当前位置:
X-MOL 学术
›
Int. J. Appl. Earth Obs. Geoinf.
›
论文详情
Our official English website, www.x-mol.net, welcomes your
feedback! (Note: you will need to create a separate account there.)
Big geo-data unveils influencing factors on customer flow dynamics within urban commercial districts
International Journal of Applied Earth Observation and Geoinformation ( IF 7.6 ) Pub Date : 2024-10-19 , DOI: 10.1016/j.jag.2024.104231 Xia Peng, Yue-yan Niu, Bin Meng, Yingchun Tao, Zhou Huang
International Journal of Applied Earth Observation and Geoinformation ( IF 7.6 ) Pub Date : 2024-10-19 , DOI: 10.1016/j.jag.2024.104231 Xia Peng, Yue-yan Niu, Bin Meng, Yingchun Tao, Zhou Huang
Commercial districts, as the epicenters of urban commerce and economic activity, largely reflect an area’s prosperity through their customer flow. However, previous research, which often relied on statistical and survey data, has typically not captured the full scope of customer flow dynamics throughout urban commercial districts and has not adequately measured the specific impacts of business district locations and their surrounding communities on customer flow. To bridge these gaps, this study utilizes multidimensional big geo-data resources, including mobile phone signaling data, Points of Interest (POI) data, and transportation network data, to uncover the underlying factors that influence customer flow within urban commercial districts. The findings suggest that several factors—the size of the commercial district, the diversity of business formats, the convenience of parking, the working and residential population in surrounding communities, and the proximity to urban centers—significantly influence the customer flow. Consumers show a preference for larger-scale, centrally-located commercial districts that offer convenient parking options, while a homogenized and uncharacteristic business format may reduce a commercial district’s appeal. Furthermore, the study reveals that industrial parks and mixed-use complexes within the 15-minute living circle surrounding the commercial district have a stronger attraction to customer flow than residential neighborhoods do. The insights from this research not only guide the strategic placement of new commercial centers but also provide a robust framework for enhancing the layout of urban commercial spaces and for the revitalization and advancement of established commercial districts.
中文翻译:
大数据揭示了城市商业区内客流动态的影响因素
商业区作为城市商业和经济活动的中心,在很大程度上通过其客流反映了一个地区的繁荣。然而,以前的研究通常依赖于统计和调查数据,通常无法捕捉到整个城市商业区的客流动态的全部范围,也没有充分衡量商业区位置及其周围社区对客流的具体影响。为了弥合这些差距,本研究利用多维地理大数据资源,包括手机信令数据、兴趣点 (POI) 数据和交通网络数据,来揭示影响城市商业区内客流的潜在因素。研究结果表明,几个因素——商业区的规模、商业业态的多样性、停车的便利性、周围社区的工作和居住人口以及靠近城市中心——都会对客流产生重大影响。消费者表现出对提供便捷停车选择的大型、位于中心位置的商业区的偏好,而同质化和不典型的商业形式可能会降低商业区的吸引力。此外,该研究表明,与住宅区相比,商业区周围 15 分钟生活圈内的工业园区和混合用途综合体对客流的吸引力更强。这项研究的见解不仅指导了新商业中心的战略布局,还为加强城市商业空间的布局以及已建立商业区的振兴和发展提供了一个强大的框架。
更新日期:2024-10-19
中文翻译:
大数据揭示了城市商业区内客流动态的影响因素
商业区作为城市商业和经济活动的中心,在很大程度上通过其客流反映了一个地区的繁荣。然而,以前的研究通常依赖于统计和调查数据,通常无法捕捉到整个城市商业区的客流动态的全部范围,也没有充分衡量商业区位置及其周围社区对客流的具体影响。为了弥合这些差距,本研究利用多维地理大数据资源,包括手机信令数据、兴趣点 (POI) 数据和交通网络数据,来揭示影响城市商业区内客流的潜在因素。研究结果表明,几个因素——商业区的规模、商业业态的多样性、停车的便利性、周围社区的工作和居住人口以及靠近城市中心——都会对客流产生重大影响。消费者表现出对提供便捷停车选择的大型、位于中心位置的商业区的偏好,而同质化和不典型的商业形式可能会降低商业区的吸引力。此外,该研究表明,与住宅区相比,商业区周围 15 分钟生活圈内的工业园区和混合用途综合体对客流的吸引力更强。这项研究的见解不仅指导了新商业中心的战略布局,还为加强城市商业空间的布局以及已建立商业区的振兴和发展提供了一个强大的框架。