Mining human mobility patterns from social geo-tagged data

被引：49
|
作者：
Comito, Carmela [1 ]
Falcone, Deborah [2 ]
Talia, Domenico [2 ]
机构：
[1] ICAR CNR, Arcavacata Di Rende, CS, Italy
[2] Univ Calabria, DIMES, Arcavacata Di Rende, CS, Italy
来源：
PERVASIVE AND MOBILE COMPUTING | 2016年 / 33卷
关键词：
Trajectory pattern mining; Geo-social data; Human mobility;
D O I：
10.1016/j.pmcj.2016.06.005
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Online social networks allow users to tag their posts with geographical coordinates collected through the GPS interface of smart phones. The time- and geo-coordinates associated with a sequence of posts/tweets manifest the spatial-temporal movements of people in real life. This paper aims to analyze such movements to discover people and community behavior. To this end, we defined and implemented a novel methodology to mine popular travel routes from geo-tagged posts. Our approach infers interesting locations and frequent travel sequences among these locations in a given geo-spatial region, as shown from the detailed analysis of the collected geo-tagged data. (C) 2016 Elsevier B.V. All rights reserved.
引用
收藏
页码：91 / 107
页数：17
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