Multi-source Data-driven Identification of Urban Functional Areas: A Case of Shenyang, China

Urban functional area (UFA) is a core scientific issue affecting urban sustainability. The current knowledge gap is mainly reflected in the lack of multi-scale quantitative interpretation methods from the perspective of human-land interaction. In this paper, based on multi-source big data include 250 m × 250 m resolution cell phone data, 1.81 × 105 Points of Interest (POI) data and administrative boundary data, we built a UFA identification method and demonstrated empirically in Shenyang City, China. We argue that the method we built can effectively identify multi-scale multi-type UFAs based on human activity and further reveal the spatial correlation between urban facilities and human activity. The empirical study suggests that the employment functional zones in Shenyang City are more concentrated in central cities than other single functional zones. There are more mix functional areas in the central city areas, while the planned industrial new cities need to develop comprehensive functions in Shenyang. UFAs have scale effects and human-land interaction patterns. We suggest that city decision makers should apply multi-sources big data to measure urban functional service in a more refined manner from a supply-demand perspective.