| 集成地理探测器与随机森林模型的城市人口分布格网模拟 |
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| 引用本文： | 成方龙,赵冠伟,杨木壮,刘月亮,李芳.集成地理探测器与随机森林模型的城市人口分布格网模拟[J].测绘通报,2020,0(1):76-81. |
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| 作者姓名： | 成方龙 赵冠伟 杨木壮 刘月亮 李芳 |
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| 作者单位： | 1. 广州大学地理科学学院, 广东 广州 510006;2. 广州大学国土资源与海岸带研究所, 广东 广州 510006 |
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| 基金项目： | 国家自然科学基金（41671175；41671430）；广东省自然科学基金（2017A030313240）；广东省高等学校优秀青年教师培养项目（YQ2015127） |
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| 摘 要： | 精细尺度的城市人口分布信息是城市资源配置和综合管理的重要依据。本文以广州市越秀区、荔湾区、天河区、海珠区、白云区及黄埔区作为研究区域，基于人口统计、夜间灯光、兴趣点及土地利用等多源数据，利用地理探测器识别人口分布的影响因子，运用随机森林模型开展人口分布空间格网模拟研究。研究结果表明，与传统的相关分析相比，地理探测器能够更为准确地识别人口空间分布的重要影响因子。基于随机森林模型的人口分布格网模拟结果与街道（镇）实际人口的相关系数为0.774，平均相对误差约为30%。相比基于线性回归模型的模拟结果，随机森林模型的精度有明显提高。
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| 关 键 词： | 人口分布 格网 模拟 随机森林 地理探测器 |
| 收稿时间： | 2019-04-08 |
| 修稿时间： | 2019-10-19 |
Simulation of urban population distribution grid by integrating geodetector and random forest model |
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| CHENG Fanglong,ZHAO Guanwei,YANG Muzhuang,LIU Yueliang,LI Fang.Simulation of urban population distribution grid by integrating geodetector and random forest model[J].Bulletin of Surveying and Mapping,2020,0(1):76-81. |
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| Authors: | CHENG Fanglong ZHAO Guanwei YANG Muzhuang LIU Yueliang LI Fang |
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| Institution: | 1. School of Geographical Science, Guangzhou University, Guangzhou 510006, China;2. Institute of Land Resources and Coastal Zone, Guangzhou University, Guangzhou 510006, China |
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| Abstract: | Fine population distribution is important to urban resource allocation and management.In this paper,we take the Yuexiu District, Liwan District, Tianhe District, Haizhu District, Baiyun District and Huangpu District of Guangzhou city as the research areas,and base on multi-source data such as demography, night lighting, interest points and land use,using the geodetector to identify the influencing factors of population distribution,and simulate the population distribution grid by using random forest model.The results show that compared with the traditional correlation analysis, the geodetector can identify the important factors of spatial distribution of population more accurately.The correlation coefficient between the results of population distribution grid simulation based on random forest model and the actual population of streets (towns) is 0.774, with an average relative error of about 30%.Compared with the simulation results based on linear regression model, the accuracy of the stochastic forest model is significantly improved. |
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| Keywords: | population distribution grid simulation random forest geodetector |
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