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

arXiv: https://arxiv.org/abs/1803.09136 · HTML: https://mlsanity.com/paper/1803.09136/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Mar 2018.

## Built on by (most cited first, 1 of 1)

- [From Cities to Series: Complex Networks and Deep Learning for Improved Spatial and Temporal Analytics*](https://mlsanity.com/paper/2206.01176/) (2022)

## Abstract (arXiv)

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.