Repositório Colecção: CEC_PT_ARI
https://hdl.handle.net/1822/1109
CEC_PT_ARI2024-03-28T16:36:51ZA spatial analysis approach for the definition of metropolitan regions: the case of Portugal
https://hdl.handle.net/1822/8322
Título: A spatial analysis approach for the definition of metropolitan regions: the case of Portugal
Autor: Ramos, Rui A. R.; Silva, Antônio Nélson Rodrigues da
Resumo: The objective of this paper is to present a combined, two-step spatial analysis approach for the definition of metropolitan regions. The proposed approach, which constitutes an option to avoid the endless confrontations that may be derived from the essentially subjective political criteria,
explores two branches of spatial analysis: spatial statistics and spatial modelling. Spatial statistics tools are used to identify the characteristics of local association and are combined with a neural network in order to build prediction models. The analyses conducted with exploratory spatial data
analysis tools and census data give a clear indication of clusters of zones with similar characteristics, which can be seen as uniform regions. Spatial models can then be used to foresee the global behaviour of regions in terms of growth, albeit the basis of local (and historical) relationships among zones.
The proposed approach is tested in a case study carried out in Portugal, where this is a timely issue.
<b>Tipo</b>: article2008-11-20T16:49:52ZA data-driven approach for the definition of metropolitan regions
https://hdl.handle.net/1822/2320
Título: A data-driven approach for the definition of metropolitan regions
Autor: Ramos, Rui A. R.; Silva, Antônio Nélson Rodrigues da
Resumo: The objective of this paper is to present a data-driven approach for the definition of
metropolitan regions. The proposed approach, which constitutes an option to avoid the endless confrontations that may be derived from the essentially subjective political criteria, explores two branches of Spatial Analyses: Spatial Statistics and Spatial Modeling. Spatial Statistics tools are used to identify the characteristics of local association and combined with Cellular Automata techniques in order to build prediction models. The analyses conducted
with Exploratory Spatial Data Analyses (ESDA) tools and census data give a clear indication of clusters of zones with similar characteristics, which can be seen as uniform regions. Spatial dynamic models can then be used to foresee the global behavior of regions in terms of growth, although based on local (and historical) relationships among zones. The proposed approach is tested in a case study carried out in Portugal, where this is a timely issue.
<b>Tipo</b>: conferencePaper2005-06-22T11:28:18Z