Spatial Autoregressive Model for Estimation of Visitors' Dynamic Agglomeration Patterns Near Event Location.

The rapid development of ubiquitous mobile computing has enabled the collection of new types of massive traffic data to understand collective movement patterns in social spaces. Contributing to the understanding of crowd formation and dispersal in populated areas, we developed a model of visitors' dynamic agglomeration patterns at a particular event using dynamic population data. This information, a type of big data, comprised aggregate Global Positioning System (GPS) location data automatically collected from mobile phones without users' intervention over a grid with a spatial resolution of 250 m. Herein, spatial autoregressive models with two-step adjacency matrices are proposed to represent visitors' movement between grids around the event site. We confirmed that the proposed models had a higher goodness-of-fit than those without spatial or temporal autocorrelations. The results also show a significant reduction in accuracy when applied to prediction with estimated values of the endogenous variables of prior time periods.