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This work presents a heuristic for describing the next best view location for an autonomous agent exploring an unknown environment. The approach considers each robot as a point mass with omnidirectional and unrestricted vision of the environment and line-of-sight communication operating in a polygonal environment which may contain holes. The number of robots in the team is always sufficient for full visual coverage of the space. The technique employed falls in the category of distributed visibility-based deployment algorithms which seek to segment the space based on each agent's field of view with the goal of deploying each agent into the environment to create a visually connected series of agents which fully observe the previously unknown region. The contributions made to this field are a technique for utilizing linear programming methods to determine the solution to the next best observation (NBO) problem as well as a method for calculating multiple NBO points simultaneously. Both contributions are incorporated into an algorithm and deployed in a simulated environment built with MATLAB for testing. The algorithm successfully deployed agents into polygons which may contain holes. The efficiency of the deployment method was compared with random deployment methods to establish a performance metric for the proposed tactic. It was shown that the heuristic presented in this work performs better the other tested strategies., (Copyright © 2018 Graves and Chakraborty.)

Many economic processes are intertwined with landscape change. A large number of individual economic decisions shape the landscape, and in turn the changes in the landscape shape economic decisions. This article describes key research questions about the economics of landscape change and reviews the state of research knowledge. The rich and varied economic-landscape interactions are an active area of research by economists, geographers, and others. Because the interactions are numerous and complex, disentangling the causal relationships in any given landscape system is a formidable research challenge. Limited data with mismatched temporal and spatial scales present further obstacles. Nevertheless, the growing body of economic research on these topics is advancing and shares fundamental challenges, as well as data and methods, with work in other disciplines.