Type Identification of Land Use in Metro Station Area Based on Spatial-Temporal Features Extraction of Human Activities

As a social carrier, a city is a place of human activities, and human activities also shape and influence the city through its dynamic demand. The in-depth understanding of urban functions is important for urban planning, and the full utilization of the spatial-temporal variation of human activities can be more effective in the identification of land functions. However, the complete extraction of time series features is one of the difficulties. To solve the above problems, the paper explores the identification of land use types based on human activity feature extraction, by taking Beijing as an example. Firstly, this paper constructs a time series that characterizing the change of passenger flow in the metro station area with AFC data, and realizes the feature extraction and type clustering of the time series. Secondly, this paper forms an index system for land use type identification by introducing POI-based indicators, which achieves a comprehensive representation of population activity data. Finally, this paper constructs a land use type identification model based on multi-source human activity data by using GBDT classifier. The results show that the model has high recognition accuracy. It is found that the fusion application of AFC and POI improves the land use recognition accuracy in the case of Consistent change in time series but different types of demand, and the different POI triggers the seemingly overall stable but random activity demand within the city. The research results promote the innovative application of open-source big data in the field of urban planning.

Automated summary

This paper explores the identification of land use types based on human activity feature extraction, using Beijing as an example. By constructing time series and introducing indicators based on POI, a comprehensive representation of population activity data is achieved. The results show that the model has high recognition accuracy and promotes the innovative application of open-source big data in the field of urban planning.