A distance-based tool-set to track inconsistent urban structures through complex-networks

TL;DR

This paper presents a set of distance-based algorithms for analyzing urban complex networks, detecting access issues, and recommending improvements to urban structures for better connectivity.

Contribution

It introduces novel distance-based pattern discovery tools and a greedy algorithm for urban structure optimization within complex network models.

Findings

Effective detection of inaccessible nodes in urban networks

Algorithmic recommendations for optimal placement of points of interest

Systematic approach to improve urban connectivity

Abstract

Complex networks can be used for modeling street meshes and urban agglomerates. With such a model, many aspects of a city can be investigated to promote a better quality of life to its citizens. Along these lines, this paper proposes a set of distance-based pattern-discovery algorithmic instruments to improve urban structures modeled as complex networks, detecting nodes that lack access from/to points of interest in a given city. Furthermore, we introduce a greedy algorithm that is able to recommend improvements to the structure of a city by suggesting where points of interest are to be placed. We contribute to a thorough process to deal with complex networks, including mathematical modeling and algorithmic innovation. The set of our contributions introduces a systematic manner to treat a recurrent problem of broad interest in cities.