@phdthesis{Spoerhase2009,
  author    = {Spoerhase, Joachim},
  title     = {Competitive and Voting Location},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-52978},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2009},
  abstract  = {We consider competitive location problems where two competing providers place their facilities sequentially and users can decide between the competitors. We assume that both competitors act non-cooperatively and aim at maximizing their own benefits. We investigate the complexity and approximability of such problems on graphs, in particular on simple graph classes such as trees and paths. We also develop fast algorithms for single competitive location problems where each provider places a single facilty. Voting location, in contrast, aims at identifying locations that meet social criteria. The provider wants to satisfy the users (customers) of the facility to be opened. In general, there is no location that is favored by all users. Therefore, a satisfactory compromise has to be found. To this end, criteria arising from voting theory are considered. The solution of the location problem is understood as the winner of a virtual election among the users of the facilities, in which the potential locations play the role of the candidates and the users represent the voters. Competitive and voting location problems turn out to be closely related.},
  subject      = {Standortproblem},
  language  = {en}
}
@phdthesis{Budig2018,
  author    = {Budig, Benedikt},
  title     = {Extracting Spatial Information from Historical Maps: Algorithms and Interaction},
  edition   = {1. Auflage},
  publisher = {W{\"u}rzburg University Press},
  address   = {W{\"u}rzburg},
  isbn      = {978-3-95826-092-4},
  doi       = {10.25972/WUP-978-3-95826-093-1},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-160955},
  school      = {W{\"u}rzburg University Press},
  pages     = {viii, 160},
  year      = {2018},
  abstract  = {Historical maps are fascinating documents and a valuable source of information for scientists of various disciplines. Many of these maps are available as scanned bitmap images, but in order to make them searchable in useful ways, a structured representation of the contained information is desirable. This book deals with the extraction of spatial information from historical maps. This cannot be expected to be solved fully automatically (since it involves difficult semantics), but is also too tedious to be done manually at scale. The methodology used in this book combines the strengths of both computers and humans: it describes efficient algorithms to largely automate information extraction tasks and pairs these algorithms with smart user interactions to handle what is not understood by the algorithm. The effectiveness of this approach is shown for various kinds of spatial documents from the 16th to the early 20th century.},
  subject      = {Karte},
  language  = {en}
}