| 南京商铺租金时空格局及影响因素分析 |
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| 引用本文： | 谷跃,王捷凯,黄琴诗,宋伟轩.南京商铺租金时空格局及影响因素分析[J].地理研究,2021,40(9):2459-2475. |
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| 作者姓名： | 谷跃 王捷凯 黄琴诗 宋伟轩 |
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| 作者单位： | 1. 中国科学院南京地理与湖泊研究所,南京 2100082. 中国科学院大学,北京 1000493. 南京大学建筑与城市规划学院,南京 2100934. 浙江科技学院土木与建筑工程学院,杭州 3100235. 中国科学院流域地理学重点实验室,南京 210008 |
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| 基金项目： | 国家自然科学基金项目(41871116);国家自然科学基金项目(41771184);国家自然科学基金项目(42171234) |
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| 摘 要： | 商铺租金是衡量城市商业活力与地段商业价值的重要指标之一,也是经济地理学关注的热点问题。以南京中心城区3294处商铺为研究对象,在通过核密度分析、莫兰指数等地理空间分析方法探究商铺租金时空格局的基础上,运用地理探测器从交通区位、周边配套、消费能力等方面挖掘商铺租金背后的影响因素。研究发现：南京商铺空间分布以新街口为核心,呈现出日益增强的中心极化态势,并与河西、东山和江北共同形成等级鲜明的商业集聚格局;主城区商铺租金的空间分布亦表现出以新街口为热点区、城北和城南为冷点区的核心边缘结构,具有显著的空间正相关性与局部空间集聚性,主副城区间租金分异程度逐年加剧;交通区位和消费潜力是影响主城区商铺租金的关键外部因素,而商业业态、商铺形象等内生因素同样会对商铺租金产生影响,在多种因素的交互作用下形成了南京商铺租金时空分异格局。通过揭示南京商铺租金空间分异的影响因素及其组合变化,期待推进中国城市商业空间研究,并为地方政府、开发商和商铺投资经营者提供一定借鉴参考。
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| 关 键 词： | 商铺租金 空间集聚 地理探测器 空间分异 南京 |
| 收稿时间： | 2020-10-19 |
The spatio-temporal pattern of shop rent and its influencing factors in Nanjing |
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| GU Yue,WANG Jiekai,HUANG Qinshi,SONG Weixuan.The spatio-temporal pattern of shop rent and its influencing factors in Nanjing[J].Geographical Research,2021,40(9):2459-2475. |
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| Authors: | GU Yue WANG Jiekai HUANG Qinshi SONG Weixuan |
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| Institution: | 1. Nanjing Institute of Geography and Limnology, CAS, Nanjing 210008, China2. University of Chinese Academy of Sciences, Beijing 100049, China3. School of Architecture and Urban Planning, Nanjing University, Nanjing 210093, China4. School of Civil Engineering and Architecture, Zhejiang University of Science and Technology, Hangzhou 310023, China5. Key Laboratory of Watershed Geographic Sciences, CAS, Nanjing 210008, China |
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| Abstract: | As one of the important indexes used to evaluate the commercial vitality of a city and the commercial value of a range, shop rent is a hot issue in the field of economic geography. Nanjing, located in the economically developed Yangtze River Delta region, has been a major commercial city for a long time. This paper examines the spatio-temporal pattern of the rent of 3294 shops in the central urban areas of Nanjing from 2010 to 2019 by means of Kernel Density, Moran’s I and other methods of geographical space analysis. On this basis, this paper taps into the factors affecting the shop rent from traffic location, peripheral supporting facilities, and consumption capacity by using the geographical detector. According to the research, the average shop rent in the central urban area of Nanjing presents an obvious spatial and temporal differentiation pattern from 2010 to 2019. The changing trend of the average shop rent in the study area is characterized by a stable growth in the early stage, a significant decline in the middle stage, and a rapid rise in the later stage. The spatial distribution of shops presents a trend of increasingly intensive spatial agglomeration by centering in Xinjiekou and the areas of Hexi, Dongshan and Jiangbei in the periphery of inner city form a hierarchical commercial agglomeration pattern. Similarly, the spatial distribution of rent in primary urban areas has significant spatial positive correlation and localized spatial agglomeration, and forms the core-periphery structure with Xinjiekou as a hot spot and the southern and northern urban areas as cold spots, aggravating the rent differentiation between the primary and secondary urban areas. The shop rent in the primary urban areas is affected not only by the key external factors including the subway accessibility, surrounding housing prices and potential consumption capacity, but also by the endogenous factors such as business form and shop image. However, dwelling density and construction age of shops have a weaker impact on shop rent. The factors influencing shop rent are complex and the interaction of many factors has led to the spatio-temporal differentiation pattern of shop rent in Nanjing. This paper reveals the leading factors causing the spatial differentiation of shop rent in Nanjing and their combinations and changes, aiming to advance the research on urban commercial space in China and provide a reference for local governments, developers and shop investors and operators. |
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| Keywords: | shop rent spatial agglomeration geographical detector spatial differentiation Nanjing |
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