Multiscale decomposition of spatial lattice data for hotspot detection

  • René Stander Department of Statistics, University of Pretoria
  • Inger Fabris-Rotelli Department of Statistics, University of Pretoria
  • Ding-Geng Chen Department of Statistics, University of Pretoria
Keywords: COVID-19, Crime, Discrete Pulse Transform, Feature detection, Hotspot detection, Ht-index, Local Getis-Ord, Mutliscale decomposition, Multiscale Ht-index, Spatial lattice data, Spatial scan statistics, Spatial statistics


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.


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