摘要
2019年12月以来,新型冠状病毒肺炎(以下简称新冠肺炎)席卷全世界,已成为国际关注的突发性公共卫生事件。利用探索性空间数据分析方法和地理加权回归模型,对福建省新冠肺炎疫情的空间分布格局以及影响因素的空间分异特征进行了研究。结果表明:①新冠肺炎疫情初期呈随机分布,后期存在显著空间正相关,但空间集聚在大幅聚集后发生小幅扩散;②新冠肺炎确诊数热点区域呈以福建中东部沿海地带为中心、向福建南部先扩大后缩小的趋势;③各影响因子对不同县域新冠肺炎疫情的影响具有明显的空间分异规律,且4个影响因子与新冠肺炎确诊数均呈负相关关系,对新冠肺炎确诊数的影响程度依次为各县到火车站的距离>每平方公里拥有医疗机构数>人均GDP>人均公共财政教育支出。研究结论可为制定高效的应急方案提供科学依据,使得疫情防控措施更加具有针对性。
Since December 2019,COVID-19 has swept the world and has become a public health emergency of international concern.We used the exploratory spatial data analysis and geographical weighted regression model to study the spatial distribution pattern of COVID-19 epidemic and the spatial differentiation characteristics of influence factors in Fujian Province.The results show that①the outbreak of COVID-19 showes a random distribution in the early stage,and there is a significant spatial positive correlation in the later stage,but the spatial agglomeration occurred a small spread after a large aggregation.②The hot spots for the diagnosis of COVID-19 are mainly distributed in the coastal zone of central and eastern Fujian,and expands to southern Fujian and then contracted.③Each influence factor has obvious spatial differentiation rules for COVID-19 epidemic in different counties,and the four impact factors are negatively correlated with the COVID-19 diagnosed number.The influence on the COVID-19 diagnosed number is in turn:the distance of each county to the railway station>number of medical institutions per square kilometer>per capita GDP>per capita expenditure on public finance education.The conclusion can provide a scientific basis for formulating an efficient emergency plan,making the epidemic prevention and control measures more targeted.
作者
贺卓文
陈楠
HE Zhuowen;CHEN Nan(Key Lab for Spatial Data Mining and Information Sharing of Education Ministry,Fuzhou University,Fuzhou 350108,China;The Academy of Digital China,Fuzhou University,Fuzhou 350108,China)
出处
《地理空间信息》
2022年第7期15-21,共7页
Geospatial Information
关键词
新冠肺炎
空间分异特征
探索性空间数据分析
地理加权回归模型
COVID-19
spatial differentiation characteristic
exploratory spatial data analysis
geographical weighted regression model
