Visual Analysis Method of Urban Regional Relationship Based on Trajectory Distribution Representation

Abstract

The invention belongs to the technical field of data analysis and modeling, and discloses a visual analysis method for urban region relations based on trajectory distribution representation; a method for dividing urban regions based on trajectory data, which divides urban geographical space into appropriate regions, and converts the trajectory into regions. Aggregation flow among them; A regional vectorization model based on word embedding technology is proposed, considering the interaction between urban locations and moving objects, vectorized coding of urban regions, and exploring the differences of different regions in the trajectory according to the similarity between regions Location information and connection relationship; design and develop a visual analysis system for analyzing area vectors and exploring crowd movement patterns, based on the area vector space, and through the linkage mechanism of visual views, it supports analysts from global overview to local crowd movement patterns Analysis, and finally to the reasoning process of exploration of contextual details. Guarantee the applicability and effectiveness when assisting urban planners to make corresponding traffic decisions.

Description

technical field [0001] The invention belongs to the technical field of data analysis and modeling, and in particular relates to a visual analysis method for urban region relations based on trajectory distribution representation. Background technique [0002] With the continuous development of location tracking technology, a large amount of spatiotemporal trajectory data in the city can be collected, and these spatiotemporal trajectories represent the movement of various moving objects, such as people and vehicles. A large amount of spatio-temporal trajectory data provides us with a lot of unprecedented information, and has been widely used in location-based social networks, intelligent transportation systems, and urban computing. technology development. Therefore, the data mining task for trajectory data has become an important research hotspot in the construction of smart cities. In terms of urban population mobility pattern exploration, it is particularly important for a...

AI Technical Summary

Problems solved by technology

If only aggregated values ​​from trajectories such as position flows or in-and-out flows between pairs of positions are used to represent and analyze trajectories, the time attributes in the space-time attributes of trajectories will be largely ignored, and the interaction between position and flow behavior is not considered. Effectively consider and analyze

[0004] At this stage, there is a lack of analysis methods that are driven by the purpose of people visiting a certain place and explore human movement patterns through the aggregate information of continuous positions in the trajectory. This method can refine the analysis tasks while efficiently aggregating trajectories, and supports dynamic analysis of crowds Movement, which can eventually enable decision makers to observe the changing laws of crowd movement patterns from the perspective of time and space

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