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

被引:0
作者
Marko Kallio
Joseph H. A. Guillaume
Matti Kummu
Kirsi Virrantaus
机构
[1] Aalto University,Research Group on Geoinformatics, School of Engineering, Department of Built Environment
[2] Aalto University,Water and Development Research Group, School of Engineering, Department of Built Environment
来源
Social Indicators Research | 2018年 / 140卷
关键词
Water Poverty Index; Geographically weighted principal component analysis; Monsoon; Water poverty; Spatio-temporal analysis; Laos;
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中图分类号
学科分类号
摘要
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.
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页码:1131 / 1157
页数:26
相关论文
共 89 条
[1]  
Brunsdon C(2002)Geographically weighted summary statistics—A framework for localised exploratory data analysis Computers, Environment and Urban Systems 97 257-267
[2]  
Fotheringham AS(2010)Simplifying the water poverty index Social Indicators Research 539 276-279
[3]  
Charlton M(2016)Fluvial sediment supply to a mega-delta reduced by shifting tropical-cyclone activity Nature 13 258-267
[4]  
Cho DI(2013)Principal component analysis on spatial data: An overview Annals of the Association of American Geographers 136 1287-1298
[5]  
Ogwang T(1989)Macro-scale water scarcity requires micro-scale approaches Natural Resources Forum 57 122-139
[6]  
Opio C(2010)Improved method to calculate a water poverty index at local scale Journal of Environmental Engineering 25 1717-1736
[7]  
Darby SE(2014)The water poverty index in rural Cambodia and Vietnam: A holistic snapshot to improve water management planning Natural Resources Forum 28 1869-1887
[8]  
Hackney CR(2006)Multi-attributed environmental index construction Ecological Economics 47 146-172
[9]  
Leyland J(2011)Geographically weighted principal components analysis International Journal of Geographical Information Science 15 98-115
[10]  
Kummu M(2014)Geographically weighted methods and their use in network re-designs for environmental monitoring Stochastic Environmental Research and Risk Assessment 115 253-277