Recognition of Urban Functions and Mixed Use Based on Residents’ Movement and Topic Generation Model: The Case of Wuhan, China

The rapid evolution of cities has brought new challenges to urban planning and management. The accurate evaluation of urban functional structure and mixed use is critical, especially at a fine scale such as by blocks. The composition and mixing of urban spatial functions calculated by remote sensing and statistics are non-quantitative and undetailed. The text topic models are often applied to process text data, but are rarely used to mine semantic information in quantitative data. Therefore, this paper attempts to carry out research on the recognition of urban functions and mixed use using a text topic generation model based on resident mobile data. First, the area within Wuhan Third Ring Road was divided into 2451 units at a grid size of 500 m × 500 m. The histogram-latent Dirichlet allocation (H-LDA) and information entropy were applied to assign different grid units to correct the functional topics and topic information entropy (TIE). Second, the functional categories of different analysis units were calculated using the point of interest (POI), frequency density (FD) and category proportion (CP) indicators, while the functional information entropy (FIE) based on the POI was calculated. Then, the urban functions and mixtures identified by the two kinds of data were compared and analyzed. Finally, referring to the geographic information in streetscape map and applying correlation analysis, the function and mixing results obtained from the experiment were verified. Studies have shown that the H-LDA model can identify bridges, which the POI data have shown is challenging to identify without attributes such as length. The function recognition accuracy of the H-LDA model is 89.3%, which is higher than K-means algorithm and Word2vec models. The correlation coefficient between FIE and TIE is 0.587, indicating that both are highly correlated. These explain the accuracy and rationality of identifying city functions and mixtures based on the H-LDA model. The H-LDA model can be applied to functional computing and fine-scale urban mixed function planning.