Generating seating plan images using clustering and concave hull algorithms

Information

• Publication Type: Bachelor Thesis
• Workgroup(s)/Project(s):
  • Visualization Group
• Date: October 2018
• Date (Start): 23. May 2018
• Date (End): 21. October 2018
• Matrikelnummer: 01327587
• First Supervisor: Hsiang-Yun Wu

Abstract

This study presents a process of generating seating plan images for the Ticket Gretchen app. The app offers the ability to buy tickets for theaters and similar venues by using an interactive seating plan. A seating plan image is a venue’s abstract visualization defined by the seating layout of a performance. It should give an impression of the spatial structure to see which seats are in reach of each other. The proposed automated solution of generating these images replaces the previously used process of creating the seating plan images manually. The image is made up of polygons representing seat groups that show the user which seats are near each other and which are separated from each other. The grouping of seats is done with the DBSCAN clustering algorithm using the seats’ 2D position, sector and box information. For the computation of the polygons two concave hull algorithms are compared.

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BibTeX

@bachelorsthesis{Deutsch-2018,
  title =      "Generating seating plan images using clustering and concave
               hull algorithms",
  author =     "Maximilian Deutsch",
  year =       "2018",
  abstract =   "This study presents a process of generating seating plan
               images for the Ticket Gretchen app. The app offers the
               ability to buy tickets for theaters and similar venues by
               using an interactive seating plan. A seating plan image is a
               venue’s abstract visualization defined by the seating
               layout of a performance. It should give an impression of the
               spatial structure to see which seats are in reach of each
               other. The proposed automated solution of generating these
               images replaces the previously used process of creating the
               seating plan images manually. The image is made up of
               polygons representing seat groups that show the user which
               seats are near each other and which are separated from each
               other. The grouping of seats is done with the DBSCAN
               clustering algorithm using the seats’ 2D position, sector
               and box information. For the computation of the polygons two
               concave hull algorithms are compared.",
  month =      oct,
  address =    "Favoritenstrasse 9-11/E193-02, A-1040 Vienna, Austria",
  school =     "Institute of Computer Graphics and Algorithms, Vienna
               University of Technology ",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2018/Deutsch-2018/",
}