13/09/2024
Spatial Interpolation Map:
The map illustrates how to use a discrete point dataset to generate a seamless continuous raster using several different geostatistical interpolation methods.
Geostatistical interpolation methods are powerful techniques used in geographic information systems (GIS), and spatial analyses, including within the QGIS environment, to estimate and predict values at various locations based on observed data. These methods are precious when dealing with spatially distributed data and can be applied to several applications. Popular geostatistical interpolation methods include kriging, Inverse Distance Weighting (IDW), and spline.
For the purpose of interpolation, firstly use Voronoi polygons (which are also known as Thiesen polygons) to define an area of influence based on the proximity of each point to one another and create a rainfall distribution map based on a point dataset. We will make use of a color ramp to visualize the polygons which shows the gradient of the variable with a clear distinction between the high and low values.
Secondly, a commonly used geostatistical interpolation method called IDW, which stands for inverse distance weighting to generate a seamless continuous raster, by predicting values for unknown areas based on the available point values in the same dataset.
Finally, we will also make use of another interpolation method which is called thin plate spline (which you can find under SAGA tools in QGIS) to perform a similar task, and then we will compare the results and see how each interpolation method differs from one another, with some thoughts on how to select a specific interpolation method for a given task at hand.
GIS and Remote Sensing Skills GIS Academy GIS Analyst Web Mapping and GIS applications Esri
