基于POI大数据的老工业区房价影响因素空间分异与实证

薛冰; 肖骁; 李京忠; 谢潇; 任婉侠; 逯承鹏; 姜璐

doi:10.13959/j.issn.1003-2398.2019.04.013

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人文地理 ›› 2019, Vol. 34 ›› Issue (4) : 106-114. DOI: 10.13959/j.issn.1003-2398.2019.04.013

经济

基于POI大数据的老工业区房价影响因素空间分异与实证

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

作者信息 +

POI-BASED ANALYSIS ON THE AFFECTING FACTORS OF PROPERTY PRICES' SPATIAL DISTRIBUTION IN THE TRADITIONAL INDUSTRIAL AREA

XUE Bing^1,2, XIAO Xiao^1,2, LI Jing-zhong^2,3, XIE Xiao^1,2, REN Wan-xia^1,2, LU Cheng-peng^1,2, JIANG Lu^1,2,4

Author information +

文章历史 +

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。

Abstract

Based on the residential POI data of the Tiexi District in Shenyang, a geographical weighted regression (GWR) model was constructed by adding three variables that could affect the traditional industrial area's property prices as well as the traditional variables, the three added variables include house age, residential density and the distance between residences and industries. The results show that:house age, residential density, public transport, public facilities and so on have a significant effect on property prices, while industrial enterprises and other factors have a certain effect on price inhibition. The relativities between all kinds of influence factors and residential prices have remarkable spatial non-stationary, showing that the intensity gradient of the regression coefficient of one-way influencing factors such as residential density, subway station distance, as well as the difference between the positive and negative effects of factors such as factory distance, bus density, shopping mall distance in the traditional and new urban areas.

导出引用

薛冰, 肖骁, 李京忠, 谢潇, 任婉侠, 逯承鹏, 姜璐. 基于POI大数据的老工业区房价影响因素空间分异与实证[J]. 人文地理. 2019, 34(4): 106-114 https://doi.org/10.13959/j.issn.1003-2398.2019.04.013

XUE Bing, XIAO Xiao, LI Jing-zhong, XIE Xiao, REN Wan-xia, LU Cheng-peng, JIANG Lu. POI-BASED ANALYSIS ON THE AFFECTING FACTORS OF PROPERTY PRICES' SPATIAL DISTRIBUTION IN THE TRADITIONAL INDUSTRIAL AREA[J]. HUMAN GEOGRAPHY. 2019, 34(4): 106-114 https://doi.org/10.13959/j.issn.1003-2398.2019.04.013

中图分类号： K902

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