Identifying Smart City Leaders and Followers with Machine Learning

Fangyao Liu, Nicole Damen, Zhengxin Chen, Yong Shi, Sihai Guan () and Daji Ergu ()
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Fangyao Liu: College of Electronic and Information, Southwest Minzu University, Chengdu 610093, China
Nicole Damen: School of Interdisciplinary Informatics, University of Nebraska at Omaha, Omaha, NE 68182, USA
Zhengxin Chen: College of Information Science and Technology, University of Nebraska at Omaha, Omaha, NE 68182, USA
Yong Shi: College of Information Science and Technology, University of Nebraska at Omaha, Omaha, NE 68182, USA
Sihai Guan: College of Electronic and Information, Southwest Minzu University, Chengdu 610093, China
Daji Ergu: College of Electronic and Information, Southwest Minzu University, Chengdu 610093, China

Sustainability, 2023, vol. 15, issue 12, 1-17

Abstract: Smart cities have been a popular topic for the city stakeholders. A smart city is the next urban lifestyle that citizens expect. Due to the hypercompetitive and globalized economy, many cities have already started or are about to start their smart city projects. There is no uniform benchmark to evaluate the smart cities’ performance. Several organizations use their own indicators to evaluate smart cities worldwide or nationwide. This research paper leverages fuzzy logic to label smart city leaders and followers based on various organization’s evaluation meta results and then uses machine learning techniques to identify the key characteristics of leaders and followers. Based on the training data performance, the Support Vector Machine (SVM) is used to predict who will be the next smart city leader or follower. According to the proposed prediction framework, we have successfully predicted 30 smart city leaders and 20 followers.

Keywords: smart city; fuzzy logic; machine learning; prediction (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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