IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i16p10450-d894861.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/16/10450/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/16/10450/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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. 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.
    5. Brian J. Reich & Murali Haran, 2018. "Precision maps for public health," Nature, Nature, vol. 555(7694), pages 32-33, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Xiaoli Zhao & Pavel Castka & Cory Searcy, 2020. "ISO Standards: A Platform for Achieving Sustainable Development Goal 2," Sustainability, MDPI, vol. 12(22), pages 1-19, November.
    2. Eric Yanchenko & Howard D. Bondell & Brian J. Reich, 2024. "Spatial regression modeling via the R2D2 framework," Environmetrics, John Wiley & Sons, Ltd., vol. 35(2), March.
    3. Kristie L. Ebi & Christopher Boyer & Kathryn J. Bowen & Howard Frumkin & Jeremy Hess, 2018. "Monitoring and Evaluation Indicators for Climate Change-Related Health Impacts, Risks, Adaptation, and Resilience," IJERPH, MDPI, vol. 15(9), pages 1-11, September.
    4. Jan Anton van Zanten & Rob van Tulder, 2020. "Beyond COVID-19: Applying “SDG logics” for resilient transformations," Journal of International Business Policy, Palgrave Macmillan, vol. 3(4), pages 451-464, December.
    5. Anoop Jain & Lia C.H. Fernald & Kirk R. Smith & S.V. Subramanian, 2019. "Sanitation in Rural India: Exploring the Associations between Dwelling Space and Household Latrine Ownership," IJERPH, MDPI, vol. 16(5), pages 1-14, February.
    6. Venera Timiryanova & Dina Krasnoselskaya & Natalia Kuzminykh, 2022. "Applying the Multilevel Approach in Estimation of Income Population Differences," Stats, MDPI, vol. 6(1), pages 1-32, December.
    7. MacFeely Steve, 2020. "Measuring the Sustainable Development Goal Indicators: An Unprecedented Statistical Challenge," Journal of Official Statistics, Sciendo, vol. 36(2), pages 361-378, June.
    8. Wang, Weiwen & Gong, Jian & Wang, Ying & Shen, Yang, 2021. "Exploring the effects of rural site conditions and household livelihood capitals on agricultural land transfers in China," Land Use Policy, Elsevier, vol. 108(C).
    9. MacFeely Steve, 2020. "Measuring the Sustainable Development Goal Indicators: An Unprecedented Statistical Challenge," Journal of Official Statistics, Sciendo, vol. 36(2), pages 361-378, June.
    10. Sanjay K. Mohanty & Guru Vasishtha, 2021. "Contextualizing multidimensional poverty in urban India," Poverty & Public Policy, John Wiley & Sons, vol. 13(3), pages 234-253, September.
    11. Katerina Zdravkova, 2023. "Personalized Education for Sustainable Development," Sustainability, MDPI, vol. 15(8), pages 1-13, April.
    12. Lorenzo Donadio & Rossano Schifanella & Claudia R Binder & Emanuele Massaro, 2021. "Leveraging insurance customer data to characterize socioeconomic indicators of Swiss municipalities," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-23, March.
    13. Ainhoa González & Shane Mc Guinness & Enda Murphy & Grainne Kelliher & Lyn Hagin-Meade, 2023. "Priorities, Scale and Insights: Opportunities and Challenges for Community Involvement in SDG Implementation and Monitoring," Sustainability, MDPI, vol. 15(6), pages 1-15, March.
    14. Håvard Hegre & Kristina Petrova & Nina von Uexkull, 2020. "Synergies and Trade-Offs in Reaching the Sustainable Development Goals," Sustainability, MDPI, vol. 12(20), pages 1-24, October.
    15. Giulia Mugellini & Jean‐Patrick Villeneuve & Marlen Heide, 2021. "Monitoring sustainable development goals and the quest for high‐quality indicators: Learning from a practical evaluation of data on corruption," Sustainable Development, John Wiley & Sons, Ltd., vol. 29(6), pages 1257-1275, November.
    16. Alberto Dello Strologo & Edoardo D’Andrassi & Niccolò Paoloni & Giorgia Mattei, 2021. "Italy versus Other European Countries: Sustainable Development Goals, Policies and Future Hypothetical Results," Sustainability, MDPI, vol. 13(6), pages 1-46, March.
    17. Cameron Allen & Graciela Metternicht & Thomas Wiedmann, 2021. "Priorities for science to support national implementation of the sustainable development goals: A review of progress and gaps," Sustainable Development, John Wiley & Sons, Ltd., vol. 29(4), pages 635-652, July.
    18. Yuewen Jiang & Chong Huang & Duoduo Yin & Chenxia Liang & Yanhui Wang, 2020. "Constructing HLM to examine multi-level poverty-contributing factors of farmer households: Why and how?," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-17, January.
    19. Suman Chakrabarti & Samuel P. Scott & Harold Alderman & Purnima Menon & Daniel O. Gilligan, 2021. "Intergenerational nutrition benefits of India’s national school feeding program," Nature Communications, Nature, vol. 12(1), pages 1-10, December.
    20. Mohajan, Haradhan, 2022. "Food Insecurity and Malnutrition of Africa: A Combined Attempt Can Reduce Them," MPRA Paper 112609, University Library of Munich, Germany, revised 10 Feb 2021.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:14:y:2022:i:16:p:10450-:d:894861. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.