## 3 Method
### Discussion  
Building functional types are important information for urban planning and management. Building functions may change over time. For example, a residential building may be changed to commercial use. Therefore, accurate identification of building functional  
Figure 9: Spatial characteristics of buildings of different functional types. types is of great significance in the assessment of urban space, population [44], facility sitting, and fire safety. The identification of the functions of urban buildings requires the classification of urban spatial uses at a finer scale than the identification of urban functional zones [45]. Most past studies on building function identification have relied on high-resolution remote sensing imagery, land survey data, cab tracks, and network heat. However, it is not too easy to obtain such types of up-to-date data due to the limitations of data service prices and confidentiality. This study proposes a method for the accurate identification of urban building functions using only POI, building footprint, and OSM road data. All of these data are relatively easy to obtain for free from internet mapping platforms. To address the lack of POIs for some buildings, we assessed the topological relationships between POIs and buildings and between road meshes and buildings, as well as the spatial similarity between buildings. We determined building functional types by calculating the maximum spatial similarity between buildings with and without POIs in the same road mesh. In previous studies, [PERSON] utilised the kernel density value of each type of POI to determine building types. This can avoid the lack of functional type density value of buildings without POIs but it does not take into account the spatial morphology characteristics of the city [29]. The functional types of buildings on both sides of the same road may be completely different, and the results of the experiment show that the accuracy of this method is low. [PERSON] considered the constraint of block range and adopted the Tyson polygon area occupancy ratio to determine the functional types of buildings without POIs; however, they only considered the single factor of distance [33]. In this paper, we synthesise location, orientation, area, and shape similarity data to identify building functional types more accurately. Experiments show that the integrated multi-feature similarity method has 10% greater identification accuracy than [PERSON]'s method. Compared with the data mining methods proposed in [34], the proposed method achieves higher identification accuracy without the need for high-resolution remote sensing images, land use surveys and other data, giving it lower needs in terms of data acquisition. Compared with the identification method fusing the spatial similarity of buildings and POI density values proposed by [PERSON] et al. [36], this paper considers the spatial relationship between the urban road network and buildings when determining the function of buildings lacking POI, which is more conducive to avoiding misidentification. The experimental results of this paper show that residential buildings and public service buildings are identified with high accuracy. Single-function residential buildings mainly carry the residential function of the public, the number and type of POIs contained in the contour are relatively small, and the use of POIs can accurately identify the functional type of the building. Due to the concentrated spatial locations and similar spatial patterns of urban residential buildings, it is easy to accurately infer the functional types of residential buildings that do not contain POIs using the spatial relationships between geographic entities. Single-function public service buildings are mainly the locations of public administration and public service organisations such as education, health, and government. The functional types of POIs they contain are more concentrated and are identified with higher accuracy.