Putting the geography into geodemographics: Using multilevel modelling to improve neighbourhood targeting – a case study of Asian pupils in London
Putting the geography into geodemographics: Using multilevel modelling to improve neighbourhood targeting – a case study of Asian pupils in London
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将地理纳入地理人口统计学：使用多层次建模来改善社区目标——伦敦亚洲学生的案例研究
DOI：
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
Yingyu Feng
中科院分区：
文献类型：
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作者：
Richard J. Harris;Yingyu Feng
本文探讨了使用多级建模为地理人口分析提供统计框架。它认为，将邻域分类与建模方法相结合，可以同时考虑地理人口层次结构的级别，识别最适合分析的级别，并允许考虑邻域类型之间的统计显着性和估计的不确定性之间的明显差异。本文展示了如何扩展该模型以创建跨分类的多尺度模型，该模型可以更好地利用可用的位置信息来提高邻域定位的效率。这些想法通过案例研究来说明，该案例研究使用数据样本和免费提供的伦敦输出区域分类来预测伦敦哪些社区的亚洲学生比例最高。多尺度模型的表现优于单独使用地理人口统计学做出的预测。
This paper explores the use of multilevel modelling to provide a statistical framework for geodemographic analysis. It argues that combining a neighbourhood classification with a modelling approach allows the levels of the geodemographic hierarchy to be considered simultaneously, identifying those that are most appropriate to the analysis and allowing the apparent differences between neighbourhood types to be considered in regard to their statistical significance, and to the uncertainty of the estimates. The paper shows how the model can be extended to create a cross-classified multiscale model that makes better use of the locational information available to improve the efficiency of the neighbourhood targeting. The ideas are illustrated with a case study using a sample of data and the freely available London Output Area Classification to predict which neighbourhoods in London have the highest percentages of Asian school pupils. The multiscale model is shown to outperform the predictions made using geodemographics alone.