Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography (한국측량학회지)

Korean Society of Surveying, Geodesy, Photogrammetry and Cartography (한국측량학회)
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Classifying Alley Markets through Cluster Analysis Using Dynamic Time Warping and Analyzing Possibility of Opening New Stores

  • Received : 2017.08.01
  • Accepted : 2017.10.26
  • Published : 2017.10.31
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Abstract

This study attempts to classify 1008 alley markets in Seoul through cluster analysis using Dynamic Time Warping, one of the methods used to analyze the similarity of time series, and evaluate the possibility of opening new stores. The sequence of the gross sales of an alley market and that of gross sales per store stand for the potential of growth and profitability of the market, respectively and are used as variables for cluster analysis. Five clusters are obtained for the gross sales and four clusters for the gross sales per store. These two types of clusters are again classified as rising and falling trends, respectively, and the combination of these trends produces four categories. These categories are used to evaluate the possibility of opening new stores in alley markets. The results show that the southeast which is relatively wealthy inferior to other regions in opening new stores. Alley markets in the northeast and the southwest are better than other regions such that opening a new store is justified. In the northwest, there are many markets with trend of gross sales and that of gross sales per store moving in opposite directions, and new store openings in these markets should be postponed.

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References

  1. Akl, A. and Valaee, S. (2010), Accelerometer-based gesture recognition via dynamic time warping, affinity propagation, & compressive sensing, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing, Dallas, TX, 2010, pp. 2270-2273.
  2. Kwon, P. and Yu, K. (2015), Trade area delimitation using AMOEBA technique, Journal of the Korean Society for Geospatial Information Science, Vol. 23, No. 2, pp. 11-16.
  3. Lee, B. (2003), Defining of trade area using spatial data mining technique in business GIS, The Journal of GIS Association of Korea, Vol. 11, No. 2, pp. 171-184.
  4. Lee, S. and Lee, Y. (2014), A study on characteristics of location decision factors of mart by using GIS - comparative analysis of large and small-medium cities, Journal of the Korean Cadaster Information Association, Vol. 30, No. 3, pp. 47-57.
  5. Lee, S. and Oh, K. (2011), Finding the optimal frequency for trade and development of system trading strategies in futures market using dynamic time warping, Journal of the Korean Data & Information Science Society, Vol. 22, No. 2, pp. 255-267.
  6. Loh, J. and Sung, J. (2015), A study on the business cycle and causal relationship in housing market, Korea Real Estate Academy Review, Vol. 61, pp. 208-221.
  7. Park, M., Park, K., and Ahn, J. (2011), A study on the regional classification method using dynamic time warping, The Geographical Journal of Korea, Vol. 45, No. 3, pp. 387-395.
  8. Sarda-Espinosa, A. (2017), Comparing time-series clustering algorithms in R using the dtwclust package, https://cran.r-project.org/web/packages/dtwclust/vignettes/dtwclust.pdf (last date accessed: 31 May 2017).
  9. Shin, W., Jung, C., and Lee, S. (2002), A study on the changing patterns of the commercial power based on retail trade area analysis, Journal of the Korea Real Estate Analysts Association, Vol. 8, No. 1, pp. 1-11.
  10. Song, M. and Chang, H. (2010), Characterization of cities in Seoul metropolitan area by cluster analysis, Journal of the Korean Society for Geo-Spatial Information System, Vol. 18, No. 1, pp. 83-88.
  11. Tae, K. and Rhim, B. (2010), A study for locating of a new store considering competition for trading area: focusing on the case of hypermarket in Seoul metropolitan area, Journal of the Economic Geographical Society of Korea, Vol. 45, No. 5, pp. 609-627.
  12. Ward, J. (1963), Hierarchical grouping to optimize an objective function, Journal of the American Statistical Association, Vol. 58, No. 301, pp. 236-244. https://doi.org/10.1080/01621459.1963.10500845