Measuring livability in peri-urban areas using machine learning: A systematic review of key indicators and measurement frameworks
DOI:
https://doi.org/10.35208/ert.1615227Keywords:
Peri-urban, sustainable development, human development clustering, environmental sustainability indexAbstract
Peri-urban areas are regions that exist between rural and urban centers and are rapidly growing urban centres that experience haphazard growth. Evaluating the livability of these areas is useful in enhancing the well-being of inhabitants as well as extending constructive development. Today, there is no universal agreement as to which indicators or frameworks should be used when assessing livability on the international level. This systematic review, therefore, aims to identify and compare essential critical livability indicators and measurement frameworks concerning rapidly growing peri-urban areas. Selected studies based on the PRISMA framework and machine learning analysis; 58 related studies were analyzed. This premise is evident based on the findings by the review, with major livability domains such as privileged access to basic services (89.66%), infrastructure resources (81.03%), housings (77.59%), and transport (74.14%). Environmental quality 68.97% as well as economic opportunities, 60.34% and social cohesiveness, 51.72% were also considered. The assessment methods ranged across surveys, reviews, ranking systems, and the construction of indexes, and always called for a combined as well as adaptable methodological framework. This study investigates the challenges of assessing livability in peri-urban areas, where traditional urban indicators often fail to capture context-specific dimensions. A systematic framework is developed that extracts and clusters livability indicators from academic literature. The study claims to provide knowledge to the urban management and policies towards designing per i-urban regions with sustainable and inclusive demands, focusing on system essentials and permanent, demanding solutions.
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