Quantifying Liveability Using Survey Analysis and Machine Learning Model

Vijayaraghavan Sujatha (), Ganesan Lavanya and Ramaiah Prakash
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Vijayaraghavan Sujatha: Department of Civil Engineering, Anna University—University College of Engineering, Ramanathapuram 623513, India
Ganesan Lavanya: Department of Civil Engineering, Anna University—University College of Engineering, Ramanathapuram 623513, India
Ramaiah Prakash: Department of Civil Engineering, Alagappa Chettiar Government College of Engineering and Technology, Karaikudi 630003, India

Sustainability, 2023, vol. 15, issue 2, 1-15

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 (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 (1)

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