plunder database
table georegions, originally 1048 records from osmtilemill shapefiles
attributes featureclass and scalerank
scale = on-screen, the number of pixels in the radius of the globe, used for realtime map drawing
scalerank = in the data, a number from 1 to 6, or from 0 to 2000, indicating the relative magnitude of the feature
| featureclass | count | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 9 | 10 | 12 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| alkaline lake | 40 | 27 | 2 | 1 | 5 | 2 | 3 | |||||
| basin | 9 | 2 | 2 | 3 | 2 | |||||||
| canal | 4 | 1 | 3 | |||||||||
| delta | 12 | 6 | 6 | |||||||||
| lake | 320 | 220 | 58 | 2 | 5 | 2 | 7 | 26 | ||||
| lake centerline | 113 | 5 | 4 | 13 | 17 | 20 | 46 | 1 | 2 | 3 | 2 | |
| reservoir* | 52 | 25 | 8 | 1 | 5 | 4 | 9 | |||||
| river* | 361 | 22 | 31 | 41 | 54 | 49 | 164 |
| featureclass | count | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 |
|---|---|---|---|---|---|---|---|---|---|
| coast | 36 | 20 | 4 | 11 | 1 | ||||
| continent | 7 | 7 | |||||||
| island | 295 | 3 | 10 | 26 | 27 | 123 | 67 | 39 | |
| island group | 167 | 4 | 5 | 12 | 31 | 35 | 57 | 22 | 1 |
| isthmus | 4 | 1 | 3 | ||||||
| geoarea | 44 | 4 | 5 | 12 | 8 | 1 | 1 | ||
| pen/cape | 55 | 4 | 6 | 11 | 11 | 21 | 2 | ||
| peninsula | 11 | 1 | 1 | 8 | 1 |
| featureclass | count | 0 | 1 | 2 | 3 | 4 | 5 | 6 |
|---|---|---|---|---|---|---|---|---|
| depression | 2 | |||||||
| desert* | 58 | |||||||
| foothills* | 3 | 1 | 2 | |||||
| gorge | 3 | 3 | ||||||
| lowland | 5 | 3 | 2 | |||||
| plain* | 30 | 4 | 10 | 4 | 3 | 9 | ||
| plateau* | 71 | 4 | 12 | 16 | 13 | 21 | 5 | |
| range/mtn* | 222 | 10 | 21 | 45 | 53 | 85 | 8 | |
| tundra* | 4 | 2 | 1 | 1 | ||||
| valley* | 6 | 1 | 2 | 2 | 1 | |||
| wetlands* | 3 | 1 | 1 | 1 |
| featureclass | count | 100 | 500 | 900 | 1000 | 2000 | |
|---|---|---|---|---|---|---|---|
| drangons-be-here | 1 | 1 | |||||
| empire* | 427 | 427 | |||||
| treasure* | 67 | 7 | 15 | 1 | 43 | 1 |
* classes used in the original plunder
| scalerank | count |
|---|---|
| 0 | 283 |
| 1 | 144 |
| 2 | 118 |
| 3 | 261 |
| 4 | 266 |
| 5 | 453 |
| 6 | 361 |
| 7 | 41 |
| 9 | 2 |
| 10 | 6 |
| 12 | 2 |
| 100 | 1 |
| 500 | 15 |
| 900 | 1 |
| 1000 | 470 |
| 2000 | 1 |
| 7 |
loaded from Natural Earth Data, 50m set
A. examine rivers 1:50m 460 rivers, scalerank 1 thru 6, 42 rows in our target geo
#all rivers combined, almost 1 MB psql -t -d voyc -U jhagstrand <rivers.sql >../json/rivers.js
select scalerank, count(*) from plunder.plunder
where featureclass = 'lake'
group by scalerank order by scalerank;
0 | 220
1 | 58
2 | 2
3 | 5
4 | 2
5 | 7
6 | 26
select scalerank, count(*) from plunder.plunder
where featureclass = 'reservoir
group by scalerank order by scalerank;
0 | 25
1 | 8
2 | 1
4 | 5
5 | 4
6 | 9
A table on this page includes names of the major seas. https://en.wikipedia.org/wiki/List_of_political_and_geographic_borders
Caspian Sea is currently missing.
Maybe needed for labeling or hit testing.
Examples
Natural Earth's Ocean file has only one polygon.
We don't currently have an oceans data. We just paint the background blue, and start drawing on top of it.
If we want to do labeling or hit testing by ocean name, then we will need a polygon for each named ocean.
3 oceans: Pacific, Atlantic, Indian
optional: Arctic, Southern
optional: North Pacific, South Pacific, North Atlantic, South Atlantic
arctic and southern oceans are each a circle, or just explicitly test for north of 80
pulled from database voyc, table fpd
| population | count |
|---|---|
| more than ten million | 40 |
| one million to ten million | 700 |
| 100,000 to one million | 4247 |
| 20,000 to 100,000 | 12,979 |
| 10,000 to 20,000 | 10,354 |
| less than 10,000 | 13,910 |
| Total | 42,180 |
| id | name | country | pop |
|---|---|---|---|
| 17463 | Tokyo | Japan | 39105000 |
| 17464 | Jakarta | Indonesia | 35362000 |
| 17465 | Delhi | India | 31870000 |
| 17466 | Manila | Philippines | 23971000 |
| 17467 | São Paulo | Brazil | 22495000 |
| 17468 | Seoul | South Korea | 22394000 |
| 17469 | Mumbai | India | 22186000 |
| 17470 | Shanghai | China | 22118000 |
| 17471 | Mexico City | Mexico | 21505000 |
| 17472 | Guangzhou | China | 21489000 |
| 17473 | Cairo | Egypt | 19787000 |
| 17474 | Beijing | China | 19437000 |
| 17475 | New York | United States | 18713220 |
| 17476 | Kolkāta | India | 18698000 |
| 17477 | Moscow | Russia | 17693000 |
| 17478 | Bangkok | Thailand | 17573000 |
| 17479 | Dhaka | Bangladesh | 16839000 |
| 17480 | Buenos Aires | Argentina | 16216000 |
| 17481 | Ōsaka | Japan | 15490000 |
| 17482 | Lagos | Nigeria | 15487000 |
| 17483 | Istanbul | Turkey | 15311000 |
| 17484 | Karachi | Pakistan | 15292000 |
| 17485 | Kinshasa | Congo (Kinshasa) | 15056000 |
| 17486 | Shenzhen | China | 14678000 |
| 17487 | Bangalore | India | 13999000 |
| 17488 | Ho Chi Minh City | Vietnam | 13954000 |
| 17489 | Tehran | Iran | 13819000 |
| 17490 | Los Angeles | United States | 12750807 |
| 17491 | Rio de Janeiro | Brazil | 12486000 |
| 17492 | Chengdu | China | 11920000 |
| 17493 | Baoding | China | 11860000 |
| 17494 | Chennai | India | 11564000 |
| 17495 | Lahore | Pakistan | 11148000 |
| 17496 | London | United Kingdom | 11120000 |
| 17497 | Paris | France | 11027000 |
| 17498 | Tianjin | China | 10932000 |
| 17499 | Linyi | China | 10820000 |
| 17500 | Shijiazhuang | China | 10784600 |
| 17501 | Zhengzhou | China | 10136000 |
| 17502 | Nanyang | China | 10013600 |