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K.3 The long and winding road

The WVS and WDB together represent more than 100 Mb of binary data and something like 20 million data points. Hence, it becomes obvious that any manipulation of these data must be automated. For instance, the reasonable requirement that no coastline should cross another coastline becomes a complicated processing step.

  1. To begin, we first made sure that all data were ``clean'', i.e. that there were no outliers and bad points. We had to write several programs to ensure data consistency and remove ``spikes'' and bad points from the raw data. Also, crossing segments were automatically ``trimmed'' provided only a few points had to be deleted. A few hundred more complicated cases had to be examined semi-manually.

  2. Programs were written to examine all the loose segments and determine which segments should be joined to produce polygons. Because not all segments joined exactly (there were non-zero gaps between some segments) we had to find all possible combinations and choose the simplest combinations. The WVS segments joined to produce more than 200,000 polygons, the largest being the Africa-Eurasia polygon which has 1.4 million points. The WDB data resulted in a smaller data base ($\sim$25% of WVS).

  3. We now needed to combine the WVS and WDB data bases. The main problem here is that we have duplicates of polygons: most of the features in WVS are also in WDB. However, because the resolution of the data differ it is nontrivial to figure out which polygons in WDB to include and which ones to ignore. We used two techniques to address this problem. First, we looked for crossovers between all possible pairs of polygons. Because of the crossover processing in step 1 above we know that there are no remaining crossovers within WVS and WDB; thus any crossovers would be between WVS and WDB polygons. Crossovers could mean two things: (1) A slightly misplaced WDB polygon crosses a more accurate WVS polygon, both representing the same geographic feature, or (2) a misplaced WDB polygon (e.g. a small coastal lake) crosses the accurate WVS shoreline. We distinguished between these cases by comparing the area and centroid of the two polygons. In almost all cases it was obvious when we had duplicates; a few cases had to be checked manually. Second, on many occasions the WDB duplicate polygon did not cross its WVS counterpart but was either entirely inside or outside the WVS polygon. In those cases we relied on the area-centroid tests.

  4. While the largest polygons were easy to identify by visual inspection, the majority remain unidentified. Since it is important to know whether a polygon is a continent or a small pond inside an island inside a lake we wrote programs that would determine the hierarchical level of each polygon. Here, level = 1 represents ocean/land boundaries, 2 is land/lakes borders, 3 is lakes/islands-in-lakes, and 4 is islands-in-lakes/ponds-in-islands-in-lakes. Level 4 was the highest level encountered in the data. To automatically determine the hierarchical levels we wrote programs that would compare all possible pairs of polygons and find how many polygons a given polygon was inside. Because of the size and number of the polygons such programs would typically run for 3 days on a Sparc-2 workstation.

  5. Once we know what type a polygon is we can enforce a common ``orientation'' for all polygons. We arranged them so that when you move along a polygon from beginning to end, your left hand is pointing toward ``land''. At this step we also computed the area of all polygons since we would like the option to plot only features that are bigger than a minimum area to be specified by the user.

  6. Obviously, if you need to make a map of Denmark then you do not want to read the entire 1.4 million points making up the Africa-Eurasia polygon. Furthermore, most plotting devices will not let you paint and fill a polygon of that size due to memory restrictions. Hence, we need to partition the polygons so that smaller subsets can be accessed rapidly. Likewise, if you want to plot a world map on a letter-size paper there is no need to plot 10 million data points as most of them will plot several times on the same pixel and the operation would take a very long time to complete. We chose to make 5 versions on the database, corresponding to different resolutions. The decimation was carried out using the Douglas-Peucker (DP) line-reduction algorithmK.2. We chose the cutoffs so that each subset was approximately 20% the size of the next higher resolution. The five resolutions are called full, high, intermediate, low, and crude; they are accessed in pscoast, gmtselect, and grdlandmask with the -D optionK.3. For each of these 5 data sets (f, h, i, l, c) we specified an equidistant grid (1$^{o}$, 2$^{o}$, 5$^{o}$, 10$^{o}$, 20$^{o}$) and split all polygons into line-segments that each fit inside one of the many boxes defined by these grid lines. Thus, to paint the entire continent of Australia we instead paint many smaller polygons made up of these line segments and gridlines. Some book-keeping has to be done since we need to know which parent polygon these smaller pieces came from in order to prescribe the correct paint or ignore if the feature is smaller than the cutoff specified by the user. The resulting segment coordinates were then scaled to fit in short integer format to preserve precision and written in netCDF format for ultimate portability across hardware platformsK.4.

  7. While we are now back to a file of line-segments we are in a much better position to create smaller polygons for painting. Two problems must be overcome to correctly paint an area:

    GMT uses a polygon-assembly routine that carries out these tasks on the fly.

next up previous contents index
Next: K.4 The Five Resolutions Up: K. The GMT High-Resolution Previous: K.2 Format required by   Contents   Index
Paul Wessel 2004-10-01