Interpolation is a technique in GIS that is used to estimate the value of a variable at a specific location based on measurements taken at other locations. In ArcGIS Pro, there are several interpolation methods that can be used to create a surface (raster) from a set of point data. In this tutorial, we will go through the steps of performing interpolation in ArcGIS Pro using the “IDW (Inverse Distance Weighted)” method.
Step 1: Open ArcGIS Pro and start a new project.
Step 2: Add your point data to the project. You can do this by going to the “Insert” menu and selecting “Add Data” or by using the “Add Data” button on the Home tab. Make sure that your point data includes the variable that you want to interpolate.
Step 3: Go to the “Analysis” tab and open the “Geoprocessing” pane. Search for the “IDW (Inverse Distance Weighted)” tool and select it.
Step 4: In the IDW tool dialog box, set the input point feature layer to the layer that contains your point data.
Step 5: Set the field that you want to interpolate as the “Z value field”
Step 6: Set the “Output raster” to a location and name of your choice. You can also choose the cell size and output extent, or choose “Same as Input” which will use the extent of the point data.
Step 7: In the “Search radius” section, you can specify the distance over which to search for points to use in the interpolation. You can specify a fixed distance, or use a variable distance.
Step 8: Run the tool by clicking “OK”
Step 9: Once the tool is finished running, the output raster will be added to the project. You can view it by going to the “View” menu and selecting “Layout” or by clicking on the “Layout” button on the Home tab.
Step 10: you can also use the symbology on the output raster layer to better visualize the data or you can use other tools such as “Elevation” to generate 3D views of the surface.
That’s it! You have just interpolated point data to create a surface in ArcGIS Pro. Keep in mind, you can use other interpolation methods as well, such as “Natural Neighbour” and “Kriging”, and you can also experiment with different parameters and settings to achieve the best results for your specific dataset and project.