零售集聚的空间范围与分类

Les Dolega
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引用次数: 0

摘要

城镇中心是许多城市地区的核心,其特点是各种社会经济活动聚集在一起,零售和相关服务是基本的。它们可以被看作是不断进化的复杂系统,因此它们的组成和空间范围可能会随着时间的推移而扩大或缩小。尽管有人认为,在全国范围内描绘零售聚集区是具有挑战性的,但购物目的地的分类和其空间范围的描绘对于更好地理解零售空间的使用与消费者行为变化之间的关系至关重要。这些挑战的解决方法如下:首先,提出了一种新的自动化方法来识别英国境内的零售集群。通过在个人业务层面采用新形式的数据和应用定制的DBSCAN方法,已经确定了3000多个零售中心。其次,基于与零售中心功能相关联的混合方法,划定零售中心的集水区。采用Huff空间相互作用模型求解便利零售目的地的集水区扩展,采用驱动次数法求解高阶比较零售目的地的集水区扩展。最后,为了解决早期尝试对购物活动集群进行分类的缺点,这些分类与等级地位的测量密切相关,并涉及零售中心从€œhighâ€到€œlowâ€的二维评分,开发了一种新的多维零售和消费空间类型。使用非分层聚类技术,从文献中得出的四个维度来理解消费空间:中心的组成、多样性、规模和功能以及经济健康状况。人们似乎一致认为,这种更全面的分类能够捕捉到零售服务供需之间的相互关系,将有助于更有效地了解城市地区零售和消费者服务在空间和时间上的作用变化,并将对各种利益相关者产生影响
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Spatial extent and classification of retail agglomerations
Town centres form the core of many urban areas and are characterized by clustering of various types of socio-economic activities with retail and related services being fundamental. They can be viewed as complex systems that constantly evolve, and therefore their composition and spatial extent is likely to expand or contract over time. Although it has been argued that depicting retail agglomerations for a national extent, is challenging, the classification of shopping destinations and delineation of their spatial extent is essential to gaining a better understanding of the relationship between use of retail space and changing consumer behaviour. These challenges have been approached as follows: Firstly, a new automated method for identification of retail agglomerations within Great Britain was proposed. By employing new forms of data at individual business level and application of a bespoke DBSCAN method over 3,000 retail centres have been identified. Secondly, delineation of catchment areas for those retail centres based on a mixed-method approach linked to their function. A Huff spatial interaction model was used to obtain catchment extends for convenience retail destination and drive times method for the higher order comparison retail destinations. Finally, to address the shortcomings of the early attempts to classify clusters of shopping activity that were closely linked to a measure of hierarchical status and involved two-dimensional scoring of retail centres from “high†to “low†, a new multidimensional typology of retail and consumption spaces was developed. Non-hierarchical clustering techniques were used to develop an understanding of consumption spaces in terms of four dimensions derived from the literature: a centre’s composition, its diversity, size and function, and its economic health. There seems to be a consensus that such more comprehensive classifications that capture the interrelationship between supply and demand for retailing services, would help to deliver more effective insights into changing role of retailing and consumer services in urban areas across space and through time and will have implicationns for a variety of stakeholders
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