ANALYSING RELATIONSHIPS BETWEEN URBAN LAND USE FRAGMENTATION METRICS AND SOCIO-ECONOMIC VARIABLES
- 1Geo-Environmental Cartography and Remote Sensing Group, Universitat Politècnica de València, Camino de Vera s/n, 46022 Valencia, Spain
- 2Department of Economic Analysis. Universitat de València, Campus de los Naranjos, Av. de los Naranjos s/n, 46022 Valencia, Spain
Keywords: Urban atlas, Urban audit, LULC, Urban fragmentation, Landscape metrics, Socio-economic variables, Europe
Abstract. Analysing urban regions is essential for their correct monitoring and planning. This is mainly accounted for the sharp increase of people living in urban areas, and consequently, the need to manage them. At the same time there has been a rise in the use of spatial and statistical datasets, such as the Urban Atlas, which offers high-resolution urban land use maps obtained from satellite imagery, and the Urban Audit, which provides statistics of European cities and their surroundings. In this study, we analyse the relations between urban fragmentation metrics derived from Land Use and Land Cover (LULC) data from the Urban Atlas dataset, and socio-economic data from the Urban Audit for the reference years 2006 and 2012. We conducted the analysis on a sample of sixty-eight Functional Urban Areas (FUAs). One-date and two-date based fragmentation indices were computed for each FUA, land use class and date. Correlation tests and principal component analysis were then applied to select the most representative indices. Finally, multiple regression models were tested to explore the prediction of socio-economic variables, using different combinations of land use metrics as explanatory variables, both at a given date and in a dynamic context. The outcomes show that demography, living conditions, labour, and transportation variables have a clear relation with the morphology of the FUAs. This methodology allows us to compare European FUAs in terms of the spatial distribution of the land use classes, their complexity, and their structural changes, as well as to preview and model different growth patterns and socio-economic indicators.