Abstract
Exploring urban travelling hotspots has become a popular trend in geographic research in recent years. Their identification involved the idea of spatial autocorrelation and spatial clustering based on density in the previous research. However, there are some limitations to them, including the unremarkable results and the determination of various parameters. At the same time, none of them reflect the influences of their neighbors. Therefore, we used the concept of the data field and improved it with the impact of spatial interaction to solve those problems in this study. First of all, an interaction-based spatio-temporal data field identification for urban hotspots has been built. Then, the urban travelling hotspots of Beijing on weekdays and weekends are identified in six different periods. The detected hotspots are passed through qualitative and quantitative evaluations and compared with the other two methods. The results show that our method could discover more accurate hotspots than the other two methods. The spatio-temporal distributions of hotspots fit commuting activities, business activities, and nightlife activities on weekdays, and the hotspots discovered at weekends depict the entertainment activities of residents. Finally, we further discuss the spatial structures of urban hotspots in a particular period (09:00–12:00) as an example. It reflects the strong regularity of human travelling on weekdays, while human activities are more varied on weekends. Overall, this work has a certain theoretical and practical value for urban planning and traffic management.
Highlights
Intra-city human activities have accelerated the urbanization process in recent years
We further compared the overlapping results between the Getis-Ord Gi* hotspot analysis, the trajectory data field, and the proposed method to investigate the effectiveness of the proposed method in a particular area
The hotspots extracted by the trajectory data field are not precise enough to be representative of the popular area
Summary
Intra-city human activities have accelerated the urbanization process in recent years. Rapid urban expansion causes imbalanced urban development, such as traffic congestion, resource shortage, and environmental degradation [1,2,3] The emergence of these problems is closely related to human mobility in the city [2]. A large number of commuting activities aggravate traffic problems and air pollution in a particular period These phenomena lead to various issues and discussions about urban structure and urban sustainable development [1,3]. There is a feasible way to study the characteristics and patterns of these travelling activities It is critical for planners and managers to understand urban structures and sustainable development for the modern city
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