Modeling the effect of scale on clustering of spatial points

Qiliang Liu, Zhilin Li, Min Deng, Jianbo Tang, Xiaoming Mei

Research output: Journal article publicationJournal articleAcademic researchpeer-review

14 Citations (Scopus)


It has been established that spatial clustering patterns are scale-dependent. However, scale is still not explicitly handled in existing methods to detect clusters in spatial points; thus, users are often puzzled by the varied clustering results obtained by different spatial clustering methods and/or parameters. To handle the effect of scale on the cluster detection of spatial points, two kinds of scales are first specified in this study: scale of data and scale of analysis. These two kinds of scales are embodied by a set of three indictors: data resolution, spatial extent, and analysis resolution. Further, a scale-driven clustering model with these three scale indicators as parameters is statistically constructed based on the Natural Principle and graph theory. A comparative study of this scale-driven clustering model with existing methods is carried out with a simulated spatial dataset. It is found that only this new method is able to discover the multi-scale spatial clustering patterns defined in the benchmarks. Further, Carex lasiocarpa population data is used to illustrate the practical value of the proposed scale-driven clustering model.
Original languageEnglish
Pages (from-to)81-92
Number of pages12
JournalComputers, Environment and Urban Systems
Publication statusPublished - 1 Jul 2015


  • Clustering
  • Spatial points
  • Spatial scale
  • The natural principle

ASJC Scopus subject areas

  • Geography, Planning and Development
  • Ecological Modelling
  • Environmental Science(all)
  • Urban Studies

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