지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- Korean Institute of Information Scientists and Engineers Journal of Computing Science and Engineering Journal of Computing Science and Engineering Vol.15 No.1
- 발행연도
- 2021.3
- 수록면
- 47 - 57 (11page)
- DOI
- 10.5626/JCSE.2021.15.1.47
이용수
초록· 키워드
오류제보하기
To understand the mobility of humans or things, it is necessary to measure the degrees of location visits in everyday mobility. In this paper, we discuss measures that can present human preferences to certain locations based on location data and analysis. From raw positioning data and the concept of location clusters, which are sets of positioning data representing location areas, several measures can be deduced. First, the location point and location area can be separated because visiting a pin point location is different from visiting a certain area. Second, the number of visits to a location and the duration of a visit to a location have different meanings. Third, the rank of the location visited is sometimes more meaningful than the absolute counts. In consideration of these aspects, we established six basic measures and two derived measures. The actual calculation of each measure requires raw positioning data to be processed. The raw positioning data were collected by volunteers over several years of their everyday lives. All measures for multiple volunteers were generated and analyzed for verification. The processing of raw positioning data to generate measures requires a vast number of calculations, like big data processing. As a solution, we implemented a generation process using the programming language R; GPGPU technology was utilized to derive numerical results within areas on able time limit with considerable speed-ups, because an undesirably large amount of time was required to process measures with CPU-only machines.
목차
Abstract
I. INTRODUCTION
II. DEFINITIONS OF MEASUREMENTS FOR LOCATION VISITS
III. APPLYING MEASUREMENTS TO REAL POSITIONING DATA SET
IV. EXECUTION TIME FOR MEASUREMENT ANALYSIS
V. CONCLUSION
REFERENCES
참고문헌 (24)
참고문헌 신청
H. Y. Song, “Probabilistic space-time analysis of human mobility patterns,” WSEAS Transactions on Computers, vol. 15, pp. 222-238, 2016.
M. C. Gonzalez, C. A. Hidalgo, and A. L. Barabasi, “Understanding individual human mobility patterns,” Nature, vol. 453, no. 7196, pp. 779-782, 2008.
H. Y. Song and J. S. Lee, “Finding a simple probability distribution for human mobile speed,” Pervasive and Mobile Computing, vol. 25, pp. 26-47, 2016.
H. Y. Song and J. S. Lee, “Detecting positioning errors and estimating correct positions by moving window,” Plos One, vol. 10, no. 12, article no. e0143618, 2015. https://doi.org/10.1371/journal.pone.0143618
S. Y. Kim, H. J. Koo, and H. Y. Song, “A study on influence of human personality to location selection,” Journal of Ambient Intelligence and Humanized Computing, vol. 7, no. 2, pp. 267-285, 2016.
M. C. Gonzalez, C. A. Hidalgo, and A. L. Barabasi, “Understanding individual human mobility patterns,” Nature, vol. 453, no. 7196, pp. 779-782, 2008.
H. Y. Song and J. S. Lee, “Finding a simple probability distribution for human mobile speed,” Pervasive and Mobile Computing, vol. 25, pp. 26-47, 2016.
H. Y. Song and J. S. Lee, “Detecting positioning errors and estimating correct positions by moving window,” Plos One, vol. 10, no. 12, article no. e0143618, 2015. https://doi.org/10.1371/journal.pone.0143618
S. Y. Kim, H. J. Koo, and H. Y. Song, “A study on influence of human personality to location selection,” Journal of Ambient Intelligence and Humanized Computing, vol. 7, no. 2, pp. 267-285, 2016.
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