Data-Driven Insights and Analytics for Measurable Sustainable Development Goals discusses the growing imperative to understand, measure, and guide actions using data-driven insights. The SDGs encompass a broad spectrum of global challenges, from eradicating poverty and hunger to preserving the environment and fostering peace. To address these issues, one should be able to measure and analyze progress. This book bridges the gap between qualitative and quantitative assessments, recognizing that goals are not solely about numbers but also encompass complex social, environmental, and economic dynamics. By merging data science with qualitative analysis, readers can explore how SDGs intersect and influence each other.
The book provides readers with an understanding of how to effectively leverage data science models and algorithms using descriptive analytics, allowing us to assess the current state of SDG performance and offering valuable insights into where we stand on these critical goals. Prescriptive analytics guides actions by offering actionable recommendations, while predictive analytics anticipates future trends and challenges, helping us navigate our path toward the SDGs effectively.
1. Introduction - p. xxvii 2. Part I: People - p. 1 3. 1. Health inequalities: the impact of social structure and mobility of risks - p. 3 4. Chapter 2. Food recommendations for reducing your impact - p. 19 5. Chapter 3. Identifying factors contributing to nutrition disparities in women within the context of Indian states - p. 37 6. Chapter 4. Mental healthcare in the times of climate change action and data science - p. 59 7. Chapter 5. Drivers of health workshop attendance among working-class women in India: a preliminary survey - p. 83 8. Part II: Planet - p. 101 9. Chapter 6. The soil guardians: role of PGPR in soil fertility and nutrient management - p. 103 10. Chapter 7. Spatiotemporal assessment of mangrove dynamics and phenological trends using Sentinel-1&2 time series - p. 135 11. Chapter 8. Wind behavior modeling towards improvement of sustainable energy generation: generative adversarial networks versus diffusion models - p. 147 12. Chapter 9. Optimizing climate action through data-driven sustainable development goal indicators - p. 163 13. Part III: Prosperity - p. 177 14. Chapter 10. Data driven insights into educational equity: Completion rates and literacy proficiency across Human Development Index tiers - p. 179 15. Chapter 11. A knowledge database discovery approach for improving quality in higher education institutions - p. 207 16. 12. Sustainable infrastructure planning and environmental impact assessment - p. 229 17. Part IV: Peace - p. 245 18. Chapter 13. Leveraging shallow learning techniques to support the methodological design of public policies in low density areas in Colombia - p. 247 19. Chapter 14. A fuzzy-based emotion detection model for determining critical hotspot urban areas in the presence of pandemic and heatwave multirisks - p. 263 20. Part V: Partnerships - p. 279 21. Chapter 15. Climate change and demographic choices - p. 281 22. 16. Enhancing climate resilience through natural resource management: the role of Mahatma Gandhi National Rural Employment Guarantee Act in Jharkhand - p. 303 23. Index - p. 315
Data-Driven Insights and Analytics for Measurable Sustainable Development Goals discusses the growing imperative to understand, measure, and guide actions using data-driven insights. The SDGs encompass a broad spectrum of global challenges, from eradicating poverty and hunger to preserving the environment and fostering peace. To address these issues, one should be able to measure and analyze progress. This book bridges the gap between qualitative and quantitative assessments, recognizing that goals are not solely about numbers but also encompass complex social, environmental, and economic dynamics. By merging data science with qualitative analysis, readers can explore how SDGs intersect and influence each other.
The book provides readers with an understanding of how to effectively leverage data science models and algorithms using descriptive analytics, allowing us to assess the current state of SDG performance and offering valuable insights into where we stand on these critical goals. Prescriptive analytics guides actions by offering actionable recommendations, while predictive analytics anticipates future trends and challenges, helping us navigate our path toward the SDGs effectively.
1. Introduction - p. xxvii 2. Part I: People - p. 1 3. 1. Health inequalities: the impact of social structure and mobility of risks - p. 3 4. Chapter 2. Food recommendations for reducing your impact - p. 19 5. Chapter 3. Identifying factors contributing to nutrition disparities in women within the context of Indian states - p. 37 6. Chapter 4. Mental healthcare in the times of climate change action and data science - p. 59 7. Chapter 5. Drivers of health workshop attendance among working-class women in India: a preliminary survey - p. 83 8. Part II: Planet - p. 101 9. Chapter 6. The soil guardians: role of PGPR in soil fertility and nutrient management - p. 103 10. Chapter 7. Spatiotemporal assessment of mangrove dynamics and phenological trends using Sentinel-1&2 time series - p. 135 11. Chapter 8. Wind behavior modeling towards improvement of sustainable energy generation: generative adversarial networks versus diffusion models - p. 147 12. Chapter 9. Optimizing climate action through data-driven sustainable development goal indicators - p. 163 13. Part III: Prosperity - p. 177 14. Chapter 10. Data driven insights into educational equity: Completion rates and literacy proficiency across Human Development Index tiers - p. 179 15. Chapter 11. A knowledge database discovery approach for improving quality in higher education institutions - p. 207 16. 12. Sustainable infrastructure planning and environmental impact assessment - p. 229 17. Part IV: Peace - p. 245 18. Chapter 13. Leveraging shallow learning techniques to support the methodological design of public policies in low density areas in Colombia - p. 247 19. Chapter 14. A fuzzy-based emotion detection model for determining critical hotspot urban areas in the presence of pandemic and heatwave multirisks - p. 263 20. Part V: Partnerships - p. 279 21. Chapter 15. Climate change and demographic choices - p. 281 22. 16. Enhancing climate resilience through natural resource management: the role of Mahatma Gandhi National Rural Employment Guarantee Act in Jharkhand - p. 303 23. Index - p. 315
