In the current study, an algorithm-based data processing, sizing, optimization, sensitivity analysis and clustering approach (DaSOSaCa) is proposed as an efficient simultaneous solar/wind assessment methodology. Accordingly, data processing is performed to obtain reliable high quality meteorological data among various datasets, which are used for hybrid photovoltaic/wind turbine/storage/converter system optimal design for consequent sites in a large region. The optimal hybrid systems are consequently simulated to meet hourly power demand in various sites. The solar/wind fraction and net present cost of the systems are then used as the technical and economic clustering variables, respectively. The clustering results are finally used as input to obtain novel hybrid solar/wind GIS maps. Iran is selected as the case study to validate the proposed methodology and detail its applicability. Ten minute annual global horizontal radiation, wind speed, and temperature data are analyzed, and the optimal, robust hybrid systems are simulated for various sites in order to classify the country. The generated GIS maps show that Iran can be efficiently clustered into four technical and five economic clusters under optimal conditions. The clustering results prove that Iran is mainly a solar country with approximately 74% solar power fraction under optimum conditions. A macroeconomic evaluation using DaSOSaCa also reveals that the nominal discount rate is recommended to be greater than 20% considering the current economic situation for the renewable energy sector in Iran. An environmental analysis results show that an average 106.68 tonCO(2)-eq/year is produced for such hybrid systems application in Iran during a cradle to grave life cycle. Thus, Iran energy sector can be eminently promoted to an environmentally efficient stage with regard to the proposed classification plan and economic considerations. (C) 2017 Elsevier Ltd. All rights reserved.