Quantify the Spatial Association between the Distribution of Catering Business and Urban Spaces in London Using Catering POI Data and Image Segmentation

Abstract

The impacts of global climate change on food systems will be broad, complex, and profoundly affected by urban context. Food-related urbanism has been investigated for decades to explore how food access influences placemaking and urban forms. With global climate change, foodscapes within urban spaces are an important consideration in urban design and planning for food security and community health. The distribution of catering businesses (restaurants and cafés), one critical method of access to food, is highly associated with urban spaces because of their high impact on diet patterns, human physical activities, travel behaviors, and the use of public spaces. This research explores the spatial associations that exist between the distribution of catering businesses and the design and planning of urban spaces in London. This quantitative research includes three parts: (1) uses Open Street Map data and the GIS spatial analysis method to study the distribution of catering businesses; (2) uses the imagery segmentation method in machine learning to categorize urban spaces into open, landscape, and conflict spaces; and (3) establishes the association between the distribution of catering businesses and the categories of urban spaces through Spearman’s correlation and a linear regression model. The results indicate that the spatial distributions of catering businesses are highly correlated with urban spaces. Conflict and landscape spaces have a significant positive influence on the distribution of catering businesses, while open space has a significant negative influence. Based on the context of global climate change, this research contributes a quantitative urban design and planning approach to promote access to food increase food options and advocate active lifestyles.

Spatial differences and underlying mechanisms in electronic word of mouth in the foodservice industry: A case of Sanya, China

“…In this study, Sanya, a tourist city, was selected as a case object to explore the spatial distribution characteristics and influencing factors of eWOM in the foodservice industry. Previous studies have focused more on the foodservice industry in economically developed megacities such as London and Beijing [ 15 , 27 ], but there is little research on small and medium-sized cities. Sanya is an internationally recognized tourist city with a rich and diverse foodservice industry.…”

“…The data used in this study from the Gaode Maps platform is all open and free. In fact, there have been many research results on electronic scoring using POI data [ 1 , 15 , 27 ].…”

“…The kernel density estimation, cold hotspot analysis, spatial clustering and other methods have been used in these studies [ 15 , 26 ]. Quantitative analysis methods such as correlation analysis, geodetector software, and geographically weighted regressions have been used to explain the impacts of various factors on the spatial pattern in the foodservice industry [ 16 , 21 , 27 ]. Recently, the Online To Offline (O2O) model has dramatically changed the spatial distribution of restaurants in China.…”

“…However, there is almost no research on combining big data technology to study eWOM in foodservice industry. Third, megacities such as London [ 27 ] and Beijing [ 15 ] have been studied. However, for some small and medium-sized cities, especially coastal cities, the research on eWOM of foodservice industry is relatively lacking.…”

Food Service Spatial Pattern After the Emergency of Online Retail

“…Stall clusters are commonly seen between residential land use and undeveloped land use. This situation diverges from the urban north like the UK, wherein the interface space accommodates many restaurants and cafés [54].…”