Introducing CANDi
A decision-support tool for Sustainable Development Goal prioritisation.
CANDi helps policymakers identify which SDGs are likely to offer the greatest opportunity for progress by analysing the experiences of countries that have faced similar development conditions and successfully improved comparable indicators.
CANDi is a novel, relational and non-linear model that transforms development data into visual networks using eigenvector centrality, highlighting the interconnected influence of each SDG indicator within the broader system. These visual representations are then analysed using a Deep Convolutional Neural Network (DCNN), enabling the model to detect complex, non-obvious similarities between countries based on their development patterns.
Country analyses of SDGs often examine relationships between indicators in isolation, without capturing the full complexity of the network of interdependencies that exists among them. A change in one indicator can have an effect across multiple domains. For example, a decrease in Oil Production can influence GDP, education, employment, and more.
By identifying nations with comparable development profiles and tracking how specific indicators have shifted in those contexts, the model highlights which indicators have the most systemic impact.
Why It Matters
Governments face difficult choices when resources are limited.
CANDi supports evidence-informed prioritisation by helping decision-makers learn from comparable national development journeys.
How It Works
- Select a country.
- Run the analysis
- Explore countries with comparable characteristics.
- Identify shared SDGs with greatest scope for progression
- Work with us to explore potential pathways to SDG progress.
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