UNDP Sudan embarked on testing a variety of Big Data sources such as household electricity consumption, night time lights and mobile phone use data to calculate proxy of poverty levels in Sudan at scales deeper than state levels. A high-correlation becomes evident between the Big Data from cellphone usage and antenna location and multi-dimensional poverty (MPI) composite index at locality level. Big Data will provide a good overview of spatial disparity – inequality across different localities based on MPI composite index. This would be groundbreaking to measure spatial inequalities at locality level, contributing to well-informed debates for public expenditure planning, donor coordination and local governance. It will be also contributing to the achievement on addressing inequalities. Furthermore, for a nation-wide scale up, UNDP Sudan will ensure gender disaggregated data to be available. At the same time, in advancing Big Data for MPI, UNDP Sudan will diminish limitations of Big Data to maintain integrity of the development policy advisory support.
New and Emerging Data