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遥感技术与应用  2020, Vol. 35 Issue (4): 820-831    DOI: 10.11873/j.issn.1004-0323.2020.4.0820
甘肃遥感学会专栏     
基于遥感和微博签到数据的住房空置空间识别
贺蕾1(),潘竟虎1(),董磊磊2
1.西北师范大学 地理与环境科学学院,甘肃 兰州 730070
2.中国科学院西北生态环境资源研究院 中国科学院黑河遥感试验研究站,甘肃省遥感重点实验室,甘肃 兰州 730000
Study on the Spatial Identification of Housing Vacancy
Lei He1(),Jinghu Pan1(),Leilei Dong2
1.College of Geography and Environmental Sciences, Northwest Normal University, Lanzhou 730070,China
2.Key Laboratory of Remote Sensing in Gansu Province,Heihe Remote Sensing Experimental Research Station, Chinese Academy of Sciences,Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences,Lanzhou 730000,China
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摘要:

房屋空置率是衡量房地产市场健康与否的重要指标。基于夜间灯光遥感数据和全国土地覆盖数据,对房屋空置率进行空间识别。利用微博签到数据,通过基于密度的聚类算法和热力分析对居民活动空间强度进行分析,从“鬼城”指数排名靠前的100个城市中随机选择30个样本城市,进行城市内部房屋空置空间识别。结果表明:2013年全国地级及以上城市基于像元的平均房屋空置率为27.3%。东部地区房屋空置率较低,中西部地区房屋空置现象明显;房屋空置在中小型城市更加突出。

关键词: 房屋空置空间识别夜间灯光微博签到空间格局    
Abstract:

Housing Vacancy Rate (HVR) is an important index in assessing the healthiness of residential real estate market. Due to lack of clear and effectively evaluation criterion, the understanding of housing vacancy in China is then rather limited. This paper quantitatively analyzed spatial identification and difference pattern of house vacancy at different scale in China by using nighttime light data and micro-blog check-in data, in order to make up the deficiency of traditional methods in the aspects of data missing and differential approach. The nighttime light intensity for non-vacancy area is estimated after removing the nighttime light intensity from non-residential sources of NPP-VIIRS light data and difference of nighttime light caused by the different urban area ratio. Then, the HVR is calculated for the spatial pattern analysis. This paper analyzed the spatial strength of residents activities by using micro-blog check-in data, based on density-based spatial clustering of applications with noise and heat map. The 30 sample cities were selected to identify house vacancy from 100 cities which ghost city index were high. The following conclusions were drawn through the study: The HVR of eastern coastal cities and regions with rapid development of economy were lower, while the phenomenon of house vacancy in central and western regions were more obvious. The HVR increased from eastern coastal regions to inland areas. What’s more, the phenomenon of house vacancy in middle and small cities were more distinct from the aspect of urban scale. The house vacancy of China were divided into five types: industry or resources driven, government planned, epitaxy expansionary, environmental constraint and speculative activate by taking the factors of natural environment, social economic development level, and population density into consideration. This may shed light on policy implications for Chinese urban development.

Key words: House vacancy    Spatial identification    Nighttime light    Micro-blog check-in    Spatial pattern
收稿日期: 2019-09-04 出版日期: 2020-09-15
ZTFLH:  TP79  
基金资助: 国家自然科学基金项目(41661025);西北师范大学青年教师科研能力提升计划(NWNU?LKQN?16?7)
通讯作者: 潘竟虎     E-mail: xkdm@qq.com;panjh_nwnu@nwnu.edu.cn
作者简介: 贺蕾(1993-),女,甘肃兰州人,硕士研究生,主要从事空间经济分析研究。E?mail:xkdm@qq.com
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引用本文:

贺蕾,潘竟虎,董磊磊. 基于遥感和微博签到数据的住房空置空间识别[J]. 遥感技术与应用, 2020, 35(4): 820-831.

Lei He,Jinghu Pan,Leilei Dong. Study on the Spatial Identification of Housing Vacancy. Remote Sensing Technology and Application, 2020, 35(4): 820-831.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2020.4.0820        http://www.rsta.ac.cn/CN/Y2020/V35/I4/820

图1  部分省市微博签到POI数据分布示意图
图2  居住区感兴趣点示意图[14]
图3  房屋空置率空间分布图
图4  微博签到核密度结果示意图
图5  常驻地识别结果示意图
图6  居民活动强度示意图
图7  城市内部房屋空置空间识别结果图
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