Considering the volatility and synergies of renewable energy sources, sufficient resource assessment is of great significance for investors and planners to reduce power fluctuations, increase integration capacity, and improve economic and social benefits. This paper proposes a tri-level framework to evaluate and visualize the spatio-temporal characteristics of regional wind and solar energy resources from the perspective of data mining. Furthermore, a free, open-source software package named Quantitative Synergy Assessment Toolbox for renewable energy sources (QSAT V1.0) has been developed with Python and hosted on GitHub, which is a useful tool for the resources assessment and preliminary regional synergetic planning. In the first level, the long-term reanalysis meteorological data of wind speed, solar irradiation and ambient temperature are acquired from MERRA, processed via virtual generation systems, and corrected by in-situ measured data. For the progressive two levels of single-site and wide-area data assessments, the data mining methods incorporating attribute construction, principal components analysis and k-means clustering are used to reduce the dimensionality and capture the temporal and spatial synergy patterns. According to the extracted patterns, the rational combinations of sub-regions can be selected as candidates to make full use of the synergies. Shandong province in China is taken as a demonstration to quantify and analyze the complementarities of solar and wind resources. The proposed method and tools can help enhance the planning of renewable energy sources.