Impact of sudden public crises on spatial distribution patterns and driving factors of the urban catering industry: a case study of Shanghai’s catering POI data before and after COVID-19

• Published in: Journal of Asian architecture and building engineering pp. 1 - 24
• Main Authors: Shao, DanDan, Zoh, KyungJin, Xie, Yanzhao
• Format: Journal Article
• Language: English
• Published: Taylor & Francis Group 26.07.2024
• Subjects: covid-19 impact; gradient boosting decision tree model (gbdt); machine learning; spatial econometric modeling; urban morphology recognition
• ISSN: 1346-7581; 1347-2852
• DOI: 10.1080/13467581.2024.2373830

This study uses Shanghai to explore the effects of the COVID-19 pandemic on the spatial distribution patterns and driving factors of the urban catering industry. Through quantitative analysis of restaurant point-of-interest data and influencing factors in Shanghai’s main urban area from 2016 to 2022 using geographic information technology, machine learning, and spatial econometric models, this study predicts spatial changes in the catering industry. The number of restaurants in Shanghai’s main urban area continuously decreased from 2016 to 2022, with a significant pre-pandemic decline. The catering industry in this area has a significant spatial correlation in the spatial distribution, showing a pattern of “one core dominance with multiple cores coexisting,” with Jing’an and Huangpu Districts having the highest densities, which is expected to continue. Target markets remain a decisive factor in restaurant location selection post-COVID. Restaurant aggregation is most favorable when the population density is between 0 –50,000 people/km2, GDP is between 0–20 billion yuan/km2, housing prices are between 20,000 –130,000 yuan/m2, school density is between 0–20/km2, hospital density is between 0–10/km2, and attraction density is between 0–40/km2. Transportation conditions have no apparent threshold; the more developed the transportation, the more favorable the conditions for the catering industry. Post-COVID-19, areas with high housing prices and around tourist attractions are less likely to attract many restaurants. Areas around hospitals maintain restaurant aggregation, although this will decrease. Areas around schools will become popular for restaurants in the future.