Study on Average Housing Prices in the Inland Capital Cities of China by Night-time Light Remote Sensing and Official Statistics Data
Study on Average Housing Prices in the Inland Capital Cities of China by Night-time Light Remote Sensing and Official Statistics Data
复制标题
利用夜间灯光遥感和官方统计数据研究中国内陆省会城市平均房价
DOI：
10.1038/s41598-020-64506-2
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发表时间：
2020-05-07
影响因子：
4.6
通讯作者：
Wu, Yijin
中科院分区：
文献类型：
--
作者：
Li, Chang;Zhu, Heli;Wu, Yijin
本文首次提出利用DMSP/OLS（Defense Meteorological Satellite Program-Operational Linescan System）夜间灯光数据的年平均值作为替代指标，对中国内陆省会城市的平均房价进行挖掘和预测。首先，基于2002 - 2013年各城市的时间序列分析，建立了平均夜间光照强度（ANLI）与平均商品住宅价格（ACRHP）之间的5个剔除粗差的回归模型。其次，选取最优模型对2014年各省会城市的农业综合生产力进行预测，并通过区间估计和相关官方统计数据进行验证。最后，实验结果表明，二次多项式回归是估计大多数省会城市未经调整的ACRHP的最优挖掘模型，除成都被高估和武汉被低估外，其余城市的预测ACRHP几乎都在其区间估计值内，而调整后的ACRHP都在预测区间内。总体而言，本文不仅提供了一个新的见解，时间序列ACRHP数据挖掘的基础上，时间序列ANLI首都城市规模，但也揭示了潜力和机制的综合ANLI表征复杂的ACRHP。此外，本文还对影响房价的其他因素，如政府政策的时间序列滞后性等进行了检验和分析。
In this paper, the annually average Defense Meteorological Satellite Program-Operational Linescan System (DMSP/OLS) night-time light data is first proposed as a surrogate indicator to mine and forecast the average housing prices in the inland capital cities of China. First, based on the time-series analysis of individual cities, five regression models with gross error elimination are established between average night-time light intensity (ANLI) and average commercial residential housing price (ACRHP) adjusted by annual inflation rate or not from 2002 to 2013. Next, an optimal model is selected for predicting the ACRHPs in 2014 of these capital cities, and then verified by the interval estimation and corresponding official statistics. Finally, experimental results show that the quadratic polynomial regression is the optimal mining model for estimating the ACRHP without adjustments in most provincial capitals and the predicted ACRHP of these cities are almost in their interval estimations except for the overrated Chengdu and the underestimated Wuhan, while the adjusted ACRHP is all in prediction interval. Overall, this paper not only provides a novel insight into time-series ACRHP data mining based on time-series ANLI for capital city scale but also reveals the potentiality and mechanism of the comprehensive ANLI to characterize the complicated ACRHP. Besides, other factors influencing housing prices, such as the time-series lags of government policy, are tested and analysed in this paper.