With steadily-declining wind and solar energy prices, many developing countries have the opportunity to leapfrog into a low carbon future and avoid investing in costly and environmentally deleterious hydropower and coal power plants. Decision makers can accelerate renewable energy (RE) deployment by identifying RE priority areas suitable for large scale development based on favorable technical, economic, environmental, and social criteria and preemptively planning transmission infrastructure, which is often the bottleneck for new RE projects. However, most countries lack openly accessible renewable resource data sets and modeling tools that can enable their decision makers to plan large-scale wind and solar buildouts. This thesis is devoted to developing the geospatial models and user-interactive platforms that can empower users (such as decision-makers) to identify the best wind and solar sites using multiple criteria that balance high resource quality with conservation and social needs. This work is part of the 2015 Multicriteria Analysis for Planning Renewable Energy (MapRE) initiative (1). First, accessible tools were developed for user-centered multicriteria allocation of renewable energy infrastructure in any region. Second, a visualization was created to further explore the potential infrastructure development areas further and weight individual parameters to choose the best-fit energy zones for a particular region and its requirements. Third, both of these tools were tested on a 12-country region known as the Southern African Power Pool (SAPP) in southern Africa as a case study with wind and solar photovoltaic (PV) as renewable energy sources. Using this case study, the factors that most affect zone choice and selection uncertainty were studied. These tools will enable decision-makers not just in southern Africa, but any part of the world, to select and develop RE sites based on multiple criteria and scale up RE power generation.