Discovery of Regular and Irregular Spatio-Temporal Patterns from Location-Based SNS by Diffusion-Type Estimation

Yoshitatsu MATSUDA; Kazunori YAMAGUCHI; Ken-ichiro NISHIOKA

doi:10.1587/transinf.2015EDP7095

Discovery of Regular and Irregular Spatio-Temporal Patterns from Location-Based SNS by Diffusion-Type Estimation

Yoshitatsu MATSUDA, Kazunori YAMAGUCHI, Ken-ichiro NISHIOKA

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In this paper, a new approach is proposed for extracting the spatio-temporal patterns from a location-based social networking system (SNS) such as Foursquare. The proposed approach consists of the following procedures. First, the spatio-temporal behaviors of users in SNS are approximated as a probabilistic distribution by using a diffusion-type formula. Since the SNS datasets generally consist of sparse check-in's of users at some time points and locations, it is difficult to investigate the spatio-temporal patterns on a wide range of time and space scales. The proposed method can estimate such wide range patterns by smoothing the sparse datasets by a diffusion-type formula. It is crucial in this method to estimate robustly the scale parameter by giving a prior generative model on check-in's of users. The robust estimation enables the method to extract appropriate patterns even in small local areas. Next, the covariance matrix among the time points is calculated from the estimated distribution. Then, the principal eigenfunctions are approximately extracted as the spatio-temporal patterns by principal component analysis (PCA). The distribution is a mixture of various patterns, some of which are regular ones with a periodic cycle and some of which are irregular ones corresponding to transient events. Though it is generally difficult to separate such complicated mixtures, the experiments on an actual Foursquare dataset showed that the proposed method can extract many plausible and interesting spatio-temporal patterns.

Publication: IEICE TRANSACTIONS on Information Vol.E98-D No.9 pp.1675-1682

Publication Date: 2015/09/01

Publicized: 2015/06/10

Online ISSN: 1745-1361

DOI: 10.1587/transinf.2015EDP7095

Type of Manuscript: PAPER

Category: Artificial Intelligence, Data Mining

Authors

Yoshitatsu MATSUDA
  The University of Tokyo
Kazunori YAMAGUCHI
  The University of Tokyo
Ken-ichiro NISHIOKA
  The University of Tokyo

Keyword

data mining, SNS, spatio-temporal pattern, diffusion-type formula, Bayesian estimation, principal component analysis

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Yoshitatsu MATSUDA, Kazunori YAMAGUCHI, Ken-ichiro NISHIOKA, "Discovery of Regular and Irregular Spatio-Temporal Patterns from Location-Based SNS by Diffusion-Type Estimation" in IEICE TRANSACTIONS on Information, vol. E98-D, no. 9, pp. 1675-1682, September 2015, doi: 10.1587/transinf.2015EDP7095.
Abstract: In this paper, a new approach is proposed for extracting the spatio-temporal patterns from a location-based social networking system (SNS) such as Foursquare. The proposed approach consists of the following procedures. First, the spatio-temporal behaviors of users in SNS are approximated as a probabilistic distribution by using a diffusion-type formula. Since the SNS datasets generally consist of sparse check-in's of users at some time points and locations, it is difficult to investigate the spatio-temporal patterns on a wide range of time and space scales. The proposed method can estimate such wide range patterns by smoothing the sparse datasets by a diffusion-type formula. It is crucial in this method to estimate robustly the scale parameter by giving a prior generative model on check-in's of users. The robust estimation enables the method to extract appropriate patterns even in small local areas. Next, the covariance matrix among the time points is calculated from the estimated distribution. Then, the principal eigenfunctions are approximately extracted as the spatio-temporal patterns by principal component analysis (PCA). The distribution is a mixture of various patterns, some of which are regular ones with a periodic cycle and some of which are irregular ones corresponding to transient events. Though it is generally difficult to separate such complicated mixtures, the experiments on an actual Foursquare dataset showed that the proposed method can extract many plausible and interesting spatio-temporal patterns.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2015EDP7095/_p

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@ARTICLE{e98-d_9_1675,
author={Yoshitatsu MATSUDA, Kazunori YAMAGUCHI, Ken-ichiro NISHIOKA, },
journal={IEICE TRANSACTIONS on Information},
title={Discovery of Regular and Irregular Spatio-Temporal Patterns from Location-Based SNS by Diffusion-Type Estimation},
year={2015},
volume={E98-D},
number={9},
pages={1675-1682},
abstract={In this paper, a new approach is proposed for extracting the spatio-temporal patterns from a location-based social networking system (SNS) such as Foursquare. The proposed approach consists of the following procedures. First, the spatio-temporal behaviors of users in SNS are approximated as a probabilistic distribution by using a diffusion-type formula. Since the SNS datasets generally consist of sparse check-in's of users at some time points and locations, it is difficult to investigate the spatio-temporal patterns on a wide range of time and space scales. The proposed method can estimate such wide range patterns by smoothing the sparse datasets by a diffusion-type formula. It is crucial in this method to estimate robustly the scale parameter by giving a prior generative model on check-in's of users. The robust estimation enables the method to extract appropriate patterns even in small local areas. Next, the covariance matrix among the time points is calculated from the estimated distribution. Then, the principal eigenfunctions are approximately extracted as the spatio-temporal patterns by principal component analysis (PCA). The distribution is a mixture of various patterns, some of which are regular ones with a periodic cycle and some of which are irregular ones corresponding to transient events. Though it is generally difficult to separate such complicated mixtures, the experiments on an actual Foursquare dataset showed that the proposed method can extract many plausible and interesting spatio-temporal patterns.},
keywords={},
doi={10.1587/transinf.2015EDP7095},
ISSN={1745-1361},
month={September},}

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TY - JOUR
TI - Discovery of Regular and Irregular Spatio-Temporal Patterns from Location-Based SNS by Diffusion-Type Estimation
T2 - IEICE TRANSACTIONS on Information
SP - 1675
EP - 1682
AU - Yoshitatsu MATSUDA
AU - Kazunori YAMAGUCHI
AU - Ken-ichiro NISHIOKA
PY - 2015
DO - 10.1587/transinf.2015EDP7095
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E98-D
IS - 9
JA - IEICE TRANSACTIONS on Information
Y1 - September 2015
AB - In this paper, a new approach is proposed for extracting the spatio-temporal patterns from a location-based social networking system (SNS) such as Foursquare. The proposed approach consists of the following procedures. First, the spatio-temporal behaviors of users in SNS are approximated as a probabilistic distribution by using a diffusion-type formula. Since the SNS datasets generally consist of sparse check-in's of users at some time points and locations, it is difficult to investigate the spatio-temporal patterns on a wide range of time and space scales. The proposed method can estimate such wide range patterns by smoothing the sparse datasets by a diffusion-type formula. It is crucial in this method to estimate robustly the scale parameter by giving a prior generative model on check-in's of users. The robust estimation enables the method to extract appropriate patterns even in small local areas. Next, the covariance matrix among the time points is calculated from the estimated distribution. Then, the principal eigenfunctions are approximately extracted as the spatio-temporal patterns by principal component analysis (PCA). The distribution is a mixture of various patterns, some of which are regular ones with a periodic cycle and some of which are irregular ones corresponding to transient events. Though it is generally difficult to separate such complicated mixtures, the experiments on an actual Foursquare dataset showed that the proposed method can extract many plausible and interesting spatio-temporal patterns.
ER -