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Although geographic patterns have been considered in statistical modelling for many years, new volunteered geographical information is opening opportunities for estimating variables of the city using the urban characteristics of places. Studies have shown the effectiveness of using Points-of-Interest (PoI) data in various predictive applications domains involving geographic data science, e.g. crime hot spots, air quality and land usage analysis. However, it is hard to find the data sources mentioned in these studies and which are the best practices of extracting useful covariates from them. In this study, we propose the Geohunter, a reproducible geographic feature engineering procedure that relies on OpenStreetMap, with a software interface to commonly used tools for geographic data analysis. We also analysed two feature engineering procedures, the quadrat method and KDE in which we conduct a qualitative and quantitative evaluation to suggest which better translate geographic patterns of the city. Further, we provide some illustrative examples of Geohunter

Geographic Feature Engineering with Points-of-Interest from OpenStreetMap