Transcript No Slide Title

c-squares - a new method for
representing, querying, displaying
and exchanging dataset spatial
extents
System concept and development by:
Tony Rees
Divisional Data Centre
CSIRO Marine Research, Australia
some example Metadatabases (Data Directories)
+ many others -- 100 < 1000? ...
Typical features: include searchability by
• text
• keywords
• spatial and time constraints
This presentation - focus on spatial searching
current “base level” representation of spatial data coverage in
metadata is by bounding box (minimum bounding rectangle, MBR)
• concept introduced in 1994 (FGDC)
• used for spatial searching, 1995 onwards
• still the primary tool for metadata spatial searches
--------- data
----- data bounding rectangle (MBR)
--------- search rectangle
How well do MBR’s represent spatial data?
(examples from our own metadata system)
MBR
actual data locations
Franklin 02/1999
hydrology data
SRTM 8-449 catch data
Catch records Hoplostethus atlanticus
alternatives to MBR’s for representation of data spatial extents ...
• bounding polygons
• multiple bounding rectangles
• defined regions - countries, administrative areas, bio- or
geo-regions …
• circles (centre point + radius)
• pre-defined path + distance (e.g. along a contour,
coastline, satellite path)
• actual point locations held in the metadata record
• grid-based system
global grid systems already available ...
• International Map of the World (IMW) rectangles (6 x 4
degrees)
• Marsden Squares (10 x 10 degrees)
• Maidenhead Squares (2 x 1 degree)
• WMO (World Meteorological Organisation) Squares (10 x 10
degrees)
• others ?
-- WMO squares eventually chosen for ease of subdivision
(base 10) and simple relationship between WMO numbers and
lat/long values
WMO 10-degree squares notation (part)
1400
3414
(Available via the web in NODC, 1998: World Ocean Database 1998 Documentation)
The “c-squares”
concept
c-squares:
Concise Spatial Query and
Representation System
“c-squares” principle
actual ship’s track - “Franklin” voyage 10/87
data “footprint” using 1 x 1 degree csquares
data “footprint” using
bounding rectangle
same using 0.5 x 0.5 degree csquares
“c-squares” numbering system
• each square is numbered according to a globally applicable system based
on recursive divisions of WMO (World Meteorological organisation) 10degree squares, e.g.:
10 degree square: 3414 (= WMO number)
5 degree square:
3414:2
1 degree square:
3414:227
0.5 degree square:
3414:227:4
0.1 degree square:
3414:227:466
(etc.)
• strings of codes represent an individual dataset extent, e.g.
3013:497|3111:468|3111:478|3111:479|3111:488|3111:489|3111:499|3112:122|3112:123|
3112:131|3112:132|3112:134|3112:141|3112:142|3112:143|3112:217|3112:218|3112:219|
3112:226|3112:235|3112:350|3112:351|3112:352|3112:353|3112:360|3112:361|3112:362|
3112:363|3112:370|3112:371|3112:380|3112:381|3112:390|3113:100|3113:101|3113:102|
3113:103|3113:104|3113:205|3113:206|3113:207|3113:216|3113:217|3113:228|3113:238|
3113:239
encodes the extent
shown in the example:
Codes have straightforward relationship with lats/longs, mapsheets, etc. ...
e.g.:
1400:458
(1-degree square with origin at
46
45 º N, 008 º E)
additional degrees E [00+8] =8
additional degrees N [40+5] = 45
45
5-degree quadrant, i.e.
3 4
1 2
tens of degrees E (i.e., 00)
tens of degrees N (i.e., 40)
global sector (1=NE, 3=SE, 5=SW, 7=NW)
44
110 km
8
9
0.5- and 0.1- degree squares
10
“quad tree” -type approach used where numerous adjacent
squares are occupied
squares can be “bulked” - example: 3212:*** instead of specifying
every 1-degree square within 10 degree square 3212.
This leads to corresponding data reduction, e.g. Australia (at 1degree resolution) can be described in 343 squares rather than 800:
Example database-level implementation of c-squares for
metadata records (e.g. at 1 degree resolution)
(etc.)
Spatial queries using c-squares
• c-squares spatial queries simply test whether a text string representing the
search box (ideally one or several c-squares) is matched anywhere in the csquares string …
example: - search square 3113:2 will match any c-squares string which
includes 3113:2 within it, e.g.:
<csquares>
3112:363|3112:370|3112:371|3112:380|3112:381|3112:390|3113:100|3113:101|3113:102|
3113:103|3113:104|3113:205|3113:206|3113:207|3113:216|3113:217|3113:228|3113:238|
3113:239
</csquares>
hierarchical naming system for c-squares means that finer resolution squares are
automatically picked up in any “coarser resolution” search
example
search
result ...
(etc.)
Viewing the full metadata record produces ...
(etc.)
with clickable link to show
dataset extent using c-squares:
Base maps for displayed data can be changed at will by the user, e.g.:
(numerous other maps
available, sample only shown)
Process invoked for web mapping
c-squares strings can be sent directly to the CMR c-squares mapper (accessible
via the web), e.g. from OBIS (Ocean Biogeographic Information System, USA):
<form action = "http://www.marine.csiro.au/cgi-bin/cs_map.pl" method="post">
<INPUT TYPE="hidden" NAME="csq" VALUE="3215:459:4|3215:459:3|3215:459:4|(etc.)">
<INPUT TYPE="hidden" NAME="title" VALUE="Global Distribution of <i>Raja</i>">
<INPUT TYPE="submit" NAME="submit" VALUE="make map ...">
</form>
c-squares strings are suitable for inclusion as a new metadata element
alongside “bounding box”, for example ...
<metadata>
<title>Franklin Voyage FR 10/87 CTD Data</title>
<custodianOrg>CSIRO Marine Research</custodianOrg>
(etc. etc.)
<boundingBox>
<northBoundingCoord>-9.0</northBoundingCoord>
<southBoundingCoord>-19.0</southBoundingCoord>
<westBoundingCoord>117.0</westBoundingCoord>
<eastBoundingCoord>145.8</eastBoundingCoord>
</boundingBox>
<csquares>3111:499:2|3112:390:1|3111:489:3|3112:380:3|3112:380:4|3112:381:1|3111:488:2|3112:381:2|
3112:371:3|3111:478:4|3112:370:4|3112:370:1|3111:478:1|3111:479:2|3111:479:1|3112:361:4|3111:468:4|311
2:363:3|3112:361:3|3111:467:2|3112:360:2|3112:363:1|3112:362:2|3112:360:1|3112:352:4|3112:352:3|3112:3
50:4|3112:352:1|3112:351:2|3112:352:2|3112:353:2|3112:353:1</csquares>
(etc.)
… would permit interoperability with both enabled and non-enabled systems
Summary - strengths and weaknesses of c-squares
Strengths ...
• “c-squares” is a concise and flexible method of encoding simple to
moderately complex forms
• automated or manual code entry (and maintenance) is straightforward
• spatial searching is simple text string matching operation (no GIS
involved)
• “c-squares mapper” utility available via simple web call
• can be used as adjunct to bounding coordinates searches
Weaknesses …
• some other numbering systems in use (Marsden Squares, Maidenhead
Locators) - needs willingness to standardise on a single system for
interoperability
• c-squares are not a fixed multiple of kilometres, miles, etc.
• strings can become quite long for large, complex regions (e.g. “Pacific
Ocean”) - need to be able to incorporate data reduction using “bulk”
method
other comments ...
• “c-squares” notation is language-independent - can be
equally useful in English, French, Italian, Japanese … also
discipline-independent
• downwards-scalability of the c-squares notation means
that it can be applied to any size region (e.g. local level)
• equally applicable to both terrestrial and marine data
• uses established standards for nomenclature, basis
already available via the web (e.g. NODC site)
c-squares current and future status...
• Implemented already in CMR’s “MarLIN” metadata system and “CAAB”
taxon dictionary
• concept is available for implementation in any other agencies’ metadata
systems without cost or technology overhead
• potential to to be recognised as a formal metadata element by relevant
user communities / national bodies
• current CMR c-squares mapper is already accessible for general use
• c-squares website constructed as a focal point for all c-squares related
materials - including:
• initial c-squares specification
• connection information to the c-squares mapper
• sample PL/SQL code (to convert lat/long pairs to c-squares)
• on-line lat/long - to - c-square converter
• example c-squares-enabled metadata records, and more
Acknowledgements …
• Miroslaw Ryba and other CMR staff for assistance with constructing the c-squares
mapper and general feedback
• “Blue Pages” Marine and Coastal Data Directory (MCDD) for the notation for
subdividing WMO squares
• Martin Dix (CSIRO Atmospheric Research) and NOAA “Globe” Project for base maps
as used in the mapper (used by permission)
Questions, comments?
website: http://www.marine.csiro.au/csquares/
(NB: handout available at this meeting)
My email: [email protected]