PROGRESS IN GEOGRAPHY ›› 2023, Vol. 42 ›› Issue (1): 104-115.doi: 10.18306/dlkxjz.2023.01.009

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Spatial measurement of commercial homogeneity of ancient town destination based on street view images: A case study of Dali ancient city

LI Xianzheng1(), ZHAO Zhenbin1,2,*(), LIU Yang1,2, ZHANG Dazhao1, ZHANG Jian1, ZHANG Yuqian1   

  1. 1. School of Geography and Tourism, Shaanxi Normal University, Xi'an 710119, China
    2. Shaanxi Key Laboratory of Tourism Informatics, Xi'an 710119, China
  • Received:2022-05-18 Revised:2022-08-06 Online:2023-01-28 Published:2023-03-28
  • Contact: ZHAO Zhenbin;
  • Supported by:
    National Natural Science Foundation of China(41971227);National Natural Science Foundation of China(42201245);China Postdoctoral Science Foundation(2022M711998);The Key Research and Development Project of Shaanxi Provincial Science and Technology Plan(2023-YBSF-029)


The commercial homogenization of ancient town tourism destination is the main manifestation of the homogenization of this type of tourism destination. Although to some extent this phenomenon promotes the economic development of the tourism destination, it also affects the tourism experience of tourists and leads to the disorder of the commercial operation of the destination. Taking Dali ancient city as an example, this study obtained the street view image data inside the ancient city through programming, and used computer vision, machine learning, and other technologies to identify the shop sign text information in the street view images. On this basis, this study constructed an index model to measure the commercial homogeneity of the case, and examined the spatial characteristics and formation mechanism of the commercial homogenization of the ancient town tourism destination. The conclusions are as follows: 1) Spatially, the overall commercial homogeneity of Dali ancient city presents the characteristics of core-periphery distribution. The commercial homogenization degree of stores is high in the core tourism area, showing the characteristics of street-level distribution, while the commercial homogenization degree of service firms is high in the peripheral tourism area, showing the characteristics of city block-level distribution. 2) The distribution of tourism resources, planning and regulation, location conditions, and capital intervention are the main factors leading to commercial homogenization. Competition and spatial aggregation are the two leading mechanisms to form commercial homogenization. 3) Using computer vision and machine learning technologies to extract the shop sign text information in the street view images and taking the repetition number and agglomeration level of the same type of shops as indicators to build a model, combined with GIS spatial analysis method, we can realize the spatial measurement of the commercial homogeneity of the ancient town tourism destination.

Key words: commercial homogenization, street view images, language landscape, computer vision, machine learning, Dali ancient city