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A new report from Credit Suisse is not your usual 'rich list'. Amid a record high for average global wealth its figures reveal striking inequalities – such as 35% of Russia's riches in the hands of 110 peopleDespite the continuing proble...
A new report from Credit Suisse is not your usual 'rich list'. Amid a record high for average global wealth its figures reveal striking inequalities – such as 35% of Russia's riches in the hands of 110 peopleDespite the continuing problems in the global economy, the ultra-rich have helped to push up average wealth in the world to an all-time high of US$51,600 per adult (£32,399). But what do those averages hide?The data, published in a new report by Credit Suisse, also reveals the countries that have seen the biggest rises in wealth, and which are likely to in the future. Here are some of the main findings:The wealthiest 32m people own more than the poorest 4.3bn put togetherThe idea of the world's wealthy 1% is still a powerful one – and graphics like the pyramid below demonstrate that stark contrast between the few and the many.32m individuals (just 0.7% of the world's population) together hold US$98.7tn (or £62,000,0000,000,000, which represents 41% of global wealth).At the other extreme, there are 3.2bn individuals at the bottom of the pyramid. Together they have 3% of global riches, despite representing 68.7% of the world population. But it might be surprising how little wealth an individual has to have to get out of that bottom tier and in with the top 33% of the world's population: US$10,000 is sufficient. 110 rich RussiansWith just 110 individuals holding 35% of the country's riches, Russia has the highest level of wealth inequality in the world (with the exception of some small Caribbean nations that have resident billionaires). There's a stark contrast between that and the world average, where billionaires hold around 1-2% of wealth.Globally, for every US$170bn in household wealth there is on average 1 billionaire. In Russia, there is just US$11bn in household wealth for every billionaire in the country. Wealth can be fleetingFortunes can go down as well as up. Using the Forbes rich list, Credit Suisse caught up with individuals that had been classified as billionaires as far back as 2001, to see how many were still among the super-rich by 2013.The financial crisis did very little to chance a consistent trend. In the first year after making it to the list of the top 100, around 33 individuals dropped out. By the time a decade had passed, more than 60 billionaires of the top 100 lost their status.Europeans can lose wealth easier than AfricansThe blues on the right-hand side of this graph show people who have increased their wealth over thirty years; the lighter the blue, the bigger the jump in wealth. The oranges and yellows on the other side indicate people who have slid down the wealth scale in their respective countries. Globally, the graphic shows that on average almost half of individuals do not significantly change their wealth status over the course of their lifetimes, and change is the least likely in Africa. So you have more chance of losing wealth if you live in Europe than in Africa. But at the other extreme, there's also a much bigger chance individuals in Europe will move up the wealth scale too. China has the world's highest level of wealth mobility. Almost 75% of the country has seen their wealth rise significantly in the past three decades. By 2018, Poland will have seen the biggest rise in billionairesBut how will wealth change in the future? Credit Suisse predicts the number of millionaires for various countries by 2018. Globally, there will be a 50% rise in the number of millionaires in the next five years. Although the US will continue to top the list with the highest number of millionaires, in terms of percentage change, Poland's population will experience the biggest leap – with 89% more billionaires in 2018 than it had in 2013.Libya has seen the biggest growth in household wealth If the past year is anything to go by, individuals in any country can see their wealth radically change as a result of sometimes unexpected national events. In Libya, household wealth leapt by more than 60% between 2012
about 4 hours ago
We're thrilled to have John Wallace and Tess Nesbitt from DataSong join our Fall webinar series tomorrow, with a great presentation on time to event models. If you're trying to predict when an event will occur (for example, a con...
We're thrilled to have John Wallace and Tess Nesbitt from DataSong join our Fall webinar series tomorrow, with a great presentation on time to event models. If you're trying to predict when an event will occur (for example, a consumer buying a product) or trying to infer why events occur (what were the factors that led to a component failing?), time-to-event models are a useful framework. These models are closely related to survival analysis in life sciences, except that the outcome of interest isn't "time to death" but time to some other event (e.g. in marketing, "time to purchase"). Also in today's applications the data sizes are much larger (often Hadoop scale) as all kinds of demographic, operational and sensor data are brought to bear to imrove the predictions. I've included the webinar abstract below, and you can register here to attend (and/or be notified when the slides and replay are available). There's no charge to attend. USING TIME TO EVENT MODELS FOR PREDICTION AND INFERENCE Presented by Revolution Analytics and DataSong Date: Thursday, October 10, 2013 Time: 9AM - 10AM Pacific Time Presenters: John Wallace, Founder and CEO & Tess Nesbitt, Senior Consultant, Statistician PhD, DataSong Companies are doing a better and better job of collecting data that explains why consumers behave the way they do. These diverse data sets cause us to rethink some of the workhorse algorithms for data analysis. Specifically, the traditional binary response model leaves much room for improvement in how it embraces time. Cross–sectional models allow much rich data to fall through the cracks. We’ll discuss real-world scenarios and how to better use data with time to event modeling. This session will cover:  Several business scenarios where time to event modeling makes better use of rich data  Time to event models for prediction  Time to event models for inference  RevoScale functions used for data analysis Revolution Analytics Webinars: Using Time to Event Models for Prediction and Inference, presented by Revolution Analytics and DataSong
about 5 hours ago
(This article was first published on Revolutions, and kindly contributed to R-bloggers) We're thrilled to have John Wallace and Tess Nesbitt from DataSong join our Fall webinar series tomorrow, with a great presentation on...
(This article was first published on Revolutions, and kindly contributed to R-bloggers) We're thrilled to have John Wallace and Tess Nesbitt from DataSong join our Fall webinar series tomorrow, with a great presentation on time to event models. If you're trying to predict when an event will occur (for example, a consumer buying a product) or trying to infer why events occur (what were the factors that led to a component failing?), time-to-event models are a useful framework. These models are closely related to survival analysis in life sciences, except that the outcome of interest isn't "time to death" but time to some other event (e.g. in marketing, "time to purchase"). Also in today's applications the data sizes are much larger (often Hadoop scale) as all kinds of demographic, operational and sensor data are brought to bear to imrove the predictions. I've included the webinar abstract below, and you can register here to attend (and/or be notified when the slides and replay are available). There's no charge to attend. USING TIME TO EVENT MODELS FOR PREDICTION AND INFERENCE Presented by Revolution Analytics and DataSong Date: Thursday, October 10, 2013 Time: 9AM - 10AM Pacific Time Presenters: John Wallace, Founder and CEO & Tess Nesbitt, Senior Consultant, Statistician PhD, DataSong Companies are doing a better and better job of collecting data that explains why consumers behave the way they do. These diverse data sets cause us to rethink some of the workhorse algorithms for data analysis. Specifically, the traditional binary response model leaves much room for improvement in how it embraces time. Cross–sectional models allow much rich data to fall through the cracks. We’ll discuss real-world scenarios and how to better use data with time to event modeling. This session will cover:  Several business scenarios where time to event modeling makes better use of rich data  Time to event models for prediction  Time to event models for inference  RevoScale functions used for data analysis Revolution Analytics Webinars: Using Time to Event Models for Prediction and Inference, presented by Revolution Analytics and DataSong To leave a comment for the author, please follow the link and comment on his blog: Revolutions. R-bloggers.com offers daily e-mail updates about R news and tutorials on topics such as: visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more...
about 5 hours ago
Filed under: Kids, pictures, Running Tagged: appletree, Argentan, fog, half-marathon, morning light, Normandy
Filed under: Kids, pictures, Running Tagged: appletree, Argentan, fog, half-marathon, morning light, Normandy
about 6 hours ago
Football-related arrests rose by 4% during the 2012-13 season according to the Home Office. See how the figures break down by club, location and offence type• Get the data• Explore the interactiveThe number of football-related arrests at...
Football-related arrests rose by 4% during the 2012-13 season according to the Home Office. See how the figures break down by club, location and offence type• Get the data• Explore the interactiveThe number of football-related arrests at international and domestic matches involving teams from, or representing England and Wales rose by 4% during the 2012-13 season. A total of 2,456 arrests were made - up by 93 on the 2011-12 total which was the lowest on record - according to the latest figures out today.Attendance at regulated matches over the season soared to more than 39m (up from 37m in 2011-12) and despite the rise in the total number of arrests, the Home Office stress that the total number of arrests represent less than 0.01% of total attendees, or one arrest for every 14,000 spectators. For the first time, the annual release also includes data on the number of football-related offence arrests made by the British Transport Police - an additional 316 arrests in 2012-13 - which when combined with the the total number of arrests made by Home Office police forces brings the total of football-related offence arrests up to 2,772. An average of less than one arrest was made per match inside and outside of stadia during the season and no arrests were made at 75% of all regulated matches according to the figures. With more than 100,000 English and Welsh club fans travelling to Champions League and Europa matches outside of England and Wales during the 2012-13 season, the Home Office have also highlighted the fact that only 20 arrests of away fans were made across 44 matches.Newcastle United recorded the highest number of arrests with 175 during the 2012-13 Premier League season - 114 of these being for violent disorder. Earlier this year football supporters clashed with police resulting in 29 arrests after a match between Newcastle and Sunderland in which Newcastle lost 3-0. The violence which included a fan punching a police horse was described by one officer as "the worst rioting I have seen in the city centre in decades."An outbreak of serious violence was also witnessed between Millwall fans earlier this year at Wembley. Millwall supporters accounted for 67 arrests during the 2012-13 season with almost half of those being attributed to public disorder.Whilst the number of arrests increased slightly on the year, the number of football banning orders dropped by 11% to 2,451 (on the 20 September compared with 2,750 on 9 November 2012). Manchester United, who recorded the highest number of arrests in the 2011-12 Premier League season, saw their figures remain relatively steady (down from 148 arrests to 145), however the largest number of banning orders issued between the 10th November 2012 and the 20th September were imposed on Manchester United supporters. A total of 139 football banning orders were imposed in the reporting period with West Ham supporters recording the second highest number at 14 and Chelsea the third highest with 12. Southampton and Fulham were the only clubs not to have any banning orders imposed on supporters during the season.The figures which cover arrests reported by police to the Football Banning Orders Authority offer an interesting break down of arrests by club supported and offence type. For example, the highest number of arrests related to ticket touting were attributed to Manchester United supporters (10 arrests) whilst nine arrests were made for racist or indecent chanting by West Ham supporters.Click on the image above to explore the Tableau interactive which allows you to see how football-related arrests break down by club supported and offence type. The table below shows how the number of arrests and banning orders have changed over time. The downloadable spreadsheet has all the data used in the interactive and figures from past releases. Download the data• DATA: download the full spreadsheetMore open dataData journalism and data visualisations from the GuardianDevelopment and aid data• Search th
about 8 hours ago
Supporters of Newcastle were arrested more times for football related offences than those of any other club in the country. See how fans of your team behaved with our interactive breaking down arrests by league, season and type of offenc...
Supporters of Newcastle were arrested more times for football related offences than those of any other club in the country. See how fans of your team behaved with our interactive breaking down arrests by league, season and type of offenceGeorge ArnettAmi Sedghi
about 8 hours ago
The Office for National Statistics is planning to reduce or scrap altogether several of the data sets it publishes. Which numbers might soon be lost?It might not be a ministry, but as a government department, the Office for National Stat...
The Office for National Statistics is planning to reduce or scrap altogether several of the data sets it publishes. Which numbers might soon be lost?It might not be a ministry, but as a government department, the Office for National Statistics (ONS) is as vulnerable as other public bodies to budget cuts. UK and EU laws mean that it has to continue to publish around 80% of what it does already - so which numbers might go from public knowledge to private guesswork? £9 millionFree and open data doesn't come cheap - the ONS is trying to cut back by £9m over the next two years. The vast majority of that will be by "streamlining" and "greater efficiencies" at the office - but reducing the "national statistics" bit of the ONS will have to account for £1m of those savings.The ONS has opened up a public consultation about how it should go about reducing its output. It lists the surveys, questionnaires and general statistics which it is not obliged by law to collect and publish. They include numbers on alcohol-related deaths, labour disputes and internet access. How are they used?If those numbers serve as a reliable evidence base for the government to make policies (and subsequently check how those policies are working) then it's worth looking at the datasets that might be stopped. So we've listed the formal name of the data, how we have written about it and how you've reacted. Health inequalities analysis• Answers questions like 'How does socioeconomic class affect mortality?', 'Has the gap narrowed between professional and routine workers since 2001?', 'Does the North have a higher mortality rate than the South?'• Our past articles on this include 'The toll of being an unpaid carer' which showed carers, especially young carers, were more likely to report being in poor health. • See past ONS data on thisInternet access quarterly update• Answers questions like 'How many people in the UK have internet access?', 'How many people have never used the internet before?', 'How does disability affect an individual's access to the internet?'• Our past articles on this include '36m Brits use the internet every day - but what are they all doing?' which looked at activities online as well as the reasons people give for not using the internet. • One reader, @The_Diddler said: "I think the over-65s increase could well be to do with the fact that under-65s that did use the internet and are used to it have aged, rather than any more older people starting."• See past ONS data on thisIntegrated household survey• Answers questions like 'How many people in the UK identify as gay, lesbian or bisexual?', 'How does that change between age groups?' and 'How has that changed over time?'• Our past articles on this include 'Gay Britain: what do the statistics say?' which showed that only 1.5% of the country would say that they are homosexual or bisexual and asks why people assume it's much higher.• One reader, @MyPoorEar (commenting in response to another who said this data was not useful) said: "Very idealistic of you, but this would be useful data for any number of reasons. Policymakers and charities identifying the prevalence of sexuality-based discrimination and strategies for dealing with it. Sociologists researching human sexuality and its links to other phenomena. Market researchers chasing a gay or bi demographic. Entrepreneurs deciding whether to open a gay bar in a local area. Loads more."• See past ONS data on thisThe consultation will remain open until 31 October 2013 and the ONS aims to publish a summary of its findings in early 2014.Do you think these are important datasets? Which numbers do you think are less relevant to understanding life in Britain and should be the target of ONS cuts? Let us know your views below.Office for National StatisticsData protectionMona Chalabitheguardian.com © 2013 Guardian News and Media Limited or its affiliated companies. All rights reserved. | Use of this content is subject to our Terms & Conditions | More Fe
about 9 hours ago
The Filmarket Hub is the first cinematographic and audiovisual online market that connects creatives, producers and investors in one single platform. Don´t miss out this great opportunity! www.filmarkethub.com
The Filmarket Hub is the first cinematographic and audiovisual online market that connects creatives, producers and investors in one single platform. Don´t miss out this great opportunity! www.filmarkethub.com
about 10 hours ago
This infographic discusses the importance of learning and teaching music along with the benefits that rise from music education.
This infographic discusses the importance of learning and teaching music along with the benefits that rise from music education.
about 10 hours ago
All about ergonomics in the workplace and the benefits of ergonomic office chairs- information for National Ergonomics Month
All about ergonomics in the workplace and the benefits of ergonomic office chairs- information for National Ergonomics Month
about 10 hours ago