QGIS Python Plugins Repository
Plugins tagged with: cluster
7 records found —
Click to toggle descriptions.
| Name | Featured | Downloads ↑ | Author | Latest Plugin Version | Created on | Stars (votes) | Stable | Exp. | |
|---|---|---|---|---|---|---|---|---|---|
|
Spatial Analyzer | — | 559 | D.J Paek | 2024-04-01T16:10:16.997936+00:00 | 2024-04-01T16:01:35.079435+00:00 | (5) |
0.1.1 | — |
| Spatial Analysis Tools | |||||||||
|
Webmap Utilities | — | 2790 | Guilherme Alexsander Pereira | 2023-09-29T12:44:52.331069+00:00 | 2023-09-29T12:44:51.660169+00:00 | (4) |
0.1 | — |
| This plugin provides tools for clustered and hierarchical visualization of vector layers, creation of Relief Shading and management of scales using zoom levels. | |||||||||
|
|
QgisMarkerCluster Plugin | — | 6341 | Matthias Ludwig - Datalyze Solutions | 2015-02-05T14:54:35.691242+00:00 | 2014-08-18T08:50:41.331687+00:00 | (37) |
— | 0.1.1 |
| Combines points in a defined distance into a cluster point. | |||||||||
|
Polygon Cluster Reducer | — | 6970 | Zoran Petrović | 2018-11-14T14:13:36.941196+00:00 | 2018-11-14T14:13:35.837541+00:00 | (4) |
0.1 | — |
| This plugin removes polygons from the cluster, based on the user parameters. | |||||||||
|
Extreme Value Classification for Polygon | — | 7166 | Prof. Jochen Schiewe, M.Sc. Juiwen Chang, Philipp Loose | 2020-07-02T08:02:50.158865+00:00 | 2018-06-26T09:33:21.386799+00:00 | (10) |
— | 1.1 |
| Task-Oriented Data Classification for Choropleth Maps <br/> Pre-request python packages: fiona, shapely, pysal, rtree, sklearn, dbf | |||||||||
|
Clusterpy - Spatially constrained clustering | — | 14175 | RISE Group Universidad EAFIT | 2014-06-08T03:10:47.111145+00:00 | 2014-06-08T03:10:46.681937+00:00 | (38) |
1.0 | — |
| Clusterpy plugin version for QGIS | |||||||||
|
ClusterPoints | — | 26690 | Johannes Jenkner | 2023-08-18T05:52:37.378747+00:00 | 2020-03-30T11:15:13.154597+00:00 | (27) |
6.1 | — |
| Cluster Points conducts clustering of points based on their mutual distance to each other or based on supplemental information from attributes. The user can select between the K-Means or Fuzzy C-Means algorithms and (agglomerative) hierarchical clustering with several different link functions. To find the best possible grouping of points, both geographical coordinates and numerical fields can be incorporated into the algorithm to a varying degree. | |||||||||
Elements per page:
Deprecated plugins are printed in red.
Sustaining Members
