Lindy Hop Webpage StatisticsStatistics
Hi Lindy Hoppers!!!
Our TENTH Million!

Thanks for tuning into Lindy Week in Review

Counter for the ENTIRE Website


website counter
  • We have been on the Net since April 1, 1997
  • We had out 10,000th visitor on March 22, 1998
  • We had our 100,000th visitor on June 6, 1999
  • We had our 200,000th visitor on September 12, 2000
  • We had our 300,000th visitor on August 17,2001
  • We had our 400,000th visitor on May 2, 2002
  • We had our 500,000th visitor on September 23, 2002
  • We had our 600,000th visitor on November 29, 2002
  • We had our 700,000th visitor on January 28, 2003
  • We had our 800,000th visitor on March 19, 2003
  • We had our 900,000th visitor on May 9, 2003
  • We had our 1,000,000th visitor on June 28, 2003
  • We had our 1,500,000th visitor on March 26, 2004
  • We had our 2,000,000th visitor on August 15, 2004
  • We had our 2,500,000th visitor on December 15, 2004
  • We had our 3,000,000th visitor on April 9, 2005
  • We had our 3,500,000th visitor on August 4, 2005
  • We had our 4,000,000th visitor on December 3,2005
  • We had our 10,000,000th visitor on April 5, 2011

In other words:

  • 355 days for the first 10,000 hits
  • 797 days for the first 100,000
  • 463 days for the second 100,000
  • 339 days for the third 100,000
  • 258 days for the fourth 100,000
  • 144 days for the fifth 100,000
  • 67 days for the sixth 100,000
  • 60 days for the seventh 100,000
  • 49 days for the eighth 100,000
  • 50 days for the ninth 100,000
  • 50 days for the tenth 100,000
  • 414 days for our second 1,000,000 hits
  • 239 Days for our third 1,000,000 hits
  • 237 days for our fourth 1,000,000 hits
  • 2313 Days to go from four to ten million

What Pages Do People Visit?

Since August 15, 2004, about 29,000 visitors per week have tuned into jitterbuzz.com. (that's about 4,200 per day...) These folks, on average, look at about 6 of our 700 pages

The site has three basic functions: (a) Swing Dancing; (b) Historical Artifacts; and (c) Other

Meet Mr. Zipf


Mr. Zip  George Kingsley Zipf
We mean the guy on the right...
"Mr. Zip" (left)helped educate the public to the Post Office's "Zone Improvement Program" in 1963

George Kingsley Zipf (right)started out educating Harvard students in Linguistics

What do words, books, beetles, oil fields and visits to a website have in common?

Zipf's law is what they have in common! Thanks to Harvard linguistic professor George Kingsley Zipf (1902-1950)we know that "frequency of occurrence of some event ( P ), as a function of the rank ( i) when the rank is determined by the above frequency of occurrence, is a power-law function Pi ~ 1/ia with the exponent a close to unity."

What?

In order to understand The Zipf Distribution, you have to learn the difference between "Types and "Tokens":

  • "Types" are part of a classification system -- they can be words, books in a library, genera of beetles, individual pages of a web site, oil fields classified by size (e.g. 1-2 bil bbl), companies classified by assets (e.g. $1-$2 Bil)
  • "Tokens" are specific instances of the type. (count 1,000,000 English words "The" occurs 75,000 times... ; look through S&P - there are 34 Oil companies with assets between $1 and $2 bil; take records from one million hits on a website and count the number of hits on each separate page in the site)

A Zipf distribution is evidenced by a linear form when the logarithm of the rank of a "type" (1st 2nd, etc) is plotted on the abscissa (x-axis, or "horizontal") and the logarithm of the number of "tokens" of that Type are plotted on the Ordinate (y-axis, or vertical). ("Straight Line on a Log-Log Plot" for you engineers) A simple description of data that follow a Zipf distribution is that they have a few elements that are very frequent and a huge number of elements that are infrequent

Zipf distributions have been shown to characterize use of words in a natural language (like English), the popularity of library books, Oil Companies by size, Oil and Gas Fields by size, Genera of Beetles by number of members. For example, a language has a few words ("the", "and", etc.) that are used extremely often, and a library has a few books that everybody wants to borrow (current bestsellers) , there are gigantic oil fields that are hard to miss because they cover a vast amount of area.

On the other hand, a language also has an abundance of words ("cthonic", "onomatopoeia",etc.) that are almost never used, and a library has piles and piles of books that are only checked our every few years (Biographies of Zoroaster, Tandy 103 Manuals etc.); most oil accumulations are very small and are below the resolution of seismic exploration tools and will only be found by accident.

The Zipf Distribution is the characteristic signature of evolution, whereby some phenomenon adapts to its environment. Beetles and Companies are examples of the process -- a few species are dominant because they can thrive in a wide variety of circumstances while others adapt to fill very small environmental niches. Thriving means the ability to reproduce (grow), to ward off predators, and to find a lot of food. In this case, companies and beetles share a lot. There is one Exxon-Mobil and lots of little one-well wildcatters. (Some of you may even remember 1998 when Exxon ate Mobil).

Language evolves in another way. Communication makes use of message and space. If "xxxx" is a word, it might get confused with "xxyx" -- words have certain dissimilarities that minimize confusion during transmission. Similarly words evolve to meet both the everyday needs of people and specialized circumstances. Inuit languages have lots of words for our "snow" and skiiers have a similarly specialized vocabulary for the same substance. Lawyers, Academics and Government invent specialized words to convey precise meanings. Libraries evolve when they jointly serve the needs of the general public and academic specialties. (Today's best seller may become the collector's rarity 30 years from now...)

Web Sites also serve the general public and evolve as well. jitterbuzz.com started in 1997 as just a swing dance calendar. Slowly, we added pages on the vintage clothing that you might wear to go dancing, and places to dance while you were traveling, and then to things that you might want to collect to enhance your dance experience. In short the site has evolved to reflect a total swing-inspired "retro lifestyle". At last count, the site had 700 separate pages. All of them evolved in one way or another from things that we discovered while swing dancing. Our ratings service allows us to keep records on the most frequent 150 pages.

Here is a graph that shows that access to jitterbuzz.com follows a Zipf distribution. The figure shows the distribution of incoming page requests during the most recent million hits. Each datapoint represents one page, with the x-axis (horizontal) showing the logarithm of the rank of that page and the y-axis (vertical) showing the log of the number of hits on that page.

Mr. Zip
The Zipf Distribution for Hits on jitterbuzz.com

Rank Hits (Tokens) Page (Type)
  1. 306370 Dance Schedule
  2. 123305 Vintage Clothes
  3. 57691 Home Page
  4. 23167 Dances of the 1950s
  5. 20253 All about Coffee
  6. 19701 Links to other Lindy Websites
  7. 16373 Link to Collectables pages
  8. 15049 Remodeling a Kitchen for Less than $1000
  9. 13852 Building a 1948 Model Airplane Kit
  10. 13744 Art Deco Furniture
  11. 12227 Cavalier Cedar Chests
  12. 10336 Kitchen Mixers
  13. 10107 Kitchen Appliances
  14. 10089 Antique Radios
  15. 9655 Taking Care of Black and White Shoes
  16. 9197 Antique Toys
  17. 9158 All About Irons and Ironing
  18. 9124 A Trip through 1947
  19. 9021 Things to do in the DC Area
  20. 8727 Antique Fans
  21. 8029 Antique Telephones

So, it looks like we have been evolving to meet the needs of our readers.

Scroll down below for the full historical detail

Date Counter Days Week Week Avg Long Term Avg
(hits) (since 4-1-97) (hits/wk) (hits/da) (hits/da)
12-7-97 5188 251 20.67
12-14-97 5420 258 232 33.14 21.01
12-21-97 5627 265 207 29.57 21.23
12-28-97 5789 272 162 23.14 21.28
Date Counter Days Week Week Avg Long Term Avg
(hits) (since 4-1-97) (hits/wk) (hits/da) (hits/da)
1-04-98 6013 279 224 32.00 21.55
1-11-98 6256 286 243 34.71 21.87
1-18-98 6564 293 308 44.00 22.40
1-25-98 6845 300 281 40.14 22.82
Date Counter Days Week Week Avg Long Term Avg
(hits) (since 4-1-97) (hits/wk) (hits/da) (hits/da)
2-01-98 7219 307 374 53.43 23.51
2-08-98 7558 314 339 48.43 24.07
2-15-98 7905 321 347 49.57 24.63
2-22-98 8312 328 407 58.14 25.34
Date Counter Days Week Week Avg Long Term Avg
(hits) (since 4-1-97) (hits/wk) (hits/da) (hits/da)
3-01-98 8681 335 369 52.71 25.91
3-08-98 9002 342 321 45.86 26.32
3-15-98 9480 349 478 68.29 27.16
3-22-98 9919 356 439 62.71 27.86
3-29-98 10328 363 409 58.43 28.45
Date Counter Days Week Week Avg Long Term Avg
(hits) (since 4-1-97) (hits/wk) (hits/da) (hits/da)
4-05-98 10732 370 404 57.71 29.01
4-12-98 11150 377 418 59.71 29.58
4-19-98 11551 384 401 57.29 30.08
4-26-98 11982 391 431 61.51 30.64
Date Counter Days Week Week Avg Long Term Avg
(hits) (since 4-1-97) (hits/wk) (hits/da) (hits/da)
5-03-98 12513 398 531 75.86 31.44
5-10-98 13002 405 489 69.86 32.10
5-17-98 13470 412 468 66.86 32.69
5-24-98 13939 419 469 67.00 33.27
5-31-98 14416 426 477 68.14 33.84
Date Counter Days Week Week Avg Long Term Avg
(hits) (since 4-1-97) (hits/wk) (hits/da) (hits/da)
6-07-98 15015 433 599 85.57 34.68
6-14-98 15804 440 789 112.71 35.92
6-21-98 16580 447 776 110.86 37.03
6-29-98 17469 454 889 127.00 38.48
Date Counter Days Week Week Avg Long Term Avg
(hits) (since 4-1-97) (hits/wk) (hits/da) (hits/da)
7-05-98 18326 461 857 122.43 39.75
7-12-98 19247 468 921 131.57 41.13
7-19-98 20161 475 914 130.57 42.44
7-26-98 21114 482 953 136.14 43.80
Date Counter Days Week Week Avg Long Term Avg
(hits) (since 4-1-97) (hits/wk) (hits/da) (hits/da)
8-2-98 22207 489 1087 155.29 45.40
8-9-98 23225 496 1024 146.29 46.86
8-16-98 24203 503 978 139.71 48.12
8-23-98 25013 509 810 135.0 49.14
8-31-98 26007 517 994 124.25 50.31
Date Counter Days Week Week Avg Long Term Avg
m-d-y (hits) (since 4-1-97) (hits/wk) (hits/da) (hits/da)
9-6-98 26915 524 908 129.71 51.36
9-13-98 27898 531 983 140.43 52.54
9-20-98 29091 538 1193 170.43 54.70
9-27-98 30270 545 1179 168.43 55.43
Date Counter Days Week Week Avg Long Term Avg
m-d-y (hits) (since 4-1-97) (hits/wk) (hits/da) (hits/da)
10-04-98 31579 552 1309 187.00 57.21
10-11-98 33066 559 1487 212.43 59.15
10-18-98 34567 566 1501 214.43 61.07
10-25-98 36078 573 1511 215.86 62.96
Date Counter Days Week Week Avg Long Term Avg
m-d-y (hits) (since 4-1-97) (hits/wk) (hits/da) (hits/da)
11-01-98 37565 580 1487 212.43 64.77
11-08-98 39277 587 1712 244.54 66.98
11-15-98 41352 594 2075 296.43 69.62
11-22-98 43226 601 1874 267.71 71.92
11-29-98 44834 608 1613 230.43 73.75
Date Counter Days Week Week Avg Long Term Avg
m-d-y (hits) (since 4-1-97) (hits/wk) (hits/da) (hits/da)
12-06-98 46742 615 1903 271.86 76.00
12-13-98 48482 622 1740 248.57 77.95
12-20-98 50109 629 1627 232.43 79.66
12-27-98 51375 636 1266 180.86 80.78
Date Counter Days Week Week Avg Long Term Avg
m-d-y (hits) (since 4-1-97) (hits/wk) (hits/da) (hits/da)
1-03-99 53014 643 1639 234.14 82.45
01-10-99 54852 650 1838 262.57 84.39
01-17-99 56857 657 2005 286.43 86.59
01-24-99 58876 664 2019 288.43 88.67
01-31-99 61159 671 2283 325.14 91.15
Date Counter Days Week Week Avg Long Term Avg
m-d-y (hits) (since 4-1-97) (hits/wk) (hits/da) (hits/da)
02-07-99 63460 678 2301 328.71 93.60
02-14-99 66039 685 2579 368.43 96.41
02-21-99 68432 692 2393 341.86 98.81
02-28-99 70572 699 2140 305.72 100.96
Date Counter Days Week Week Avg Long Term Avg
m-d-y (hits) (since 4-1-97) (hits/wk) (hits/da) (hits/da)
03-07-99 73182 706 2610 372.86 103.66
03-14-99 75685 713 2503 357.57 106.15
03-21-99 77844 720 2159 308.43 108.60
03-28-99 80043 727 2199 314.14 110.10
Date Counter Days Week Week Avg Long Term Avg
(hits) (since 4-1-97) (hits/wk) (hits/da) (hits/da)
04-4-99 82387 734 2344 334.86 112.29
04-11-99 84708 741 2321 331.57 114.32
04-18-99 86869 748 2161 308.71 116.14
04-25-99 89112 755 2243 320.43 118.03
Date Counter Days Week Week Avg Long Term Avg
(hits) (since 4-1-97) (hits/wk) (hits/da) (hits/da)
05-02-99 91524 762 2412 344.57 120.11
05-09-99 93567 769 2133 304.71 121.79
05-16-99 95918 777 2261 323.00 123.61
05-23-99 98037 784 2119 302.71 125.21
05-30-99 99866 791 1829 261.29 126.42
Date Counter Days Week Week Avg Long Term Avg
(hits) (since 4-1-97) (hits/wk) (hits/da) (hits/da)
06-6-99 101695 798 1829 261.29 127.60
06-13-99 103576 805 1881 268.71 128.83
06-20-99 105362 812 1786 255.14 129.92
06-27-99 107069 819 1707 243.86 130.89
Date Counter Days Week Week Avg Long Term Avg
(hits) (since 4-1-97) (hits/wk) (hits/da) (hits/da)
07-04-99 109013 826 1944 277.71 132.14
07-11-99 110949 833 1981 283.00 133.41
07-18-99 112863 840 1914 273.43 134.52
07-25-99 114769 847 1906 272.29 135.66
Date Counter Days Week Week Avg Long Term Avg
(hits) (since 4-1-97) (hits/wk) (hits/da) (hits/da)
08-01-99 116,706 853 1937 276.71 136.82
08-08-99 118,659 860 1953 279.00 137.98
08-15-99 120,391 867 1732 247.4 138.38
08-22-99 122,071 874 1680 240.0 139.67
08-29-99 123,730 881 1659 237.0 140.4
Date Counter Days Week Week Avg Long Term Avg
(hits) (since 4-1-97) (hits/wk) (hits/da) (hits/da)
09-05-99 125,450 888 1720 245.7 141.3
09-12-99 127,140 895 1690 241.43 142.06
09-19-99 128,805 902 1665 237.86 142.87
09-26-99 130,420 909 1615 230.71 143.48
09-20-01 307,959 1,636 *** *** 188.23
10-04-01 312,852 1,650 *** *** 189.61
10-11-01 314,711 1,657 *** *** 189.93
10-22-01 317,809 1,668 *** *** 190.53
11-02-01 320,937 1,679 *** *** 191.15
11-09-01 323,956 1,686 *** *** 192.14
11-16-01 327,003 1,693 *** *** 193.15
01-09-02 341,952 1,756 *** *** 194.73
01-22-02 346,329 1,769 *** *** 195.67


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