After logging into the Website using the Username and the Password, first step is the area of study such as “State” and “Local Government” to be selected.
Next step is to choose the SDG targets by checking the relevant box next to it.
The Indicators’ “Weight” can be modified by users manually depending on the purpose of study. However the overall value for each target should be equal to “1”.
Pressing the “Run” button will run the analysis.
SDG Analytic Results will appear. There would be four analytics categories available on this page such as:
1-Interlinkages Matrix
This symmetrical matrix is a graphical representation that displays the interlinkage between two variables, which are represented on both the X and Y axes . The correlation value is indicated by the degree of similarity between the variables, and ranges from -100, which represents a strong negative correlation, to 100, which represents a strong positive correlation.
2- Interlinkage by Target
The second tab in the user interface displays the interlinkage strength between a selected target and all other targets that have been chosen, sorted in descending order from strong positive to strong negative correlation. This tab provides a comprehensive overview of the relationships between the selected target and the other variables in the dataset. By examining the interlinkage strength values and their corresponding correlation coefficients, users can gain insights into the complex relationships between the variables and make informed decisions based on the data
3- Causality Analysis by target.
The results of the analysis are displayed in the form of P values, which range from 0 to 1. A smaller P value indicates a more statistically significant causal relationship between the target or indicator on the X-axis and the target on the Y-axis.
This information is valuable for understanding the relationships between different SDG targets and indicators and identifying potential causal factors. By examining the P values, users can identify the strongest causal relationships and prioritize interventions accordingly.
4-Prediction model
The final step involves conducting the prediction analysis. In the given scenario as it is shown on the above figure, three grey lines are depicted in the prediction graph, representing the identified causes. Additionally, the violet line represents the observed data, while the blue line represents the predicted values.
The blue line provides predictions that can be used as a basis for making informed decisions. These predictions offer insights into the potential outcomes or developments of the selected target based on the established causal relationships.
By employing the prediction analysis, decision-makers can utilise the blue line’s projected values to guide their decision-making processes, helping them anticipate future changes and align their actions accordingly. This enables proactive planning and strategic interventions to achieve desired outcomes related to the selected target and its associated goals.