1 |
Kim, K. and Lee, G. (2016). A study on improvement of estimating de facto population using mobile telecommunications big data, Journal of The Korean Urban Geographical Society, 19, 181-196.
DOI
|
2 |
Kim, S. and Seong, B. (2011). Intervention analysis of Korea tourism data, The Korean Journal of Applied Statistics, 24, 735-743.
DOI
|
3 |
Paparrizos, J. and Gravano, L. (2016). k-shape: Efficient and accurate clustering of time series, ACM SIGMOD Record, 45, 69-76.
DOI
|
4 |
Sarda-Espinosa, A. (2019). Time-Series clustering in R using the dtwclust package, The R Journal, 11, 1-22.
DOI
|
5 |
Scherl, M. (2010). Benchmarking of cluster indices (Diploma Thesis), Department of Statistics, Ludwig-Maximilians-University Munich, Germany.
|
6 |
Son, C. (2020). The present and future of Seoul's management of new infectious diseases through responses to COVID-19, Policy Report, 104, 1430-1439.
|
7 |
Statistics Korea (2019). Korean Social Indicators in 2018, http://kostat.go.kr/portal/korea/index.action
|
8 |
Wiradinata, S. A., Yendra, R., Suhartono, and Gamal, M. D. H. (2017). Multi-Input intervention analysis for evaluating of the domestic airline passengers in an international airport, Science Journal of Applied Mathematics and Statistics, 5, 110-126.
DOI
|
9 |
Won, Y. (2018). Seoul living population data estimation, Local Informatization, 113, 19-23.
|
10 |
Seo, J., Jung, W., and Shim, K. (2019). Improving the upper bound of the dynamic time warping for sparse and long time sequences, Journal of KIISE, 299, 1-36.
|
11 |
Jung, J-H. and Nam, J. (2019). Types and characteristics analysis of human dynamics in Seoul using location-based big data, Journal of Korea Planning Association, 54, 75-90.
DOI
|