The three case studies demonstrating the application of geospatial data analysis at the neighborhood level will address (1) locations to spark investment in low income communities, (2) investing in the well-being of communities and (3) AI to predict behavior for disaster mitigation.
How Big Data Can Quantify Community Social and Climate Risks – from neighborhoods to country levels
Wednesday, October 16, 2019
1:15 to 2:45 PM EDT
Poverty rates, education attainment, food insecurity, English fluency, greenspace, health and wellness, air and water quality, and other ESG (Environment, Social, and Governance) factors can now be captured at the neighborhood level and are used to evaluate the stability, resiliency, and economic vitality of a community. The application of big data at the neighborhood level is an emerging field of analysis.
Who should care? Municipalities, investors, philanthropies, corporations, and others that routinely rely on risk analysis to make investment, planning and community well-being decisions.
Join SSF and ESGAnalytics.Ai Team’s Suchi Gopal and Josh Pitts, in a demonstration of their analytics using three case studies. Andrew Teras and Michael Bonanno, senior credit analysts from Breckinridge Capital Advisors, will discuss how they have used ESGAnalytics.Ai to evaluate the risks in their firm’s municipal bond portfolios.Register for the Webinar