%0 Journal Article
%T Modelling urban form: A multidimensional typology of urban occupation for spatial analysis
%A Eduarda Marques da Costa
%A Eduardo Gomes
%A Jorge Rocha
%A Nuno Costa
%A Patr¨ªcia Abrantes
%A Paulo Morgado
%J Environment and Planning B: Urban Analytics and City Science
%@ 2399-8091
%D 2019
%R 10.1177/2399808317700140
%X The conceptual and methodological debate on urban form has grown in the last decades to recognize that social, economic, demographic and political processes can contribute to the development of new urban forms, especially those related to urban sprawl, as well as to find alternative methodologies for measuring them. Spatial metrics derived from landscape ecology arise as principal indicators to measure urban form. This paper proposes a typology of the urban occupation of Portuguese municipalities. It uses land use/cover data from 1990 and 2006 to extract built-up areas, and it presents five spatial metrics alongside seventeen statistical indicators from 1991 to 2011 most commonly used in the literature to characterize urban occupation. It uses a self-organising map as a visual tool to identify trends and relationships among variables and to cluster municipalities. Based on the self-organising map¡¯s visual clustering, six types of urban occupation of Portuguese municipalities are proposed. In addition, the paper discusses the added value of using indicators that describe both the patterns and the characteristics of the municipalities for making spatial planning decisions in Portugal. The observed results show that spatial metrics are particularly adequate for measuring peri-urban municipalities (urban sprawl areas). These results represent the first multidimensional and systematic analysis of Portuguese urban occupation and they can be the first step in the integration of spatial metrics as indicators that are suitable for the analysis of spatial planning, and also for comparative purposes at a broader geographical scale
%K Land use/cover
%K spatial planning
%K spatial metrics
%K self-organising-maps
%K Portugal
%U https://journals.sagepub.com/doi/full/10.1177/2399808317700140