[Benchmarking] Styling gnis_pop
Andrea Aime
aaime at opengeo.org
Thu Sep 3 05:21:17 EDT 2009
Hi,
the point layer we have on the benchmarking server is gnis_pop.
I've loaded it up on my local postgis to see what the styling potential
is.
Its structure, as reported by postgis, is:
gid | integer | not null default
fid | bigint |
name | character varying(100) |
class | character varying(21) |
state | character varying(2) |
county | character varying(29) |
elevation | bigint |
map | character varying(30) |
the_geom | geometry |
The class is interesting, it contains quite a number of
different point types, 64 to be precise.
The distribution is as follows:
select class, count(*) as cnt from gnis_names_pg group by class order by cnt
class | cnt
-----------------------+--------
Sea | 11
Isthmus | 18
Geyser | 118
Lava | 169
Unknown | 183
Crater | 239
Plain | 283
Slope | 361
Arch | 445
Arroyo | 456
Levee | 540
Woods | 643
Harbor | 652
Tunnel | 697
Bench | 723
Rapids | 1089
Glacier | 1090
Reserve | 1262
Forest | 1287
Military (Historical) | 1772
Pillar | 2030
Area | 2321
Beach | 2322
Range | 2395
Falls | 2449
Bend | 2783
Channel | 3911
Basin | 4267
Cliff | 4395
Gut | 4542
Oilfield | 4858
Bar | 5713
Bridge | 6213
Swamp | 7386
Gap | 8362
Crossing | 10088
Flat | 10471
Trail | 10537
Hospital | 11958
Bay | 12732
Ridge | 14946
Cape | 16108
Tower | 16656
Island | 18484
Airport | 19434
Canal | 20570
Post Office | 25012
Mine | 32915
Spring | 35847
Well | 38232
Civil | 38237
Dam | 56929
Park | 62308
Building | 65132
Lake | 68012
Summit | 70223
Valley | 71857
Reservoir | 75260
Cemetery | 126167
Locale | 132971
Populated Place | 180279
School | 181041
Church | 188695
Stream | 241440
I guess we could take some 10 of the most popular
classes and have them symbolized with a png or
ttf marker and have a label drawn below it.
How does this sound? With some varied zoom
levels it should exercise also label conflict
detection.
The inherent filtering would make it evident
what system can do to filter data (I hear that
ESRI can index the DBF attributes for example).
Database wise we would add an index on the
"class" column.
Oh, the data set is complete USA wise and contains
almost 2 million points, to make it comparable with
the roads layers maybe we should filter out only
the points in Texas? That would leave us with a much
smaller data set, only 95k points and the following
distribution:
class | cnt
-----------------------+-------
Crater | 1
Bench | 1
Slope | 2
Tunnel | 2
Arch | 5
Rapids | 5
Plain | 6
Forest | 9
Reserve | 11
Woods | 17
Arroyo | 19
Harbor | 19
Pillar | 21
Beach | 27
Area | 27
Falls | 29
Bar | 55
Mine | 56
Post Office | 57
Crossing | 61
Range | 63
Basin | 65
Military (Historical) | 67
Levee | 87
Ridge | 110
Bridge | 130
Channel | 146
Gap | 157
Flat | 180
Cliff | 185
Swamp | 231
Gut | 259
Bend | 259
Island | 261
Civil | 262
Cape | 279
Bay | 283
Canal | 299
Trail | 504
Hospital | 579
Well | 942
Tower | 1052
Spring | 1243
Oilfield | 1294
Airport | 1757
Lake | 1780
Summit | 2117
Valley | 2844
Building | 3795
Park | 4008
Dam | 5947
Cemetery | 6016
Locale | 7980
Populated Place | 8511
Reservoir | 8542
School | 8756
Stream | 11640
Church | 12072
Cheers
Andrea
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