Function Description
The point clustering function is to divide the points with dense spatial distribution into a cluster according to the Density Clustering algorithm, and form a polygon with the same cluster of points. After point clustering, a "ResultType" field will be generated in the Source Dataset to count the clustering category information.
Application scenario
This function is applicable to the large amount of data. It is used to classify and remove noise points according to the closeness of the spatial relationship of the points. It can also be used to construct a cluster of points with close geographical location as an area object. The specific application scenarios are as follows:
- Remove noise points from Point Cloud Data: The acquisition of Point Cloud Data will be affected and interfered by human factors, instruments, environment, measurement methods and other factors, and the acquired Point Cloud Data will contain noise points. The obtained point cloud of the measured object is usually continuously distributed along the surface, and the noise points are generally located outside the point cloud, so the noise points can be removed through the Aggregate Points function.
- Construct the area with weak signal according to the monitoring data of communication signal: the communication monitoring system will monitor the strength of communication signal in real time, and extract the point where the signal is lower than a certain strength according to the strength of the signal, and construct the area with weak signal through the Density Clustering function, which can be used as a reference for the site selection of the new signal tower.
Function entrance
- Data tab-> Data Processing-> Vector-> Point Cluster.
- Toolbox-> Data Processing-> Vector-> Point Clustering.
Parameter Description
- Source Data: used to display and set the Point Dataset for Density Clustering and the Datasource where it is located.
- Cluster Radius: Set the radius of Aggregate Points, which means that when the number of points within the specified radius is Is no less than threshold, it means that these points are a category, and the attribute values in the new ResultType field in the original Dataset are the same.
- Distance unit: a unit for setting the cluster radius.
- Threshold of number of points: used to display and set the minimum number of points clustered as one cluster. The value must be Greater than or equal to 2. The larger the threshold is, the more stringent the condition of clustering into a cluster is. The recommended value is 4.
- Result Data: used to display and set the Result Dataset and the Datasource to be saved.
After successful execution, the Output Window will give a corresponding prompt and prompt the field name of the cluster category saved in the Dataset. The resulting Aggregate Result is shown in the following figure. As shown in the figure, the points that cannot form a face are discrete points (noise points), that is, the dark green points in the right figure, whose ResultType attribute value is 0.
Related topics