Activity network analysis is a widely used tool for managing project risk. Traditionally, this type of analysis is used to evaluate task criticality by assuming linear cause‐and‐effect phenomena, where the size of a local failure (e.g. task delay) dictates its possible global impact (e.g. project delay). Motivated by the question of whether activity networks are subject to non‐linear cause‐and‐effect phenomena, a computational framework is developed and applied to real‐world project data to evaluate project systemic risk. Specifically, project systemic risk is viewed as the result of a cascading process which unravels across an activity network, where the failure of a single task can consequently affect its immediate, downstream task(s). As a result, we demonstrate that local failures are capable of triggering failure cascades of intermittent sizes. In turn, a modest local disruption can fuel exceedingly large, systemic failures. In addition, the probability for this to happen is much higher than anticipated. A systematic examination of why this is the case is subsequently performed, with results attributing the emergence of large‐scale failures to topological and temporal features of activity networks. Finally, local mitigation is assessed in terms of containing these failures cascades – results illustrate that this form of mitigation is both ineffective and insufficient. Given the ubiquity of our findings, our work has the potential of deepening our current theoretical understanding on the causal mechanisms responsible for large‐scale project failures.

The domino effect: an empirical exposition of systemic risk across project networks

Christos Ellinas

Production and Operations Management

https://doi.org/10.1111/poms.12890 

Source: onlinelibrary.wiley.com

Technological advances have led to a strong increase in the number of data collection efforts aimed at measuring co-presence of individuals at different spatial resolutions. It is however unclear how much co-presence data can inform us on actual face-to-face contacts, of particular interest to study the structure of a population in social groups or for use in data-driven models of information or epidemic spreading processes. Here, we address this issue by leveraging data sets containing high resolution face-to-face contacts as well as a coarser spatial localisation of individuals, both temporally resolved, in various contexts. The co-presence and the face-to-face contact temporal networks share a number of structural and statistical features, but the former is (by definition) much denser than the latter. We thus consider several down-sampling methods that generate surrogate contact networks from the co-presence signal and compare them with the real face-to-face data. We show that these surrogate networks reproduce some features of the real data but are only partially able to identify the most central nodes of the face-to-face network. We then address the issue of using such down-sampled co-presence data in data-driven simulations of epidemic processes, and in identifying efficient containment strategies. We show that the performance of the various sampling methods strongly varies depending on context. We discuss the consequences of our results with respect to data collection strategies and methodologies.

Can co-location be used as a proxy for face-to-face contacts?
Mathieu Génois and Alain Barrat
EPJ Data Science 2018 7:11
https://doi.org/10.1140/epjds/s13688-018-0140-1

Source: epjdatascience.springeropen.com

Urban economists have put forward the idea that cities that are culturally interesting tend to attract “the creative class” and, as a result, end up being economically successful. Yet it is still unclear how economic and cultural dynamics mutually influence each other. By contrast, that has been extensively studied in the case of individuals. Over decades, the French sociologist Pierre Bourdieu showed that people’s success and their positions in society mainly depend on how much they can spend (their economic capital) and what their interests are (their cultural capital). For the first time, we adapt Bourdieu’s framework to the city context. We operationalize a neighborhood’s cultural capital in terms of the cultural interests that pictures geo-referenced in the neighborhood tend to express. This is made possible by the mining of what users of the photo-sharing site of Flickr have posted in the cities of London and New York over 5 years. In so doing, we are able to show that economic capital alone does not explain urban development. The combination of cultural capital and economic capital, instead, is more indicative of neighborhood growth in terms of house prices and improvements of socio-economic conditions. Culture pays, but only up to a point as it comes with one of the most vexing urban challenges: that of gentrification.

The New Urban Success: How Culture Pays

Desislava Hristova, Luca M. Aiello and Daniele Quercia

Front. Phys., 09 April 2018 | https://doi.org/10.3389/fphy.2018.00027

Source: www.frontiersin.org

Multifractal analysis offers a number of advantages to measure spatial economic segregation and inequality, as it is free of categories and boundaries definition problems and is insensitive to some shape-preserving changes in the variable distribution. We use two datasets describing Kyoto land prices in 1912 and 2012 and derive city models from this data to show that multifractal analysis is suitable to describe the heterogeneity of land prices. We found in particular a sharp decrease in multifractality, characteristic of homogenisation, between older Kyoto and present Kyoto, and similarities both between present Kyoto and present London, and between Kyoto and Manhattan as they were a century ago. In addition, we enlighten the preponderance of spatial distribution over variable distribution in shaping the multifractal spectrum. The results were tested against the classical segregation and inequality indicators, and found to offer an improvement over those.

Salat H, Murcio R, Yano K, Arcaute E (2018) Uncovering inequality through multifractality of land prices: 1912 and contemporary Kyoto. PLoS ONE 13(4): e0196737. https://doi.org/10.1371/journal.pone.0196737

Source: journals.plos.org