Understanding the urban space for better governance: use of nontraditional data for real time disaggregated decision making

Achieving Sustainable development goals by 2030 is the UN agenda that countries have pledged to achieve. This requires the stakeholders of the nation-states to be actively participating in the action plans set forth by individual governments. Understanding the issues, visualizing the challenges, and monitoring progress are keys to achieve these goals. SDG is about ensuring quality. Hence, evidence-based data-centric decision making gained traction in the policy frameworks. With the advent of computational capacity, advances of knowledge streams such as machine learning, data mining, statistical inference, and prevalence of technologies such as social networks and IoT devices, data has become ubiquitous. These rapidly generating data are now getting integrated into the core decision making sphere as complementary mechanisms of traditional data sources. This project is designed to harness the capability of data and computational science to aid to achieve SDG goals. The project will have both theoretical and practical aspects. Theories of social science, environmental science, and earth sciences will be applied in conjunction with current research in computer science. Publicly available heterogeneous non-traditional dataset and state of the art computational knowledge will be used to formulate methods to address SDG. The project will have three distinct goals- conducting research, building capacity, and designing tools to aid data for policy analysis.