infrastructures Article Smart City Ranking System: A Supporting Tool to Manage Migration Trends for Australian Cities Muhammad Atiq Ur Rehman Tariq 1,2, * , Maha Hussein 1 and Nitin Muttil 1,2, *   Citation: Tariq, M.A.U.R.; Hussein, M.; Muttil, N. Smart City Ranking System: A Supporting Tool to Manage Migration Trends for Australian Cities. Infrastructures 2021, 6, 37. https://doi.org/10.3390/ infrastructures6030037 Academic Editors: Ahmed W. A. Hammad, Assed N. Haddad and Carlos A. P. Soares Received: 9 February 2021 Accepted: 2 March 2021 Published: 8 March 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). 1 College of Engineering and Science, Victoria University, Melbourne, VIC 8001, Australia; Maha.Hussein@live.vu.edu.au 2 Institute for Sustainable Industries & Livable Cities, Victoria University, P.O. Box 14428, Melbourne, VIC 8001, Australia * Correspondence: Atiq.Tariq@yahoo.com (M.A.U.R.T.); Nitin.Muttil@vu.edu.au (N.M.) Abstract: A key driver of Australia’s economic development is through promoting migration. A strong bottleneck to achieve the targets is a disproportional concentration of population in the metropolitan cities. To avoid congestion in these cities, emphasis is being given at the government level to promote the regional cities. With different city ranking systems, this study tries to identify linkage between the city ranking and people’s preference to live there. The proposed ranking system uses six components, namely, economy, mobility, environment, people, living, and governance. A comparison is done between the ranking systems by first assigning the same weightage to the six components and then assigning different weightages based on people’s preferences. This study considered 112 Australian cities, which were ranked by considering their performance based on the non-weighted and weighted parameters. Analytical Hierarchy Process is then used to assign the priorities/preferences of the components, factors, and indicators. The study also incorporates clustering technique to address the issue of missing data/information that is a typical problem with small cities where missing data is a common issue. The results of the comparison demonstrate that assigning weightage to ranking parameters makes the city ranking closer to the preference of people to live in a city. It is also recommended that the city ranking system and urban governance should have closer connection to each other. The lowest performing city ranking parameter should be given higher preferences in urban management and development plans. Keywords: smart cities; preferred cities; smart city ranking; Australian regional cities; imputation of missing data; analytical hierarchy process; data clustering; z-score 1. Introduction The world population is expected to double with 70% of the population living in cities by 2050 [1,2]. As urbanization continues to grow, the demand for improvements in the cities services increases to accommodate for these predicted urban growths. Through the concept of developing smarter cities, the concern of urbanization can be addressed as the factors covered by smarty cities, ensures a sustainable urban development [3–5]. It can be concluded that population increase creates opportunities, yet challenges will also be faced if not prepared for it correctly. Cities are not just a place to live in and work, they are “area of emotional attachment” as each city has their own “personalities”, “traditions”, and “attractions” [6]. Therefore, it can be concluded that the concept of urban sustainability and smart city have been a potential solution to solve the problems concerning urbanization [7]. 1.1. The Concept of Smart Cities and Rankings The definition of a Smart City has “no universally accepted definition” as it means “different things to different people”. Kuru et al. [8] indicate that the agreed definition of smart city is not available as there is no “best formula” to transform each city into a smart city [2,8–10]. The general concept of Smart Cities seems to be a simple one, however it is far Infrastructures 2021, 6, 37. https://doi.org/10.3390/infrastructures6030037 https://www.mdpi.com/journal/infrastructures