地球信息科学学报 ›› 2022, Vol. 24 ›› Issue (1): 38-49.doi: 10.12082/dqxxkx.2022.210212

• 第二届中国空间数据智能学术会议(SpatialDI 2021)优秀论文 • 上一篇    下一篇

基于多源数据的夜间经济时空分布格局研究方法

曾磊鑫1,2,3(), 刘涛1,2,3,*(), 杜萍1,2,3   

  1. 1.兰州交通大学测绘与地理信息学院,兰州 730070
    2.地理国情监测技术应用国家地方联合工程研究中心,兰州 730070
    3.甘肃省地理国情监测工程实验室,兰州 730070
  • 收稿日期:2021-04-21 修回日期:2021-07-17 出版日期:2022-01-25 发布日期:2022-03-25
  • 通讯作者: * 刘涛(1981— ),男,湖北随州人,博士,教授,主要从事空间关系理论;GIS、RS应用与开发。 E-mail: ltaochina@foxmail.com
  • 作者简介:曾磊鑫(1997— ),男,湖南益阳人,硕士生,主要从事时空数据挖掘。E-mail: 364030102@qq.com
  • 基金资助:
    国家重点研发计划课题(2016YFC0803106);国家自然科学基金项目(41761088)

Research Method of Temporal and Spatial Distribution Pattern of Night-time Economy based on Multi-source Data

ZENG Leixin1,2,3(), LIU Tao1,2,3,*(), DU Ping1,2,3   

  1. 1. Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China
    2. National-Local Joint Engineering Research Center of Technologies and Applications for National Geographic State Monitoring, Lanzhou 730070, China
    3. Gansu Provincial Engineering Laboratory for National Geographic State Monitoring, Lanzhou 730070, China
  • Received:2021-04-21 Revised:2021-07-17 Online:2022-01-25 Published:2022-03-25
  • Supported by:
    National Key Research and Development Program of China(2016YFC0803106);National Natural Science of China(41761088)

摘要:

夜间经济是一个城市经济发展和消费水平的重要表征。目前国内外研究者对夜间经济的研究多停留在理论层面,或基于市场调研和问卷调查的小范围精细化研究。本文融合多源数据为夜间经济提供了新的视角,相较于传统的调查数据,具有更加快速、高效、广泛的特点,适合于夜间经济大范围研究。本文基于夜间灯光、POI、OD流等多源数据,采用DBSCAN、K-Means++等空间聚类算法和研究供需关系的盈亏法,分别从消费者角度和商户角度识别厦门市夜间活动热点区域和夜间服务设施分布区域,分析厦门市夜间经济时空分布格局及相关性。研究表明:① 厦门市夜间活动在空间上呈多环状分布并向四周递减,夜间活动热点区域分布受假期的影响因地而异;② 厦门市部分区域已有服务设施未能很好地服务于夜间经济,现有的照明、夜景等夜间灯光基础设施存在供给不足之处;③ 居住人口密度与夜间活动密度呈中度正相关,研究结果具有有效性,夜间服务设施盈亏值及数量、夜间灯光与夜间活动密度呈中、弱度相关,并且餐饮设施更加依赖于夜间灯光。最后,为厦门市未来夜间经济建设提出了根据不同的消费人群和心理提供不同的夜间服务、加强夜间灯光基础设施建设以及市场扶持的举措。研究结论对促进社会就业、增强基础设施使用率有积极意义,同时也能够为城市夜间经济发展和政策制定提供参考。

关键词: 多源数据, 夜间经济, DBSCAN, K-Means++, 时空分布, 供需关系, 相关性分析, 厦门市

Abstract:

Night-time economy refers to the related economic activities mainly in the services taking place in urban space and at night, which is an important representation of a city's economic development and consumption level. Currently, researchers at home and abroad mostly rest on the theoretical level, or small-scale refined research based on market research and questionnaire survey, lacking in-depth mining using models and mathematical statistics methods, and rarely intuitively show the specific temporal and spatial distribution of large-scale night-time economy. With the development of information technology, night lighting data and perception big data provide new data sources for quantitative research of night-time economy. This paper provides a new perspective for night-time economy by fusing multi-source data. Compared with the traditional survey data, it is more rapid, efficient, and extensive, which is suitable for large-scale research of night-time economy. Based on taxi OD flow, this paper uses spatial clustering algorithms such as DBSCAN and K-Means ++ to identify hot areas of night-time activities in Xiamen City from the perspective of consumers. Based on the night-time lighting image and POI, this paper analyzes the supply and demand relationship by the method of profit and loss and identifies the distribution area of night service facilities from the perspective of merchants. Then we analyze the temporal and spatial distribution pattern of night-time economy in Xiamen City. The results show that: ① The spatial distribution of night activities in Xiamen City is multi ring and decreases to the surrounding areas. The distribution of hot spots of night activities varies from place to place; ② The existing service facilities in some areas of Xiamen City fail to serve the night economy well, and the existing lighting infrastructure, such as lighting and nightscape, is insufficient; ③ There is a moderate positive correlation between residential population density and night activity density, and the results are valid. The profit and loss value and quantity of night service facilities, night lighting, and night activity density are moderately and weakly correlated, and catering facilities are more dependent on night lighting. Finally, we put forward some suggestions for Xiamen's future night-time economic construction, such as providing different night-time services according to different consumer groups and psychology, strengthening the construction of night light infrastructure and market support. The research conclusions are of positive significance to promote social employment, and enhance the utilization rate of infrastructure. At the same time, they can also provide reference for urban economic development and policy formulation.

Key words: multi source data, night-time economy, DBSCAN, K-Means++, temporal and spatial distribution, supply and demand, correlation analysis, Xiamen City