## 2 Literature Review
### Application of Big Data Tools in Urban Spatial Research  
The complexity of urban space makes the direction and dimensions of urban research more diverse, and the amount of data available on various attributes of cities is relatively large. Traditional urban planning data primarily comes from spatial geographic information and urban planning management systems, which sometimes have lagging and regional restrictions. Supported by computer technology, big data approaches make up for the needs of primary urban data in terms of data volume and accuracy. Currently, the most widely used data sources for urban spatial street research mainly include street-view maps [27], urban facility POI [28], and cell phone big data [29]. Street View Map is an Internet-based live-view electronic map service that provides a new type of map experience centered around the human perspective [30]. The most commonly used street-view imagery processing technology is image segmentation based on deep learning, and image segmentation contains various types of technologies such as panoramic segmentation [31],semantic segmentation [32], instance segmentation [33], and so on. Among them, semantic segmentation technology is widely used, being among the principal technical methods utilized in this paper. Semantic segmentation is processed by classifiers based on specific theories to categorize the pixel points in the image. This method is then combined with image feature extraction techniques to obtain the segmentation results.  
The semantic segmentation of images based on convolutional neural networks is a leap forward from traditional semantic segmentation. [PERSON] et al. used semantic segmentation and statistical modeling of Google Street View images to assess streetscape walkability. The results of the study found that the walking vigor of older women has a higher correlation with streetscape walkability than older men [34]. [PERSON] et al. used image segmentation techniques to generate a street-level SVFf map to assess the urban thermal environment and propose more targeted urban planning measures [35]. Many studies have confirmed the higher feasibility of street-level images in urban spatial research. Urban POI data, which is mainly acquired through the map API interface, the urban big data open platform, and field research data integration, is the most commonly used data source for urban spatial research. POI is an important data source for spatio-temporal big data in the fields of urban spatial structure [36], human activities [37], and ecological development [38], and also has a significant reference value for urban spatial planning and infrastructure service construction. Mobile big data has considerable advantages in studying crowd mobility and social patterns. For example, [PERSON] et al. compared travel patterns in selected monocentric and polycentric urban areas in Germany using mobile big data to test hypotheses of transit-oriented regional development and congestion risk in transport networks [39]. Many studies have combined the above research methods to achieve more comprehensive data coverage, and this study aims to assist in understanding spatial and temporal mobility patterns in different city types.