A Graph-Based Visual Query Method for Massive Human Trajectory Data

Zhongshuai Wang,Yuan Yuan,Liang Chang,Xiyan Sun,Xiaonan Luo

doi:10.1109/access.2019.2948304

Abstract

With the advance of location acquisition technologies nowadays, the human trajectory data, such as vehicles, phones and bicycles, is growing vastly and extending our imagination of new users. These trajectories have shown great values in supporting situation-aware exploration and prediction of human mobility, discovering movement patterns and monitoring the traffic situations. Query is an essential task for trajectory exploration. To do this, users should input spatial, temporal and other types of query conditionals to obtain a group of filtered trajectories. However, current visual query methods for urban data suffer from two main problems. First, it is a challenge for users without background knowledge to specify query conditions, such as selecting a popular entertainment area on the map as spatial condition. Second, there still needs a way to fuse the multi-source, heterogeneous, and large-scale urban data to partition the urban area, extract their semantic information, and provide intuitive, efficient trajectory queries. In this paper, we propose CBRG(cellular-based region growing) algorithm to make a preliminary region partition which tends to be a certain collection of similar regions. Then, we generate a semantic node for graph and provide a set of feasible clues and signs to explore and mine data from the aspects of spatio-temporal characteristics, inherent coupling of data, and human mobility. It is worth mentioning that our methodology is also universal for other heterogeneous data with spatio-temporal characteristics.

Highlights

The city, evolved from human activities, is a highly developed product of crowd social activities, cultural activities, economic activities, and other factors
We explore multi-source data which includes points of interest (POI) data, base station sites data, and human trajectory data to construct a graph-based visual query system
We propose a celluar-based growing region (CBGR) algorithm

Summary

INTRODUCTION

The city, evolved from human activities, is a highly developed product of crowd social activities, cultural activities, economic activities, and other factors. For large-scale data visualization, massive trajectory query, or other analysis tasks, the modeling methods covering fluid mechanics [25], [26], graph theory [27], [28] and machine learning [29], [30] and so on, are exploited to excavate mobility pattern and city functional zones. Different from previous work, this paper focuses on the high-dimensional semantic structure identification based on voronoi cell constructed by POI and station sites data. The urban node map constructed based on POI can effectively visualize the flow between regions and analyze the regularity of trajectory distribution, so as to speculate the movement mode of large crowds.

ANALYTIC AND QUERY TASK

VISUAL DESIGN