Space-time cluster detection with cross-space-time relative risk functions

Hyeongmo Koo, Monghyeon Lee, Yongwan Chun, Daniel A. Griffith

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


Space-time kernel density estimation (STKDE) commonly is used for space-time cluster detection. But, this technique might be limited because it does not take into account an underlying population at risk for observed events. A space-time relative risk function (STRRF) can help overcome this limitation by allowing a comparison of each kernel density of observations with that of controls. This paper proposes a cross-STRRF to identify spatio-temporal locations that experience statistically significant changes in their density of events. With events organized in a space-time voxel structure, the cross-STRRF evaluates space-time patterns by comparing event occurrences at a spatial location in a previous time period with ones in its future as well as with its spatial neighbors in its contemporaneous time period. The test statistics of the cross-STRRF values in each voxel are obtained with a permutation test in which cases and controls are shuffled within each time period to maintain the space-time envelope of events. An application to assault crime incidents in the city of Plano, Texas between 2008 and 2012 illustrates that the cross-STRRF and its significance test results emphasize spatio-temporal changes in event density rather than constantly focusing on high density regions, which STKDE does.

Original languageEnglish
Pages (from-to)67-78
Number of pages12
JournalCartography and Geographic Information Science
Issue number1
StatePublished - 2 Jan 2020


  • Space-time cluster detection
  • assault crime
  • space-time kernel density estimation
  • space-time relative risk function
  • volume rendering


Dive into the research topics of 'Space-time cluster detection with cross-space-time relative risk functions'. Together they form a unique fingerprint.

Cite this