The SONIC Speaker Series presents

Céline Rozenblat

University of Lausanne (UNIL), Switzerland

A Multi-Level Network Perspective on Complex Urban Systems

SONIC Lab is proud to welcome Dr. Céline Rozenblat of the University of Lausanne. She will speak on Tuesday, May 21st at 10 am in Frances Searle Building, Room 1-483. Please contact Brent Hoagland with any questions.

Abstract: In the context of knowledge and information societies, new tendencies in the long/medium term evolution of urban systems, together with new data and methods, require that existing theoretical assumptions and conceptualizations be challenged as global urban hierarchies are reconfigured. The connection between urban systems at different levels of organization becomes more and more relevant for understanding urban systems and their transformations. But the current inter-urban perspective is not sufficient to encompass these dynamics. Other cognitive, social, and institutional proximities in the innovation processes combine with spatial proximities. It leads us to consider cities in several dimensions of proximities in a multilayer perspective. The evolution of power distributions inside and between cities reshapes the world organization of central/peripheral cities and the complexity of the global urban system. Actors as multinational firms, or high-level innovation centers, participate actively in these reconfigurations that concentrate wealth, control, innovation, and attractiveness in a few cities. In the complexity of this multi-level system, how is regionalization of the world reshaping in a multipolar urban world? How does the multi-level perspective highlight some resilience properties? The methodologies derived from complex systems sciences bring new forms of intelligibility to worldwide urban dynamics.

Céline Rozenblat, is professor of Urban Geography at the University of Lausanne, Switzerland and president of the urban commission of the International Geographical Union (IGU). She studies systems of cities at European and world scales, multinational firm networks, inter-urban dynamics, comparative urban data, mapping and visualization of networks in geography, and spatial analysis. For several years she has worked on the relations between the evolution of multi-level urban processes and dynamics in city-system networks. To study these topics comparatively, she has built many databases on large European and worldwide cities and the networks they form. In particular, she has dealt since 1990 with databases on multinational firm networks and on city properties and evolution in a multi-dimensional and long temporal approach. Diachronic and dynamic studies supply materials to develop spatial and dynamic models and visualizations. She participated in European projects like ESPON FOCI 2008-2011, FP7 FET Insite (2011-2013) and Multiplex (2012-2016). She also participates to the EuropeAid project with China MEDIUM (2015-2019) on medium size cities in China and launched two projects LOGIICCS (FNS 2015-2018) and MEDIUM (SWISS-Conf. 2016-2018) on modeling Indian and Chinese cities’ integration in global networks of multinational firms and innovation.

The SONIC Speaker Series presents

Jake Fisher

Survey Research Center at the University of Michigan’s Institute for Social Research

Social Space Diffusion: Applications of a Latent Space Model to Diffusion with Uncertain Ties

SONIC Lab is proud to welcome Dr. Jake Fisher of the University of Michigan. He will speak on Monday, April 15th, 2019 at 10 am in Frances Searle Building, SONIC Conference Room 1-459. Please contact Brent Hoagland with any questions.

Abstract:

Social networks represent two different facets of social life: (1) stable paths for diffusion, or the spread of something through a connected population, and (2) random draws from an underlying social space, which indicate the relative positions of the people in the network to one another. The dual nature of networks creates a challenge: if the observed network ties are a single random draw, is it realistic to expect that diffusion only follows the observed network ties? This study takes a first step toward integrating these two perspectives by introducing a social space diffusion model. In the model, network ties indicate positions in social space, and diffusion occurs proportionally to distance in social space. Practically, the simulation occurs in two parts. First, positions are estimated using a statistical model (in this example, a latent space model). Then, second, the predicted probabilities of a tie from that model—representing the distances in social space—or a series of networks drawn from those probabilities—representing routine churn in the network—are used as weights in a weighted averaging framework. Using longitudinal data from high school friendship networks, the author explores the properties of the model. The author shows that the model produces smoothed diffusion results, which predict attitudes in future waves 10 percent better than a diffusion model using the observed network and up to 5 percent better than diffusion models using alternative, non-model-based smoothing approaches.

The forthcoming paper is available online at: https://doi.org/10.1177%2F0081175018820075