Measuring Urban Quality of Life Through Spatial Analytics and Machine Learning: A Data-Driven Framework for Sustainable Urban Planning and Development

Abstract

Today, the expansion of cities and rapid urbanization have led to unsustainable development and reduced quality of life (QOL) in urban ecosystems. This research aimed to establish a new framework for measuring QOL in a city by using spatial data and integrating the fuzzy analytical hierarchy process (FAHP) with support vector machine (SVM) methods. Four main components, including socioeconomic level, urban land use, urban environment, and natural environment; eleven subcomponents; and seventeen spatial indicators were defined. To produce quality-of-life maps of Mashhad City, the components, subcomponents, and indicators were integrated using weights obtained via the FAHP method. Then, SVM was applied to semi-automatically produce QOL maps. The results showed that Regions 2, 3, 4, 6, 10, and 11 displayed lower QOL scores, especially regarding the environmental and socioeconomic indicators. Regions 1 and 7, as well as Districts 0902, 0903, 0501, and 0502, showed average QOL in regard to the natural environment and socioeconomic indicators. Regions 8 and 12, along with District 0901 and Samen City, obtained better QOL scores in regard to nearly all indicators, except for access to land uses and the NDVI index. The results show that using the SVM method, a QOL map—with a kappa coefficient of 0.97 and an overall accuracy of 98%—can be successfully created with significant time, cost, and effort savings.