| Grant number: | 17/08376-0 |
| Support type: | Scholarships in Brazil - Doctorate |
| Effective date (Start): | March 01, 2019 |
| Effective date (End): | July 31, 2021 |
| Field of knowledge: | Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques |
| Principal researcher: | José Fernando Rodrigues Júnior |
| Grantee: | Gabriel Spadon de Souza |
| Home Institution: | Instituto de Ciências Matemáticas e de Computação (ICMC). Universidade de São Paulo (USP). São Carlos , SP, Brazil |
| Associated research grant: | 16/17078-0 - Mining, indexing and visualizing Big Data in clinical decision support systems (MIVisBD), AP.TEM |
| Associated scholarship(s): | 19/04461-9 - Advancing medical prognosis based on graph concepts and artificial neural networks, BE.EP.DR |
Abstract Complex networks contribute toward computational research and analysis through their ability to design systems modeled by vertices and edges, and by the attributes of their elements. They provide means to describe urban structures through their street mesh, expressing predicates that refer to the flow and transportation in an urban zone. The analysis of such structures is potentialized when they are linked with problematic issues, such as slowness, traffic jam and lack of road access that can be caused by ill-conceived urban decisions. Towards the analysis of information from the street networks, and through metrics inherent from its elements, this research aims to develop techniques to guide more effective planning actions. This investigation will contribute through applications that can optimize street meshes and characterize cities, by using computational techniques that focus on structural, topological and geometrical properties of street networks; it is also our intention to extract features from different cities, aiming to analyze their similarity, and consequently, providing for the understanding of the urban space. In this sense, the methodology of this study is based on the use of digital maps, which are widely produced nowadays; through them, we provide methods for extraction, computational representation, and preprocessing, which are followed by the development and/or improvement of algorithms. Specifically, we will use complex-network tools, clustering algorithms, similarity analysis and retrieval methods, besides mathematical techniques - derived from the optimization theory related to submodular-functions. Through this set of methods, we foresee to provide theoretical and practical results as methods and tools for urban analysis, design, and optimization. (AU) | |
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