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Spatial Variation in Seasonal Water Poverty Index for Laos: An Application of Geographically Weighted Principal Component Analysis

Author

Listed:
  • Marko Kallio

    (Aalto University
    Aalto University)

  • Joseph H. A. Guillaume

    (Aalto University)

  • Matti Kummu

    (Aalto University)

  • Kirsi Virrantaus

    (Aalto University)

Abstract

Water poverty, defined as insufficient water of adequate quality to cover basic needs, is an issue that may manifest itself in multiple ways. Extreme seasonal variation in water availability, such as in Laos, located in Monsoon Asia, results in large differences in water poverty conditions between dry and wet seasons. In this study, seasonal Water Poverty Indices (WPI) are developed for 8215 villages in Laos. WPI is a multidimensional composite index integrating five dimensions of water: resource availability, access to safe water, capacity to manage the resource, its use and environmental requirements. Principal Component Analysis (PCA) and Geographically Weighted PCA (GWPCA) were used to examine drivers of water poverty and to derive different weighting schemes. Three major drivers were identified: poverty, commercial/subsistence agriculture and village location. The least water poor areas are located around the capital city and along the Mekong River Valley while the highest water poverty is found in sparsely populated mountainous areas. Wet season WPI is on average more than 12 index points higher than in the dry season, but in some villages monsoon rain does not improve the situation. The results indicate large spatial and temporal differences in WPI within Laos. In analysis of WPI components, a mean–variance scaled PCA is recommended due to its capacity for uncovering processes driving water poverty. Extending to GWPCA is recommended when information on local differences is of interest.

Suggested Citation

  • Marko Kallio & Joseph H. A. Guillaume & Matti Kummu & Kirsi Virrantaus, 2018. "Spatial Variation in Seasonal Water Poverty Index for Laos: An Application of Geographically Weighted Principal Component Analysis," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 140(3), pages 1131-1157, December.
    • Handle: RePEc:spr:soinre:v:140:y:2018:i:3:d:10.1007_s11205-017-1819-6
    DOI: 10.1007/s11205-017-1819-6
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    References listed on IDEAS

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    Cited by:

    1. Matheus Pereira Libório & João Francisco Abreu & Petr Iakovlevitch Ekel & Alexei Manso Correa Machado, 2023. "Effect of sub-indicator weighting schemes on the spatial dependence of multidimensional phenomena," Journal of Geographical Systems, Springer, vol. 25(2), pages 185-211, April.
    2. Alfredo Cartone & Domenica Panzera, 2021. "Deprivation at local level: Practical problems and policy implications for the province of Milan," Regional Science Policy & Practice, Wiley Blackwell, vol. 13(1), pages 43-61, February.
    3. Matheus Pereira Libório & Alexandre Magno Alvez Diniz & Douglas Alexandre Gomes Vieira & Petr Iakovlevitch Ekel, 2024. "Subjective–Objective Method of Maximizing the Average Variance Extracted From Sub-indicators in Composite Indicators," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 175(2), pages 613-637, November.
    4. Anderson, Heather K. & Price, Heather & Staddon, Sam, 2023. "Water poverty in a ‘Hydro Nation’: Exploring distributional and recognitional water injustice in Scotland," Utilities Policy, Elsevier, vol. 85(C).

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