Exploring neighborhood daily activity patterns from the perspective of mobility using location-based services data: A case study of Shanghai

The relationship between residents’ daily activities and the built environment has long been a central theme in urban studies, offering valuable insights for planning and neighborhood development. However, conventional static methods often fail to reflect the dynamic nature of these interactions, limiting their relevance for neighborhood renewal. This study addresses this gap by adopting a mobility-based approach that integrates spatial and functional dimensions to explore evolving neighborhood activity patterns. Using Shanghai as a case study, the research first uses Location-Based Services (LBS) data to trace neighborhood travel trajectories and construct a spatial flow network. It then incorporates Points of Interest (POIs) and Areas of Interest (AOIs) data to identify activity types and distributions, building a functional network of neighborhood activities. The study finds that, 1) spatial activity patterns fall into four types: high-density with low aggregation (35 %), low-density with low aggregation (27 %), low-density with high aggregation (23 %), and high-density with high aggregation (15 %); 2) functional patterns also fall into four categories: single-function core (40 %), residential-business dual-core (20 %), multi-function core (25 %), and tourism-driven core (15 %); 3) neighborhoods with similar spatial patterns tend to exhibit corresponding functional characteristics. These spatial and functional patterns help explain how different types of activities are connected and reflected in the physical layout. Aligning spatial design with activity patterns can improve how neighborhoods support residents’ daily needs. The findings offer practical guidance for resident-centered neighborhood renewal, particularly in the design and enhancement of public space elements. • Analyzing neighborhood daily activity patterns from a dynamic perspective. • Coordinating the analysis across both spatial and functional dimensions. • Neighborhoods with similar spatial activity patterns show uniform functional traits. • Classifying neighborhoods into four main types based on activity patterns. • Providing a basis for neighborhood renewal that based on residents' actual activities.