Analysing Trends of Computational Urban Science and Data Science Approaches for Sustainable Development

Abstract

Urban computing with a data science approaches can play a pivotal role in understaning and analyzing the potential of these
methods for strategic, short-term, and sustainable planning. The recent development in urban areas have progressed towards
the data-driven smart sustainable approaches to resolve the complexities around urban areas. The urban system faces severe
challenges and these are complicated to capture, predict, resolve and deliver. The cur- rent study advances an unconventional
decision-support framework to integrate the complexities of science, urban sustainability theories, and data science, with a
data-intensive science to incorporate grassroots initiatives for a top- down policies. This work will influence the urban data
analytics to optimize the designs and solutions to enhance sustainability, efficiency, resilience, equity, and quality of life. This
work emphasizes the significant trends of data- driven and model-driven decision support systems. This will help to address
and create an optimal solution for mul- tifaceted challenges of an urban setup within the analytical framework. The analytical
investigations includes the research about land use prediction, environmental monitoring, transportation modelling, and social
equity analysis. The fusion of urban computing, intelligence, and sustainability science is expected to resolve and contribute
in shap- ing resilient, equitable, and future environmentally sensible eco-cities. It examines the emerging trends in the domain
of computational urban science and data science approaches for sustainable development being utilized to address urban challenges
including resource management, environmental impact, and social equity. The analysis of recent improvements and case
studies highlights the potential of data-driven insights with computational models for promoting resilient sustainable urban
environments, towards more effective and informed policy-making. Thus, this work explores the integration of computational
urban science and data science methodologies to advance sustainable development.