Shown here is a globe visualization of world-wide Google searches, categorized by one of 21 languages. The visualization is created with WebGL toolkit and bundled data from Chrome Experiments.
I have annotated the data with geographical information from MaxMind, to include city, region, and country for each search location. The closest city was determined by finding the entry in the MaxMind data set (2.8M cities) with the smallest haversine distance to the coordinates of the search term. Note that latitude and longitude were provided to 3 decimal places in the original data file but are available to 7 decimal places in the MaxMind set.
The annotated data file includes new fields
rank(1-indexed rank of magnitude of search data point)
cumulative_value(fractional total of all search terms with equal or smaller magnitude)
language_name(name of the search language)
city(closest city to latitude/longitude of search data point)
region(region of closest city)
country(country of closest city)
city_latitude, city_longitude(coordinates of closest city)
Thanks to Evan Applegate from UC Davis for requesting an explanation of the additional fields. They were not obvious.
View all languages or individual data for the following languages: Arabic Belgian Chinese Dutch English Finnish French German Indonesian Italian Japanese Korean Norwegian Polish Portuguese Romanian Russian Spanish Swedish Thai Turkish
View search density.
Showing search density.
The color legend was created based on the color scheme used in the original webgl-globe code.
There are 11 locations in the US with searches in Spanish: Dillard, Douglas, Flint Hill, Floyds Knobs, Great Falls, Orrs Island, Redwood Estates, Simpsonville, Spanish Fork, Spanish Fort, and Washington. Conspicuously, Los Angeles is missing.
The northern-most town in Mexico with a Spanish search is Mexicali (Baja Californa, lat 32.65 long -115.47).
The Chinese takeover has been largely overestimated. Only two towns in the US participate in Chinese language searches: Williamsport and Evensville.
With the exception of Albouystown (Demerara-Mahaica, Guyana) and Paramaribo (Suriname), South America shows no English searches.
Asia shows interesting patterns. Namely, no English searches are seen from China. No doubt, political firewalls are the cause. By country, India leads with 82 searches, followed by Malaysia (64) and Pakistan (11). The full list is India (82), Malaysia (64), Pakistan (11), United (5), Bangladesh (4), Sri (3), Philippines (3), Nepal (3), Korea (3), Japan (2), Iran (2), Singapore (1), Papua (1), Myanmar (1), Maldives (1), Cambodia (1), Brunei (1), Bhutan (1), Afghanistan (1).
There are 25 locations with English language searches at latitude ≥ 60°. There are 15 cities in Alaska with searches (Anchorage, Barrow, Bethel, Cordova, Delta Junction, Eagle River, Fairbanks, Kenai, Nome, North Pole, Palmer, Seward, Soldotna, Valdez, Wasilla), of which Barrow is furthest north (lat 71.29°). The other 10 cities are mostly in Canada: Lerwick (Shetland Islands, United Kingdom, lat 60.160°), Whitehorse (Yukon Territory, Canada, lat 60.720°), Jarstad (Sogn og Fjordane, Norway, lat 61.360°), Fort Providence (Northwest Territories, Canada, lat 61.380°), Yellowknife (Northwest Territories, Canada, lat 62.450°), Frobisher Bay (Nunavut, Canada, lat 63.750°), Keflavík Gullbringusysla Iceland lat 64.010°), Inuvik (Northwest Territories, Canada, lat 68.340°), Gjoa Haven (Nunavut, Canada, lat 68.630°), Igloolik (Nunavut, Canada, lat 69.380°).
New Zealand and Australia dominate search loations in the far south. The southermost English search is from Invercargill (Southland, New Zealand, lat -46.4° — compare this to the northmost search from Barrow in Alaska at lat 71.29°). In Australia, the southermost search is from Davenport (Tasmania, Australia, lat -41.17°). In South Africa, the southermost search is from Hermanus (Western Cape, South Africa, lat -34.42°).
What is the most remote search location? Here, I define distance between locations by the haversine distance.
I tabulate three types of remote locations, by language, by finding
Cities, by language, most distant from their closest city.
The most remote search location of alll is Papeete, whose closest search data point is 2,287 km away — Fusi in American Samoa. Also interesting is the Belgian-speakinng Westerschelling in the Netherlands, which has the smallest maximum distance to its nearest city, by language. It is 25 km from Harlingen, Netherlands.
Cities, by language, most distant from their closest city, in which people speak (i.e. search) in the same language.
English searches are the most spread out on the globe. Of all search languuages, Mahe in Seychelles is furthest from its same-language nearest loccation of all other languages. It is 1,347 from Hamar in Somalia, in which English searches are found.
Cities, by language, most distant from their closest city, which is foreign (i.e. searching in a different language).
About 10% of all searches come from the top 10 locations.
I am surprised to see Miami here (bored retirees?) as well as Istanbul — I don't have a theory for that one.
38% of all searches come from the top 100 locations (out of 22,826), with English dominating (33/100) followed by Spanish (11/100).
The full breakdown for the top 100 locations by language is English (33), Spanish (11), German (8), Japanese (6), Dutch (6), Portuguese (5), French (5), Turkish (4), Italian (4), Chinese (4), Russian (3), Arabic (3), Polish (2), Thai (1), Swedish (1), Romanian (1), Korean (1), Indonesian (1), Finnish (1).
By country, the top 100 locations fall in United States (11), Germany (6), India (6), Japan (6), Brazil (5), United Kingdom (5), Italy (4), Turkey (4), Australia (3), France (3), Mexico (3), Russian Federation (3), Canada (2), China (2), Colombia (2), Poland (2), Saudi Arabia (2), Spain (2), Vietnam (2), Algeria (1), Argentina (1), Austria (1), Chile (1), Egypt (1), Finland (1), Greece (1), Hong Kong (1), Hungary (1), Indonesia (1), Ireland (1), Israel (1), Korea (1), Malaysia (1), Peru (1), Philippines (1), Romania (1), Serbia (1), Singapore (1), Sweden (1), Switzerland (1), Taiwan (1), Thailand (1), Tunisia (1), Ukraine (1), United Arab Emirates (1), Venezuela (1)
The top 100 locations are
To achieve a `k` index for a movement you must perform `k` unbroken reps at `k`% 1RM.
The expected value for the `k` index is probably somewhere in the range of `k = 26` to `k=35`, with higher values progressively more difficult to achieve.
In my `k` index introduction article I provide detailed explanation, rep scheme table and WOD example.
The effect is intriguing and facetious—yes, those are real words.
But these are not: necronology, abobionalism, gabdologist, and nonerify.
These places only exist in the mind: Conchar and Pobacia, Hzuuland, New Kain, Rabibus and Megee Islands, Sentip and Sitina, Sinistan and Urzenia.
And these are the imaginary afflictions of the imagination: ictophobia, myconomascophobia, and talmatomania.
And these, of the body: ophalosis, icabulosis, mediatopathy and bellotalgia.
Want to name your baby? Or someone else's baby? Try Ginavietta Xilly Anganelel or Ferandulde Hommanloco Kictortick.
When taking new therapeutics, never mix salivac and labromine. And don't forget that abadarone is best taken on an empty stomach.
And nothing increases the chance of getting that grant funded than proposing the study of a new –ome! We really need someone to looking into the femome and manome.
An exploration of things that are missing in the human genome. The nullomers.
Julia Herold, Stefan Kurtz and Robert Giegerich. Efficient computation of absent words in genomic sequences. BMC Bioinformatics (2008) 9:167
We've already seen how data can be grouped into classes in our series on classifiers. In this column, we look at how data can be grouped by similarity in an unsupervised way.
We look at two common clustering approaches: `k`-means and hierarchical clustering. All clustering methods share the same approach: they first calculate similarity and then use it to group objects into clusters. The details of the methods, and outputs, vary widely.
Altman, N. & Krzywinski, M. (2017) Points of Significance: Clustering. Nature Methods 14:545–546.
Lever, J., Krzywinski, M. & Altman, N. (2016) Points of Significance: Logistic regression. Nature Methods 13:541-542.
Lever, J., Krzywinski, M. & Altman, N. (2016) Points of Significance: Classifier evaluation. Nature Methods 13:603-604.