Tessellated spatial Poisson point process models
- Authors: Nicoletta D'Angelo
- Publication year: 2025
- Type: Contributo in atti di convegno pubblicato in volume
- OA Link: http://hdl.handle.net/10447/678404
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.