Function2vec: A Geographic Knowledge Graph Model of Urban Function Evolution and Its Application

Abstract

Urban function evolution (UFE) has become more and more complex in emerging cities. However, insufficient theoretical support exists for the visual expression of the spatial correlation between UFE patterns. In order to fill this gap, we use the 2013 and 2022 Point-of-Interest (POI) data of Shenzhen city to implement the funtion2vec model based on the node2vec model and urban tree theory. In this model, we first divide UFE patterns into three categories: Function Replace (FR), Function Newly Added (FNA), and Function Vanishing (FV). Then, we calculate the correlation between those UFE patterns using their functional vectors, resulting in a graph structure representing the urban function evolution network (UFEN). Based on our case study, we obtained the following conclusions: (1) From 2013 to 2022, the UFE in Shenzhen was primarily dominated by FR (89.44%). (2) FV and FNA exhibit a long-tailed distribution, adhering to the 20–80 law. (3) Through the UFEN based on FR, healthcare services are well suited to form mutual complementarities with other functions; science, education, and cultural services demand a higher complementarity with other functions; administrative offices exhibit a strong diversity in their evolutionary patterns; and the integration of transportation hubs with other functions results in a significantly deviating urban function evolution from its original pattern. The above conclusions suggest that function2vec can well express UFE in emerging cities by adding spatial correlation in UFE.

Keywords

• urban function evolution
• urban tree theory
• function2vec
• emerging cities