地球信息科学学报 ›› 2019, Vol. 21 ›› Issue (2): 168-177.doi: 10.12082/dqxxkx.2019.180508
收稿日期:
2018-10-09
修回日期:
2018-11-27
出版日期:
2019-02-20
发布日期:
2019-01-30
作者简介:
作者简介:姚 尧(1987-),男,广东梅州人,副教授,研究方向为空间大数据和智慧城市。E-mail:
基金资助:
Yao YAO1,2(), Shuliang REN1, Junyi WANG1, Qingfeng GUAN1,*(
)
Received:
2018-10-09
Revised:
2018-11-27
Online:
2019-02-20
Published:
2019-01-30
Contact:
Qingfeng GUAN
Supported by:
摘要:
随着中国城市化进程的加快,城市人口的大规模集聚带来了住房紧张的问题,房价政策制定的时效性与正确性也时刻吸引着社会的关注,因此在微观尺度下对房价进行精细化制图变得愈发重要。由于数据可获取性和现有模型精度的限制,目前已有研究均较少涉及微观尺度。本研究通过将房价数据和遥感影像相融合,构建了一种基于卷积神经网络(CNN)和随机森林(RF)的遥感影像挖掘模型,以实现在不考虑其他数据的情况下,精确、合理地进行房价的微观尺度制图。本文以武汉市作为研究区,在仅有房价数据和遥感影像的情况下,利用本文所构建的模型成功得到武汉市中心城区5 m精度的精细房价图。此外,还利用其他数据源以及挖掘技术与本文所构模型进行了对比分析。结果显示,本文所构建的模型获得了最高的房价模拟拟合优度(R2=0.805),相比传统方法中的最高拟合优度(R2=0.653)其精度提升了23.28%,其制图结果可为政府部门规划决策及武汉市经济分布研究提供基础支撑。
姚尧, 任书良, 王君毅, 关庆锋. 卷积神经网络和随机森林的城市房价微观尺度制图方法[J]. 地球信息科学学报, 2019, 21(2): 168-177.DOI:10.12082/dqxxkx.2019.180508
Yao YAO, Shuliang REN, Junyi WANG, Qingfeng GUAN. Mapping the Fine-Scale Housing Price Distribution by Integrating a Convolutional Neural Network and Random Forest[J]. Journal of Geo-information Science, 2019, 21(2): 168-177.DOI:10.12082/dqxxkx.2019.180508
表4
武汉中心区域不同地区房价的平均值、标准差和总体准确度
区域类别 | 区域 | 真实/(元/m2) | 预测/(元/m2) | 准确度/% | ||
---|---|---|---|---|---|---|
平均值 | 标准差 | 平均值 | 标准差 | |||
主城区 | 江汉区 | 19 577.900 | 4612.090 | 19 261.333 | 3913.542 | 98.38 |
江岸区 | 20 845.530 | 7071.716 | 20 337.013 | 5188.628 | 97.56 | |
洪山区 | 20 384.442 | 4577.304 | 19 654.007 | 3639.945 | 96.42 | |
武昌区 | 22 129.308 | 6297.948 | 20 964.847 | 3467.340 | 94.74 | |
汉阳区 | 16 674.734 | 3868.972 | 17 708.541 | 3544.656 | 93.80 | |
青山区 | 15 822.054 | 4327.896 | 16 813.957 | 3255.780 | 93.73 | |
远城区 | 江夏区 | 18 222.980 | 4833.506 | 18 423.225 | 3861.028 | 98.90 |
蔡甸区 | 15 347.774 | 4090.231 | 17 043.074 | 2801.621 | 88.95 | |
东西湖区 | 14 127.650 | 3076.744 | 16 367.463 | 2822.753 | 84.15 | |
黄陂区 | 12 825.273 | 2654.790 | 16 298.700 | 2303.813 | 72.92 |
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