IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v19y2022i12p7179-d836662.html
   My bibliography  Save this article

Prediction of the Old-Age Dependency Ratio in Chinese Cities Using DMSP/OLS Nighttime Light Data

Author

Listed:
  • Yue Li

    (Institute of Population Research, Peking University, Beijing 100871, China)

  • Chengmeng Zhang

    (Institute of Population Research, Peking University, Beijing 100871, China)

  • Yan Tong

    (Institute of Population Research, Peking University, Beijing 100871, China)

  • Yalu Zhang

    (Institute of Population Research, Peking University, Beijing 100871, China)

  • Gong Chen

    (Institute of Ageing Studies, Peking University, Beijing 100871, China)

Abstract

The old-age dependency ratio (ODR) is an important indicator reflecting the degree of a regional population’s aging. In the context of aging, this study provides a timely and effective method for predicting the ODR in Chinese cities. Using the provincial ODR from the Seventh National Population Census and Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) nighttime light data, this study aims to predict and analyze the spatial correlation of the municipal ODR in Chinese cities. First, the prediction model of the ODR was established with curve regression. Second, the spatial structure of the municipal ODR was investigated using the Moran’s I method. The experimental results show the following: (1) the correlation between the sum of the nighttime light and ODR is greater than the mean of nighttime light in the study areas; (2) the Sigmoid model fits better than other regression models using the provincial ODR in the past ten years; and (3) there exists an obvious spatial agglomeration and dependence on the municipal ODR. The findings indicate that it is reasonable to use nighttime light data to predict the municipal ODR in large and medium-sized cities. Our approach can provide support for future regional censuses and spatial simulations.

Suggested Citation

  • Yue Li & Chengmeng Zhang & Yan Tong & Yalu Zhang & Gong Chen, 2022. "Prediction of the Old-Age Dependency Ratio in Chinese Cities Using DMSP/OLS Nighttime Light Data," IJERPH, MDPI, vol. 19(12), pages 1-23, June.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:12:p:7179-:d:836662
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/19/12/7179/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/19/12/7179/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Richard Verdugo, 2006. "Workers, workers’ productivity and the dependency ratio in Germany: analysis with implications for social policy," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 25(5), pages 547-565, December.
    2. Ana Maria Santacreu, 2016. "Long-Run Economic Effects of Changes in the Age Dependency Ratio," Economic Synopses, Federal Reserve Bank of St. Louis, issue 17, pages 1-2.
    3. Wanchun Leng & Guojin He & Wei Jiang, 2019. "Investigating the Spatiotemporal Variability and Driving Factors of Artificial Lighting in the Beijing-Tianjin-Hebei Region Using Remote Sensing Imagery and Socioeconomic Data," IJERPH, MDPI, vol. 16(11), pages 1-20, June.
    4. Tao Zhang & Jing Liu & Chaojie Liu, 2019. "Changes in Perceived Accessibility to Healthcare from the Elderly between 2005 and 2014 in China: An Oaxaca–Blinder Decomposition Analysis," IJERPH, MDPI, vol. 16(20), pages 1-12, October.
    5. Han Lin Shang & Heather Booth & Rob Hyndman, 2011. "Point and interval forecasts of mortality rates and life expectancy: A comparison of ten principal component methods," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 25(5), pages 173-214.
    6. Ying Li & Yiyang Pan & Yuan Chen & Pingyu Cui, 2021. "Important Dependency-Associated Community Resources among Elderly Individuals with a Low Level of Social Support in China," IJERPH, MDPI, vol. 18(5), pages 1-11, March.
    7. Yizhen Wu & Mingyue Jiang & Zhijian Chang & Yuanqing Li & Kaifang Shi, 2020. "Does China’s Urban Development Satisfy Zipf’s Law? A Multiscale Perspective from the NPP-VIIRS Nighttime Light Data," IJERPH, MDPI, vol. 17(4), pages 1-26, February.
    8. Lianxia Wu & Zuyu Huang & Zehan Pan, 2021. "The spatiality and driving forces of population ageing in China," PLOS ONE, Public Library of Science, vol. 16(1), pages 1-18, January.
    9. Han, Xuehui & Cheng, Yuan, 2020. "Consumption- and productivity-adjusted dependency ratio with household structure heterogeneity in China," The Journal of the Economics of Ageing, Elsevier, vol. 17(C).
    10. Zhongwei Zhao & Hongbo Jia & Mengxue Chen, 2020. "Major Socioeconomic Driving Forces of Improving Population Health in China: 1978–2018," Population and Development Review, The Population Council, Inc., vol. 46(4), pages 643-676, December.
    11. Doll, Christopher N.H. & Pachauri, Shonali, 2010. "Estimating rural populations without access to electricity in developing countries through night-time light satellite imagery," Energy Policy, Elsevier, vol. 38(10), pages 5661-5670, October.
    12. Xin Xu & Yuan Zhao & Xinlin Zhang & Siyou Xia, 2018. "Identifying the Impacts of Social, Economic, and Environmental Factors on Population Aging in the Yangtze River Delta Using the Geographical Detector Technique," Sustainability, MDPI, vol. 10(5), pages 1-15, May.
    13. Qingxu Huang & Yang Yang & Yajing Li & Bin Gao, 2016. "A Simulation Study on the Urban Population of China Based on Nighttime Light Data Acquired from DMSP/OLS," Sustainability, MDPI, vol. 8(6), pages 1-13, May.
    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. Jie Liu & Qingshan Yang & Jian Liu & Yu Zhang & Xiaojun Jiang & Yangmeina Yang, 2020. "Study on the Spatial Differentiation of the Populations on Both Sides of the “Qinling-Huaihe Line” in China," Sustainability, MDPI, vol. 12(11), pages 1-25, June.
    2. Imam, M. & Jamasb, T. & Llorca, M. & Llorca, M., 2018. "Power Sector Reform and Corruption: Evidence from Electricity Industry in Sub-Saharan Africa," Cambridge Working Papers in Economics 1801, Faculty of Economics, University of Cambridge.
    3. López-González, A. & Domenech, B. & Ferrer-Martí, L., 2018. "Formative evaluation of sustainability in rural electrification programs from a management perspective: A case study from Venezuela," Renewable and Sustainable Energy Reviews, Elsevier, vol. 95(C), pages 95-109.
    4. Ahbab Mohammad Fazle Rabbi & Stefano Mazzuco, 2021. "Mortality Forecasting with the Lee–Carter Method: Adjusting for Smoothing and Lifespan Disparity," European Journal of Population, Springer;European Association for Population Studies, vol. 37(1), pages 97-120, March.
    5. Tania García-Sánchez & Arbinda Kumar Mishra & Elías Hurtado-Pérez & Rubén Puché-Panadero & Ana Fernández-Guillamón, 2020. "A Controller for Optimum Electrical Power Extraction from a Small Grid-Interconnected Wind Turbine," Energies, MDPI, vol. 13(21), pages 1-16, November.
    6. Ardi Novra & Adriani & Fatati, 2021. "Farming Household Readiness for Smallholder Palm Oil Replanting (SPR) Program in Jambi Province, Indonesia: Is there a need for empowerment?â€," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 5(08), pages 292-302, August.
    7. Bhanot, Jaya & Jha, Vivek, 2012. "Moving towards tangible decision-making tools for policy makers: Measuring and monitoring energy access provision," Energy Policy, Elsevier, vol. 47(S1), pages 64-70.
    8. Jian-Zhou Wei & Kai Zheng & Feng Zhang & Chao Fang & Yu-Yu Zhou & Xue-Cao Li & Feng-Min Li & Jian-Sheng Ye, 2019. "Migration of Rural Residents to Urban Areas Drives Grassland Vegetation Increase in China’s Loess Plateau," Sustainability, MDPI, vol. 11(23), pages 1-18, November.
    9. Igawa, Moegi & Managi, Shunsuke, 2022. "Energy poverty and income inequality: An economic analysis of 37 countries," Applied Energy, Elsevier, vol. 306(PB).
    10. OlaOluwa Simon Yaya & Luis Alberiko Gil-Alana, 2020. "Modelling Long-Range Dependence and Non-linearity in the Infant Mortality Rates of African Countries," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 26(3), pages 303-315, August.
    11. Konstantin Ash & Nick Obradovich, 2020. "Climatic Stress, Internal Migration, and Syrian Civil War Onset," Journal of Conflict Resolution, Peace Science Society (International), vol. 64(1), pages 3-31, January.
    12. Francis Rathinam & Sayak Khatua & Zeba Siddiqui & Manya Malik & Pallavi Duggal & Samantha Watson & Xavier Vollenweider, 2021. "Using big data for evaluating development outcomes: A systematic map," Campbell Systematic Reviews, John Wiley & Sons, vol. 17(3), September.
    13. López-González, A. & Ferrer-Martí, L. & Domenech, B., 2019. "Sustainable rural electrification planning in developing countries: A proposal for electrification of isolated communities of Venezuela," Energy Policy, Elsevier, vol. 129(C), pages 327-338.
    14. Feng, Lingbing & Shi, Yanlin & Chang, Le, 2021. "Forecasting mortality with a hyperbolic spatial temporal VAR model," International Journal of Forecasting, Elsevier, vol. 37(1), pages 255-273.
    15. Ugofilippo Basellini & Søren Kjærgaard & Carlo Giovanni Camarda, 2020. "An age-at-death distribution approach to forecast cohort mortality," Working Papers axafx5_3agsuwaphvlfk, French Institute for Demographic Studies.
    16. Man Chung Fung & Gareth W. Peters & Pavel V. Shevchenko, 2016. "A unified approach to mortality modelling using state-space framework: characterisation, identification, estimation and forecasting," Papers 1605.09484, arXiv.org.
    17. Tickle Leonie & Booth Heather, 2014. "The Longevity Prospects of Australian Seniors: An Evaluation of Forecast Method and Outcome," Asia-Pacific Journal of Risk and Insurance, De Gruyter, vol. 8(2), pages 259-292, July.
    18. Yaya, OlaOluwa S & Adekoya, Oluwasegun B. & Babatunde, Oluwagbenga T., 2021. "Testing Fractional Persistence and Nonlinearity in Infant Mortality Rates of Asia Countries," MPRA Paper 109368, University Library of Munich, Germany.
    19. Javier Muro & Leo Zurita-Arthos & José Jara & Esteban Calderón & Richard Resl & Andreas Rienow & Valerie Graw, 2020. "Earth Observation for Settlement Mapping of Amazonian Indigenous Populations to Support SDG7," Resources, MDPI, vol. 9(8), pages 1-17, August.
    20. Montalvo-Navarrete, Juan M. & Lasso-Palacios, Ana P., 2024. "Energy access sustainability criteria definition for Colombian rural areas," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PA).

    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:jijerp:v:19:y:2022:i:12:p:7179-:d:836662. 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.