Spacial data - QGIS and QGIS2threejs in action
Grand Canyon 3D models
All models were created using the Quantum GIS and QGIS plugin 2threejs and based on the dataset spatial coordinates obtained using Google Maps and Google Earth. Presents the final results of two methods described below TIN and IDW interpolation . My interests are concentrated on finding indirect methods used for this type of spatial mapping, depending on the type of CRS and the data layers.
[1] Inverse Distance Weighted (IDW) In the IDW interpolation method, the sample points are weighted during interpolation such that the influence of one point relative to another declines with distance from the unknown point you want to create (see figure_idw_interpolation).
Inverse Distance Weighted interpolation based on weighted sample point distance (left). Interpolated IDW surface from elevation vector points (right). Image Source: Mitas, L., Mitasova, H. (1999).
Weighting is assigned to sample points through the use of a weighting coefficient that controls how the weighting influence will drop off as the distance from new point increases. The greater the weighting coefficient, the less the effect points will have if they are far from the unknown point during the interpolation process. As the coefficient increases, the value of the unknown point approaches the value of the nearest observational point.
It is important to notice that the IDW interpolation method also has some disadvantages: the quality of the interpolation result can decrease, if the distribution of sample data points is uneven. Furthermore, maximum and minimum values in the interpolated surface can only occur at sample data points. This often results in small peaks and pits around the sample data points as shown in figure_idw_interpolation.
In GIS, interpolation results are usually shown as a 2 dimensional raster layer. In figure_idw_result, you can see a typical IDW interpolation result, based on elevation sample points collected in the field with a GPS device.
IDW interpolation result from irregularly collected elevation sample points (shown as black crosses).
Triangulated Irregular Network (TIN) TIN interpolation is another popular tool in GIS. A common TIN algorithm is called Delaunay triangulation. It tries to create a surface formed by triangles of nearest neighbour points. To do this, circumcircles around selected sample points are created and their intersections are connected to a network of non overlapping and as compact as possible triangles (see figure_tin_interpolation).
Delaunay triangulation with circumcircles around the red sample data. The resulting interpolated TIN surface created from elevation vector points is shown on the right. Image Source: Mitas, L., Mitasova, H. (1999).
The main disadvantage of the TIN interpolation is that the surfaces are not smooth and may give a jagged appearance. This is caused by discontinuous slopes at the triangle edges and sample data points. In addition, triangulation is generally not suitable for extrapolation beyond the area with collected sample data points (see ).
Delaunay TIN interpolation result from irregularly collected rainfall sample points (blue circles)
[1]- http://docs.qgis.org/2.6/en/docs/gentle_gis_introduction/spatial_analysis_interpolation.html