In this work, we extend the Local Indicators of Spatio-Temporal Association (LISTA) functions (Siino et al. 2018) to the non-Euclidean space of linear networks. We introduce the local version of some inhomogeneous second-order statistics for spatio-temporal point processes on linear networks (Morandi and Mateu, 2019), namely the K-function and the pair correlation function. Following the work of Adelfio et al. (2019) for the Euclidean case, we employ the proposed LISTA functions to assess the goodness-of-fit of different spatio-temporal models fitted to point patterns occurring on linear networks. Indeed, the peculiar lack of homogeneity in a network discourages the usage of traditional spatial and spatio-temporal methods based on stationary processes. Therefore, the weighted second-order statistics are appropriate diagnostic tools since they directly apply to data without assuming homogeneity. We provide simulation studies, by generating both inhomogeneous and self-exiting spatio-temporal point processes on networks, and by carrying out diagnostics on different fitted intensities. By comparing the values of the LISTA functions and their theoretical values, we show that the LISTA can correctly identify the true intensity when this is constrained on a network.

nicoletta d'angelo; giada adelfio; jorge mateu (17 - 19 february 2021).Local indicators of spatio-temporal association on linear networks.

Local indicators of spatio-temporal association on linear networks

nicoletta d'angelo
;
giada adelfio;

Abstract

In this work, we extend the Local Indicators of Spatio-Temporal Association (LISTA) functions (Siino et al. 2018) to the non-Euclidean space of linear networks. We introduce the local version of some inhomogeneous second-order statistics for spatio-temporal point processes on linear networks (Morandi and Mateu, 2019), namely the K-function and the pair correlation function. Following the work of Adelfio et al. (2019) for the Euclidean case, we employ the proposed LISTA functions to assess the goodness-of-fit of different spatio-temporal models fitted to point patterns occurring on linear networks. Indeed, the peculiar lack of homogeneity in a network discourages the usage of traditional spatial and spatio-temporal methods based on stationary processes. Therefore, the weighted second-order statistics are appropriate diagnostic tools since they directly apply to data without assuming homogeneity. We provide simulation studies, by generating both inhomogeneous and self-exiting spatio-temporal point processes on networks, and by carrying out diagnostics on different fitted intensities. By comparing the values of the LISTA functions and their theoretical values, we show that the LISTA can correctly identify the true intensity when this is constrained on a network.
Local indicators of spatio-temporal association; point processes on linear networks
nicoletta d'angelo; giada adelfio; jorge mateu (17 - 19 february 2021).Local indicators of spatio-temporal association on linear networks.
File in questo prodotto:
File Dimensione Formato  
abstract_STSS.pdf

Solo gestori archvio

Descrizione: Abstract
Tipologia: Versione Editoriale
Dimensione 126.35 kB
Formato Adobe PDF
126.35 kB Adobe PDF   Visualizza/Apri   Richiedi una copia

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/10447/479607
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact