| 中国男性儿童呼气高峰流量参考值地理分布 |
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| 引用本文： | 葛淼,薛然尹,何进伟,胡燕宇.中国男性儿童呼气高峰流量参考值地理分布[J].地理研究,2014,33(3):451-466. |
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| 作者姓名： | 葛淼 薛然尹 何进伟 胡燕宇 |
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| 作者单位： | 陕西师范大学旅游与环境学院健康地理研究所, 西安710119 |
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| 基金项目： | 国家自然科学基金项目（40971060） |
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| 摘 要： | 为弥补健康男性儿童肺部呼气高峰流量参考值制定时忽略地理因素的影响，分析了健康男性儿童肺部呼气高峰流量参考值与地理因素的关系。收集中国各地健康男性儿童呼气高峰流量参考值，运用相关分析将健康男性儿童肺部呼气高峰流量参考值与选取的25项地理因素指标进行了研究，提取其中存在相关性的10项地理因素进行进一步分析。以空间自相关Moran’s指数分析数据，确定数据与空间及地理因素存在关系。用选定的10项指标进行BP人工神经网络与地理要素模拟分析。通过5层神经网络，选取含9个隐含层的1000次训练自学习建立模拟规则，用此规则模拟健康男性儿童呼气高峰流量参考值与地理环境的神经网络模型。运用ArcGIS地统计分析对数据进行分布检测，选取析取克里金法进行插值并输出参考值的地理分布图。研究表明神经网络预测与地统计插值可以很好的结合进行插值出图，分析出中国男性儿童肺部呼气高峰流量参考值与经度、海拔高度、年平均气温、年平均相对湿度、年平均风速、表土石砾含量、表土有机质含量、表土（粘土）阳离子交换量、表土（粉土）阳离子交换量、表土总可交换量存在关系。同时，分析了地理因素与医学指标间的关系，对其影响机制进行了讨论。
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| 关 键 词： | 呼气高峰流量参考值 BP人工神经网络 地统计分析 Moran&rsquo s指数 空间分析 |
| 收稿时间： | 2013-01-15 |
| 修稿时间： | 2013-10-24 |
Geographic distribution of reference value of boys’ peak expiratory flow rate based on the artificial neural networks |
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| GE Miao,XUE Ranyin,HE Jinwei,HU Yanyu.Geographic distribution of reference value of boys’ peak expiratory flow rate based on the artificial neural networks[J].Geographical Research,2014,33(3):451-466. |
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| Authors: | GE Miao XUE Ranyin HE Jinwei HU Yanyu |
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| Institution: | Health Geography Institute, College of Tourism and Environment, Shaanxi Normal University, Xi'an 710119, China |
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| Abstract: | To improve the situation in which geographical factors are ignored when healthy boys PEFR values are estimated, this article aims to analyze the relationship between their PEFR reference values and geographical factors. Correlation analysis was adopted in the process of collecting Chinese healthy boys' PEFR values to explore the data and the selected 25 geographical factors. After that, those 10 geographical factors which have correlation with the data were extracted for further analysis. Furthermore, spatial autocorrelation (Moran's index) shows that the data is correlated with spatial and geographic factors. The artificial neural networks were created to analyze the simulation of the selected indicators of geographical elements. This research chooses 5 layer neural networks and selects 9 hidden layers and 1000 times of training to build a simulation rule, and this rule thereafter was used to simulate the relationship between healthy boys' PEFR reference values and geographical environment. The distribution map of reference values was generated by using Arcgis' statistical analysis to test the data's distribution and choosing the disjunctive kriging interpolating. It is indicated that the artificial neural network and geostatistical analyst can be combined to generate a better interpolation map and that the Chinese boys' PEFR values have some correlation with longitude, altitude, annual average temperature, annual average relative humidity, wind speed, average annual soil gravel content, soil organic matter content, soil cation exchange capacity (clay), soil cation exchange capacity (silt), and soil total exchangeable amount. Meanwhile, this article analyzes the relationship of the geographical factors and the medical indicators and discusses the effect of these factors on Chinese healthy boys' PEFR values. |
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| Keywords: | PEFR reference value BP neural network geostatistical analyst Moran’s index spatial analysis |
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