Computes a concave hull containing a set of features according to the algorithm described in detail by Adriano Moreira and Maribel Yasmina Santos (2007). Prior construction of the hull the data may optionally clustered using a Shared Nearest Neighbor Clustering algorithm after Ertoz, Steinbach & Kumar (2003).
Computes a minimum area containing a set of features. The concave hull is supposed to best describe that area occupied by the given set of points. The resulting polygon geometries are stored in an existing polygon layer or new memory layer. Prior computation of the hull the given data can optionally be divided into clusters.
Specify an initial number k of nearest neighbors for the algorithm to inspect. If no valid hull can be constructed the algorithm will restart with an increased number of neighbors. It proceeds until all features are enclosed by a non self-intersecting polygon.
Select one or more vector layers containing points, lines, or polygons. Any mixture of geometry types is allowed, since all objects will be converted to points.
If at least one layer contains selected features you can direct the plugin to use selected features only. Note that in this case only selected features will be processed, layers without any selected feature will be ignored.
The dataset can be clustered optionally based on location prior construction of the concave hull. This plugin implements a sheared nearest neighbor algorithm. The algorithm finds clusters with at least the given number of neighbors being closer to one another than to other points. Points not being part of any cluster are considered as noise, and are not included in any cluster.
Output can go to a new memory layer, an existing map layer, or a new shape file. Type in the name of the new memory layer. In this case a new memory layer containing the concave hull is created and added to the legend. The CRS of the new layer is being set to the project CRS. If you select an output layer from the dropdown list all created polygon features will be appended to this layer. If you select output to shapefile browse to the directory where the shapefile should be saved, and type in the filename. An existing shapefile will be overwritten.
Hull polygons get the attributes id and count. Count receives the number of points falling inside this polygon.
Included with this plugin are two test datasets with similar point distribution as describe in Moreira and Santos (2007). These are the results:
Point distribution for example 1
Concave hull without clustering, starting with k=3
Concave hull for a subset of points, starting with k=3
Concave hull for the same subset of points, starting with k=10
First clustered (5 neighbors), then concave hull with k=3
Point distribution for example 2
First clustered (5 neighbors), then concave hull with k=5
First clustered by location and field (categories represented by point color), then concave hull based on clusterId
Concave hull and clustering algorithm are both accessible in the QGIS processing framework. The algorithms can be used individually and can be combined with other geoprocessing tools to build more complex workflows.
The concave hull algorithm can build a single concave hull polygon like the plugin version, as well as many hulls based on an attribute. This works like a group by clause. In this case the field grouping is based on will be added to the hulls.
The clustering algorithm can build clusters based on location like the plugin version, and additionally grouped by an attribute. The clustered points get an attribute clusterId containing the id of the cluster the point belongs to. In case of grouping the field grouping is based on will be added to the points.
Building concave hulls based on clusterId of location based clustered input geometries achieves the same result as with the plugin version.
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In general the concave hull computed by this algorithm is more concave then that computed by the alpha-shape algorithm.
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The algorithm cannot deal with holes.
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Although you can use arbitrary input geometries, this may lead to unexpected results. This is especially true for if the input data are vertices of polygons with holes. If a hole is lying near to the polygon boundary the algorithm may include the boundary of the hole and thus leading to misinterpretation of the covered area.
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If segment lengths of input polygons and lines are very different you may consider to add additional vertices (densify), otherwise the algorithm may pass over these segments leading to eroded boundaries.
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Since the algorithm increases the number k of nearest neighbors, a slightly different data distribution might lead to very different hull geometries. In the second and third example above, if all points will be used the hull geometry is more generalized than processing only the upper point cloud. Especially if you cluster the input data each cluster gets its own concave hull, and therefore can be more or less detailed than other hulls. To avoid this, you may start with a greater neighborhood k.
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When selecting a large value for k the hull geometry equals the convex hull.
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Construction of concave hulls based on points grouped by an attribute may lead to overlapping polygons, since these groups are processed independently. Overlapping concave hulls may be an indicator for interspersed distribution of variables.
The clustering method is based on python code written by some guy named jonno (http://www.georeference.org/forum/t76320). Visited in November, 2014.
Thanks to Joel Lawhead and his book: Learning Geospatial Analysis with Python. Especially for the implementation of the ray-casting algorithm.
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Optimization of code in terms of running time and readability
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Processing of PostGIS-features takes a much too long running time.
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Version 1.0.2 (2015/02/10): added algorithms to processing framework, standard attributes, grouping based on fields
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Version 1.0.1 (2015/01/10): added UI event handlers, more user feedback, and standard file selection dialog
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Version 1.0.0 (2015/01/07): first version
Levent Ertoz, Michael Steinbach and Vipin Kumar (2003): Finding Clusters of Different Sizes, Shapes, and Densities in Noisy, High Dimensional Data. In Proceedings of the Second SIAM International Conference on Data Mining, San Francisco, CA, USA, May
Adriano Moreira and Maribel Yasmina Santos (2007): CONCAVE HULL: A K-NEAREST NEIGHBOURS APPROACH FOR THE COMPUTATION OF THE REGION OCCUPIED BY A SET OF POINTS. GRAPP 2007 - International Conference on Computer Graphics Theory and Applications, pp. 61-68