Where Am I? Location Archetype Keyword Extraction from Urban Mobility Patterns

被引：7

作者：

Kostakos, Vassilis ^{[1
]}

Juntunen, Tomi ^{[1
]}

Goncalves, Jorge ^{[1
]}

Hosio, Simo ^{[1
]}

Ojala, Timo ^{[1
]}

机构：

[1] Univ Oulu, Dept Comp Sci & Engn, Oulu, Finland

来源：

PLOS ONE | 2013年 / 8卷 / 05期

基金：

芬兰科学院;

关键词：

D O I：

10.1371/journal.pone.0063980

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

Can online behaviour be used as a proxy for studying urban mobility? The increasing availability of digital mobility traces has provided new insights into collective human behaviour. Mobility datasets have been shown to be an accurate proxy for daily behaviour and social patterns, and behavioural data from Twitter has been used to predict real world phenomena such as cinema ticket sale volumes, stock prices, and disease outbreaks. In this paper we correlate city-scale urban traffic patterns with online search trends to uncover keywords describing the pedestrian traffic location. By analysing a 3-year mobility dataset we show that our approach, called Location Archetype Keyword Extraction (LAKE), is capable of uncovering semantically relevant keywords for describing a location. Our findings demonstrate an overarching relationship between online and offline collective behaviour, and allow for advancing analysis of community-level behaviour by using online search keywords as a practical behaviour proxy.

引用

页数：10

共 14 条

[1]

[Anonymous], GEOSPATIAL ANAL MODE, DOI DOI 10.1007/978-90-481-8572-63

[2]

[Anonymous], 2011, Google correlate whitepaper

[3] Twitter mood predicts the stock market [J].