Road networks are characterised by several structural and geometrical properties. The topological structure determines partially the hierarchical arrangement of roads, but since these are networks that are spatially constrained, geometrical properties play a fundamental role in determining the network’s behaviour, characterising the influence of each of the street segments on the system. In this work, we apply percolation theory to the UK’s road network using the relative angle between street segments as the occupation probability. The appearance of the spanning cluster is marked by a phase transition, indicating that the system behaves in a critical way. Computing Shannon’s entropy of the cluster sizes, different stages of the percolation process can be discerned, and these indicate that roads integrate to the giant cluster in a hierarchical manner. This is used to construct a hierarchical index that serves to classify roads in terms of their importance. The obtained classification is in very good correspondence with the official designations of roads. This methodology hence provides a framework to consistently extract the main skeleton of an urban system and to further classify each road in terms of its hierarchical importance within the system.

 

The angular nature of road networks
Carlos Molinero, Roberto Murcio & Elsa Arcaute
Scientific Reports 7, Article number: 4312 (2017)
doi:10.1038/s41598-017-04477-z

Source: www.nature.com

How much knowledge is there in an economy? In recent years, data on the mix of products that countries export has been used to construct measures of economic complexity that estimate the knowledge available in an economy and predict future economic growth. Here we introduce a new and simpler metric of economic complexity (ECI+) that measures the total exports of an economy corrected by how difficult it is to export each product. We use data from 1973 to 2013 to compare the ability of ECI+, the Economic Complexity Index (ECI), and Fitness complexity, to predict future economic growth using 5, 10, and 20-year panels in a pooled OLS, a random effects model, and a fixed effects model. We find that ECI+ outperforms ECI and Fitness in its ability to predict economic growth and in the consistency of its estimators across most econometric specifications. On average, one standard deviation increase in ECI+ is associated with an increase in annualized growth of about 4% to 5%. We then combine ECI+ with measures of physical capital, human capital, and institutions, to find a robust model of economic growth. The ability of ECI+ to predict growth, and the value of its coefficient, is robust to these controls. Also, we find that human capital, political stability, and control of corruption; are positively associated with future economic growth, and that income is negatively associated with growth, in agreement with the traditional growth literature. Finally, we use ECI+ to generate economic growth predictions for the next 20 years and compare these predictions with the ones obtained using ECI and Fitness. These findings improve the methods available to estimate the knowledge intensity of economies and predict future economic growth.

 

Improving the Economic Complexity Index
Saleh Albeaik, Mary Kaltenberg, Mansour Alsaleh, Cesar A. Hidalgo

Source: arxiv.org

Viral videos can reach global penetration traveling through international channels of communication similarly to real diseases starting from a well-localized source. In past centuries, disease fronts propagated in a concentric spatial fashion from the the source of the outbreak via the short range human contact network. The emergence of long-distance air-travel changed these ancient patterns. However, recently, Brockmann and Helbing have shown that concentric propagation waves can be reinstated if propagation time and distance is measured in the flight-time and travel volume weighted underlying air-travel network. Here, we adopt this method for the analysis of viral meme propagation in Twitter messages, and define a similar weighted network distance in the communication network connecting countries and states of the World. We recover a wave-like behavior on average and assess the randomizing effect of non-locality of spreading. We show that similar result can be recovered from Google Trends data as well.

 

Video Pandemics: Worldwide Viral Spreading of Psy’s Gangnam Style Video
Zsofia Kallus, Daniel Kondor, Jozsef Steger, Istvan Csabai, Eszter Bokanyi, Gabor Vattay

Source: arxiv.org

Is Chinese urbanization going to take a long time, or can its development goal be achieved by the government in a short time? What is the highest stable urbanization level that China can reach? When can China complete its urbanization? To answer these questions, this paper presents a system dynamic (SD) model of Chinese urbanization, and its validity and simulation are justified by a stock-flow test and a sensitivity analysis using real data from 1998 to 2013. Setting the initial conditions of the simulation by referring to the real data of 2013, the multi-scenario analysis from 2013 to 2050 reveals that Chinese urbanization will reach a level higher than 70% in 2035 and then proceed to a slow urbanization stage regardless of the population policy and GDP growth rate settings; in 2050, Chinese urbanization levels will reach approximately 75%, which is a stable and equilibrium level for China. Thus, it can be argued that Chinese urbanization is a long social development process that will require approximately 20 years to complete and that the ultimate urbanization level will be 75–80%, which means that in the distant future, 20–25% of China’s population will still settle in rural regions of China.

 

Chinese urbanization 2050: SD modeling and process simulation
GU Chao Lin, GUAN Wei Hua, LIU He Lin

SCIENCE CHINA Earth Sciences 60(6), 1067-1082(2017);  10.1007/s11430-016-9022-2

Source: engine.scichina.com

The massive spread of fake news has been identified as a major global risk and has been alleged to influence elections and threaten democracies. Communication, cognitive, social, and computer scientists are engaged in efforts to study the complex causes for the viral diffusion of digital misinformation and to develop solutions, while search and social media platforms are beginning to deploy countermeasures. However, to date, these efforts have been mainly informed by anecdotal evidence rather than systematic data. Here we analyze 14 million messages spreading 400 thousand claims on Twitter during and following the 2016 U.S. presidential campaign and election. We find evidence that social bots play a key role in the spread of fake news. Accounts that actively spread misinformation are significantly more likely to be bots. Automated accounts are particularly active in the early spreading phases of viral claims, and tend to target influential users. Humans are vulnerable to this manipulation, retweeting bots who post false news. Successful sources of false and biased claims are heavily supported by social bots. These results suggests that curbing social bots may be an effective strategy for mitigating the spread of online misinformation.

 

The spread of fake news by social bots

Chengcheng Shao, Giovanni Luca Ciampaglia, Onur Varol, Alessandro Flammini, Filippo Menczer

Source: arxiv.org