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. / Doctor of Philosophy / In many countries, 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. To promote 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, 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. In this presentation, we propose algorithmic techniques for solving the school redistricting problem. Our approaches include (i) clustering, (ii) local search, and (iii) memetic algorithms. We also show that MCMC-based techniques can aid in enabling exact methods to operate on this problem. Lastly, we briefly highlight ethical considerations involved in the process of school redistricting and throw light on some ways to devise more ethically-aware strategies for doing school redistricting. The results indicate that the proposed methods could be a valuable decision-making tool for school officials.

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. / Ph. D.

This study aims to elucidate firm and performance attributes of a population of small, elite firms that assume prominent positions in their respective technological spaces and product markets. More specifically, this study addresses the role and impact of industrial agglomeration on the location and performance characteristics of serial innovator firms. The dissertation was conceived as a collection of three distinct but related essays. The first essay on the geographical location of firms with high levels of innovative prowess i.e. serial innovator firms vis-à-vis technology clusters and research universities indicates that these firms are not necessarily located in Metropolitan Statistical Areas (MSA) with higher average levels of industry clustering than non-serial innovator firms of similar size. Serial innovator firms and their less innovative counterparts appear to have the same need and capacity to absorb knowledge spillovers in technology clusters. Further analysis, however, revealed that serial innovator firms in the Pharmaceutical & Biotechnology and IT hardware industries are located in MSA's with significantly higher levels of regional specialization than non-serial innovator firms in that industry which suggests an asymmetric need for knowledge spillovers by these firms. Furthermore, serial innovator firms seem to be located in MSA's with a significantly higher number of research universities than a non-serial innovator firm although differences across industries can be noted. This again indicates an asymmetric use and need for academic knowledge spillovers and pecuniary advantages offered by these institutions. The analysis in the second essay reveals that serial innovator firms located in MSAs (Metropolitan Statistical Areas) with elevated levels of industrial clustering announce significantly more new products than their counterparts located in MSA areas with low levels of industrial clustering. However, no differences in the pace of technological progress of the technologies developed by serial innovator firms located in technology clusters and those outside of clusters was found. Finally, the research reported in the third essay indicates that the level of industrial agglomeration has a positive impact on the export performance of serial innovator firms and that these firms benefit proportionately more from technology clusters than non-serial innovator firms.