A novel framework for the local modelling of spatial point processes is proposed by extending segmented regression models to spatial contexts. The approach consists of a two-step procedure: first, a spatial segmentation algorithm identifies a spatial tessellation using geographically weighted regression estimates; then, a log-linear Poisson model is fitted within the identified non-overlapping regions. This methodology can serve for spatial breakpoint detection or as a local spatial modeling tool. The method is illustrated by a case study on seismicity.
Nicoletta D'Angelo (2025). Tessellated spatial Poisson point process models. In Book of short papers - SIS 2025 - Statistics for innovation [10.1007/978-3-031-95995-0_14].
Tessellated spatial Poisson point process models
Nicoletta D'Angelo
2025-01-01
Abstract
A novel framework for the local modelling of spatial point processes is proposed by extending segmented regression models to spatial contexts. The approach consists of a two-step procedure: first, a spatial segmentation algorithm identifies a spatial tessellation using geographically weighted regression estimates; then, a log-linear Poisson model is fitted within the identified non-overlapping regions. This methodology can serve for spatial breakpoint detection or as a local spatial modeling tool. The method is illustrated by a case study on seismicity.
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