# Detecting Urban Mobility Structure and Learning Functional Distribution with Multi-Scale Features
## 1 Introduction  
Urban spatial structure represents the relative spatial relationships and distribution patterns of geographical elements within a certain geographic area [1]. On the one hand, the spatial structure of a city, as the core representation of its functional layout, plays a significant role in guiding and shaping human activities [2]. For instance, commercial districts often attract residents during leisure time, encouraging them to engage in consumption activities such as shopping and dining. However, areas with high levels of noise pollution may deter pedestrian movement and outdoor activities, thereby affecting the functionality and attractiveness of certain urban zones [3, 4]. On the other hand, the dynamic changes in human mobility also feed back into the urban spatial structure, prompting continuous optimization and adjustment of the city's functional layout [5]. As urban residents' lifestyles and consumption needs evolve, the hotspots of human activity may shift or expand, driving the urban spatial layout to adapt to better meet demands. Therefore,through the study of urban spatial structure, researchers can grasp the current distribution and interrelations of various functional zones, optimize the functional layout, enhance the efficiency of the transportation system, and ensure the equitable distribution of public service resources. This has significant implications for urban planning [6], traffic management [7], and public transportation route selection [8]. With the rapid development of mobile positioning technology, transportation devices such as taxis, buses, and subways can record detailed movement trajectories of individuals in different areas, generating a large amount of human mobility data [9; 10]. The rise of micromobility infrastructure has further diversified the sources and scale of human mobility data [11]. These data establish a connection between urban spatial structures and human activities, leading to a key shift in the perspective of urban spatial structure research from static to dynamic. For example, with the help of daily travel data such as subway transit data and check-in information, we can accurately assess the functional attributes and living environment quality of different urban areas, allowing for a more comprehensive and in-depth understanding of urban spatial structures [12]. Human mobility plays a significant role in driving the optimization and adjustment of urban functional layouts, thereby having a broad and profound impact on urban spatial structures. Given the important driving force of human behavior in shaping changes in urban functional layouts, numerous scholars have initiated related research and introduced the concept of \"urban mobility structure,\" which aims to analyze the spatial and temporal distribution, combination, and interrelationships of various activities within the city [13]. The urban mobility structure reflects the mobility patterns of urban residents in different regions and time periods, including areas for work, residence, leisure, and entertainment [14]. To capture the changes in urban layout and network attributes induced by human activities, researchers have explored various network structure partitioning methods. Among these, modularity-based network substructure partitioning methods, such as Louvain [15], FastNewman [16], and InfoMap [17], are widely used for effectively identifying community structures in urban activities. However, with the increasing scale and complexity of data, these traditional methods face challenges in terms of computational efficiency and accuracy. In recent years, with the rapid development of deep learning technologies, data-driven approaches have provided new perspectives and solutions for urban mobility structure partitioning. These methods process massive data, enabling models to automatically learn the intrinsic patterns, features, and regularities within the data, thereby allowing for the precise segmentation of urban activity structures.