Public transit, compared with passenger cars, can effectively help conserve energy, reduce air pollution, and optimize flow on roadways. In recent years, Battery Electric Bus (BEB) is receiving an increasing amount of attention from transit vehicle industry and transit agencies due to recent advances in battery technologies and the direct environmental benefits it can offer (e.g. zero emission, less noise). However, limited efforts have been attempted on the effective deployment planning of BEB system due to the unique spatiotemporal features associated with the system itself (e.g. driving range, bus scheduling). In this project, we propose to develop an innovative spatiotemporal analytical framework and web-based visualization platform to assist transit agencies in identifying the optimal deployment strategies for the BEB system by using a combination of mathematical programming methods, GIS-based analysis, and multi-objective optimization techniques. The framework will allow transit agencies to optimally phase in BEB infrastructure and deploy the BEB system in a way that can minimize the capital and operational cost of BEB system while maximizing its environmental benefits (i.e. emission reduction). We will engage three transit agencies - the Utah Transit Authority (UTA), TriMet, and Riverside Transit Agency (RTA), with the first two already in the planning phase of BEB deployment - to evaluate the usability of the platform. The web-based visualization platform will operationalize the framework and make it accessible to transit planners, decision-makers and the public. The proposed project fits the NITC theme on increasing access to opportunities, improving multi-modal planning, and developing data, models, and tools for better decision-making. The research will help transit agencies develop optimal deployment strategies for BEB systems, allowing planners and decision makers to create transportation systems that better serve livable and sustainable communities.