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DOI:	10.1371/journal.pone.0149105
论文题名:	Inferring Stop-Locations from WiFi
作者:	David Kofoed Wind; Piotr Sapiezynski; Magdalena Anna Furman; Sune Lehmann
刊名:	PLOS ONE
ISSN:	1932-6203
出版年:	2016
发表日期:	2016-2-22
卷:	11, 期:2
语种:	英语
英文关键词:	Human mobility ; Data visualization ; Cell phones ; Behavior ; Algorithms ; Clustering algorithms ; Social epidemiology ; Social systems
英文摘要:	Human mobility patterns are inherently complex. In terms of understanding these patterns, the process of converting raw data into series of stop-locations and transitions is an important first step which greatly reduces the volume of data, thus simplifying the subsequent analyses. Previous research into the mobility of individuals has focused on inferring ‘stop locations’ (places of stationarity) from GPS or CDR data, or on detection of state (static/active). In this paper we bridge the gap between the two approaches: we introduce methods for detecting both mobility state and stop-locations. In addition, our methods are based exclusively on WiFi data. We study two months of WiFi data collected every two minutes by a smartphone, and infer stop-locations in the form of labelled time-intervals. For this purpose, we investigate two algorithms, both of which scale to large datasets: a greedy approach to select the most important routers and one which uses a density-based clustering algorithm to detect router fingerprints. We validate our results using participants’ GPS data as well as ground truth data collected during a two month period.
URL:	http://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0149105&type=printable
Citation statistics:
资源类型:	期刊论文
标识符:	http://119.78.100.158/handle/2HF3EXSE/23255
Appears in Collections:	过去全球变化的重建影响、适应和脆弱性科学计划与规划气候变化与战略全球变化的国际研究计划气候减缓与适应气候变化事实与影响

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File Name/ File Size	Content Type	Version	Access	License
journal.pone.0149105.PDF（3725KB）	期刊论文	作者接受稿	开放获取	CC BY-NC-SA	View Download

作者单位:

DTU Compute, Technical University of Denmark, Copenhagen, Denmark;DTU Compute, Technical University of Denmark, Copenhagen, Denmark;DTU Compute, Technical University of Denmark, Copenhagen, Denmark;DTU Compute, Technical University of Denmark, Copenhagen, Denmark

Recommended Citation:

David Kofoed Wind,Piotr Sapiezynski,Magdalena Anna Furman,et al. Inferring Stop-Locations from WiFi[J]. PLOS ONE,2016-01-01,11(2)

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