Citation: Sujatha, V.; Lavanya, G.; Prakash, R. Quantifying Liveability Using Survey Analysis and Machine Learning Model. Sustainability 2023, 15, 1633. https://doi.org/10.3390/ su15021633 Academic Editor: Antonino Marvuglia Received: 30 November 2022 Revised: 9 January 2023 Accepted: 11 January 2023 Published: 13 January 2023 Copyright: © 2023 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/). sustainability Article Quantifying Liveability Using Survey Analysis and Machine Learning Model Vijayaraghavan Sujatha 1, * , Ganesan Lavanya 1 and Ramaiah Prakash 2 1 Department of Civil Engineering, Anna University—University College of Engineering, Ramanathapuram 623513, India 2 Department of Civil Engineering, Alagappa Chettiar Government College of Engineering and Technology, Karaikudi 630003, India * Correspondence: suja.3051@gmail.com Abstract: Liveability is an abstract concept with multiple definitions and interpretations. This study builds a tangible metric for liveability using responses from a user survey and uses Machine Learning (ML) to understand the importance of different factors of the metric. The study defines the liveability metric as an individual’s willingness to live in their current location for the foreseeable future. Stratified random samples of the results from an online survey conducted were used for the analysis. The different factors that the residents identified as impacting their willingness to continue living in their neighborhood were defined as the “perception features” and their decision itself was defined as the “liveability feature”. The survey data were then used in an ML classification model, which predicted any user’s liveability feature, given their perception features. ‘Shapley Scores’ were then used to quantify the marginal contribution of the perception features on the liveability metric. From this study, the most important actionable features impacting the liveability of a neighborhood were identified as Safety and Access to the Internet/Organic farm products/healthcare/Public transportation. The main motivation of the study is to offer useful insights and a data-driven framework to the local administration and non-governmental organizations for building more liveable communities. Keywords: urban planning; liveability; supervised machine learning; online user survey 1. Introduction Liveability is an abstract concept with multiple definitions and interpretations. Live- ability is the degree to which a place fulfills the expectations of its residents for their well-being and quality of life. Myers [1] mentioned that liveability could be expressed as sustainability, quality of life, the “character” of place, the health of communities, etc. Live- ability is an “ensemble concept”. Balsas [2] measured the ‘city-center liveability’ through a set of Key Performance Indicators (KPIs). The online survey for this study was designed on the perception of liveability factors, such as preserving green spaces, reduction of traffic congestion, restoring community, promoting neighboring community’s collaboration, and enhancing competitiveness at an economic level. Emphasis is given to safety, affordability of housing, and transportation options as important aspects of liveability. Tyce and Re- becca [3], in their research initiative, reviewed 237 sources related to liveability and found that the three most common categories used to define liveability were Transportation, Development, and Community features. Philips, a global leader in energy technology and electronics, analyses regional trends in liveability, health, and well-being based on five factors: employment, community, physi- cal health, emotional health, and family/friends [4]. This survey reveals regional liveability patterns. This survey’s flaw is that it cannot rank cities. In 2008, Organization for Economic Cooperation and Development (OECD) developed a worldwide project for measuring Sustainability 2023, 15, 1633. https://doi.org/10.3390/su15021633 https://www.mdpi.com/journal/sustainability