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ANALYSING RELATIONSHIPS BETWEEN URBAN LAND USE FRAGMENTATION METRICS AND SOCIO-ECONOMIC VARIABLES

机译:分析城市土地利用碎片指标与社会经济变量的关系

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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.
机译:分析城市地区对其正确的监测和规划至关重要。这主要占城区人民人民的急剧增加,因此,需要管理它们。与此同时,使用空间和统计数据集(如城市地图集)的使用情况升高,提供从卫星图像获得的高分辨率城市土地使用地图,以及提供欧洲城市的统计数据和城市审计他们的周围环境。在这项研究中,我们分析了来自城市地图集数据集的土地利用和土地覆盖(LULC)数据的城市碎片指标之间的关系,以及来自城市审计的社会经济数据2006年和2012年。我们进行了分析在六十八个功能城市地区的样本(FUAS)。为每个FUA,土地使用课程和日期计算一次性和基于两日的碎片指数。然后应用相关性测试和主成分分析以选择最具代表性的指标。最后,测试了多元回归模型以探索社会经济变量的预测,利用不同的土地使用度量作为解释性变量,在给定日期和动态上下文中。结果表明,人口统计,生活条件,劳动力和运输变量与FUAS的形态有明确的关系。这种方法使我们能够在土地使用类别,复杂性及其结构变化的空间分布方面比较欧洲富士,以及预览和模拟不同的增长模式和社会经济指标。

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