Tuesday, 12 February 2019

Better and Faster Interior Point for Polygons in JTS/GEOS

A humble workhorse of geospatial processing is the ability to compute a point which is guaranteed to lie in the interior of a polygon.  In the OGC Simple Features for SQL specification (and hence in PostGIS) this is known as ST_PointOnSurface.  In JTS/GEOS it is called getInteriorPoint, for historical reasons [1].

Interior points for country boundaries

There are some important use cases for this capability:
  • Constructing a "proxy point" for a polygon to use in drill-down spatial query.  This has all kinds of applications:
  • Cartographic rendering including:
    • Creating leader lines
    • Placing labels for polygons (for which it is a quick solution but not necessarily a quality one.  More on this in a later post)
There is a variety of ways that have been proposed to compute interior points: triangulation, sampling via random or grid points, medial axis transform, etc.  These all involve trade-offs between location quality and performance [2].  JTS uses an approach which optimizes performance, by using a simple scan-line algorithm:

JTS Scan-Line Interior Point Algorithm
  1. Determine a Y-ordinate which is distinct to every polygon vertex Y-ordinate and close to the centre of the vertical extent
  2. Draw a scan line across the polygon and determine the segments of intersection
  3. Choose the interior point as the midpoint of the widest intersection segment
Locating a polygon interior point along a scan-line

The current code has been in the JTS codebase since the release of the very first version back in 2001.  It is elegantly simple, but is quite non-optimal, since it uses the overlay intersection algorithm.  This is overkill for the computation of the scan-line intersection segments.  It also has a couple of serious drawbacks: slow performance, and the requirement that the input polygon be valid.  These are not just theoretical concerns.  They have been noticed in the user community, and have caused client projects to have to resort to awkward workarounds.  It's even documented as a known limitation in PostGIS.

Thanks to Crunchy Data recognizing the importance of geospatial, we've been able to look into fixing this.  It turns out that a relatively simple change makes a big improvement.  The scan-line intersections can be computed via a linear-time scan of the polygon edges.  This works even for invalid input (except for a few pathological situations).
Interior points of invalid polygons
(LH invalid polygon shows suboptimal point placement)

Best of all, it's much faster - providing performance comparable to the (less useful) centroid computation.  The performance results speak for themselves:

Dataset # polys # points Time Prev
time
Improvement
World countries
244
366,951 25 ms 686 ms
x 27
Land Cover 64,090 366,951 78 ms 6.35 s
x 81

This has been committed to JTS.  It will be ported to GEOS soon, and from there should show up in PostGIS (and other downstream projects like QGIS, Shapely, GDAL, etc etc).

More Ideas

Some further improvements that could be investigated:
  • Use the centroid to provide the Y-ordinate.  This is probably better in some situations, and worse in others.  But perhaps there's a fast way to choose the best one?
  • Use multiple scan-lines (both vertical and horizontal)
  • Provide better handling of short/zero-width scan-line intersections
  • Support clipping the interior point to a rectangle.  This would provide better results for cartographic labelling



[1] JTS was based on the original OGC SFS specification (version 1.1).  The spec actually does include a method Surface.pointOnSurface.  The reason for the different choice of name is lost to time, but possibly the reasoning was that the JTS function is more general, since it handles all types of geometry.  (One of the design principles of JTS is Geometric Uniformity, AKA No Surprises.  Wherever possible spatial operations are generalized to apply to all geometric types. There is almost always a sensible generalization that can be defined, and it's often quite useful.)

[1a] Also, the acronym IPA is much better than the one for PointOnSurface.

[2] Apparently Oracle has decided to provide blazingly fast performance by simply returning a point on the boundary of the polygon.  Thus proving the maxim that for every problem there is an solution which is simple, fast, and dead wrong.




Saturday, 2 February 2019

Joining Crunchy Data to work on PostGIS

I'm happy to announce that I am taking a position with Crunchy Data as a Senior Geospatial Engineer.  I'm working alongside fellow Victorian geospatial maven extraordinaire Paul Ramsey, as the core of a proposed Geospatial Data Centre of Excellence.

Our mission statement is simple:  make PostGIS bigger, better, faster!

  • Bigger - more spatial algorithms and functions
  • Better - enhance existing functionality to make it easier to use, more powerful, and more robust
  • Faster - keep looking for algorithmic optimizations and ways to use the power of Postgres to make spatial processing faster

A lot of this work will involve enhancements to the core GEOS geometry library.  Part of the goal is to keep JTS and GEOS aligned, so this should produce a nice boost to JTS as well.

Having been lurking in the background for many years now, I'm stoked to be (finally) able to work directly on PostGIS.  And I'm excited to be part of the Crunchy team. They have some of the leading Postgres experts in-house, so I'm expecting that it will be a great learning experience.  And their client list promises to expose us to some fascinating large-scale use cases for spatial data processing, which can only be good for the power and robustness of PostGIS.

I'm also looking forward to re-engaging with the geospatial open source community, and learning more about the (even bigger) open source Postgres community.  Great things lie ahead!



Monday, 28 January 2019

Hilbert and Morton Curves in JTS

I just landed a JTS pull request for Hilbert and Morton (Z-order) codes and curves.
Hilbert Curve of level 3
Morton Curve of level 3

Apart from pretty pictures of fractals, the goal is to support experimenting with Packed Hilbert R-trees, as an alternative to the current Sort-Tile-Recursive packing strategy (implemented as STRtree in JTS).  STRtrees are heavily used to speed up spatial algorithms inside JTS (and externally as per recent report). So if Hilbert curve-based packing provides better performance that would be a big win.


Thursday, 17 January 2019

Fun wit JEQL: Hilbert Curves

Hilbert Curve of order 4:


Hilbert Curve of order 6:
Code is in the JEQL script repo.

import jeql.std.function.HashFunction;

hilbertOrder = 6;
side = Val.toInt( Math.pow(2, hilbertOrder) );
count = side * side;

radius = 1;

t = select * from Generate.sequence( 0, count-2 );

t = select i, geom: Geom.buffer(hilbertEdge, 0.4)
let 
hilbertPt1 = HashFunction.hilbertPoint(hilbertOrder, i),
hilbertPt2 = HashFunction.hilbertPoint(hilbertOrder, i+1),
hilbertEdge = Geom.createLineFromPoints( hilbertPt1, hilbertPt2 )
from t;

t1 = select *,
styleFill: clr, styleStroke: clr, styleStrokeWidth: 1
let
clr = Color.toRGBfromHSV(Val.toDouble(i) / count, 1, 1)
from t;
Mem t1;

The function hilbertPoint uses the efficient algorithm from http://threadlocalmutex.com/.  Code is on Github.