| 小区域癌症数据典型空间插值方法比较研究 |
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| 引用本文： | 王士博,王勇.小区域癌症数据典型空间插值方法比较研究[J].地理研究,2021,40(7):2102-2118. |
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| 作者姓名： | 王士博 王勇 |
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| 作者单位： | 1.中国科学院地理科学与资源研究所 资源与环境信息系统国家重点实验室,北京 1001012.中国科学院大学,北京 100049 |
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| 基金项目： | 中国科学院战略性先导科技专项(A类)(XDA19040103);中国科学院重点部署项目(ZDRW-KT-2020-2-2) |
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| 摘 要： | 癌症已成为危害全球居民健康的重大民生问题,选取合适的空间插值方法分析小区域癌症数据的空间特征可对区域性癌症防控工作的有效开展提供依据。本研究以湖南省苏仙区2012和2016年以村为单位的肺癌死亡率数据为研究对象,以平均误差和均方根误差为评价指标,对反距离加权（IDW）、普通克里金（OK）、趋势面分析（TSA）、多元线性回归（MLR）与协同克里金（CK）五种典型空间插值方法进行精度效果对比及参数优选,并结合不同插值方法的优缺点,确定癌症数据的最优插值方法。结果表明：插值精度方面,CK法的均方根误差最小、插值精度最高,OK、IDW（幂值=1）和MLR次之,TSA（阶数=5）最低;插值效果方面,五种插值方法的实测值和预测值均显著相关,除CK外,其它四种方法均对死亡率低估程度较大,CK和OK插值结果的空间分布效果更好。同时考虑空间因素和影响因子的CK方法是小区域苏仙区2012年、2016年肺癌死亡率最优插值方法,应用该方法可对区域性癌症防控工作的有效开展提供最优的技术支撑。本论文的研究思路也可为小区域癌症数据空间插值方法及参数优选提供参考。
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| 关 键 词： | 小区域 癌症死亡率 空间插值 |
| 收稿时间： | 2020-07-20 |
Comparative research on typical spatial interpolation methods for cancer data in small regions |
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| WANG Shibo,WANG Yong.Comparative research on typical spatial interpolation methods for cancer data in small regions[J].Geographical Research,2021,40(7):2102-2118. |
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| Authors: | WANG Shibo WANG Yong |
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| Institution: | 1. State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China2. University of Chinese Academy of Sciences, Beijing 100049, China |
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| Abstract: | Cancer has become a major livelihood problem endangering the health of global residents. And selecting an appropriate spatial interpolation method to analyze the spatial characteristics of cancer data in a small area can provide a basis for the effective development of regional cancer prevention and control efforts. In this study, we took the 2012 and 2016 lung cancer mortality data in Suxian District of Hunan Province as the research object, and used the average error and the root mean square error as the evaluation indicators. Five typical spatial interpolation methods, namely, inverse distance weighting (IDW), ordinary kriging (OK), trend surface analysis (TSA), multiple linear regression (MLR) and co-kriging (CK), were compared in terms of accuracy and effectiveness and parameter optimization. And then the optimal interpolation method for cancer data was determined in combination of the advantages and disadvantages of different interpolation methods. The results showed that: in terms of interpolation accuracy, the co-kriging (CK) method had the lowest root mean square error of lung cancer mortality in any year, with the interpolation accuracy being the highest, followed by ordinary kriging (OK), inverse distance weighting (IDW) (power=1) and multiple linear regression (MLR), while trend surface analysis (TSA) (order=5) had the lowest root mean square error; in terms of interpolation effect, the measured and predicted values of the five interpolation methods were significantly correlated, except for co-kriging (CK), the other four methods had a greater degree of underestimation of mortality, and the spatial distribution of co-kriging (CK) and ordinary kriging (OK) interpolation results were better. The co-kriging (CK) method, which considers both spatial factors and impact factors, is the optimal interpolation method for lung cancer mortality in 2012 and 2016 in the study area. The application of this method can provide the optimal technical support for the effective implementation of regional cancer prevention and control. This paper can also provide a great reference for the spatial interpolation method and parameter optimization of small area cancer data. |
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| Keywords: | small area cancer mortality spatial interpolation method |
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