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Effects of missing data and locational errors on spatial concentration measures based on Ripley’s K-function

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Listed:
  • Giuseppe Arbia
  • Giuseppe Espa
  • Diego Giuliani
  • Maria Michela Dickson

Abstract

Effects of missing data and locational errors on spatial concentration measures based on Ripley’s K-function. Spatial Economic Analysis. Measures based on Ripley’s K-function are the preferred tools to test the concentration of individual agents in an economic space. In many empirical cases, however, the datasets contain different inaccuracies due to missing data or uncertainty about the location of the agents. Little is known thus far about the effects of these inaccuracies on the K-function. This paper sheds light on the problem through a theoretical analysis supported by Monte Carlo experiments. The results show that patterns of clustering or inhibition may be observed not as genuine phenomena but only as the effect of data imperfections.

Suggested Citation

  • Giuseppe Arbia & Giuseppe Espa & Diego Giuliani & Maria Michela Dickson, 2017. "Effects of missing data and locational errors on spatial concentration measures based on Ripley’s K-function," Spatial Economic Analysis, Taylor & Francis Journals, vol. 12(2-3), pages 326-346, July.
    • Handle: RePEc:taf:specan:v:12:y:2017:i:2-3:p:326-346
    DOI: 10.1080/17421772.2017.1297479
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