Poverty as a Function of Space: Understanding the Spatial Configuration of Poverty in Malaysia for Sustainable Development Goal Number One

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Spatial analysis, Poverty, Spatial autocorrelation, Geographically weighted regression (GWR), Sustainable development goal

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Poverty is one of the most common problems throughout the world. For this reason, United Nations Sustainable Development Goal (SDG) number one aims at its eradication from all countries around the globe. While this is an ambitious goal, it has reinforced the commitment of many countries, including Malaysia, towards increased poverty alleviation efforts. Peninsular Malaysia has over the past decade been making many efforts to reduce poverty among its population. Like many socioeconomic problems, poverty is a function of space, and spatial analysis can be key to deeper understanding and implementation of effectual intervention policies. In the era of advancement of geospatial techniques, many of our socio-economic problems can be explained through spatial analysis, mapping, and visualization. This study’s main objective is to illuminate the spatial distribution of poverty and assess the factors that most contribute to the spatial configuration, using hotspot analysis and geographically weighted regression. While the results demonstrate the complexity of poverty as an issue in Malaysia, they demonstrate a clear spatial pattern. Poverty rates in Malaysia are significantly clustered (p < 0.001), as most of the high poverty rate sub-districts are located in the northeastern states of Kelantan and Terengganu. Even though the SDG number one is an ambitious one, this paper has revealed important spatial dynamics that are worthy of consideration as the government implement policies that will eradicate poverty.

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GeoJournal, v. 84, p. 1317-1336