Exposing Emerging Trends in Smart Sustainable City Research Using Deep Autoencoders-Based Fuzzy C-Means

Anne Parlina, Kalamullah Ramli and Hendri Murfi
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Anne Parlina: Department of Electrical Engineering, Universitas Indonesia, Depok, Jawa Barat 16424, Indonesia
Kalamullah Ramli: Department of Electrical Engineering, Universitas Indonesia, Depok, Jawa Barat 16424, Indonesia
Hendri Murfi: Department of Mathematics, Universitas Indonesia, Depok, Jawa Barat 16424, Indonesia

Sustainability, 2021, vol. 13, issue 5, 1-28

Abstract: The literature discussing the concepts, technologies, and ICT-based urban innovation approaches of smart cities has been growing, along with initiatives from cities all over the world that are competing to improve their services and become smart and sustainable. However, current studies that provide a comprehensive understanding and reveal smart and sustainable city research trends and characteristics are still lacking. Meanwhile, policymakers and practitioners alike need to pursue progressive development. In response to this shortcoming, this research offers content analysis studies based on topic modeling approaches to capture the evolution and characteristics of topics in the scientific literature on smart and sustainable city research. More importantly, a novel topic-detecting algorithm based on the deep learning and clustering techniques, namely deep autoencoders-based fuzzy C-means (DFCM), is introduced for analyzing the research topic trend. The topics generated by this proposed algorithm have relatively higher coherence values than those generated by previously used topic detection methods, namely non-negative matrix factorization (NMF), latent Dirichlet allocation (LDA), and eigenspace-based fuzzy C-means (EFCM). The 30 main topics that appeared in topic modeling with the DFCM algorithm were classified into six groups (technology, energy, environment, transportation, e-governance, and human capital and welfare) that characterize the six dimensions of smart, sustainable city research.

Keywords: smart city; sustainable city; smart sustainable city; text mining; topic detection; topic modeling (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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