TY - JOUR
T1 - Mining periodic behaviors of object movements for animal and biological sustainability studies
AU - Li, Zhenhui
AU - Han, Jiawei
AU - Ding, Bolin
AU - Kays, Roland
N1 - Funding Information:
Acknowledgement The work was supported in part by the NSF IIS-1017362, NSF CNS-0931975, NASA NRA-NNH10ZDA001N, U.S. Air Force Office of Scientific Research MURI award FA9550-08-1-0265, Boeing company, and by the U.S. Army Research Laboratory under Cooperative Agreement Number W911NF-09-2-0053 (NS-CTA). The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Laboratory or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation here on.
PY - 2012/3
Y1 - 2012/3
N2 - Periodicity is one of the most frequently occurring phenomena for moving objects. Animals usually have periodic movement behaviors, such as daily foraging behaviors or yearly migration behaviors. Such periodic behaviors are the keys to understand animal movement and they also reflect the seasonal, climate, or environmental changes of the ecosystem. However, periodic behaviors could be complicated, involving multiple interleaving periods, partial time span, and spatiotemporal noises and outliers. In this paper, we address the problem of mining periodic behaviors for moving objects. It involves two sub-problems: how to detect the periods in complex movements, and how to mine periodic behaviors. A period is usually a single value, such as 24 h. And a periodic behavior is a statistical description of the periodic movement for one specific period. For example, we could describe an animal's daily behavior in the way that "From 6 pm to 6 am, it has 90% probability staying at location A and from 7 am to 5 pm, it has 70% probability staying at location B and 30% probability staying at location C". So our tasks is to first detect the periods and then describe each periodic behavior according to different periods. Our main assumption is that the observed movement is generated from multiple interleaved periodic behaviors associated with certain reference locations. Based on this assumption, we propose a two-stage algorithm, Periodica, to solve the problem. At the first stage, the notion of reference spot is proposed to capture the reference locations. Through reference spots, multiple periods in the movement can be retrieved using a method that combines Fourier transform and autocorrelation. At the second stage, a probabilistic model is proposed to characterize the periodic behaviors. For a specific period, periodic behaviors are statistically generalized from partial movement sequences through hierarchical clustering. Finally, we show two extensions to the Periodica algorithm: (1) missing data interpolation, and (2) future movement prediction. Empirical studies on both synthetic and real data sets demonstrate the effectiveness of the proposed method.
AB - Periodicity is one of the most frequently occurring phenomena for moving objects. Animals usually have periodic movement behaviors, such as daily foraging behaviors or yearly migration behaviors. Such periodic behaviors are the keys to understand animal movement and they also reflect the seasonal, climate, or environmental changes of the ecosystem. However, periodic behaviors could be complicated, involving multiple interleaving periods, partial time span, and spatiotemporal noises and outliers. In this paper, we address the problem of mining periodic behaviors for moving objects. It involves two sub-problems: how to detect the periods in complex movements, and how to mine periodic behaviors. A period is usually a single value, such as 24 h. And a periodic behavior is a statistical description of the periodic movement for one specific period. For example, we could describe an animal's daily behavior in the way that "From 6 pm to 6 am, it has 90% probability staying at location A and from 7 am to 5 pm, it has 70% probability staying at location B and 30% probability staying at location C". So our tasks is to first detect the periods and then describe each periodic behavior according to different periods. Our main assumption is that the observed movement is generated from multiple interleaved periodic behaviors associated with certain reference locations. Based on this assumption, we propose a two-stage algorithm, Periodica, to solve the problem. At the first stage, the notion of reference spot is proposed to capture the reference locations. Through reference spots, multiple periods in the movement can be retrieved using a method that combines Fourier transform and autocorrelation. At the second stage, a probabilistic model is proposed to characterize the periodic behaviors. For a specific period, periodic behaviors are statistically generalized from partial movement sequences through hierarchical clustering. Finally, we show two extensions to the Periodica algorithm: (1) missing data interpolation, and (2) future movement prediction. Empirical studies on both synthetic and real data sets demonstrate the effectiveness of the proposed method.
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U2 - 10.1007/s10618-011-0227-9
DO - 10.1007/s10618-011-0227-9
M3 - Article
AN - SCOPUS:84856599429
SN - 1384-5810
VL - 24
SP - 355
EP - 386
JO - Data Mining and Knowledge Discovery
JF - Data Mining and Knowledge Discovery
IS - 2
ER -