Multiscale decomposition of spatial lattice data for hotspot detection

René Stander, Inger Fabris-Rotelli, Ding Geng Chen

Research output: Contribution to journalArticlepeer-review

Abstract

Hotspot detection in spatial analysis identifies geographic areas with elevated event rates, facilitating more effective policy interventions aimed at reducing such incidents. In the current literature, several methods have been used to detect hotspots such as measures for local spatial association and spatial scan methods. However, the performance of these methods is limited for small-scale hotspots as well as spatial domains where the number of areas is small. In this work, we propose a new approach, making use of the Discrete Pulse Transform (DPT) to decompose spatial lattice data along with the multiscale Ht-index and the spatial scan statistic as a measure of saliency on the extracted pulses to detect significant hotspots. The proposed method outperforms the well-used local Getis-Ord statistic in a simulation study, especially on small-scale hotspots. The method is also illustrated on South African COVID-19 cases and South African crime data.

Original languageEnglish (US)
Pages (from-to)57-79
Number of pages23
JournalSouth African Statistical Journal
Volume58
Issue number1
DOIs
StatePublished - May 1 2024
Externally publishedYes

Keywords

  • COVID-19
  • Crime
  • Discrete Pulse Transform
  • Feature detection
  • Hotspot detection
  • Ht-index
  • Local Getis-Ord
  • Multiscale Ht-index
  • Mutliscale decomposition
  • Spatial lattice data
  • Spatial scan statistics
  • Spatial statistics

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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