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 Compulsory education - Wikipedia, the free encyclopedia
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{{cite web | title = Compulsory education - Wikipedia, the free encyclopedia | url = http://en.wikipedia.org/wiki/Compulsory_education | date = 2015-02-25 | archiveurl = http://archive.today/BNqes | archivedate = 2015-02-25 }}
(Redirected from Compulsory schooling)
Compulsory education refers to a period of education that is required of persons, imposed by law. In some countries the education needs to take place at a registered school. Other countries allow the education to happen outside of school, for example via homeschooling.
1 Antiquity to Medieval Era
1.1 Early Modern Era
1.2 Modern Era
2 Variation in countries
Antiquity to Medieval Era[edit]
Although Plato's The Republic is credited with having popularized the concept of compulsory education in Western intellectual thought, every parent in Judea since ancient times was required to teach their children at least informally. Over the centuries, as cities, towns and villages developed, a class of teachers called Rabbis evolved. According to the Talmud (tractate Bava Bathra 21a), which praises the sage Joshua ben Gamla with the institution of formal Jewish education in the 1st century AD, Ben Gamla instituted schools in every town and made formal education compulsory from the age of 6 or 7.[1]
The Aztec Triple Alliance, which ruled from 1428 to 1521 in what is now central Mexico, is considered to be the first state to implement a system of universal compulsory education.[2][3]
Early Modern Era[edit]
The Reformation prompted the establishment of compulsory education for boys and girls. Most important was Martin Luther's text 'An die Ratsherren aller Städte deutschen Landes,' (1524) with the call for establishing schools.[4] Especially the Protestant South-West of the Holy Roman Empire of the German Nation with cities like Strassburg became pioneers in educational questions. Under the influence of Strasbourg in 1592 the German Duchy Pfalz-Zweibrücken became the first territory of the world with compulsory education for girls and boys.[5] The South German Duchy Wuerttemberg installed a compulsory education already in 1559, but for boys only.[6]
In Scotland the Education Act of 1496 had obliged the children of noblemen and freeholders to attend school, but the School Establishment Act of 1616 commanded every parish with the means to establish a school paid for by parishioners. The Parliament of Scotland confirmed this with the Education Act of 1633 and created a local land-based tax to provide the required funding. The required majority support of parishioners, however, provided a tax evasion loophole which heralded the Education Act of 1646. The turmoil of the age meant that in 1661 there was a temporary reversion to the less compulsory 1633 position. However, in 1696 a new Act re-established the compulsory provision of a school in every parish with a system of fines, sequestration, and direct government implementation as a means of enforcement where required.
During the Reformation in 1524, Martin Luther advocated compulsory schooling so that all parishioners would be able to read the Bible themselves, and Palatinate-Zweibrücken passed accordant legislation in 1592, followed by Strasbourg—then a free city of the Holy Roman Empire— in 1598.
Prussia implemented a modern compulsory system in 1763 which was widely recognised and copied. It was introduced by the Generallandschulreglement, a decree of Frederick the Great in 1763-5.[7] The Generallandschulreglement, authored by Johann Julius Hecker, asked to educate all young citizens, girls and boys, to be educated from the fifth till at age 13 or 14 and to provide them with a basic outlook on (Christian) religion, singing, reading and writing based on a regulated, state provided curriculum of text books. The teachers, often former soldiers, were asked to cultivate silk worms to make a living besides contributions from the local citizens and municipalities.[8] Funding and training of the teachers was slowly expanded and received funding till teachers gained full academic status in the 20th century. This provided a working model for other states to copy; the clearest example of direct copying is probably Japan in the period of the Meiji Restoration.[9]
In Austria, Hungary and in the Lands of the Bohemian Crown (Czech lands), mandatory primary education was introduced by Empress Maria Theresa in 1774.[7]
Modern Era[edit]
Compulsory school attendance based on the Prussian model gradually spread to other countries. It was quickly adopted by the governments in Norway and Sweden, and also in Finland, Estonia and Latvia within the Russian Empire, but was rejected in Russia itself.[10][11] France and the United Kingdom did not, until the 1880s, introduce compulsory education: France due to conflicts between a radical secular state and the Catholic church, and the UK due to the upper class defending its educational privileges and turfs.[12]
One of the last areas in Europe to adopt a compulsory system was England and Wales, where the Elementary Education Act of 1870 paved the way by establishing school boards to set up schools in any places that did not have adequate provision. Attendance was made compulsory until age 10 in 1880. The Education Act of 1996 made it an obligation on parents to require children to have a full-time education from the age of five to the age of sixteen. However, attendance at school itself is not compulsory; Section 7 of the Act allows for "education otherwise" than at a school i.e. home education
Following Luther and other Reformers, the Separatist Congregationalists who founded Plymouth Colony in 1620, obliged parents to teach their children how to read and to write so that they were able to read the Bible for themselves.[13] In Massachusetts Bay Colony, founded by Puritans in 1628, a law obliged parents to teach their children reading and writing in 1642. Five years later, the first steps were taken to require free elementary instruction in the towns. The Puritan zeal for learning was reflected in the early and rapid rise of educational institutions. Harvard College was founded in 1636.[14] The American Commonwealth of Massachusetts was the first state to pass a compulsory education law which occurred in 1852. These laws continued to spread to other states until finally, in 1918, Mississippi was the last state to enact a compulsory attendance law.[15] Massachusetts had originally enacted the first compulsory education law in the American colonies in 1647. In 1852, the Massachusetts General Court passed a law requiring every town to create and operate a grammar school. Fines were imposed on parents who did not send their children to school and the government took the power to take children away from their parents and apprentice them to others if government officials decided that the parents were "unfit to have the children educated properly".[16]
Compulsory education was not part of early American society;[citation needed] which relied instead on church-run private schools that mostly charged fees for tuition.[citation needed] The spread of compulsory attendance in the Massachusetts tradition throughout America, especially for Native Americans, has been credited to General Richard Henry Pratt.[17] Pratt used techniques developed on Native Americans in a prisoner of war camp in Fort Marion, Augustine, Florida, to force demographic minorities across America into government schools.[17] His prototype was the Carlisle Indian Industrial School in Pennsylvania.
Variation in countries[edit]
Some kind of education is compulsory to all people in most countries, but different localities vary in how many years or grades of education they require and in whether it needs to be in a school or can be provided at home. Due to population growth and the proliferation of compulsory education, UNESCO calculated in 2006 that over the subsequent 30 years more people would receive formal education than in all prior human history.[18] It is possible in many countries for parents to provide education for children by homeschooling, although this is often monitored for adherence to national standards.
Argentina 6-14
Australia 5-15/17 Upper age limit varies among states. Waived if pursuing full-time employment or full-time education
Belgium 6-18
Brazil 7-14
Canada 6-16 Except Ontario and New Brunswick: 6-18. Some provinces have exemptions at 14.
China 6-15
Egypt 6-14
Finland approx 7-15 Beginning age is negotiable ± 1 year. Ends after graduation from comprehensive school, or at least 9 years.
France 6-16
Germany 6-16 Varies slightly between states.[19][20]
Haiti 6-11 The Haitian Constitution mandates that education be free of charge. However, even public schools charge substantial fees. 80% of children go to private schools.
Hong Kong 6-17
Hungary 6-16
India 6-14 The Right of Children to Free and Compulsory Education Act in August 2009 made education free and compulsory for children between 6 and 14.
Indonesia grades 1-9
Italy 6-16
Jamaica 5-16 Parents could faces charges of Child Neglect if they prevent their children from going to school without valid reasons. Not enforced.
Malaysia 6-12[21]
Mexico Schooling is required through upper secondary school (Preparatoria).[22]
Morocco 6-15
Netherlands 5-18 Students are allowed to leave early after obtaining their 'start qualification' (MBO level 2, HAVO or VWO degree)
Norway 6-15 A total of ten years, where Primary school is year 1-7 (without grades), and Lower Secondary school (with grades) is year 8-10.[23]
Poland 7-18 Polish law distinguishes between compulsory school (obowiązek szkolny) and compulsory education (obowiązek nauki).
Portugal 6-18
Russia 7-18 Student may leave after age 15 with the approval of a parent and the local authority.[24]
Slovenia 6-15
Singapore 7-12[25] Children with special needs are excluded from compulsory education
Spain 6-16
Syria 6-15 Typical ages for 9 years of compulsory education from grade 1 to grade 9.
Sweden 7-16[26]
Taiwan 7-15 Typical ages for 9 years of compulsory education. 12-year compulsory education starting from 2014.
United Kingdom 5-17 Will rise to 18 in 2015.[27] Requirement is for a full-time education, but attendance at a school is not compulsory (section 7 of The Education Act 1996).
United States about 6-17 Varies by state. Beginning age varies 5-8, ending age varies 15-18.[28] Some states allow early leave with parental approval. Education does not need to be at a school and can be provided in the home.
Uruguay 6-14
Zimbabwe 6-16 Typical ages for 11 years of compulsory education.
Criticism[edit]
Compulsory education has been criticized on various grounds:
The belief that it encroaches on the rights of children[29]
The belief that it encroaches on the rights of parents[30]
The belief that, historically, compulsory education is not guided by altruism[31]
The belief that it implicitly teaches authoritarianism[32]
The belief that the variety of children's individual growth cannot be supported within an imposed structure[33]
Public school (government funded)
Home education in the United Kingdom
Anti-schooling activism
Raising of school leaving age
Home School Legal Defense Association
Democratic education
Jump up ^ Wikipedia: Jewish education#Primary schooling
Jump up ^ Jacques Soustelle (11 November 2002). Daily life of the Aztecs: on the eve of the Spanish Conquest. Courier Dover Publications. p. 173. ISBN 978-0-486-42485-9. Retrieved 27 November 2012.
Jump up ^ Wikipedia: Aztec#Education
Jump up ^ http://books.google.de/books?id=LmOsRpe0nC0C&pg=PA70&lpg=PA70&dq=%27An+die+Ratsherren+aller+St%C3%A4dte+deutschen+Landes,&source=bl&ots=Cbol9QTBZu&sig=NoTvTICsLBMYPYMisSmAphXiMDA&hl=de&sa=X&ei=sfJPU6j_I4LAtQb16oGAAw&ved=0CDMQ6AEwAA#v=onepage&q=%27An%20die%20Ratsherren%20aller%20St%C3%A4dte%20deutschen%20Landes%2C&f=false
Jump up ^ Emil Sehling (ed.), Die evangelischen Kirchenordnungen des 16. Jahrhunderts. Vol 18: Rheinland-Pfalz I. Tübingen 2006, p. 406.
Jump up ^ "Große Kirchenordnung", 1559, Oliver Geister, Die Ordnung der Schule. Zur Grundlegung einer Kritik am verwalteten Unterricht. Münster 2006, p. 145.
^ Jump up to: a b James van Horn Melton. "Absolutism and the Eighteenth-Century Origins of Compulsory Schooling in Prussia and Austria". p. xiv.
Jump up ^ 250 Jahre Volksschule in Preußen, Lesen, Schreiben und Beten (250 years of primary education in Prussia) 12.08.2013 Tagesspiegel Berlin, Barbara Kerbel, in German
Jump up ^ Wikipedia: Education in Japan
Jump up ^ Cubberley, 1920
Jump up ^ Yasemin Nuhoglu Soysal, and David Strang, "Construction of the First Mass Education Systems in Nineteenth-Century Europe," Sociology of Education (1989) 62#4 pp. 277-288 in JSTOR
Jump up ^ Construction of the First Mass Education Systems in Nineteenth-Century Europe Yasemin Nuhoglu Soysal and David Strang, Sociology of Education, Vol. 62, No. 4 (Oct., 1989), pp. 277-288 Published by: American Sociological Association
Jump up ^ John Demos (1970), A Little Commonwealth: Family Life in Plymouth Colony. Oxford University Press, New York, N.Y., pp. 104, 142-144
Jump up ^ Clifton E. Olmstead (1960), History of Religion in the United States. Englewood Cliffs, N.J, pp. 79-80
Jump up ^ Katz, Michael S. "A History of Compulsory Education Laws". ERIC - Institute of Education Sciences. ERIC. Retrieved 19 December 2014.
Jump up ^ Rothbard, Murray Rothbard. "The Puritans 'Purify': Theocracy in Massachusetts". Conceived in Liberty. Arlington House Publishers.
^ Jump up to: a b Witte, Daniel E. and Paul T. Mero. "Removing Classrooms from the Battlefield: Liberty, Paternalism, and the Redemptive Promise of Educational Choice, 2008 BYU Law Review 377" (PDF). Retrieved 2010-10-06.
Jump up ^ Schools Kill Creativity. TED Talks, 2006, Monterey, CA, USA.
Jump up ^ "Schulpflicht" (in German). Retrieved October 2, 2010.
Jump up ^ "Where home schooling is illegal". BBC News. March 22, 2010.
Jump up ^ "Pelaksanaan pendidikan wajib di peringkat rendah 2003".
Jump up ^ "''Calderón firma decreto de preparatoria obligatoria'' Laura Casillas". Azteca Noticias. Retrieved 2012-02-08.
Jump up ^ "About Education in Norway". udir.no. Foreign Ministry of Norway. Retrieved 29 July 2014.
Jump up ^ Federal law of Russia "On education", article 19.6
Jump up ^ "COMPULSORY EDUCATION ACT (CHAPTER 51)". Singapore Statutes Online.
Jump up ^ Hans Högman. "Den svenska skolans historia" (in Swedish). Hasses hemsida. Retrieved 6 September 2014.
Jump up ^ "Education leaving age". Politics.co.uk. Retrieved 2013-05-15.
Jump up ^ Age range for compulsory school attendance and special education services, and policies on year-round schools and kindergarten programs.. Retrieved November 28, 2009.
Jump up ^ John Holt
Jump up ^ Branden, N. (1963). Public Education, Should Education be Compulsory and Tax Supported, as it is Today? Chapter 5, Common Fallacies About Capitalism, Ayn Rand, Capitalism: The Unknown Ideal, p. 89.
Jump up ^ Murray Rothbard
Jump up ^ Hidden curriculum
Jump up ^ Herbert Read, The Education of Free Men (London: Freedom Press, 1944), pp. 27 — 28.
Coleman, J. S., et al. (1966). Equality of Educational Opportunity. Washington: U.S. Government Printing Office.
Dunlap, Knight. "Is Compulsory Education Justified?," The American Mercury, February 1929.
Epstein, R. (2007). Let's abolish high school. Education Week. Retrieved April 18, 2007, from http://www.edweek.org/ew/articles/2007/04/04/31epstein.h26.html
Gatto, J. T. (2003). The Underground History of American Education. New York: The Oxford Village Press.
Holt, J. (1974). Escape from childhood. In Noll, J.W. (Ed.), Taking Sides: Clashing Views on Educational Issues (pp. 25–29). Dubuque, IA: McGraw-Hill.
Illich, I. (1970). Deschooling Society. New York: Harper & Row.
O'Keeffe, D. (2004). Libertarian Alliance. Compulsory education: An oxymoron of modernity. Retrieved April 16, 2007, from http://www.libertarian.co.uk/lapubs/educn/educn036.htm
Rothbard, M. (1978). Public and compulsory schooling. In For a New Liberty (chap. 7). Retrieved April 12, 2007, from http://www.mises.org/rothbard/newliberty6.asp
Van Horn Melton, J. (1988). Absolutism and the Eighteenth-Century Origins of Compulsory Schooling in Prussia and Austria Cambridge: Cambridge University Press.
West, E. G. (1974). The economics of compulsion. In The Twelve-Year Sentence. Retrieved April 11, 2007, from http://www.ncl.ac.uk/egwest/pdfs/economics%20of%20compulsion.pdf
http://www.nhliberty.org/bills/view/2011/HB542
Wikisource has the text of a 1920 Encyclopedia Americana article about Compulsory education.
The Principle and Practice of Compulsion in Education
A discussion of compulsory education as a human right (Right to education Project)
From enforced schooling to self-directed learning A survey and a critique of compulsory education.
Why Education is Broken Author Isamu Fukui shares his thoughts on the educational system and why it doesn't work.
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text:
Les Paravents est une pièce de théâtre de Jean Genet écrite en 1961 et représentée pour la première fois le  au théâtre de l'Odéon par la compagnie Renaud-Barrault.

La pièce, en tableaux, se déroule pendant la guerre d'Algérie (1954-1962) ; l'auteur y brosse un portrait très sévère de l'armée française.

Réactions
La création des Paravents en 1966, quatre ans après la fin de la guerre d'Algérie, est perçue par différents mouvements nationalistes et ultranationalistes comme un atteinte à l'image virile de la France coloniale. Sa représentation au théâtre de l'Odéon est l'occasion de violentes manifestations du groupuscule d'extrême droite Occident et de certains anciens combattants d'Afrique du Nord et d'Indochine, autour et parfois à l'intérieur même du théâtre. Alain Madelin et Patrick Devedjian participent quotidiennement à ces protestations, allant jusqu'à jeter des rats et du gaz lacrymogène sur scène pour réclamer l'arrêt des représentations. Même à l'Assemblée, certains remous se font sentir, qu'André Malraux, alors ministre de la Culture de la V République, parvient à calmer.

Distribution d'origine
 Jacques Alric : Le Gendarme
 Amidou : Saïd
 Paule Annen : Leila
 Jean-Louis Barrault : Si Slimane
 Annie Bertin : Malika
 Maria Casarès : La Mère
 Gabriel Cattand : Le Lieutenant
 Marie-Hélène Dasté : Mme Blankensee
 Paul Descombes : Sir Harold
 François Gabriel : Le Fils de sir Harold
 Jean-Pierre Granval : Habib
 Germaine Kerjean : Kadidja
 Jane Martel
 Michèle Oppenot
 Régis Outin : M. Blankensee
 Marcelle Ranson : Ommou
 Madeleine Renaud : Warda
 Bernard Rousselet : Le Sergent
 Dominique Santarelli
 Georges Sellier : Malik
 Tania Torrens : La Vamp
 André Weber : Pierre

Liens externes
 Discours d'André Malraux à l'Assemblée nationale, après la première des Paravents.

Notes

Pièce de théâtre française
Pièce de théâtre de 1966
Pièce de théâtre de Jean Genet
Pièce de théâtre se déroulant à l'époque contemporaine
Œuvre ayant pour thème la guerre d'Algérie
1966 en France

redpajama_set_name: RedPajamaWikipedia


text:
As mentioned before, the Saksunarvegur route eventually leads to Saksun and while on the face of it, Saksun might sound like your regular tint little village, once you arrive here, you soon realise why it's so special and unlike any other village you've ever visited.
Reason – the view here is absolutely incredible!
We're talking towering mountains, draped in rugged grass, which dips into a lake (of sorts), laced with a beach and fed by the sea. If you're even lucky, it might be foggy (or even snowing) when you visit which only makes it more incredible!
The most 'famous' building to see here is down at the old farm and is one of the most photographed sights on the islands.
There's barely anyone in sight here, pretty much zero phone service and it truly feels like a perfect place to escape from the hustle and bustle of city life.
Moving on from Saksun, we made our way over to Eiði (I can barely pronounce it – it sounds kinda like "Ah-e-yeah" if you say it really fast). This wasn't actually planned but seeing as we'd had a picnic packed for lunch, it seemed as good a place to stop as any.
If you drive further up the road here, you even find a picnic spot boasting some spectacular views across the islands and even a gigantic stone chair and table set!
Lunch over and done with, we headed off in search of Gjógv, a village named after the sea-filled gorge in it.
The only thing with this though is that you have to drive way into the mountains before slowly descending to get to it so, despite the fog lifting slowly, the climb into the mountain slowly leaves you surrounded by nothing but fog as far as you can see.
You get a few sheep to start with of course…
…though once you find yourself in deep fog, you'll probably notice a considerable temperature change. How Game of Thrones hasn't been set here, I have no idea! (As I wrote this, I quickly googled it just to be sure and as it turns out, quite a few GoT scenes have been shot here).
^ Wow! Talk about an unflattering jumper! I look like I'm hiding several sheep underneath that thing. Still, it's fairly warm and, as I keep telling myself, that's what really counts. 🙂 😉
By the time, we arrived in Gjógv, the fog had cleared a lot more and we got to explore the reason behind the name of the village!
We also got to make friend with a rather friendly and very well trained local sheep dog. Seriously, this little fella could turn anything into a game. He's pick up stones and drop them at your feet, just waiting for you to play in a game of fetch. As it turns out, every person (and animal) on the islands has resourcefulness built into them (even the sheep walk effortlessly up almost vertical mountain drops to graze and relax).
Eventually, it was time to say farewell to our new friend and make our way back to Torshavn.
Not before an important stop though! Again, totally unplanned (but absolutely necessary to see when you're in the Faroe Islands) is Fossá – the islands highest waterfall.
When you visit, you need to park slightly up the road and walk back to towards it as there's no parking right exactly where it is. The walk is only like 2 minutes so it's not a major hardship, by the way.
Arriving at the waterfall reminded me again of how amazing these islands are (not that I needed much reminding to be fair).
If you're feeling like something of a sure-footed mountain goat, you can even make your way closer to the waterfalls though it'd be safe to say you should exercise some common sense and caution should you decided to do so. 🙂
Much as we could have spent quite a while longer taking in this amazing view, dinner was calling back at Torshavn and so we hot footed it back to the capital city, in search of food!
It's gotta be said though, despite the increasing lack of time to make it back to the city for our dinner reservation, no journey in the Faroe islands is every straight forward as you'll always find some reasons to stop and take in another amazing sight/view… It's truly one of those things that makes these islands so special.
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text:
Women and Leadership Development in Australian Sport Organizations
Marissa Banu-Lawrence * , 1 , Stephen Frawley * , 1 and Larena Hoeber * , 2
1 University of Technology Sydney
2 University of Regina
diversity; gender; inclusion; stakeholders
There has been growing interest in gender diversity and the leadership development of women in recent years within the broader field of management studies. Understanding leadership development processes is important for the sport industry, in which organizations are becoming increasingly professional and commercially focused. Despite the increased attention on gender diversity and leadership development within the sport industry to date, the scope and application of organizational gender and leadership development theory within an Australian sport context has been limited. As such, the purpose of this study was to explore the leadership development practices adopted by key stakeholders of the Australian sports industry, with the intention to uncover how they impact the role of women in different organizations. Specifically, the research investigated the practices of three organizations that have a major stake in Australian professional sport.
* Banu-Lawrence and Frawley are with the University of Technology Sydney, Lindfield, New South Wales, Australia. Hoeber is with the University of Regina, Regina, Saskatchewan, Canada.
Frawley (Stephen.Frawley@uts.edu.au) is corresponding author.
Leadership Development and Gender
Leadership, Gender, and Sport
Financial Services Organization
Sport Media Organization
Professional Sport Organization
Gender Policy Engagement
Gender Quota Tensions
Gender-Specific Leadership Development
Gender Reporting
Formal and Informal Approaches to Development
Adriaanse, J. (2017). Gender diversity in the governance of international sport federations. In N. Schulenkorf & S. Frawley (Eds.), Critical issues in global sport management (pp. 23–37). New York, NY: Routledge.
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Marissa Banu-Lawrence
Stephen Frawley
Larena Hoeber


text:
Renamed jazz series begins Sept. 24 with Russell Gunn
Staff2 min readAugust 23, 2016
Jazz at the G has a new name – Straight A Jazz – and the first concert is coming up fast.
Two-time Grammy nominee Russell Gunn, a contemporary neo-bop jazz trumpeter/flugelhornist/composer, is set to headline the first concert under the banner. He'll be performing with the Gray Mayfield Trio.
The concert is scheduled for 6 p.m. on Saturday, Sept. 24, at the D. Douglas Barnard Amphitheatre on the Summerville Campus of Augusta University. The South Carolina-based Reggie Sullivan Trio and the Augusta University Jazz Ensemble under the direction of Dr. Rob Foster are also on the bill.
Gunn is a native of East St. Louis, Illinois, but currently operates from a base in Atlanta. Weaned on rap, Gunn turned to jazz in his professional pursuits, although hip-hop remains a primary influence on his work. He first attracted the attention of critics and audiences through his contributions to Wynton Marsalis' Blood on the Fields. Gunn also backed the likes of Jimmy Heath, Roy Hargrove, James Moody, and R&B hit-maker Maxwell. During a career that has spanned more than 20 years, he has released more than a dozen recordings, including Krunk Jazz, Russell Gunn Plays Miles, and Ethnomusicology, vols. 1–4.
Straight A Jazz is a continuation of the groundbreaking Jazz At The G concert series, produced by GPB/Augusta in partnership with the Augusta University Department of Music, Garden City Jazz, and the Greater Augusta Arts Council. The new moniker became necessary when the university's name changed to Augusta University late last year.
Tickets are available now at the Maxwell Theatre Box Office and on-line . Tickets are $10 for the general public; $5 for Augusta University employees and active military; and free for all students.
Tagged asDepartment of MusicPamplin CollegeReggie Sullivan TrioRussell GunnStraight A Jazz
Augusta University Staff is a collection of talented writers, photographers, students and professionals; all working together to promote and support the amazing impacts and every day wins of Augusta University and the people that make up JagNation.
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In India, we have very few Valuepickr ace Investors. Rakesh Jhunjhunwala, Porinju Veliyath, Vijay Kedia, Daljeet Kohli and Dolly Khanna are among them. These Valuepickr Investors are kind enough to share their value pick stocks and investment strategy with us. It is us who need to take maximum benefit of their advice and recommendation for wealth generation. In this post, I will share value pick stocks by Valuepickr investors of India.
What is Value Pick Stock?
Value Pick stock is stock with strong fundamentals, future potential, and trading at a low price. This stock is undervalued (wrongly valued) stock by the market and contains potential to increase in share price. A success of value stock picking relies on the strict screening process.
The first value pick stock of valuepickr investor Rakesh Jhunjhunwala is Delta Corp. Delta Corp is largest and only listed company in the gaming and hospitality segment in India. In addition to gaming and entertainment Delta Corp is also in the real estate business. Delta Corp is a pioneer and they have no completion in gaming space. The company has shown a very good financial performance and expected to perform better in the future. The stock has strong fundamental and at CMP of Rs. 175 (As on April,2017) it is a good stock pick.
Titagarh Wagons is value pick stock by Porinju Veliyath. Titagarh Wagons is in the business of making wagons, bridges and heavy earth moving equipment. The company has very good order book. Looking at upcoming metro train projects in multiple cities this company is expected to get good business in the future. Strong fundamentals and the healthy order book is a positive side of Titagarh Wagons. At a current level of Rs.120 (As on April,2017) it is a very good stock pick.
Valuepickr Investor Vijay Kedia is bullish on banking and financial sector. As per Vijay Kedia stock of Karnataka Bank is a value pick. He has aggressively increased holding in this stock. Karnataka Bank is available at very low price compares to its growth potential. Once upon a time, ICICI bank has attempted to buy Karnataka bank and a possibility of ICICI making another similar attempt cannot be ruled out. At a current level of Rs.160 (As on April,2017) it is grossly undervalued stock with very good potential.
The last value pick stock by valuepickr Dolly Khanna is Tata Metaliks. Although Tata Metaliks has given a very good return in past still there is a juice in this stock. Tata Metaliks is in the fastest growing business of DI pipes with limited completion and solid demand. The stock is undervalued with very good potential to show a hefty gain in the future.
What is your call on above Value Picks?
Too Good value pick advisable stock investment purpose .Be continuous for us these type of advisable stock for good fundamental, potential to earn future prospective.


Congratulations! You've just found the perfect little crossbody bag! This two-toned bag is constructed from butter yellow vegan leather with soft gray vegan leather on the bottom.
OUTSIDE: This smaller crossbody bag is perfect for the everyday outings that we all have. It features a convenient back zipper to throw your keys or phone in to keep them safe, but accessible.
INSIDE: This purse has a zippered closure on top. The inside is crafted from high quality quilting cotton that has a gorgeous yellow poppies on gray pattern, and is big enough to fit your wallet and other important items. There is a zippered pocket inside to hide your super secret stuff as well as a slip pocket to stuff your grocery list or coupons in to keep them organized.
STRAP: The strap is adjustable and can be shortened to sit on your shoulder, or lengthened to wear across your body.
Every part of this bag was lovingly designed and crafted by me in my home studio. There are many different colors available, make sure to check out them out.
If a larger bag is more your style, I have those too, in both cork and vegan leather options. Thanks for visiting and I appreciate your interest - I love what I do!


New Slumgullion! Yes, it's time for our so-late-nobody-gives-a-damn-about-the-spoilers-anymore review of Logan. We drop all pretense of format and genre ecumenism, and plunge in-depth to this, the meatiest, the bloodiest, and most albino-y of the Marvel movies.
This episode, Scott and Jeff are joined by the New Movie Crew (John Szura, Blanche Ramirez, and smutty book author Indy McDaniel). But first, Jeff and Mary do that weird thing where they pretend they're characters from Happy Days and chat about the TV show Bones, which I'm happy to say is coming to the end of its run soon, because I never have a clue what they're talking about (who the hell are "Buck and Wanda"?).
Stick around after the closing theme to hear Mary's take on the movie. She generally shies away from films with violence and gore, so she probably hated Logan, right? Or did she...?


text:
Our Spaces to Play
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Home > Take Part > Playgrounds > Crumbles Castle Adventure Playground
Come and play in the grounds of a real castle!
Get transported out of the city and into a nature-filled wonderland
Our staff are on hand to provide hands-on experiences
An exciting and safe space for children to really jump, climb and run
A unique space for children to explore
Built from the old buried courtyards of Crumbles' tenements
A place filled with imagination and adventure
Crumbles Castle Adventure Playground
Dear parents, families and friends of the playground,
We are working hard to make sure that the children of Islington still get access to excellent play opportunities whilst they manage a second lockdown. After School Provision has closed and the playground is open as an Outdoor Playground only throughout the lockdown period. To keep the children safe we have a maximum capacity of 30 children on site at any one time, there is regular hand washing and social distancing in place. All food and drink are banned on site during the lockdown and we are asking that all parents remind children that they must keep their distance from others whilst on the playground. Please also make sure your children are dressed for playing outside – it is getting colder! The playground operates on a one in/ one out basis and we are very sorry that we cannot allow more children in. We hope you understand the restrictions imposed by the government and will abide by the lockdown rules. We hope to reopen as the adventure playgrounds you know and love very soon.
Crumbles Castle is overlooks Bingfield Park. as you enter the playground you will find yourself transported out of the city and into a nature-filled wonderland with hidden swings and secret places to climb and explore.
Crumbles Castle provides an exciting and safe space for children to jump, climb, run and enjoy playing out, meet neighbours and friends and explore the world of their imagination.
Islington Play Association has worked closely with Islington Council to extend the playground and to adapt an empty building on the park into a welcoming space for young people that now houses offices and a play space.
The Crumbles' was the name given to the local Beaconsfield Buildings which were regarded as the worst slums in Islington and demolished in 1971. Soon after, the Bemerton Adventure Playground Committee asked architecture students from Regent Street Polytechnic to design what we know today as Crumbles Castle. Cobbles from the old buried courtyards of Crumbles' tenements and the market site on York Way can be found in the tower and walls of the castle. Local children and their parents helped build the castle between 1968 and 1971; it was recognised that 'building' was an extension of the children's play throughout that time. Crumbles Castle Adventure Playground has welcomed generations of Islington's children to play.
Please note, in line with local authority COVID-19 guidelines, we are limited to 30 children on site at one time
Term-Time:
Crumbles Castle is open Tuesday to Friday after school from 3.30-6.30pm in term time. These play sessions are free for children aged 6 and over.
We are also open during term-time on Saturdays between 12.00-5.00pm. At the moment these session are only open to children aged 6 years old and over, parents and carers will not be allowed on site.
Half-Term Holidays:
Monday 26th-Friday 30th October 2020, 10:30am-5:30pm. First-come-first-served basis
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\section{Introduction}
The objective of this study is to reveal and evaluate early indicators for COVID-19 spread risk in cities during the initial stages of the outbreak using measures of human activities derived from digital trace data. An arguably unprecedented global pandemic, the coronavirus disease 2019 (COVID-19) has infected millions of people worldwide with a mortality rate of 6.6\% and a high infection rate \cite{WHO}\cite{keni2020covid}. Since the spread of COVID-19 is highly dependent on human activities, incidence of infection could be contained by restricting human activities and mobility \cite{gollwitzer2020connecting}. Many countries and authorities have implemented various non-pharmaceutical interventions (e.g., shelter-in-place orders, regional lockdowns, and travel restrictions), which were undertaken to slow the spread of disease by disrupting transmission chains by restricting human mobility and activities. Such social distancing and activity reduction interventions have proven to be critical in slowing down the spread of pandemics both in previous epidemics \cite{caley2008quantifying} and during COVID-19 \cite{anderson2020will}\cite{tian2020investigation}\cite{ramchandani2020deepcovidnet}.

While reduction in human activities is considered an effective measure for containing epidemics and pandemics, there are limited reliable, proven, real-time leading indicators related to human activities that could provide early insights about the risk of disease spread in a region to inform proactive policy making. One reason for this limitation has been the absence of quantitative measures and data that could be examined to proactively evaluate human activities. With advancements in location intelligence data technologies, however, information derived from cellular devices offers a large depository of digital trace data related to human activities have been have increasingly been adapted and analyzed to promote understanding of and to quantify human activity and mobility in pandemic analysis, as well as in other applications \cite{asgari2013survey}\cite{balcan2009multiscale}\cite{barbosa2018human}. For example, in the context of COVID-19, the radius of gyration, which captures the mobility of individuals using human movement trajectories, was adopted to analyze the COVID-19 spread in Japan \cite{yabe2020non}. Daily step-counts (gathered from smartphones) were used to estimate and predict decreased movement of individuals within the United States during COVID-19 \cite{gollwitzer2020connecting}. Two of the most important aspects of human activities during an epidemic are agglomeration of activities and intensity of activities.

Although previous research reveals insights regarding human activities in the context of COVID-19, the relationship between human activities and disease-spreading risk has not been fully explored, and leading indicators of human activities to proactively assess the risk of disease spread during the early stages of pandemics are lacking. The majority of research studies \cite{chang2020mobility}\cite{cintia2020relationship}\cite{gao2020mapping}\cite{li2020disparate} focus on quantifying and analyzing the changes in human activities as a consequence of the outbreak of the virus and in response to protective policies (such as shelter-in-place policies). The time-lag relationship between these human activity metrics and the spread of virus, which can be generally described by the basic reproduction number ($R_0$), has not yet been fully examined. The basic reproduction number, $R_0$, is defined as the number of secondary cases produced by one previous case in a completely susceptible population \cite{dietz1993estimation}. Although research studies \cite{lampos2020tracking}\cite{lu2020internet} have focused on leading indicators obtained from users' online search behavior, the decrease of online search frequencies may not have direct impact on the spread of virus. Hence, the previous indicators cannot be utilized for proactive assessment of disease spread risk in a proactive manner.

In addition, the nature of human activities—such as activities in public indoor venues versus in residences—may have differing effects on the spread risk of a disease. The measures of human activities should distinguish between the nature of activities to provide useful leading insights for decision making and policy formulation.

In this study, we adopted the Venables distance ($D_v$) index \cite{louail2014mobile} and also created the activity density ($D_a$) index to serve as two real-time indicators to examine the spatial and temporal patterns of human activities across 193 counties in the United States using Mapbox high-resolution temporal-spatial activity index data from January 1 to March 31, 2020. The Venables distance captures the average distance (i.e., concentration) of human activities across a city or county (less distance between persons might indicate a greater contact risk). The activity density captures the intensity level of overall activities in a county or city (higher activity levels might indicate a greater spread risk). Human activities were examined in four categories—social, traffic, work, and other—based on the location and time of activities. Accordingly, we analyzed the correlation between the two metrics ($D_v$ and $D_a$) and the basic reproduction number for 193 counties with the highest number of confirmed COVID-19 cases. The rest of this paper is organized into three sections. The first section discusses the description of the two datasets (Mapbox data and total confirmed cases number data), as well as the analysis methods. The second section describes the results of time-lag correlation analysis between the two metrics and the basic reproduction number. The last section presents the results and the implications of the findings for future work.

\section{Methods}
In this section, we describe the two datasets—Mapbox data and total confirmed cases number data—and the procedures for human activity categorization. Also covered in this section are definitions and equations related to the Venables distance ($D_v$), the activity density ($D_a$), and the basic reproduction number ($R_0$). The time-lag cross-correlation analysis method is presented at the end of this section.

\subsection{Data Source and Preprocessing}
We utilized digital trace telemetry data obtained from Mapbox from January 1 to March 30, 2020. The dataset contains a metric of telemetry-based human activity, $a_{tile,t}$, which varies across spatial tiles and time t. The partition of tiles is based on Mercantile, a Python library, which is capable of creating spatial-resolution grids all worldwide. The $a_{tile,t}$ is collected, aggregated, and normalized by Mapbox from geography information updates from users' cell phone locations by time flows. The more users located in a tile at time t, the higher the human activity (i.e., $a_{tile,t}$). The dataset comprises the United States and the District of Columbia; however, in this study, we examined only 193 counties whose cumulative confirmed cases were greater than 100 as of March 31, 2020 (shown in \ref{fig:1}). In the raw data, the temporal resolution is 4 hours. Each tile represents about 100 by 100 square meters for spatial resolution. Since the data is derived from cell phone activity, data may not exist for all cells at all times. For example, a park opens during the day but is closed at night would not generate any data at midnight. Also, for protecting users' privacy and the data aggregation process, tiles with a small number of users are reported without any activity data. It is also noteworthy that the data is aggregated and normalized for each month, so the absolute values of activity indices for different months cannot be directly compared.
To reveal the time-lag relationship between metrics and spread of the virus, the total number of confirmed cases was used. We obtained the data from the COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University \cite{Hopkins}. The data in this repository were gathered and aggregated from various sources, such as the World Health Organization (WHO) and the Centers for Disease Control and Prevention (CDC).  We extracted the total number of confirmed cases $c_{i,j}$ from the CSSE repository, where i represents each date and j represents each county.

\begin{figure}[!ht]
\centering
\includegraphics[width=\linewidth]{Figure_1.jpg}
\caption{193 selected counties whose cumulative confirmed cases were greater than 100 as of March 31, 2020.}
\label{fig:1}
\end{figure}

\subsection{Tile Categorization}
The nature of an activity might put its participants at a higher risk level for contracting the virus. For example, activities in public common areas, such as grocery stores or gyms, would lead to greater risk of disease spread compared to the activities in residential areas, such as working from home or walking a dog in the community. The fine granularity of the spatial resolution enables classification of each tile into one of the four categories: (1) social tiles, (2) traffic tiles, (3) work tiles, and (4) other tiles. Categorization is based on the following characteristics: (1) social tiles are the location of at least one point of interest; (2) traffic tiles include a road; (3) work tiles show no activity in the evening; and (4) other tiles are located in residential areas. The analysis in this paper examines human activities for social, work, and traffic tiles separately. Example tile maps related to each category are shown in \ref{fig:2} for Harris County, Texas.

\begin{figure}[!ht]
\centering
\includegraphics[width=0.7\linewidth]{Figure_2.jpg}
\caption{Maps of four different tile categories (Social, Traffic, Work, and Other) in Harris County, Texas.}
\label{fig:2}
\end{figure}

\subsection{Venables Distance}

To quantify the agglomeration of human activities, we used the Venables distance ($D_v$) as a weighted average distance of human activities. The Venables Distance aggregates the spatial distribution of $a_{tile,t}$ in a county and captures the urban spatial structure of human activities (Louail et al. 2014). The $D_v$ is calculated using Equation \ref{eq:1}:

\begin{equation}
\label{eq:1}
D_v(t) = \frac{\sum_{tile_1 \neq tile_2}{a_{tile_1,t}\times a_{tile_2,t}\times d_{tile_1,tile_2}}}{\sum_{tile_1 \neq tile_2}{a_{tile_1,t}\times a_{tile_2,t}}}
\end{equation}

where, $a_{tile_1,t}$ and $a_{tile_2,t}$ are the metrics of human activities in $tile_1$ and $tile_2$ at time t, respectively, and $d_{tile_1,tile_2}$ ) is the distance from the centroids between these two tiles. In Harris County, Texas, there are more than 70K unique tiles, which makes it computational expensive to analyze all pairs of existing tiles. To reduce the computational burden, we aggregated the 100 by 100-square-meter tiles, $a_{tile,t}$, to square cells 2 kilometers in length using Equation \ref{eq:2}:

\begin{equation}
\label{eq:2}
a_{k,t} = \frac{\sum_{\textrm{for all } tile \textrm{ in cell } k}{a_{tile,t}}}{A_k}
\end{equation}

where, $a_{k,t}$ is the intensity of human activity in cell $k$, at time $t$, $A_k$ is the area of the cell $k$. By aggregating human activity into a larger spatial cell, we reduced the computational efforts, maintaining a meaningful spatial resolution without losing important characteristics in the raw data. Accordingly, the modified Venables distance is derived as shown in Equation \ref{eq:3}:

\begin{equation}
\label{eq:3}
D_v(t) = \frac{\sum_{k_1 \neq k_2}{a_{k_1,t}\times a_{k_2,t}\times d_{k_1,k_2}}}{\sum_{k_1 \neq k_2}{a_{k_1,t}\times a_{k_2,t}}}
\end{equation}

where, $a_{k_1,t}$ and $a_{k_2,t}$ are the intensity of human activities in cells $k_1$ and $k_2$ at time $t$, respectively, and $d_{k_1,k_2}$  is the distance from the centroids between these two cells. In Equation \ref{eq:3}, the values of the activity intensity ($a_{k,t}$) are used as weights to calculate a human activity-weighted distance for the whole area. In other words, the relative values of $a_{k,t}$ were used to examine changes in agglomeration of activities. We calculated $D_v(t)$ for each county $j$, which is denoted as $D_v(j,t)$  for all cells in the county $j$.

\subsection{Activity Density}
Although $D_v$ captures the agglomeration of human activities, the density of activities is also critical for examining population contact. To make the raw data ($a_{tile,t}$) from Mapbox comparable among different months, we de-normalized the activity index to the contact activity metric $ca_{tile,t}$ for each tile and each month (where $t$ is time). In the de-normalization process, the assumption was that, in each month, the minimum human activity intensity among tiles is the same. First, the tile with minimum human activity index ($a_{tile,t}$) in each month is found. Then we set the value of contact activity $ca_{tile,t}$ in that low-activity tile as 5, and de-normalized the values for other tiles based on this value. For each county, the activity density at time $t$ ($D_a(t)$) was calculated using Equation \ref{eq:4}:

\begin{equation}
\label{eq:4}
D_a(t) = \sqrt{\frac{1}{N}\sum_{tile = 1}^{N}{ca_{tile,t}^{2}}}
\end{equation}

\subsection{Basic Reproduction Number Estimation}
The basic reproduction number ($R_0$) (the number of secondary cases arising from one previous case) is a critical parameter in epidemic modeling for understanding the speed of disease spread \cite{dietz1993estimation}\cite{gatto2020spread}, as well as the risk of virus spread \cite{aleta2020modeling}\cite{giordano2020modelling}\cite{liu2018measurability}\cite{newman2002spread}. $R_0$ is calculated using Equation \ref{eq:5}:

\begin{equation}
\label{eq:5}
c_{i+t} = c_i\cdot R_0^{t/\tau}
\end{equation}
where, $c_i$ and $c_{i+t}$ represent the total confirmed cases in day $i$ and day $i+t$, respectively; $\tau$ is a constant parameter. We estimated $R_0$ using CDC data ($c_{i,j}$) in Equation \ref{eq:6}:

\begin{equation}
\label{eq:6}
R_{0i,j} = e^{\tau\frac{\ln{c_{i,j}}-\ln{c_{i-t,j}}}{t}}
\end{equation}

where $R_{0i,j}$ is the basic reproduction number at date $i$ in county $j$. Because the CDC total confirmed cases data fluctuates within the course of a week (i.e., more reported cases in weekdays and less reported cases during weekends), the time interval $t$ was set to 7 days. Based on the existing literature and simulation models related to COVID-19 \cite{fan2020effects}\cite{zhang2020changes}\cite{zhang2020evolving}, the constant parameter $\tau$ was set to 5.1 days. Accordingly, the $R_0$ was calculated for all 193 counties for the analysis period.

\subsection{Time Lagged Cross-Correlation Analysis}
In the next step, we examined the correlation between the two human activity indicators and the basic reproduction number across all counties. Since these variables are a time series, we used time-lagged cross-correlation analysis to assess the synchrony of time series data sets. The cross-correlation coefficient was calculated using Equation \ref{eq:7}:

\begin{equation}
\label{eq:7}
\rho_{A_1,A_2}(\delta) = \frac{Cov(A_1(t),A_2(t+\delta))}{\sigma_{A_1}\sigma_{A_2}}
\end{equation}
where $\rho_{A_1,A_2}$ is the cross-correlation coefficient for two time series data $A_1$ and $A_2$; $\delta$ is the time offset of $A_2$; $Cov(X,Y)$ is the function calculating the covariance of two variables; $\sigma_{A_1}$ and $\sigma_{A_2}$ are the standard deviation of data $A_1$ and $A_2$, respectively. Based on the definition, $\rho_{A_1,A_2}$ represents the correlation between two variables and $|\rho_{A_1,A_2}|\leq1$ ( $|\rho_{A_1,A_2}|=1$ happens if and only if $A_1=mA_2+n$, where $m$ and $n$ are constants). Then, by iteratively calculating the $\rho_{A_1,A_2}(\delta)$ with different $\delta$, the correlation coefficient would reach its peak when $\delta=T$, and $T$ was determined as the time lag between two variables.

\section{Results}

This section presents the results related to the calculation of the two human activity metrics and their time-lagged correlation with the basic reproduction number across 193 counties during the initial stage of the COVID-19 outbreak in the United States.

\subsection{Evaluation of Human Activity for Each Category among Counties}

In this study, the Venables distance ($D_v$), and the activity density ($D_a$) were calculated to assess the human activities at the county level using data from Mapbox. The very first four weeks (January 1 to 28) were considered as the baseline, and $D_v$ and $D_a$ values were compared with the average baseline in corresponding weekdays. For example, the $D_v$ values on March 1 (Sunday) were compared with the mean value of $D_v$ values on Sundays between January 1 and 28. Three different ways of daily activity aggregation were used: peak, average, and noon. The peak (largest), average, or the noon (11 a.m. to 3 p.m.) values of human activities $a_{tile,t}$ were selected and set as the representative value of each tile at each day. Figure \ref{fig:3} and Figure \ref{fig:4} show the percentage of $D_v$ and $D_a$ change for social, traffic, and work activity categories and for three types of daily tile aggregation.

\begin{figure}[!ht]
\centering
\includegraphics[width=\linewidth]{Figure_3.jpg}
\caption{The percentage change of $D_v$ for 193 counties in March 2020. The height of each bar is the average percentage change of all 193 counties. The error bar indicates the standard deviation among all counties. The three tile categories of social, traffic, and work are shown in each column, and three daily tile aggregations of peak, average, and noon are shown in each row.}
\label{fig:3}
\end{figure}

\begin{figure}[!ht]
\centering
\includegraphics[width=\linewidth]{Figure_4.jpg}
\caption{The percentage change of $D_a$ for 193 counties in March 2020. The height of each bar is the average percentage change of all 193 counties, and the error bar indicates the standard deviation among all counties. The three tile categories of social, traffic, and work are shown in each column, and three daily tile aggregations of peak, average, and noon are shown in each row.}
\label{fig:4}
\end{figure}

The increasing trend of Venables distance ($D_v$) implies declining concentration and rising distance among people, and the decreasing trend of activity density ($D_a$) indicates less human activity compared with the beginning of this year. Due to the shelter-in-place policies, residents changed their daily activity patterns. More and more people reduced the non-essential outdoor activities (e.g., shopping in supermarkets, exercising in gyms, and eating at restaurants). Such changes in daily human activity patterns led to the change of $D_v$ and $D_a$. For the three categories, significant change can be seen in both social and traffic tiles, while the change in work tiles is not obvious. This is because the activities in work tiles could be essential activities. The $D_v$ increased the most, around 15\%, in social tiles, while the $D_a$ fell the most in traffic tiles, which is around 25\%. Differences among the three types of daily tile aggregation were not significant. The percentage change of peak values is slightly greater than the other two values, indicating that peak values are influenced more by COVID-19, while average values are more stable. The following analysis uses peak values to calculate $D_v$ and $D_a$.
Histograms of average percentage change of $D_v$ and $D_a$ for each county are shown in Figure \ref{fig:5}. The average percentage change is calculated during the last week of March. The $D_v$ values in the majority of counties increased, and $D_a$ values decreased for social and traffic categories, while the work category shows more even distribution around 0\% for both $D_v$ and $D_a$. These histogram plots are consistent with the claim that human activities in work tiles are more essential than other two (social and traffic), which did not show significant change during the COVID-19 study period.

\begin{figure}[!ht]
\centering
\includegraphics[width=\linewidth]{Figure_5.jpg}
\caption{Histogram plots of average percentage change of $D_v$ and $D_a$ (each row) for three different categories (each column).}
\label{fig:5}
\end{figure}

While $D_v$ and $D_a$ describe the different global characteristics of human activity—the $D_v$ captures spatial distribution of human activity, and the $D_a$ focuses on the intensity of human activity—they all reveal the insight of massive human activity patterns, which could have a quite significant influence to the spread of the virus. The correlation analysis between these two metrics and the basic reproduction number $R_0$ becomes critical.

\subsection{Time-Lagged Correlation Analysis}
The spread of the coronavirus is closely related to the human activity patterns. In the previous section, we showed that the average distance between human activities ($D_v$) increased by 10\% to 15\%, and the average human activity intensity ($D_a$) decreased 5\% to 10\% for social tiles during March 2020 compared with the baseline period of January 2020. This result provides a good indication of the reduction in human activities in response to social distancing policies. In this section, we examine the extent to which the change in human activity metrics was related with the change in reproduction rate of corona virus in the 193 counties under study. Hence, we conducted the time-lagged correlation analysis for the two human activity metrics calculated based on social, traffic, and work tile activity categories.
Figure \ref{fig:6} shows the time offset results between the Venables distance ($D_v$) change and the basic reproduction number ($R_0$) for social, traffic, and work activity categories. Since the number of confirmed cases follows a skewed distribution during March 2020, the log scale is used to illustrate the results. The results show that, in majority of typical counties, the decline in the basic reproduction number ($R_0$) happens 20 to 40 days after the increase in Venables distance. This result is consistent across all three activity categories. In the right column of Figure \ref{fig:6}, the bar charts show the correlation between the offset $D_v$ and $R_0$ within different P-value intervals. The average correlation coefficients (with P-values less than 0.05) are around 0.8 for each category, indicating a significant relationship between the increased distance among human activities and the decline in the virus spread speed. For the P-values greater than 0.05 (indicating no sufficient evidence to prove the correlation between two variables), the correlation indices are correspondingly smaller. The number of counties in each P-value interval show that about 50\% of the counties have P-values less than 0.001 for Venables Distance calculated based on social and traffic activity tiles. The results related to work tiles, however, show a significant correlation between the two variables in a smaller number of counties.

\begin{figure}[!ht]
\centering
\includegraphics[width=\linewidth]{Figure_6.jpg}
\caption{The time-lagged correlation analysis between Venables distance ($D_v$) change and reproduction number ($R_0$). The left column shows the number of cases and the number of counties for different offset days, and the right column shows the correlation index and the number of counties with different P-value intervals. Each row presents one of the three tile activity categories: social, traffic and work.}
\label{fig:6}
\end{figure}

\begin{figure}[!ht]
\centering
\includegraphics[width=\linewidth]{Figure_7.jpg}
\caption{The time-lagged correlation analysis result between activity density ($D_a$) change and reproduction number ($R_0$). The left column shows the number of cases and the number of counties for different offset days, and the right column shows the correlation index and the number of counties with different P-value intervals. Each row presents one of the three tile activity categories: social, traffic and work.}
\label{fig:7}
\end{figure}

Figure \ref{fig:7} shows the time offset result between activity density ($D_a$) change and reproduction number ($R_0$) for the three activity categories. The results show that the decline in the basic reproduction number happens 6 to 17 days after the reduction of the activity intensity ($D_a$); a similar result exists in all activity categories. The time lag is less than the one obtained for the Venables distance ($D_v$), which means that the spread of virus responds to human activity intensity reduction more quickly than to human agglomeration reduction. In the right column of Figure \ref{fig:7}, the bar charts show the correlation of offset $D_a$ and $R_0$ in different P-value intervals. The average correlation indices for P-values less than 0.05 are around 0.9 for tile activity categories. This result indicates a significant relationship between the human activity intensity reduction and the decline in the virus spread speed. For P-values greater than 0.05, the correlation indices are smaller as well. The number of counties in each P-value interval show about 50\% of counties have P-values less than 0.01 for social and traffic tiles, while the work tiles result shows P-values between 0.1 and 1.0 (indicating a non-significant correlation).

\subsection{Heterogeneity for Different Features}

In the next step, we examined the variation of findings across counties with different population sizes, number of confirmed cases, and date of first confirmed cases. The goal is to examine the extent to which the correlation between the two metrics of human activities and the reproduction number is sensitive to these county features. The 193 counties were divided into three uniform categories according to population size and confirmed cases (on March 18, 2020) labeled high, medium, and low. Similarly, the first case dates were labeled as early, mid-range, and late for each one third of counties. Then, the changes in $D_v$ and $D_a$ (on March 31, 2020) were examined for each label in each tile category, and the results are plotted in Figure \ref{fig:8}. As shown in Figure \ref{fig:8}, for all three tile activity categories, the $D_a$ declined more in counties with larger population, more confirmed cases, and earlier first-case date. This result indicates a greater recognition and response to the pandemic risks in more populous counties with early confirmed cases.

\begin{figure}[!ht]
\centering
\includegraphics[width=\linewidth]{Figure_8.jpg}
\caption{. Change in Venables distances ($D_v$) and activity density ($D_a$) across counties with different population size, confirmed cases number, and the date of first confirmed case in the three tile activity categories (social, traffic, and work).}
\label{fig:8}
\end{figure}

\section{Discussion}

This study shows the utility of two human activity metrics (the Venables distance ($D_v$) and the activity density ($D_a$)) as leading indicators for the spread speed of COVID-19 in the early stages of the outbreak. These metrics were derived from digital trace data obtained from Mapbox high-resolution temporal-spatial datasets. The results provide statistical evidence regarding the time-lag correlation between these two metrics and the basic reproduction number ($R_0$) in the context of COVID-19. The results regarding the significant leader-follower relationship between human activities and the rate of spread of viral infections could provide valuable implications for authorities to monitor and control the transmission of COVID-19 and future pandemics.  For example, time lag indicates that the spread of virus responds to human activity intensity reduction more quickly than to human agglomeration reduction. Hence, the proposed indicators can be calculated using digital trace telemetry data in near real time to proactively assess the risk of virus spread.
This study has some limitations which need to be improved in future studies. First, the tile activity categorization—social, work, and traffic—is not precise. One tile could be labeled as both social and work. In this study and due to data availability limitations, however, we classified tiles into only one of the three categories. Second, the CDC confirmed-cases data had limitations due to testing availability. In this study, we did not adjust the confirmed case data based on the extent of testing in different counties. A lack of testing in some areas resulted in the underestimation of the total cases.

\section*{Acknowledgements}

The authors would also like to acknowledge that Mapbox provided digital trace telemetry data of human activity. The authors would like to thank Kieran Gupta, Sofia Heisler, Ruggero Tacchi from Mapbox for providing technical support.

\section*{Funding}

This work was supported by several grants including from the United States National Science Foundation RAPID project \#2026814: Urban Resilience to Health Emergencies: "Revealing Latent Epidemic Spread Risks from Population Activity Fluctuations and Collective Sense-making," and Microsoft AI for Health COVID-19 Grant for cloud computing resources.

\section*{Author contributions statement}

Research design and conceptualization: X. G., C. F., A. M.; Data collection, processing, analysis, and visualization: X. G., C. F., Y. Y., S. L., Q. L; Writing: X. G., A. M.; Reviewing and revising: all authors.

\bibliographystyle{unsrt}

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No. 15 Sailing Coeds Close Out Fall Season at Atlantic Coast Tournament
The Crimson conclude its fall schedule with a 13th-place finish (photo courtesy Athletic Communications).
NEW LONDON, Conn. - The No. 15 Harvard sailing coed team closed out its fall season with a 14th-place performance at the Atlantic Coast Tournament hosted by Connecticut College Nov. 16-17.
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Q: Maven dependency is imported but still i get a "Unresolved reference" error - Intellij

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Live from CES 2020
The Wrap – Tech You Can Look Forward To January 17, 2020
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Aussies turn social media data into prediction tool
byLeigh :) Stark
Social media services like Twitter and Facebook don't just have to be used for communication. They can also be used to predict epidemics.
The next time you post to Facebook or Twitter, or any other social media service, you might want to think about how you use your words.
While these words represent you, they may do a little more than that in the coming years. They might be used as an indication of what's to come, as researchers tap into the data science behind social media.
It's something that has come out of Australia's government science organisation, the CSIRO, with its data science arm, Data61, developing a tool to scour social media and work out events ahead of time.
Developed by Data61 in collaboration with UNSW's Kirby Institute, the team has built a tool that uses artificial intelligence to scan for tweets that match specific words and phrases in locations after an event, and then process the informational with natural language processing to highlight situations that can be grouped together. The tool processes the words with AI and works with time modelling to work out which social media posts might be referring to a specific event.
For the test, the tool was used as way to detect an asthma epidemic that it Melbourne three years ago. The asthma attack in question saw over 8000 hospital admissions on November 21, 2016 following a local thunderstorm, with a type of asthma known as "thunderstorm asthma". While the technique being used to track this specific condition is being used in a post-dated way, because posts made to Twitter are archived typically so that everyone can see them (unless user privacy settings are set to high), the team from Data61 and UNSW Sydney was able to analyse over three million tweets using words like "breath" and "coughing" related to asthma to test its tool.
Using this process, the team was able to prove its system could have predicted the medical event ahead of time, and could be used as a forecasting tool to help hospitals and organisations be as prepared as possible.
"In future, this system can be used to provide health authorities and the community early warning of a serious and sudden health event," said Professor Raina MacIntyre, Head of the Biosecurity Research Program for UNSW Sydney's Kirby Institute.
"Early detection could significantly improve our capability to mitigate the impact of epidemics."
While it's not yet known if Australian organisations will use or embrace the method, it adds to Australia's growing contributions to the world of science and technology, and highlights the great ideas the country has added to the world.
Leigh :) Stark
A technology journalist working out of Sydney, Australia, Leigh has written for publications including The Australian Financial Review, GadgetGuy, Popular Science, APC, PC & Tech Authority, as well as for radio and TV since 2007.
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