基于多元数据的中国地理空间疫情风险评估探索——以2020年1月1日至4月11日COVID-19疫情数据为例
方云皓(1995— ),男,安徽合肥人,硕士生,主要从事地理信息系统方面研究。E-mail: 1198321182@qq.com |
收稿日期: 2020-05-31
修回日期: 2020-08-22
网络出版日期: 2021-04-25
基金资助
安徽省自然科学基金面上项目(2008085ME160)
版权
Exploration on Geospatial Risk Assessment in China based on Multiple Data: A Case Study of COVID-19 Data from January 1 to April 11, 2020
Received date: 2020-05-31
Revised date: 2020-08-22
Online published: 2021-04-25
Supported by
Natural Science Foundation of Anhui Province(2008085ME160)
Copyright
新冠肺炎(COVID-19)在空间上具有一定的传播风险,对城市的安全健康构成了威胁,防止疫情传播成为紧迫的任务。在2020年1月1日至4月11日,COVID-19疫情经历了发生、迅速发展和趋于稳定的发展过程,利用初期的COVID-19数据进行宏观层面的疫情风险评估,为防疫控制措施提供一定的参考。因此本研究基于行政区划、定点医院、疫情小区以及道路交通等多元数据,在宏观层面提出并构建全国地理空间疫情风险性评估,对疫情风险分布探讨的同时进行评估效果验证,并根据构建指标揭示影响风险的因素及其机理,主要结论: ① 地理空间风险评估具有有效的可行性。② 地理空间疫情风险分布全局Moran's I指数为0.758,具有显著的空间集聚特征。同时,不同的省区市之间的局部LISA值呈现空间差异性,其中高—高聚类省区市占比全国25.81%,风险程度较高,主要分布在湖北、河南、湖南、江西、安徽、浙江、江苏、上海,低—低聚类省区市占比全国9.68%,风险程度较低,主要分布在青海、西藏、新疆。③ 地理空间疫情风险分布与地理区位、道路交通、医疗卫生、疫情现状指标均存在一定的相关性。根据统计学的Pearson相关性分析,其相关指标R 2存在差异,在数值上由高到低依次为疫情现状、地理区位、道路交通、医疗卫生,在属性上其相关因子存在正负2种效应,地理空间疫情风险与武汉市地理距离、定点医院密度以及居民-医院地理距离呈现显著的负相关,其R 2分别为0.813、0.545、0.436,与铁路网密度、公路网密度以及疫情小区密度呈现显著的正相关,其R 2分别为0.751、0.792、0.825。④ 地理空间疫情风险的构成因素错综复杂,其受到多种因子的共同作用,根据空间分层异质性分析,不同因子之间均存在交互作用,其中居民—医院地理距离与公路网密度、铁路网密度交互作用较强,q值分别为0.9842、0.9837。本研究在宏观层面为城市管理中重大疫情的空间资源分配以及区域空间的联防联控策略提供了相应的依据。
方云皓 , 顾康康 . 基于多元数据的中国地理空间疫情风险评估探索——以2020年1月1日至4月11日COVID-19疫情数据为例[J]. 地球信息科学学报, 2021 , 23(2) : 284 -296 . DOI: 10.12082/dqxxkx.2021.200273
COVID-19 has a spatial transmission risk and poses a threat to the safety and health of the city. Preventing the spread of COVID-19 is therefore the urgent need for society now. The COVID-19 experienced a process that occurred, developed rapidly and stabilized from January 1 to April 11, 2020. Using the initial COVID-19 data for macro-level epidemic risk assessment can provide a certain reference for epidemic prevention and control measures. In this study, by using multiple data, including administrative division data, designated hospital data, epidemic community data, and road traffic data, we proposed a macro-level geospatial risk assessment and validated its effectiveness in China. Based on this, this study also analyzed the construction factors of geospatial risk assessment in order to explore its distribution rules. The following four conclusions showed that: ① Geospatial risk assessment of Covid-19 in China is effective to some extent. ② The spatial distribution of global Moran's I index of geospatial risk in China was 0.758, which had significant spatial agglomeration characteristics. At the same time, the LISA index in different provinces showed spatial differences. Some regions, including Hubei, Henan, Hunan, Jiangxi, Anhui, Zhejiang, Jiangsu, and Shanghai, were identified as high-high clusters and accounted for 25.81% of the provinces in China. The geospatial risk of these provinces was higher. Regions like Qinghai, Tibet, and Xinjiang, with a low degree of geospatial risk, accounted for 9.68% of the provinces in the country. ③ Some indicators, including geographic location indicators, road traffic indicators, medical and health indicators, and epidemic status indicators, were related to the distribution of geospatial risk. According to the statistical Pearson correlation analysis, there were differences in the correlation index R 2. In terms of numerical values, the epidemic status indicators, geographic location indicators, road traffic indicators, and medical and health indicators were ranked from highest to lowest. Different secondary factors were composed of four indicators, and they exhibited two effects of positive and negative correlation. Specifically, the factor of geographic distance from Wuhan, the designated hospital density factor, and the resident-hospital geographical distance factor showed significant negative correlations, with R 2 of 0.813, 0.545, and 0.436, respectively. However, the remaining factors showed significant positive correlations, including railway network density factor, road network density factor, and epidemic community density factor, and their R 2 were 0.751, 0.792, and 0.825, respectively. ④ The components of geospatial risk were intricate and complicated by multiple factors. According to the spatial stratified heterogeneity analysis, we found that there were interactions between different factors. Among them, the resident-hospital geographic distance factor interacted strongly with the railway network density factor and the road network density factor, and their q values were 0.9842 and 0.9837, respectively. This study not only explored the spatial resource allocation of major epidemics in urban management, but also provided a basis for regional spatial prevention and control strategies.
表1 中国综合空间风险评估因子及其权重Tab. 1 Comprehensive spatial risk assessment factors and their weights in China |
评估因子 | 地理区位 | 道路交通 | 医疗卫生 | 疫情现状 | ||
---|---|---|---|---|---|---|
与武汉市地理距离 | 公路网密度 | 地铁网密度 | 定点医院密度 | 居民-医院地理距离 | 疫情小区密度 | |
权重 | 0.250 | 0.125 | 0.125 | 0.125 | 0.125 | 0.250 |
表2 中国疾控机构资源指标及其权重Tab. 2 Disease control agency resource indicators and their weights in China |
指标 | 达标情况 | 平均使用面积 | 机构数 | 总编制人数 | 使用仪器情况 |
---|---|---|---|---|---|
属性 | + | + | + | + | + |
权重 | 0.2 | 0.2 | 0.2 | 0.2 | 0.2 |
注:表中“+”表示其指标与全国疾控机构资源呈正相关。 |
表3 2020年1月1日至4月11日中国综合空间风险指数(CSRI)与各变量对比统计Tab. 3 Comparative statistics of Comprehensive Spatial Risk Index (CSRI) and various variables in China from January 1 to April 11, 2020 |
地区 | CSRI | 排名 | 疾控机构资源指数 | 残差 | 归一化患病率指数 | 残差 | 归一化死亡率指数 | 残差 |
---|---|---|---|---|---|---|---|---|
湖北 | 8.69 | 1 | 0.49 | -6 | 0.62 | 0 | 0.45 | 0 |
河南 | 8.35 | 2 | 0.59 | 0 | 0.39 | -2 | 0.17 | 0 |
安徽 | 8.32 | 3 | 0.33 | -14 | 0.38 | -4 | 0.10 | -6 |
江西 | 8.05 | 4 | 0.40 | -9 | 0.39 | -3 | 0.06 | -11 |
湖南 | 7.81 | 5 | 0.49 | -3 | 0.28 | -20 | 0.08 | -7 |
江苏 | 7.48 | 6 | 0.57 | +2 | 0.36 | -5 | 0.00 | -21 |
浙江 | 7.18 | 7 | 0.54 | +2 | 0.40 | +5 | 0.06 | -7 |
上海 | 6.76 | 8 | 0.61 | +7 | 0.37 | 0 | 0.11 | +2 |
山东 | 6.68 | 9 | 0.44 | -1 | 0.36 | -3 | 0.11 | +2 |
北京 | 6.68 | 10 | 0.53 | +4 | 0.38 | +4 | 0.12 | +6 |
广东 | 6.33 | 11 | 0.49 | +2 | 0.40 | +8 | 0.11 | +6 |
福建 | 6.33 | 12 | 0.31 | -9 | 0.33 | -2 | 0.06 | -5 |
重庆 | 6.13 | 13 | 0.37 | -1 | 0.37 | +4 | 0.10 | +3 |
山西 | 5.79 | 14 | 0.25 | -9 | 0.30 | -6 | 0.00 | -14 |
陕西 | 5.58 | 15 | 0.31 | -7 | 0.32 | -1 | 0.06 | -3 |
广西 | 5.45 | 16 | 0.37 | +1 | 0.31 | -2 | 0.04 | -6 |
贵州 | 5.33 | 17 | 0.25 | -7 | 0.29 | -6 | 0.04 | -7 |
河北 | 5.14 | 18 | 0.41 | +6 | 0.32 | +3 | 0.10 | +7 |
辽宁 | 4.95 | 19 | 0.33 | +1 | 0.28 | -7 | 0.04 | -7 |
天津 | 4.89 | 20 | 0.33 | +1 | 0.31 | +1 | 0.07 | +7 |
四川 | 4.83 | 21 | 0.58 | +18 | 0.35 | +8 | 0.06 | +5 |
宁夏 | 4.67 | 22 | 0.13 | -6 | 0.27 | -6 | 0.00 | -7 |
海南 | 4.32 | 23 | 0.10 | -6 | 0.32 | +6 | 0.11 | +15 |
吉林 | 4.13 | 24 | 0.36 | +8 | 0.27 | -3 | 0.06 | +4 |
云南 | 3.86 | 25 | 0.44 | +14 | 0.29 | +3 | 0.04 | +2 |
甘肃 | 3.75 | 26 | 0.20 | -1 | 0.29 | +2 | 0.04 | +1 |
黑龙江 | 3.66 | 27 | 0.32 | +7 | 0.37 | +17 | 0.15 | +24 |
内蒙古 | 3.53 | 28 | 0.22 | 2 | 0.30 | +7 | 0.06 | +9 |
青海 | 3.00 | 29 | 0.04 | -1 | 0.19 | -1 | 0.00 | -1 |
西藏 | 2.45 | 30 | 0.00 | -1 | 0.07 | -1 | 0.00 | -1 |
新疆 | 2.02 | 31 | 0.23 | 6 | 0.25 | +2 | 0.06 | +10 |
注:残差表示与CSRI指数排名差异;各变量指数均经过归一化处理,非真实数字;由于数据获取困难,本研究不包括港香港、台湾和澳门。 |
表4 2020年1月1日至4月11日中国地理空间疫情风险评估因子探测结果Tab. 4 Detection results of geospatial epidemic risk factors in China from January 1 to April 11, 2020 |
因子 | 与武汉市地理距离 | 铁路网密度 | 公路网密度 | 定点医院密度 | 居民-医院地理距离 | 疫情小区密度 |
---|---|---|---|---|---|---|
q 统计值 | 0.8355 | 0.8103 | 0.8214 | 0.7649 | 0.6053 | 0.8139 |
p 值 | 0.0000 | 0.0215 | 0.0343 | 0.0000 | 0.0127 | 0.0000 |
注:p<0.05具有统计意义。 |
表5 2020年1月1日至4月11日中国地理空间疫情风险评估因子交互作用结果Tab. 5 Interaction results of geospatial epidemic risk factors in China from January 1 to April 11, 2020 |
因子 | 与武汉市地理距离 | 铁路网密度 | 公路网密度 | 定点医院密度 | 居民-医院地理距离 | 疫情小区密度 |
---|---|---|---|---|---|---|
与武汉市地理距离 | 0.8355 | |||||
铁路网密度 | 0.9252(Y) | 0.8103 | ||||
公路网密度 | 0.9309(Y) | 0.9034(N) | 0.8214 | |||
定点医院密度 | 0.9210(Y) | 0.8934(Y) | 0.8957(Y) | 0.7649 | ||
居民-医院地理距离 | 0.9687(Y) | 0.9837(Y) | 0.9842(Y) | 0.9781(Y) | 0.6053 | |
疫情小区密度 | 0.9102(Y) | 0.8834(N) | 0.8908(N) | 0.8755(Y) | 0.9803(Y) | 0.8139 |
注:Y代表有显著性差异,N代表没有显著性差异。 |
[1] |
|
[2] |
|
[3] |
朱斌, 刘锦林, 毛瑛. 中国典型法定报告传染病发病率空间关联性分析[J]. 中国公共卫生, 2018,34(1):4-8.
[
|
[4] |
丁晓彤, 余卓渊, 宋海慧, 等. 基于信息熵的中国自然疫源性疾病分布特征研究[J]. 地球信息科学学报, 2019,21(12):1877-1887.
[
|
[5] |
高芳旭, 齐秀英. 天津市和平区细菌性痢疾流行特征及疫情预测[J]. 现代预防医学, 2015,42(11):1951-1953.
[
|
[6] |
陈会宴, 廖一兰, 张宁旭, 等. 山西省原平市神经管畸形时空分析[J]. 地球信息科学学报, 2017,19(4):502-510.
[
|
[7] |
张湘雪, 王丽, 尹礼唱, 等. 京津唐地区HFMD时空变异分析与影响因子探测[J]. 地球信息科学学报, 2019,21(3):398-406.
[
|
[8] |
|
[9] |
叶莹, 范威, 王海峰, 等. 河南省新型冠状病毒肺炎聚集性疫情流行病学特征分析[J]. 中国公共卫生, 2020,36(4):465-468.
[
|
[10] |
|
[11] |
唐燕. 新冠肺炎疫情防控中的社区治理挑战应对:基于城乡规划与公共卫生视角[J]. 南京社会科学, 2020(3):8-14,27.
[
|
[12] |
杨俊宴, 史北祥, 史宜, 等. 高密度城市的多尺度空间防疫体系建构思考[J]. 城市规划, 2020,44(3):17-24.
[
|
[13] |
中华人民共和国国家卫生健康委员会. 截至4月11日24时新型冠状病毒肺炎疫情最新情况[EB/OL]. http://www.nhc.gov.cn/,2020-04-12.
[ National Health Commission, PRC. COVID-19 update as of 11 April 24:00[EB/OL]. http://www.nhc.gov.cn/,2020-04-12.]
|
[14] |
National data[DB/OL]. http://data.stats.gov.cn/search.htm?s/.
|
[15] |
The Data-center of China Public Health Science[DB/OL]. http://www.phsciencedata.cn/Share/en/index.jsp.
|
[16] |
Openstreetmap[DB/OL]. http://download.geofabrik.de/.
|
[17] |
China Data Lab Dataverse[DB/OL]. https://dataverse.harvard.edu/.
|
[18] |
刘耀宝, 曹俊. 我国境外输入性疟疾防控策略对当前新型冠状病毒肺炎防控工作的启示[J]. 中国血吸虫病防治杂志, 2020,32(2):113-118.
[
|
[19] |
|
[20] |
许小可, 文成, 张光耀, 等. 新冠肺炎爆发前期武汉外流人口的地理去向分布及影响[J]. 电子科技大学学报, 2020,49(3):324-329.
[
|
[21] |
张宇, 田万利, 吴忠广, 等. 基于改进SEIR模型的新冠肺炎疫情沿交通线路传播机制[J]. 交通运输工程学报, 2020,20(3):150-158.
[
|
[22] |
李欣, 周林, 贾涛, 等. 城市因素对COVID-19疫情的影响——以武汉市为例[J]. 武汉大学学报·信息科学版, 2020,45(6):826-835.
[
|
[23] |
|
[24] |
李刚, 李建平, 孙晓蕾, 等. 主客观权重的组合方式及其合理性研究[J]. 管理评论, 2017,29(12):17-26,61.
[
|
[25] |
郑斓, 任红艳, 施润和, 等. 珠江三角洲地区登革热流行风险空间模拟与预测[J]. 地球信息科学学报, 2019,21(3):407-416.
[
|
[26] |
张永树, 杨振凯, 訾璐, 等. 中国艾滋病空间格局和时空演化分析[J]. 地球信息科学学报, 2020,22(2):198-206.
[
|
[27] |
张新, 林晖, 朱长明, 等. COVID-19疫情早期中国确诊时间的时空特征及动态过程分析[J]. 武汉大学学报·信息科学版, 2020,45(6):791-797.
[
|
[28] |
|
[29] |
张婷, 程昌秀. 顾及空间集聚程度的中国高温灾害危险性评价[J]. 地球信息科学学报, 2019,21(6):865-874.
[
|
[30] |
陈芳, 吴家兵, 姜静静, 等. 安徽省新型冠状病毒肺炎聚集性疫情流行特征与防控措施分析[J]. 中国公共卫生, 2020,36(4):469-472.
[
|
[31] |
刘勇, 杨东阳, 董冠鹏, 等. 河南省新冠肺炎疫情时空扩散特征与人口流动风险评估——基于1243例病例报告的分析[J]. 经济地理, 2020,40(3):24-32.
[
|
[32] |
|
/
〈 | 〉 |