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

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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.

Details

Original languageEnglish
Pages (from-to)1131-1157
Number of pages27
JournalSocial Indicators Research
Volume140
Issue number3
Early online date13 Dec 2017
Publication statusPublished - 1 Dec 2018
MoE publication typeA1 Journal article-refereed

    Research areas

  • Water Poverty Index, Geographically weighted principal component analysis, Monsoon, Water poverty, Spatio-temporal analysis, Laos

ID: 16579689