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dc.contributor.authorMUBAREKA Sarahen_GB
dc.contributor.authorKOOMEN Ericen_GB
dc.contributor.authorESTREGUIL Christineen_GB
dc.contributor.authorLAVALLE Carloen_GB
dc.identifier.citationLANDSCAPE AND URBAN PLANNING vol. 103 no. 3-4 p. 303-317en_GB
dc.description.abstractThis paper introduces a composite index to characterise urban expansion patterns based on four associated indices that describe the degree of compactness of urban land: nuclearity, ribbon development, leapfrogging and branching processes. Subsequently, principal component and cluster analysis are applied to build the composite index. Two baseline scenarios and three hypothetical policy alternatives, run from 2000 to 2030 using the pan-European EU-ClueScanner 1 km resolution land use model are then used to test the sensitivity and robustness of the composite index in large urban zones (LUZs). The second part of the paper is dedicated to the spatial analysis of a subset of large urban zones with the largest area growth in all the model runs for the year 2030. The landscape context of all built-up land in the year 2000 is analysed for the newly created urban land. It is characterised according to the proportion of natural, agricultural, built-up areas within a 7 km radius. A stepwise multiple regression analysis relating the landscape mosaic types and the composite index allowed us to understand whether or not the landscape surrounding the existing urban cores acts as the driving force responsible for the more “successful” policy alternatives in terms of urban compactness. Modellers may consider the landscape mosaic as one possible proxy to determine which urban areas are more likely to have less compact urban expansion patterns for scenarios with an increase in land claims for built-up areas.en_GB
dc.description.sponsorshipJRC.H.7-Land management and natural hazardsen_GB
dc.titleDevelopment of a composite index of urban compactness for land use modelling applicationsen_GB
dc.typeArticles in periodicals and booksen_GB
JRC Directorate:Sustainable Resources

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