Topological indexes and community structure for urban mobility networks: Variations in a business day

Abstract

Topological analysis and community detection in mobility complex networks have an essential role in many contexts, from economics to the environmental agenda. However, in many cases, the dynamic component of mobility data is not considered directly. In this paper, we study how topological indexes and community structure changes in a business day. For the analyzes, we use a mobility database with a high temporal resolution. Our case study is the city of São José dos Campos (Brazil)—the city is divided into 55 traffic zones. More than 20 thousand people were asked about their travels the day before the survey (Origin-Destination Survey). We generated a set of graphs, where each vertex represents a traffic zone, and the edges are weighted by the number of trips between them, restricted to a time window. We calculated topological properties, such as degree, clustering coefficient and diameter, and the network’s community structure. The results show spatially concise community structures related to geographical factors such as highways and the persistence of some communities for different timestamps. These analyses may support the definition and adjustment of public policies to improve urban mobility. For instance, the community structure of the network might be useful for defining inter-zone public transportation.

Flood risk map from hydrological and mobility data: A case study in São Paulo (Brazil)

“…Furthermore, the tool produces vector data with the resulting network to make it possible to analyze it in a GIS environment (Jorge et al, 2017). The (geo)graph's approach is applied in a variety of different domains (Ceron et al, 2020;Lamosa et al, 2021;Lima Santos et al, 2019;Santos et al, 2017Santos et al, , 2019.…”

Optimal rain gauge network to reduce rainfall impacts on urban mobility – a spatial sensitivity analysis

“…noise is added simultaneously to each point in the dataset, allowing for a sensitivity analysis over all the points and their interactions. This process is represented by equation (Lamosa et al, 2021), where a matrix M AIO represents the coordinates and δ is a matrix of random numbers of the same dimensionality of the M AIO matrix, which can assume any distribution (Pantus et al, 2008). In order to perform the proposed SSA, the maximal covering problem was implemented in AMPL (Fourer et al, 1995) version 3.1.0, and CPLEX was used as a solver.…”

“…Climate change is critical in this context: extreme meteorological events will be even Rain gauge networksensitivity analysis 2261 more frequent and severe (Pachauri et al, 2014;UNISDR, 2015). Additionally, vulnerability to those extreme meteorological events is even more severe in developing countries (Nur and Shrestha, 2017), and impacts on transportation infrastructure and urban mobility are high and increasing (Santos et al, 2017;Carvalho et al, 2018;Lamosa et al, 2021). In Brazil, for example, there are approximately 40 thousand areas at risk of events triggered by rainfall, such as floods and landslides.…”

Study of links between people in urban areas based on mobility data for the city of São Paulo

“…A mobility network consists of locations connected by the flow of people [6], which is essential for understanding virus transmission on a large scale, particularly in a vast country like Brazil [4]. Such networks provide insight into complex systems by treating regions as nodes and movements between them as edges in a graph [9].…”