Abstract

This study proposes two innovative methods to measure transport-related exclusion in rural areas caused by the lack of access to basic opportunities (e.g. healthcare, education, jobs, etc). The Spatial Accessibility Poverty (SAP) indices, that are developed, are tailored for rural areas of the Global South and particularly illustrated in a case study in Northeast Brazil, where spatial data is most scarce. Gravity-based models are proposed based on travel impedance methods derived from i) Friction surface datasets, and ii) Kernel density maps. The spatial information is then aggregated at a municipality level creating indices that conjugate factors of severity and extent of SAP. The findings show that factors like deprivation of housing facilities (electricity and sanitation) and low population density are associated with critical SAP levels. The results also point out to higher prevalence of SAP in inner areas than the coastal areas of Northeast Brazil. The sensitivity analysis shows how data-poor contexts present a particularly complex environment to develop robust SAP measures. The findings emphasise the importance of considering sensitivity analysis and complementary factor analysis when applying SAP indicators for planning transport and for action prioritisation purposes. Since access to services and opportunities has been claimed to be a key difference between those who have escaped chronic poverty those still trapped in it, SAP indicators tailored to areas where poverty is most spread are of primary importance to promote a new standard of transport development strongly committed to eradicating poverty.

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