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

Abstract

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.

Space-Time Pattern of Coupling Coordination between Environmental Regulation and Green Water Resource Efficiency in China

“…There is an interactive phenomenon between environmental regulation and GWRE, and a certain agglomeration pattern has gradually formed in space [49]. In fact, the coupling and coordination between environmental regulation and GWRE not only reflects the differences in space and driving mechanisms, but also shows a certain degree of spatial regularity.…”

Spatial and deep learning analyses of urban recovery from the impacts of COVID-19

“…Dong et al 22 conducted a recovery analysis of Beijing at a spatial scale of 1.0 km × 1.0 km. Related to Nagoya, socioeconomic factors and land use patterns at the zip-code level are revealed to influence the frequency of visiting green infrastructures during the COVID-19 34 ; using mobile spatial statistics (with a minimal mesh of 1.0 km × 1.0 km) to evaluate the characteristics of population at different stages of COVID-19, it is found that population recovery is more robust at Nagoya Station and Sakae on weekdays and holidays 35 (note that in Nagoya, its central area was the most visited place before the pandemic 36 , 37 , similar to other cities in Japan; but no study had not been done on the influences of pre-pandemic social-economic activity patterns on urban recovery). Similar cross-space associations may exist in the urban recovery that needs to be realized over wider areas; however, little has been done from such a perspective.…”