Urban Functional Zone Classification Based on POI Data and Machine Learning

Abstract

The identification of urban spatial functional units is of great significance in urban planning, construction, management, and services. Conventional field surveys are labour-intensive and time-consuming, while the abundant data available via the internet provide a new way to identify urban spatial functions. A major issue is in determining point of interest (POI) weights in urban functional zone identification using POI data. Along these lines, this work proposed a recognition method based on POI data combined with machine learning. First, the relationship between POI data and urban spatial function types was mapped, and the density of each type of POI was calculated. Then, the density values of each type of POI in the study unit were used as feature vectors and combined with the Kstar algorithm to identify urban spatial functions. Finally, the identification results were validated by combining multiple sources of POI data. From the acquired sampling results, it was demonstrated that the proposed method achieved an accuracy of 86.50%. The problem of human bias was also avoided in determining POI weights. High recognition accuracy was achieved, making urban spatial function recognition more accurate and automatable.