Heuristics in spatial analysis: A genetic algorithm for coverage maximization

Daoqin Tong, Alan Murray, Ningchuan Xiao

Research output: Contribution to journalArticlepeer-review

57 Scopus citations


Many government agencies and corporations face locational decisions, such as where to locate fire stations, postal facilities, nature reserves, computer centers, bank branches, and so on. To reach such location-related decisions, geographical information systems (GIS) are essential for providing access to spatial data and analysis tools. Moreover, geographic insights can be gained from GIS as they enable capabilities for better reflecting problems of interest in location modeling. The resulting models can be complex, however, and hence computationally challenging to solve. This article examines an important model for regional service coverage maximization. This model is solved heuristically using a genetic algorithm. The new heuristic innovatively incorporates problemspecific knowledge by exploring the geographical structure of the problem under study. Comparative application results demonstrate important nuances of the new genetic algorithm, enhancing overall performance.

Original languageEnglish (US)
Pages (from-to)698-711
Number of pages14
JournalAnnals of the Association of American Geographers
Issue number4
StatePublished - Oct 2009
Externally publishedYes


  • Facility location
  • Genetic algorithm
  • Heuristics
  • Maximal coverage
  • Spatial analysis

ASJC Scopus subject areas

  • Geography, Planning and Development
  • Earth-Surface Processes


Dive into the research topics of 'Heuristics in spatial analysis: A genetic algorithm for coverage maximization'. Together they form a unique fingerprint.

Cite this