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范玉,张庆勋,韩姝伊,罗静,何宏轩.2020.基于GIS技术对虎病例的回顾性分析.动物学杂志,55(4):491-512.
基于GIS技术对虎病例的回顾性分析
A Retrospective Analysis of Tiger Case Reports Based on GIS Technology
投稿时间:2019-12-13  修订日期:2020-06-24
DOI:10.13859/j.cjz.202004011
中文关键词:  回顾性分析  风险因素  虎病例  可视化  地理信息系统(GIS)
英文关键词:Retrospective analysis  Risk factors  Tiger case report  Visualization  Geographic information system, GIS
基金项目:国家重点研发计划项目(No. 2017YFD0501702)
作者单位E-mail
范玉 中国科学院动物研究所 北京 100101 中国科学院大学生命科学学院 北京 100049 fanyu@ioz.ac.cn 
张庆勋 中国科学院动物研究所 北京 100101 hehx@ioz.ac.cn 
韩姝伊 中国科学院动物研究所 北京 100101 hehx@ioz.ac.cn 
罗静 中国科学院动物研究所 北京 100101 hehx@ioz.ac.cn 
何宏轩 中国科学院动物研究所 北京 100101 hehx@ioz.ac.cn 
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中文摘要:
      疫病对珍稀濒危物种虎的(Panthera tigris)生存构成重大威胁。先前的许多研究都将疫病的出现与社会经济、环境和生态因素联系起来,但还没有研究明确分析这些因素与虎病例发生之间的关系。本研究从虎病例的分类统计情况、空间分布情况、时间相关性三个方面分析了1909至2019年间全球范围内的551个虎病例。运用地理信息系统(GIS)及SPSS统计分析软件针对中国范围内(未统计台湾省以及香港和澳门特别行政区)虎病例的发生情况进行了回归分析,分析结果表明,虎病例的发生与人口因素存在显著的相关性,提示相关工作人员应注意加强对人口密度较大地区的虎疫病防控工作力度。该结果为建立一个用于预测虎疫病最有可能发生地区的模型提供了基础。
英文摘要:
      Diseases represent a significant threat to the survival of tigers (Panthera tigris). Many previous studies have linked the emergence of the diseases to socio-economic, environmental and ecological factors, but none of these studies specifically analyzed the relationship between these factors and tiger case reports. This study used Excel software to make statistics and present the classification statistics of tiger case reports in the world (Table 1), as well as time-dependent change (Fig. 4, 5). The Poisson regression of SPSS software was used to analyze correlation between case reports’ number and time quantitatively. ArcGIS software was used to present the geographical distribution of tiger case reports worldwide (Fig. 1) and in China (Fig. 2). Furthermore, this study used ArcGIS software to open the spatial interpolation database derived from the ‘Resource and Environment Data Cloud Platform’ which is created by Chinese Academy of Sciences. The properties of grids where tiger distribute in China (Taiwan Province, Hong Kong and Macao special administrative regions were not counted) were export to an excel sheet, including whether the grid was tiger case reports positive, the population interpolation of the grid, the precipitation interpolation of the grid, and the air temperature interpolation of this grid. The logistic regression model in SPSS was used to quantify the association’s strength among grids where tiger case reports occurred and factors hypothesized. The response variable (yes, no) was whether the grids occurred tiger case reports. Candidate variables to fit the prediction were population, precipitation and air temperature. The result of analysis confirms that there is a significant positive correlation between tiger case reports and demographic factor (Table 2), suggesting that we should pay more attention to the prevention of tiger diseases in areas with high population density (Fig. 3). This study provides a basis for building a model to predict regions where new tiger diseases are most likely to occur.
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