## 2 Overview and Current Status for Urban Functional Zones
### Data Sources of Urban Functional Zone Research  
Land use and land cover data [18], urban cadastral data [19], and thematic maps can be used for analyzing urban functional zones. However, it may be difficult to obtain these data, and the limitations of these data lead to low processing efficiency and a long research period. Remote sensing images are efficient data for analyzing urban space, especially in the research of extracting urban land cover by using the spectral, shape, and texture information of ground objects in the image. However, the division of urban functional zones mainly reflects the socio-economic of the region [20; 21]. While the social and economic functions of the region cannot be obtained directly from the physical features of remote sensing images. It is difficult to meet the realistic needs of urban planning. Nighttime light data can reflect economic activities in urban areas, but its wide suitability is affected by acquisition costs, spatial resolution, and data processing complexity [22]. With increasing use of big data, cell phone data [23; 24], social media data [25; 26], traffic travel data [27; 28], point of interest (POI) data [29; 30], and other location-based data have provided new opportunities for the study of urban internal functions. These data have the advantages of the low acquisition cost, comprehensive data coverage, high temporal and spatial resolution, and high sensitivity to social functional attributes [31].  
There were 102 articles analyzed (Figure 2). The literature using a single data source accounted for 50.98%, of which 35 articles only used POI data as the data source, accounting for 34.31% of all articles, followed by only image data as the data source. Meanwhile, 49.02% of the articles used multi-source data, in which the conjunction of POI and image data and the combination of POI and traffic travel data accounting for 13.73%, respectively. POI data, used by 74.51% of articles, was the most widely used data. We used the keywords from 102 articles and imported the information into WordArt ([[https://wordart.com](https://wordart.com)]([https://wordart.com](https://wordart.com))) to obtain a word cloud image (Figure 3). The image of the word cloud also proves it.  
The main reason is that POI data are closely related to the land-use type, with few privacy issues and relatively easy data processing [32]. POI data can reflect the spatial distribution of industries and public institutions. However, it cannot directly describe the spatial scale of industries [4; 33; 34; 35]. Cell phone data can objectively reflect the real-time distribution of urban populations without functional semantic information [36]. Social media data have plenty of semantic information with the disadvantage of feature sparseness and processing difficulties [37; 38]. Traffic travel data can indicate the activity characteristics of citizens, but the analysis accuracy depends largely on the built-in algorithm of Internet map service providers [39]. The acquisition of these socio-economic data includes web crawlers, open API interfaces, and purchases from service providers. Since possible privacy issues limit wide applicability, the above are seldom used as the single data source [40; 41]. The identification of urban functional zones based on multi-source data increases the  
Figure 1: The statistics of the publication year of 306 papers.  
semantic information, improves the spatial and temporal resolution, and provides more accurate results for urban functional zone identification [29; 40; 42].