Evolution and entropy in the organization of urban street patterns

The street patterns of cities are the result of long-term evolution and interaction between various internal, social and economic, and external, environmental and landscape, processes and factors. In this article, we use entropy as a measure of dispersion to study the effects of landscapes on the evolution and associated street patterns of two cities: Dundee in Eastern Scotland and Khorramabad in Western Iran, cities which have strong similarities in terms of the size of their street systems and populations but considerable differences in terms of their evolution within the landscape. Landscape features have strong effects on the city shape and street patterns of Dundee, which is primarily a shoreline city, while Khorramabad is primarily located within mountainous and valley terrain. We show how cumulative distributions of street lengths when graphed as log-log plots show abrupt changes in their straight-line slopes at lengths of about 120 m, indicating a change in street functionality across scale: streets shorter than 120 m are primarily local streets, whereas longer streets are mainly collectors and arterials. The entropy of a street-length population varies positively over its average length and length range which is the difference between the longest and the shortest streets in a population. Similarly, the entropies of the power law tails of the street populations of both cities have increased during their growth, indicating that the distribution of street lengths has gradually become more dispersed as these cities have expanded. © 2013 Copyright Taylor and Francis Group, LLC

Online Popularity and Topical Interests through the Lens of Instagram

Online socio-technical systems can be studied as proxy of the real world to investigate human behavior and social interactions at scale. Here we focus on Instagram, a media-sharing online platform whose popularity has been rising up to gathering hundred millions users. Instagram exhibits a mixture of features including social structure, social tagging and media sharing. The network of social interactions among users models various dynamics including follower/followee relations and users' communication by means of posts/comments. Users can upload and tag media such as photos and pictures, and they can "like" and comment each piece of information on the platform. In this work we investigate three major aspects on our Instagram dataset: (i) the structural characteristics of its network of heterogeneous interactions, to unveil the emergence of self organization and topically-induced community structure; (ii) the dynamics of content production and consumption, to understand how global trends and popular users emerge; (iii) the behavior of users labeling media with tags, to determine how they devote their attention and to explore the variety of their topical interests. Our analysis provides clues to understand human behavior dynamics on socio-technical systems, specifically users and content popularity, the mechanisms of users' interactions in online environments and how collective trends emerge from individuals' topical interests.Comment: 11 pages, 11 figures, Proceedings of ACM Hypertext 201

Robustness and Closeness Centrality for Self-Organized and Planned Cities

Street networks are important infrastructural transportation systems that cover a great part of the planet. It is now widely accepted that transportation properties of street networks are better understood in the interplay between the street network itself and the so called \textit{information} or \textit{dual network}, which embeds the topology of the street network navigation system. In this work, we present a novel robustness analysis, based on the interaction between the primal and the dual transportation layer for two large metropolis, London and Chicago, thus considering the structural differences to intentional attacks for \textit{self-organized} and planned cities. We elaborate the results through an accurate closeness centrality analysis in the Euclidean space and in the relationship between primal and dual space. Interestingly enough, we find that even if the considered planar graphs display very distinct properties, the information space induce them to converge toward systems which are similar in terms of transportation properties

Spatial information and the legibility of urban form: Big data in urban morphology

Urban planning and morphology have relied on analytical cartography and visual communication tools for centuries to illustrate spatial patterns, propose designs, compare alternatives, and engage the public. Classic urban form visualizations – from Giambattista Nolli’s ichnographic maps of Rome to Allan Jacobs’s figure-ground diagrams of city streets – have compressed physical urban complexity into easily comprehensible information artifacts. Today we can enhance these traditional workflows through the Smart Cities paradigm of understanding cities via user-generated content and harvested data in an information management context. New spatial technology platforms and big data offer new lenses to understand, evaluate, monitor, and manage urban form and evolution. This paper builds on the theoretical framework of visual cultures in urban planning and morphology to introduce and situate computational data science processes for exploring urban fabric patterns and spatial order. It demonstrates these workflows with OSMnx and data from OpenStreetMap, a collaborative spatial information system and mapping platform, to examine street network patterns, orientations, and configurations in different study sites around the world, considering what these reveal about the urban fabric. The age of ubiquitous urban data and computational toolkits opens up a new era of worldwide urban form analysis from integrated quantitative and qualitative perspectives