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Capturing the heterogeneity of urban growth in South Korea using a latent class regression model
Publication year - 2018
Publication title -
transactions in gis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.721
H-Index - 63
eISSN - 1467-9671
pISSN - 1361-1682
DOI - 10.1111/tgis.12492
Subject(s) - geography , latent class model , class (philosophy) , citation , library science , cartography , regional science , demography , computer science , artificial intelligence , sociology , machine learning