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Sustainable Development01:43

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As the human population continues to grow and use resources, we must be mindful of our planet’s natural limits. Sustainable development provides a pathway to maintain and improve human life now while also ensuring that future generations will have the resources that they need. The long-term success of sustainability efforts rests on understanding the interplay between human actions and ecological systems.
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Design Example: Analyzing Capacity Contours for Flood Risk Assessment01:17

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Flood risk assessment involves careful planning and analysis to ensure the safety of communities near water retention structures. Capacity contours are a vital tool in this process, as they illustrate the potential spread of water at specific levels in a given area. In the context of building a bund across a small valley, these contours play a critical role in evaluating the safety of nearby residential areas.In this example, the bund is intended to store stormwater in the valley. The engineers...
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Levels of Use of a GIS01:29

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Geographic Information Systems (GIS) operate across three levels of application, each representing an increasing degree of complexity: data management, analysis, and prediction. These levels reflect the expanding functionality and versatility of GIS technology in handling spatial data for diverse purposes.Data ManagementAt its foundational level, GIS serves as a tool for data management, enabling the input, storage, retrieval, and organization of spatial data. This level is often employed in...
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Response Surface Methodology01:16

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Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
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Causality in Epidemiology01:21

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Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...
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SBAR II: Application of SBAR01:14

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SBAR is an effective communication tool used by healthcare professionals to communicate patient information accurately. SBAR stands for Situation, Background, Assessment, and Recommendation. For a better understanding, an example is given below.
SBAR Report from a Nurse to a Health Care Provider
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Related Experiment Video

Updated: Dec 28, 2025

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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Data-driven Bayesian network modelling to explore the relationships between SDG 6 and the 2030 Agenda.

David Requejo-Castro1, Ricard Giné-Garriga2, Agustí Pérez-Foguet1

  • 1Engineering Sciences and Global Development (EScGD), Department of Civil and Environmental Engineering, Universitat Politècnica de Catalunya · BarcelonaTech (UPC), Jordi Girona, 1-3, 08034 Barcelona, Spain.

The Science of the Total Environment
|February 14, 2020
PubMed
Summary

This study introduces a Bayesian network (BN) approach to map Sustainable Development Goals (SDGs) interlinkages. The data-driven method effectively identifies complex relationships, particularly for SDG 6 (water and sanitation), enhancing global development monitoring.

Keywords:
Bayesian networksData-drivenInterlinkagesSDG 6Sustainable Development Goals

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Area of Science:

  • Environmental Science
  • Data Science
  • Sustainable Development Studies

Background:

  • The Sustainable Development Goals (SDGs) are recognized as interconnected and indivisible.
  • Conventional indicator-based SDG monitoring frameworks often overlook the complex links and interdependencies between goals and targets.
  • A need exists for advanced analytical approaches to capture and interpret these interlinkages effectively.

Purpose of the Study:

  • To propose and validate a data-driven Bayesian network (BN) approach for identifying and interpreting interlinkages among the SDGs.
  • To specifically analyze the interlinkages of SDG 6 (water and sanitation) within the broader 2030 Agenda.
  • To demonstrate the utility of the BN approach in uncovering and understanding complex SDG relationships.

Main Methods:

  • Utilized a data-driven Bayesian network (BN) model.
  • Employed global SDG data for 179 countries, covering 16 goals, 28 targets, and 44 indicators.
  • Validated the BN approach by assessing the robustness of indicator relationships across different country sample sizes and comparing results with UN-Water studies.

Main Results:

  • The Bayesian network approach robustly identifies significant interlinkages between SDG indicators.
  • The analysis revealed previously undocumented interlinkages, expanding upon existing literature.
  • The findings demonstrate the coherency and added value of the BN method in mapping SDG connections, with a specific focus on SDG 6.

Conclusions:

  • The proposed data-driven Bayesian network approach is a valuable tool for analyzing and interpreting the complex interdependencies within the Sustainable Development Goals.
  • This method enhances the understanding of SDG linkages, offering insights beyond traditional indicator-based frameworks.
  • The approach provides a robust foundation for more integrated SDG monitoring and policy development, particularly concerning water and sanitation (SDG 6).