发展邻里类型学和理解城市不平等:一种数据驱动的方法

IF 1.7 Q2 GEOGRAPHY Regional Studies Regional Science Pub Date : 2022-10-20 DOI:10.1080/21681376.2022.2132180
Halfdan Lynge, J. Visagie, Andreas Scheba, I. Turok, David Everatt, C. Abrahams
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引用次数: 0

摘要

社区影响着人们的生计,因此推动和调解城市内部的不平等和转型。虽然社区长期以来一直被认为是一个重要的分析单位,但令人惊讶的是,对不同社区类型的系统研究很少,尤其是在全球南方快速发展的城市。在本文中,我们采用k-means聚类(一种常见的机器学习算法)来开发南非八个最大城市的社区类型。利用人口普查数据,我们确定并描述了八种社区类型,每种类型都具有独特的人口、社会经济、结构和基础设施特征。接下来是围绕关键变量的邻里类型的关系比较,在这里我们展示了住宅不平等的持久和多维性。除了为南非城市的内部结构提供新的视角外,该论文还通过应用归纳、数据驱动的方法来开发社区类型学,从而促进对全球南方城市更复杂和细致入微的理解,从而做出了重要贡献。
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Developing neighbourhood typologies and understanding urban inequality: a data-driven approach
ABSTRACT Neighbourhoods affect people’s livelihoods, and therefore drive and mediate intra-urban inequalities and transformations. While the neighbourhood has long been recognized as an important unit of analysis, there is surprisingly little systematic research on different neighbourhood types, especially in the fast-growing cities of the Global South. In this paper we employ k-means clustering, a common machine-learning algorithm, to develop a neighbourhood typology for South Africa’s eight largest cities. Using census data, we identify and describe eight neighbourhood types, each with distinct demographic, socio-economic, structural and infrastructural characteristics. This is followed by a relational comparison of the neighbourhood types along key variables, where we demonstrate the persistent and multi-dimensional nature of residential inequalities. In addition to shedding new light on the internal structure of South African cities, the paper makes an important contribution by applying an inductive, data-driven approach to developing neighbourhood typologies that advances a more sophisticated and nuanced understanding of cities in the Global South.
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来源期刊
CiteScore
3.00
自引率
15.80%
发文量
49
审稿时长
18 weeks
期刊介绍: Regional Studies, Regional Science is an interdisciplinary open access journal from the Regional Studies Association, first published in 2014. We particularly welcome submissions from authors working on regional issues in geography, economics, planning, and political science. The journal features a streamlined peer-review process and quick turnaround times from submission to acceptance. Authors will normally receive a decision on their manuscript within 60 days of submission.
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