Web-based data visualization and optimization methods for applications in urban planning

Abstract

This thesis deals with the problem of enhancing the understanding of urban information through interactive data-driven visualization and planning tools. In the first part of the thesis, I present a series of web-based data visualizations through maps to provide users with thorough understanding of their living environments, and highlight patterns and trends to raise their awareness of how cities evolve over time. In the second part, I focus on tools that support the planning of interventions on the urban level. Specifically, I tackle the problem of selecting the most optimal locations for placing new entities within the city using a modified k-means algorithm.