The Facility Location Problem is an important research topic in spatial analysis. This paper focuses on the Static and Mobile Facility Location (SMFL) Problem, which aims to identify those static and mobile facility locations that serve a target area most efficiently and equally. This paper formalizes the SMFL problem as a bi-objective model and then solves the model by using a novel heuristic algorithm, named Static and Mobile Facility Location Searching (SMFLS). The algorithm consists of two steps: static facility location searching and mobile facility location searching. In order to solve the model for large datasets efficiently, a clustering-based heuristic method is proposed for the static facility location searching while the mobile facility location searching is implemented using a greedy heuristic method. Experiments on synthetic datasets demonstrate the efficiency of the SMFLS algorithm. In addition, with the aim of conducting facility location decision-making conveniently and efficiently, in this paper, an interactive geospatial analysis platform, named Geospatial Analysis Platform using Interactive Maps (GAPIM) is proposed by combining the bi-objective models and the SMFLS algorithm with an interactive map. Experiments on Alberta public health service data are conducted, with the results demonstrating the efficiency and practicality of the platform.