Spatial Interpolation with GDAL in Python #1: Nearest Neighbor and Moving Average

In this tutorial, I will give an introduction to the spatial interpolation algorithms nearest neighbor and moving average. We will use gdal.Grid() from the geospatial data abstraction library GDAL to create a regular grid from scattered point data in Python.

Chapters:
0:00 Introduction
1:33 Nearest Neighbor
14:14 Moving Average

Code:

from osgeo import gdal
from osgeo import ogr

layer = pts.GetLayer()

gt = dem.GetGeoTransform()

ulx = gt[0]
uly = gt[3]
res = gt[1]

xsize = dem.RasterXSize
ysize = dem.RasterYSize

lrx = ulx + xsize * res
lry = uly - ysize * res

dem = None

# nearest neighbor interpolation
pts = layer = None
algorithm = "nearest", outputBounds = [ulx,uly,lrx,lry],
width = xsize, height = ysize)
nn = None

# moving average
algorithm = "average:radius1=1000:radius2=800:angle=20",
outputBounds = [ulx,uly,lrx,lry],
width = xsize, height = ysize)
ma = None

[anantharamaniyer]

Good to see you back! Thank you for this.

[ca-scholars]

Thank you very much, everything is clear and helped to apply the algorithm.

[jacobokomo]

kindly share us the raster image data you've used to make it easy to follow along with you.
-Thank you in advance

[柳風-xf]

5:10 Can you please mention how have you done this step?

[xiaozhenma]

Thanks a lot, Can you tell me how did you extract the elevations from dem raster file with giving points?