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

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.Comment: Paper to be published on the International Conference on Computational Science (ICCS), 201

Complex Network Tools to Understand the Behavior of Criminality in Urban Areas

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.Comment: 7 pages, 2 figures, 14th International Conference on Information Technology : New Generation

Enhancing Global Maritime Traffic Network Forecasting with Gravity-Inspired Deep Learning Models

Aquatic non-indigenous species (NIS) pose significant threats to biodiversity, disrupting ecosystems and inflicting substantial economic damages across agriculture, forestry, and fisheries. Due to the fast growth of global trade and transportation networks, NIS has been introduced and spread unintentionally in new environments. This study develops a new physics-informed model to forecast maritime shipping traffic between port regions worldwide. The predicted information provided by these models, in turn, is used as input for risk assessment of NIS spread through transportation networks to evaluate the capability of our solution. Inspired by the gravity model for international trades, our model considers various factors that influence the likelihood and impact of vessel activities, such as shipping flux density, distance between ports, trade flow, and centrality measures of transportation hubs. Accordingly, this paper introduces transformers to gravity models to rebuild the short- and long-term dependencies that make the risk analysis feasible. Thus, we introduce a physics-inspired framework that achieves an 89% binary accuracy for existing and non-existing trajectories and an 84.8% accuracy for the number of vessels flowing between key port areas, representing more than 10% improvement over the traditional deep-gravity model. Along these lines, this research contributes to a better understanding of NIS risk assessment. It allows policymakers, conservationists, and stakeholders to prioritize management actions by identifying high-risk invasion pathways. Besides, our model is versatile and can include new data sources, making it suitable for assessing international vessel traffic flow in a changing global landscape

Maritime Tracking Data Analysis and Integration with AISdb

Efficiently handling Automatic Identification System (AIS) data is vital for enhancing maritime safety and navigation, yet is hindered by the system's high volume and error-prone datasets. This paper introduces the Automatic Identification System Database (AISdb), a novel tool designed to address the challenges of processing and analyzing AIS data. AISdb is a comprehensive, open-source platform that enables the integration of AIS data with environmental datasets, thus enriching analyses of vessel movements and their environmental impacts. By facilitating AIS data collection, cleaning, and spatio-temporal querying, AISdb significantly advances AIS data research. Utilizing AIS data from various sources, AISdb demonstrates improved handling and analysis of vessel information, contributing to enhancing maritime safety, security, and environmental sustainability efforts

Topological street-network characterization through feature-vector and cluster analysis

Complex networks provide a means to describe cities through their street mesh, expressing characteristics that refer to the structure and organization of an urban zone. Although other studies have used complex networks to model street meshes, we observed a lack of methods to characterize the relationship between cities by using their topological features. Accordingly, this paper aims to describe interactions between cities by using vectors of topological features extracted from their street meshes represented as complex networks. The methodology of this study is based on the use of digital maps. Over the computational representation of such maps, we extract global complex-network features that embody the characteristics of the cities. These vectors allow for the use of multidimensional projection and clustering techniques, enabling a similarity-based comparison of the street meshes. We experiment with 645 cities from the Brazilian state of Sao Paulo. Our results show how the joint of global features describes urban indicators that are deep-rooted in the network's topology and how they reveal characteristics and similarities among sets of cities that are separated from each other.Comment: Paper to be published on the International Conference on Computational Science (ICCS), 201

Das Cidades às Séries: Redes Complexas e Aprendizado Profundo para Aprimorar Análises Espaciais e Temporais

The relationship between different entities is a property that can be represented as a graph, structured sets formed by entities (i.e., vertices) and relationships (i.e., edges). Graphs have often been used to answer questions about the interaction between entities from the real world by analyzing their vertices and edges (i.e., the graphs topology). On the other hand, complex networks are known to be graphs of non-trivial topology, capable of representing human phenomena such as cities urbanization, peoples movement, and migration, besides epidemic processes. However, graph theory and network science, the research fields that oversee the study of graphs and complex networks, have also been traversed in the realm of artificial intelligence, in which the analysis of the interaction between different entities is transposed to the internal learning process of algorithms. In this sense, this thesis introduces complex networks and supervised learning (classification and regression) techniques to improve understanding of human phenomena inherent to street networks, pendular migration, and pandemics progression through computational analysis and modeling. Accordingly, we contribute with: (i) techniques for identifying inconsistencies in the urban plan while tracking the most influential vertices; (ii) a methodology for analyzing and predicting links in the scope of human mobility between cities through machine learning algorithms; and (iii) a new neural network architecture capable of modeling dynamic processes observed in spatial and temporal data with applications on different domains. These results reiterate the potential of graphs and complex networks in solving problems related to analyzing human phenomena and modeling their evolutive processes across space and time when used together with articial intelligence learning algorithms.A relação entre diferentes entidades de um conjunto de dados é uma propriedade passível de ser representada por um grafo, os quais são conjuntos estruturados formados por entidades (i.e., vértices) e relacionamentos (i.e., arestas). Por muitas vezes grafos foram utilizados para responder questionamentos sobre a interação entre entidades do mundo real pela análise de seus vértices e arestas (i.e., topologia do grafo). As redes complexas, por outro lado, ficaram conhecidas por serem grafos de topologia não trivial. Entre suas aplicações, destaca-se a representação de fenômenos humanos como a urbanização de cidades, o movimento migratório de populações, e a propagação de pandemias. A teoria dos grafos e a ciência de redes, os campos de pesquisa que regem o estudo de grafos e redes complexas, tem sido explorados com sinergia no âmbito da inteligencia artificial, no qual transpõe-se a análise da interação entre diferentes entidades para o processo interno de aprendizado computacional dos algoritmos. Neste sentido, a presente tese introduz um ferramental de redes complexas juntamente com técnicas de aprendizado supervisionado de classificação e regressão de modo a contribuir com o entendimento de fenômenos humanos inerentes às malhas viárias, migrações pendulares, e progressões pandêmicas por meio de modelagem e análise computacional. Entre os resultados alcançados, estão: (i) técnicas de identificação de falhas de planejamento urbano ao mesmo tempo em que se auxilia na análise da topologia da rede complexa para diferenciar os vértices mais influentes; (ii) uma metodologia de análise e predição de links em redes complexas no âmbito de mobilidade humana entre cidades por meio de aprendizado de máquina; e, (iii) uma nova arquitetura de rede neural capaz de modelar processos dinâmicos observados em dados variantes no espaço e no tempo, com aplicações de alcance a diferentes domínios. Tais resultados reiteram o potencial dos grafos e das redes complexas na solução de problemas conectados à análise de diferentes fenômenos humanos, bem como a previsão de seus processos evolutivos no espaço e no tempo, quando utilizados conjuntamente com os algoritmos de aprendizado computacional provenientes da inteligência artificial