Characterization of mobility patterns and collective behavior through the analytical processing of real-world complex networks (original) (raw)

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

14th International Conference on Information Technology: New Generations, 2017

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.

The Spatial Structure of Crime in Urban Environments

It is undoubtedly cliché to say that we are in the Age of Big Data Analytics or Data Science; every computing and IT publication you find talks about Big Data and companies no longer are interested in software engineers and analysts but instead they are looking for Data Scientists! In spite of the excessive use of the term, the truth of the matter is that data has never been more available and the increase in computation power allows for more sophisticated tools to identify patterns in the data and on the networks that governs these systems (complex networks). Crime is not different, the open data phenomena has spread to thousand of cities in the world, which are making data about crime activity available for any citizen to look at. Furthermore, new criminology studies argue that criminals typically commit crimes in areas in which they are familiar, usually close to home. Using this information we propose a new model based on networks to build links between crimes in close physical proximity. We show that the structure of the criminal activity can be partially represented by this spatial network of sites. In this paper we describe this process and the analysis of the networks we have constructed to find patterns in the underlying structure of criminal activity.

The network analysis of urban streets: a primal approach

Environment and Planning B: Planning and Design, 2006

The network metaphor in the analysis of urban and territorial cases has a long tradition especially in transportation/land-use planning and economic geography. More recently, urban design has brought its contribution by means of the "space syntax" methodology. All these approaches -though under different terms like "accessibility", "proximity", "integration" "connectivity", "cost" or "effort" -focus on the idea that some places (or streets) are more important than others because they are more central. The study of centrality in complex systems, however, originated in other scientific areas, namely in structural sociology, well before its use in urban studies; moreover, as a structural property of the system, centrality has never been extensively investigated metrically in geographic networks as it has been topologically in a wide range of other relational networks like social, biological or technological.

Urbanity: automated modelling and analysis of multidimensional networks in cities

npj Urban Sustainability

Urban networks play a vital role in connecting multiple urban components and developing our understanding of cities and urban systems. Despite the significant progress we have made in understanding how city networks are connected and spread out, we still have a lot to learn about the meaning and context of these networks. The increasing availability of open data offers opportunities to supplement urban networks with specific location information and create more expressive urban machine-learning models. In this work, we introduce Urbanity, a network-based Python package to automate the construction of feature-rich urban networks anywhere and at any geographical scale. We discuss data sources, the features of our software, and a set of data representing the networks of five major cities around the world. We also test the usefulness of added context in our networks by classifying different types of connections within a single network. Our findings extend accumulated knowledge about how...

The Science of Networks: Urban Movement Design, Analytics, and Navigation

Ćirić, D. 2023. The Science of Networks: Urban Movement Design, Analytics, and Navigation. In On Architecture - Challenges in Design Conference Proceedings, edited bz Ružica Bogdanović, pp. 110-129. Belgrade: STRAND., 2023

The science of networks, a relatively young field of research that appeared in such a form and definition at the beginning of the 21st century (as a distinctive, officially approved and accepted scientific discipline (Barabási, 2016)), represents a very powerful area considering the range of subjects to which it contributes and is applied. This science is key for complex systems analysis or analytics (when referring to the (big) data science framework, which now mostly defines its methods and resources (Betty, 2019)) based on the claim that networks encode the interactions between the system’s components (Barabási, 2016) and thus provide insights into the ways complex systems behave, or control the behaviour of the artificially created systems (emphasis added). The area represented here, through its analytical methods and forms (network graphs and related operations), is the urban transportation system — the Grand Paris rail system, including all the categories with their existing lines and extensions currently either in construction and planned, or under consideration in the long term. The network has been created as a background topological environment and geometry for various research operations and generative design tasks. Some of them, such as urban movement path generation or the network’s incremental growth and reconfiguration as a system and the geometry of possible moves (“legal actions”), will be presented in more detail. The network can be considered both an abstract and real-world environment and situation, susceptible to the research of both gaming strategies for any constructed scenario and designed spatial situation (academic gaming, operational gaming, and heuristic gaming) and problem-solving strategies related to identified real-world design issues. Thus, the main question addresses the ways in which its presented graph can be operationalised and the methods through which this can be achieved, with special regard to AI.

Topological analysis of urban street networks

Environment and Planning B-planning & Design, 2004

Network analysis has long been a basic function of geographic information systems (GIS) for a variety of applications in, for example, hydrology, facilities management, transportation engineering, and business and service planning. In network GIS, computational modelling of an urban network (for example, street network or underground) is based on a graph view in which the intersections of linear features are regarded as nodes, and connections between pairs of nodes are represented as edges. Common network operations include computational processes to find the shortest, least-cost, or most-efficient path ( pathfinding), to analyse network connectivity (tracing), and to assign portions of a network to a location based on some given criteria (allocation) .

Complex Networks and Fundamental Urban Processes

SSRN Electronic Journal, 2019

Networks provide a particularly useful analytical framework for modeling many distinct phenomena in cities, from social and economic contact and exchange, to physical infrastructure and to migration flows and trade. Here, I provide a primer on the importance of networks in cities as the bases for quantitative modeling and theory. I emphasize a number of social, economic and infrastructural characteristics of these networks, and their role in expressing general phenomena such as the division of labor and knowledge, epidemics of infectious diseases and information, spatial topology and densification, mobility, congestion and migration. I also provide a framework for understanding networks at different scales in space and time from neighborhoods, to cities and to urban systems. I finish with a discussion of open questions for urban theory and network science and point to the particular multifaceted, subtle and dynamical roles of networks in cities as rich grounds for new research developments.

ANALYSING AND VISUALIZING TOPOLOGICAL STRUCTURE OF AN URBAN STREET NETWORK

ABSTRACT Recently there seems to be a revival in the network analysis and visualization due to the availability of large network data sets such as the web, the Internet, scientific citation networks, and social networks. This paper reports experiments we did for analysing and visualizing the topology of an urban street network. The topology of an urban street network is based on a straightforward rule, ie take named street as nodes and street intersections as links of a graph.

Methods and Measures for Analyzing Complex Street Networks and Urban Form

Complex systems have been widely studied by social and natural scientists in terms of their dynamics and their structure. Scholars of cities and urban planning have incorporated complexity theories from qualitative and quantitative perspectives. From a structural standpoint, the urban form may be characterized by the morphological complexity of its circulation networks – particularly their density, resilience, centrality, and connectedness. This dissertation unpacks theories of nonlinearity and complex systems, then develops a framework for assessing the complexity of urban form and street networks. It introduces a new tool, OSMnx, to collect street network and other urban form data for anywhere in the world, then analyze and visualize them. Finally, it presents a large empirical study of 27,000 street networks, examining their metric and topological complexity relevant to urban design, transportation research, and the human experience of the built environment.