A Visual Decision-Guided Tool: Integrating Optimization Programming into Geo-Data Visualization for Finding the Viable Shortest Path

We propose a Visual Decision-Guided Tool that integrates optimization programming into geo-data visualization to determine the best path for rescue and recovery missions. First, we will develop the Top-k Objected-oriented Smoothest Paths model which captures the object dynamics of geospatial temporal network in a terrain over a time horizon. These objects include stationary entities, mobile objects, and route segments. Second, we will extend the Smoothest Path Algorithm (SPA) to be a dynamic learning algorithm, i.e., the Time-varying Smoothest Path Algorithm, which integrates the object dynamics to learn the top-k smoothest routes at each instance of time. The main advantage offered by the SPA extension is its lower logarithmic time complexity, i.e., O(NlogN), where N is the number of nodes in a terrain. Finally, we will develop a new design of visual displays that enable military operators to analyze other crucial factors, such as vehicle types, weather severity, and soldiers´ specialty levels, which are required to be interpreted by human perception, cognition, and knowledge to select the best path among the top-k smoothest routes at each instance of time for rescue and recovery missions.