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projects:voyc:data_analysis

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
projects/voyc/data_analysis.txt · Last modified: 2023/03/13 03:33 by jhagstrand

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