Data visualization and optimization methods for placing entities within urban areas
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Sepandar D. Kamvar.
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In the first part of this thesis, I present a portfolio of web-based visualizations that illustrate different data-driven ideas about urban environments. These visualizations are intended to provide the user with unique perspectives about cities and the way they function. I detail the conceptualization, data aspects, and implementation of each of these map visualizations. In the second part of this thesis, I describe an interesting optimization problem of placing entities such as trees or shops within a city. The location of these placements needs to conform to certain constraints enforced by spatial distributions of variables such as population, income, travel times, etc. I then present a heuristic-based optimization strategy, that combines some aspects of Gradient-ascent and Simulated Annealing, to address this problem and attempt to generalize this approach to finding the optimal placements of any entity within a given city. I present some initial results of my optimization algorithm and discuss ways in which it can be further improved.
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2014.Cataloged from PDF version of thesis.Includes bibliographical references (page 83).
DepartmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology
Electrical Engineering and Computer Science.