摘要
近年房价高企成为城市居民居住生活面临的棘手问题,受到了学者的广泛关注。大数据时代背景下,房价研究的详细数据资料更为全面,且获取便捷。研究借助数据抓取工具,以2017年3月苏州市主城区普通二手商品房房价(POI)数据为研究对象,运用探索性空间数据分析和空间插值方法,模拟苏州主城区房价空间分布特征,探索分布规律。同时,应用地理加权回归模型建立房价与公共服务设施之间的线性联系,分析不同公共服务设施对房价的影响程度。结果表明,各类影响因素与房价的关系在空间上呈现正负相关交替的现象,各类设施影响程度不同,房价空间差异是受多种因子共同作用的结果。
Housing price is among the most pressing issues in urban China in recent years,and it has been widely concerned by scholars.In the past,detailed data on housing price were difficult to obtain.In the background of big data,it is more convenient and comprehensive to acquire.With the help of data acquisition tools,this study takes the data of ordinary second-hand commercial housing price(POI)in the main urban area of Suzhou in March 2017 as the research object,and simulates housing price spatial distribution feature to reveal regularities by using exploratory spatial data analysis and spatial interpolation method.Meanwhile,geographically weighted regression(GWR)model is used to establish the linear relationship between housing price and public service facilities,aiming to study the extent of the impact of different public service facilities on housing price.The result shows that the relationship between the various factors and the price of house is positive and negative by turns and the impact of various types of facilities is different.Spatial differences of housing price are the results of a combination of multiple factors.
作者
邱煜卿
张振龙
QIU Yuqing;ZHANG Zhenlong
出处
《上海城市规划》
2018年第6期102-107,136,共7页
Shanghai Urban Planning Review
关键词
住房价格
公共服务设施
地理加权回归
苏州市主城区
Housing price
Public service facilities
Geographically weighted regression
Main urban area in Suzhou
作者简介
邱煜卿,苏州科技大学,建筑与城市规划学院硕士研究生;张振龙,苏州科技大学,建筑与城市规划学院副教授,硕士生导师
