text: Complex networks are nowadays employed in several applications. Modeling urban street networks is one of them, and in particular to analyze criminal aspects of a city. Several research groups have focused on such application, but until now, there is a lack of a well-defined methodology for employing complex networks in a whole crime analysis process, i.e. from data preparation to a deep analysis of criminal communities. Furthermore, the “toolset” available for those works is not complete enough, also lacking techniques to maintain up-to-date, complete crime datasets and proper assessment measures. In this sense, we propose a threefold methodology for employing complex networks in the detection of highly criminal areas within a city. Our methodology comprises three tasks: (i) Mapping of Urban Crimes; (ii) Criminal Community Identification; and (iii) Crime Analysis. Moreover, it provides a proper set of assessment measures for analyzing intrinsic criminality of communities, especially when considering different crime types. We show our methodology by applying it to a real crime dataset from the city of San Francisco—CA, USA. The results confirm its effectiveness to identify and analyze high criminality areas within a city. Hence, our contributions provide a basis for further developments on complex networks applied to crime analysis.
  @cite_9: In the fourth book, Sabrina and Daphne's adventures continue in their hometown of New York City as they bring Puck to Faerie to cure the wounds sustained in Book Three. With the help of Granny Relda, Mr. Canis and Sheriff Hamstead, the girls must figure out who killed Puck's father, King Oberon, while navigating the warring factions of Everafters that make their homes in every neighbourhood of New York City, including the pirates of Wall Street, the Yahoos of the Lower East Side and many more. Wherever they go, they learn something new about their mother, who wasn't the sweet, average lady they remember. Their mother's name and the girls' street smarts give them an advantage over their older friends and the Sisters Grimm really come into their own as detectives. If only Sabrina can embrace her Grimm inheritance and give up her dream of being normal.
  @cite_4: 
  @cite_3: Advancements in mobile technology and computing have fostered the collection of a large number of civic datasets that capture the pulse of urban life. Furthermore, the open government and data initiative has led many local authorities to make these datasets publicly available, hoping to drive innovation that will further improve the quality of life for the city-dwellers. In this paper, we develop a novel application that utilizes crime data to provide safe urban navigation. Specifically, using crime data from Chicago and Philadelphia we develop a risk model for their street urban network, which allows us to estimate the relative probability of a crime on any road segment. Given such model we define two variants of the SafePaths problem where the goal is to find a short and low-risk path between a source and a destination location. Since both the length and the risk of the path are equally important but cannot be combined into a single objective, we approach the urban-navigation problem as a biobjective shortest path problem. Our algorithms aim to output a small set of paths that provide tradeoffs between distance and safety. Our experiments demonstrate the efficacy of our algorithms and their practical applicability.
summary: Other works include the proposals of Galbrun @cite_3 , who focus on crime mapping to estimate the probability of a crime occurrence within a street segment; of Fitterer @cite_4 , who use a statistical model to predict crimes in the city of Vancouver; and of Bogomolov @cite_9 , who predict in London by using mobile data. Our work differs from theirs because (i) we consider crimes within a more complete and up-to-date period; (ii) we map crimes directly, there is no space summarization in the crime mapping phase; and (iii) we benefit from complex networks, which are a reliable toolset to design georeferenced data.