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Spatial Variations of Village-Level Environmental Variables from Satellite Big Data and Implications for Public Health–Related Sustainable Development Goals

Author

Listed:
  • Xue Liu

    (Center for Geographic Analysis, Harvard University, Cambridge, MA 02138, USA)

  • Rockli Kim

    (Division of Health Policy and Management, College of Health Science, Korea University, Seoul 02841, Korea
    Interdisciplinary Program in Precision Public Health, Department of Public Health Sciences, Graduate School of Korea University, Seoul 02841, Korea)

  • Weixing Zhang

    (Harvard Center for Population and Development Studies, Cambridge, MA 02138, USA)

  • Weihe Wendy Guan

    (Center for Geographic Analysis, Harvard University, Cambridge, MA 02138, USA)

  • S. V. Subramanian

    (Harvard Center for Population and Development Studies, Cambridge, MA 02138, USA
    Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA)

Abstract

The United Nations Sustainable Development Goals (SDGs) include 17 interlinked goals designed to be a blueprint for the world’s nations to achieve a better and more sustainable future, and the specific SDG 3 is a public health–related goal to ensure healthy living and promote well-being for all population groups. To facilitate SDG planning, implementation, and progress monitoring, many SDG indicators have been developed. Based on the United Nations General Assembly resolutions, SDG indicators need to be disaggregated by geographic locations and thematic environmental and socioeconomic characteristics for achieving the most accurate planning and progress assessment. High-resolution data such as those captured at the village level can provide comparatively more precise insights into the different socioeconomic and environmental factors relevant to SDGs, therefore enabling more effective sustainable development decision-making. Using India as our study area and the child malnutrition indicators stunting, underweight, and wasting as examples of public health–related SDG indicators, we have demonstrated a process to effectively derive environmental variables at the village level from satellite big datasets on a cloud platform for SDG research and applications. Spatial analysis of environmental variables regarding vegetation, climate, and terrain have shown spatial grouping patterns across the entire study area, with each village group having different statistics. Correlation analysis between these environmental variables and stunting, underweight, and wasting indicators show a meaningful relationship between these indicators and vegetation index, land surface temperature, rainfall, elevation, and slope. Identifying the spatial variation patterns of environmental variables at the village level and their correlations with child malnutrition indicators can be an invaluable tool to facilitate a clearer understanding of the causes of child malnutrition and to improve area-specific SDG 3 implementation planning. This analysis can also provide meaningful support in assessing and monitoring SDG implementation progress at the village level by spatially predicting SDG indicators using available socioeconomic and environmental independent variables. The methodology used in this study has the potential to be applied to other similar regions, especially low-to-middle income countries where a high number of children are severely affected by malnutrition, as well as to other environmentally related SDGs, such as Goal 1 (No Poverty) and Goal 2 (Zero Hunger).

Suggested Citation

  • Xue Liu & Rockli Kim & Weixing Zhang & Weihe Wendy Guan & S. V. Subramanian, 2022. "Spatial Variations of Village-Level Environmental Variables from Satellite Big Data and Implications for Public Health–Related Sustainable Development Goals," Sustainability, MDPI, vol. 14(16), pages 1-14, August.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:16:p:10450-:d:894861
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    References listed on IDEAS

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    1. Kim, Rockli & Mohanty, Sanjay K. & Subramanian, S.V., 2016. "Multilevel Geographies of Poverty in India," World Development, Elsevier, vol. 87(C), pages 349-359.
    2. Scott F. Dowell & David Blazes & Susan Desmond-Hellmann, 2016. "Four steps to precision public health," Nature, Nature, vol. 540(7632), pages 189-191, December.
    3. Kofi Annan, 2018. "Data can help to end malnutrition across Africa," Nature, Nature, vol. 555(7694), pages 7-7, March.
    4. Brian J. Reich & Murali Haran, 2018. "Precision maps for public health," Nature, Nature, vol. 555(7694), pages 32-33, March.
    5. Steve MacFeely, 2019. "The Big (data) Bang: Opportunities and Challenges for Compiling SDG Indicators," Global Policy, London School of Economics and Political Science, vol. 10(S1), pages 121-133, January.
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