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In countries like the US, public school systems function through school districts, which are geographical areas where schools share the same administrative structure and are often coterminous with the boundary of a city or a county. School districts play an important role in the functioning of society. In a well-run school district with safe and well-functioning schools, graduating enough students can enhance the quality of life in its area. Conversely, a poorly run district may cause growth in the area to be far less than surrounding areas, or even a decline in population over time. To promote the efficient functioning of the school district, the boundaries of public schools are redrawn from time to time by the school board/planning officials. In the majority of the cases, this process of redrawing the school boundaries, also called school redistricting or school boundary formation, is done manually by the planners and involves hand-drawn maps. Given the rapid advancements in GIS made in the last decade and the availability of high-quality geospatial data, we opine that an objective treatment of the school redistricting problem by a data-driven model can assist the school board/ decision-makers by providing them with automated plans. These automated plans may serve as possible suggestions to the planners, who can adapt them to prepare their own plans in the way they see fit based on their subjective knowledge and expertise. In this dissertation, we propose algorithmic techniques for solving the problem of (school) redistricting, which is an NP-hard problem. We primarily investigate optimization-based algorithms for solving the problem. Our approaches include (i) clustering, (ii) local search, and (iii) memetic algorithms. We also propose ways of solving the problem using exact methods and fair redistricting techniques based on ethical considerations. The techniques developed here are generic enough to be applied to other redistricting problems with some degree of modification in the objective function and constraint-handling techniques. The source code and corresponding datasets are available at https://github.com/subhodipbiswas/schoolredistricting.

The purpose of this study is to investigate implications of geographic location for firm strategy and for the competitive climate in emerging higher technology industries. Hypotheses are generated based on concepts from institutional theory, transaction costs economics, economic geography, and strategic management. Specifically, tests are conducted to determine whether there is an association between establishments' geographic locations and the incidence of two collective strategies: strategic isomorphism and strategic complementarity. These tests are performed with respect to the U. S. flberoptics industry at three-year intervals during the period 1976-1994. Tests are also performed (using 1994 data) to assess the influence that research institutes and economically dominant firms have on collective strategy formation. The study's summary finding is that, to date, there is little, if any, empirical support for an association between geographic location and strategic posture in the fiberoptics industry. While it is possible that the proposed phenomena do not occur in this industry, for all of the hypotheses there are several alternative explanations for the results. First, several of the findings suggest that too little time has elapsed for the proposed phenomena to be fully manifested in the fiberoptics industry. Second, some of the phenomena might be observable by changing sampling or measurement procedures. Third, certain characteristics of emerging higher technology industries might affect the strength of some hypothesized relationships. Based on the findings of this study, a number of suggestions are offered for further studies of the subject.