基于街景图像的古镇旅游地商业同质化空间测度——以大理古城为例

doi:10.18306/dlkxjz.2023.01.009

Abstract

Abstract:

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

LI Xianzheng, ZHAO Zhenbin, LIU Yang, ZHANG Dazhao, ZHANG Jian, ZHANG Yuqian. Spatial measurement of commercial homogeneity of ancient town destination based on street view images: A case study of Dali ancient city[J].PROGRESS IN GEOGRAPHY, 2023, 42(1): 104-115.

Figures/Tables 7

Fig.1

Fig.2

Fig.3

Tab.1

Fig.4

Fig.5

Fig.6

References 44

[1]	Harvey D. The condition of postmodernity: An enquiry into the origins of cultural change[M]. Oxford, UK: Wiley- Blackwell, 1989: 343.
[2]	居伊·德波. 景观社会[M]. 张新木, 译. 南京: 南京大学出版社, 2020: 97-132.
	[Guy Debord. The society of the spectacle. Translated by Zhang Xinmu. Nanjing, China: Nanjing University Press, 2020: 97-132. ]
[3]	Bernhard B, Duccio C. Copysites: Tourist attractions in the age of their architectural reproducibility[J]. Journal of Tourism and Cultural Change, 2019, 17(1): 13-26. doi: 10.1080/14766825.2019.1558020
[4]	Bosker B. Original copies: Architectural mimicry in contemporary China[M]. Honolulu, USA: University of Hawai'i Press, 2013: 2.
[5]	Yin C Z, Qian X Y. The spatial production of simulacrascape in urban China: Economic function, local identity and cultural authenticity[J]. Cities, 2020, 104: 102806. doi: 10.1016/j.cities.2020.102806. doi: 10.1016/j.cities.2020.102806
[6]	周大鸣. 树立文化多元理念, 避免民族旅游中的同质化倾向[J]. 旅游学刊, 2012, 27(11): 16-17.
	[Zhou Daming. Establish the concept of cultural diversity and avoid the tendency of homogenization in ethnic tourism. Tourism Tribune, 2012, 27(11): 16-17. ]
[7]	翟俊. 应对环境同质化的景观实践[J]. 中国园林, 2012, 28(5): 113-115.
	[Zhai Jun. Landscape practice coping with the homogenization of environment. Chinese Landscape Architecture, 2012, 28(5): 113-115. ]
[8]	李倩, 吴小根, 汤澍. 古镇旅游开发及其商业化现象初探[J]. 旅游学刊, 2006, 21(12): 52-57.
	[Li Qian, Wu Xiaogen, Tang Shu. Preliminary study on tourism development and commercialization in ancient towns. Tourism Tribune, 2006, 21(12): 52-57. ]
[9]	保继刚, 苏晓波. 历史城镇的旅游商业化研究[J]. 地理学报, 2004, 59(3): 427-436.
	[Bao Jigang, Su Xiaobo. Studies on tourism commercialization in historic towns. Acta Geographica Sinica, 2004, 59(3): 427-436. ] doi: 10.11821/xb200403013
[10]	孙小龙, 林璧属. 基于网络文本分析的旅游商业化符号表征研究: 以西江苗寨为例[J]. 旅游学刊, 2017, 32(12): 28-36.
	[Sun Xiaolong, Lin Bishu. A study of the representation of tourism commercialization symbols based on internet text content analysis: A case of Xijiang Miao Village. Tourism Tribune, 2017, 32(12): 28-36. ]
[11]	萧清碧, 林岚, 林泓, 等. 三坊七巷历史文化街区旅游商业意象感知及年际变化研究[J]. 福建师范大学学报(自然科学版), 2019, 35(3): 96-108.
	[Xiao Qingbi, Lin Lan, Lin Hong, et al. Study on the overall and annual features of the Three Lanes and Seven Alleys historical and cultural block tourism commercial image perception. Journal of Fujian Normal University ( Natural Science Edition), 2019, 35(3): 96-108. ]
[12]	王坤, 刘康. 贵州省乡村旅游同质化及其创新策略[J]. 贵州大学学报(社会科学版), 2019, 37(5): 109-115.
	[Wang Kun, Liu Kang. Homogeneity of rural tourism in Guizhou Province and its innovative development strategy. Journal of Guizhou University (Social Sciences), 2019, 37(5): 109-115. ]
[13]	饶勇, 林雪琼. 民族村寨旅游发展中的同质化竞争困境及其成因研究[J]. 贵州民族研究, 2018, 39(12): 52-55.
	[Rao Yong, Lin Xueqiong. A study on the homogeneous competition and its cause mechanism in ethnic village tourism. Guizhou Ethnic Studies, 2018, 39(12): 52-55. ]
[14]	许业辉, 韩磊, 谢双玉, 等. 乡村振兴背景下乡村旅游同质化问题解构与评价体系构建[J]. 中南林业科技大学学报(社会科学版), 2021, 15(6): 108-114, 122.
	[Xu Yehui, Han Lei, Xie Shuangyu, et al. Deconstruction and evaluation system construction of the homogenization of rural tourism under the background of rural revitalization. Journal of Central South University of Forestry & Technology (Social Sciences), 2021, 15(6): 108-114, 122. ]
[15]	Zhang F, Wu L, Zhu D, et al. Social sensing from street-level imagery: A case study in learning spatio-temporal urban mobility patterns[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2019, 153: 48-58. doi: 10.1016/j.isprsjprs.2019.04.017
[16]	He N, Li G. Urban neighbourhood environment assessment based on street view image processing: A review of research trends[J]. Environmental Challenges, 2021, 4: 100090. doi: 10.1016/j.envc.2021.100090. doi: 10.1016/j.envc.2021.100090
[17]	Zhang F, Zhou B L, Liu L, et al. Measuring human perceptions of a large-scale urban region using machine learning[J]. Landscape and Urban Planning, 2018, 180: 148-160. doi: 10.1016/j.landurbplan.2018.08.020
[18]	李智轩, 何仲禹, 张一鸣, 等. 绿色环境暴露对居民心理健康的影响研究: 以南京为例[J]. 地理科学进展, 2020, 39(5): 779-791. doi: 10.18306/dlkxjz.2020.05.007
	[Li Zhixuan, He Zhongyu, Zhang Yiming, et al. Impact of greenspace exposure on residents' mental health: A case study of Nanjing City. Progress in Geography, 2020, 39(5): 779-791. ] doi: 10.18306/dlkxjz.2020.05.007
[19]	Yao Y, Wang J L, Hong Y, et al. Discovering the homogeneous geographic domain of human perceptions from street view images[J]. Landscape and Urban Planning, 2021, 212: 104125. doi: 10.1016/j.landurbplan.2021.104125. doi: 10.1016/j.landurbplan.2021.104125
[20]	徐红罡, 任燕. 旅游对纳西东巴文语言景观的影响[J]. 旅游学刊, 2015, 30(1): 102-111.
	[Xu Honggang, Ren Yan. Tourism impact on the Naxi Dongba linguistic landscape. Tourism Tribune, 2015, 30(1): 102-111. ]
[21]	张捷, 张宏磊, 唐文跃. 中国城镇书法景观空间分异及其地方意义: 以城镇商业街区为例[J]. 地理学报, 2012, 67(12): 1675-1685.
	[Zhang Jie, Zhang Honglei, Tang Wenyue. Spatial differentiation and place identity of Chinese calligraphic landscape. Acta Geographica Sinica, 2012, 67(12): 1675-1685. ] doi: 10.11821/xb201212009
[22]	尚国文, 赵守辉. 语言景观研究的视角、理论与方法[J]. 外语教学与研究, 2014, 46(2): 214-223, 320.
	[Shang Guowen, Zhao Shouhui. Linguistic landscape studies: Perspectives, theories and approaches. Foreign Language Teaching and Research, 2014, 46(2): 214-223, 320. ]
[23]	张帆, 刘瑜. 街景影像: 基于人工智能的方法与应用[J]. 遥感学报, 2021, 25(5): 1043-1054.
	[Zhang Fan, Liu Yu. Street view imagery: Methods and applications based on artificial intelligence. National Remote Sensing Bulletin, 2021, 25(5): 1043-1054. ]
[24]	Long Y, Liu L. How green are the streets? An analysis for central areas of Chinese cities using Tencent Street View[J]. PLoS One, 2017, 12(2): e0171110. doi: 10.1371/journal.pone.0171110. doi: 10.1371/journal.pone.0171110
[25]	Zeng L Y, Lu J, Li W Y, et al. A fast approach for large-scale Sky View Factor estimation using street view images[J]. Building and Environment, 2018, 135: 74-84. doi: 10.1016/j.buildenv.2018.03.009
[26]	Hankey S, Zhang W W, Le H T K, et al. Predicting bicycling and walking traffic using street view imagery and destination data[J]. Transportation Research Part D: Transport and Environment, 2021, 90: 102651. doi: 10.1016/j.trd.2020.102651. doi: 10.1016/j.trd.2020.102651
[27]	Ma R X, Wang W, Zhang F, et al. Typeface reveals spatial economical patterns[J]. Scientific Reports, 2019, 9: 15946. doi: 10.1038/s41598-019-52423-y. doi: 10.1038/s41598-019-52423-y pmid: 31685908
[28]	Zhang F, Zu J Y, Hu M Y, et al. Uncovering inconspicuous places using social media check-ins and street view images[J]. Computers, Environment and Urban Systems, 2020, 81: 101478. doi: 10.1016/j.compenvurbsys.2020.101478. doi: 10.1016/j.compenvurbsys.2020.101478
[29]	Li X J, Zhang C R, Li W D, et al. Assessing street-level urban greenery using Google Street View and a modified green view index[J]. Urban Forestry & Urban Greening, 2015, 14(3): 675-685.
[30]	Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation[C]// Navab N, Hornegger J, Wells W, et al. Medical image computing and computer-assisted intervention: MICCAI 2015. Cham, Switzerland: Springer International Publishing, 2015: 234-241.
[31]	Qin X B, Zhang Z C, Huang C Y, et al. U²-Net: Going deeper with nested U-structure for salient object detection[J]. Pattern Recognition, 2020, 106: 107404. doi: 10.1016/j.patcog.2020.107404. doi: 10.1016/j.patcog.2020.107404
[32]	荆涛, 王仲. 光学字符识别技术与展望[J]. 计算机工程, 2003, 29(2): 1-2, 80.
	[Jing Tao, Wang Zhong. A survey of optical character recognition. Computer Engineering, 2003, 29(2): 1-2, 80. ]
[33]	陈诚, 赵振斌, 黄燕. 西部乡村旅游社区社会景观敏感度分析: 以甘南郎木寺镇为例[J]. 地理研究, 2017, 36(5): 899-912. doi: 10.11821/dlyj201705008
	[Chen Cheng, Zhao Zhenbin, Huang Yan. Social landscape sensitivity analysis of western tourism community: A case study of Langmusi Town in Gannan. Geographical Research, 2017, 36(5): 899-912. ] doi: 10.11821/dlyj201705008
[34]	Silverman B W. Density estimation for statistics and data analysis[M]. London, UK: Chapman and Hall, 1986: 76.
[35]	保继刚, 林敏慧. 历史村镇的旅游商业化控制研究[J]. 地理学报, 2014, 69(2): 268-277. doi: 10.11821/dlxb201402010
	[Bao Jigang, Lin Minhui. Study on control of tourism commercialization in historic town and village. Acta Geographica Sinica, 2014, 69(2): 268-277. ] doi: 10.11821/dlxb201402010
[36]	中国人民政治协商会议大理白族自治州委员会文史资料委员会. 大理州文史资料第9辑:手工业、工业专辑[M]. 昆明: 云南人民出版社, 1997: 1-9.
	[Literature and History Data Committee of Dali Bai Autonomous Prefecture Committee of the Chinese People's Political Consultative Conference. Dali cultural and historical material Volume 9:Handicraft and industry album. Kunming, China: Yunnan People's Publishing House, 1997: 1-9. ]
[37]	李一玮, 夏林根. 国内城镇居民旅游消费结构分析[J]. 旅游科学, 2004(2): 30-32, 38.
	[Li Yiwei, Xia Lingen. An analysis of tourism consuming structure of China's urban residents. Tourism Science, 2004(2): 30-32, 38. ]
[38]	杨德爱. 旅游与被旅游: 大理“洋人街”由来及变迁[D]. 北京: 中央民族大学, 2012.
	[Yang De'ai. Visiting and being visited: The origin and change of "Foreigner Street" in Dali. Beijing, China: Minzu University of China, 2012. ]
[39]	刘瑜. 社会感知视角下的若干人文地理学基本问题再思考[J]. 地理学报, 2016, 71(4): 564-575. doi: 10.11821/dlxb201604003
	[Liu Yu. Revisiting several basic geographical concepts: A social sensing perspective. Acta Geographica Sinica, 2016, 71(4): 564-575. ]
[40]	李钢, 陈未雨, 杨兰, 等. 武汉市快递自提点的空间格局与集聚模式研究[J]. 地理科学进展, 2019, 38(3): 407-416. doi: 10.18306/dlkxjz.2019.03.010
	[Li Gang, Chen Weiyu, Yang Lan, et al. Spatial pattern and agglomeration mode of parcel collection and delivery points in Wuhan City. Progress in Geography, 2019, 38(3): 407-416. ] doi: 10.18306/dlkxjz.2019.03.010
[41]	薛冰, 李京忠, 肖骁, 等. 基于兴趣点(POI)大数据的人地关系研究综述: 理论、方法与应用[J]. 地理与地理信息科学, 2019, 35(6): 51-60.
	[Xue Bing, Li Jingzhong, Xiao xiao, et al. Overview of man-land relationship research based on POI data: Theory, method and application. Geography and Geo-Information Science, 2019, 35(6): 51-60. ]
[42]	陈洪星, 杨德刚, 李江月, 等. 大数据视角下的商业中心和热点区分布特征及其影响因素分析: 以乌鲁木齐主城区为例[J]. 地理科学进展, 2020, 39(5): 738-750. doi: 10.18306/dlkxjz.2020.05.004
	[Chen Hongxing, Yang Degang, Li Jiangyue, et al. Distribution characteristics and influencing factors of commercial center and hotspots based on big data: A case of the main urban area of Urumqi City. Progress in Geography, 2020, 39(5): 738-750. ] doi: 10.18306/dlkxjz.2020.05.004
[43]	张丽英, 裴韬, 陈宜金, 等. 基于街景图像的城市环境评价研究综述[J]. 地球信息科学学报, 2019, 21(1): 46-58. doi: 10.12082/dqxxkx.2019.180311
	[Zhang Liying, Pei Tao, Chen Yijin, et al. A review of urban environmental assessment based on street view images. Journal of Geo-information Science, 2019, 21(1): 46-58. ]
[44]	钟士恩, 章锦河, 丁蕾, 等. 江南水乡游客满意度的多维度影响因素测量模型[J]. 地理科学, 2016, 36(11): 1715-1721. doi: 10.13249/j.cnki.sgs.2016.11.014
	[Zhong Shien, Zhang Jinhe, Ding Lei, et al. Multi-dimension influential factors of tourist satisfaction in the ancient waterfront towns in the south of the Changjiang River. Scientia Geographica Sinica, 2016, 36(11): 1715-1721. ] doi: 10.13249/j.cnki.sgs.2016.11.014

序号	店铺类型	数量/家
1	客栈	302
2	民族服饰	193
3	云南小吃	137
4	银器	118
5	茶叶	96
6	租车行	81
7	玉器	70
8	鲜花饼	67
9	酒吧	58
10	饮品	55
11	旅行社	53

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

RichHTML

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 7

References 44

Related Articles 6

Metrics

Comments

Recommended 0

[1]	WANG Chenggang, WANG Bo, WANG Qizhi, LEI Yaqin. Nonlinear associations between urban vitality and built environment factors and threshold effects: A case study of central Guangzhou City [J]. PROGRESS IN GEOGRAPHY, 2023, 42(1): 79-88.
[2]	ZHANG Fan, ZHANG Yongyong, CHEN Junxu, ZHAI Xiaoyan, HU Qingfang. Performance of multiple machine learning model simulation of process characteristic indicators of different flood types [J]. PROGRESS IN GEOGRAPHY, 2022, 41(7): 1239-1250.
[3]	LIU Jixiang, XIAO Longzhu, ZHOU Jiangping, GUO Yuanyuan, YANG Linchuan. Non-linear relationships between the built environment and walking to school: Applying extreme gradient boosting method [J]. PROGRESS IN GEOGRAPHY, 2022, 41(2): 251-263.
[4]	ZHANG Peng, HU Shougeng, YANG Shengfu, CHENG Peikun. Modeling urban residential land price distribution using multi-source data and ensemble learning: A case of Wuhan City [J]. PROGRESS IN GEOGRAPHY, 2021, 40(10): 1664-1677.
[5]	WU Kejie, WU Jidong, YE Mengqi. A review on the application of social media data in natural disaster emergency management [J]. PROGRESS IN GEOGRAPHY, 2020, 39(8): 1412-1422.
[6]	LI Zhixuan, HE Zhongyu, ZHANG Yiming, JIN Shuangshuang, WANG Xuemei, ZHU Jie, LIU Shicen. Impact of greenspace exposure on residents’ mental health: A case study of Nanjing City [J]. PROGRESS IN GEOGRAPHY, 2020, 39(5): 779-791.