https://github.com/centrogeo/hierarchicalgeoclustering
https://github.com/centrogeo/hierarchicalgeoclustering
Last synced: about 1 year ago
JSON representation
- Host: GitHub
- URL: https://github.com/centrogeo/hierarchicalgeoclustering
- Owner: CentroGeo
- License: apache-2.0
- Created: 2021-12-01T00:18:16.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2025-02-27T00:41:34.000Z (over 1 year ago)
- Last Synced: 2025-02-27T01:19:28.757Z (over 1 year ago)
- Language: Jupyter Notebook
- Size: 10.7 MB
- Stars: 0
- Watchers: 8
- Forks: 2
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Citation: CITATION.cff
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README
Hierarchical Geo Clustering
================
## Install
The library uses the cgal library, if the library is use inside a conda
enviroment the installation procedure should create the enviroment and
install the cgal library using `conda install -c conda-forge cgal`.
To install the library can be simply:
`pip install git+https://github.com/CentroGeo/HierarchicalGeoClustering.git`
## How to use
### Build a TreeCluster
We can build a hierarchical geographical cluster, the `TreeCluster`
class allows to create te structure and then populate it.
``` python
HGC = TreeClusters(3, random_seed= 12)
```
``` python
HGC.populate_tree(number_per_cluster=100)
```
``` python
HGC.print_structure()
```
Root
Root
├── Root_l_0_n_0
│ ├── Root_l_0_n_0_l_1_n_0
│ │ ├── Root_l_0_n_0_l_1_n_0_l_2_n_0
│ │ ├── Root_l_0_n_0_l_1_n_0_l_2_n_1
│ │ ├── Root_l_0_n_0_l_1_n_0_l_2_n_2
│ │ ├── Root_l_0_n_0_l_1_n_0_l_2_n_3
│ │ └── Root_l_0_n_0_l_1_n_0_l_2_n_4
│ └── Root_l_0_n_0_l_1_n_1
│ ├── Root_l_0_n_0_l_1_n_1_l_2_n_0
│ └── Root_l_0_n_0_l_1_n_1_l_2_n_1
├── Root_l_0_n_1
│ ├── Root_l_0_n_1_l_1_n_0
│ │ └── Root_l_0_n_1_l_1_n_0_l_2_n_0
│ ├── Root_l_0_n_1_l_1_n_1
│ │ ├── Root_l_0_n_1_l_1_n_1_l_2_n_0
│ │ ├── Root_l_0_n_1_l_1_n_1_l_2_n_1
│ │ └── Root_l_0_n_1_l_1_n_1_l_2_n_2
│ ├── Root_l_0_n_1_l_1_n_2
│ ├── Root_l_0_n_1_l_1_n_3
│ │ ├── Root_l_0_n_1_l_1_n_3_l_2_n_0
│ │ └── Root_l_0_n_1_l_1_n_3_l_2_n_1
│ └── Root_l_0_n_1_l_1_n_4
│ ├── Root_l_0_n_1_l_1_n_4_l_2_n_0
│ ├── Root_l_0_n_1_l_1_n_4_l_2_n_1
│ └── Root_l_0_n_1_l_1_n_4_l_2_n_2
└── Root_l_0_n_2
├── Root_l_0_n_2_l_1_n_0
│ ├── Root_l_0_n_2_l_1_n_0_l_2_n_0
│ └── Root_l_0_n_2_l_1_n_0_l_2_n_1
├── Root_l_0_n_2_l_1_n_1
│ ├── Root_l_0_n_2_l_1_n_1_l_2_n_0
│ ├── Root_l_0_n_2_l_1_n_1_l_2_n_1
│ ├── Root_l_0_n_2_l_1_n_1_l_2_n_2
│ ├── Root_l_0_n_2_l_1_n_1_l_2_n_3
│ └── Root_l_0_n_2_l_1_n_1_l_2_n_4
├── Root_l_0_n_2_l_1_n_2
│ ├── Root_l_0_n_2_l_1_n_2_l_2_n_0
│ ├── Root_l_0_n_2_l_1_n_2_l_2_n_1
│ └── Root_l_0_n_2_l_1_n_2_l_2_n_2
└── Root_l_0_n_2_l_1_n_3
├── Root_l_0_n_2_l_1_n_3_l_2_n_0
├── Root_l_0_n_2_l_1_n_3_l_2_n_1
├── Root_l_0_n_2_l_1_n_3_l_2_n_2
├── Root_l_0_n_2_l_1_n_3_l_2_n_3
└── Root_l_0_n_2_l_1_n_3_l_2_n_4
``` python
fig, axs = plt.subplots( figsize=(8,8))
HGC.visualize(axs, polygon=True)
```
``` python
HGC.root.polygon_cluster
```
``` python
HGC.get_deepth()
```
4
### Extract the points and clusterize
To clusterize we extract the points and use the clusterize tool
implemented in the `Clustering` module
``` python
original_points= HGC.get_points_tree()
X_2=np.array([[p.x,p.y] for p in original_points])
dic_points={'points':[X_2], 'parent':''}
```
``` python
HGC_adapta_DBSCAN = recursive_clustering_tree(dic_points,
levels_clustering = 3,
algorithm = 'adaptative_DBSCAN'
)
```
``` python
fig, axs = plt.subplots( figsize=(8,8))
HGC_adapta_DBSCAN.visualize(axs, polygon=True)
```
### Get the Similarity Shape Measurement
To obtain the SSM the clusters tree has to be used, this due to the use
of the geometric shape in the metric. Therefore the polygons of each
cluster on each level has to be able to compare.
``` python
form_metric=[]
for l in range(0, 4):
d = { 'Level': l,
'adaptive DBSCAN':SSM(HGC.levels_nodes[l],
HGC_adapta_DBSCAN.levels_nodes[l])
}
form_metric.append(d)
```
``` python
form_metric
```
[{'Level': 0, 'adaptive DBSCAN': 0.6046098496677557},
{'Level': 1, 'adaptive DBSCAN': 0.6195370749041116},
{'Level': 2, 'adaptive DBSCAN': 0.4080125145736268},
{'Level': 3, 'adaptive DBSCAN': 0.22732397098600046}]
``` python
df_metric_form = pd.DataFrame(form_metric)
```
``` python
df_metric_form
```
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Level
adaptive DBSCAN
0
0
0.604610
1
1
0.619537
2
2
0.408013
3
3
0.227324
A detail
[documentation](https://centrogeo.github.io/HierarchicalGeoClustering/)
is available