Detecting and mapping slums using open data: a case study in Kenya

Detecting and mapping slums using open data: a case study in Kenya

The worldwide slum population currently stands at over one billion, with substantial growth expected in the coming decades. Traditionally, slums have been mapped using information derived mainly from either physical indicators using remote sensing data, or socio-economic indicators using census data...

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Bibliographic Details
Main Authors: Ron Mahabir, Peggy Agouris, Anthony Stefanidis, Arie Croitoru, Andrew T. Crooks
Format: Article
Language:English
Published: Taylor & Francis Group 2020-06-01
Series:International Journal of Digital Earth
Subjects:
slums
remote sensing
socio-economic
urban sustainability
data mining
kenya
Online Access:http://dx.doi.org/10.1080/17538947.2018.1554010

Internet

http://dx.doi.org/10.1080/17538947.2018.1554010

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