School of Statistics, Shanxi University of Finance and Economics, Wucheng Road 696, Taiyuan 030006, China.
College of Architecture and Civil Engineering, Taiyuan University of Technology, Yingze Street 79, Taiyuan 030024, China.
Int J Environ Res Public Health. 2019 Feb 4;16(3):454. doi: 10.3390/ijerph16030454.
Currently, more and more remotely sensed data are being accumulated, and the spatial analysis methods for remotely sensed data, especially big data, are desiderating innovation. A deep convolutional network (CNN) model is proposed in this paper for exploiting the spatial influence feature in remotely sensed data. The method was applied in investigating the magnitude of the spatial influence of four factors-population, gross domestic product (GDP), terrain, land-use and land-cover (LULC)-on remotely sensed PM2.5 concentration over China. Satisfactory results were produced by the method. It demonstrates that the deep CNN model can be well applied in the field of spatial analysing remotely sensed big data. And the accuracy of the deep CNN is much higher than of geographically weighted regression (GWR) based on comparation. The results showed that population spatial density, GDP spatial density, terrain, and LULC could together determine the spatial distribution of PM2.5 annual concentrations with an overall spatial influencing magnitude of 97.85%. Population, GDP, terrain, and LULC have individual spatial influencing magnitudes of 47.12% and 36.13%, 50.07% and 40.91% on PM2.5 annual concentrations respectively. Terrain and LULC are the dominating spatial influencing factors, and only these two factors together may approximately determine the spatial pattern of PM2.5 annual concentration over China with a high spatial influencing magnitude of 96.65%.
目前，越来越多的遥感数据正在被积累，遥感数据的空间分析方法，尤其是大数据，需要创新。本文提出了一种深度卷积神经网络（CNN）模型，用于挖掘遥感数据中的空间影响特征。该方法应用于研究人口、国内生产总值（GDP）、地形、土地利用和土地覆盖（LULC）四个因素对中国遥感 PM2.5 浓度的空间影响程度。该方法产生了令人满意的结果。结果表明，深度 CNN 模型可以很好地应用于空间分析遥感大数据领域。并且与基于地理加权回归（GWR）的方法相比，深度 CNN 的准确性要高得多。结果表明，人口空间密度、GDP 空间密度、地形和 LULC 可以共同决定 PM2.5 年浓度的空间分布，整体空间影响程度为 97.85%。人口、GDP、地形和 LULC 对 PM2.5 年浓度的单独空间影响程度分别为 47.12%和 36.13%、50.07%和 40.91%。地形和 LULC 是主要的空间影响因素，只有这两个因素共同作用，才能大致确定中国 PM2.5 年浓度的空间分布，空间影响程度高达 96.65%。
