City2vec: Urban knowledge discovery based on population mobile network

Abstract

Due to the needs of social and economic development, population movements between cities often occur on a large scale. Spontaneous population movements between cities constitute a Mobile Network of enormous scale, and although a considerable amount of research has been conducted to parse this structure using algorithms related to complex networks, it is not possible to quantitatively describe the differences and similarities between different cities. We use graph embedding algorithm that extends the traditional complex network approach of one-dimensional cognition of cities to two-dimensional cognition. It could project city information into a high-dimensional mathematical space by learning the population flow relationships and computing a unique vector representation. We parsed the cell phone number data in about 80 million POIs in 334 cities across China provided by Tencent eMap, and constructed a city Mobile Network containing 2,662,596 directed edges that could effectively capture long-term, long-distance population migrations. The city embedding is actually a reduced dimensional representation of the mobile network, which can retain richer original information. Our research method can not only accomplish the tasks that traditional complex network analysis methods can do well, but also achieve higher dimensional cognition of cities (e.g., spatial relationships between cities and maturity of urban agglomerations), which is an effective complement to traditional graph theory methods.