K and k london: K+K Hotels – First class Hotels in European Top Destinations

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K+K Hotel George, London, United Kingdom. Rates from GBP83.

The K+K Hotel George is a contemporary and modern boutique property hiding behind a beautiful Victorian facade.

The hotel overlooks its private garden – a real island of peace in the hustle & bustle of the city of London. The K+K Hotel George is ideally located in the city centre but in a quiet residential street.

Dining facilities at K&K Hotel George include a restaurant. Breakfast is available – a surcharge will apply. There is also room service available. The hotel boasts a 24-hour front desk service. Public areas at K&K Hotel George are comfortably air-conditioned. Hotel guests will appreciate the welcoming services of our multilingual staff. Other hotel amenities include garden and patio. Leisure amenities: Guests can also enjoy the following spa/wellness facilities: sauna. Other services: babysitting or child care, complimentary newspapers in lobby, concierge services, dry cleaning/laundry service, elevator/lift, express check-out, laundry facilities, safe-deposit box – front desk and tour assistance.

Business/Internet: K&K Hotel George features a well-equipped business centre and conference/meeting room(s). The following Internet options are available at the hotel: access in public areas is complimentary.

K&K Hotel George offers transportation to/from the airport for an additional fee. For guest convenience, secure hotel guest parking is offered. Limo/Town car services can be arranged.

Hotel Services and Facilities

Free WiFi Free parking Free breakfast

Hotel facilities
  • 24 hour front desk
  • Babysitting Services
  • Business Center
  • Concierge
  • Dry cleaning service
  • Fitness Center
  • Internet Access – Free Public Access
  • Lounge/Bar
  • Non-Smoking Facility
  • Parking – Self
  • Room Service
  • Property is cleaned with disinfectant
  • Staff wears personal protective equipment
  • Acrylic shield between guests and staff in main contact areas
  • Guests are provided with free hand sanitizer
  • Social distancing measures are in place
  • Property confirms they are implementing enhanced health & safety measures
  • Individually-wrapped food options are available
  • Individually-wrapped food options are available for breakfast
  • Gap period enforced between guest stays
  • Property confirms they are implementing guest safety measures

Guest reviews

Click here to leave your opinion >>

Susanne Seemann from Stuttgart, Germany

stayed in June, 2012

Guest Score 10. 0 out of 10

This year we had our second time in this lovely and clean hotel with kind staff and (very important for us) absolutely nice breakfast buffet. Thank you!

Trish from Manchester

stayed in July, 2009

Guest Score 10.0 out of 10

The hotel was lovely. It’s location was ideal. Staff were really friendly and helpful. Lovely buffet breakfast, a nice garden to sit out in if wanted to and the bar area was excellent. I would recommend this hotel to anyone. A definate 10!!!

The room I stayed in was a single room with a single bed and a bit small. I have stayed in single rooms before and always had a double bed.

Cheryl Ciolino from New Orleans, United States

stayed in January, 2009

Guest Score 9.5 out of 10

I give this hotel a 10 for cleanliness, a 9 for location, a 10 for an absolutely fabulous breakfast buffet, a 9. 5 for their pleasant back garden( a rarity in London), a 9 for their staff and an 8 for the small size of their single rooms. I stayed there for 6 nights in 8/2000, and 5/2005. It’s only one block from a tube station. Overall, a great London hotel!

The small size of the single rooms was the only reason I didn’t rate this hotel a 10.

Lucy from Greece

stayed in March, 2009

Guest Score 9.5 out of 10

Wonderful service,everyone was very polite! The room wasn’t very big but it was still comfortable. The hotel was very clean and the price reasonable. I would definitely stay there again.

FAQ

What are the check-in and check-out times at K+K Hotel George?

The guests can check in from 14:00. Check-out time is 12:00.

What is the nearest airport to K+K Hotel George?

London, England, UK (LCY-London City) is located 10.57mi from the property.

Does K+K Hotel George provide free wi-fi?

We provide complimentary internet access in public areas.

What are the parking options at K+K Hotel George?

Self Parking is provided on-site.

Is smoking allowed on the premises?

The establishment is entirely non-smoking.

Is there a fitness center?

A state-of-the-art fitness center, equipped with weight-training machines, can be accessed by all guests.

Is there a lounge or bar at K+K Hotel Georgel?

Guests may find a large selection of both alcoholic and non-alcoholic beverages at the in-house bar/lounge.

Does K+K Hotel George have business center?

Property has facilities onsite with computers and/business services.

Is the front desk open 24 hours?

Our helpful 24-hour front desk is ready to assist you with any inquiry.

Does K+K Hotel George provide babysitting services?

We can arrange qualified babysitting services (fee may apply).

Is there dry cleaning available at K+K Hotel George?

Dry cleaning services can be found on the premises.

Is there a concierge available at K+K Hotel George?

A friendly and helpful concierge is always ready to assist with any inquiries.

Will room service be provided?

Room service can be arranged for guests staying at the hotel.

How to book

Making your reservation in K+K Hotel George is easy and secure. Simply select your dates of stay and click on the “Check Rates” button to submit the form.

After room types and rates are returned to your screen, you will be able to choose the rate you desire by reading the room information in the center of the screen, then clicking on the “Book Now” button on the right side of the screen. After you have selected your desired room type, you will be taken to a “Complete your booking” form. Then you will need to review the “Details of your stay” and fill in the blanks on the “Guest Details” section. Be sure your name and email address are spelled correctly.

After you fill out all of the needed information, press the “Complete reservation” button. To ensure your security, we use a secure commerce server, which encrypts the credit card number and encodes it into a non-usable form.

K+K Hotel George- First Class London, England Hotels- GDS Reservation Codes: Travel Weekly

Classification
7 Superior First Class
Commission Rooms Rates
10% 154 $223-$375 (USD)

Overview

Modern hotel set in 1880 Victorian with multilingual staff, restaurant & bar; situated in Kensington area.

  • Details
  • Year Built: 1995
  • Year Last Renovated: 2012
  • Check in Time: 3:00 PM
  • Check out Time: 12:00 PM
  • Number of Floors: 7
  • Chain: K+K Hotels
  • Chain Website: www.kkhotels.com
  • K+K Hotel George GDS Codes
  • Amadeus GDS: WV LONGEO
  • Galileo/Apollo GDS: WV 07200
  • Sabre GDS: WV 015398
  • WorldSpan GDS: WV 04031

K+K Hotel George Rates & Policies

  • Rate Policy: Daily in USD
  • Standard Room: from $223-$375 (USD)
  • Credit Cards: Credit Cards Are Accepted
  • Reservation Policy: Reservations must be guaranteed with a credit card
  • Included Meals: Full Breakfast Included
  • Deposit Policy: Valid credit card to guarantee 1st night
  • K+K Hotel George Cancellation Policy: 24 HRS

K+K Hotel George Room Amenities

Amenities are in all rooms unless noted otherwise.

  • Air Conditioning
  • Coffeemaker
  • Daily Maid Service
  • Mini-Bar
  • Free Newspaper
  • Rooms for Non-smokers
  • Telephone
  • Safe (In Room)
  • Cable/Satellite Television
  • Television
  • Internet Access (Wireless)

K+K Hotel George Recreation

  • On-Site Activities
  • Health Club
  • Health Spa/Massage
  • Nearby Activities
  • Boating/Sailing
  • Golf Course
  • Horseback Riding
  • Jogging Trails
  • Shopping Mall/Area

K+K Hotel George Hotel Services & Facilities

  • Guest Services
  • Car Rental
  • Complimentary Coffee
  • Concierge Services
  • Crib/Rollaway Bed
  • Laundry/Dry Cleaning Service
  • Multi-lingual Staff
  • Paid Onsite Parking
  • Room Service
  • Common Area Internet Access (High Speed & Wireless)
  • Security Services
  • Electronic Key/Lock
  • Parking Area Well Lit
  • Smoke Alarm in Rooms
  • Surveillance Cameras On Site

Kamala London Newton

Kamala London Newton

 

PhD, University of Wyoming, 2001
Postdoctoral fellowship, Johns Hopkins Medical School, 2001-2005
Professor

Personal website: https://kamalalondon. com

Office: UH 1880B
Phone: 419-530-2352
Fax: (419) 530-8479
Email: [email protected]

Mailing Address:
Department of Psychology
University of Toledo
2801 West Bancroft St.
Toledo, Ohio 43606-3390

Please click here for Dr. London’s CV.
Please click here for lab webpage.

Dr. London will be accepting students for the Fall of 2022.

Research Interests
  • forensic developmental psychology
  • autobiographical memory and suggestibility in children
  • disclosure of child maltreatment (including human trafficking)
  • statistics
Selected Publications

London, K. , Bruck, M., Miller, Q. C., & Ceci, S. J. (2020). Analyzing the scientific foundation of child sexual abuse accommodation syndrome: A reply to Lyon et al. (2020). Behavioral Sciences & the Law.

McGuire, K., & London, K. (2020). A retrospective approach to examining child abuse disclosure.Child Abuse & Neglect, 99. doi.org/10.1016/j.chiabu.2019.104263

Capparelli, A.L., Miller, Q.C., Wright, D.B., & London, K. (2019). Canine-assisted interviews bolster informativeness for negative autobiographical memories. Psychological Reports, 123, 159-178. doi.org/10.1177/0033294119851803

Lawson, M., Rodriguez-Steen, L. & London, K. (2018). A systematic review of the reliability of children’s event reports after discussing experiences with a naïve, knowledgeable, or misled parent.   Developmental Review, 49, 62-79. doi:10.1016/j.dr.2018.06

Lawson, M., & London, K. (2017). Children’s memory for conversations after a one-year delay. Journal of Applied Research in Memory and Cognition.

London, K., Hall, A., & Lytle, N. (2017). Does it help, hurt, or something else: The effect of an “or something else” option on children’s performance on forced-choice questions. Psychology, Public Policy, & the Law, 23, 281-289

McGuire, K. & London, K. (2017). Common beliefs about child sexual abuse and disclosure:  A college sample.  Journal of Child Sexual Abuse, 26, 175-194.

Surnames beginning ‘K’ | British History Online

Surnames beginning ‘K’

Kaid, Wm, app, 47. 5

Kalant, John, 52.6

Kaley, Sar, 11.14

Kallendrine, John; Mary, w, 20.17

Kalloway, John, app, 49.10

Kam, Ric, ser, 59.13

Karwood, John, 71.13

Kate, :
Lancelott, ser, 74.12
Thos, bach, 43.19

Kater, :
Mary, 44.2
Ric, 9.57

Kates, John; Mary, w; Francis, s; John, s, 17.36

Katherine, John, ser, 81.5

Katon, Mary, ser, 70.9

Kattenby, -, wid, 55.14

Keane, :
Hen; Jane, w; Mary, d, 54.3
Margery, ser, 36.18

Keare, Mary, ser, 41.10

Keartly, Edw, ser, 59. 6

Keason, Isaac, ser, 33.10

Keate, :
Anne, 30.11
Gilbert; Eliz, mother; Frances, sis; Eliz, sis, 76.2
Sar, ser, 12.4
Thos, child, 12.15
Wm, ser, 41.1

Keats, :
Hen; Eliz, w, 1.39
Wm; Mary, w, 47.4

Keay, :
-, 65.15
John, 38.5,assr, 38.26;Melior, w, 38.5

Keble, Thos; Jud, w; Thos, s; Rob, s; John, s, 64.4

Kechin, -, Madam, wid, 12.19

Keck, :
John, 20.24
John, gent; Marg, w, 1.30

Keckwick, :
Joyce, ser, 30.6
Sam; Eliz, w, 30.6

Kedder, Eliz, ser, 68.3

Kedg, Jaine, ser, 80.7

Keeble, :
Cary; Jane, w; Cary, s; Jane, d, 74. 7
Rob; Mary, w; Wm, s; Cath, d; Mary, d; Eliz, d; Han, d, 43.16

Keeblewhite, Edw, ser, 48.2

Keech, Keach:
Marg, ser, 26.23
Thos; Isabell, w; Peter, s, 38.7

Keek, Mary, ser, 28.14

Keelett, Ann, ser, 41.2

Keeling, :
John; Mary, w; Josiah, s; John, s, 17.12
Sam, bach, 56.9

Keely, Wm; Martha, w, 28.4

Keen(e), :
-, Mr. gent, bach, 32.4
Abigall, ser, 34.2
Anne, ser, 31.14,59.4
Chas, bach, 59.24
Clement; Susan, w, 1.36
Eliz, 17.113
John; Sar, w, 12.5
Marg, nurse, 62.1
Margery, ser, 33.3
Sam, ser, 44.23
Ste, 51.7
Ste, £600, assr; Sar, w, 21.17
Wm; Dot,w; Anne Phillis, d, 31.14
Wm, barber, 23. 9
Wm, plasterer; Marg, w; Eliz, d; Han, d; Thos, s, 66.7

Keepe, :
Alyce, ser, 64.6
Edw, ser, 32.9

Keeps, Ste, ser, 12.19

Kees, Ann, ser, 47.1

Keesley, Han, ser, 28.5

Keet, Thos, £600; Eliz, w; Honoria, d; Philadelphia, d, 14.14

Keever, Wm, clothdrawer; Eliz, w; Eliz, d; Cath, d; Sar, d; Jud, d, 43.23

Kegell, Francis, bach, 75.18

Keiling, :
Ralph, £600; Ralph, s; Coppinger, s; Eliz, d, 2.8
Ralph, bach, ser, 2.8

Keilsby, John; Juliana, w, 63.5

Keire, Marg, 79.12

Keister, :
Thos, jr; Eliz, w, 35.32
Thos, sr, widr, £600, 35.32

Kelleson, Sar, 73. 17

Kellett, :
Hodgkins; Martha, w, 80.10
John, ser, 68.2

Kellinghall, Sar, ser, 80.6

Kellison, Thos; Lucy, w, 37.14

Kelloway, Alice, 54.15

Kellum, :
Anne, ser, 38.24
Mary, Mrs, 70.9

Kelly, :
Eliz, ser, 35.1
John, 49.22
John, ser, 22.3
Susann, ser, 58.16

Kelsall, Jas; Mary, w; Sar, d, 17.6

Kelse, Thos; Mary, w, 59.10

Kelsey, :
Anne, 44.6
Han, app, 31.19
Inglebird; Mary, w, 17.84
Mary, 44.6
Reb, 68.6

Kelson, :
Joa, sre, 6.14
John; Eliz, w; Mary, d, 20.20
John, chairmaker; Eliz, w; Jos, s, 15. 9

Kemball, Nic; Mary, w, 52.5

Kembell, -; -, w, 14.17

Kember, Nic, assr, 52.6

Kemble, :
Ric, £600; Mary, w; Ric, ?s, 72.6
Thos, widr, 53.8

Kemery, Mary, wid, 16.11

Kemish, Eliz, 26.7

Kemm, Benj, bach, 24.6

Kemp, Cemp:
Ann, ser, 7.17,12.13
Ann, spin, 12.2
Edw, 31.16
Grace, 17.66
Hope, wid; Hope, d, 47.5
Jas, ser, 24.2
Marg, 17.40
Mary, ser, 13.7
Roger, 19.9
Sam; Mary, w; Sam, s; Wm, s; Sar, d, 81.12
Thos; Ann, w, 12.10
Thos, bach, 29.20
Thos, ser, 35.18
Wm; Mary, w, 18.4
Wm, ser, 28.5
Wm, tinman; Eliz, w; Eliz, d; Susa, d; Isabella, d, 39.2

Kempson, Kemson:
Anne, ser, 45. 4
Frederick, 39.7

Kempster, :
Edw, 66.12
Eliz, ser, 18.10
Sam, bach, 2.10
Susa, ser, 23.8
Wm; Mary, w; Chris, s; Wm, s; Sar, d, 38.25

Kempstone , Jane, ser, 68.1

Kempton, :
Ann, ser, 32.13
Francis, ser, 58.14
Sar, ser, 45.2

Kendall, :
Ann, 34.4
Benj, gent; Eliz, w, 12.8
Cath, ser, 21.4
Ellen, 53.3
Han, ser, 21.4
Jeremiah, 44.30
Jonathan, bach, 44.30
Moses; Sar, w; Moses, s; Sar, d, 27.7
Sam, £600; Mary, w; Mary Cole, ?grand-d, 73.17
Thos; Joa, w; Han Guidon, d, 6.17
Thos; Mary, w; Sar, d; John, s; Mary, d, 17.66
Wm; Mary, w; Wm, s; Art, s, 38.19
Wm, ser, bach, 79.1

Kendle, Ann, ser, 55.18

Kendrick, :
Edw; Frances, w, 8. 13bElenor, poor wid; Hen, ?s; Wm, ?s, 62.2
Mary, ser, 14.10

Kenelme, Eliz, ser, 14.12

Keneston, Mary, 35.24

Kening, Chas; Eliz, w; Mary, d; Eliz, d, 19.6

Keniston, :
And; Jone, w; Sar, d, 9.60
Elinor, ser, 75.2

Kenn, Rose; Martha, d, 67.5

Kennell, :
John; Arabella, w; John, s, 7.34
Wm; Eliz, w; Wm, s; Alice, d; Jane, d, 7.17
Wm, app, 29.20

Kenner, Thos, 29.10

Kenney, :
Wm; Susan, w; Hen, s; Wm, s, 7.24
Wm, £600; Marg, w; Mary, d; Susa, d, 29.21

Kennison, Mary, maid, 12.16

Kent, :
Abraham, ser, 61.1
Chas, victualler; Mary, w; Ann, d; Mary, d; Wm, s, 79. 11
Elinor, wid; Mary, d; Jobe, ?s, 16.19
Eliz, ser, 59.2
Eliz, wid of John, 73.9
Eliz, wid; Eliz, d; Mary, ?d, 35.27
Geo, 49.22
Hester, ser, 49.31
John; Bridg, w, 14.18
John, ser, 13.14,68.3,78.10
John, turner; Anne, mother, 61.4
Mary, ser, 80.9
Mic, ser, 63.8
Rob, 7.25
Rob; Patience, w; Edw, s, 1.7
Sam, seaman; Martha,w, 42.24
Sar, ser, 74.3
Ste, bach, 73.9
Susa, wid, 29.16
Thos, 74.6
Thos; Eliz, w, 9.8
Thos; Dyonesius, w, 19.9
Thos, ser, 28.2
Wal, £600, merchant, 73.9,assr, 73.29;Ellinor, w, 73.9
Wm, gent; Eliz, w; Bridg, d; Mary, d; Eliz, d, 7.18
Wm, ser, 36.19

Kentish, Thos; Jane, w, 75.8

Kenton, :
Eliz, ser, 64.4
Thos, ser, 63.6

Kentswell, Geo, bach, 31.11

Ker, And, bach, 33. 10

Kerby, :
Ann, child, 75.8
Chas; Susa, w, 9.6
Edw; Anne, w, 13.23
Jas; Mary, w; Mary, d, 29.25
Jas, bach, 69.4
Mat, tailor; Mary, w; Mary, d, 42.22
Wm; Sar, w, 16.21

Kerfoot, Nat; Bridg, w, 36.22

Kerrison, Sam; Eliz, w, 49.19

Kerry, Sam, ser, 75.17

Kersell, Rob, 55.12

Kershaw, Han, ser, 21.14

Kersley, Kath; Eliz, d; Grace, d, 1.10

Kerton, :
Ashur, ser, 35.29
Susa, chambermaid, 73.22

Kestermain, Peter, merchant, £600; Sar, w; Wm, s; Jas, s, 45.3

Kesterton, Anne, ser, 71.8

Ketch, :
John; Han, w; Han, d, 9.37
John; Ann, w; Crispin, s; Ann, d, 47.12

Ketchell, Thos, bach, £600, 29.36

Ketcher, John, bach, journeyman, 62.14

Kettell, Clement; Bridg, w; Jeremiah, s, 36.25

Kettelthorpe, Jos, app, 6.8

Ketter, Jesper, app, 47.10

Kettle, Ketle:
Ann, chambermaid, 73.9
Hen, 59.8
John, hatter; Mary, w, 15.14
Wm, ser, 74.10

Kettlebey, :
Geo; Eliz, w, 9.28
Wal, gent; Ann, w; Ric, s, 32.4

Kettlewell, :
John, 17.2
John; Eliz, w; John, s; Lucy, d, 76.13

Kevett, Eliz, ser, 56.3

Kew, :
Ric; Eliz, w; Ric, s; Eliz, d, 36.5
Thos, app, 43.5

Key, :
Alice, ser, 35.8
Ann, ser, 33.4
Han, wid; Han, d; Sar, d, 6.3
Jas; Joa, w; John, s; Joa, d; Kath, d; Mary, d, 57.10
John, packer, £600; Jane, w; Geo, s; Marg, d, 49.10
Mary, 7.36
Mary, ser, 22.4
Nat, bach, 43.18
Ric, ser, 44.3
Rob, 20.9
Rob; Ann, w; Rob, s; Eleanor, d, 63.11
Rob, linen draper; Eliz, w, 62.15
Sar, ser, 20.9,49.1
Thos, ser, 42.13

Keybourne, Thos, app, 54.10

Keyd, Francis, ser, 11.5

Keymer, Kath, ser, 44.4

Key(e)s, :
Ant, 37.16
Eliz, ser, 56.12
John; Mary, w; Ric, s; Eliz, d, 7.26
Mat, widr; Rob, s, 34.5
Sar, spin, 15.6
Susan, 9.34
Thos, ser, 44.32

Keyser, :
Balthasar; Mary, w, 17.38
Garret, ser, 17.19
John, £600; Han, w, 44.3

Kibber, Jud, 9.25

Kibblewhite, :
Wm, 32.3
Wm, bookseller; Elenor, w, 32.3

Kible, Thos, bach, 74.20

Kichcock, Wm, ser, 64.13

Kidd, :
Anne; Anne, d, 29.11
Hester, ser, 32.2
John, 77.9,assr, 77.12;Dot, w; John, s; Jas, s, 77.9
Peter, £600; Marg, w; Sar, d, 55.2
Peter, bach, 74.12

Kidder, :
Han, ser, 50.3
Jac; Sar, w, 9.24
Marg, 9.63
Mary, ser, 53.3

Kidditt, Jas; Anne, w, 78.7

Kidley, :
John, app, 43.11
Wal; Eliz, w; Ann, d; Eliz, d; Bridg, d, 72.1

Kidwalliter, Mary, ser, 12.2

Kiffen, Jos, ser, £600, 24.4

Kiffitt, John; Hester, w, 13.13

Kiffton, Isaack, bach, 69.4

Kift, Kifft:
Hen, £600, 33.7,assr, 33.11;Allce, w; Allce, d, 33.7
Ric; Sar, w, 32.27
Rob, ser, 33.7

Kilby, :
Eliz, ser, 35.18,35.40
John; Cath, w, 35.24
John; Mary, w; Eliz, d, 64.11
Lancelott, ser, 20.1
Lawrence; Eliz, w, 5.7
Mary, ser, 36.9
Sar, ser, 36.15

Kildimore, Eliz, 25.6

Kilgowe, Dot, 17.69

Kilinghall, Eliz, 2.12

Kilinghorne, Eliz, ser, 60.20

Killegrew, Nic, app, 9.13

Killett, Sar, 31.15

Killey, Eliz, ser, 5.5

Killingsley, Mary, wid, 77.2

Killingsworth, Ann, 69.7

Killington, John, ser, bach, 9.12

Killison, Frances, ser, 41.3

Killman, Rowland, Dutchman, 43.15

Killner, Kilner:
Thos, gent; Eliz, w; Marg, d; Eliz, d; Mary, d; Sar, d; Jas, s, 29.2
Wm; Mary, w; Wm, s, 7.13

Killum, Jas, 57.14

Kilmister, Hen; Jane, w, 9.11

Kilpin, :
Mary, ser, 26.19
Ric; Sar, w; Sar, d; Mary, d, 11.6

Kilsbe, :
Eliz, ser, 8.10
Rob; Susa, w, 17.6
Wm; Jud, w, 29.7

Kimber, Ellenor, wid; Ellenor, d, 29.30

Kimberly, Mary, ser, 5.2

Kimble, Eliz, ser, 55.1

Kime, Sar, 9.45

Kimpton, John; Susa, w; Thos, s; Susa, d; Eliz, d; Ann, d, 33.9

Kinch, :
Josua, £600; Marg, w, 48.7
Rob, ser, 47.3

Kindall, Jane, ser, 13.2

Kindar, Eliz, grand-d of Eliz Nash, 31.6

Kinderman, Mic; Sar, w, 78.5

Kindrell, John; Sar, w, 17.49

Kinebrooke, John, bach, 65.8

King, :
-, bach, 74.18
-, wid, 22.5
Abraham, app, 1.34
Alex, 30.9
Ann, ser, 3.9,9.1,42.28
Benj, bach, 12.10
Chas; Eliz, w; Chas, s; John, s, 35.12
Chris, 4.5
Dan, 32.11
David; Mary, w; Wm, s; Gerrard, s; Hezekiah, s; David, s; Ric, s; Mary, d, 54.22
Dot, ser, 73.27
Edw, £600; Eliz, w; Edw, s, 49.26
Edw, bach, 21.14
Edw, bach, gent, 49.20
Edw, ser, 32.1
Elias, ser, 35.11
Elisha, 54.25
Eliz, 65.8
Eliz, ser, 28.3,47.3,74.18
Ellenor, 14.10
Frances, ser, 1.41
Gregory, esq; Anne, w, 23.7
Grigory, app, 9.24
Han, ser, 49.22,71.11
Hen, £600; Lewcy, w; Hen, s, 60.3
Hen, bach, 17.91
Hen, carman; Mary, w; John, s; Tobia, ?s, 15.10
Hen, ser 25.5,44.2
Hezekiah, 27.13,assr, 27.15;Esther, w 27.13
Isibella, wid, 29.26
Jas, 32.11
Jas; Marg, w, 59.20
Jeane, 65.8
Jeremiah, ser, 5.3
John, 51.3
John; Anne, w; Eliz, d, 17.32
John; Anne, w, 38.15
John, bach, ser, 50.4
John, chairmaker; Mary, w; Eliz, d; Mary, d; Sar, d; John, s, 32.1
John, flasmaker; Kath, w; Mary, d; John, s, 73.8
John, ser, 1.19,7.8,22.7,32.3,50.6,79.3
John, weaver; Susa, w; Susa, d, 23.9
John, widr, 54.2
Jos, 75.21
Jos; Mary, w; John, s, 60.13
Jos, £600, bach, 65.14,common councilman, assr, 65.15
Jos, bach, 60.3
Kath, 47.8,57.6
Magdalen, 32.11
Marg, cook-maid, 73.2
Margery, ser, 50.6
Martha, ser, 32.14,81.8
Mary, 47.16
Mary; Sar, d, 51.6
Mary, cook-maid, 73.23
Mary, ser, 20.12,20.22,36.5,43.4,55.6
Mary, wid, 38.14
Meriam, wid; Meriam, d, 9.2
Mic, £600; Mary, w; Samson, s, 18.18
Mic, ser, 81.9
Moses, 43.14
Oliver; -, w, 68.3
Ric, 54.6
Ric, ser, 25.3,26.4,41.9
Rob; Anne, w, 9.44
Rob; Mary, w, 16.10
Rob; Joa, w, 30.13
Rob, app, 18.15
Rob, ser, 32.21
Sam, 56.2
Sam; Mary, w, 17.63
Sam, bach, 74.11
Sar, 20.13,33.1
Sar, pensioner, 16.2
Sar, ser, 2.7,18.2,42.23
Simon; Marg, w, 70.8
Ste, ser, 64.10
Susan, ser, 47.9
Susa, 28.14,65.8,65.14
Susa, ser, 64.1
Thos; Easter, w, 9.54
Thos; Mary, w, 9.4
Thos; Ann, w; Ann, d; Han, d, 9.59
Thos; Alce, w, 16.4
Thos; Eliz, w; Francis, s, 28.11
Thos, £600; Ann, w; Ric, s; Ann, d; Martha, d; Martha, d ; Ellen, d; Eliz, d, 28.14
Thos, £600; Mary, w; Eliz, d; Jane, d; Thos, s, 29.7
Thos, app, 12.25
Thos, bach, 5.14
Thos, ser, 31.6
Wm, 76.13
Wm; Sar, w, 12.8
Wm; Emma, w, 14.6
Wm; Anne, w; John, s; Martha, d, 14.18
Wm; Easter, w; -, d, 9.23
Wm; Dot, w, 46.3
Wm; Mary, w; Edw, s; Ric, s, 78.9
Wm, Dr of Laws, bach, (changed in St Benets)23.17,35.26
Wm, hatter, widr, 15.1
Wm, porter; Eliz, w; Mary, d; Ann, d, 42.11
Wm, ser, 13.22,37.3,50.6
Wm, wine cooper, £600, 42.17,collr, 42.32;Sar, w, 42.17

Kingdon, Jos; Mary, w; John, s, 36.4

Kingham, Edith, ser, 79.11

Kingle(?), Ralph, bach, 77.10

Kingley, Edw, ser, 39.19

Kingman, Chas, ser, 74.17

Kingsburrough, Mary, ser, 61.2

Kingsbury, Kingsbree:
Ann, ser, 62.8
Mary, 35.23
Thos; Grace, w; Jas, s; Susan, d; Grace, d, 49.22

Kingsford, Dot, ser, 6.2

Kingsley, :
Ant, 9.63
Thos, 54.9

Kingsman, Eliz, 32.14

Kingston(e), :
Gartrud, ser, 33.7
Mat, ser, 22.2
Ric; Ruth, w, 1.42
Thos, ser, 59.14
Wm; Marg, w; Benj, s, 47.9

Kinkaid, Mic, bach, £600, 27.9

Kinnard, :
-, wid, 12.25
John; Mary, w; Wm, s; Sam, s; Mary, d; Reb, d, 1.26

Kinner, Wm, bach, ser, 62.8

Kinnerson, Mary, ser, 77.9

Kinnion, Christian, ser, 75.5

Kinns, John, £600; Amy, w, 36.19

Kinsman, Thos, ser, 48.5

Kipley, Thos, 15.1

Kipping, Jos, bach, 48.14

Kirbray, Isabella, ser, 6.8

Kirby, :
Argulus, widr, 5.6
Eliz, wid, 5.14
Grace, Mrs; Eliz Jarvis, d, 63.6
Han, 30.7
Jas, ser, 6.9
John, bricklayer; Martha Clifford, w, 23.10
Reb, ser, 35.6
Sar, ser, 6.14
Susa; Eliz, w; Reb, d, 35.34
Tomasin, ser, 3.13
Wm, 5.6
Wm, app, 12.4

Kirke, :
Alice, ser, 35.24
Anne, 44.33
Barnabas, 76.7
Eliz, 9.63,35.19
Eliz, ser, 54.19
Hugh, app, 17.121
John, app, 69.3
Jonathan, widr, 58.6
Mary, 6.12,7.3,57.5
Nic; Eliz, w; Eliz, d, 55.11
Reb, ser, 43.24
Ric, 17.14
Rob; Eliz, w; Eliz, d, 37.4
Sam; Eliz, w; Mary, d, 57.2
Thos; Mary, w; Thos, s, 36.13

Kirkham, Kurkham:
Jane, ser, 29.26
John, app, 31.14

Kirpatrick, Thos, bach, £600, 49.23

Kirton, Wm; Eliz, w, 57.7

Kiser, :
John, £600; Mary, w, 33.2
Tim, bach, £600, 33.6

Kitchenham, John; Ann, w; John, s, 43.18

Kitchin, :
Eliz, ser, 32.9
John, 17.3
Nic; Martha, w, 1.18
Ralph; Mary, w; Geo, s; Thos, s; John, s, 78.7
Ric, ser, 11.5

Kitchinman, Chas; Alice, w; Mary Peate, her d; Thos Peate, her s, 63.7

Kitchins, Chas; Mary, w; Mary, d, 54.19

Kite, :
Alice, ser, 9.47
Ant, 7.9

Kiteridge, Anne, ser, 49.3

Kit(t)son, :
Edw, ser, 26.8
Ric, ser, 4.3

Kitterman, Hen; Eleanor, w; Marg, d, 17.94

Kittle, :
Ann, 1.12
Randall; Martha, w; John Williams, grand-s, 1.33

Kittleby, John; Eliz, w; Martha, d; Francis, s, 52.2

Knackson, Han, ser, 35.16

Knapman, Grace, 38.23

Knapp, :
Anne, Mrs, maiden ,70.11
Eliz, wid; Thos, s, 1.11
Geo, 19.1,assr, 19.10;Mary, w; Jerom, s; Geo, s; Mary, d; Cordelia, d, 19.1
John; Jane, w; Han, d, 29.35
John, £600; Mary, w; John, s; Susa, d, 64.8
John, £50 p a, 70.11,assr, 70.13;Dot, w; Thos, s; John, s; Anne, d; Martha, d, 70.11
John, merchant, commr; Kath, w; Mat, s; Eliz, d; Rob, s; Geo, s; Kath, d; Anne, d; Frances, d, 73.27
Jos, app, 54.27
Sam, captain; Sar, d, 9.49

Knapton, :
Jas, bookseller; Hester, w; John, s, 32.4
Wm, 32.4

Knewstub, :
Martha, ser, 37.5
Paul; Eliz, w; Dot, d, 74.14
Susan, ser, 37.5

Knighley, Mary, ser, 9.40

Knight, Night, Nite:
Ambrose; Sar, w; Reb, d, 36.14
Ann, 7.24,32.10
Ann, ser, 33.10,35.16,59.21,77.3
Art; Sar, w; Thos, s, 32.22
Elinor, ser, 33.6
Eliz, 44.16,74.9
Eliz, ser, 48.3,60.9,64.2
Frances, 14.7
Han, 15.9,17.16,27.11
Han, ser, 77.5
Hen, ser, 13.16
Hester, ser, 20.21
Isaac; Eliz, w; Isaac, s, 7.19-20
Isaac, gent; Mary, w; Sar, d, 12.21
Jaine, ser, 73.27
Jane, wid, 75.15
Joa, ser, 64.1
John, 32.10
John; Susa, w; John, s; Thos, s; Ann, d, 8.10bJohn; Bridg, w; Eliz, d, 48.7
John; Mary, w, 56.6
John, £600; Sar, w; Sar, d; Susa, d; Anne, d; Thos, s, 22.4
John, porter; Esther, w; John, s, 79.12
John, ser, 30.6,36.16,50.3
Jos, ser, 41.1
Marg, ser, 19.3,73.25
Mary, 15.9,57.2
Mary, ser, 6.16,17.23
Peter, ser, 26.3
Ric, ser, 74.16
Rob; Marg, w; Rose, d, 1.17
Rob; Eliz, w; Kath, d; Rob, s, 7.3
Rob, £600; Jane, w; Rob, s; Anne, d, 80.1
Sar, ser, 14.19
Simon, bach, 41.1
Thos, ser, 16.11
Wm; Jane, w; Sam, s, 69.6
Wm; Eliz, w; Wm, s, 81.11
Wm, £600, plumber; Mary, w; Mary, d, 49.12
Wm, app, 77.4
Wm, ser, 55.1

Knighting, Wm, 59.10

Knightl(e)y, :
Alice, 9.39
Lucy, bach, merchant, £600, 49.9

Knighton, :
Bridg, ser, 59.17
Chris, ser, 45.1
Francis, app, 29.28
John; Eliz, w; Job, s; Kath, d, 59.17

Knightsmith, Joa, wid, 8.13

Knipe, :
Eliz; Eliz, d, 49.6
John, app, 11.19

Kniveton, John, tallow-chandler; Hen, s; Ann, d, 42.2

Knobbs, Hen, ser, 55.10

Knock, Rob, ser, 78.2

Knocker, :
Eliz, ser, 8.10
Francis; Eliz, w; Zachary, s; Mary, d; Sar, d; Martha, d, 7.36
Jos; Hester, sis, 37.8
Jos, ser, 75.14

Knocks, Martha, child, 66.7

Knocksnell, John, ser, 16.2

Knotsford, Wm; Anne, w; Anne Munday, her d, 81.10

Knott, :
Chris, ser, 63.5
Geo, 43.16
Jane, 26.15
Jeremiah, ser, 30.4
Thos; -, Dame, w, 14.11
Wm, ser, 32.8

Knowler, Gregory, bach, 28.24

Knowles, :
Hen; Kath, w, 17.112
Hester, 30.6
Israel, carpenter, £600; Jas, s; Martha, d, 66.4
Jas, app, 18.7
Sar, ser, 77.5
Thos; Hester, w, 36.13
Thos; Eliz, w; Jane, d; John, s; Mary, d; Sar, d; Eliz, d, 49.28
Wm, victualler; Alice, w; Anne, d, 79.14

Knweight, John, ser, 28.12

Kong, John, ser, 58.7

Kroger, Val, bach, £600, 28.8

Kynder, Gilbert, ser, 1.8

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Professor Peter Austin | Staff

Biography

Prof Austin retired on 1st January 2019.

My research interests cover documentary, descriptive, theoretical, historical and applied linguistics. I have extensive fieldwork experience since 1972 on Australian Aboriginal languages (northern New South Wales, northern South Australia, and north-west Western Australia) and co-authored with David Nathan the first fully page-formatted hypertext dictionary on the World Wide Web, a bilingual dictionary of Gamilaraay (Kamilaroi), northern New South Wales, as well as publishing seven bilingual dictionaries of Aboriginal languages. Since 2011 I have been working with the Dieri Aboriginal Corporation on revitalisation of the Dieri language spoken in South Australia (see Dieri WordPress).

Since 1995 I have been carrying out research on Sasak and Samawa, Austronesian languages spoken on Lombok and Sumbawa islands, eastern Indonesia, in collaboration with colleagues at Mataram University and Frankfurt University. My theoretical research is mainly on syntax and focuses on Lexical Functional Grammar, morpho-syntactic typology, computer-aided lexicography and multi-media for endangered languages. I have also published on historical and comparative linguistics, typology, and Aboriginal history and biography. I am currently working with Dr Julia Sallabank and with colleagues at University of Warsaw and Leiden University on an EU Horizon2020 Twinning project called Engaged Humanities, and with Professor Stefanie Pillai, University of Malaya, on a British Academy-funded collaborative research project in Malaysia.

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Nathan, David and Austin, Peter (2014) ‘Editor’s Introduction’. Language Documentation and Description, (12), pp 4-16.

Austin, Peter (2014) ‘And still they speak Diyari: the life history of an endangered language’. Ethnorema, (10), pp 1-17.

Austin, Peter and Sallabank, Julia (2013) ‘Endangered Languages: An introduction’. Journal of Multilingual and Multicultural Development, (34) 4, pp 313-316.

McGill, Stuart (2009) ‘Documenting grammatical tone using Toolbox: an evaluation of Buseman’s interlinearisation technique’. Language Documentation and Description, (6), pp 236-250.

Austin, Peter K. and Sallabank, Julia, (eds.), (2014) Endangered Languages: Beliefs and Ideologies in Language Documentation and Revitalization. London: British Academy. (Proceedings of the British Academy Vol. 199).

Nathan, David and Austin, Peter, (eds.), (2014) Language Documentation and Description, Volume 12. London: EL Publishing.

Austin, Peter K. and Sallabank, Julia, (eds.), (2014) Endangered Languages: Beliefs and Ideologies in Language Documentation and Revitalization. Oxford: Oxford University Press / British Academy. (Proceedings of the British Academy).

Austin, Peter and Sallabank, Julia, (eds.), (2013) Endangered Languages. Special issue of Journal of Multilingual and Multicultural Development, Vol 34. No. 4. London: Taylor & Francis.

Austin, Peter and McGill, Stuart, (eds.), (2011) Critical Concepts in Linguistics: Endangered Languages. London: Routledge. (Critical Concepts in Lingusitics).

Austin, Peter K. and Sallabank, Julia, (eds.), (2011) Cambridge Handbook of Endangered Languages. Cambridge: Cambridge University Press. (Cambridge Handbooks in Language and Linguistics).

Austin, Peter and Bond, Oliver and Marten, Lutz and Nathan, David, (eds.), (2011) Proceedings of the 3rd Conference on Language Description and Theory. London: SOAS.

Austin, Peter and Bond, Oliver and Charette, Monik and Nathan, David and Sells, Peter, (eds.), (2009) Proceedings of Conference on Language Documentation and Linguistic Theory 2. London: Department of Linguistics, SOAS.

Austin, Peter and Musgrave, Simon, (eds.), (2008) Transitivity and Voice in Austronesian Languages. Stanford: CSLI Publications. (Studies in Constraint-Based Lexicalism).

Austin, Peter, (ed.), (2008) 1000 languages. The worldwide history of living and lost tongues. London: Thames & Hudson.

Austin, Peter K. and Bond, Oliver and Nathan, David, (eds.), (2007) Proceedings of Conference on Language Documentation & Linguistic Theory. London, UK: Department of Linguistics, SOAS.

Austin, Peter and Sallabank, Julia (2018) ‘Language Documentation and Language Revitalization: Some Methodological Considerations’. In: Hinton, Leanne and Huss, Leena and Roche, Gerald, (eds.), The Routledge Handbook of Language Revitalization. Abingdon: Routledge.

Austin, Peter (2016) ‘Language documentation 20 years on’. In: Filipovic, Luna and Pütz, Martin, (eds.), Endangered Languages: Issues of Ecology, Policy and Documentation. Amsterdam: John Benjamins, pp 147-170.

Austin, Peter (2014) ‘Going, going, gone? The ideology and politics of language revitalisation in eastern Australia’. In: Austin, Peter K. and Sallabank, Julia, (eds.), Endangered Languages: Beliefs and Ideologies in Language Documentation and Revitalisation. London: British Academy, pp 109-124.

Austin, Peter (2008) ‘Survival of Language’. In: Shuckburgh, E., (ed.), Survival. Cambridge: Cambridge University Press, pp 80-98.

Austin, Peter K. and Bond, Oliver and Nathan, David (2007) ‘Introduction to Proceedings of Conference on Language Documentation & Linguistic Theory’. In: Austin, Peter K. and Bond, Oliver and Nathan, David, (eds.), Proceedings of Conference on Language Documentation & Linguistic Theory. London, UK: Department of Linguistics, SOAS, pp 1-2.

Austin, Peter (2006) ‘Data and Language Documentation’. In: Gippert, J. and Himmelmann, N. P. and Mosel, U., (eds.), Essentials of Language Documentation. Berlin: Mouton de Gruyter, pp 87-112.

Austin, Peter (2006) ‘Content Questions in Sasak, Eastern Indonesia: An Optimality Theoric Syntax Account’. In: Schulze, F. and Warnk, H., (eds.), Insular Southeast Asia. Linguistic and Cultural Studies in Honour of Bernd Nothofer.. Wiesbaden: Harrassowitz, pp 1-12.

 

This list was last generated on Monday, 6th December 2021, 08:41 Europe/London.

Creating a k-means model to cluster London bicycle hires dataset

Introduction

Your data may contain natural groupings or clusters of data. You may want to identify these groupings descriptively in order to make data-driven decisions. For example, a retailer may want to identify natural groupings of customers who have similar purchasing habits or locations. This process is known as customer segmentation.

Data you use to perform customer segmentation might include the store they visited, what items they bought, how much they paid, and so on. You would create a model to try to understand what these groups of customer personas are like so that you can design items that appeal to group members.

You could also find product groups among the items purchased. In this case, you would cluster items based on who purchased them, when they were purchased, where they were purchased, and so on. You would create a model to determine the characteristics of a product group so that you can make informed decisions such as how to improve cross-selling.

In this tutorial, you use BigQuery ML to create a k-means model that clusters London Bicycle Hires data based on bike station attributes.

Creating your k-means model consists of the following steps.

  • Step one: Create a dataset to store your model.
    The first step is to create a dataset that stores your model.
    Note: Because the London Bicycle Hires dataset is stored in the EU multi-region location, your dataset must also reside in the EU.
  • Step two: Examine your training data.
    The next step is to examine the data you use to train your clustering model by running a query against the london_bicycles table. Because k-means is an unsupervised learning technique, model training does not require labels nor does it require you to split the data into training data and evaluation data.
  • Step three: Create a k-means model.
    Step three is to create your k-means model. When you create the model, the clustering field is station_name, and you cluster the data based on station attribute, for example the distance of the station from the city center.
  • Step four: Use the ML.PREDICT function to predict a station’s cluster.
    Next, you use the ML.PREDICT function to predict the cluster for a given set of stations. You predict clusters for all station names that contain the string Kennington.
  • Step five: Use your model to make data-driven decisions.
    The final step is to use the model to make data-driven decisions. For example, based on the model results, you can determine which stations would benefit from extra capacity.

Step one: Create your dataset

The first step is to create a BigQuery dataset to store your model. To create your dataset:

  1. In the Cloud Console, go to the BigQuery page.

    Go to the BigQuery page

  2. In the navigation panel, in the Resources section, click your project name.

  3. On the right side, in the details panel, click Create dataset.

  4. On the Create dataset page:

    • For Dataset ID, enter bqml_tutorial.
    • For Data location, choose European Union (EU). The London Bicycle Hires public dataset is stored in the EU multi-region location. Your dataset should be in the same location.

  5. Leave all of the other default settings in place and click Create dataset.

Step two: Examine your training data

Next, you examine the data used to train your k-means model. In this tutorial, you cluster bike stations based on the following attributes:

  • Duration of rentals
  • Number of trips per day
  • Distance from city center

The following query compiles your training data. This query is included in your CREATE MODEL statement later in this tutorial.

WITH
  hs AS (
  SELECT
    h.start_station_name AS station_name,
    IF
    (EXTRACT(DAYOFWEEK
      FROM
        h.start_date) = 1
      OR EXTRACT(DAYOFWEEK
      FROM
        h.start_date) = 7,
      "weekend",
      "weekday") AS isweekday,
    h.duration,
    ST_DISTANCE(ST_GEOGPOINT(s.longitude,
        s.latitude),
      ST_GEOGPOINT(-0.1,
        51.5))/1000 AS distance_from_city_center
  FROM
    `bigquery-public-data.london_bicycles.cycle_hire` AS h
  JOIN
    `bigquery-public-data.london_bicycles.cycle_stations` AS s
  ON
    h.start_station_id = s.id
  WHERE
    h.start_date BETWEEN CAST('2015-01-01 00:00:00' AS TIMESTAMP)
    AND CAST('2016-01-01 00:00:00' AS TIMESTAMP) ),
  stationstats AS (
  SELECT
    station_name,
    AVG(duration) AS duration,
    COUNT(duration) AS num_trips,
    MAX(distance_from_city_center) AS distance_from_city_center
  FROM
    hs
  GROUP BY
    station_name )
SELECT
  *
FROM
  stationstats
ORDER BY
  distance_from_city_center ASC

Query details

This query extracts data on cycle hires, including start_station_name, and duration and joins it against station information, including distance-from-city-center. Then, it computes attributes of the station in stationstats, including the average duration of rides and the number of trips, and passes through the station attribute distance_from_city_center.

This query uses the WITH clause to define subqueries. The query also uses the ST_DISTANCE and ST_GEOGPOINT geospatial analytics functions. For more information on these functions, see Geography Functions. For more information on geospatial analytics, see Introduction to geospatial analytics.

Run the query

To run the query that compiles the training data for your model:

  1. In the Cloud Console, click the Compose new query button.

  2. Enter the following standard SQL query in the Query editor text area.

    WITH
      hs AS (
      SELECT
        h.start_station_name AS station_name,
        IF
        (EXTRACT(DAYOFWEEK
          FROM
            h.start_date) = 1
          OR EXTRACT(DAYOFWEEK
          FROM
            h.start_date) = 7,
          "weekend",
          "weekday") AS isweekday,
        h.duration,
        ST_DISTANCE(ST_GEOGPOINT(s.longitude,
            s.latitude),
          ST_GEOGPOINT(-0.1,
            51.5))/1000 AS distance_from_city_center
      FROM
        `bigquery-public-data.london_bicycles.cycle_hire` AS h
      JOIN
        `bigquery-public-data.london_bicycles.cycle_stations` AS s
      ON
        h.start_station_id = s.id
      WHERE
        h.start_date BETWEEN CAST('2015-01-01 00:00:00' AS TIMESTAMP)
        AND CAST('2016-01-01 00:00:00' AS TIMESTAMP) ),
      stationstats AS (
      SELECT
        station_name,
        AVG(duration) AS duration,
        COUNT(duration) AS num_trips,
        MAX(distance_from_city_center) AS distance_from_city_center
      FROM
        hs
      GROUP BY
        station_name )
    SELECT
      *
    FROM
      stationstats
    ORDER BY
      distance_from_city_center ASC
    
    
  3. Click Run.

  4. When the query is complete, click the Results tab below the query text area. The results tab shows the columns you queried that are used to train your model: station_name, duration, num_trips, distance_from_city_center. The results should look like the following.

Step three: Create a k-means model

Now that you have examined your training data, the next step is to create a k-means model using the data.

You can create and train a k-means model using the CREATE MODEL statement with the option model_type=kmeans. The following query adds a CREATE MODEL statement to the previous query and removes the id fields in the data.

CREATE OR REPLACE MODEL
  bqml_tutorial.london_station_clusters OPTIONS(model_type='kmeans',
    num_clusters=4) AS
WITH
  hs AS (
  SELECT
    h.start_station_name AS station_name,
  IF
    (EXTRACT(DAYOFWEEK
      FROM
        h.start_date) = 1
      OR EXTRACT(DAYOFWEEK
      FROM
        h.start_date) = 7,
      "weekend",
      "weekday") AS isweekday,
    h.duration,
    ST_DISTANCE(ST_GEOGPOINT(s.longitude,
        s.latitude),
      ST_GEOGPOINT(-0.1,
        51.5))/1000 AS distance_from_city_center
  FROM
    `bigquery-public-data.london_bicycles.cycle_hire` AS h
  JOIN
    `bigquery-public-data.london_bicycles.cycle_stations` AS s
  ON
    h.start_station_id = s.id
  WHERE
    h.start_date BETWEEN CAST('2015-01-01 00:00:00' AS TIMESTAMP)
    AND CAST('2016-01-01 00:00:00' AS TIMESTAMP) ),
  stationstats AS (
  SELECT
    station_name,
    isweekday,
    AVG(duration) AS duration,
    COUNT(duration) AS num_trips,
    MAX(distance_from_city_center) AS distance_from_city_center
  FROM
    hs
  GROUP BY
    station_name, isweekday)
SELECT
  * EXCEPT(station_name, isweekday)
FROM
  stationstats

Query details

The CREATE MODEL statement specifies the desired number of clusters — four. In the SELECT statement, the EXCEPT clause excludes the station_name column because station_name is not a feature. The query creates a unique row per station_name, and only the features are mentioned in the SELECT statement.

If you omit the num_clusters option, BigQuery ML will choose a reasonable default based on the total number of rows in the training data. You could also perform hyperparameter tuning to find a good number. To determine an optimal number of clusters, you would run the CREATE MODEL query for different values of num_clusters, find the error measure, and pick the point at which the error measure is at its minimum value. You can obtain the error measure by selecting your model and clicking the Evaluation tab. This tab shows the Davies–Bouldin index.

Run the

CREATE MODEL query

To run the query that creates your k-means model:

  1. In the Cloud Console, click the Compose new query button.

  2. Enter the following standard SQL query in the Query editor text area.

    CREATE OR REPLACE MODEL
      bqml_tutorial.london_station_clusters OPTIONS(model_type='kmeans',
        num_clusters=4) AS
    WITH
      hs AS (
      SELECT
        h.start_station_name AS station_name,
      IF
        (EXTRACT(DAYOFWEEK
          FROM
            h.start_date) = 1
          OR EXTRACT(DAYOFWEEK
          FROM
            h.start_date) = 7,
          "weekend",
          "weekday") AS isweekday,
        h.duration,
        ST_DISTANCE(ST_GEOGPOINT(s.longitude,
            s.latitude),
          ST_GEOGPOINT(-0.1,
            51.5))/1000 AS distance_from_city_center
      FROM
        `bigquery-public-data.london_bicycles.cycle_hire` AS h
      JOIN
        `bigquery-public-data.london_bicycles.cycle_stations` AS s
      ON
        h.start_station_id = s.id
      WHERE
        h.start_date BETWEEN CAST('2015-01-01 00:00:00' AS TIMESTAMP)
        AND CAST('2016-01-01 00:00:00' AS TIMESTAMP) ),
      stationstats AS (
      SELECT
        station_name,
        isweekday,
        AVG(duration) AS duration,
        COUNT(duration) AS num_trips,
        MAX(distance_from_city_center) AS distance_from_city_center
      FROM
        hs
      GROUP BY
        station_name, isweekday)
    SELECT
      * EXCEPT(station_name, isweekday)
    FROM
      stationstats
    
  3. Click Run.

  4. In the navigation panel, in the Resources section, expand your project name, click bqml_tutorial, and then click london_station_clusters.

  5. Click the Schema tab. The model schema lists the four station attributes that BigQuery ML used to perform clustering. The schema should look like the following.

  6. Click the Evaluation tab. This tab displays visualizations of the clusters identified by the k-means model. Under Numerical features, bar graphs display up to 10 of the most important numerical feature values for each centroid. You can select which features to visualize from the drop-down menu.

Step four: Use the

ML.PREDICT function to predict a station’s cluster

To identify the cluster to which a particular station belongs, use the ML.PREDICT function. The following query predicts the cluster of every station that has the string “Kennington” in its name.

WITH
  hs AS (
  SELECT
    h.start_station_name AS station_name,
    IF
    (EXTRACT(DAYOFWEEK
      FROM
        h.start_date) = 1
      OR EXTRACT(DAYOFWEEK
      FROM
        h.start_date) = 7,
      "weekend",
      "weekday") AS isweekday,
    h.duration,
    ST_DISTANCE(ST_GEOGPOINT(s.longitude,
        s.latitude),
      ST_GEOGPOINT(-0.1,
        51.5))/1000 AS distance_from_city_center
  FROM
    `bigquery-public-data.london_bicycles.cycle_hire` AS h
  JOIN
    `bigquery-public-data.london_bicycles.cycle_stations` AS s
  ON
    h.start_station_id = s.id
  WHERE
    h.start_date BETWEEN CAST('2015-01-01 00:00:00' AS TIMESTAMP)
    AND CAST('2016-01-01 00:00:00' AS TIMESTAMP) ),
  stationstats AS (
  SELECT
    station_name,
    AVG(duration) AS duration,
    COUNT(duration) AS num_trips,
    MAX(distance_from_city_center) AS distance_from_city_center
  FROM
    hs
  GROUP BY
    station_name )
SELECT
  * EXCEPT(nearest_centroids_distance)
FROM
  ML.PREDICT( MODEL bqml_tutorial.london_station_clusters,
    (
    SELECT
      *
    FROM
      stationstats
    WHERE
      REGEXP_CONTAINS(station_name, 'Kennington')))

Query details

This query uses the REGEXP_CONTAINS function to find all entries in the station_name column that contain the string “Kennington”. The ML.PREDICT function uses those values to predict which clusters would contain those stations.

Run the

ML.PREDICT query

To run the ML.PREDICT query:

  1. In the Cloud Console, click the Compose new query button.

  2. Enter the following standard SQL query in the Query editor text area.

    WITH
      hs AS (
      SELECT
        h.start_station_name AS station_name,
        IF
        (EXTRACT(DAYOFWEEK
          FROM
            h.start_date) = 1
          OR EXTRACT(DAYOFWEEK
          FROM
            h.start_date) = 7,
          "weekend",
          "weekday") AS isweekday,
        h.duration,
        ST_DISTANCE(ST_GEOGPOINT(s.longitude,
            s.latitude),
          ST_GEOGPOINT(-0.1,
            51.5))/1000 AS distance_from_city_center
      FROM
        `bigquery-public-data.london_bicycles.cycle_hire` AS h
      JOIN
        `bigquery-public-data.london_bicycles.cycle_stations` AS s
      ON
        h.start_station_id = s.id
      WHERE
        h.start_date BETWEEN CAST('2015-01-01 00:00:00' AS TIMESTAMP)
        AND CAST('2016-01-01 00:00:00' AS TIMESTAMP) ),
      stationstats AS (
      SELECT
        station_name,
        AVG(duration) AS duration,
        COUNT(duration) AS num_trips,
        MAX(distance_from_city_center) AS distance_from_city_center
      FROM
        hs
      GROUP BY
        station_name )
    SELECT
      * EXCEPT(nearest_centroids_distance)
    FROM
      ML.PREDICT( MODEL bqml_tutorial.london_station_clusters,
        (
        SELECT
          *
        FROM
          stationstats
        WHERE
          REGEXP_CONTAINS(station_name, 'Kennington')))
    
    
  3. Click Run.

  4. When the query is complete, click the Results tab below the query text area. The results should look like the following.

Step five: Use your model to make data-driven decisions

Evaluation results can help you to interpret the different clusters.

In this example, Cluster#3 shows a busy city station that is close to the city center. Cluster#2 shows the second city station which is less busy. Cluster#1 shows a less busy suburban substation, with longer duration rentals. Cluster#4 shows another suburban station with trips that are shorter. Based on these results, you can use the data to inform your decisions. For example:

  • Assume that you need to experiment with a new type of lock. Which cluster of stations should you choose as a subject for this experiment? The stations in Cluster#1, Cluster#2 or Cluster#4 seem like logical choices because they are not the busiest stations.

  • Assume that you want to stock some stations with racing bikes. Which stations should you choose? Cluster#1 is the group of stations that are far from the city center, and they have the longest trips. These are likely candidates for racing bikes.

Imperial College London – Imperial College London, UK

Imperial College London or Imperial College has a rich history that can be traced back to 1907. On July 1 of this year, the list of members of the Governing Body was published, and the bylaws forming the Imperial College were presented on July 8. It is this date that is considered the official founding day of Imperial College. This was preceded by a report of the Committee on Education, published in January 1906, which recommended the amalgamation of three educational institutions – the Royal School of Science, the Royal School of Mines and the City & Guilds College. , which were founded in the middle of the XIX century, – in one institution.And exactly two years after the establishment of the Imperial College on July 8, 1909, Edward VII laid the foundation stone of the new building.

Further, for 100 years, the college was part of the University of London, which united all the major educational institutions of the British capital, including LSE, King’s College, Birbeck and University College UCL.

In 2007, the college celebrates its centenary and is separated from the University of London, becoming a separate independent educational institution.

Imperial College London is ranked among the top ten universities in the world with an international reputation for teaching and research. Educational excellence is at the heart of the Imperial ethos and is reflected in UK and international rankings. According to The Times Higher Education World University Rankings, Imperial College was ranked 3rd in Europe and 10th in the world in 2020. Also in the ranking of the Most Innovative Universities in the World 2019, Imperial College was ranked 1st in the UK, 2nd in Europe, 10th in the world.

Imperial has the largest amount of high-impact research of any major UK university. He is the recipient of the Silver Athena Swan Award, which recognizes the advancement of women in science, technology, engineering, mathematics, and medicine in academia.

The College’s distinguished members include 14 Nobel Prize winners and three Fields Medal winners. Famous college alumni include British bacteriologist Thomas Fleming, musician and member of the Queen band Brian May, English writer Herbert Wells and many other prominent personalities.

Imperial ranks among the best universities in the world and strives to develop the next generation of researchers, scientists and academics through collaborations in various fields. Located in the heart of London, Imperial College is an interdisciplinary teaching and research space that uses science and innovation to solve global problems.

Imperial is home to approximately 17,000 students and 8,000 employees. Imperial College London awards approximately 6,700 degrees annually.It is an international community that attracts students from over 125 countries. The College’s excellence – consistently ranked among the top ten universities in the world – is due to its talented students and staff from around the world, as well as its support of a global network of alumni and staff. It is an international academic community in which people from different cultures share their views, new ideas and fresh approaches to solving complex problems. 60% of Imperial students come from outside the UK, including 20% ​​from other European countries.Two-thirds of Imperial’s research is conducted with international collaborators, including scientists from more than 130 countries.

The college specializes in four main disciplines: science, engineering, medicine and business, and is known for applying these skills to industry and enterprises. The college has 4 faculties: engineering, medicine, natural sciences and a business school. Among the college programs there are 114 undergraduate programs, 153 master’s programs and 50 doctoral programs.

The college’s scientific achievements include the development of a magnetometer for the NASA Solar Orbiter spacecraft, a rapid HIV test, clinical trials of a successful drug for multiple myeloma, the discovery of a way to convert sunlight into matter, extensive research into Parkinson’s disease, and much more.

The College strives to attract the brightest and best students and offers a wide range of scholarships. The President’s PhD Scholarship provides an excellent opportunity for prospective graduate students to work in their chosen field of study with the support of an academic advisor.Successful applicants will receive full tuition fees funding for up to 3.5 years, a scholarship of £ 21,600 per year for living expenses, and a fee of £ 2,000 per year for supplies for the first 3 years of study.

International students can apply for President’s Undergraduate Scholarships, which is 1000 pounds for each academic year of the undergraduate program (4 years), World Scientific Scholarship (5000 pounds per year), Chevening Scholarships, which covers the full or partial cost of studying abroad, full content, some additional costs and a return flight to the country of residence, as well as many other more specialized scholarships for citizens of certain countries, athletes, musicians, as well as researchers in narrow fields of science.

The college participates in more than 80 Erasmus programs with various universities around the world and works directly on student exchange programs with a number of educational institutions, including the University of Melbourne, University of British Columbia, Hong Kong Polytechnic University, Tokyo Institute of Technology, Columbia University, MIT and many others.

The college also has a career center that helps students navigate their professional environment, prepare a resume correctly, provides career advice, conducts tests, trainings, master classes and various events that contribute to the adaptation of students to the professional community.

The huge territory of the college has the richest infrastructure. The grounds have tennis courts, swimming pools, gyms, soccer and basketball fields, an excellent central library and several on-campus libraries, 29 retail outlets including cafes, restaurants and a mini market.

There are also many research facilities on the territory:

  • A fully functional flight simulator used to investigate the handling characteristics of existing and future aircraft.
  • Pilot Carbon Capture Plant – Spans over four stories and is built to industrial standards.
  • Laboratory of hydrodynamics for measuring waves and their impact.
  • KPMG Data Observatory (DO), the largest of its kind in Europe, with a circular wall of 64 monitors connected to 32 computers providing 313-degree circular vision.
  • Test flight equipment for hybrid unmanned aerial robots in a laboratory equipped with 16 high-speed 3D cameras capable of tracking robots in flight and eight more for tracking underwater movement.
  • Wind tunnels providing speeds from a few meters per second to Mach 9.

Advantages:

  1. Imperial College London is in the top 10 best universities in the world and in the top three in Europe;
  2. Imperial college London is a member of the most prestigious university associations: Russell Group, G5, Association of Commonwealth Universities (ACU), League of European Research Universities (LERU) and Golden Triangle;
  3. The College has graduated 14 Nobel Prize winners, 3 Fields Prize winners and 70 members of the Royal Society of London;
  4. Student / teacher ratio (the ratio of the number of students to the number of teachers) is 7: 1;
  5. Imperial College comprises 20 campuses in London and beyond, several gyms, two student bars and 7 major libraries;
  6. The college has several scientific laboratories and research centers, and is also known for its scientific achievements throughout the world;
  7. The College offers a wide variety of undergraduate scholarships and has partnerships with many universities around the world.

Enrollment dates: October 2021

Flight to London (LON) | Find and book a flight to London

Visit the city of unforgettable attractions. Buckingham Palace, Wembley Stadium, Big Ben, Regent’s Park, Camden Town, Notting Hill, London Underground – the British capital is endlessly multifaceted. (And to see as much as possible, ride the London Eye, a giant Ferris wheel).

Fortunately, the city is pedestrian-friendly, and those who don’t like walking can rent a bike under the Santander Cycles scheme introduced by Mayor Boris Johnson (there are 700 rental bike stands in London). And of course, you cannot fly to the British capital and not take a ride on the famous London Underground or the double-decker bus that has become the symbol of the city.

Guests will find something amazing from history or modernity here on every corner. Whether you walk into one of Shoreditch’s hipster boutiques or visit the fashionable Kensington restaurant, there is a unique charm and spirit everywhere.

The architecture here is as varied as the neighborhoods, from St. Paul’s Cathedral with its classic splendor to the ultra-modern The Shard soaring skyward. London is refreshing with vast green spaces: for a break from the hustle and bustle of the city, go to Hyde Park or Regent’s Park (which is home to the famous London Zoo).

The city is split in two by the majestic Thames, on one side of which the Westminster complex rises, and on the other there are the Tate Modern and Shakespeare’s Globe Theater.Beyond Tower Bridge, the river forms a bend: it spans Canary Wharf, and on the other side is Greenwich Wharf.

The banks of the Thames and the surrounding area are dotted with hundreds of theaters, galleries, boutiques, restaurants and markets, each with its own flavor.

You will land at Heathrow, Gatwick or Stansted airport: each of them is no more than an hour and a half from the center of this amazing city.

Money is fleeing Brexit: Amsterdam bypassed London in stock trading

  • Yuri Wendik
  • BBC Russian Service

Photo author, Getty Images

Photo caption,

British Government promises that London as a financial center has a bright global future after Brexit.But for now, the time has come for him vague uncertainty

In the first month after the final Brexit, Amsterdam toppled the City of London from the pedestal of the financial capital of Europe – albeit only in one area, in stock trading.

According to the analytical company Cboe Global Markets, in January the volume of trading in shares on all exchanges of Amsterdam jumped four times compared to December and amounted to 9.2 billion euros per day, while in London it fell by half, to 8.6 billion euros per day.

These data do not mean yet that the City of London as a whole ceases to be the main financial center of Europe. Stock trading is only a small part of the financial market.

“But this is an illustration of how the dominant position of the ‘square mile’ that dominated European finance for decades is eroding after Brexit. Wall Street, “Bloomberg writes.The City of London is figuratively called the “square mile”.

Money is leaving the City due to the fact that Britain and the European Union, before the final Brexit, which took place on this New Year’s Eve, did not agree on relations in the service sector, including financial services, although services account for about 80% of Britain’s GDP.

The post-Brexit relationship agreement, which the parties negotiated in dire time pressure in December, deals primarily with (relatively) free trade in goods – but not services.

In the field of finance, all power was in the hands of the EU government, the European Commission: it decides whether to allow British financial companies to enter the single market of the European Union. EC

issues these permits on the basis of the so-called “equivalence” – that is, the commission recognizes that the rules of a particular country in a particular sector of the financial industry are “equivalent”, that is, no less strict than the EU rules, and then admits firms from this country and from this industry to Europe.

Photo author, PA Media

Photo caption,

Stock trading fled from London to Amsterdam (archived photo of the London Stock Exchange)

Until most of the companies from the City received these permits – and European clients and assets are leaving them in Amsterdam, Frankfurt , Paris, Milan and Dublin.

London and Brussels are currently in talks and hope to sign a “memorandum of understanding” by March, on the basis of which the European Commission is supposed to recognize the “equivalence” of British rules and allow City financiers to enter its market.

Experts, however, have big doubts that everything will be so and in a month or two the City’s troubles will end.

Cutting Britain

“Is the EU going to cut off Britain? There are now signs that there is such an intention, but I think it would be a mistake,” Bank of England Governor Andrew Bailey said Wednesday in his annual speech to City officials.

According to Bailey, Brussels wants guarantees that London will continue to essentially obediently copy the rules of the European Union, while London, for obvious reasons, does not agree.

Former member of the European Commission from Britain Lord Jonathan Hill is generally confident that Brussels will not give British firms the right of “equivalence” – he recently told the Financial Times.

But even if Lord Hill turns out to be wrong, British financiers will no longer see the same freedom on the continent.

Without confidence in the future

Firstly, the European Commission issues work permits based on “equivalence” at its discretion and not forever, but for a while, and can take them away, notifying about it just a month in advance.That is, the foreigners, whom the British financiers have now turned into in the European Union, will no longer have confidence in the future.

Secondly, the “equivalence” mechanism does not apply to all branches of finance. Retail banking, for example, does not fall under it – and British banks operating in Europe have already notified their clients on the continent that their accounts are being closed.

Many experts suggest that the European Commission will only let British companies back into those workshops of the giant financial industry of the European Union, in which they cannot yet be replaced by competitors from the EU countries.

So, even before the final Brexit, the European Commission gave the British permission to work in two areas: in clearing (but only for a year and a half) and in settlements on Irish assets (but only for six months).

The British government, wishing to support the City, allowed Europeans to stay and work in it without unnecessary formalities for another three years after Brexit.

Macron urged London to take seriously the situation with the transit of migrants across the English Channel – International Panorama

PARIS, 26 November./ TASS /. France calls on the UK to “seriously cooperate” to resolve the crisis with migrants crossing the English Channel. This was stated on Friday by French leader Emmanuel Macron during a press conference with Italian Prime Minister Mario Draghi in Rome.

“Migrants do not want to stay in France. They want to get to the UK at any cost,” said Macron, whose performance was broadcast by the Elysee Palace. “The real answer must be serious cooperation in order to neutralize the networks of illegal migrant traffic,” the French leader said.

Commenting on a public letter from British Prime Minister Boris Johnson criticizing the actions of Paris, Macron said he was “surprised at the frivolity of London’s approach.” “In relations between the leaders of countries, it is not customary to communicate through tweets or open letters,” the French president said.

He noted that in Paris “they are in favor of an effective solution to the migration problem.” “We must cooperate with the countries of origin of migrants, protect our borders, prevent illegal traffic of migrants and assist those who entered France legally,” Macron said.”It is necessary to act in the spirit of humanism and in compliance with the existing rules,” he said.

Earlier, the Prime Minister of Great Britain sent a letter to the President of France, in which he presented a five-point plan to solve the problem of illegal migrants. He published his text on his Twitter page. Johnson demanded the return to France of migrants who entered the UK illegally. After this letter, the French side, considering London’s approach frivolous, decided to cancel the meeting with British Interior Minister Priti Patel, which was supposed to take place in Calais on Sunday.Meanwhile, as Macron said, Paris will continue to discuss the situation with European partners.

Victims of illegal traffic

On Wednesday, a boat carrying fifty illegal migrants sank in the Calais area. They expected to cross the English Channel to reach Great Britain. Rescuers continue to search for the missing. The Dunkirk Port Prosecutor’s Office announced the start of an investigation under the article “manslaughter with aggravated circumstances.” As a result of the boat crash, according to the Ministry of Internal Affairs, about 30 people were killed.French law enforcement agencies have detained five people suspected of organizing the transfer of migrants from France to the UK through the English Channel.

London and Paris have been jointly fighting the problem of illegal immigrants for several years, but the number of migrants trying to cross the English Channel and Pas-de-Calais is constantly growing. More than 25,000 people have illegally entered the United Kingdom this year using this route. Several thousand migrants this year were detained by the French authorities before they had time to leave French waters.

90,000 London threatened with sanctions for Nord Stream 2 over Ryanair flight – RBK

There is no exact information about Russia’s involvement in the incident with the Ryanair flight, but it is hard to believe that this could have happened without Moscow’s participation, the head of the British Foreign Ministry said.

Photo: Sean Gallup / Getty Images

London will consider the possibility of imposing sanctions against Nord Stream 2 due to the incident with the Ryanair plane, British Foreign Minister Dominic Raab said, the Guardian reports.

Tom Tugendhat, head of the House of Commons Foreign Affairs Committee, spoke with a corresponding idea during the minister’s speech to parliament: “Will [Raab] take another step and call for a freeze of the Nord Stream 2 gas pipeline and [operation] of the Yamal gas pipeline that passes through Belarus, from which comes the money to support this tyrannical regime [Lukashenka]? ” – he said after Raab’s speech.

Video

Raab said in his speech that it was “hard to believe” that the incident with the Ryanair flight in Minsk happened “without the tacit consent of Moscow,” the Guardian reports.At the same time, he stressed that there is no exact information about Russia’s involvement in the incident. Nevertheless, London will think about putting sanctions pressure on Russia, the minister said. “The Nord Stream and other opportunities [to exert pressure from the sanctions] were mentioned. We will consider this issue and consult with our partners in order to understand what further steps they are also willing to take, “he said after talking with the deputies (quoted by RIA Novosti).

MEP, head of the delegation on relations with Belarus Robert Bedron on May 24 said that Russians may be involved in the incident with the Ryanair plane in Minsk, noting, however, that this information has not been confirmed.“From our information it follows that the KGB agents were on board the plane and left after landing in Minsk. These are, probably, including the citizens of Russia who remained in Minsk after the landing. A lot of information indicates this, “he said, reports RIA Novosti. Ryanair CEO Michael O’Leary also argued that the number of passengers on board the plane could have included intelligence officers and that “KGB officers” could have gotten off the flight at the Minsk airport. However, he did not mention the presence of Russian citizens in Minsk.

London sent warships to the island in the English Channel | News from Germany about Europe | DW

The post-Brexit conflict between London and Paris over fishing rights in the English Channel continues to escalate. Two ships of the Royal Navy of Great Britain arrived at the island of Jersey on Thursday, May 6. We are talking about the patrol ships HMS Severn and HMS Tyne, which will “watch the situation”.

Prime Minister Boris Johnson ordered their dispatch in response to France’s threats to block almost 60 fishing vessels in Saint Helier, the port and main city of Jersey, AFP news agency notes.France has also sent two patrol vessels to the waters.

British Prime Minister: the blockade of the English Channel is “completely unfounded”

As pointed out in the British Defense Department, this is a “purely preventive measure”. Johnson, in turn, has already called the blockade of the English Channel “completely unjustified.”

Meanwhile, French Secretary of State for European Affairs Clement Bon stressed that his country will not “let itself be intimidated.” A spokesman for the European and Foreign Office demanded a “swift and full implementation of the Brexit agreement.”Maritime Minister Annick Girardin, in particular, has threatened “retaliatory measures” if London continues to restrict fishing rights. France could cut off under-cable electricity supplies for more than 100,000 Jersey residents, she said.

EU-UK Trade Agreement

Fishing activity is one of the contentious issues in the EU-UK trade agreement, which entered into force in full on 1 May.In accordance with the agreements, the British should admit to their waters only those fishing vessels that have had the right to sail there since 2012. According to the French side, this is not easy to prove. From 2026, European fishermen will be forced to give up a quarter of their catch in British waters, which translates into revenue losses of around € 650 million a year.

Jersey is a British Crown possession, which, however, is not part of the UK. In particular, London is responsible for protecting this largest of the Channel Islands.

See also:

  • Brexit: UK divorce from EU going hard

    May survives second vote of no confidence

    MPs voted on no confidence after UK parliament failed Theresa May. The Prime Minister won the victory thanks to the support of Conservative MPs and allies from the Democratic Union Party.

  • Brexit: UK divorce from EU going hard

    Brexit defeat in British parliament

    The lower house of the British parliament on January 15 rejected the agreement to withdraw the UK from the European Union. 432 deputies spoke against the document, 202 supported it. To pass the agreement, at least 320 votes were needed. London is to submit an alternative Brexit plan within three days.

  • Brexit: UK divorce from EU going hard

    Conservatives failed to pass a vote of no confidence in Theresa May

    A number of Conservative MPs in the UK have initiated a vote of no confidence in Prime Minister Theresa May. The reason was the “Brexit” plan agreed by May with the EU, which is disadvantageous, according to the conservatives, for Great Britain. May retained her post as prime minister and postponed the parliamentary vote on Brexit, previously scheduled for December 11, to mid-January.

  • Brexit: UK-EU divorce is going hard

    Brussels and London agree to leave the UK from the EU

    On November 25, at an extraordinary summit in Brussels, the heads of state and government of the 27 EU countries approved a Brexit agreement with the UK. The 585-page agreement on Britain’s withdrawal from the European Union was supported by the leaders of all European countries. European Commission President Jean-Claude Juncker described the deal as “the best possible” and called it a fair compromise. “

  • Brexit: UK divorce from EU going hard

    May for soft Brexit

    Theresa May, who took over as British Prime Minister after the resignation of David Cameron, who opposed the country’s exit from the EU, supports the soft Brexit model. In particular, after the government agreed on a collective position on July 6, 2018, May announced that the UK was in favor of creating a free trade area with the EU after Brexit.

  • Brexit: Great Britain’s divorce with the EU is going hard

    Dissenters have resigned

    David Davis, the UK’s exit minister, resigned first. Then – the Minister of Foreign Affairs, supporter of “Brexit” Boris Johnson. They nullified Prime Minister Theresa May’s attempt to rally her cabinet to implement a soft exit from the EU. In turn, Johnson said that the implementation of “Brexit” requires the Prime Minister to take a tougher stance towards the EU.

  • Brexit: Divorce between Great Britain and the European Union is going hard

    Foreign Ministry headed by Brexit adversary

    The post of British Foreign Minister Boris Johnson was replaced by Jeremy Hunt, who previously served as Minister of Health and Social Affairs of the country. Unlike Johnson, who advocated a complete “divorce” from the European Union, Hunt was previously known as an opponent of the country’s exit from the EU. Davis, who also retired, was replaced by Dominic Raab.

  • Brexit: Divorce between Great Britain and the European Union is going hard

    What is soft Brexit

    Soft Brexit declares no checks on the border between Ireland and Northern Ireland. It is also expected that the EU and the UK will develop a “joint institutional framework” for the consistent interpretation of legal agreements between them. Finally, the May cabinet approved a principle to avoid customs checks after March 29, 2019, the entry into force of Brexit.

  • Brexit: UK divorce with EU going hard

    Brussels welcomes London decision

    EU head of the Brexit negotiation Michelle Barnier on Twitter has already welcomed the UK government’s agreements, but added: “We will appreciate these suggestions and see how practical and realistic they are. ” Brussels expected London to voice concrete proposals at the recent EU summit, but this did not happen.

  • Brexit: UK-EU divorce is going hard

    Historical decision

    The referendum on the UK’s withdrawal from the EU was held on June 23, 2016. 51.9 percent of the voters opted for Brexit. More than 33.5 million Britons took part in the referendum, the turnout was 72.1 percent. Before, no state has left the EU, which was established in 1992.

  • Brexit: UK divorce with EU going hard

    How the UK voted on Brexit

    The UK is made up of England, Scotland, Wales and Northern Ireland.The outcome of the vote in them turned out to be heterogeneous. If in England (53.4%) and Wales (52.5%) they voted in favor of leaving the EU, then in Scotland and Northern Ireland the majority was in favor of maintaining Britain’s membership in the European Union – 55.8% and 62% of the local population, respectively. …

  • Brexit: UK divorce from EU going hard

    Metropolitan view

    Greater London is the only one of nine regions in England to vote to retain UK membership in the EU.59.9 percent of the participants in the referendum here were in favor of the UK remaining part of the European Union.

  • Brexit: UK divorce from EU going hard

    Young Brits against Brexit

    75% of Britons aged 18-24 who participated in the referendum voted against leaving the EU. On the other hand, 61% of voters over 65 supported Brexit. In the 25-49 age group, 56% were supporters of EU membership, while the same percentage of Britons aged 50-64, on the contrary, voted for Brexit.

    Artist: Ilya Koval, Natalya Pozdnyakova, Marina Baranovskaya


City of London | UK | Prices | Tours | Flights | Hotels | Climate

London – the largest city in Europe, the political, financial and cultural capital of Great Britain, located on the banks of the River Thames in the southern part of the country. Historically London is divided into several parts – administrative, financial, industrial, aristocratic and entertainment.

In its center are the main financial institutions, large international companies and stock exchanges, a little further, in the popular Soho area, there are shops of famous brands, shopping centers and entertainment venues.

London is a cosmopolitan city where traditions and customs of different cultural and ethnic groups have been mixed together. About a third of the capital’s residents are foreigners – they come from African countries, Pakistan, India and Bangladesh. Here, in addition to English, more than 300 languages ​​are spoken, and national dishes from the cuisines of different peoples of the world are prepared every day.

Cost of living and prices of goods in London

Restaurants
Lunch Inexpensive restaurant 1 person 18 USD
Lunch Middle class restaurant 2 persons 67 USD
Lunch McMeal at McDonald’s 1 PC. 6 USD
Drinks Local beer 0.5 liters 5 USD
Drinks Imported beer 0.33 liters 5 USD
Drinks Coke / Pepsi 0.33 liters 1 USD
Drinks Water 0.33 liters 1 USD
Drinks Cappuccino Serving 3 USD
Supermarkets
Products Water 1.5 liters 1 USD
Products White fresh bread 500 g 1 USD
Products Eggs 12 pcs. 3 USD
Products Milk 1 liter 1 USD
Products Bow 1 kg. 1 USD
Products Cabbage 1 head 1 USD
Products White rice 1 kg. 2 USD
Products Potato 1 kg. 1 USD
Products Tomatoes 1 kg. 2 USD
Products Local cheese 1 kg. 7 USD
Meat Chicken fillet 1 kg. 8 USD
Fruit Apples 1 kg. 2 USD
Fruit Bananas 1 kg. 1 USD
Fruit Oranges 1 kg. 2 USD
Alcohol Middle class wine 1 bottle 10 USD
Alcohol Local beer 0.5 liters 2 USD
Alcohol Imported beer 0.33 liters 2 USD
Cigarettes Marlboro 1 pack 12 USD
Transport
Public transport One-way trip 1 ticket 3 USD
Public transport Transport Travel card 158 USD
Taxi Starting rate Landing 4 USD
Taxi Trip 1 km. 4 USD
Taxi Waiting 1 hour 61 USD
Fuel Gasoline 1 liter 1 USD
Car, purchase Volkswagen Golf 1.4 90 KW Trendline 1 PC. 21.922 USD
Entertainment
Sports Fitness center, subscription 1 month 59 USD
Sports Tennis court, rent 1 hour 14 USD
Rest Cinema, international film 1st place 15 USD
Products
Clothes Jeans, Levis 501 1 pair 77 USD
Clothes Summer dress in Zara, H&M 1 PC. 39 USD
Shoes Nike Running Shoes 1 pair 78 USD
Shoes Leather office shoes 1 pair 94 USD
Accommodation
Rental housing Apartment in the city center 1-room 2,047 USD
Rental housing Apartment not in the center 1-room 1.418 USD
Rental housing Apartment in the city center 3-room 3.914 USD
Rental housing Apartment not in the center 3-room 2,345 USD
Utilities Electricity, heating… 85 sq. meters 1 month 187 USD
Communication Mobile communications 1 minute 0 USD
Communication Unlimited Internet 1 month 30 USD
Home purchase Apartment in the city center 1 sq.meter 19.017 USD
Home purchase Apartment not in the center 1 sq. meter 9.545 USD
Salary Average salary after taxes 1 month 2,512 USD
Mortgage Interest rate 1 year 3.40%

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Weather, climate, temperature by months in London

Season Fall Winter Spring Summer
Month Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug
Average maximum ° C 19 15 10 7 7 7 11 13 17 20 22 22 14
Average temperature ° C 15 11 8 5 5 5 7 9 12 15 18 18 11
Average minimum ° C 11 8 5 3 2 2 4 5 8 11 13 13 7
Rain Days 9 11 11 11 12 10 10 10 10 9 9 9 120
Snow Days 0 0 0 1 4 5 2 1 0 0 0 0 13
Fog Days 12 13 12 12 9 7 7 7 8 8 6 9 110
Thunderstorm Days 1 1 0 0 0 0 0 1 2 3 2 3 13
Precipitation level mm. 54 64 59 52 48 38 40 42 48 48 58 59 610
Air humidity % 73 79 84 84 83 79 75 71 68 67 67 68 75
Wind Speed ​​ Km / h 8 8 9 10 12 12 11 11 10 10 11 9 10
Day length Hours 13 11 10 9 9 11 13 15 16 17 17 15 13

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London – Closest airports

Total 10 records.

Cities close to London

Showing entries 1-10 of 1 495 .

.7 km
Country Region City Population Expenditures Quality of Life Climate Safety
1 Month 1-10 1-10 1-10
0 km UK England London 7,556,900 1,077 USD 9.0 6.6
300 km Great Britain Scotland Glasgow 610.268 589 USD 8.3 8.6 6 9034 9034 9034 9034 Scotland Edinburgh 435.791 664 USD 9.0 8.6 8.2
88 km United Kingdom England Birmingham .7 4.7 9.0
141 km United Kingdom England Manchester 395.515 585 USD 6.9 8.9 9034 90 England Oxford 154.566 845 USD 9.0 7.9 9.7
151 km UK England York 9039.0 9.3 7.9
147 km Great Britain England Leeds 455.123 558 USD 8.9 6 7.4 km 9034 Northern Ireland Belfast 274.770 513 USD 8.2 7.0 4.4
95 km United Kingdom England Nottingham 9034 9034.

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