Spatial time-series modeling: A review of the proposed methodologies

Yiannis Kamarianakis, Poulicos Prastacos

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

This paper discusses three modelling techniques, which apply to multiple time series data that correspond to different spatial locations (spatial time series). The first two methods, namely the Space-Time ARIMA (STARIMA) and the Bayesian Vector Autoregressive (BVAR) model with spatial priors apply when interest lies on the spatio-temporal evolution of a single variable. The former is better suited for applications of large spatial and temporal dimension whereas the latter can be realistically performed when the number of locations of the study is rather small. Next, we consider models that aim to describe relationships between variables with a spatio-temporal reference and discuss the general class of dynamic space-time models in the framework presented by Elhorst (2001). Each model class is introduced through a motivating application.

Original languageEnglish (US)
Title of host publicationProceedings 2005 - The 8th AGILE International Conference on Geographic Information Science, AGILE 2005
StatePublished - 2005
Externally publishedYes
Event8th AGILE International Conference on Geographic Information Science, AGILE 2005 - Estoril, Portugal
Duration: May 26 2005May 28 2005

Other

Other8th AGILE International Conference on Geographic Information Science, AGILE 2005
CountryPortugal
CityEstoril
Period5/26/055/28/05

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Keywords

  • Bayesian Vector Autoregressions
  • Space-time models
  • Spatial time-series
  • STARIMA

ASJC Scopus subject areas

  • Information Systems
  • Geography, Planning and Development

Cite this

Kamarianakis, Y., & Prastacos, P. (2005). Spatial time-series modeling: A review of the proposed methodologies. In Proceedings 2005 - The 8th AGILE International Conference on Geographic Information Science, AGILE 2005