以湖北省武汉市为中心，向周围城市蔓延扩散且随机分布的空间格局，国内疫情主要以高-低集聚为主，高-高集聚集中在香港和澳门特别行政区，而湖北省一直处于高-低集聚模式，并且在研究时间段内高-低集聚模式只有湖北省一个省份，低-低集聚模式主要集中在黑龙江省和西藏自治区；T₁-T₃时间段疫情以武汉市为中心向西北方向急剧扩散，T₄-T₆时间段范围内疫情逐渐转为向西南方向发展扩散，T₇-T₉时间段内疫情向东北方向扩散蔓延。结论在今后的疫情防控工作中应更注重疫情空间扩散模式的研究，探索影响扩散的因素，为后期精准防疫措施的制定提供有力的理论支撑。
- COVID-19 /
- 空间自相关 /
- 重心轨迹迁移算法 /
- 空间格局 /
- 时空演化
Abstract: Objective To better control the spread of COVID-19, it is important to understand the spatial pattern of COVID-19 and its spatiotemporal evolution characteristics, and explore its distribution and diffusion laws. Methods In this study, based on the daily incidence data of COVID-19 in China from January 22 to May 26, 2020, spatial autocorrelation was used to analyze the spatial pattern of COVID-19, and center of gravity trajectory migration algorithm was used to explore spatial-temporal evolution process. Results In the study period, COVID-19 had strong spatial dependence at the provincial scale. From January 22 to May 26, 2020, the global spatial correlation of COVID-19 showed a trend of increasing from strong to weak. Moran's I was negative in the range of (-0.04, -0.02) and had a small fluctuation range. The COVID-19 epidemic in China showed a general pattern with Wuhan City in Hubei Province as the center, spreading to the surrounding cities and random distribution. The domestic epidemic were mainly high-low clusters, and high-high clusters were distributed in Hong Kong and Macao Special Administrative Regions, while Hubei Province had been in the high-low clusters. In the study period, the high-low cluster was only one province in Hubei Province, and the low-low clusters were mainly distributed in Heilongjiang Province and Tibet Autonomous Region. During the T₁-T₃ period, the epidemic spread rapidly from Wuhan City to the northwest. During the T₄-T₆ period, the epidemic gradually spread to the southwest. During the T₇-T₉ period, the epidemic spread to the northeast. Conclusions In the future epidemic prevention and control work, we should pay more attention to the study of epidemic spatial diffusion model, explore the factors affecting diffusion, so as to provide strong theoretical support for the formulation of precise epidemic prevention measures.
- COVID-19 /
- Spatial autocorrelation /
- Center of gravity trajectory migration algorithm /
- Spatial pattern /
- Spatio-temporal evolution

HTML全文

图 1 2020年1月22日-5月26日中国COVID-19疫情逐期发病率全局Moran's I时序

注：T₁：1月22日-2月4日；T₂：2月5日-2月18日；T₃：2月19日-3月3日；T₄：3月4日-3月17日；T₅：3月18日-3月31日；T₆：4月1日-4月14日；T₇：4月15日-4月28日；T₈：4月29日-5月12日；T₉：5月13日-5月26日。

Figure 1. Global Moran's I sequence of periodic incidence of COVID-19 in China from January 22 to May 26, 2020

下载: 全尺寸图片幻灯片

图 2 2020年1月22日-5月26日中国COVID-19发病率LISA集聚图

注：该图基于国家测绘地理信息局标准地图服务网站下载的审图号为GS(2020)4632的标准地图制作，底图无修改。图中红色和蓝色分别表示该地区的COVID-19发病率存在空间正相关；浅红色和浅蓝色分别表示该地区COVID-19发病率存在空间负相关。

Figure 2. LISA cluster maps of the distribution of COVID-19 incidence rate in China from January 22 to May 26, 2020

下载: 全尺寸图片幻灯片

图 3 2020年1月22日-5月26日中国COVID-19发病率局部空间自相关显著性检验图

注：该图基于国家测绘地理信息局标准地图服务网站下载的审图号为GS(2020)4632的标准地图制作，底图无修改。

Figure 3. The local spatial autocorrelation significance test chart of the incidence of COVID-19 in China from January 22 to May 26, 2020

下载: 全尺寸图片幻灯片

图 4 2020年1月22日-5月26日中国COVID-19疫情发病率重心迁移轨迹

注：该图基于国家测绘地理信息局标准地图服务网站下载的审图号为GS(2020)4632的标准地图制作，底图无修改。

Figure 4. Center of gravity migration trajectory of incidence of COVID-19 in China from January 22 to May 26, 2020

下载: 全尺寸图片幻灯片

表 1 2020年1月22日-5月26日中国COVID-19发病率重心轨迹迁移计算结果

Table 1. The calculation results of gravity center trajectory migration of the incidence of COVID-19 in China from January 22 to May 26, 2020

时间阶段	阶段观测属性值	阶段发病变化强度	重心迁移方向(°)	重心迁移距离(km)
T₁	6.24	-	-	-
T₂	37.04	30.80	西北118.44	39.00
T₃	55.77	18.73	西北176.30	8.35
T₄	58.10	2.32	西南257.54	2.44
T₅	61.32	3.23	西南287.15	38.77
T₆	65.77	4.45	西南292.31	44.50
T₇	67.37	1.60	东北42.47	12.72
T₈	67.63	0.26	东北53.41	2.07
T₉	67.84	0.21	东北38.96	1.81

下载: 导出CSV

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