Regional Coverage Maximization

Alternative Geographical Space Abstraction and Modeling

Daoqin Tong, Ran Wei

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

Analysis results are often found to vary with the way we abstract geographical space. When continuous geographic phenomena are abstracted, processed, and stored in a digital environment, some level of discretization is often employed. Information loss in a discretization process brings about uncertainty/error, and as a result research findings may be highly dependent on the particular discretization method used. This article examines one spatial problem concerning how to achieve the maximal regional coverage given a limited number of service facilities. Two widely used geographical space abstraction approaches are examined, the point-based representation and the area-based representation, and issues associated with each representation scheme are analyzed. To accommodate the limitations of the existing representation schemes, a mixed representation strategy is proposed along with a new maximal covering model. Experiments are conducted to site warning sirens in Dublin, Ohio. Results demonstrate the effectiveness of the mixed representation scheme in finding high-quality solutions when the regional coverage level is medium or high.

Original languageEnglish (US)
Pages (from-to)125-142
Number of pages18
JournalGeographical Analysis
Volume49
Issue number2
DOIs
StatePublished - Apr 1 2017

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abstraction
coverage
modeling
experiment
research results
service provider
uncertainty
loss
analysis
method
services

ASJC Scopus subject areas

  • Geography, Planning and Development
  • Earth-Surface Processes

Cite this

Regional Coverage Maximization : Alternative Geographical Space Abstraction and Modeling. / Tong, Daoqin; Wei, Ran.

In: Geographical Analysis, Vol. 49, No. 2, 01.04.2017, p. 125-142.

Research output: Contribution to journalArticle

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