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基于地理探测器的南京市住宅租金空间分异格局及驱动因素研究
引用本文:尹上岗,李在军,宋伟轩,马志飞.基于地理探测器的南京市住宅租金空间分异格局及驱动因素研究[J].地球信息科学,2018,20(8):1139-1149.
作者姓名:尹上岗  李在军  宋伟轩  马志飞
作者单位:1. 南京师范大学地理科学学院,南京 2100232. 扬州大学苏中发展研究院,扬州 2250093. 中国科学院南京地理与湖泊研究所 流域地理学重点实验室,南京 210008
基金项目:国家自然科学基金项目(41671155、41771184);北部湾环境演变与资源利用教育部重点实验室系统基金项目(2015BGERLKF06)
摘    要:以南京市“一主三副”住宅小区为研究单元,运用GIS中的渔网(Fishnet)分析和探索性空间数据分析(ESDA)对“一主三副”住宅租金的空间分布进行模拟和估计,并利用地理探测器模型从住宅小区的区位特征、建筑特征和邻里特征3个方面探究住宅租金空间分异的影响机制。结果表明:① 南京市住宅租金总体呈上升趋势,空间上表现出主城向副城递减的中心外围模式,住宅租金空间结构逐渐由单核向双核发展,且住宅租金存在显著的空间异质性;② 住宅租金呈现出明显的空间正相关性和区域集聚性,热点区自内城核心区至副城趋于弱化,冷热点空间格局呈圈层结构;③ 交通位势和中心位势是对一主三副住宅租金解释力最大的因素,商务配套、金融设施和住宅房龄的解释力次之,特征因素对主城副城租金的影响强度各异。

关 键 词:住宅租金  空间格局  探索性空间数据分析  地理探测器  南京市  
收稿时间:2018-01-25

Spatial Differentiation and Influence Factors of Residential Rent in Nanjing Based on Geographical Detector
YIN Shanggang,LI Zaijun,SONG Weixuan,MA Zhifei.Spatial Differentiation and Influence Factors of Residential Rent in Nanjing Based on Geographical Detector[J].Geo-information Science,2018,20(8):1139-1149.
Authors:YIN Shanggang  LI Zaijun  SONG Weixuan  MA Zhifei
Institution:1. School of Geography Science, Nanjing Normal University, Nanjing 210023, China2. Research Institute of Central Jiangsu Development, Yangzhou University, Yangzhou 225009, China3. Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences Nanjing 210008, China
Abstract:Urban residential rent space differentiation has always been one of the core contents of urban geography and urban economics. The residential rent has the inherent regularity in the spatial distribution, and explores this law information and its influence mechanism, which helps the government to formulate reasonable price regulation and land use policy. Taking Nanjing "one main city and three subsidiary cities" residential quarters as research unit, this paper simulates and estimates the spatial distribution of "one main city and three subsidiary cities" residential rents by using fishnet analysis and Exploratory Spatial Data Analysis (ESDA) , and explores the influence mechanism of residential rent space difference from three aspects of residential area's location feature, architectural feature and neighborhood feature by using the geographical detector model. The results show that: (1) Residential rent in Nanjing generally shows an upward trend, showing a peripheral pattern of decentering from the main city to the vice city in space, and the spatial structure of residential rent gradually develops from single nuclear to dual nuclear with significant spatial heterogeneity in residential rent. (2) The residential rent shows obvious positive spatial correlation and regional agglomeration. The hot spots tend to weaken from the inner core to the vice cities, and the hot and cold spot spatial patterns are in a circle. (3) The traffic trend and central tendency are the factors that explain the rent of one house and three houses the most, and the second one is the explanation power of commercial facilities, financial facilities and residential houses. The influence intensity of the characteristic factors on the rents of the main city and vice-cities varies. Therefore, research on urban residential rental space distribution can promote the differentiation of space and an important driving mechanism, and can predict the basic trend in the future time city residential rental space distribution pattern evolution, can provide a forward-looking perspective and analysis tools to the study of urban social space.
Keywords:residential rent  spatial pattern  exploratory spatial data analysis  geographical detector  Nanjing  
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