A linear tessellation model for the identification of "food desert": A case study of Shanghai, China.

The "food desert" problem has been treated under a national strategy in the United States and other countries. At present, there is little research on the phenomenon of "food desert" in China. This study takes Shanghai as the research area and proposes a multiscale analysis method using a linear tessellation model that splits the street network into homogeneous linear units. Firstly, the network kernel density estimation using a linear tessellation model is used to measure the travel-mode-based food accessibility. Considering the actual travel constraints, the GPS trajectory data of four travel modes (walking, bicycle, metro and taxi) are applied to calculate the speed of each linear unit. Secondly, the "food desert" phenomenon in Shanghai are identified combing with the results of the network K-function. Finally, the resident income conditions in different modes are fitted based on the housing price data and the spatial distribution of four "food desert" patterns are detected by the overlay analysis of food accessibility and resident income conditions. The experimental results show that fifty percent of Shanghai is characterized by low food accessibility, and half of these areas are disadvantaged and low-income areas in suburbs, which are the locations experiencing the "food desert" phenomenon. Comparing the results of the proposed method and that of the traditional planar method, the identification results for all modes based on the traditional planar method underestimate the severity of the "food desert", especially for the bicycle and taxi modes. This study also provides corresponding decision-making reference for the alleviation and resolution of "food desert" issues. Moreover, the proposed method provides a new research perspective for urban research under the street network space.