Function Description
The function of point rarefaction is to take a point in Dataset as the center according to the specified rarefaction radius, all points in the circle will be rarefied, and then use a point to represent all points. The rarefied point is not necessarily the center point of the rarefied point set, which has a certain randomness.
Application scenario
It can be used for mapping in a small scale. If the points in the point data are dense, the display in a small scale will be overlapped with each other. Through this function, the point objects can be thinned out, which can improve the performance and Display Effects of the map while reflecting the overall information of the point data.
Function entrance
- Data tab-> Data Processing-> Vector-> Point Thinning.
- Toolbox-> Data Processing-> Vector-> Point Thinning.
Parameter Description
- Source Data: used to display and set the Point Dataset and its Datasource to be rarefied.
- Thinning Radius: It is used to set the radius of the thinning point, indicating that only one point is randomly reserved within the radius circle. The larger the radius is, the sparser the point objects in the Result Dataset are.
- Statistics Type: select a Statistics TypeRecalculate for the original field value of the result point retained within the rarefaction radius, and add a Statistic Field in the Result Dataset. Assign Calculate Result to this field. There are 8 supported Statistics Types, including average, maximum, minimum, Sample Standard Deviation, Sample Variance, standard deviation, variance, and sum.
- Average: when Statistics Type is Average, the value of Statistic Field is the average of Property Field values of all points within the rarefaction radius.
- Maximum value: when Statistics Type is Maximum value, the value of Statistic Field is the maximum value of Property Field values of all points within the rarefaction radius.
- Minimum value: when Statistics Type is Minimum value, the value of Statistic Field is the minimum value of Property Field values of all points within the rarefaction radius.
- Sample Standard Deviation: When Statistics Type is Sample Standard Deviation, The Statistic Field value is the Sample Standard Deviation of the Property Field values for all points within the rarefaction radius.
- Sample Variance: When Statistics Type is Sample Variance, The Statistic Field value is the Sample Variance of the Property Field values for all points within the rarefaction radius.
- Standard Deviation: when Statistics Type is Standard Deviation, the value of Statistic Field is the standard deviation of Property Field values of all points within the rarefaction radius.
- And: When Statistics Type is And, the value of Statistic Field is the sum of Property Field values of all points within the rarefaction radius.
- Variance: when Statistics Type is Variance, the value of Statistic Field is the variance of Property Field values of all points within the rarefaction radius.
- Randomly Save Rare Points: Select the Randomly Save Rare Points, and select a random point from the rarefaction radius range to save; otherwise, select the point with the minimum sum of the distances from the point set within the rarefaction radius range.
- Keep Source Field: if Keep Source Field is checked, the Property Field in the source data will be kept.
- Statistic Field: In the Statistic Field list box, the Property Field with Integer Type contained in the thinned Point Dataset is displayed. You can select and set the attribute value field of the Point Dataset participating in rarefaction and the attribute Field and Statistics Type of the Result Dataset after rarefaction.
- Result Data: used to display and set the Result Dataset and the Datasource to be saved.
After setting the above parameters, click the Run button to perform rarefaction on the specified Point Dataset. After the execution is successful, the Output Window will give a execution completed prompt, and the obtained Rarefy Points Result is as shown in the following figure. The left figure is the thermodynamic diagram made according to Point Dataset before thinning. It can be seen that there is a phenomenon of capping where the point data is dense, and the rendering effect of the thermodynamic diagram is not obvious; The figure on the right is the thermodynamic diagram made according to the Point Dataset after thinning. Compared with the figure on the left, the point objects are sparser, and there is no mutual capping phenomenon, and the rendering effect is better.
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