Uma geografia temporal do encontro

A integração de redes sociais e espaciais é fundamental para novas abordagens a cidades como sistemas de interacção. Neste artigo, propomos uma maneira de analisar as condições espaciais e temporais do encontro como condição da formação de redes sociais. Reunindo abordagens clássicas como a geografia temporal de Hägerstrand e o conceito de segregação como ‘restrição de contato’ de Freeman, e explorações recentes de dados de localização via mídia digital, analisamos a estrutura espaço-temporal de encontros potenciais nas trajetórias urbanas de usuários do Twitter diferenciados por níveis de renda no Rio de Janeiro. Esta abordagem permite estimar as posições dos usuários, visualizar grupos de renda e suas trajetórias no espaço urbano, identificar espaços de encontro potencial e os níveis de diversidade e segregação nos espaços públicos. O artigo conclui com uma discussão dos achados empíricos e a utilidade desta ‘geografia temporal dos encontros’ potenciais na cidade, possível a partir da introdução de novas tecnologias de comunicação digital móvel

Social Interaction and the City: The Effect of Space on the Reduction of Entropy

How can individual acts amount to coherent systems of interaction? In this paper, we attempt to answer this key question by suggesting that there is a place for cities in the way we coordinate seemingly chaotic decisions. We look into the elementary processes of social interaction exploring a particular concept, “social entropy,” or how social systems deal with uncertainty and unpredictability in the transition from individual actions to systems of interaction. Examining possibilities that (i) actions rely on informational differences latent in their environments and that (ii) the city itself is an information environment to actions, we propose that (iii) space becomes a form of creating differences in the probabilities of interaction. We investigate this process through simulations of distinct material scenarios, to find that space is a necessary but not sufficient condition for the reduction of entropy. Finally, we suggest that states and fluctuations of entropy are a vital part of social reproduction and reveal a deep connection between social, informational, and spatial systems

A temporal geography of encounters

Integrating social and spatial networks will be critical to new approaches to cities as systems of interaction. In this paper, we focus on the spatial and temporal conditions of encounters as a key condition for the formation of social networks. Drawing on classic approaches such as Freeman’s concept of segregation as ‘restriction on contact’, Hägerstrand’s time-geography, and recent explorations of social media locational data, we analysed the space-time structure of potential encounters latent in the urban trajectories of people with different income levels in Rio de Janeiro, Brazil. This approach allows us to estimate trajectories examining spatiotemporal positions in tweets, and assess spaces of potential encounter and levels of social diversity on the streets. Finally, we discuss the utility and limitations of an approach developed to grasp how clusters of encounters between groups with different income levels are produced

A model of urban scaling laws based on distance dependent interactions

Socio-economic related properties of a city grow faster than a linear relationship with the population, in a log–log plot, the so-called superlinear scaling. Conversely, the larger a city, the more efficient it is in the use of its infrastructure, leading to a sublinear scaling on these variables. In this work, we addressed a simple explanation for those scaling laws in cities based on the interaction range between the citizens and on the fractal properties of the cities. To this purpose, we introduced a measure of social potential which captured the influence of social interaction on the economic performance and the benefits of amenities in the case of infrastructure offered by the city. We assumed that the population density depends on the fractal dimension and on the distance-dependent interactions between individuals. The model suggests that when the city interacts as a whole, and not just as a set of isolated parts, there is improvement of the socio-economic indicators. Moreover, the bigger the interaction range between citizens and amenities, the bigger the improvement of the socio-economic indicators and the lower the infrastructure costs of the city. We addressed how public policies could take advantage of these properties to improve cities development, minimizing negative effects. Furthermore, the model predicts that the sum of the scaling exponents of social-economic and infrastructure variables are 2, as observed in the literature. Simulations with an agent-based model are confronted with the theoretical approach and they are compatible with the empirical evidences

Scaling Laws in Cities

Ao longo da história, diversas foram as tentativas da ciência em sistematizar o conhecimento sobre as cidades. Um conjunto de recentes descobertas empíricas deu início a uma nova ciência urbana, fundamentada nos sistemas complexos( BATTY , 2013). Uma das raízes empíricos dessa ciência está nas análises de escalamento entre diversas variáveis urbanas com a população das cidades. Como hipótese fundamental, propõe-se que por mais diferentes que sejam, parece existir um padrão muito claro de escalamento entre a população dos centros urbanos com variáveis de produção socioeconômica (superlinear) e variáveis infraestruturais (sublinear). Um modelo de campo médio proposto por Bettencourt( BETTENCOURT , 2013) parece apresentar uma prossível explicação para a origem dessas leis de escala nas interações sociais. Essa hipótese ainda carece, entretanto, de maiores comprovações empíricas e de maiores explorações do modelo proposto. O presente trabalho apresenta os resultados de uma exploração das leis de escala entre as cidades brasileiras e uma tradução do modelo de Bettencourt em uma estrutura de modelagem por agentes. Os padrões de escalamento das cidades brasileiras parecem seguir aqueles encontrados em outras cidades do mundo com certa robustez. Escalamentos de variáveis de arrecadação e de despesas foram estudados e as cidades brasileiras parecem otimizá-los a medida que crescem. O modelo se mostrou coerente com os fatos observados empiricamente e indicou que cidades muito desiguais tendem a ter menores produções socioeconômicas e que áreas de interações maiores e custos de transporte menores tendem a produzir mais interações socioeconômicasThroughout history science has tried in many ways to sistematyze the knowledge about cities. Recent empirical discoveries started a new urban science, based on complex systems and data science( BATTY , 2013). One of the empirical foundation of this science is the scaling laws of diferent urban variables in relation to the urban population size. As a main hypothesis, it is sugested that as diferent as cities may be, there seems to be an evident scaling pattern between population size and socioeconomic production (superlinear) and infrastrufture variables (sublinear). A mean field model proposed by Bettencourt( BETTENCOURT , 2013) appears to present a plausible explanation for the origin of scaling laws in social interactions. However, this hypothesis still lacks more concluding empirical proof and further study of the model itself. This paper presents the results of an exploration of the scaling laws among Brazilian cities and a translation of Bettencourts model in a ABM framework. The scaling patterns of Brazilian cities appear to follow those found in other cities in the world with a certain robustness. Tax revenues and costs scaling were studied and Brazilian cities seem to optimize them as they grow. The model proved to be consistent with the facts observed empirically and indicated that very unequal city tend to have lower socioeconomic productions and greater areas of interactions and lower transportation costs tends to imply greater socioeconomic production

THE FABRIC OF ENCOUNTER: Integration and segregation in the spatiotemporal structure of social networks

Integrating social and spatial networks will be critical to new approaches to cities as material systems of interaction. In this paper, we propose a way of doing so by focusing on the spatial and temporal conditions of formation of social networks – namely, on ‘encounters’ as a key social event. Drawing on classic approaches such as Freeman’s concept of segregation as ‘restriction on contact’ and Hägerstrand’s time-geography, and recent explorations of social media locational data, we analysed the space-time structure of potential encounters latent in the urban trajectories of agents differentiated by income levels in Rio de Janeiro, Brazil. This approach allows us to estimate agents’ urban trajectories examining geographic spatiotemporal positions in tweets, visualise income groups as potentially overlapping class networks, assess spaces of potential encounter and levels of social diversity on the streets. Finally, we discuss our findings and the utility and limitations of this approach in grasping a temporal ‘geography of potential encounters’ and segregated networks

More from Less? Environmental Rebound Effects of City Size

Global sustainability relies on our capacity of understanding and guiding urban systems and their metabolism adequately. It has been proposed that bigger and denser cities are more resource-efficient than smaller ones because they tend to demand less infrastructure, consume less fuel for transportation and less energy for cooling/heating in per capita terms. This hypothesis is also called Brand’s Law. However, as cities get bigger, denser and more resource-efficient, they also get richer, and richer inhabitants consume more, potentially increasing resource demand and associated environmental impacts. In this paper, we propose a method based on scaling theory to assess Brand’s Law taking into account greenhouse gas (GHG) emissions from both direct (energy and fuels locally consumed) and indirect (embedded in goods and services) sources, measured as carbon footprint (CF). We aim at understanding whether Brand’s Law can be confirmed once we adopt a consumption-based approach to urban emissions. By analyzing the balance between direct and indirect emissions in a theoretical urban system, we develop a scaling theory relating carbon footprint and city size. Facing the lack of empirical data on consumption-based emissions for cities, we developed a model to derive emission estimations using well-established urban metrics (city size, density, infrastructure, wealth). Our results show that, once consumption-based CF is considered, Brand’s Law falls apart, as bigger cities have greater purchase power, leading to greater consumption of goods and higher associated GHG. Findings also suggest that a shift in consumption patterns is of utmost importance, given that, according to the model, each new monetary unit added to the gross domestic product (GDP) or to other income variables results in a more than proportional increase in GHG emissions. This work contributes to a broader assessment of the causes of emissions and the paradigm shift regarding the assumption of efficiency in the relationship of city size and emissions, adding consumption behavior as a critical variable, beyond Brand’s Law