Evolution Characteristics and Causes—An Analysis of Urban Catering Cluster Spatial Structure

Studying the development characteristics of the urban catering industry holds significant importance for understanding the spatial patterns of cities. In this manuscript, according to the characteristics of the distribution of catering points and based on catering point of interest (POI) data of 106 cities in China in 2016 and 2022, we propose the Natural Nearest Neighbor Single Branch Model (NNSBM) to identify catering points by adaptive clustering, which improves the efficiency of identifying catering clusters. Subsequently, a catering spatial structure division model is constructed to classify the spatial structure of catering clusters into 3 major categories and 17 subcategories, and the evolution pattern of urban catering clusters is analyzed. In addition, based on the population density raster data, a bivariate spatial autocorrelation model is employed to analyze the complex relationship between the distribution of urban catering clusters and population density, revealing the distinctive characteristics of urban catering cluster evolution. The results showed that (1) In the initial stage of catering cluster formation, catering activities tend to gather first in a specific area of the city, giving rise to the main catering cluster. However, as the catering industry progresses, the phenomenon of “central fading” occurs within the main catering cluster. (2) The overall trend of the catering spatial structure of most cities showed an evolution toward low primacy–high concentration (Lp-Hc), and cities at different stages of catering capacity exhibited different evolution characteristics of catering clusters. (3) The influence of population density on catering distribution was staged, with a varying impact on cities with different types of catering spatial structures.

[1]  Zeyang Li,et al.  Spatio-temporal agglomeration and morphological causes of Shanghai catering clusters , 2023, Applied Geography.

[2]  Berhanu Woldetensae,et al.  Special economic zones location decision and quality of life in Ethiopia: the case of Bole Lemi-1 and Eastern Industry Zone , 2023, GeoJournal.

[3]  Kaiqi Zhang,et al.  The Impact of Road Functions on Road Congestions Based on POI Clustering: An Empirical Analysis in Xi’an, China , 2023, Journal of Advanced Transportation.

[4]  Yanbin Chen,et al.  Street centrality and vitality of a healthy catering industry: A case study of Jinan, China , 2022, Frontiers in Public Health.

[5]  Yuetao Wu,et al.  Correlation between Urban Commercial Nodes and the Development of Sci-Tech Enterprises in Hangzhou West High-Tech Corridor, China , 2022, Land.

[6]  Hongqiang Jiang,et al.  The influence of the neighbourhood environment on peer-to-peer accommodations: A random forest regression analysis , 2022, Journal of Hospitality and Tourism Management.

[7]  L. Liu,et al.  Analysis of the Characteristics and Spatial Pattern of the Catering Industry in the Four Central Cities of the Yangtze River Delta , 2022, ISPRS Int. J. Geo Inf..

[8]  Ty Choi Ethnic enclaves in immigrant entrepreneurship: Korean immigrant entrepreneurship in Australia , 2022, Journal of the International Council for Small Business.

[9]  Yishao Shi,et al.  Business Circle Identification and Spatiotemporal Characteristics in the Main Urban Area of Yiwu City Based on POI and Night-Time Light Data , 2021, Remote. Sens..

[10]  Ci Song,et al.  Roles of locational factors in the rise and fall of restaurants: A case study of Beijing with POI data , 2021 .

[11]  Yuechen Li,et al.  Hotspot Detection and Spatiotemporal Evolution of Catering Service Grade in Mountainous Cities from the Perspective of Geo-Information Tupu , 2021, ISPRS Int. J. Geo Inf..

[12]  Shifei Ding,et al.  Chameleon algorithm based on improved natural neighbor graph generating sub-clusters , 2021, Applied Intelligence.

[13]  G. Pavlidis,et al.  Gastronomic tourism in Greece and beyond: A thorough review , 2020, International Journal of Gastronomy and Food Science.

[14]  Wang Ling-en,et al.  Spatial Distribution Pattern of the Catering Industry in a Tourist City: Taking Lhasa City as a Case , 2020, Journal of Resources and Ecology.

[15]  Guangdong Wu,et al.  Rethinking the Identification of Urban Centers from the Perspective of Function Distribution: A Framework Based on Point-of-Interest Data , 2020, Sustainability.

[16]  Yansheng Liu,et al.  ‘Take the Essence, Discard the Dregs’: A Perspective on Blockchain Technology in China , 2020, Management and Organization Review.

[17]  Tianhui Fan,et al.  Consumer clusters detection with geo-tagged social network data using DBSCAN algorithm: a case study of the Pearl River Delta in China , 2019, GeoJournal.

[18]  Carlo Ratti,et al.  Predicting neighborhoods’ socioeconomic attributes using restaurant data , 2019, Proceedings of the National Academy of Sciences.

[19]  Geoffrey J. D. Hewings,et al.  Understanding urban sub-centers with heterogeneity in agglomeration economies—Where do emerging commercial establishments locate? , 2019, Cities.

[20]  S. Jang,et al.  To cluster or not to cluster?: Understanding geographic clustering by restaurant segment , 2019, International Journal of Hospitality Management.

[21]  Zhenhong Du,et al.  Identifying Urban Functional Zones Using Public Bicycle Rental Records and Point-of-Interest Data , 2018, ISPRS Int. J. Geo Inf..

[22]  Fernando E. García-Muiña,et al.  Formal and Informal Institutional Differences Between Home and Host Country and Location Choice: Evidence from the Spanish Hotel Industry , 2018, Management International Review.

[23]  R. Aswini,et al.  A Novel Optimization Algorithm Based on Stinging Behavior of Bee , 2018, IAES International Journal of Artificial Intelligence (IJ-AI).

[24]  M. Henning Time should tell (more): evolutionary economic geography and the challenge of history , 2018, Regional Studies.

[25]  David W. S. Wong,et al.  Comparing implementations of global and local indicators of spatial association , 2018, TEST.

[26]  Avory Bryant,et al.  RNN-DBSCAN: A Density-Based Clustering Algorithm Using Reverse Nearest Neighbor Density Estimates , 2018, IEEE Transactions on Knowledge and Data Engineering.

[27]  A. Rodríguez‐Pose The Revenge of the Places that Don't Matter (and What to Do About it) , 2017 .

[28]  W. Roehl,et al.  Understanding and projecting the restaurantscape: The influence of neighborhood sociodemographic characteristics on restaurant location , 2017 .

[29]  S. Fan,et al.  CURE-SMOTE algorithm and hybrid algorithm for feature selection and parameter optimization based on random forests , 2017, BMC Bioinformatics.

[30]  Nathan Schiff,et al.  Cities and product variety: evidence from restaurants , 2015 .

[31]  D. Kogler Editorial: Evolutionary Economic Geography – Theoretical and Empirical Progress , 2015 .

[32]  Juan Alcacer,et al.  Location strategies for agglomeration economies , 2014 .

[33]  Andres Sevtsuk Location and Agglomeration , 2014 .

[34]  K. Murota,et al.  Self-organization of hexagonal agglomeration patterns in new economic geography models , 2014 .

[35]  M. O'Neill,et al.  Factors Driving Consumer Restaurant Choice: An Exploratory Study From the Southeastern United States , 2013 .

[36]  Timothy F. Leslie,et al.  The spatial food environment of the DC metropolitan area: Clustering, co-location, and categorical differentiation , 2012 .

[37]  Scott Stern,et al.  Clusters and Entrepreneurship , 2010 .

[38]  Sal Kukalis Agglomeration Economies and Firm Performance: The Case of Industry Clusters , 2010 .

[39]  Edward Feser,et al.  Clusters and Economic Development Outcomes , 2008 .

[40]  Glenn Ellison,et al.  What Causes Industry Agglomeration? Evidence from Coagglomeration Patterns , 2007 .

[41]  A. D. Diez Roux,et al.  Associations of neighborhood characteristics with the location and type of food stores. , 2006, American journal of public health.

[42]  H. Bathelt,et al.  Clusters and knowledge: local buzz, global pipelines and the process of knowledge creation , 2004 .

[43]  Stéphane Bégin The geography of a tourist business: Hotel distribution and urban development in Xiamen, China , 2000 .

[44]  Cindy Claycomb,et al.  The influence of brand recognition on retail store image , 1997 .

[45]  K. Small,et al.  URBAN SPATIAL STRUCTURE. , 1997 .

[46]  P. Dasgupta,et al.  Learning-by-doing, Market Structure and Industrial and Trade Policies , 1988 .

[47]  Zhibang Xu,et al.  Temporal and Spatial Variation Characteristics of Catering Facilities Based on POI Data: A Case Study within 5th Ring Road in Beijing , 2018 .

[48]  L. Möller Bemerkungen zur Hydrographie der Gewässer , 1948 .

[49]  Fatin Hamadah Rahman,et al.  EnTruVe: ENergy and TRUst-aware Virtual Machine allocation in VEhicle fog computing for catering applications in 5G , 2022, Future Gener. Comput. Syst..