Sunday, 24 July 2022

Polygonal Coverages and Operations in JTS

An important concept in spatial data modelling is that of a coverage.  A coverage models a two-dimensional region in which every point has a value out of a range (which may be defined over one or a set of attributes).  Coverages can be represented in both of the main physical spatial data models: raster and vector.  In the raster data model a coverage is represented by a grid of cells with varying values.  In the vector data model a coverage is a set of non-overlapping polygons (which usually, but not always, cover a contiguous area).  

This post is about the vector data coverage model, which is termed (naturally) a polygonal coverage. These are used to model regions which are occupied by discrete sub-regions with varying sets of attribute values.  The sub-regions are modelled by simple polygons.  The coverage may contain gaps between polygons, and may cover multiple disjoint areas.  The essential characteristics are:

  • polygon interiors do not overlap
  • the common boundary of adjacent polygons has the same set of vertices in both polygons.

There are many types of data which are naturally modelled by polygonal coverages.  Classic examples include:

  • Man-made boundaries
    • parcel fabrics
    • political jurisdictions
  • Natural boundaries
    • vegetation cover
    • land use
A polygonal coverage of regions of France

Topological and Discrete Polygonal Coverages

There are two ways to represent polygonal coverages: as a topological data structure, or as a set of discrete polygons.  
A coverage topology consists of linked faces, edges and nodes. The edges between two nodes form the shared boundary between two faces.  The coverage polygons can be reconstituted from the edges delimiting each face.  
The discrete polygon representation is simpler, and aligns better with the OGC Simple Features model.  It is simply a collection of polygons which satisfy the coverage validity criteria given above.
Most common spatial data formats support only a discrete polygon model, and many coverage datasets are provided in this form.  However, the lack of inherent topology means that datasets must be carefully constructed to ensure they have valid coverage topology.  In fact, many available datasets contain coverage invalidities.  A current focus of JTS development is to provide algorithms to detect this situation and provide the locations where the polygons fail to form a valid coverage.

Polygonal Coverage Operations

Operations which can be performed on polygonal coverages include:

  • Validation - check that a set of discrete polygons forms a valid coverage
  • Gap Detection - check if a polygonal coverage contains narrow gaps (using a given distance tolerance)
  • Cleaning - fix errors such as gaps, overlaps and slivers in a polygonal dataset to ensure that it forms a clean, valid coverage
  • Simplification - simplify (generalize) polygon boundary linework, ensuring coverage topology is preserved
  • Precision Reduction - reduce precision of polygon coordinates, ensuring coverage topology is preserved
  • Union - merge all or portions of the coverage polygons into a single polygon (or multipolygon, if the input contains disjoint regions)
  • Overlay - compute the intersection of two coverages, producing a coverage of resultant polygons 

Implementing polygonal coverage operations is a current focus for development in the JTS Topology Suite.  Since most operations require a valid coverage as input, the first goal is to provide Coverage Validation.  Cleaning and Simplification are priority targets as well.  Coverage Union is already available, as is Overlay (in a slightly sub-optimal way).  In addition,  a Topology data structure will be provided to support the edge-node representation.  (Yes, the Topology Suite will provide topology at last!).  Stay tuned for further blog posts as functionality is rolled out.

As usual, the coverage algorithms developed in JTS will be ported to GEOS, and will thus be available to downstream projects like PostGIS.

Tuesday, 21 June 2022

JTS 1.19 Released

JTS 1.19 has just been released!  There is a great deal of new, improved and fixed functionality in this release - see the GitHub release page or the Version History for full details.

This blog has several posts describing new functionality in JTS 1.19:

New Functionality


Many of these improvements have been ported to GEOS, and will appear in the soon-to-appear version 3.11.  In turn this has provided the basis for new and enhanced functions in the next PostGIS release, and will likely be available in other platforms via the many GEOS bindings and applications.

Monday, 30 May 2022

Algorithm for Concave Hull of Polygons

The previous post introduced the new ConcaveHullOfPolygons class in the JTS Topology Suite.  This allows computing a concave hull which is constrained by a set of polygonal geometries.  This supports use cases including:

  • generalization of groups of polygon
  • joining polygons
  • filling gaps between polygons

A concave hull of complex polygons

The algorithm developed for ConcaveHullOfPolygons is a novel one (as far as I know).  It uses several features recently developed for JTS, including a neat trick for constrained triangulation.  This post describes the algorithm in detail.

The construction of a concave hull for a set of polygons uses the same approach as the existing JTS ConcaveHull implementation.  The space to be filled by a concave hull is triangulated with a Delaunay triangulation.  Triangles are then "eroded" from the outside of the triangulation, until a criteria for termination is achieved.  A useful termination criteria is that of maximum outside edge length, specified as either an absolute length or a fraction of the range of edge lengths.

For a concave hull of points, the underlying triangulation is easily obtained via the Delaunay Triangulation of the point set.  However, for a concave hull of polygons the triangulation required is for the space between the constraint polygons.  A simple Delaunay triangulation of the polygon vertices will not suffice, because the triangulation may not respect the edges of the polygons.  

Delaunay Triangulation of polygon vertices crosses polygon edges

What is needed is a Constrained Delaunay Triangulation, with the edge segments of the polygons as constraints (i.e. the polygon edge segments are present as triangle edges, which ensures that other edges in the triangulation do not cross them).  There are several algorithms for Constrained Delaunay Triangulations - but a simpler alternative presented itself.  JTS recently added an algorithm for computing Delaunay Triangulations for polygons.  This algorithm supports triangulating polygons with holes (via hole joining).  So to generate a triangulation of the space between the input polygons, they can be inserted as holes in a larger "frame" polygon.  This can be triangulated, and then the frame triangles removed. Given a sufficiently large frame, this leaves the triangulation of the "fill" space between the polygons, out to their convex hull. 

Triangulation of frame with polygons as holes

The triangulation can then be eroded using similar logic to the non-constrained Concave Hull algorithm.  The implementations all use the JTS Tri data structure, so it is easy and efficient to share the triangulation model between them. 

Triangulation after removing frame and eroding triangles

The triangles that remain after erosion can be combined with the input polygons to provide the result concave hull.  The triangulation and the input polygons form a polygonal coverage, so the union can be computed very efficiently using the JTS CoverageUnion class.  If required, the fill area alone can be returned as a result, simply by omitting the input polygons from the union.

Concave Hull and Concave Fill

A useful option is to compute a "tight" concave hull to the outer boundary of the input polygons.  This is easily accomplished by removing triangles which touch only a single polygon.  

Concave Hull tight to outer edges

Concave Hull of complex polygons, tight to outer edges.

Like the Concave Hull of Points algorithm, holes are easily supported by allowing erosion of interior triangles.

Concave Hull of Polygons, allowing holes

The algorithm performance is determined by the cost of the initial polygon triangulation.  This is quite efficient, so the overall performance is very good.

As mentioned, this seems to be a new approach to this geometric problem.  The only comparable implementation I have found is the ArcGIS tool called Aggregate Polygons, which appears to provide similar functionality (including producing a tight outer boundary).  But of course algorithm details are not published and the code is not available.  It's much better to have an open source implementation, so it can be used in spatial tools like PostGIS, Shapely and QGIS (based on the port to GEOS).  Also, this provides the ability to add options and enhanced functionality for use cases which may emerge once this gets some real-world use.

Friday, 20 May 2022

Concave Hulls of Polygons

A common spatial need is to compute a polygon which contains another set of polygons.  There are numerous use cases for this; for example:

  • Generalizing groups of building outlines (questions: 1, 2
  • Creating "district" polygons around block polygons (questions: 1)
  • Removing gaps between sets of polygons (questions: 1, 2, 3, 4)
  • Joining two polygons by filling the space between them (questions: 1, 2)     
This post describes a new approach for solving these problems, via an algorithm for computing a concave hull with polygonal constraints.  The algorithm builds on recent work on polygon triangulation in JTS, and uses a neat trick which I'll describe in a subsequent post.  

Approach: Convex Hull

The simplest way to compute an area enclosing a set of geometries is to compute their convex hull.  But the convex hull is a fairly coarse approximation of the area occupied by the polygons, and in most cases a better representation is required.  

Here's an example of gap removal between two polygons.  Obviously, the convex hull does not provide anything close to the desired result: 

Approach: Buffer and Unbuffer

A popular suggestion is to buffer the polygon set by a distance sufficient to "bridge the gaps", and then "un-buffer" the result inwards by the same (negative) distance.  But the buffer computation can "round off" corners, which usually produces a poor match to the input polygons.  It also fills in the outer boundary of the original polygons.

Approach: Concave Hull of Points

A more sophisticated approach is to use a concave hull algorithm.  But most (or all?) available concave hull algorithms use points as the input constraints. The vertices of the polygons could be used as the constraint points, but since the polygon boundaries are not respected, the computed hull may cross the polygon edges and hence not cover the polygons.  

Densifying the polygon boundaries helps, but introduces another problem - the computed hull can extend beyond the outer boundaries of individual polygons.  And it introduces new vertices not present in the original data.

Solution: Concave Hull of Polygons

What is needed is a concave hull algorithm that accepts polygons as constraints, and thus respects their boundaries.  The JTS Topology Suite now provides this capability in a class called ConcaveHullOfPolygons (not a cute name, but descriptive).  It provides exactly the solution desired for the gap removal example:  

The Concave Hull of Polygons API

Like concave hulls of point sets, concave hulls of polygons form a sequence of hulls, with the amount of concaveness determined by a numeric parameter. ConcaveHullOfPolygons uses the same parameters as the JTS ConcaveHull algorithm.  The control parameter determines the maximum line length in the triangulation underlying the hull.  This can be specified as an absolute length, or as a ratio between the longest and shortest lines.  

Further options are:

  • The computed hull can be kept "tight" to the outer boundaries of the individual polygons.  This allows filling gaps between polygons without distorting their original outer boundaries.  Otherwise, the concaveness of the outer boundary will be decreased to match the distance parameter specified (which may be desirable in some situations).
  • Holes can be allowed to be present in the computed hull
  • Instead of the hull, the fill area between the input polygons can be computed.  
As usual, this code will be ported to GEOS, and from there it can be exposed in the downstream libraries and projects.

Examples of Concave Hulls of Polygons

Here are examples of using ConcaveHullOfPolygons for the use cases above:

Example: Generalizing Building Groups

Using the "tight" option allows following the outer building outlines.

Example: Aggregating Block Polygons

The concave hull of a set of block polygons for an oceanside suburb.  Note how the "tight" option allows the hull to follow the convoluted, fine-grained coastline on the right side.

Example: Removing Gaps to Merge Polygons

Polygons separated by a narrow gap can be merged by computing their concave hull using a small distance and keeping the boundary tight.

Example: Fill Area Between Polygons

The "fill area" portion of the hull between two polygons can be computed as a separate polygon. This could be used to provide an "Extend to Meet" construction by unioning the fill polygon with one of the input polygons.  It can also be used to determine the "visible boundary", provided by the intersection of the fill polygon with the input polygon(s).

Wednesday, 4 May 2022

Using Outer Hulls for Smoothing Vectorized Polygons

The electrons were hardly dry on the JTS Outer and Inner Polygon Hull post when another interesting use case popped up on GIS StackExchange.  The question was how to remove aliasing artifacts (AKA "jaggies") from polygons created by vectorizing raster data, with the condition that the result should contain the original polygon.  

A polygon for Vancouver Island vectorized from a coarse raster dataset.  Aliasing artifacts are obvious.

This problem is often handled by applying a simplification or smoothing process to the "jaggy" polygon boundary.  This works, as long as the process preserves polygonal topology (e.g. such as the JTS TopologyPreservingSimplifier). But generally this output of this process does not contain the input polygon, since the simplification/smoothing can alter the boundary inwards as well as outwards.  

Simplification using TopologyPreservingSimplifier with distance = 0.1.  Artifacts are removed, but the simplified polygon does not fully contain the original.

In contrast, the JTS Polygon Outer Hull algorithm is designed to do exactly what is required: it reduces the number of vertices, while guaranteeing that the input polygon is contained in the result.  It is essentially a simplification method which also preserves polygonal topology (using an area-based approach similar to the Visvalingham-Whyatt algorithm).

Outer Hull using vertex ratio = 0.25.  Artifacts are removed, and the original polygon is contained in the hull polygon.

Here's a real-world example, taken from the GADM dataset for administrative areas of Germany.  The coastline of the state of Mecklenburg-Vorpommern appears to have been derived from a raster, and thus exhibits aliasing artifacts.  Computing the outer hull with a fairly conservative parameter eliminates most of the artifacts, and ensures polygonal topology is preserved.

A portion of the coastline of Mecklenburg-Vorpommern showing aliasing artifacts.  The Outer Hull computed with vertex ratio = 0.25 eliminates most artifacts, and preserves the coastline topology.

Future Work

A potential issue for using Outer Hull as a smoothing technique is the choice of parameter value controlling the amount of change.  The algorithm provides two options: the ratio of reduction in the number of vertices, or the fraction of change in area allowed.  Both of these are scale-independent, and reflect natural goals for controlling simplification.  But neither relate directly to the goal of removing "stairstep" artifacts along the boundary.  This might be better specified via a distance-based parameter. The parameter value could then be determined based on the known artifact size (i.e. the resolution of the underlying grid).  Since the algorithm for Outer Hull is quite flexible, this should be feasible to implement. 

Monday, 25 April 2022

Outer and Inner Concave Polygon Hulls in JTS

The JTS Topology Suite recently gained the ability to compute concave hulls.  The Concave Hull algorithm computes a polygon enclosing a set of points using a parameter to determine the "tightness".  However, for polygonal inputs the computed concave hull does not always respect the polygon boundaries.  So the concave hull may not actually contain the input polygon.

It would be useful to be able to compute the "outer concave hull" of a polygon.  This is a valid polygon formed by a subset of the vertices of the input polygon which fully contains the input polygon.  Vertices can be eliminated as long as the resulting boundary does not self-intersect, and does not cross into the original polygon.

An outer concave hull of a polygon representing Switzerland

As with point-set concave hulls, the vertex reduction is controlled by a numeric parameter. This creates a sequence of hulls of increasingly larger area with smaller vertex counts.  At an extreme value of the parameter, the outer hull is the same as the convex hull of the input.
A sequence of outer hulls of New Zealand's North Island

The outer hull concept extends to handle holes and MultiPolygons.  In all cases the hull boundaries are constructed so that they do not cross each other, thus ensuring the validity of the result.

An outer hull of a MultiPolygon for the coast of Denmark.  The hull polygons do not cross.

It's also possible to construct inner hulls of polygons, where the constructed hull is fully within the original polygon.

An inner hull of Switzerland

Inner hulls also support holes and MultiPolygons.  At an extreme value of the control parameter, holes become convex hulls, and a polygon shell reduces to a triangle (unless blocked by the presence of holes).

An inner hull of a lake with islands.  The island holes become convex hulls, and prevent the outer shell from reducing fully to a triangle

A hull can provide a significant reduction in the vertex size of a polygon for a minimal change in area. This could allow faster evaluation of spatial predicates, by pre-filtering with smaller hulls of polygons.

An outer hull of Brazil provides a 10x reduction in vertex size, with only ~1% change in area.

This has been on the JTS To-Do list for a while (I first proposed it back in 2009).  At that time it was presented as a way of simplifying polygonal geometry. Of course JTS has had the TopologyPreservingSimplifier for many years.  But it doesn't compute a strictly outer hull.  Also, it's based on Douglas-Peucker simplification, which isn't ideal for polygons.  

It seems there's quite a need for this functionality, as shown in these GIS-StackExchange posts (1, 2, 3, 4).  There's even existing implementations on Github: rdp-expansion-only and simplipy (both in Python) - but both of these sound like they have some significant issues. 

Recent JTS R&D (on concave hulls and polygon triangulation) has provided the basis for an effective, performant polygonal concave hull algorithm.  This is now released as the PolygonHull class in JTS.

The PolygonHull API

Polygon hulls have the following characteristics:

  • Hulls can be constructed for Polygons and MultiPolygons, including holes.
  • Hull geometries have the same structure as the input.  There is a one-to-one correspondence for  elements, shells and holes.
  • Hulls are valid polygonal geometries.
  • The hull vertices are a subset of the input vertices.

The PolygonHull algorithm supports computing both outer and inner hulls. 

  • Outer hulls contain the input geometry.  Vertices forming concave corners (convex for holes) are removed.  The maximum outer hull is the convex hull(s) of the input polygon(s), with holes reduced to triangles.
  • Inner hulls are contained within the input geometry.  Vertices forming convex corners (concave for holes) are removed.   The minimum inner hull is a triangle contained in (each) polygon, with holes expanded to their convex hulls.  

The number of vertices removed is controlled by a numeric parameter.  Two different parameters are provided:

  • the Vertex Number Fraction specifies the desired result vertex count as a fraction of the number of input vertices.  The value 1 produces the original geometry.  Smaller values produce simpler hulls.  The value 0 produces the maximum outer or minimum inner hull.
  • the Area Delta Ratio specifies the desired maximum change in the ratio of the result area to the input area.  The value 0 produces the original geometry.  Larger values produce simpler hulls. 
Defining the parameters as ratios means they are independent of the size of the input geometry, and thus easier to specify for a range of inputs.  Both parameters are targets rather than absolutes; the validity constraint means the result hull may not attain the specified value in some cases. 

Algorithm Description

The algorithm removes vertices via "corner clipping".  Corners are triangles formed by three consecutive vertices in a (current) boundary ring of a polygon.  Corners are removed when they meet certain criteria.  For an outer hull, a corner can be removed if it is concave (for shell rings) or convex (for hole rings).  For an inner hull the removable corner orientations are reversed.  

In both variants, corners are removed only if the triangle they form does not contain other vertices of the (current) boundary rings.  This condition prevents self-intersections from occurring within or between rings. This ensures the resulting hull geometry is topologically valid.  Detecting triangle-vertex intersections is made performant by maintaining a spatial index on the vertices in the rings.  This is supported by an index structure called a VertexSequencePackedRtree.  This is a semi-static R-tree built on the list of vertices of each polygon boundary ring.  Vertex lists typically have a high degree of spatial coherency, so the constructed R-tree generally provides good space utilization.  It provides fast bounding-box search, and supports item removal (allowing the index to stay consistent as ring vertices are removed).

Corners that are candidates for removal are kept in a priority queue ordered by area.  Corners are removed in order of smallest area first.  This minimizes the amount of change for a given vertex count, and produces a better quality result.  Removing a corner may create new corners, which are inserted in the priority queue for processing.  Corners in the queue may be invalidated if one of the corner side vertices has previously been removed; invalid corners are discarded. 

This algorithm uses techniques originated for the Ear-Clipping approach used in the JTS PolgyonTriangulator implementation. It also has a similarity to the Visvalingham-Whyatt simplification algorithm.  But as far as I know using this approach for computing outer and inner hulls is novel. (After the fact I found a recent paper about a similar construction called a Shortcut Hull [Bonerath et al 2020], but it uses a different approach).

Further Work

It should be straightforward to use this same approach to implement a Topology-Preserving Simplifier variant using the corner-area-removal approach (as in Visvalingham-Whyatt simplification).  The result would be a simplified, topologically-valid polygonal geometry.  The simplification parameter  limits the number of result vertices directly, or the net change in area.  The resulting shape should be a good approximation of the input, but would not necessarily be either wholly inside or outside.

Thursday, 27 January 2022

Cubic Bezier Curves in JTS

As the title of this blog indicates, I'm a fan of linearity.  But sometimes a little non-linearity makes things more interesting.  A convenient way to generate non-linear curved lines is to use Bezier Curves.  Bezier Curves are curves defined by polynomials.  Bezier curves can be defined for polynomials of any degree, but a popular choice is to use cubic Bezier curves defined by polynomials of degree 3.  These are relatively easy to implement, visually pleasing, and versatile since they can model ogee or sigmoid ("S"-shaped) curves.

A single cubic Bezier curve is specified by four points: two endpoints forming the baseline, and two control points.  The curve shape lies within the quadrilateral convex hull of these points.

Note: the images in this post are created using the JTS TestBuilder.

Cubic Bezier Curves, showing endpoints and control points

A sequence of Bezier curves can be chained together to form a curved path of any required shape. There are several ways to join composite Bezier curves.  The simplest join constraint is C0-continuity: the curves touch at endpoints, but the join may be a sharp angle.  C1-continuity (differentiable) makes the join smooth. This requires the control vectors at a join point to be collinear and opposite.  If the control vectors are of different lengths there will be a different radius of curvature on either side.  The most visually appealing join is provided by C2-continuity (twice-differentiable), where the curvature is identical on both sides of the join.  To provide this the control vectors at a vertex must be collinear, opposite and have the same length.

Bezier Curve with C2-continuity

A recent addition to the JTS Topology Suite is the CubicBezierCurve class, which supports constructing Bezier Curves from LineStrings and Polygons.  JTS only supports representing linear geometries, so curves must be approximated by sequences of line segments. (The buffer algorithm uses this technique to approximate the circular arcs required by round joins.)   

Bezier Curve approximated by line segments

Bezier curves can be generated on both lines and polygons (including holes):
Bezier Curve on a polygon

There are two ways of specifying the control points needed to define the curve:  

Alpha (Curvedness) Parameter

The easiest way to define the shape of a curve is via the parameter alpha, which indicates the "curvedness".  This value is used to automatically generate the control points at each vertex of the baseline.  A value of 1 creates a roughly circular curve at right angles.  Higher values of alpha make the result more curved; lower values (down to 0) make the curve flatter.

Alpha is used to determine the length of the control vectors at each vertex.  The control vectors on either side of the vertex are collinear and of equal length, which provides C2-continuity.  The angle of the control vectors is perpendicular to the bisector of the vertex angle, to make the curve symmetrical.  

Bezier Curve for alpha = 1
Bezier Curve for alpha = 1.3

Bezier Curve for alpha = 0.3

Explicit Control Points

Alternatively, the Bezier curve control points can be provided explicitly.  This gives complete control over the shape of the generated curve.  Two control points are required for each line segment of the baseline geometry, in the same order.  A convenient way to provide these is as a LineString (or MultiLineString for composite geometries) containing the required number of vertices.

Bezier Curve defined by control points, with C2 continuity

When using this approach only C0-continuity is provided automatically.  The caller must enforce C1 or C2-continuity via suitable positioning of the control points.

Bezier Curve defined by control points showing C0 and C1 continuity

Further Ideas

  • Allow specifying the number of vertices used to approximate each curve
  • Add a function to return the constructed control vectors (e.g. for display and analysis purposes)
  • Make specifying explicit control points easier by generating C2-continuous control vectors from a single control point at each vertex

Tuesday, 18 January 2022

Concave Hulls in JTS

A common spatial need is to find a polygon that accurately represents a set of points.  The convex hull of the points often does not provide this, since it can enclose large areas which contain no points.  What is required is a non-convex hull, often termed the concave hull.  

The Convex Hull and a Concave Hull of a point set

A concave hull is generally considered to have some or all of the following properties:

  • The hull is a simply connected polygon
  • It contains all the input points
  • The vertices in the hull polygon boundary are all input points
  • The hull may or may not contain holes
For a typical point set there are many polygons which meet these criteria, with varying degrees of concaveness.  Concave Hull algorithms provide a numeric parameter which controls the amount of concaveness in the result.  The nature of this parameter is particularly important, since it affects the ease-of-use in practical scenarios.  Ideally it has the following characteristics:

  • Simple geometric basis: this allows the user to understand the effect of the parameter and aids in determining an effective value
  • Scale-free (dimensionless): this allows a single parameter value to be effective on varying sizes of geometry, which is essential for batch or automated processing
  • Local (as opposed to global):  A local property (such as edge length) gives the algorithm latitude to determine the concave shape of the points. A global property (such as area) over-constrains the possible result. 
  • Monotonic area:  larger (or smaller) values produce a sequence of more concave areas
  • Monotonic containment :the sequence of hulls produced are topologically nested
  • Convex-bounded: an extremal value produces the convex hull

This is a well-studied problem, and many different approaches have been proposed.  Some notable ones are:

Of these, Delaunay Erosion (Chi-shapes) offers the best set of features.  It is straightforward to code and is performant.  It uses the control parameter of Edge Length Ratio, a fraction of the difference between the longest and shortest edges in the underlying Delaunay triangulation.  This is easy to reason about, since it is scale-free and corresponds to a simple property of the point set (that of distance between vertices).  It can be extended to support holes.  And it has a track record of use, notably in Oracle Spatial.  

ConcaveHull generated by Delaunay Erosion with Edge Length Ratio = 0.3

Recently the Park-Oh algorithm has become popular, thanks to a high-quality implementation in Concaveman project (which has spawned numerous ports).  However, it has some drawbacks.  It can't support holes (and likely not disconnected regions and discarding outlier points).  As the paper points out and experiment confirms, it produces rougher outlines than the Delaunay-based algorithm.  Finally, the control parameter for Delaunay Erosion has a simpler geometrical basis which makes it easier to use.

Given these considerations, the new JTS ConcaveHull algorithm utilizes Delaunay Erosion. The algorithm ensures that the computed hull is simply connected, and contains all the input points.  The Edge Length Ratio is used as the control parameter. A value of 1 produces the convex hull; 0 produces a concave hull of minimal size.  Alternatively the maximum edge length can be specified directly. This allows alternative strategies to determine an appropriate length value; for instance, another possibility is to use a fraction of the longest edge in the Minimum Spanning Tree of the input points.  

The recently-added Tri data structure provides a convenient basis for the implementation,.  It operates as follows:

  1. The Delaunay Triangulation of the input points is computed and represented as a set of of Tris
  2. The Tris on the border of the triangulation are inserted in a priority queue, sorted by longest boundary edge
  3. While the queue is non-empty, the head Tri is popped from the queue.  It is removed from the triangulation if it does not disconnect the area.  Insert new border Tris into the queue if they have a boundary edge length than the target length
  4. The Tris left in the triangulation form the area of the Concave Hull 

Thanks to the efficiency of the JTS Delaunay Triangulation the implementation is quite performant, approaching the performance of a Java port of Concaveman.  

Concave Hull of Ukraine dataset; Edge Length Ratio = 0.1

Optionally holes can be allowed to be present in the hull polygon (while maintaining a simply connected result).  A classic demonstration of this is to generate hulls for text font glyphs:

This algorithm is in the process of being ported to GEOS.  The intention is to use it to enhance the PostGIS ST_ConcaveHull function, which has known issues and has proven difficult to use.

Further Ideas

  • Disconnected Result - It is straightforward to extend the algorithm to allow a disconnected result (i.e. a MultiPolygon).  This could be provided as an option.
  • Outlier Points - It is also straightforward to support discarding "outlier" points.
  • Polygon Concave Hull - computing a concave "outer hull" for a polygon can be used to simplify the polygon while guaranteeing the hull contains the original polygon.  Additionally, an "inner hull" can be computed which is fully contained in the original.  The implementation of a Polygon Concave Hull algorithm is well under way and will be released in JTS soon. 

Monday, 3 January 2022

JTS Offset Curves

Offset curves (also known as parallel curves) are an oft-requested feature in JTS.  They are a natural extension to the concept of buffering, and are useful for things like placing labels along rivers.  As far as I know there is no hard-and-fast definition for how an offset curve should be constructed, but a reasonable semantic would seem to be "a line lying on one side of another line at a given offset distance"

Here's an image of offset curves on both sides of a river reach from a US rivers dataset:

GEOS Implementation

GEOS acquired an implementation to produce offset curves a few years ago.  However, it has some known bugs (such as producing disconnected curves, and including extraneous segments).  It also has the feature (or quirk?) of potentially producing a result with multiple disjoint curves for a single input line. 

The GEOS implementation is based on the concept that an offset curve is just a portion of the corresponding buffer boundary.  So to generate an offset curve the algorithm extracts a portion of the buffer polygon boundary.  The trick is deciding which portion!  

The GEOS implementation generates a raw offset curve (potentially full of self-intersections and unwanted linework) and then determines the intersection of that curve with the buffer boundary.  However, the use of intersection on linework is always tricky.  The buffer geometry is necessarily only a (close) approximation, and the buffer algorithm takes advantage of this to use various heuristics to improve the quality and robustness of the generated buffer linework.  This can cause the buffer linework to diverge from the raw offset curve.  The divergence makes the intersection result susceptible to errors caused by slight differences between the generated curves. The two issues above are caused by this limitation. 

JTS Implementation

Instead of using intersection, an effective technique is to match geometry linework using a distance tolerance.  This is the approach taken in the new JTS Offset Curve algorithm. The high-level design is

  1. The buffer is generated at the offset distance
  2. The raw offset curve is generated at the same distance
  3. The raw curve segments are matched to the buffer boundary using a distance tolerance
  4. The offset curve is the section of the buffer boundary between the first and last matching points.
To make the matching efficient a spatial index is created on the buffer curve segments.  

This algorithm provides the following semantics:
  • The offset curve of a line is a single LineString
  • The offset curve lies on one side of the input line (to the left if the offset distance is positive, and to the right if negative)
  • The offset curve has the same direction as the input line
  • The distance between the input line and the offset curve equals the offset distance (up to the limits of curve quantization and numerical precision)
This algorithm does a fine job at generating offset curves for typical simple linework.  The image below shows a family of offset curves on both sides of a convoluted line.  
This resolves both of the GEOS code issues.  It also supports parameters for join style (round, bevel, and mitre), number of quadrant segments (for round joins) and mitre limit:
Join Style = MITRE
Join Style = ROUND; Quadrant Segments = 2

There are also a few nuances required to handle some tricky situations; in particular, cases when the input line string curves back on itself and when it self-intersects.  These do not have a clear definition for what the result should be.  Moreover, the algorithm itself imposes some constraints on how these cases can be handled.  The images below show how the algorithm behaves in these cases.

"Loopbacks"produce offset curves representing only the "exposed" sections of the input linework: 
Offset Curves of a Line with "loop-backs"

For a self-intersecting input line, the offset curve starts at the beginning of the input line on the specified side, and continues only up to where the line side changes due to the crossing.  The length of the offset curve is also reduced by the requirement that it be no closer than specified offset distance to the input line: 
Offset Curves of a Line with a self-intersection

This algorithm is now in JTS, and has been ported to GEOS.

Wednesday, 22 December 2021

Christmas Wrapping

 Every so often I produce an image in the JTS TestBuilder which strikes me as worthy of capture.  Here's one that seems pretty seasonal:

It is generated like this:
  1. Produce two sets of 1000 random points roughly aligned with a grid
  2. Compute their fully-eroded Convex Hulls
  3. Compute the intersection of the two hulls
  4. Theme the intersection with random fill

Monday, 1 November 2021

JTS Polygon Triangulation, at last

A (long) while ago I posted about "soon-to-be-released" JTS code for polygon triangulation using Ear Clipping.  It turned out it was actually in the category of "never-to-be-released".  However, later  I worked with a student, Dan Tong, on a coding exercise sponsored by Facebook. We decided to tackle polygon triangulation.  By the end of the project he implemented a functional algorithm, including handling holes (via the hole-joining technique described by Eberly).  To make it release-ready the code needed performance and structural improvements.  This has finally happened and the code is out!  

Polygon Triangulation

The new codebase has been substantially rewritten to improve modularity and the API.  A new data structure to represent triangulations has been introduced, called a Tri  It is simpler, more memory efficient, and easier to work with than the QuadEdge data structure previously used in JTS. It provides an excellent basis for developing further algorithms based on triangulations (of which more below)

Most of the reworking involved implementing key performance optimizations, utilizing existing and new JTS spatial index structures: 

  • the Hole Joining phase now uses a spatial index to optimize the test for whether a join line is internal to a polygon
  • the Ear-Clipping phase has to check whether a candidate ear contains any vertices of the polygon shell being clipped.  This has been made performant by using a new spatial index structure called the VertexSequencePackedRtree

Performance must be benchmarked against an existing implementation.  Geometry maestro Vladimir Agafonkin has developed an Ear Clipping algorithm in Javascript called Earcut.  It's claimed to be the fastest implementation in Javascript.  Indeed the performance comparison shows Earcut to be over 4 times faster than some other Javascript triangulation implementations.  Earcut has been ported to Java as earcut4j, which allows comparing JTS to it.   

DatasetSizeJTS TimeEarcut4j Time
Lake Superior3,478 vertices / 28 holes18 ms13 ms
Canada10,522 vertices35 ms26 ms
Complex polygon44,051 vertices / 125 holes1.1 s0.64 s
North America with Lakes115,206 vertices / 1,660 holes13.3 s5.4 s

So JTS ear clipping is not quite as fast as Earcut4j.  But it's still plenty fast enough for production use!

The triangulation algorithm is exposed as the PolygonTriangulator class (deliberately named after the effect, rather than the algorithm producing it).  Here's an example of the output:

Ear-Clipping Triangulation of Lake Superior

Constrained Delaunay Triangulation

The output of PolygonTriangulator is guaranteed to be a valid triangulation, but in the interests of performance it does not attempt to produce a high-quality one.  Triangulation quality is measured in terms of minimizing the number of "skinny" triangles (or equivalently, maximizing the sum of the interior angles).  Optimal quality is provided by Constrained Delaunay Triangulation (CDT).  In addition to being optimal in terms of triangle shape, the CDT has the nice properties of being (essentially) unique, and of accurately representing the structure of the input polygon.  It also removes the influence that Hole Joining holes has on the raw triangulation provided by Ear-Clipping.

I originally proposed that a CDT can be achieved by a Delaunay Improvement algorithm based on iterated triangle flipping.  It turned out that this technique is not only effective, but also pleasingly performant.  This is implemented in the ConstrainedDelaunayTriangulator class.  Running it on the example above shows the improvement obtained.  Note how there are fewer narrow triangles, and their arrangement more closely represents the structure of the polygon.

Constrained Delaunay Triangulation of Lake Superior

This code will appear in the next JTS version (1.18.3 or 1.19), and has already been released in GEOS 3.10.

Future Work

It seems possible to adapt the machinery for Ear Clipping to provide two other useful constructions:

  • Polygon Concave Hull ("Outer Hull") - this requires a triangulation of the area between the polygon and its convex hull.  That can be obtained by running Ear Clipping outwards, rather than inwards. A Concave Hull can be constructed by limiting the number of triangles generated based on some criteria such as area, longest edge, or number of vertices.  The result is a simplified polygon with fewer vertices which is guaranteed to contain the original polygon. 
  • Polygon Inner Hull - the triangulation constructed by Ear Clipping can be "eroded" to provide a simpler polygon which is guaranteed to be contained in the original.
The property of the Constrained Delaunay Triangulation of representing the structure of the input polygon could provide a basis for computing the following constructions: 
  • Approximate Skeleton construction (also known as the Medial Axis) by creating edges through the CDT triangles
  • Equal-Area Polygon Subdivision, by partitioning the graph induced by the CDT
The Tri data structure provides a simple representation of a triangulation.  I expect that it will facilitate the development of further JTS triangulation algorithms, such as:
  • Concave Hull of Point Set
  • Constrained Delaunay Triangulation of Lines and Points