基于POI大数据的西安市住宅租金空间分异特征与影响因素研究  

Research on Spatial Differentiation Characteristics and Influence Effects of Housing Rent in Xi′an Based on POI Big Data

作　　者：高园园 GAO Yuanyuan(School of Management,Xi’an University of Architecture and Technology,Xi’an 710055,China)

机构地区：[1]西安建筑科技大学管理学院,陕西西安710055

出　　处：《测绘与空间地理信息》2022年第7期177-180,184,共5页Geomatics & Spatial Information Technology

摘　　要：住宅租金呈现明显的空间分异特征,而定量挖掘影响因素对住宅租金空间分异特征影响效应的研究还存在欠缺。本文通过网络爬虫法抓取西安市住宅租金及POI数据,运用GWR模型定量探究影响因素规模大小对住宅租金空间分异的驱动机理。结果表明:1)从平均水平来看,风景名胜对西安市住宅租金影响最大,科教文化服务影响最小;医疗保健服务、商务住宅、科教文化对住宅租金具有负向边际效应,其他因素对住宅租金具有正向边际效应。2)从空间异质性来看,影响效应因区域而呈现出明显的正负差异性。Residential rent shows obvious spatial differentiation characteristics,but there is still a lack of research on the effect of quantitative mining of influencing factors on the spatial differentiation characteristics of residential rent.In this paper,the web crawler method is used to capture the data of housing rent and POI in Xi′an city,and the GWR model is used to quantitatively explore the driving mechanism of the scale of influencing factors on the spatial differentiation of housing rent.The results show that:(1)From the average level,the impact of scenic spots on Xi′an housing rent is the greatest,the impact of science,education and cultural services is the least;Medical care services,commercial housing,science,education and culture have negative marginal effects on housing rent,while other factors have positive marginal effects on housing rent.(2)From the perspective of spatial heterogeneity,the effect of different regions showed obvious positive and negative differences.

关 键 词：POI大数据 住宅租金 空间分异 地理加权回归 影响因素 

分 类 号：P208[天文地球—地图制图学与地理信息工程]