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This research proposes a model to predict the location of the most deprivedareas in a city using data from the census. Census data is veryhigh-dimensional and needs to be simplified. We use the diffusion map algorithmto reduce dimensionality and find patterns. Features are defined byeigenvectors of the Laplacian matrix that defines the diffusion map. Theeigenvectors corresponding to the smallest eigenvalues indicate specificcharacteristics of the population. Previous work has found qualitatively thatthe second most important dimension for describing the census data in Bristol,UK is linked to deprivation. In this research, we analyse how good thisdimension is as a model for predicting deprivation by comparing it with therecognised measures. The Pearson correlation coefficient was found to begreater than 0.7. The top 10 per cent of deprived areas in the UK, which arealso located in Bristol, are extracted to test the accuracy of the model. Thereare 52 of the most deprived areas, and 38 areas are correctly identified bycomparing them to the model. The influence of scores of IMD domains that do notcorrelate with the models and Eigenvector 2 entries of non-deprived OutputAreas cause the model to fail the prediction of 14 deprived areas. The modeldemonstrates strong performance in predicting future deprivation in the projectareas, which is expected to assist in government resource allocation andfunding greatly. The codes can be accessed here:https://github.com/junegoo94/diffusion_maps

Data-Driven Socio-Economic Deprivation Prediction via Dimensionality Reduction: The Power of Diffusion Maps