Abstract
Beijing1 was the most prevalent city of SARS in China in 2003. The study on the spatial distribution and clustering characteristics is helpful to deeply understand the epidemic of SARS in Beijing. In this paper, the home addresses of SARS patients accquired by investigation were considered as the spatial location, deriving 2321 cases of the spatial distribution and incidence rate of infected patients. Kernel estimation method is used to obtain the density distribution of SARS patients. The results indicate that the distribution density of infected people is gradually attenuated from the center of the city to the suburbs. Ripley'K function is also used to explore the spatial clustering characteristics of SARS infection. In addition, the influence of gender, contact history and SARS Beijing Xiaotangshan Hospital towards the spatial clustering of patients are analyzed and thus shows that the spatial clustering of patients is the strongest at 11km distance. Gender and history of exposure to SARS infection in the spatial clustering are of a small impact, while SARS Beijing Xiaotangshan Hospital on SARS infection in the spatial clustering are of a strong impact. The clustering characteristics are significantly weaker after the establishment of the hospital that shows the importance of the establishment of the hospital on prevent and control of SARS epidemic in Beijing.
Original language | English |
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Title of host publication | Proceedings of the 3rd ACM SIGSPATIAL International Workshop on the Use of GIS in Emergency Management, EM-GIS 2017 |
Place of Publication | New York, U. S. A. |
Publisher | Association for Computing Machinery |
ISBN (Electronic) | 9781450354936 |
DOIs | |
Publication status | Published - 7 Nov 2017 |
Event | 3rd ACM SIGSPATIAL International Workshop on the Use of GIS in Emergency Management, EM-GIS 2017 - Redondo Beach, USA United States Duration: 7 Nov 2017 → 7 Nov 2017 |
Conference
Conference | 3rd ACM SIGSPATIAL International Workshop on the Use of GIS in Emergency Management, EM-GIS 2017 |
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Country/Territory | USA United States |
City | Redondo Beach |
Period | 7/11/17 → 7/11/17 |
Keywords
- Beijing
- Kernel estimation
- Point pattern analysis
- Ripley'K function
- SARS
ASJC Scopus subject areas
- Computer Networks and Communications
- Geography, Planning and Development
- Information Systems
- Information Systems and Management