Two fundamental issues surrounding research on Zipf's law regarding city sizes are whether and why Zipf's law holds. This paper does not deal with the latter issue with respect to why, and instead investigates whether Zipf's law holds in a global setting, thus involving all cities around the world. Unlike previous studies, which have mainly relied on conventional census data, and census- bureau-imposed definitions of cities, we adopt naturally and objectively delineated cities, or natural cities, to be more precise, in order to examine Zipf's law. We find that Zipf's law holds remarkably well for all natural cities at the global level, and remains almost valid at the continental level except for Africa at certain time instants. We further examine the law at the country level, and note that Zipf's law is violated from country to country or from time to time. This violation is mainly due to our limitations; we are limited to individual countries, and to a static view on city-size distributions. The central argument of this paper is that Zipf's law is universal, and we therefore must use the correct scope in order to observe it. We further find that this law is reflected in the distribution of cities: the number of cities in individual countries follows an inverse power relationship; the number of cities in the first largest country is twice as many as that in the second largest country, three times as many as that in the third largest country, and so on.
Zipf's Law for All the Natural Cities around the World Bin Jiang, Junjun Yin, Qingling Liu
Cities around the world are growing faster than you can say megalopolis. More than half the world lives in cities, and by 2050, it will be two-thirds. In China alone, 300 million people will move to the city within the next 15 years, and to serve them, China must build the equivalent of the entire built infrastructure of the United States by 2028. At the same time, 250 million new urban dwellers are expected in India and 380 million in Africa. Even though cities will soon account for 90 percent of population growth, 80 percent of global CO2, and 75 percent of energy consumption, more and more, it’s where people want to live. Why? Because it’s where 80 percent of the wealth is created, and it’s where people find opportunities, especially women in the developing world. But beyond basic needs from housing to jobs, how do we enjoy the benefits of the city—like cafes, art galleries, restaurants, cultural facilities—without the traffic, crowding, crime, pollution, and disease?
Social networks readily transmit information, albeit with less than perfect fidelity. We present a large-scale measurement of this imperfect information copying mechanism by examining the dissemination and evolution of thousands of memes, collectively replicated hundreds of millions of times in the online social network Facebook. The information undergoes an evolutionary process that exhibits several regularities. A meme's mutation rate characterizes the population distribution of its variants, in accordance with the Yule process. Variants further apart in the diffusion cascade have greater edit distance, as would be expected in an iterative, imperfect replication process. Some text sequences can confer a replicative advantage; these sequences are abundant and transfer "laterally" between different memes. Subpopulations of the social network can preferentially transmit a specific variant of a meme if the variant matches their beliefs or culture. Understanding the mechanism driving change in diffusing information has important implications for how we interpret and harness the information that reaches us through our social networks.
Information Evolution in Social Networks Lada A. Adamic, Thomas M. Lento, Eytan Adar, Pauline C. Ng
With public information becoming widely accessible and shared on today's web, greater insights are possible into crowd actions by citizens and non-state actors such as large protests and cyber activism. We present efforts to predict the occurrence, specific timeframe, and location of such actions before they occur based on public data collected from over 300,000 open content web sources in 7 languages, from all over the world, ranging from mainstream news to government publications to blogs and social media. Using natural language processing, event information is extracted from content such as type of event, what entities are involved and in what role, sentiment and tone, and the occurrence time range of the event discussed. Statements made on Twitter about a future date from the time of posting prove particularly indicative. We consider in particular the case of the 2013 Egyptian coup d'etat. The study validates and quantifies the common intuition that data on social media (beyond mainstream news sources) are able to predict major events.
Predicting Crowd Behavior with Big Public Data Nathan Kallus
Many species dream, yet there remain many open research questions in the study of dreams. The symbolism of dreams and their interpretation is present in cultures throughout history. Analysis of online data sources for dream interpretation using network science leads to understanding symbolism in dreams and their associated meaning. In this study, we introduce dream interpretation networks for English, Chinese and Arabic that represent different cultures from various parts of the world. We analyze communities in these networks, finding that symbols within a community are semantically related. The central nodes in communities give insight about cultures and symbols in dreams. The community structure of different networks highlights cultural similarities and differences. Interconnections between different networks are also identified by translating symbols from different languages into English. Structural correlations across networks point out relationships between cultures. Similarities between network communities are also investigated by analysis of sentiment in symbol interpretations. We find that interpretations within a community tend to have similar sentiment. Furthermore, we cluster communities based on their sentiment, yielding three main categories of positive, negative, and neutral dream symbols.
António F Fonseca's insight:
Very interesting research work based on web content.
Recent advances on human dynamics have focused on the normal patterns of human activities, with the quantitative understanding of human behavior under extreme events remaining a crucial missing chapter. This has a wide array of potential applications, ranging from emergency response and detection to traffic control and management. Previous studies have shown that human communications are both temporally and spatially localized following the onset of emergencies, indicating that social propagation is a primary means to propagate situational awareness. We study real anomalous events using country-wide mobile phone data, finding that information flow during emergencies is dominated by repeated communications. We further demonstrate that the observed communication patterns cannot be explained by inherent reciprocity in social networks, and are universal across different demographics.
Quantifying Information Flow During Emergencies Liang Gao, Chaoming Song, Ziyou Gao, Albert-László Barabási, James P. Bagrow & Dashun Wang
Animal behavior isn't complicated, but it is complex. Nicolas Perony studies how individual animals -- be they Scottish Terriers, bats or meerkats -- follow simple rules that, collectively, create larger patterns of behavior. And how this complexity born of simplicity can help them adapt to new circumstances, as they arise.
NEWS.com.au Google raises concerns with purchase of 'strong' artificial intelligence developer allvoices Mountain View claims they are building on a chance to break into the field of industry automation, not our collective consciousness.
António F Fonseca's insight:
The owner of a significant portion of earth's curiosity patrimony is turning into deep AI.