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The MOVES model was developed by the U.S. Environmental Protection Agency (U.S. EPA) to estimate emissions from on-road mobile sources and nonroad sources in the United States. Coupling high-resolution on-road vehicle activity data with appropriate MOVES emission rates further advances research efforts designed to assess the environmental impacts of transportation design and operation strategies. However, the complicated MOVES interface and slow performance makes it difficult to assess large, regional scale transportation networks and to undertake analyses of large-scale systems that are dynamic in nature. The MOVES-Matrix system develops an initial Large Matrix of MOVES outputs by running MOVES 146,853 times on the PACE high performance computing cluster to generate more than 90 billion emission rates to populate the matrix for a single area with one fuel regime and one inspection and maintenance program. A total of 117 such Large Matrices would be needed for the entire United States. The MOVES-Matrix system developed can be used to conduct the emissions modeling 200-times faster than using MOVES. The hypothetical case study shows that MOVES-Matrix is able to generate the exact same emission results as the MOVES model to ensure the validity for regulatory analysis. The resulting matrix allows users to link emission rates to big data projects and to evaluate changes in emissions for dynamic transportation systems in near-real-time. MOVES-Matrix does not currently estimate emissions from starts, hoteling or evaporative emissions, and the research team is working on MOVES-Matrix version 2 that supports incorporating off-network modeling. Implications: MOVES-Matrix should be of interest to a broad readership including those interested in vehicle emission modeling, near-road air quality modeling, transportation conformity analysis. The paper should also interest engineers who are involved in transportation regulatory and conformity analysis, state implementation plan, and who are seeking an efficient way of conducting regulatory emission modeling and air quality analysis in the United States. [ABSTRACT FROM AUTHOR]

This study is an interpretation of the early computer industry in America from the perspective of evolutionary economic geography. We analyze the County Business Patterns microdata files for the 1970s and identify a variety of early computer clusters. We explore the economic factors that led to the formation of these clusters, including preexisting conditions and Marshallian externalities, and suggest that a combination of these factors goes a long way toward explaining the location patterns we observe in the data. Part 1 of our study analyzes the clustering patterns of the early computer industry. Part 2 develops explanations for those patterns. That article will be published in the next issue of Information & Culture. [ABSTRACT FROM AUTHOR]