Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Decoding Urban Inequality
The Applications of Machine Learning for Mapping Inequality in Cities of the Global South
According to the United Nations, by the year 2050, 68% of the world's population will live in cities. It is important to note that, while the world is undergoing this immense change in its ecology, we are also experiencing a data revolution which is characterised by a rapid growth in data availability as well as a growing interest in data science techniques such as machine learning (ML). The research explored two ML methods: Model 1: supervised ML to map living conditions for small areas in Nairobi (random forest) and Model 2: unsupervised ML to develop neighbourhood typologies for spatial planning (k‐means). The first stage of the research involved reviewing relevant literature on ML, urban poverty and slums, remote sensing, residential fragmentation, and spatial and land use planning. The findings of the research suggest that, indeed, ML techniques can enrich our understanding of spatial inequality in the Global South.
Decoding Urban Inequality
The Applications of Machine Learning for Mapping Inequality in Cities of the Global South
According to the United Nations, by the year 2050, 68% of the world's population will live in cities. It is important to note that, while the world is undergoing this immense change in its ecology, we are also experiencing a data revolution which is characterised by a rapid growth in data availability as well as a growing interest in data science techniques such as machine learning (ML). The research explored two ML methods: Model 1: supervised ML to map living conditions for small areas in Nairobi (random forest) and Model 2: unsupervised ML to develop neighbourhood typologies for spatial planning (k‐means). The first stage of the research involved reviewing relevant literature on ML, urban poverty and slums, remote sensing, residential fragmentation, and spatial and land use planning. The findings of the research suggest that, indeed, ML techniques can enrich our understanding of spatial inequality in the Global South.
Decoding Urban Inequality
The Applications of Machine Learning for Mapping Inequality in Cities of the Global South
Carta, Silvio (Herausgeber:in) / Khan, Kadeem (Autor:in)
Machine Learning and the City ; 625-629
21.05.2022
5 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
Smart cities and urban inequality
| Taylor & Francis Verlag | 2022
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