  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.
