地理科学进展 ›› 2014, Vol. 33 ›› Issue (5): 636-646.doi: 10.11820/dlkxjz.2014.05.005

• 人口与健康地理 • 上一篇    下一篇

组合预测模型在女性呼气高峰流量参考值地理分布研究中的应用

薛然尹, 葛淼, 何进伟, 胡燕宇, 谷琳琳, 杨绍芳   

  1. 陕西师范大学旅游与环境学院健康地理研究所, 西安710119
  • 收稿日期:2013-10-01 修回日期:2013-12-01 出版日期:2014-05-25 发布日期:2014-05-25
  • 通讯作者: 葛淼(1960-),男,研究员,博士生导师,主要从事健康地理研究,E-mail:gemiao@snnu.edu.cn E-mail:gemiao@snnu.edu.cn
  • 作者简介:薛然尹(1988-),男,河北承德人,硕士生,主要研究方向为健康地理学及GIS分析方法,E-mail:lyws2007@qq.com。
  • 基金资助:
    国家自然科学基金项目(40971060)。

Application of combination forecasting model in geographic distribution of reference value of women’s peak expiratory flow rate

XUE Ranyin, GE Miao, HE Jinwei, HU Yanyu, GU Linlin, YANG Shaofang   

  1. Health Geography Institute of Tourism and Environment College, Shaanxi Normal University, Xi'an 710119, China
  • Received:2013-10-01 Revised:2013-12-01 Online:2014-05-25 Published:2014-05-25

摘要: 随着地理学的发展以及人们对健康问题的日益关注,医学地理学得到迅速发展。鉴于目前医学参考值制定时仍存在忽略地理因素的影响,本文收集中国各地3809 例健康成年女性呼气高峰流量参考值,分析地理因素对其的影响,计算不同地区参考值的数值差异,探究地理因素对医学参考值产生影响的机理,其中,纬度、海拔高度、年平均气温、年平均相对湿度、年降水量、表土砂砾百分率、表土参考容量共7项地理因素存在显著的相关性。利用ArcGIS中的Moran's I指数对数据进行分析,确定数据与空间及地理因素存在关系。并通过岭回归分析,建立回归方程,并进行插值。研究结果表明,中国健康成年女性肺部呼气高峰流量与纬度与海拔、气候、土壤等地理因素之间存在着显著的关系,同时证明,岭回归与支持向量机组合模型的地理分布差异预测结果优于单独预测方法。

关键词: 地理分布, 呼气高峰流量, 岭回归, 支持向量机, 中国, 组合预测模型

Abstract: With the development of geography and people's overall health concerns, medical geography as an emerging discipline has also experienced rapid developments. Given that most of the existing medical reference values take into little consideration the influence of geographical factors, a more comprehensive and scientific method should be developed to take these into account. This article takes healthy adult women's lung peak expiratory flow rate reference value as an example, using 3809 cases of healthy adult women's peak expiratory flow reference value collected throughout China to analyze the impact of geographic factors, calculate the differences of different regions' reference values, and explore the mechanism of geographical factor's influence on medical reference value in an effort to improve the methods of medical reference value analysis through analyzing the relationship between geographical factors and medical reference values. As a first step, correlation analysis was used to analyze the relationship between peak expiratory flow value of adult women and the selected 25 indicators of geographical factors. Based on the result, seven geographic indicators (latitude, altitude, average temperature, annual average relative humidity, annual rainfall, topsoil gravel percentage, and topsoil reference capacity) that have significant correlation with peak expiratory flow reference value were extracted for further analysis. Second, Moran's I (spatial autocorrelation module), one of the ArcGIS software's analytical tools, was used to determine if this group of data is impacted by spatial and geographical factors. Third, using the data for the seven selected indicators, ridge regression analysis and SVR (support vector regression) were used to create two regression models and interpolate values. Then the results of these two prediction models were given different weights to establish the optimal combination forecasting model of spatial differences. Student's T test was used to compare the accuracy of ridge regression analysis, SVR and the combination forecasting model. Meanwhile, differences between the true values and results of the above three models were also considered for evaluating the performance of the models. Finally, a spatial difference prediction map was made. Based on this map and the results of correlation analyses, this article discusses why and how these geographical factors influence human tissues/organs and medical reference values. The output of this study indicates that the relationship between geographical factors and healthy adult Chinese women's lung peak expiratory flow rate should not be overlooked. The selected geographical factors (classify into terrain, climatic and soil factors) affect the lung tissue, especially the structure and function of the bronchi, because different living environments impact human tissues and organs differently, and humans living in different regions develop some differences in tissues and organs. The result of this research also shows that the combination forecasting model, which combined ridge regression and SVR, performed better than the individual prediction methods. Combination forecasting model not only can be used in traditional prediction exercises using temporal data: it is also possible to use this method to predict differences in geographic distribution or spatial data. It has the potential to be further expanded and utilized.

Key words: China, combination forecasting model, geographical distribution, peak expiratory flow rate, ridge regression, support vector regression

中图分类号: 

  • R188