基于POI大数据的老工业区房价影响因素空间分异与实证

被引：49

作者：

薛冰 ^{[1
,2
]}

肖骁 ^{[1
,2
]}

李京忠 ^{[2
,3
]}

谢潇 ^{[1
,2
]}

任婉侠 ^{[1
,2
]}

逯承鹏 ^{[1
,2
]}

姜璐 ^{[1
,2
,4
]}

机构：

[1] 中国科学院污染生态与环境工程重点实验室(沈阳应用生态研究所)

[2] 辽宁省环境计算与可持续发展重点实验室

[3] 许昌学院城乡规划与园林学院

[4] 兰州大学资源环境学院

来源：

人文地理 | 2019年 / 34卷 / 04期

关键词：

人地系统; 房价; GWR模型; 空间异质性; 老工业区;

D O I：

10.13959/j.issn.1003-2398.2019.04.013

中图分类号：

F299.23 [城市经济管理];

学科分类号：

120405 ;

摘要：

在采集沈阳市铁西区2017年住宅、工厂、地铁站等兴趣点数据的基础上,将工厂距离、房龄以及住宅密度三个变量与传统变量共同参与构建地理加权回归模型,揭示房价影响因素的空间异质性及形成机制。结果表明:在全区范围内,房龄、住宅密度、公共交通、公共配套设施等对房价有显著的提升作用,而工业企业等对房价有一定的抑制作用;新老城区对比来看,所筛选的影响因素与房价的相关性具有显著的空间非平稳性,具体表现在工厂距离、公交密度、商场距离等因素在新老城区的正负影响差异,以及住宅密度、地铁站距离等单向影响因子回归系数的强度渐变;从研究方法来看,基于POI与GWR集成分析,可以有效克服房价实时更新慢、准确度低及数据清洗困难等传统难题,从而为构建和发展新数据环境下的经济地理研究提供参考。

引用

页码：106 / 114

页数：9

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