Curriculum

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====== Data Analysis ======

  * personality traits of the land
    * fixed
      * altitude: basin, lowland, highland, plateaux (high plain, tableland)
      * relief:
      * flatness: plain, hills, foothills, mountain range
      * features: mountain peak, rift, canyon, 
      * water: wet, dry
      * size, boundaries, isolation: continent, island, peninsula, coastal, inland
    * changing:
      * plate tectonics, volcanos, erosion
    * weather (average, seasonal)
      * rainfall 
      * temperature: hot, cold (altitude)
      * wind
    * soil: erosion, sand, rock, dirt, topsoil: 
    * plant cover: forest, steppe (grassland), scrub, desert, tundra, ice
    * animal cover: 
    * canals, man-made chokepoints




plunder database

table georegions, originally 1048 records from osmtilemill shapefiles

attributes featureclass and scalerank

==== 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

=== scalerank by featureclass ===

== Water ==
^ 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|   |  |   |   |

== Land ==
^ 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|   |

== Terrain ==
^ 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|   |

== Political ==
^ 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 ====

^ 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 |

===== water =====

loaded from Natural Earth Data, 50m set

  * oceans
  * seas
  * lakes
  * rivers

==== rivers ====

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

==== lakes ====
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


==== seas ====

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
  * Mediterranean
  * Bay of Bengal
  * Arabian Sea
  * Carribean

==== oceans ====

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


===== Political data ======

pulled from database voyc, table fpd
  * empire - polygon
  * treasure - point

==== Cities ====

^ 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 |