Using bundling to visualize multivariate urban mobility structure patterns in the São Paulo Metropolitan Area

Abstract

Internet-based technologies such as IoT, GPS-based systems, and cellular networks enable the collection of geolocated mobility data of millions of people in large metropolitan areas. In addition, large, public datasets are made available on the Internet by open government programs, providing ways for citizens, NGOs, scientists, and public managers to perform a multitude of data analysis with the goal of better understanding the city dynamics to provide means for evidence-based public policymaking. However, it is challenging to visualize huge amounts of data from mobility datasets. Plotting raw trajectories on a map often causes data occlusion, impairing the visual analysis. Displaying the multiple attributes that these trajectories come with is an even larger challenge. One approach to solve this problem is trail bundling, which groups motion trails that are spatially close in a simplified representation. In this paper, we augment a recent bundling technique to support multi-attribute trail datasets for the visual analysis of urban mobility. Our case study is based on the travel survey from the São Paulo Metropolitan Area, which is one of the most intense traffic areas in the world. The results show that bundling helps the identification and analysis of various mobility patterns for different data attributes, such as peak hours, social strata, and transportation modes.

User-centered analysis of a safe bus routing strategy

“…The authors describe a metric con sisting of the spatial distribution of students, the school lo cations, and the public transport vehicles that serve the in fluence area of schools. Martins et al (2021) also use data from the metropolitan area of São Paulo, due to its intense traffic, to create a trajectory visualization solution with Trail Bundling, a technique to group trajectories next to each other in a simplified representation, differing from the concept of mobility flow in the way trajectories are grouped.…”