Identification and spatio-temporal characterization of urban functional areas based on POI data

Abstract

Accurate identification of urban functional areas is of great significance to the scientific management and intelligent services of cities. Aiming at the problems of low accuracy and subjectivity of current identification methods, a quantitative identification method combining random forest and frequency density algorithm is proposed. Firstly, the frequency density calculation method is constructed by assigning weights to 19 POI types through random forest, and then the spatiotemporal feature analysis is performed on the distribution of urban functional areas identified in 2016 and 2021, respectively; secondly, the influence of rank scaling is considered, and Jingjiang Palace Street is used as an example for functional area identification; finally, a test set is selected for validation and comparison experiments. The results show that the accuracy rate of the method in this paper reaches 85.55%.