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 ; Applications of machine learning for mapping inequality in cities of the Global South
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. ; Thesis: M.C.P., Massachusetts Institute of Technology, Department of Urban Studies and Planning, 2019 ; Cataloged from student-submitted PDF version of thesis. ; Includes bibliographical references (pages 64-67). ; According to the United Nations, by the year 2050, 68% of the world's population will live in cities. However, the UN also estimates that 1 in 8 people in the world currently live in slums; furthermore, slum populations are growing at a rate of 4.5% per year. Nairobi, the capital of Kenya, is known for having large slum settlements and a high degree of spatial inequality. While slums are expanding at a rapid rate, cities in the Global South lack the crucial data to monitor deepening spatial inequalities. Current urban poverty assessments rely on census data, poverty maps or slum demarcation maps, however, for city planning, these are subject to limitations. 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 characterized by a rapid growth in data availability as well as a growing interest in data science techniques such as machine learning (ML). Acknowledging these significant trends, this thesis applies ML to generate useful insights on spatial inequality in Nairobi. The research incorporates data from multiple sources including: census, satellite imagery and data derived from calculations in GIS. The research explored two ML methods. The first method attempted to map living conditions for small areas in the city. Moreover, the second method produced residential typologies or zones for equitable investment and land management in the city. One of the overall aims of the research is to contribute to the wider conversation on how ML may be applied in the realms of policy and city planning in the Global South. ; by Kadeem Khan. ; M.C.P. ; M.C.P. Massachusetts ...
Decoding urban inequality : the applications of machine learning for mapping inequality in cities of the Global South ; Applications of machine learning for mapping inequality in cities of the Global South
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. ; Thesis: M.C.P., Massachusetts Institute of Technology, Department of Urban Studies and Planning, 2019 ; Cataloged from student-submitted PDF version of thesis. ; Includes bibliographical references (pages 64-67). ; According to the United Nations, by the year 2050, 68% of the world's population will live in cities. However, the UN also estimates that 1 in 8 people in the world currently live in slums; furthermore, slum populations are growing at a rate of 4.5% per year. Nairobi, the capital of Kenya, is known for having large slum settlements and a high degree of spatial inequality. While slums are expanding at a rapid rate, cities in the Global South lack the crucial data to monitor deepening spatial inequalities. Current urban poverty assessments rely on census data, poverty maps or slum demarcation maps, however, for city planning, these are subject to limitations. 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 characterized by a rapid growth in data availability as well as a growing interest in data science techniques such as machine learning (ML). Acknowledging these significant trends, this thesis applies ML to generate useful insights on spatial inequality in Nairobi. The research incorporates data from multiple sources including: census, satellite imagery and data derived from calculations in GIS. The research explored two ML methods. The first method attempted to map living conditions for small areas in the city. Moreover, the second method produced residential typologies or zones for equitable investment and land management in the city. One of the overall aims of the research is to contribute to the wider conversation on how ML may be applied in the realms of policy and city planning in the Global South. ; by Kadeem Khan. ; M.C.P. ; M.C.P. Massachusetts ...
Decoding urban inequality : the applications of machine learning for mapping inequality in cities of the Global South ; Applications of machine learning for mapping inequality in cities of the Global South
01.01.2019
1140507239
Hochschulschrift
Elektronische Ressource
Englisch
Global cities of the South: Emerging perspectives on growth and inequality
| Online Contents | 2007
Smart cities and urban inequality
| Taylor & Francis Verlag | 2022
| Online Contents | 2009