A Novel Algorithm to Enrich Geographical Information Systems with Hybrid Data for Better Informed Decisions

Land use and census data are of a significant value to urban planners, decision-makers, and investors. Important decisions are made based upon historical data, i.e, inductive reasoning. Such data become increasingly valuable at a fine-grain level to decision-makers in businesses where audience segmentation is needed. In rapidly developing countries, land use and census data are widely available at a coarse-grain level. However, accurate fine-grain mapping of such data is a very challenging task. In this paper, we propose a hierarchical methodology that enriches Geographical Information Systems (GIS) by utilizing existing datasources, rule-based algorithms, and workers (mechanical turks) to identify and map socioeconomic data at a fine-grain level. The proposed methodology was tested and evaluated on multiple cities in Saudi Arabia, with an overall accuracy of 90%. The output of this work, a GIS enriched with socioeconomic data and population count, is of high importance to decision makers and can be utilized in different problems to help make a better informed decision.