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 top 20 locations.
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
Decision trees classify data by splitting it along the predictor axes into partitions with homogeneous values of the dependent variable. Unlike logistic or linear regression, CART does not develop a prediction equation. Instead, data are predicted by a series of binary decisions based on the boundaries of the splits. Decision trees are very effective and the resulting rules are readily interpreted.
Trees can be built using different metrics that measure how well the splits divide up the data classes: Gini index, entropy or misclassification error.
When the predictor variable is quantitative and not categorical, regression trees are used. Here, the data are still split but now the predictor variable is estimated by the average within the split boundaries. Tree growth can be controlled using the complexity parameter, a measure of the relative improvement of each new split.
Individual trees can be very sensitive to minor changes in the data and even better prediction can be achieved by exploiting this variability. Using ensemble methods, we can grow multiple trees from the same data.
Krzywinski, M. & Altman, N. (2017) Points of Significance: Classification and regression trees. Nature Methods 14:757–758.
Lever, J., Krzywinski, M. & Altman, N. (2016) Points of Significance: Logistic regression. Nature Methods 13:541-542.
Altman, N. & Krzywinski, M. (2015) Points of Significance: Multiple Linear Regression Nature Methods 12:1103-1104.
Lever, J., Krzywinski, M. & Altman, N. (2016) Points of Significance: Classifier evaluation. Nature Methods 13:603-604.
Lever, J., Krzywinski, M. & Altman, N. (2016) Points of Significance: Model Selection and Overfitting. Nature Methods 13:703-704.
Lever, J., Krzywinski, M. & Altman, N. (2016) Points of Significance: Regularization. Nature Methods 13:803-804.
The artwork was created in collaboration with my colleagues at the Genome Sciences Center to celebrate the 5 year anniversary of the Personalized Oncogenomics Program (POG).
The Personal Oncogenomics Program (POG) is a collaborative research study including many BC Cancer Agency oncologists, pathologists and other clinicians along with Canada's Michael Smith Genome Sciences Centre with support from BC Cancer Foundation.
The aim of the program is to sequence, analyze and compare the genome of each patient's cancer—the entire DNA and RNA inside tumor cells— in order to understand what is enabling it to identify less toxic and more effective treatment options.
Principal component analysis (PCA) simplifies the complexity in high-dimensional data by reducing its number of dimensions.
To retain trend and patterns in the reduced representation, PCA finds linear combinations of canonical dimensions that maximize the variance of the projection of the data.
PCA is helpful in visualizing high-dimensional data and scatter plots based on 2-dimensional PCA can reveal clusters.
Altman, N. & Krzywinski, M. (2017) Points of Significance: Principal component analysis. Nature Methods 14:641–642.
Altman, N. & Krzywinski, M. (2017) Points of Significance: Clustering. Nature Methods 14:545–546.
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.