基于多源数据和集成学习的城市住宅地价分布模拟——以武汉市为例

doi:10.18306/dlkxjz.2021.10.005

地理科学进展 ›› 2021, Vol. 40 ›› Issue (10): 1664-1677.doi: 10.18306/dlkxjz.2021.10.005

基于多源数据和集成学习的城市住宅地价分布模拟——以武汉市为例

张鹏¹^,²(), 胡守庚¹^,²^,^*(), 杨剩富¹^,², 成佩昆¹^,³

1.中国地质大学(武汉) 公共管理学院,武汉 430074
2.自然资源部法治研究重点实验室,武汉 430074
3.西安市临潼区投资合作局,陕西临潼 710600

收稿日期:2020-11-22 修回日期:2021-06-15 出版日期:2021-10-28 发布日期:2021-12-28
通讯作者: *胡守庚(1978— ),男,浙江庆元人,博士,教授,博士生导师,主要从事土地利用与城乡发展研究。E-mail: husg2009@gmail.com
作者简介:张鹏(1993— ),男,河南泌阳人,博士生,主要从事土地利用时空信息分析与模拟研究。E-mail: zhangpeng_cug@163.com
基金资助:
国家社会科学基金重大项目(18ZDA053)

Modeling urban residential land price distribution using multi-source data and ensemble learning: A case of Wuhan City

ZHANG Peng¹^,²(), HU Shougeng¹^,²^,^*(), YANG Shengfu¹^,², CHENG Peikun¹^,³

1. School of Public Administration, China University of Geosciences, Wuhan 430074, China
2. Key Laboratory for Rule of Law Research, Ministry of Natural Resources, Wuhan 430074, China
3. Xi'an Lintong Bureau of Investment Cooperation, Lintong 710600, Shaanxi, China

Received:2020-11-22 Revised:2021-06-15 Online:2021-10-28 Published:2021-12-28
Supported by:
Major Program of National Social Science Foundation of China(18ZDA053)

摘要/Abstract

摘要：

精准刻画城市住宅地价分布特征,对于科学引导城市空间布局规划、有效实现城市精明增长等具有重要意义。而城市住宅地价与其潜在影响因素之间的复杂非线性关系,给地价分布精细模拟带来了挑战。论文旨在探索基于地理大数据和集成学习的城市住宅地价分布模拟方法体系,以满足快速、精准监测地价动态变化的需要。选取武汉市为典型区,以住宅用地交易样点、兴趣点(points of interest, POI)和夜间灯光影像为数据源,以500 m分辨率网格为估价单元,提取POI核密度和夜间灯光强度作为住宅地价预测变量,采用机器学习算法和bagging、stacking集成方法构建住宅地价预测模型,并对比分析其精度。研究发现：① 单个机器学习算法中,支持向量回归(support vector regression, SVR)预测精度最高,接下来依次是k最近邻算法(k-nearest neighbor algorithm, k-NN)、高斯过程回归(Gaussian process regression, GPR)和BP神经网络(back propagation neural networks, BP-NN);② 在提升单个算法预测精度方面,stacking方法的性能优于bagging方法,使用stacking集成SVR和k-NN的地价预测模型精度最高,其平均绝对百分误差仅为8.29%,拟合优度R²达0.814;③ 基于论文所构建模型生成的城市住宅地价分布图能有效表征价格圈层分布特征和局部奇异性。研究结果可为城市住宅地价评估提供新的思路和方法借鉴。

关键词: 城市住宅地价, 土地价格分布, 机器学习, POI, 夜间灯光影像, 武汉

Abstract:

Characterizing the spatial distribution of urban residential land prices (RLPs) is essential for timely improving urban planning and management, as well as for effectively realizing urban smart growth. However, mapping urban RLPs at a fine scale remains challenging, due to the complex nonlinear relationship between RLPs and their potential determinants. This study developed a grid-level urban RLP mapping method based on big geo-data and ensemble learning technology to meet the needs of rapid and accurate monitoring of urban RLP dynamics. Using ensemble learning technology, combined with predictor variables extracted from points of interest (POIs) and NPP-VIIRS nighttime light images, the fine-scale RLPs in Wuhan City in 2018 were mapped through the following steps. First, the kernel density of POIs and the intensity of nighttime lights were extracted and aggregated at the 500 m×500 m grid level as the predictor variables of RLPs. Second, several RLP prediction models were established using four individual machine learning algorithms (MLAs) and bagging and stacking ensemble methods. Finally, the prediction accuracy or errors of different models were evaluated and compared, and the best performing model was selected to estimate the RLPs of the grids with no observations in Wuhan City. The results show that: 1) Among all the individual MLAs, the support vector regression (SVR) algorithm has the best prediction performance, followed by the k-nearest neighbor algorithm (k-NN), Gaussian process regression (GPR), and back propagation neural network (BP-NN) algorithms. 2) In terms of improving the prediction accuracy of individual MLAs, the performance of the stacking method is better than that of the bagging method. The stacking #1 model that integrates the SVR and k-NN algorithms has the smallest prediction error, with %MAE of 8.29%, and R² of 0.814. 3) The RLP map generated by the proposed methodological framework can effectively reveal the circular characteristics and local singularity of the RLP distribution. This study provides new ideas, methods, and technical means for rapidly and accurately mapping urban RLPs, which is conducive to the improvement of urban RLP monitoring systems in the era of big data.

Key words: urban residential land price, land price distribution, machine learning, POI, nighttime light, Wuhan City

张鹏, 胡守庚, 杨剩富, 成佩昆. 基于多源数据和集成学习的城市住宅地价分布模拟——以武汉市为例[J]. 地理科学进展, 2021, 40(10): 1664-1677.

ZHANG Peng, HU Shougeng, YANG Shengfu, CHENG Peikun. Modeling urban residential land price distribution using multi-source data and ensemble learning: A case of Wuhan City[J]. PROGRESS IN GEOGRAPHY, 2021, 40(10): 1664-1677.

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类别	变量	数据源	参考文献	类别	变量	数据源	参考文献
商服繁华度	商场核密度	POI	[15,35]	医疗条件	医院核密度	POI	[33,38]
	超市核密度	POI	[15,35]	交通便捷度	公交站核密度	POI	[15,33]
	生活设施核密度	POI	[33,35]	交通便捷度	地铁站核密度	POI	[15,41]
教育条件	大学核密度	POI	[29,38]	居住环境	公园广场核密度	POI	[34,38]
	中学核密度	POI	[29,41]	居住环境	距最近水体距离	土地利用现状数据	[29,42]
	小学核密度	POI	[15,41]	夜间灯光	夜间灯光像素值	NPP-VIIRS影像	[41,43]

评价指标	GPR	k-NN	BP-NN	SVR
MAE	1760.91	1360.68	1964.62	1304.41
%MAE/%	11.66	9.06	13.26	8.73
RMSE	2514.09	2066.58	2677.31	1961.69
%RMSE/%	17.47	14.27	18.59	13.60
P	0.885	0.909	0.872	0.913
R²	0.676	0.760	0.626	0.803

评价指标	B_GPR	B_k-NN	B_BP-NN	B_SVR
MAE	1862.48	1429.18	1844.68	1363.82
%MAE/%	12.19	9.40	12.65	9.11
RMSE	2685.98	2127.50	2498.64	2003.45
%RMSE/%	18.68	14.72	17.37	13.89
P	0.880	0.905	0.876	0.910
R²	0.631	0.754	0.678	0.796

评价指标	stacking #1	stacking #2	stacking #3
MAE	1231.06	1245.11	1249.50
%MAE/%	8.29	8.37	8.41
RMSE	1871.29	1889.08	1892.24
%RMSE/%	12.94	13.07	13.09
P	0.918	0.917	0.917
R²	0.814	0.811	0.810

运行次数	B_GPR		B_k-NN		B_BP-NN		B_SVR		stacking #2		stacking #3
运行次数	MAE差异	秩	MAE差异	秩	MAE差异	秩	MAE差异	秩	MAE差异	秩	MAE差异	秩
1	-610.55	1	-200.33	6	-600.41	1	-139.96	2	-10.22	8	-5.49	3
2	-629.66	5	-190.31	3	-635.75	7	-183.10	5	-7.02	5	-8.26	5
3	-625.41	2	-216.81	9	-611.44	3	-171.72	3	-10.82	9	-5.77	4
4	-630.42	6	-218.38	10	-647.00	9	-183.25	6	-8.86	7	-14.84	7
5	-631.42	7	-198.12	5	-613.62	4	-132.76	1	-14.05	10	-18.44	9
6	-654.50	10	-213.26	8	-606.55	2	-219.31	10	-4.47	2	-1.98	1
7	-625.84	4	-205.48	7	-639.70	8	-192.83	9	-8.12	6	-17.64	8
8	-625.73	3	-179.01	1	-623.23	5	-183.57	7	-4.83	3	-3.83	2
9	-636.82	8	-181.17	2	-635.56	6	-176.81	4	-1.46	1	-10.56	6
10	-642.45	9	-194.39	4	-649.68	10	-186.16	8	-5.32	4	-22.88	10
R⁺	0		0		0		0		0		0
R^-	55		55		55		55		55		55
Z值	–2.803		–2.803		–2.803		–2.803		–2.803		–2.803

基于多源数据和集成学习的城市住宅地价分布模拟——以武汉市为例

Modeling urban residential land price distribution using multi-source data and ensemble learning: A case of Wuhan City

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