从精细尺度数据揭示多尺度隔离效应:以巴黎两个社区为例

IF 1.1 Q3 DEMOGRAPHY

Spatial Demography Pub Date : 2020-07-13 DOI:10.1007/s40980-020-00065-4

Madalina Olteanu, Cecile de Bezenac, William Clark, Julien Randon-Furling

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引用次数: 1

摘要

精细尺度数据对于多标量偏析现象的分析尤为重要。使用来自欧盟数据挑战的分解数据，我们在这里展示了如何应用最近开发的方法，该方法可以在多个尺度上测量隔离，并提供跨尺度和空间的隔离水平的可视化。我们用巴黎市两组公民移民的结果来说明这种技术。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Revealing Multiscale Segregation Effects from Fine-Scale Data: A Case Study of Two Communities in Paris

Fine-scale data is particularly important for the analysis of multiscalar segregation phenomena. Using dis-aggregated data from an EU data challenge, we show here how to apply a recently developed method that measures segregation at multiple scales and provides a visualization of the levels of segregation across scale and space. We illustrate the technique with results for two groups of citizen migrants in the city of Paris.

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来源期刊

Spatial Demography DEMOGRAPHY-

自引率

0.00%

发文量

期刊介绍： Spatial Demography focuses on understanding the spatial and spatiotemporal dimension of demographic processes. More specifically, the journal is interested in submissions that include the innovative use and adoption of spatial concepts, geospatial data, spatial technologies, and spatial analytic methods that further our understanding of demographic and policy-related related questions. The journal publishes both substantive and methodological papers from across the discipline of demography and its related fields (including economics, geography, sociology, anthropology, environmental science) and in applications ranging from local to global scale. In addition to research articles the journal will consider for publication review essays, book reviews, and reports/reviews on data, software, and instructional resources.