Data Science for Social Good: A Review of Applications in Poverty Alleviation, Disaster Management, and Intelligent Transportation (KEY IJP************392)

• Manish Ramlani,Bharat Thathera

Abstract

Data science has emerged as a transformative force in addressing some of the most pressing challenges faced by society today. Traditionally, efforts to tackle issues like poverty alleviation, disaster management, and urban mobility relied heavily on manual analysis and heuristic approaches, which often suffered from subjectivity, inefficiency, and limited scalability. With the advent of advanced data science techniques, these domains are undergoing a paradigm shift, leveraging big data, machine learning, and predictive analytics to drive data-informed decision-making and optimize resource utilization. For example, in helping to reduce poverty, data science helps us find areas that need more support and makes sure resources are given where they can make the biggest difference in people's lives. In emergencies, collecting data quickly and using maps helps those who respond to predict problems, plan how to move people to safety, and send help where it's needed most. In cities, using data helps make traffic better, keeps people safe, and makes traveling around easier. This paper reviews how data science is used in these three important areas, showing its ability to promote social benefits. By combining information from different studies, it explains the methods, difficulties, and effects on society related to data science. The results show the great potential in using data science to develop solutions that are not only new but also fair and sustainable.