Author(s)

Yitian Liu¹, Guangping Chen^1,2*

¹School of Earth Sciences, Zhejiang University, Hangzhou, China.
²Institute for Geography & Spatial Information, Zhejiang University, Hangzhou, China.

The retail food environment (RFE) has a significant impact on people’s dietary behavior and diet-related outcomes. Although RFE research has received a lot of attention, there are very few studies that shed light on the foodscape and assessment methodologies in the China context. Based on open data obtained from Dianping.com and AutoNavi map, we classified all food outlets into six types. Geographic Information Systems (GIS) techniques were employed to create two network buffer areas (1-km and 3-km) and calculate the absolute measures and relative measures (i.e., mRFEI and Rmix). We modified the calculation of relative measures by adding items and assigning weights. The mean mRFEI using the 1-km and 3-km buffer sizes across the communities were 10.45 and 20.12, respectively, while the mean mRmix of the two buffer sizes were 20.97 and 58.04, indicating that residents in Wuhan have better access to fresh and nutritious food within 3-km network buffers. Residents in urban areas are more likely to be exposed to an unhealthy food environment than those in rural areas. Residents in Xinzhou and Qiaokou districts are more likely to be subjected to unfavorable neighborhood RFE. The open data-driven methods for assessing RFE in Wuhan, China may guide community-level food policy interventions and promote active living by shifting built environments to increase residents’ access to healthy food.

Retail Food Environment (RFE), Diet Quality, Geographic Information Systems (GIS), Density, Big Data

Liu, Y. and Chen, G. (2023) Assessment of the Retail Food Environment Using Integrated GIS and Modified Measures in Wuhan, China. Journal of Geographic Information System, 15, 421-439. doi: 10.4236/jgis.2023.155021.