Inferring region significance by using multi-source spatial data

被引:0
作者
Shunzhi Zhu
Dahan Wang
Lijuan Liu
Yan Wang
Danhuai Guo
机构
[1] Xiamen University Technology,School of Computer and Information Engineering
[2] Chinese Academy of Sciences,CNIC
来源
Neural Computing and Applications | 2020年 / 32卷
关键词
Region; Trajectory; Density; Recommendation; Ranking; Spatial data mining;
D O I
暂无
中图分类号
学科分类号
摘要
The ranking and recommendation of regions of interest are increasingly important in recent years. In this light, we propose and study a novel and interesting problem of inferring region significance using multi-source spatiotemporal data. In our study, POIs, locations, regions, trajectories, and spatial networks are taken into account. Given a set of regions R and a set of trajectories T, we seek for the top-k most attractive regions to users, i.e., regions with the top-k highest spatial-density correlations to the trajectories of travelers. This study is useful in many mobile applications such as urban computing, region recommendation, and location-based service in general. This problem is challenging due to two reasons: (1) how to model the spatial-density correlation effectively and practically and (2) how to process the problem in interactive time. To overcome the challenges, we design a novel spatial-density correlation function to evaluate the relationship between regions and trajectories, and the density of POIs and network distance are taken into account. Then, we develop a series of optimization techniques to accelerate the query efficiency. Furthermore, we develop a parallel mechanism to support big spatial data. Finally, we conduct extensive experiments on real and synthetic spatial data sets to show the efficiency and effectiveness of developed algorithms.
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页码:6523 / 6531
页数:8
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