Barter Edmund, Gross Thilo
Merchant Venturers Building, Woodland Road, Bristol, BS8 1UB, UK.
Proc Math Phys Eng Sci. 2019 Jan;475(2221):20180615. doi: 10.1098/rspa.2018.0615. Epub 2019 Jan 9.
In the twenty-first century, ongoing rapid urbanization highlights the need to gain deeper insights into the social structure of cities. While work on this challenge can profit from abundant data sources, the complexity of this data itself proves to be a challenge. In this paper, we use diffusion maps, a manifold learning method, to discover hidden manifolds in the UK 2011 census dataset. The census key statistics and quick statistics report 1450 different statistical features for each census output area. Here, we focus primarily on the city of Bristol and the surrounding countryside, comprising 3490 of these output areas. Our analysis finds the main variables that span the census responses, highlighting that university student density and poverty are the most important explanatory variables of variation in census responses.
在21世纪,持续快速的城市化凸显了深入洞察城市社会结构的必要性。虽然应对这一挑战的工作可以从丰富的数据来源中获益,但数据本身的复杂性被证明是一项挑战。在本文中,我们使用扩散映射(一种流形学习方法)来发现英国2011年人口普查数据集中隐藏的流形。人口普查关键统计数据和快速统计数据报告了每个普查输出区域的1450个不同统计特征。在这里,我们主要关注布里斯托尔市及其周边乡村,其中包括3490个这样的输出区域。我们的分析找到了涵盖人口普查响应的主要变量,突出表明大学生密度和贫困是人口普查响应变化中最重要的解释变量。