Accurately identifying an urban functional zone (UFZ) is crucial for rationally allocating urban land resources and optimizing urban spatial structure. Existing research based on Points of Interest (POIs) mostly uses the relationship between the number of various types of POIs as the basis for identification. However, this approach neglects the difference of physical surface property of urban functional zones—imperviousness. Based on the FD-CR method, this study proposes the RFD-ECR identification method by combining TF-IDF and ISI. This study divides research units according to OpenStreetMap (OSM), and reclassifies POI data. It then uses the Term Frequency-Inverse Document Frequency (TF-IDF) algorithm to highlight the dominant function of study units and incorporates the impervious surface index (ISI) as a correction to recognize urban functional zones. Experiments conducted in the central urban area of Chengdu demonstrate that this method is effective in identifying urban functional zones, achieving an accuracy rate of 80.21%. Comparison with the Frequency Density-Category Ratio (FD-CR) method reveals that this method, through the TF-IDF algorithm and the impervious surface index constraint, effectively improves the classification accuracy of mixed commercial UFZs. This method broadens the scope of research on urban functional zone identification based on POI data, and also provides a valuable reference for other cities undertaking functional zone identification.
Zhao et al. (Fri,) studied this question.