GIS analysis isn't just about creating maps to show pattern - it's about making informed decisions that impact policies, investments, and people's lives. But how do we go beyond simply correlating patterns to truly understand the cause-and-effect relationships in our data? And how can we make sure that decision-makers trust our findings even when there is some uncertainty in the data?
This session introduces two important advancements in spatial analysis: understanding cause-and-effect relationships (causal inference analysis) and checking how sensitive our analysis results are to imperfections in our data (assessing sensitivity to attribute uncertainty). By exploring cause-and-effect, it helps us understand why things happen the way they do, not just that they happen. At the same time, new tools help us evaluate and communicate how reliable our results are, even when we know the data might not be perfect.