## 3 Identification and Division of Urban Functional Zones
### Urban Functional Zone Division Based on Cluster Analysis  
Cluster analysis refers to feature extraction of static or dynamic urban data (such as cell phone data, traffic travel data, and check-in data) to partition urban functional land and identify functional zones according to the functional characteristics of the region or the temporal and spatial characteristics of human mobility. K-means algorithm is a common clustering method in urban functional zone identification. Many studies use different data  
Figure 6: Diagram of kernel density estimation for POI data. (The figure was drawn by the author). based on this method to classify urban functional zones, such as mobile phone data [23; 76], taxi GPS data [77], and check-in data [78], etc. The advantages of this method are fast speed, simple calculation, and excellent clustering effect, but the number of clusters (K) needs to be manually determined and the initial cluster center has a great influence on the clustering effect [79]. The K-medoids method is different from the central point selection of K-means, which involves lower data requirements than K-means. [PERSON], et al. [80] proposed a dynamic time warping (DTW) distance-based k-medoids method to delineate urban functional zones with building-level social media data. Partition around medoids (PAM) is the basic algorithm of K-medoids. The results show that the PAM algorithm can be applied to the identification of urban functional areas, and the identification accuracy of functional areas is up to 86% [81]. Fuzzy c-means clustering (FCM) is the extension of K-means. Besides the number of clusters, the selection of weighted index also has a certain influence on the clustering result. [PERSON], et al. [4] and [PERSON], et al. [82] used fuzzy c-means clustering to identify urban regions with different functions from POI data and successfully divided and analyzed urban areas. Spectral clustering is generally superior to K-means. [PERSON] and [PERSON] [83] used a spectral clustering algorithm and self-organizing map (SOM) to classify land use in urban areas by clustering geographical regions with similar tweeting activity patterns. Gaussian mixture model (GMM) can provide stronger description ability than K-means. [PERSON], et al. [84] distinguished urban functional zones by integrating GPS movement trajectories and an unsupervised Gaussian mixture model (GMM). [PERSON], et al. [31] improved this method and designed a multi-feature weighted decision algorithm based on call detail records (CDR) data to identify urban functional zones with a supervised GMM. When the dataset is dense, the density-based spatial clustering of applications with noise (DBSCAN) algorithm can be considered. It does not need to input the number of clusters and can find outliers while clustering [79]. [PERSON], et al. [28] used the iterative DBSCAN algorithm to extract functional zones and manually labeled the features of taxi GPS traces to classify land use. A support vector machine (SVM) was used, achieving a recognition accuracy of 95%. Ordering points to identify the clustering structure (OPTICS) clustering is similar to DBSCAN, but in particular, it can obtain clusters of different densities. [PERSON], et al. [26] combined the spatial correlation and semantic information of POI data with location-based check-in data and proposed a feature model of POI data that considered human activity characteristics by OPTICS. [PERSON], et al. [85] used an ant colony clustering algorithm to divide urban block functions based on the activity characteristics of smart card data (SCD) and the POI category information. This method has high optimization performance, but a slow convergence speed. The classifier classifies the source data based on the class knowledge formed by the training samples. K-nearest neighbor (KNN) algorithm is one of the simplest classification algorithms. [PERSON], et al.